Order 932343: Does exercise and fitness affect cognitive health

requiredcriteria.htmleditDraft.ResearchProposal.SekinahWALLS xDraft.ResearchProposal.SekinahWALLS xArticle6 Article5-2 Article1 Article2 Article3 Article4
 

Save Time On Research and Writing
Hire a Pro to Write You a 100% Plagiarism-Free Paper.
Get My Paper
  • Type of paperResearch Proposal
  • SubjectHealthcare
  • Number of pages15
  • Format of citationAPA
  • Number of cited resources15
  • Type of serviceRewriting

I have posted what I have thus far. Its is not all correct and needs to be rewritten. I post the draft and the grading rubic that has what is required to be on my paper. I have already found 6 articles but I do need 9 more I have to have 15 articles in total. The 15 pages cannot include the reference page and/or title page.

Additional Service

i

1-page abstract 

Save Time On Research and Writing
Hire a Pro to Write You a 100% Plagiarism-Free Paper.
Get My Paper

An abstract is a paragraph (150-300 words) that summarizes the main aspects of the paper in a prescribed sequence that includes 1) the overall purpose of the study and the research problem(s) you investigated; 2) the basic design of the study; 3) major findings or trends found as a result of your analysis; and, 4) a brief summary of your interpretations and conclusions.

Mississippi College

 

  • Apply
  • Request Info
  • Give
  • Contact
  • MyMC
  • Search
  • Admissions
  • Undergraduate

    Freshmen

    Transfers

    Adult

    Accelerated

    International Students

    Graduate

    Degree Programs

    Law

    Physician Assistant

    International

    Online

    Why MC Online?

    Degree Programs

    Tuition & Fees

  • About

    Get to Know MC

    MC at a Glance

    Vision & Mission

    History

    Traditions

    Contact

    Visit MC

  • Schedule a Visit
  • Directions

    Parking

    Campus Map

    About Clinton

    University Leadership

    Board of Trustees

    President Lee G. Royce

    President’s Council

  • Academics

    Programs

  • Majors & Degrees
  • Schools & Departments

    Graduate Studies

    Accelerated Degree

    Law School

    International

    Information

    Accreditation

    Internships

    Study Abroad

    Tutoring

  • Academic Calendar
  • Academic Catalogs
  • Resources

  • Library
  • Class Schedules

    Commencement

    Service-Learning Initiative

    Bookstore

    Faculty Homepages

  • Offices

    Campus Services

    Career Services

    Computer Services

    Food Services

    Health Services

    Residence Life

    Counseling Services

    Administrative

    Business Office

    Financial Aid

    Human Resources

    Giving & Development

    Registrar

    Continuing Education

    Other Offices

    Christian Development

    Community Service

    Public Relations

    Public Safety

    Student Success

    Event Services

  • Athletics

    Choctaw Athletics

    Athletics Website

    Athletic News

    Athletic Staff

    Sports Information

    Calendar

    Tickets

    Other Athletics

    Alumni Pool

    Archery

    Equestrian Team

    Bass Fishing

    Clay Shooting

    Table Tennis

    For Alumni

    M Club

    Sports Hall of Fame

    Tailgating

  • Student Life

    Activities

    Student Organizations

    Clubs & Tribes

    Student Government

    Event Registration

    Intramural Sports

    Providence Hill Farm

    Student Services

    Career Services

    Computer Services

    Residence Life

    Health Services

    Food Services

    Counseling Services

    Other Resources

    Academic Catalogs

    Student Handbook

    Student Success

    The Mississippi Collegian

    Veterans Affairs

MyMC Login

Username

@mc.edu

Password

Help

  • Students can look up their MCnet username and password using the Student Account Lookup tool
  • If you are no longer an enrolled student and need access to Banner Web, Click Here
  • For assistance with problems logging in to My MC, please contact Computer Services at [email protected] or 601-925-3939.

MCNet Account

  • What is MyMC?
  • Student Account Lookup
  • Change Password
  • MCnet App Status

    Latest Status Information

  • MCnet App Status
    • Prospective Students
    • Admissions

    • Tuition & Financial Aid
    • Majors & Degrees

      Schedule a Visit

      Apply

    • Current Students
    • MyMC

      Academic Calendar

      Academic Catalogs

      Library

    • Faculty Webpages
    • Faculty & Staff
    • Committees & Councils
    • Faculty & Staff Meetings
    • University Policies
    • Strategic Planning
    • Insitutional Research
    • Alumni & Friends
    • Get Involved
    • MC Athletics
    • Beacon Magazine
    • Give to MC
    • Alumni Chapters
    • © 2015 Mississippi College
    • 601.925.3000
    • 200 S. Capitol St. Clinton, MS 39056
    • Privacy Statement
    • Employment
    • Institutional Data
    • Federal Disclosures
    • Facebook
    • Twitter
    • Instagram
    • Pinterest

    1

    Runninghead:

    DOES EXERCISE AND FITNESS HELP WITH COGNITIVE HEALTH

    6

    DOES EXERCISE AND FITNESS HELP WITH COGNITIVE HEALTH

    Does exercise and fitness help with cognitive health

    Sekinah Walls

    Mississippi College

    Abstract

    Older adults are experiencing the deterioration of physical functions and also suffering from declining brain and cognitive functions. Normal aging includes the degradation of cognitive processes, including memory, reasoning, and information processing speed. Older adults are at risk for osteoporosis and they are at high risk for falling. It is a huge problem when an older adult falls because the body does not heal as fast and easy like and young adult. Older adults need to be active and continue to be active to help increase the blood flow throughout their bodies and promote a healthy life physically and mentally. Living a healthy lifestyle helps cognitive skills for both young and old adults. Childhood obesity, cardiovascular disease, diabetes type 2, and certain cancers can possibly be prevented with exercise and fitness. Regular physical activity has been shown to be productive against the development of these diseases and many others. Not all children have adequate and effective brain development throughout childhood, therefore leading them to having health conditions that have causes. Exercise and fitness is suggested to indirectly improve cognitive and brain health by reducing the risks for many disease processes. Exercise and fitness in the adolescence and adults will help with physical as well as cognitive and brain health Comment by User: This sentence is wordy, consider revising.

    Statement of the Problem

    Older adults are experiencing the deterioration of physical functions and also suffering from declining brain and cognitive functions. Normal aging includes the degradation of cognitive processes, including memory, reasoning, and information processing speed. Older adults are at risk for osteoporosis and they are at high risk for falling. Cognitive function and exercise and fitness is directly related to many issues older and young adults deal with. It is a huge problem when and older adults falls because the body does not heal as fast and easy like and young adult. Older adults need to be active and continue to be active to help increase the blood flow throughout their bodies and promote a healthy life physically and mentally. Cognitive health is an important factor in living an effective and productive lifestyle. Comment by User: “as fast and easy” is fairly informal, try using different descriptors here.

    Purpose of the Study

    This research purpose is to review studies and examine the cross sectional and experimental relationship between exercise and fitness and executive control function. Since the brain does not fully develop until one has reached their twenties, exercise and fitness maybe an important guiding principle to have for children early on and young adults. A proper and healthy exercise and fitness lifestyle affects the whole body, mind, and soul. This study is to see if exercise and fitness affects the cognitive functions positively. Medically thinking, exercise and fitness is naturally good for the body physically and cognitively, because it promotes good blood flow throughout the body. Having good flow throughout the body is necessary when relating it to cognitive functions. The body has to have proper blood flow in order to receive an adequate

    amount of oxygen that the body requires to function properly and effectively. In this study I will review researches and based off of those and the results from the studies will determine and prove that exercise and fitness does have a positive effect on cognitive function. Comment by User: Try not to use first person here. This section is meant to be very clear and concise, but I’m not sure what you are wanting to do. You can start this section by saying: The purpose of this study is…and tell me.

    Research Question/Hypothesis Comment by User: I need this section to be set up with a numbered list of questions.

    What exercises and fitness regimens are recommended to help with cognitive function in both children, young adults, middle age adults, and older adults. Exercise and fitness helps the blood to flow better in the body. Therefore, people’s brains are getting well oxygenated and people are able to make smarter decisions more effectively. Does exercise and fitness have an effect of the cognitive function in older adults? What different cognitive functions are measured, implying that the cognition type plays a moderating role in the relationship between acute exercise and cognition? Whether the effects of acute exercise have general or specific effects on the different cognition types and whether fitness status moderates the magnitude of favorable acute exercise effects on these cognitive performances, particularly in older adults. This research study will investigate how cardiovascular fitness moderates two type of cognitive function assessed by the Stroop test, following an acute bout of moderate aerobic exercise in an older population. Acute exercise not only reduced the response times for both Stroop test conditions but also diminished the interface, suggesting that acute exercise led to both general and specific improvements in cognitive functions. How does exercise and fitness affect the cognitive functions?

    Review of Literature

    Klan and Hillman reviewed the brain development mental trajectory and evaluated observational and interventional studies examining relationship between physical activity and fitness with cognitive performance and brain health in childhood. The relationship between physical activity, fitness, and academic achievement has received attention in recent years due to increased prevalence of children who overweight and not active. Studies in humans suggest that physical activity may be protective against age related brain tissue loss and may be positively associated with brain health and cognitive functions in children. Further research is needed to determine to what extent genetics, motivation, personality characteristics, nutrition, and intellectual stimulation plays a role in mediating the fitness brain relationship observed in cross sectional studies. Longitudinal studies are needed to better examine the association between changes in fitness, physical activity, brain, and cognition. According to Klan and Hillman, evidence from MRI studies suggest that physical activity may influence the modulation of neural activity supporting executive control in pre-puberty children, but also suggests that further work is needed to better determine the differential patterns of activation across various cognitive tasks. Childhood physical activity and aerobic fitness helps brain function and cognition. Despite the fact that the brain achieves 95% of its maximum size by the age of six, the processes underling functional connectivity continues throughout life which is why physical activity helps brain function and cognition. The purpose of this research was to find out whether exercise and fitness has an effect on the cognitive function in older adults. The examination of whether the positive effects of acute exercise extend to older adults has been limited, with unclear findings. This study examined the effects of acute exercise on two types of cognitive process derived from the Stroop test, where the Stroop incongruent condition is believed to engage a greater amount of executive control than does the Stroop congruent condition, which reflects more basic information processing. A Stroop test is when you compare congruent conditions with incongruent conditions. Acute exercise not only reduced the response times for both Stroop conditions, but also diminished the interference, suggesting that acute exercise led to both general and specific improvements in cognitive functions.

    Reference

    Boa Sorte Silva, N. C., Gregory, M. A., Gill, D. P., & Petrella, R. J. (2017). Multiple-modality exercise and mind-motor training to improve cardiovascular health and fitness in older adults at risk for cognitive impairment: A randomized controlled trial. Archives Of Gerontology And Geriatrics, 68149-160. doi:10.1016/j.archger.2016.10.009

    Chiu, C., Ko, M., Wu, L., Yeh, D., Kan, N., Lee, P., & … Ho, C. (2017). Benefits of different intensity of aerobic exercise in modulating body composition among obese young adults: a pilot randomized controlled trial. Health And Quality Of Life Outcomes, 15(1), 168. doi:10.1186/s12955-017-0743-4

    Chu, C., Chen, A., Hung, T., Wang, C., & Chang, Y. (2015). Exercise and fitness modulate cognitive function in older adults. Psychology And Aging, 30(4), 842-848. doi:10.1037/pag0000047

    Khan, N. A., & Hillman, C. H. (2014). The relation of childhood physical activity and aerobic fitness to brain function and cognition: a review. Pediatric Exercise Science, 26(2), 138-146. doi:10.1123/pes.2013-0125

    Lowe, B. D., Swanson, N. G., Hudock, S. D., & Lotz, W. G. (2015). Unstable sitting in the workplace–are there physical activity benefits? American Journal Of Health Promotion: AJHP, 29(4), 207-209. doi:10.4278/ajhp.140331-CIT-127

    Ludyga, S., Gerber, M., Brand, S., Holsboer-Trachsler, E., & Pühse, U. (2016). Acute effects of moderate aerobic exercise on specific aspects of executive function in different age and fitness groups: A meta-analysis. Psychophysiology, 53(11), 1611-1626. doi:10.1111/psyp.12736

    1

    Runninghead:

    DOES EXERCISE AND FITNESS HELP WITH COGNITIVE HEALTH

    1
    DOES EXERCISE AND FITNESS HELP WITH COGNITIVE HEALTH

    Does exercise and fitness help with cognitive health

    Sekinah Walls

    Mississippi College

    Abstract

    Older adults are experiencing the deterioration of physical functions and also suffering from declining brain and cognitive functions. Normal aging includes the degradation of cognitive processes, including memory, reasoning, and information processing speed. Older adults are at risk for osteoporosis and they are at high risk for falling. It is a huge problem when and older adults falls because the body does not heal as fast and easy like and young adult. Older adults need to be active and continue to be active to help increase the blood flow throughout their bodies and promote a healthy life physically and mentally. Living a healthy lifestyle helps cognitive skills for both young and old adults. Childhood obesity, cardiovascular disease, diabetes type 2, and certain cancers can possibly be prevented with exercise and fitness. Regular physical activity has been shown to be productive against the development of these diseases and many others. Not all children have adequate and effective brain development throughout childhood, therefore leading them to having health conditions that have causes. Exercise and fitness is suggested to indirectly improve cognitive and brain health by reducing the risks for many disease processes. Exercise and fitness in the adolescence and adults will help with physical as well as cognitive and brain health

    Statement of the Problem

    Older adults are experiencing the deterioration of physical functions and also suffering from declining brain and cognitive functions. Normal aging includes the degradation of cognitive processes, including memory, reasoning, and information processing speed. Older adults are at risk for osteoporosis and they are at high risk for falling. Cognitive function and exercise and fitness is directly related to many issues older and young adults deal with. It is a huge problem when and older adults falls because the body does not heal as fast and easy like and young adult. Older adults need to be active and continue to be active to help increase the blood flow throughout their bodies and promote a healthy life physically and mentally. Cognitive health is an important factor in living an effective and productive lifestyle.

    Purpose of the Study

    This research purpose is to review studies and examine the cross sectional and experimental relationship between exercise and fitness and executive control function. Since the brain does not fully develop until one has reached their twenties, exercise and fitness maybe an important guiding principle to have for children early on and young adults. A proper and healthy exercise and fitness lifestyle affects the whole body, mind, and soul. This study is to see if exercise and fitness affects the cognitive functions positively. Medically thinking exercise and fitness is naturally good the body physically and cognitively, because it promotes good blood flow throughout the body. Having good flow throughout the body is necessary when relating it to cognitive functions. The body has to have proper blood flow in order to receive an adequate

    amount of oxygen that the body requires to function properly and effectively. In this study I will review researches and based off of those and the results from the studies will determine and prove that exercise and fitness does have a positive effect on cognitive function.

    Research Question/Hypothesis

    What exercises and fitness regimens are recommended to help with cognitive function in both children, young adults, middle age adults, and older adults. Exercise and fitness helps the blood to flow better in the body. Therefore, people’s brains are getting well oxygenated and people are able to make smarter decisions more effectively. Does exercise and fitness have an effect of the cognitive function in older adults? What different cognitive functions are measured, implying that the cognition type plays a moderating role in the relationship between acute exercise and cognition? Whether the effects of acute exercise have general or specific effects on the different cognition types and whether fitness status moderates the magnitude of favorable acute exercise effects on these cognitive performances, particularly in older adults. This research study will investigate how cardiovascular fitness moderates two type of cognitive function assessed by the Stroop test, following an acute bout of moderate aerobic exercise in an older population. Acute exercise not only reduced the response times for both Stroop test conditions but also diminished the interface, suggesting that acute exercise led to both general and specific improvements in cognitive functions. How does exercise and fitness affect the cognitive functions?

    Review of Literature

    Klan and Hillman reviewed the brain development mental trajectory and evaluated observational and interventional studies examining relationship between physical activity and fitness with cognitive performance and brain health in childhood. The relationship between physical activity, fitness, and academic achievement has received attention in recent years due to increased prevalence of children who overweight and not active. Studies in humans suggest that physical activity may be protective against age related brain tissue loss and may be positively associated with brain health and cognitive functions in children. Further research is needed to determine to what extent genetics, motivation, personality characteristics, nutrition, and intellectual stimulation plays a role in mediating the fitness brain relationship observed in cross sectional studies. Longitudinal studies are needed to better examine the association between changes in fitness, physical activity, brain, and cognition. According to Klan and Hillman, evidence from MRI studies suggest that physical activity may influence the modulation of neural activity supporting executive control in pre-puberty children, but also suggests that further work is needed to better determine the differential patterns of activation across various cognitive tasks. Childhood physical activity and aerobic fitness helps brain function and cognition. Despite the fact that the brain achieves 95% of its maximum size by the age of six, the processes underling functional connectivity continues throughout life which is why physical activity helps brain function and cognition. The purpose of this research was to find out whether exercise and fitness has an effect on the cognitive function in older adults. The examination of whether the positive effects of acute exercise extend to older adults has been limited, with unclear findings. This study examined the effects of acute exercise on two types of cognitive process derived from the Stroop test, where the Stroop incongruent condition is believed to engage a greater amount of executive control than does the Stroop congruent condition, which reflects more basic information processing. A Stroop test is when you compare congruent conditions with incongruent conditions. Acute exercise not only reduced the response times for both Stroop conditions, but also diminished the interference, suggesting that acute exercise led to both general and specific improvements in cognitive functions.

    Reference

    Boa Sorte Silva, N. C., Gregory, M. A., Gill, D. P., & Petrella, R. J. (2017). Multiple-modality exercise and mind-motor training to improve cardiovascular health and fitness in older adults at risk for cognitive impairment: A randomized controlled trial. Archives Of Gerontology And Geriatrics, 68149-160. doi:10.1016/j.archger.2016.10.009

    Chiu, C., Ko, M., Wu, L., Yeh, D., Kan, N., Lee, P., & … Ho, C. (2017). Benefits of different intensity of aerobic exercise in modulating body composition among obese young adults: a pilot randomized controlled trial. Health And Quality Of Life Outcomes, 15(1), 168. doi:10.1186/s12955-017-0743-4

    Chu, C., Chen, A., Hung, T., Wang, C., & Chang, Y. (2015). Exercise and fitness modulate cognitive function in older adults. Psychology And Aging, 30(4), 842-848. doi:10.1037/pag0000047

    Khan, N. A., & Hillman, C. H. (2014). The relation of childhood physical activity and aerobic fitness to brain function and cognition: a review. Pediatric Exercise Science, 26(2), 138-146. doi:10.1123/pes.2013-0125

    Lowe, B. D., Swanson, N. G., Hudock, S. D., & Lotz, W. G. (2015). Unstable sitting in the workplace–are there physical activity benefits? American Journal Of Health Promotion: AJHP, 29(4), 207-209. doi:10.4278/ajhp.140331-CIT-127

    Ludyga, S., Gerber, M., Brand, S., Holsboer-Trachsler, E., & Pühse, U. (2016). Acute effects of moderate aerobic exercise on specific aspects of executive function in different age and fitness groups: A meta-analysis. Psychophysiology, 53(11), 1611-1626. doi:10.1111/psyp.12736

    ©Journal of Sports Science and Medicine (2015) 14, 716-722

    http://www.jssm.org

    Influence of Two Different Exercise Programs on Physical Fitness and Cognitive
    Performance in Active Older Adults: Functional Resistance-Band Exercises vs.
    Recreational Oriented Exercises

    Hernán Ponce-Bravo 1,2, Christian Ponce 1, Belén Feriche 1 and Paulino Padial 1
    1 Department of Physical Education and Sport, Faculty of Sport Sciences, University of Granada, Granada, Spain
    2 Faculty of Health Sciences, School of Physical Culture, National University of Chimborazo, Ecuador

    Abstract
    This study examines the impact of a resistance-band functional
    exercise program, compared with a recreational exercise pro-
    gram, on physical fitness and reaction times in persons older
    than 60 years. Fifty-four community-dwelling volunteers (71.76
    ± 6.02 years) were assigned to a specific exercise program:
    Functional activity program (focused on resistance-band multi-
    joint activities; experimental group, EG), or recreational physi-
    cal activity program (with gross motor activities of ludic con-
    tent; control group, CG). Before and after the intervention, we
    determined cognitive capacity in terms of simple reaction time
    (S-RT), choice reaction time (C-RT) and fitness. In both groups
    physical performance improved, though this improvement was
    more marked in the EG for grip strength, arm strength and gross
    motor abilities (p < 0.05). Reaction times were better only in EG (S-RT = 10.70%, C-RT = 14.34%; p < 0.05) after the corre- sponding physical training intervention. The training period showed no effect on the moderate relationship between both RT and gross motor abilities in the CG, whereas the EG displayed an enhanced relationship between S-RT and grip-strength as well as the C-RT with arm strength and aerobic capacity (r ~ 0.457; p < 0.05). Our findings indicate that a functional exercise program using a resistance band improves fitness and cognitive performance in healthy older adults. Key words: Aging, reaction time, physical activity program, cognitive capacity.

    Introduction

    According to estimates by the World Health Organization,
    elderly persons make up the most rapidly growing section
    of the population worldwide. It is forecast that by 2050,
    around 2000 million people –or one in four– will be older
    than 60 years of age.

    The physical and cognitive decline that occurs dur-
    ing aging translates to an inability to carry out daily living
    tasks with consequent impacts on social relationships and
    quality of life. This has prompted the design of programs
    for this population sector targeted at improving functional
    health and promoting the independence of the elderly in
    their environment. In this context, routine physical exer-
    cise plays a major role in the life quality and expectancy
    of older adults (Blain et al., 2000; Katula et al., 2008;
    Poon and Fung, 2008; Vogel et al. 2009).

    A lack of agility and dynamic balance (gross mo-
    tor abilities) is a significant risk factor for loss of inde-
    pendence and increases the risk of falls. Balance also

    affects daily living activities such as standing, bending,
    climbing stairs, walking or responding to external stimuli
    (Sturnieks et al., 2008). The reason for this is the
    progressive deterioration in neurophysical skills that
    occurs with age, impairing sensory-motor functions and
    producing deficiencies in perception, muscle and
    cognitive function, and thus affecting balance and the risk
    of falling (Sturnieks et al., 2008). Regularly practicing
    some form of physical exercise reduces the risk of falls
    (Howe et al., 2007), essentially because of improved
    muscular strength, agility, dynamic balance and coordina-
    tion (Blain et al., 2000; Karinkanta et al., 2009; Orr et al.,
    2008) and also helps preserve cognitive skills (Angevaren
    et al., 2008; Blain et al., 2000; Brisswalter et al., 2002;
    Colcombe and Kramer, 2003; Liu-Ambrose and Don-
    aldson., 2009; Williamson et al. 2009). Indeed, different
    exercise programs seem to achieve proportional changes
    in physical condition and cognition in older adults (Ber-
    ryman et al, 2014). However, the physiological pathway
    of these results displays a large variety of mechanisms
    liked with their cognitive impact, which nowadays should
    be clarified (Berryman et al., 2014; Voelcker-Rehage et
    al., 2010).

    The available data indicates a direct link between
    improved cognitive performance and training programs
    designed to improve cardiovascular fitness (Colcombe &
    Kramer, 2003), strength and balance (Araya, 2011). How-
    ever, there is no general consensus regarding the details of
    the most appropriate fitness training program (including
    the best intervention type, length of exercise program,
    session duration, etc.) or of its effects on indicators of
    cognitive function. Some of these indicators are simple
    reaction time (S-RT) (Dustman et al. 1984), choice reac-
    tion time (C-RT) (Van Boxtel et al., 1997), or visual-
    spatial tasks (Shay and Roth, 1992), which are considered
    as key markers of the functional independence of the
    elderly (Colcombe and Kramer, 2003). Training programs
    for older adults have been mostly based on exercises
    designed to improve cardiovascular fitness and muscular
    strength (de Vreede et al., 2005; Kalapotarakos et al.,
    2006; Katula, et al. 2008; Liu-Ambrose and Donaldson,
    2009; Van Boxtel et al., 1997). However, it has not al-
    ways been possible to correlate such improvements with a
    capacity to better carry out daily living tasks. Some au-
    thors propose the inclusion of functional exercises (multi-
    joint motor tasks that involve several body parts) in inter-
    ventions planned for older adults (de Bruin and Murer,
    2007; de Vreede et al. 2005) though their effects on cog-

    Research article

    Received: 10 June 2015 / Accepted: 29 July 2015 / Published (online): 24 November 2015

    Ponce-Bravo et al.

    717

    nitive performance and general fitness have not yet been
    established.

    This study was designed to determine the impact
    of a program of functional exercises using elastic bands
    versus a recreational activity program on fitness and cog-
    nitive performance in active elderly participants older
    than 60 years of age.

    Methods

    Participants
    Fifty-four participants were recruited from the community
    among older adults attending regular physical activity
    classes organized by the town hall (age 70.57 ± 5.46
    years; weight 72.96 ± 10.54 kg; height 1.56 ± 0.09 m; 6
    men, 48 women). Exclusion criteria were: A diagnosis of
    a progressive somatic or psychiatric disease, or any illness
    preventing participation in physical activities. The study
    protocol adhered to the tenets of the Declaration of Hel-
    sinki and received institutional review board approval.
    Written informed consent was obtained from each partici-
    pant.

    The participants enrolled were assigned to two
    groups matched in terms of their baseline reaction times
    and physical fitness, as well as the compatibility training
    schedule : an experimental group (EG; n = 22) and a con-
    trol group (CG; n = 32). Participants in each group com-
    pleted a 4-week training program consisting of 5 weekly
    sessions of 50 min each. Before and after the training
    intervention, fitness and cognitive performance were
    assessed in each participant.

    Study design
    In a longitudinal-experimental study, pre- and post-
    intervention data were compared in an experimental
    (functional training with resistance elastic-bands; EG) and
    control (recreational training; CG) group. The effects of
    both training programs on physical fitness and cognitive
    performance (simple and choice reaction times) were
    determined by intra- and intergroup comparisons of
    means. Sampling was performed via an intentional non-
    probabilistic convenience procedure.

    Both types of training included a multidimensional
    activity program (endurance, strength, balance, gross
    motor, and flexibility training), which is considered opti-
    mal for health and functional benefits in older people
    (Cress et al., 2005). Differences between training groups
    came from the nature of the exercises (multi-joint vs ana-
    lytical) and the focus towards which they were directed
    (physical condition improvement or ludic orientation): 1)
    Resistance-band functional training sessions combined
    several objectives simultaneously by means of exercise
    that incorporated multi-joint motor tasks that involved
    several body parts (functional exercises; de Bruin and
    Murer, 2007; de Vreede et al., 2005). These exercises
    were executed with low-resistance elastic-bands in order
    to highlight the strength content in each session (Cress et
    al., 2005); 2) Recreational training also combined several
    objectives simultaneously but used more analytical low-
    load exercises applied in a ludic form.

    The variables used as indicators of physical fitness
    were: leg strength (LS) using the Chair-stand test;
    right/left arm strength (R/L AS) using the Arm curl test;
    and gross motor abilities (AG; 8-Foot up-and-go). For all
    tests, we followed the procedures and recommendations
    described for the Senior Fitness Test (SFT) battery (Rikli
    and Jones, 2001). The aerobic endurance test was reduced
    to 2 min, and performance subsequently estimated in the 6
    min-test according to the procedure of Butland et al,
    (1982). Finally, expected aerobic performance (EAP) was
    calculated as the difference between aerobic performance
    and the expected minimum distance (Jones and Rikli,
    2002).

    Additionally, handgrip strength was determined in
    a maximum dynamometry test (dynamometer T.K.K.
    5401 Grip-D, Tokyo Japan). From a standing position
    with arms extended on both sides and hands facing the
    thighs, participants were instructed to exert as much pres-
    sure as possible for 3-5 s on the grip adapted to hand-
    breadth. After 3 attempts with each hand, lower values
    were eliminated. Performance was calculated as the sum
    of the kg generated using both hands (HGS).

    Cognitive performance was assessed by recording
    reaction times (RT) in the simple reaction (S-RT) and
    choice (C-RT) tests using the system Whole Body Reac-
    tion Measuring Equipment (FT-3130, TKK Takei &
    Company, LTD, Tokyo, Japan). This system comprises a
    table with three buttons and three light bulbs (red, yellow
    and blue). To determine S-RT, the participants sit at the
    table with hands on each side of the buttons. The tests
    consist of pressing any button with either hand as quickly
    as possible after a bulb lights up. When participant is
    ready, the evaluator presses a button and a random time
    between 1 and 3 s is set automatically by the system be-
    fore the light turns on. The test result is recorded in milli-
    seconds. To determine C-RT, participants respond to the
    three possible colored bulbs by pressing a different button
    (left, middle, right) for each color. Three attempts were
    allowed per test and the lowest value recorded as the
    result.

    Testing procedure
    Before and after the training interventions, all measure-
    ments were taken and tests performed on a single day.
    Each testing session commenced with the measurements:
    height (Holtain, Dyffed, UK), weight (Tanita TBF-300a,
    IL, USA) and waist/hip measurements. Body mass index
    (BMI) was determined using the Quetelet procedure as
    the ratio between weight (kg) and height squared (m2).
    The waist-hip ratio was recorded using the corresponding
    measurements in cm. Anthropometric measurements were
    followed by the RT tests and then by the physical fitness
    tests in the order: strength tests (legs, arms and handgrip),
    gross motor and aerobic performance test.

    Intervention programs
    The intensity and difficulty of the experimental and con-
    trol programs were increased as the participants adapted
    to each level of exercise. Sessions were always supervised
    by a sports expert.

    Functional training and cognitive performance

    718

    In the experimental group, each training session
    commenced with 5 min of introduction, organization and
    warm-up (functional and stretching exercises). In the
    main part of the session (40 min), participants performed
    the functional exercises with elastic bands: aerobic (8
    min), gross motor activities, action/reaction speed (7 min)
    and floor exercises (25 min). Each session finished with 5
    min of active relaxation exercises. Training loads were
    adjusted according to the recommendations of Chodzko-
    Zajko et al. (2009). In weeks 1 and 4, loads were 8 repeti-
    tions of each exercise performed at an intensity of 5-6 on
    a scale of 0-10. This intensity was recorded and pre-
    scribed according to the expert’s perception of each train-
    ing session. In weeks 2 and 3, the load was increased to
    12 repetitions and intensity to 7-8.

    Training sessions for participants in the control
    group commenced with a warm up (10 min) of games and
    mixed exercises. The main part of each session (30 min)
    consisted of traditional aerobic exercises (20 min) and
    recreational (10 min) activities. All sessions ended with
    10 min of relaxation exercises. Training loads were ad-
    justed as in EG. In weeks 1 and 4, the load was 6 repeti-
    tions per exercise at an intensity of 3-4 on a scale of 0-10.
    In weeks 2 and 3, this volume was increased to 8 repeti-
    tions at an intensity of 5-6 (Chodzko-Zajko et al., 2009).

    Statistical analysis
    Data are provided as the mean and standard deviation
    (SD). The Shapiro-Wilk test was used to determine the
    distribution of data. To assess the effect of treatment on
    the measures of physical fitness, body composition and
    cognitive capacity in each group, we used a comparison
    of means test for paired data (pre vs. post intervention), or
    the Wilcoxon test for variables not normally distributed.
    The Student t-test for independent samples or Mann-
    Whitney U test were used to compare the difference pro-
    duced in each variable (post-intervention value minus pre-
    intervention value) between CG and EG. The homogenei-
    ty of groups was determined using the Levene test. For
    non-homogeneous comparisons, the Welch test was em-
    ployed. Correlations between physical condition variables
    and reaction times (S-TR and C-RT) were quantified
    through Pearson’s product-moment correlation coefficient
    (r). Qualitative interpretations of the r coefficients as

    defined by Hopkins (2002) (0–0.09 trivial; 0.1–0.29
    small; 0.3–0.49 moderate; 0.5–0.69 large; 0.7–0.89 very
    large; 0.9–0.99 nearly perfect; 1 perfect) were provided
    for all significant correlations. Statistical tests were per-
    formed using the software SPSS version 20.0 (SPSS,
    Chicago, IL, USA) and Microsoft Excel 2007. The confi-
    dence interval was set at 95%.

    Results

    Before the physical activity program, the experimental
    and control groups were homogenous in terms of body
    composition and indicators of fitness and cognitive per-
    formance (p > 0.05).

    The pre-post comparisons of the variables recorded
    in each group may be seen in Table 1. Intragroup compar-
    isons revealed increases in leg and arm strength and im-
    proved aerobic performance in both groups (p < 0.05). The CG participants also showed a slight reduction in BWI (p < 0.05). Additionally, in EG, handgrip strength and gross motor abilities also improved (p < 0.01) and significant enhancements were detected in reaction times in response to the training program (S-RT: -1.06 ± 14.99% vs. 10.70 ± 15.38%; C-RT: 3.63 ± 15.48% vs. 14.34 ± 15.58% for CG and EG respectively; p < 0.01) (Table 1).

    The comparison of the two training programs for
    the variables examined is detailed in Table 2. Compared
    to CG, the EG showed greater improvements in response
    to training in handgrip strength (16.31%; p < 0.01), arm strength (R-AS: 14.37%; L-AS: 16.17%; p < 0.05), and gross motor abilities (15.96%; p < 0.001). On average, functional training led to an 11% improvement in cogni- tive performance over recreational training (S-RT: 11.76%; C-RT: 10.71%; p < 0.05) (Table 2).

    Tables 3 and 4 show the relationship between the
    S-RT or C-RT and the fitness variables before and after
    the training period. In pre- intervention, Pearson´s prod-
    uct-moment correlation coefficients showed a wake-
    moderate relationship between S-RT and C-RT and gross
    motor abilities in the control group, there were however
    no relationships of significance detected for experimental
    group. In post- intervention, while no changes in correla-
    tions were observed in the control group with respect to

    Table 1. Response to the training program recorded in the experimental and control groups. Data expressed as mean (SD).

    CG EG
    PRE POST P-value PRE POST P-value
    BMI (kg∙m-2) 30.31 (4.65) 30.12 (4.56) .300 30.10 (4.61) 30.0 (4.71) .254
    WH-r (%) .91 (.09) .89 (.06) .116 .91 (.06) .90 .05) .129
    S-RT (ms) .51 (.08) .52 (.08) .874 .47 (.09) .41 (.08) .006
    C-RT (ms) .58 (.11) .55 (.11) .119 .56 (.14) .48 (.12) .001
    HGS (kg) 39.91 (11.94) 41.59 (13.86) .207 44.42 (12.41) 52.25 (14.68) <.001 LS (rep) 11.75 (2.13) 14.31 (2.15) <.001 12.00 (2.56) 14.86 (2.77) <.001 R-AS(rep) 15.39 (2.64) 18.48 (2.84) <.001 14.68 (2.34) 19.55 (3.20) <.001 L-AS (rep) 16.13 (2.75) 19.13 (3.00) <.001 15.09 (2.37) 20.27 (3.33) <.001 AG (s) 6.13 (1.20) 6.27 (.97) .210 5.91 (.98) 5.11 (.72) .001 EAP (m) -9.10 (62.26) 30.54 (51.82) .001 5.66 (65.26) 29.36 (73.92) .046

    CG = control group; EG = experimental group; PRE = pre-intervention; POST = post-intervention; BMI = body mass index;
    WH-r = waist-hip ratio; S-RT = simple reaction time; C-RT = choice reaction time; HGS = handgrip strength; LS = leg
    strength; R-AS = right arm strength; L-AS = left arm strength; AG = gross motor abilities; EAP = expected aerobic perfor-
    mance; P-value = statistical significance at 95% CI (values in bold indicate a significant difference).

    Ponce-Bravo et al.

    719

    Table 3. Correlation between reaction time (Simple and Choice) and physical performance variables in the control group.

    PRE POST
    S-RT C-RT S-RT C-RT

    r P-value r P-value r P-value r P-value
    HGS (kg) -.21 .239 -.20 .273 -.27 .128 -.27 .138
    LS (rep) -.23 .204 -.08 .679 -.16 .368 -.29 .106
    R-AS (rep) -.24 .181 -.13 .469 -.24 .200 -.21 .260
    L-AS (rep) -.32 .075 -.18 .316 -.27 .149 -.21 .265
    AG (s) .57 .001 .36 .041 .54 .001 .44 .013
    EAP (m) -.21 .245 -.08 .646 -.23 .280 -.24 .255

    r = Pearson’s linear correlation coefficient; HGS = handgrip strength; LS = leg strength; R-AS = right arm
    strength; L-AS = left arm strength; AG = gross motor abilities; EAP = expected aerobic performance; P-value =
    statistical significance at the 95% CI (values in bold indicate a significant difference).

    Table 4. Correlation between reaction time (Simple and Choice) and physical performance variables in the ex-
    perimental group.

    PRE POST
    S-RT C-RT S-RT C-RT

    r P-value r P-value r P-value r P-value
    HGS (kg) -.03 .905 -.32 .144 -.53 .012 -.29 .182
    LS (rep) .04 .868 -.07 .744 -.04 .865 -.02 .923
    R-AS (rep) .19 .378 -.24 .275 -.37 .085 -.43 .047
    L-AS (rep) .33 .135 -.09 .693 -.41 .058 -.46 .033
    AG (s) -.20 .359 -.12 .600 .32 .146 .37 .094
    EAP (m) -.08 .747 -.25 .256 -.41 .058 -.50 .021

    r = Pearson’s linear correlation coefficient; HGS = handgrip strength; LS = leg strength; R-AS = right arm
    strength; L-AS = left arm strength; AG = gross motor abilities; EAP = expected aerobic performance; P-value =
    statistical significance at the 95% CI (values in bold indicate a significant difference).

    pre-intervention results, the experimental group displayed
    a moderate correlation between S-RT and handgrip
    strength and between C-RT and arms strength and the
    expected aerobic performance (p < 0.05). Table 2. Comparison of training programs outcome. Data expressed as mean (SD).

    Diff CG Diff EG p-value
    BMI (kg∙m-2) -.19 (.49) -.10 (.42) .485
    WH-r (%) -.02 (.08) -.01 (.03) .515
    S-RT (ms) .00 (.08) -.06 (.09) .036
    C-RT (ms) -.03 (.09) -.09 (.10) .030
    HGS (kg) 1.68 (7.03) 7.84 (8.50) .005

    LS (rep) 2.56 (1.78) 2.86 (2.10) .572
    R-AS (rep) 2.56 (3.79) 4.86 (3.33) .025
    L-AS (rep) 1.78 (5.47) 5.18 (3.25) .005
    AG (s) 014 (.77) -.79 (.89) <.001 EAP (m) 39.64 (48.27) 23.70 (52.33) .288

    CG = control group; EG = experimental group; Diff CG = post-
    pre training difference recorded in CG; Diff EG = post-pre
    training difference recorded in EG; BMI = body mass index;
    WH-r = waist-hip ratio; S-RT = simple reaction time; C-RT =
    choice reaction time; HGS = handgrip strength; LS = leg
    strength; R-AS = right arm strength; L-AS = left arm strength;
    AG = gross motor abilities; EAP = expected aerobic perfor-
    mance; P-value = statistical significance at the 95% CI (values
    in bold indicate a significant difference).

    Discussion

    This study sought to compare the effects of two physical
    exercise programs (functional with elastic bands vs. rec-
    reational) on the cognitive performance of adults older
    than 60 years measured in terms of reaction times (S-RT
    and C-RT). Our main finding was that 20 sessions of

    either training mode, despite considerable content and
    workload differences, showed beneficial effects on over-
    all leg/arm strength and aerobic capacity (p < 0.05), though no appreciable impacts were produced on body composition. However, a functional exercise program using an elastic exercise band led to additional improve- ments over those produced in the control group in arm strength (Δ 15.27%; p < 0.05), handgrip strength (~ 16.32%; p < 0.001), gross motor abilities (~ 15.95%; p < 0.01), and cognitive performance (S-RT: 10.70%, C-RT: 14.34%; p < 0.01). Additionally, only the EG improved the relationship between reaction times and fitness varia- bles, confirming that better cognitive processes can be achieved as physical condition improves. However, the design used in this study did not allow us to determine if the mechanism responsible for this result is due to a min- imum level of change in physical performance, or if it is inherent in the training method employed in this study (combination of functional exercises and elastic bands).

    The available literature is replete with reports of
    training programs for individuals older than 60 years
    targeted at improving both physical (Blain et al., 2000;
    Karinkanta et al., 2009; Orr et al., 2008) and cognitive
    skills (Angevaren et al., 2008; Berryman et al., 2014;
    Brisswalter et al., 2002; Colcombe and Kramer 2003;
    Forte et al., 2013; Liu-Ambrose and Donaldson, 2009;
    Williamson et al., 2009). Baseline physical fitness and
    reaction time data for our study population is consistent
    with those reported for the same age group in similar
    studies (Jones and Rikli, 2002; Van Boxtel et al., 1997).
    In the present study, the control program was designed to
    represent the more traditional interventions with a high
    recreational component and low workload. In contrast, the
    experimental program consisted of combined objective

    Functional training and cognitive performance

    720

    sessions incorporating motor tasks simultaneously involv-
    ing several body parts (functional exercises) (de Bruin
    and Murer, 2007; de Vreede et al., 2005). These compo-
    nents address in a single session a large number of com-
    ponents implicated in cognitive decline (Voelcker-Rehage
    et al., 2010). The use of elastic bands increases strength
    gains as reflected by the improved effects of the experi-
    mental intervention over the control intervention on
    handgrip strength (16.31%; p < 0.01) and arm strength (R-AS = 14.37%; L-AS = 16.17%; p < 0.05). The shorter simple reaction time recorded in the EG and lack of change in this variable observed in the control group intervention concurs with the recommendation by Col- combe & Kramer (2003) that cardiovascular and re- sistance training should be combined in order to benefit cognitive skills (Tables 1 and 2). Our findings indicate that 20 sessions of recrea- tional physical training produced fitness but not cognitive benefits (Table 1). In contrast, a similar program involv- ing 20 sessions of resistance-band functional training induced a greater impact on fitness and a mean 11% im- provement in reaction times (simple and choice) (Tables 1 and 2). After 3 months (2 sessions/week) of multicompo- nent (neuromuscular coordination, balance, agility, and cognitive executive control) or progressive resistance training for strength gains, Forte et al (2013) observed that the beneficial effects of a resistance program on cog- nitive function were mediated by gains in muscular strength. However, the benefits of multicomponent train- ing displayed a direct cognitive stimulation by a direct influence on neuromuscular coordination and perceptual motor adaptations. Reports in the current literature have described both beneficial (Kalapotharakos et al., 2006) and inappreciable (Paas et al., 1994; Powell, 1983) effects of physical activity programs on reaction times. Reaction time decreases with age (Sturnieks, et al., 2008; Van Boxtel et al., 1997) independently of gender (Silverman, 2006; Wellmon, 2012) and moderately dependently on changes in aerobic capacity (Colcombe and Kraemer, 2003; Van Boxtel et al., 1997). The participants of our study, with a mean age of 70, showed no impairment in cognitive function and their aerobic performance was just at the lower expected limit. In contrast with other reports (Barella et al., 2010; Kalapotharacos et al., 2006), a bene- ficial effect was detected on aerobic capacity in response to both training interventions (p < 0.05), although only the experimental intervention led to improved reaction times (p < 0.01). Contrary with the findings of Van Boxtel et al. (1997), a positive change in correlation was detected between the C-RT and aerobic performance in EG (Table 4).

    Other authors have reported cognitive function im-
    provements ranging from 13.4% to 9.6% in response to a
    12-week recreational activity program of 2 and 3 sessions
    per week respectively in participants with a mean age of
    79 years (p < 0.001) (Gálvez, 2012; Pereira, 2011). De- spite the similarity with our control intervention, the dif- ferent findings of these studies may be attributed to the mild cognitive impairment described for the participants of the studies by Gálvez and Pereira (scores of ~19 out of 30 in the Minimental test; MSSE; Folstein et al., 1975),

    and the 9-year difference in age with our study partici-
    pants. These differences along with the different training
    regimens (20 vs. 24 vs. 36 sessions), could in part explain
    the different physical performance gains recorded in the
    different studies and our control group. Studies that have
    linked improved cognitive function to gains in cardiovas-
    cular capacity seem to suggest greater dedifferentiation in
    neuron activation pathways in younger adults (Colcombe
    and Kramer, 2003). Thus, recreational type activity ses-
    sions do not seem to offer a stimulus that is able to im-
    prove both the physical and cognitive capacities of older
    persons with no cognitive impairment.

    Other studies have shown a direct relationship be-
    tween improved cognitive performance and cardiovascu-
    lar fitness, strength or balance training (Araya, 2011;
    Colcombe and Kramer, 2003). Araya et al. (2012), in
    response to a 12-week physical training program (3 ses-
    sions per week) completed by 33 women with a mean age
    of 72 years and with mild cognitive decline (MSSE = 24
    out of 30), noted the improved fitness of the participants
    along with a cognitive capacity improvement of 7.4% (p
    < 0.05). In agreement with these findings, we observed improvements in the fitness (strength and gross motor abilities; p < 0.05) and cognitive skills (11% in RT, p < 0.05) of participants in the functional training group com- pared to the recreational intervention group (Table 2). Hence, in only 20 sessions, it seems that a functional resistance-band exercise program is able to improve both physical fitness and cognitive function in elderly partici- pants with no cognitive deficiency. Such improvements are likely to play a role in preventing cognitive decline and maintaining independence. Finally, despite the apparent contribution of physical activity programs in ameliorating cognitive skills (Colcombe and Kramer, 2003; Forte et al, 2013; Voelcker-Rehage et al., 2010), only a moderate correla- tion was observed between handgrip-strength and the S- RT and between arm-strength and endurance and the C- RT (p < 0.05) in EG. No such correlation was found in the CG despite the improvements in leg and arm strength and aerobic performance. Considering the greater im- provements in strength and coordination following re- sistance band exercises when compared to recreational exercises, our results concur with other studies which reveal that the combination of physical and cognitive training maximizes cognitive benefits in the elderly (Fos- ter et al., 2013; Oswal et al, 2006). Other studies have also described the correlation between C-RT and aerobic capacity (Rikli and Edwards, 1991). However, in our study the combination of functional exercises with an elastic band in the EG did not allow us to determine if the mechanism responsible for these findings results from a minimum change in physical performance, or if this bene- fit is inherent to the experimental training method used. Indeed, the mechanisms that cause the cortical changes linked to cognitive performance seem to differ depending on the intervention (Berryman et al., 2014; Voelcker- Rehage et al., 2010). Our results indicate a need for gains greater than 20-23% in handgrip, arm and leg strength for the neurophysiological mechanisms that regulate sensory- motor function to produce a beneficial impact on percep-

    Ponce-Bravo et al.

    721

    tion and reaction time, improving the gross motor abilities
    (such as agility and balance), and thus reducing the risk of
    falls (Sturnieks et al., 2008).

    Conclusion

    In summary, our findings indicate that 20 sessions of
    functional resistance or recreational training lead to gen-
    eral upper and lower body strength and aerobic capacity
    gains in adults older than 60 years. However, the im-
    proved cognitive function observed, as assessed through
    reaction times, seems more linked to the workload and
    resistance component of the training program. Thus, pro-
    grams involving functional exercises with an elastic band
    improve both simple and choice reaction times and lead to
    greater gains in gross motor abilities, handgrip and arm
    strength over the improvement achieved by a more recre-
    ational type program. In turn, these latter gains correlate
    to improved C-RT. Collectively our findings indicate that
    exercise sessions of more recreational type contents do
    not seem to constitute a stimulus that is able to improve
    both physical and cognitive performance in healthy active
    older adults. We therefore recommend the incorporation
    of functional elastic-band exercises in physical activity
    programs designed for this population group.

    Acknowledgements
    The authors thank the elderly of the village of Maracena (Granada,
    Spain) and the Maracena Town Hall for their enthusiasm and willing-
    ness to participate in this study. The study was financed by Research
    group SEJ-438 of the Junta de Andalucía.

    References

    Angevaren, M., Aufdemkampe, G., Verhaar, H.J., Aleman, A. and

    Vanhees, L. (2008) Physical activity and enhanced fitness to
    improve cognitive function in older people without known
    cognitive impairment. Cochrane Database of Systematic
    Reviews 3 (CD005381).

    Araya, S. (2011) Effect of physical activity on fitness and cognitive
    ability of adult-more women in the community of Iquique,
    Chile. Doctoral Thesis, University of Granada, Spain. (In Span-
    ish: English abstract). Available from URL:
    http://hdl.handle.net/10481/21006.

    Araya, S., Padial, P., Feriche, B., Gálvez, A., Pereira, J. and Mariscal-
    Arcas, M. (2012) Effect of a physical activity program on the
    anthropometric and physical fitness of women over 60 years.
    Nutrición Hospitalaria 27, 1472-1479.

    Barella, L.A., Etnier, J.L. and Chang, Y.K. (2010) The immediate and
    delayed effects of an acute bout of exercise on cognitive
    performance of healthy older adults. Journal of Aging and
    Physical Activity 18, 87-98.

    Berryman, N., Bherer, L., Nadeau,S., Lauziere, S., Lehr, L., Bobeuf,F.,
    Lussier, M., Kergoat, M.J., Minh Vu , T.T. and Bosquet, L.
    (2014) Multiple roads lead to Rome: combined high-intensity
    aerobic and strength training vs. gross motor activities leads to
    equivalent improvement in executive functions in a cohort of
    healthy older adults. AGE 36, 9710.

    Blain, H., Vuillemin, A., Blain, A. and Jeandel, C. (2000) The
    preventive effects of physical activity in the elderly. Presse
    Médicale 29, 1240-1248.

    Brisswalter, J., Collardeau, M. and René, A. (2002) Effects of acute
    physical exercise characteristics on cognitive performance.
    Sports Medicine 32, 555-566.

    Butland, R.J., Pang, J., Gross, E.R., Woodcock, A.A. and Geddes, D.M.
    (1982) Two-, six-, and 12-minute walking tests in respiratory
    disease. British Medical Journal 284, 1607-1608.

    Chodzko-Zajko, W., Proctor, D., Fiatarone S. M., Minson, C., Nigg, C.,
    Salem, G. and Skinner, J. (2009) Exercise and Physical Activity

    for Older Adults. Medicine & Science in Sports & Exercise 41,
    1510-1530.

    Colcombe, S. and Kramer, A.F. (2003) Fitness effects on the cognitive
    function of older adults: a meta-analytic study. Psychological
    Science 14, 125-130.

    Cress, M.E., Buchner, D.M., Prohaska, T., Rimmer, J., Brown, M.,
    Macera, C., DiPietro, L. and Chodzko-Zajko,W. (2005) Best
    Practices for Physical Activity Programs and Behavior Coun-
    seling in Older Adult Populations. Journal of Aging and Physi-
    cal Activity 13, 6-74

    de Bruin, E.D. and Murer, K. (2007) Effect of additional functional
    exercises on balance in elderly people. Clinical Rehabilitation
    21, 112-121.

    de Vreede, P.L., Samson, M.M., van Meeteren, N.L., Duursma, S.A. and
    Vehaar, H.J. (2005) Functional-Task Exercise versus resistance
    strength exercise to improve daily function in older women: A
    randomized, controlled trial. American Geriatrics Society 53, 2-
    10.

    Dustman, R.E., Ruhling, R.O., Russell, E.M., Shearer, D.E., Bonekat,
    W. and Shigeoka, J.W. (1984) Aerobic exercise training and
    improved neurophysiological function of older adults. Neurobi-
    ology of Aging 5, 35-42.

    Folstein, M.F., Folstein, S.E. and Mchugh, P.R. (1975) Minimental state.
    A practical method for grading the cognitive state of patients
    for the clinician. Journal of Physchiatric Research 12, 189-198.

    Forte, R., Boreham, C., Costa, J., De Vito, G., Brennan, L., Gibnet, E.R.
    and Pesce, C. (2013). Enhancing cognitive functioning in the
    elderly: multicomponent vs resistance training. Clinical Inter-
    ventions in Aging 8, 19-27.

    Gálvez, A. (2012) Effect of physical activity on cognition performance
    in patients over 60 years residents into a geriatric center. Doc-
    toral Thesis, University of Granada, Spain. (In Spanish: English
    abstract). Available from URL: http://hdl.handle.net/10481/
    21742

    Hopkins, W.G. (2002) A scale of magnitudes for effect statistics. A new
    view of statistics. Available from URL: http://sportsci.org/ re-
    source/stats/effectmag.html

    Howe, T.E., Rochester, L., Jackson, A., Banks, P.M. and Blair, V.A.
    (2007) Exercise for improving balance in older people.
    Cochrane Database of Systematic Reviews 4, CD004963.

    Jones, C.J. and Rikli, R.E. (2002) Measuring functional fitness of older
    adults. The Journal on Active Aging 1, 24-30.

    Kalapotarakos, V.I., Michalopoulos, M., Strimpakos, N., Diamantopou-
    los, K. and Tokmakidis, S.P. (2006). Functional and Neuromo-
    tor Performance in Older Adults: Effect of 12 Weeks of Aero-
    bic Exercise. American Journal of Physical Medicine and Re-
    habilitation 85, 61-67.

    Karinkanta, S., Heinonen, A., Sievänen, H., Uusi-Rasi, K., Fogelholm,
    M. and Kannus, P. (2009) Maintenance of exercise-induced
    benefits in physical functioning and bone among elderly
    women. Osteoporosis International 20, 665-674.

    Katula, J.A., Rejeski, W.J. and Marsh, A.P. (2008) Enhancing quality of
    life in older adults: A comparison of muscular strength and
    power training. Health and Quality of Life Outcomes 6, 45-53.

    Liu-Ambrose, T. and Donaldsonm, M.G. (2009) Exercise and cognition
    in older adults: is there a role for resistance training
    programmes? British Journal of Sports Medicine 43, 25-27.

    Orr, R., Raymond, J. and Fiatarone Singh, M. (2008) Efficacy of
    progressive resistance training on balance performance in older
    adults: a systematic review of randomized controlled trials.
    Sports Medicine 38, 317-343.

    Oswald, W., Gunzelmann, T., Rupprecht, T. and Hagen, B. (2006)
    Differential effects of single versus combined cognitive and
    physical training with older adults: the SimA study in a 5-year
    perspective. European Journal of Ageing 3, 179-192.

    Paas, F.G., Adam, J.J., Jansen, G.M., Vrenken, J.G. and Bovens, A.M.
    (1994) Effect of a 10-month endurance-training program on
    performance of speeded perceptual-motor tasks. Perceptual and
    Motor Skills 78, 1267-1273.

    Pereira, J. (2011) Physical activity and cognition in human aging.
    Doctoral Thesis. University of Granada, Spain. (In Spanish:
    English abstract). Available from URL: http://hdl.handle.net/
    10481/21024.

    Poon, C.Y. and Fung, H.H. (2008) Physical activity and psychological
    well-being among Hong Kong Chinese older adults: exploring
    the moderating role of self-construal. The International Journal

    Functional training and cognitive performance

    722

    of Aging and Human Development 66, 1-19.
    Powell, R.R. (1983) Reaction time changes following aerobic

    conditioning. Journal of Human Movement Studies 9, 145-150.
    Rikli, R.F. and Edwards, D.J. (1991) Effects of three years exercise

    program on motor function and cognitive processing speed in
    older women. Research Quarterly for Exercise and Sport 62,
    61-67.

    Rikli, R. and Jones, C. (2001) Senior Fitness Test Manual. Human
    Kinetics, Champaign, Illinois.

    Shay, K. and Roth, D. (1992) Association between aerobic fitness and
    visuospatial performance in healthy older adults. Psychology
    and Aging 7, 15-24.

    Silverman, I.W. (2006) Sex differences in simple visual reaction time: A
    historical meta-analysis. Sex Roles 54, 57-68.

    Sturnieks, D.L., George, R. and Lord, S.R. (2008) Balance disorders in
    the elderly. Neurophysiologie Clinique 38, 467-478.

    Van Boxtel, M.P., Paas, F.G., Houx, P.J., Adam, J.J., Teeken, J.C. and
    Jolles, J. (1997) Aerobic capacity and cognitive performance in
    a cross-sectional aging study. Medicine & Sciencie in Sports
    and Exercise 29, 1357-1365.

    Voelcker-Rehage, C., Godde, B. and Staudinger, U.M. (2010) Physical
    and motor fitness are both related to cognition in old age.
    European Journal of Neuroscience 31, 167-176.

    Vogel, T., Brechat, P., Leprêtre, P.M., Kaltenbach, G., Berthel, M. and
    Lonsdorfer, J. (2009) Health benefits of physical activity in
    older patients: a review. International Journal of Clinical
    Practice 63, 303-320.

    Wellmon, R. (2012) Does the attentional demand of walking differ for
    older men and women living independently in the community?
    Journal of Geriatric Physical Therapy 35, 55-61.

    Williamson, J.D., Espeland, M., Kritchevsky, S.B., Newman, A.B.,
    King, A.C., Pahor, M. and Miller, M.E. (2009) Changes in
    cognitive function in a randomized trial of physical activity:
    results of the lifestyle interventions and independence for elders
    pilot study. Journal of Gerontology: Medical Sciences 64, 688-
    694.

    Key points

    • Better cognitive processes can be achieved as phys-

    ical condition improves

    • Exercise sessions of a more recreational type do not
    seem to constitute a stimulus able to improve both
    physical and cognitive performance in healthy ac-
    tive older adults

    • The improvement of cognitive function, as assessed
    through reaction times, seems more linked to the
    workload and strength component of the training
    program.

    AUTHOR BIOGRAPHY

    Hernán PONCE-BRAVO
    Employment
    Lecturer in Sports Training, Swimming
    Training. Department of Physical Education
    and Sport Training, National University of
    Chimborazo- Ecuador. PhD student. De-
    partment of Physical Education and Sport,
    University of Granada. Spain.
    Degree
    MSc
    Research interests
    Physical activity and health.
    E-mail: herpobraunach@hotmail.com

    Christian PONCE
    Employment
    PhD student. Department of Physical Edu-
    cation and Sport, Faculty of Sport Sciences,
    University of Granada, Granada, Spain.
    Degree
    MSc
    Research interests
    Physical Activity and health.
    E-mail: christian.natacion@gmail.com

    Belén FERICHE Employment
    Lecturer in Sport Training. Department of
    Physical Education and Sport, University of
    Granada, Spain.
    Degree
    PhD
    Research interests
    Sport training, training assessment, altitude
    training.
    E-mail: mbelen@ugr.es

    Paulino PADIAL
    Employment
    Lecturer in Sport Training. Department of
    Physical Education and Sport, University of
    Granada, Spain.
    Degree
    PhD
    Research interests
    Strength training, sport performance and
    training in combat sports.
    E-mail: ppadial@ugr.es

     Paulino Padial
    Department of Physical Education and Sport, Faculty of Sport
    Sciences, University of Granada, Granada, Spain

    Copyright of Journal of Sports Science & Medicine is the property of Hakan Gur, Journal of
    Sports Science & Medicine and its content may not be copied or emailed to multiple sites or
    posted to a listserv without the copyright holder’s express written permission. However, users
    may print, download, or email articles for individual use.

    RESEARCH ARTICLE

    The Association of Childhood Fitness to
    Proactive and Reactive Action Monitoring
    Keita Kamijo1*, Seongryu Bae2, Hiroaki Masaki2

    1 Faculty of Sport Sciences, Waseda University, Tokorozawa, Saitama, Japan, 2 Department of Functioning
    Activation, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan

    * k-kamijo@aoni.waseda.jp

    Abstract
    Several studies have claimed that the positive association between childhood fitness and

    cognitive control is attributable to differences in the child’s cognitive control strategy, which

    can involve either proactive or reactive control. The present study tested this hypothesis by

    manipulating the probability of trial types during a modified flanker task. Preadolescent chil-

    dren performed mostly congruent and mostly incongruent conditions of the flanker task, with

    post-error task performance and error negativity/error-related negativity (Ne/ERN) being

    assessed. Results indicated that greater aerobic fitness was related to greater post-error accu-

    racy and larger Ne/ERN amplitudes in the mostly congruent condition. These findings suggest

    that higher-fit children might be able to transiently upregulate cognitive control by recruiting

    reactive control in the mostly congruent condition. Further, greater fitness was related to

    greater modulation of Ne/ERN amplitude between conditions, suggesting that higher-fit chil-

    dren engaged in more proactive control in the mostly incongruent condition. This study sup-

    ports the hypothesis that greater childhood fitness is associated with a more flexible shift

    between reactive and proactive modes of cognitive control to adapt to varying task demands.

    Introduction
    For over a decade and a half, it has become increasingly clear that regular physical activity can
    promote brain health and improve cognitive function in adult populations [1–3]. More
    recently, research has started to examine this relationship in child populations, demonstrating
    that greater physical activity levels and greater aerobic fitness are positively associated with
    cognitive functioning (see [4, 5] for reviews). Although most previous studies have employed
    cross-sectional designs that compared cognitive performance across lower-fit and higher-fit
    children, recent longitudinal randomized controlled intervention studies have provided evi-
    dence of a causal link between physical activity and changes in cognitive function [6–8]. Specif-
    ically, these longitudinal studies indicated that a physical activity intervention leading to
    increases in aerobic fitness improves higher-order cognitive function, also known as cognitive
    control, in preadolescent children. These findings emphasize the importance of physical activ-
    ity, not only for prevention of metabolic syndrome but also for cognitive development and
    brain health. In light of the worldwide epidemic of childhood inactivity [9, 10], the link
    between childhood fitness and cognitive control should be further clarified.

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 1 / 13

    OPEN ACCESS

    Citation: Kamijo K, Bae S, Masaki H (2016) The
    Association of Childhood Fitness to Proactive and
    Reactive Action Monitoring. PLoS ONE 11(3):
    e0150691. doi:10.1371/journal.pone.0150691

    Editor: Francesco Di Russo, University of Rome,
    ITALY

    Received: October 27, 2015

    Accepted: February 18, 2016

    Published: March 3, 2016

    Copyright: © 2016 Kamijo et al. This is an open
    access article distributed under the terms of the
    Creative Commons Attribution License, which permits
    unrestricted use, distribution, and reproduction in any
    medium, provided the original author and source are
    credited.

    Data Availability Statement: All relevant data are
    within the paper and its Supporting Information files.

    Funding: This work was supported by a Grant-in-Aid
    to KK for Research Activity Start-up (24800066) from
    the Japan Society for the Promotion of Science and
    the MEXT-Supported Program for the Strategic
    Research Foundation at Private Universities, 2015–
    2019, from the Ministry of Education, Culture, Sports,
    Science and Technology (S1511017). The funders
    had no role in study design, data collection and
    analysis, decision to publish, or preparation of the
    manuscript.

    http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0150691&domain=pdf

    http://creativecommons.org/licenses/by/4.0/

    Cognitive control refers to the ability to coordinate thought and action to execute goal-
    directed behaviors [11]. It has been well documented that the prefrontal cortex (PFC), which
    shows protracted maturation [12], plays a critical role in the effective regulation of cognitive
    control [11]. Although the underlying mechanisms for the beneficial effects of aerobic fitness
    are still controversial, nonhuman animal models have suggested that aerobic exercise increases
    nerve growth factors such as brain-derived neurotrophin factor [13], which promotes synaptic
    plasticity and neurogenesis [14, 15]. It is likely that exercise-induced increases in aerobic fitness
    are specifically associated with the development of higher-order cognitive function (i.e., cogni-
    tive control), which is supported by one of the last brain regions to mature (i.e., the PFC).
    Stated differently, aerobic fitness may be essential for development of the cognitive control net-
    work, including the PFC, during childhood. The present study was designed to provide addi-
    tional insight into the association between childhood fitness and cognitive development by
    focusing on cognitive control strategy use, which is linked to PFC activity [16, 17].

    Action monitoring has been considered one of the key aspects of cognitive control, since
    individuals need to monitor and correct their actions during subsequent environmental inter-
    action to optimize goal-directed behaviors. Several neuroelectric studies using error negativity
    (Ne) [18] or error-related negativity (ERN) [19] have indicated that aerobic fitness is associated
    with action monitoring system efficiency in preadolescent children [20, 21]. The Ne/ERN is a
    negative-going component of the response-locked event-related brain potential (ERP) which is
    elicited following commission errors during a cognitive task. The Ne/ERN has a fronto-central
    scalp distribution, and is thought to reflect activity of the anterior cingulate cortex (ACC) [22–
    24], which plays a crucial role in action monitoring [22, 25]. Hillman and his colleagues have
    indicated that higher-fit children exhibit smaller Ne/ERN amplitude with superior task perfor-
    mance relative to their lower-fit counterparts [20, 21]. These results suggest that greater child-
    hood fitness is associated with less ACC activation, which is thought to reflect more efficient
    action monitoring (i.e., superior cognitive control).

    Several researchers have proposed a possible explanation for the association between child-
    hood fitness and cognitive control [8, 21, 26, 27], which is based on the dual mechanisms of con-
    trol (DMC) theory [16, 17]. They propose that the association can be attributed to differences in
    the child’s cognitive control strategy, which can involve either proactive or reactive control. Pro-
    active control is associated with sustained lateral PFC activation, which is linked to decreased
    transient ACC activation, to anticipate and prevent interference before it occurs. By contrast,
    reactive control is associated with transient activation of the lateral PFC and a wider brain net-
    work including the ACC to detect and resolve interference on an as-needed basis. Based on this
    theory, smaller Ne/ERN amplitude (i.e., less ACC activation) for higher-fit children is hypothe-
    sized to reflect more efficient action monitoring due to their utilization of a proactive control
    strategy. Using a cue-probe task (i.e., an AX-continuous performance task), Chatham, Frank [28]
    indicated that young children (3 years) used more probe-driven reactive control, whereas older
    children (8years) engaged in more cue-driven proactive control. An fMRI study that compared
    Stroop task related brain activity across adolescents (14–17 years) and young adults (18–25
    years) indicated that the neural systems underlying proactive control are still underdeveloped in
    adolescents [29]. Thus, these developmental studies suggest that proactive control mechanisms
    show protracted maturation relative to reactive control mechanisms, and that they continue to
    mature through childhood and into early adulthood. As mentioned earlier, the findings of longi-
    tudinal studies [6–8] imply that childhood fitness is associated with development of the cognitive
    control network involving the PFC. In accordance with these findings, a hypothesized association
    between childhood fitness and cognitive control strategy use appears to be reasonable.

    To test this hypothesis, the present study examined the association between childhood fit-
    ness and action monitoring by manipulating the probability of trial types during a modified

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 2 / 13

    Competing Interests: The authors have declared
    that no competing interests exist.

    flanker task. In neuroelectric studies which reported a difference in Ne/ERN amplitude
    between lower-fit and higher-fit children [20, 21], congruent and incongruent trials were pre-
    sented equally during the flanker task. An fMRI study [30] manipulated the probability of trial
    types during the Stroop task and indicated that young adult participants exhibited a smaller
    interference effect on reaction time (RT; i.e., incongruent RT minus neutral RT) in the mostly
    incongruent (MI; 70% of trials were incongruent) condition, relative to the mostly congruent
    (MC; 70% of trials were congruent) condition, with increased sustained lateral PFC activation
    and decreased transient ACC activation. Such a pattern of results suggests that participants are
    biased toward adoption of a proactive control strategy in the MI condition. In the MC condi-
    tion, by contrast, there was decreased sustained activity in the lateral PFC and increased tran-
    sient activity in the PFC and ACC (De Pisapia & Braver, 2006), which is thought to reflect a
    greater utilization of reactive control. In line with this, it has been suggested that interference
    expectancy can influence reliance on proactive versus reactive modes of cognitive control [31].
    Specifically, young adult participants should engage in more proactive control when interfer-
    ence is frequent and expected, whereas reactive control should be dominant when interference
    is infrequent and unexpected. Thus, it is likely that young adults change cognitive control strat-
    egy based on the probability of trial types, in order to adapt to varying task demands. If child-
    hood fitness is associated with cognitive development, higher-fit children should show a
    pattern more similar to that observed in young adults relative to lower-fit children, based on
    trial type probabilities.

    An fMRI study compared brain activity between lower-fit and higher-fit children during the
    MC and MI conditions of a modified flanker task [26]. This study found greater ACC activity
    for higher-fit children, relative to lower-fit children, in the MC condition, whereas no such
    group difference was observed in the MI condition. This pattern of results, coupled with a
    smaller interference effect on accuracy for higher-fit children, suggests that higher-fit children
    engaged in more reactive control strategies in the MC condition relative to lower-fit children
    and flexibly changed cognitive control strategies to adapt to the increased task demands in the
    MI condition. However, higher-fit children also exhibited an increased sustained PFC activa-
    tion relative to lower-fit children in the MC condition [26], which contradicts the DMC theory.
    One possible source of this discrepancy is that this fMRI study used a block design, which
    could not dissociate transient from sustained brain activation.

    The current study further examined the association of childhood fitness to cognitive control
    strategy using the Ne/ERN, which is thought to be suitable to assess transient brain activation
    due to its high temporal resolution. We predicted that greater aerobic fitness would be related
    to smaller Ne/ERN amplitudes in the MI condition, reflecting a greater utilization of a proac-
    tive control strategy, whereas such a relationship should be attenuated or even reversed in the
    MC condition. Additionally, we used post-error accuracy and post-error slowing as behavioral
    indices of action monitoring. It is well known that individuals show reduced response speed
    following an error of commission [32], possibly to prevent subsequent errors during cognitive
    tasks. That is, greater post-error behavioral adjustments are considered to reflect greater utili-
    zation of a reactive control strategy. Accordingly, we predicted that greater aerobic fitness
    would be related to greater post-error behavioral adjustments in the MC condition, whereas
    such a relationship should be attenuated in the MI condition.

    Methods

    Participants
    Fifty preadolescent children participated in this study. The participants also performed an AX-
    continuous performance task in order to investigate a different cognitive process (i.e., task

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 3 / 13

    preparation), and as such, we will report these results elsewhere. Given that weight status has
    been associated with cognitive control during childhood [33], data from three obese children
    and one underweight child, as defined by the national cutoff points [34], were excluded from
    the analyses. Additionally, data from four participants were discarded due to an insufficient
    number of error trials to compute Ne/ERN averages (< 6 trials) [35, 36]. Thus, analyses were conducted for 42 participants (mean age = 10.5 years, SD = 1.1). Table 1 summarizes demo- graphic and fitness information for this sample. Prior to testing, legal guardians reported that their children were free of neurological diseases or physical disabilities and had normal or cor- rected-to-normal vision. All participants provided written assent and their legal guardians pro- vided written informed consent in accordance with the Ethics Committee on Human Research of Waseda University.

    Laboratory Procedure
    After providing informed consent, participants had their height and weight measured using a
    Tanita WB-3000 digital scale (Tanita Corp., Tokyo, Japan). Participants’ legal guardians com-
    pleted the Attention Deficit Hyperactivity Disorder Rating Scale IV [37] and the Physical
    Activity Readiness Questionnaire [38] to screen for any previous health issues that might be
    exacerbated by exercise. Additionally, given that socioeconomic status has been associated with
    cognitive control [39] and fitness [40], maternal educational attainment was assessed as a
    proxy for socioeconomic status. Maternal education was assessed using a five-point scale rang-
    ing from 1, indicating that they did not complete high-school, to 5, indicating an advanced
    degree. Participants were then fitted with a 64-channel headcap with Ag/AgCl active electrodes
    (BioSemi ActiveTwo system, Amsterdam, the Netherlands) and seated in a sound-attenuated
    room where the flanker task took place. Participants were then given instructions, afforded the
    opportunity to ask questions, and practiced the task prior to the start of testing. The 20-m shut-
    tle run test was conducted on a different day.

    Flanker Task
    A modified flanker task consisting of five left- or right-oriented fish was used. This task asked
    participants to press a button with their index fingers as accurately and quickly as possible cor-
    responding to the direction of a centrally presented target fish and to ignore the flanking fish.
    The target fish was surrounded by flanking fish that either pointed in the same direction (con-
    gruent trials) or the opposite direction (incongruent trials). The flanker task was performed
    under two conditions in which the probability of congruent and incongruent trials was

    Table 1. Mean (SD) values for participant demographics and fitness data.

    Measure All participants Girls Boys

    No. of participants 42 19 23

    Mean age (years) 10.5 (1.1) 10.2 (0.9) 10.7 (1.1)

    20-m shuttle run (#laps) 51.3 (22.0) 42.9 (19.5) 58.2 (21.9)

    20-m shuttle run (percentile) 53.3 (32.3) 54.1 (32.9) 52.7 (32.6)

    Body mass index (kg/m2) 16.8 (1.6) 16.5 (1.2) 17.0 (1.9)

    Maternal education 2.8 (0.9) 2.8 (0.8) 2.8 (1.0)

    ADHDa 7.8 (6.3) 7.1 (6.9) 8.4 (5.8)

    aRaw scores on the Attention Deficit Hyperactivity Disorder Rating Scale IV.

    doi:10.1371/journal.pone.0150691.t001

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 4 / 13

    manipulated. In the MC condition, 70% of trials were congruent and 30% were incongruent,
    whereas in the MI condition, 30% of trials were congruent and 70% were incongruent. The
    order of conditions was counterbalanced across the participants. After 40 practice trials, partic-
    ipants completed 320 experimental trials (160 trials × 2 blocks) in each condition. The viewing
    distance was 1 m and the stimuli subtended a horizontal visual angle between the two outside
    positions of 9.7° and a vertical visual angle of 1.2°. Stimulus duration was 200 ms, with a ran-
    domized stimulus onset asynchrony (SOA) between 1500 and 1900 ms (mean = 1700 ms).

    Task Performance
    Post-error accuracy was defined as the percentage of correct responses following trials with
    errors of commission. Post-error slowing was defined as the mean RT for correct trials follow-
    ing an error of commission trial minus the mean RT for correct trials following a correct trial.
    Post-error task performance was calculated across congruency due to an insufficient number
    of errors of commission for the congruent trials.

    ERP Recording
    EEG activity was measured from 64 electrode sites arranged in an extended montage based on
    the International 10–10 system [41], as well as two electrodes placed on the right and left mas-
    toids. Electrooculographic activity was collected from electrodes placed above and below the
    right orbit and on the outer canthus of each eye to record bipolar eye movements. Continuous
    data were recorded with a bandwidth of DC to 208 Hz (–3 dB/octave), using the BioSemi
    Active Two system and sampled at 1024 Hz. Offline EEG processing, which was performed
    using Brain Vision Analyzer 2 software (Brain Products, Gilching, Germany), included eye
    movement correction using the procedure described by Gratton, Coles [42], re-referencing to
    average mastoids, creation of response-locked epochs (–450 to 650 ms relative to response
    onset), baseline correction (−100 to 0 ms relative to response onset), bandpass filtering (1–12
    Hz, 24 dB⁄octave), and artifact rejection (epochs with signals that exceeded ± 100 μV were
    rejected). Average ERP waveforms were created for error of commission trials (i.e., Ne/ERN)
    and correct trials (i.e., correct negativity/correct response negativity: Nc/CRN) [43]. Trials with
    an error of omission were rejected and the waveforms were averaged across congruency. Across
    conditions, a mean of 31 and 278 trials were averaged for Ne/ERN and Nc/CRN, respectively.
    Ne/ERN amplitude was assessed at the FCz electrode site, where it reached its topographic
    maximum (see Fig 1B), and was quantified as the mean voltage within a 20 to 80 ms latency
    window relative to response onset.

    Fitness Assessment
    Participants’ fitness was assessed using the multistage 20-m shuttle run test (also known as the
    Progressive Aerobic Cardiovascular Endurance Run: PACER). The 20-m shuttle run test was
    performed according to Leger, Mercier [44]. Participants were required to run back and forth
    between two lines 20 m apart paced by a tone on a CD player signaling when they should reach
    the opposite line. The initial speed was set at 8.5 km/h, with the speed increasing by 0.5 km/h
    every minute. The test was continued until the participant stopped due to fatigue or could not
    reach the end lines concurrent with the audio signals on two consecutive occasions. The fin-
    ished lap number was recorded. To exclude age- and sex-related differences, in this study the
    age- and sex-specific percentile score was calculated as an index of aerobic fitness based on nor-
    mative data provided by the Japanese Ministry of Education, Culture, Sports, Science and
    Technology [45].

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 5 / 13

    Statistical Analysis
    Initial Pearson product-moment correlations were computed between demographic factors
    and dependent variables (i.e., post-error task performance and Ne/ERN amplitude). Hierarchi-
    cal linear regression analyses were then performed for each dependent variable. Age, sex
    (dummy coded, 0 = girls, 1 = boys), and maternal education were included in step 1 as control
    variables, and aerobic fitness (20-m shuttle run test percentile score) was then added to step 2
    of the analysis. The significance of the change in the R2 value between the two steps was used

    Fig 1. A: Grand averaged response-locked ERP waveforms for error trials (Ne/ERN) and correct trials (Nc/CRN) for each condition at FCz electrode site. B:
    Topographical maps of the Ne/ERN amplitudes for each condition.

    doi:10.1371/journal.pone.0150691.g001

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 6 / 13

    to judge the independent contribution of the fitness measure for explaining variance in the
    dependent variables. All statistical analyses were conducted using a significance level of p = .05.

    Results

    Task Performance
    Results of the correlational analysis for post-error task performance and Ne/ERN amplitude
    are provided in Table 2. The correlation analysis revealed that greater fitness was related to
    greater post-error accuracy for the MC condition, r = .28, p = .035 (one-tailed), whereas no
    such relationship was observed for the MI condition, r = .12, p = .23. The regression analysis
    yielded no significant relationship between fitness and post-error accuracy for both MC and
    MI conditions. The correlation and regression analyses of post-error slowing revealed no sig-
    nificant relationship with fitness for either the MC or MI condition.

    Ne/ERN
    Fig 1A illustrates grand averaged response-locked ERP waveforms for error trials (Ne/ERN)
    and correct trials (Nc/CRN) for each condition at the FCz electrode site, on which a median
    split was performed on the 20-m shuttle run test percentile scores within each sex to visualize
    the association between fitness and Ne/ERN amplitude. Preliminary Bonferroni-corrected t-
    tests were conducted within each condition comparing Ne/ERN amplitude with Nc/CRN
    amplitude. Results indicated larger Ne/ERN amplitude relative to Nc/CRN amplitude across
    conditions, ts(42) � 7.0, ps � .001, confirming the expected accuracy effect.

    A median split was performed on the 20-m shuttle run test percentile scores within each sex
    to visualize the association between fitness and Ne/ERN amplitude.

    The correlation analysis revealed that greater fitness was related to larger Ne/ERN ampli-
    tude for the MC condition, r = –.43, p = .004, whereas no such relationship was observed for
    the MI condition, r = –.18, p = .27 (Table 2). A summary of the regression analyses for Ne/ERN
    amplitude for each condition is provided in Table 3. The regression analysis for the MC condi-
    tion yielded a significant change in R2 at step 2, F(1, 37) = 13.3, p = .001, indicating that greater
    fitness was associated with larger Ne/ERN amplitude. For the MI condition, no significant

    Table 2. Pearson product-moment correlation coefficients between variables.

    Variable 1 2 3 4 5 6 7 8 9 10

    1. Fitness —

    2. Age –.20 —

    3. Sexa –.02 .26 —

    4. Maternal education .10 –.45* –.04 —

    5. MC. Post-error accuracy .28† –.26† –.24 .31* —

    6. MI. Post-error accuracy .12 –.13 –.24 .16 .66* —

    7. MC. Post-error slowing .05 –.16 –.21 .08 .01 –.07 —

    8. MI. Post-error slowing .19 .00 –.34* –.19 .17 .42* .25 —

    9. MC. Ne/ERN amplitude –.43* –.24 –.19 .24 –.06 .05 .24 –.04 —

    10. MI. Ne/ERN amplitude –.18 –.27† –.16 .18 .15 .01 .25 –.16 .64* —

    aSex was dummy coded, 0 = girls, 1 = boys.

    *Two-tailed p � .05.
    †One-tailed p � .05.

    doi:10.1371/journal.pone.0150691.t002

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 7 / 13

    fitness influence was present at step 2, F(1, 37) = 2.3, p = .14. An additional multiple hierarchi-
    cal regression analysis was conducted to examine the difference in Ne/ERN amplitude between
    the MC and MI conditions (i.e., MC–MI). This analysis tested for a possible relationship
    between fitness and differences in ACC activity, which is thought to reflect the flexibility to
    change cognitive control strategy use based on the probability of trial types. This analysis
    yielded a significant change in the R2 at step 2, F(1, 37) = 4.0, p = .05, indicating that greater fit-
    ness was associated with greater modulation of Ne/ERN amplitude between the MC and MI
    conditions.

    Discussion
    The main findings here were that greater aerobic fitness was related to larger Ne/ERN ampli-
    tude, which reflects increased transient ACC activation, in the MC condition. Additionally,
    although the regression analysis revealed no relationship between fitness and post-error task
    performance, correlation analyses implied that greater aerobic fitness was associated with
    greater post-error accuracy in the MC condition. Based on the DMC theory [16, 17, 30], these
    findings suggest that higher-fit children might be able to maintain greater post-error accuracy
    in the MC condition relative to lower-fit children by recruiting reactive control, as denoted by
    larger Ne/ERN amplitude. This interpretation can also be accounted for by conflict monitoring
    theory [46]. This theory proposes that the ACC monitors response conflict on error trials and
    provides a signal to the dorsolateral PFC to upregulate cognitive control in support of behav-
    ioral adjustments on subsequent trials [25, 46]. Accordingly, we suggest that the observed
    larger Ne/ERN amplitude and greater post-error accuracy in the MC condition for higher-fit
    children represent transient intensification of cognitive control due to greater utilization of
    reactive control.

    More importantly, greater fitness was associated with greater modulation of Ne/ERN ampli-
    tude between the MC and MI conditions. Stated differently, higher-fit children had smaller Ne/
    ERN amplitude in the MI condition relative to the MC condition, whereas such a relationship
    was attenuated for lower-fit children. Based on the DMC theory [16, 17, 30], it is plausible that
    the decreased Ne/ERN amplitude (i.e., less ACC activation) in the MI condition for higher-fit
    children is due to increased sustained PFC activation, reflecting a greater utilization of proac-
    tive control. Thus, these findings suggest that greater childhood fitness is associated with a
    more flexible shift between reactive and proactive modes of cognitive control based on the
    probability of trial types.

    However, it is noteworthy that, contrary to our hypothesis, aerobic fitness was not related to
    Ne/ERN amplitude in the MI condition. Pontifex, Raine [21] manipulated stimulus-response

    Table 3. Summary of regression analyses for variables predicting Ne/ERN amplitude.

    MC MI MC–MIa

    ΔR2 β ΔR2 β ΔR2 β

    Step1 .10 .09 .02

    Age –.12 –.21 .11

    Sex –.16 –.10 –.07

    Maternal education .18 .08 .13

    Step 2 .24* .05 .10*

    Fitness –.50* –.24 –.32*

    aA difference in Ne/ERN amplitude between the MC and MI conditions.

    *p � .05.

    doi:10.1371/journal.pone.0150691.t003

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 8 / 13

    compatibility during a modified flanker task, and observed smaller Ne/ERN amplitude and
    superior task performance, which may reflect recruitment of proactive control by higher-fit
    children, relative to lower-fit children, in the stimulus-response compatible condition. Higher-
    fit children also exhibited superior task performance in the stimulus-response incompatible
    condition, but had increased Ne/ERN amplitude that did not differ from lower-fit children.
    These findings imply that higher-fit children engaged more in reactive control in the more
    effortful condition (i.e., incompatible condition) to meet the increased task demands. Although
    it remains unclear whether proactive control and reactive control are fully independent, they
    may be able to be engaged simultaneously [16, 17]. Taken together, in the present study, we
    speculate that higher-fit children might engage in both proactive and reactive control simulta-
    neously in the more demanding condition (i.e., MI condition), and therefore aerobic fitness
    was not related to Ne/ERN amplitude. Given that this is merely speculation, further studies are
    required to confirm this interpretation using fMRI techniques to assess both transient and sus-
    tained brain activation.

    Given the cross-sectional design used here, this study does not demonstrate a causal rela-
    tionship between changes in childhood fitness and cognitive control strategy use. A random-
    ized controlled intervention study [8] showed that a physical activity program, one that lead to
    increases in aerobic fitness, improved working memory performance on a modified Sternberg
    task. Further, the program improved the effectiveness of task preparation, as assessed by con-
    tingent negative variation (CNV). The CNV is a negative slow potential that develops during
    the foreperiod between warning and imperative stimuli, and is composed of at least two differ-
    ent components, the early and late CNVs [47, 48]. Kamijo, Pontifex [8] indicated that the phys-
    ical activity program selectively enhanced early but not late CNV amplitude. An fMRI study
    manipulated participants’ cognitive control strategy (i.e., proactive or reactive) during the
    Sternberg task, and observed increased sustained PFC activation during the foreperiod with
    superior task performance when participants engaged in proactive control, relative to reactive
    control [49]. Based on this study, it is plausible that the selective effect of the physical activity
    program on the early CNV, which was accompanied by improvement in task performance,
    indicates a strategy shift from reactive control to proactive control due to increased fitness.
    Thus, although this longitudinal study did not manipulate cognitive control strategies [8],
    these findings imply a causal relationship between fitness and cognitive control strategy use
    during childhood.

    A cross-sectional study has supported the association between childhood fitness and cogni-
    tive control strategy by focusing on the effectiveness of task preparation [27]. Berchicci, Ponti-
    fex [27] assessed the Bereitschaftspotential (BP) and the prefrontal negativity (pN) during a
    modified flanker task. The BP and pN are negative slow potentials that develop around 1.5 to 1
    s before movement onset, and believed to reflect responses [50] and cognitive preparation [27,
    51], respectively. Berchicci, Pontifex [27] indicated that higher-fit children exhibited larger pN
    relative to their lower-fit counterparts, whereas no such difference was observed for the BP.
    The larger pN observed in higher-fit children might reflect more effective cognitive preparation
    by their utilization of a proactive control strategy, which is consistent with the above described
    CNV study [8]. Note that although we also assessed the BP and pN in the present study, no
    association between fitness and pN/BP amplitude was observed (S1 Text). The implication of
    this discrepancy might be related to the difference in SOA used for the flanker task. The SOA
    was randomized in the present study, whereas it was fixed in the previous study [27]. A ran-
    domized SOA might make it more difficult to prepare for upcoming stimuli. We believe that in
    the present study fitness was not associated with cognitive preparation processes (i.e., pN
    amplitude) for this reason.

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 9 / 13

    As stated in the introduction, animal studies suggest that exercise induced increases in
    nerve growth factors relate to increases in the number of synaptic connections and the develop-
    ment of new neurons that support learning and memory [14, 15]. If these changes were to
    occur in the human brain, it is no wonder that cognitive control strategies are susceptible to the
    effect of aerobic fitness, given that the neural networks underlying cognitive control strategies
    are still underdeveloped during childhood [29]. It is reasonable to suggest that the present
    results for neuroelectric (i.e., Ne/ERN) and behavioral (i.e., post-error accuracy) measures of
    action monitoring indicate differences in children’s cognitive control strategies based on their
    fitness level. Nonetheless, additional longitudinal studies are needed to examine how changes
    in physical activity and fitness influence cognitive control strategies during childhood.

    Another limitation is that findings of previous neuroelectric studies [20, 21] that observed
    smaller Ne/ERN amplitudes for higher-fit relative to lower-fit children would appear to be
    inconsistent with the present results. In the previous studies’ flanker task, congruent and
    incongruent trials were presented equally often. Given that we did not employ a 50/50 condi-
    tion in this present study and did find that the association between childhood fitness and action
    monitoring differed based on the probability of trial types, it is difficult to compare and con-
    trast study outcomes among these neuroelectric studies.

    Conclusion
    On the basis of results from neuroelectric and behavioral measures of action monitoring, we
    suggest that higher-fit children showed a strategic shift from use of reactive control in the MC
    condition to increasing use of proactive control in the MI condition. The shift in the cognitive
    control mode based on the probability of trial types for higher-fit children is similar to that
    observed in young adults in a previous study [30]. Thus, the current findings suggest that child-
    hood fitness may be associated with development of the neural network underlying cognitive
    control strategies. In conclusion, this study suggests that greater childhood fitness is associated
    with a more flexible shift between reactive and proactive modes of cognitive control to adapt to
    varying task demands.

    Supporting Information
    S1 Dataset. Demographic, fitness, behavioral, and neuroelectric data for the 42 partici-
    pants.
    (XLSX)

    S1 Fig. A: Grand averaged stimulus-locked ERP waveforms for each condition at Fpz (pN)
    and Cz (BP) electrode site. B: Topographical maps of the pN/BP amplitudes for each condi-
    tion.
    (PDF)

    S1 Text. pN/BP analysis and results.
    (DOCX)

    Author Contributions
    Conceived and designed the experiments: KK HM. Performed the experiments: KK SB. Ana-
    lyzed the data: KK. Contributed reagents/materials/analysis tools: KK HM. Wrote the paper:
    KK HM SB.

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 10 / 13

    http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0150691.s001

    http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0150691.s002

    http://www.plosone.org/article/fetchSingleRepresentation.action?uri=info:doi/10.1371/journal.pone.0150691.s003

    References
    1. Colcombe S, Kramer AF. Fitness effects on the cognitive function of older adults: A meta-analytic

    study. Psychol Sci. 2003; 14:125–30. doi: 10.1111/1467-9280.t01-1-01430 PMID: 12661673.

    2. Hillman CH, Erickson KI, Kramer AF. Be smart, exercise your heart: Exercise effects on brain and cog-
    nition. Nat Rev Neurosci. 2008; 9:58–65. Epub 2007/12/21. doi: 10.1038/nrn2298 PMID: 18094706.

    3. Kramer AF, Hahn S, Cohen NJ, Banich MT, McAuley E, Harrison CR, et al. Ageing, fitness and neuro-
    cognitive function. Nature. 1999; 400:418–9. doi: 10.1038/22682 PMID: 10440369.

    4. Hillman CH, Kamijo K, Scudder M. A review of chronic and acute physical activity participation on neu-
    roelectric measures of brain health and cognition during childhood. Prev Med. 2011; 52 Suppl 1:S21–8.
    Epub 2011/02/02. doi: 10.1016/j.ypmed.2011.01.024 PMID: 21281669.

    5. Khan NA, Hillman CH. The relation of childhood physical activity and aerobic fitness to brain function
    and cognition: A review. Pediatr Exerc Sci. 2014; 26:138–46. doi: 10.1123/pes.2013-0125 PMID:
    24722921.

    6. Chaddock-Heyman L, Erickson KI, Voss MW, Knecht AM, Pontifex MB, Castelli DM, et al. The effects
    of physical activity on functional MRI activation associated with cognitive control in children: A random-
    ized controlled intervention. Front Hum Neurosci. 2013; 7:72. Epub 2013/03/15. doi: 10.3389/fnhum.
    2013.00072 PMID: 23487583.

    7. Hillman CH, Pontifex MB, Castelli DM, Khan NA, Raine LB, Scudder MR, et al. Effects of the FITKids
    randomized controlled trial on executive control and brain function. Pediatrics. 2014; 134:e1063–71.
    doi: 10.1542/peds.2013-3219 PMID: 25266425.

    8. Kamijo K, Pontifex MB, O’Leary KC, Scudder MR, Wu CT, Castelli DM, et al. The effects of an after-
    school physical activity program on working memory in preadolescent children. Dev Sci. 2011;
    14:1046–58. Epub 2011/09/03. doi: 10.1111/j.1467-7687.2011.01054.x PMID: 21884320.

    9. Chen X, Sekine M, Hamanishi S, Wang H, Gaina A, Yamagami T, et al. Lifestyles and health-related
    quality of life in Japanese school children: A cross-sectional study. Prev Med. 2005; 40:668–78. doi:
    10.1016/j.ypmed.2004.09.034 PMID: 15850863.

    10. Guthold R, Cowan MJ, Autenrieth CS, Kann L, Riley LM. Physical activity and sedentary behavior
    among schoolchildren: A 34-country comparison. J Pediatr. 2010; 157:43–9 e1. doi: 10.1016/j.jpeds.
    2010.01.019 PMID: 20304415.

    11. Miller EK, Cohen JD. An integrative theory of prefrontal cortex function. Annu Rev Neurosci. 2001;
    24:167–202. doi: 10.1146/annurev.neuro.24.1.167 PMID: 11283309.

    12. Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC, et al. Dynamic mapping of
    human cortical development during childhood through early adulthood. Proc Natl Acad Sci U S A.
    2004; 101:8174–9. doi: 10.1073/pnas.0402680101 PMID: 15148381.

    13. Neeper SA, Gomez-Pinilla F, Choi J, Cotman C. Exercise and brain neurotrophins. Nature. 1995;
    373:109. doi: 10.1038/373109a0 PMID: 7816089.

    14. Lu B, Chow A. Neurotrophins and hippocampal synaptic transmission and plasticity. J Neurosci Res.
    1999; 58:76–87. doi: 10.1002/(SICI)1097-4547(19991001)58:1<76::AID-JNR8>3.0.CO;2–0 PMID:
    10491573.

    15. van Praag H, Christie BR, Sejnowski TJ, Gage FH. Running enhances neurogenesis, learning, and
    long-term potentiation in mice. Proc Natl Acad Sci U S A. 1999; 96:13427–31. doi: 10.1073/pnas.96.23.
    13427 PMID: 10557337.

    16. Braver TS. The variable nature of cognitive control: A dual mechanisms framework. Trends Cogn Sci.
    2012; 16:106–13. doi: 10.1016/j.tics.2011.12.010 PMID: 22245618.

    17. Braver TS, Gray JR, Burgess GC. Explaining the many varieties of working memory variation: Dual
    mechanisms of cognitive control. In: Conway ARA, Jarrold C, Kane MJ, Miyake A, Towse JN, editors.
    Variation in working memory. New York: Oxford University Press; 2007. p. 76–106.

    18. Falkenstein M, Hohnsbein J, Hoormann J, Blanke L. Effects of crossmodal divided attention on late
    ERP components. II. Error processing in choice reaction tasks. Electroencephalogr Clin Neurophysiol.
    1991; 78:447–55. Epub 1991/06/01. doi: 10.1016/0013-4694(91)90062-9 PMID: 1712280.

    19. Gehring WJ, Goss B, Coles MGH, Meyer DE, Donchin E. A neural system for error detection and com-
    pensation. Psychol Sci. 1993; 4:385–90. doi: 10.1111/j.1467-9280.1993.tb00586.x

    20. Hillman CH, Buck SM, Themanson JR, Pontifex MB, Castelli DM. Aerobic fitness and cognitive devel-
    opment: Event-related brain potential and task performance indices of executive control in preadoles-
    cent children. Dev Psychol. 2009; 45:114–29. Epub 2009/02/13. doi: 10.1037/a0014437 PMID:
    19209995.

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 11 / 13

    http://dx.doi.org/10.1111/1467-9280.t01-1-01430

    http://www.ncbi.nlm.nih.gov/pubmed/12661673

    http://dx.doi.org/10.1038/nrn2298

    http://www.ncbi.nlm.nih.gov/pubmed/18094706

    http://dx.doi.org/10.1038/22682

    http://www.ncbi.nlm.nih.gov/pubmed/10440369

    http://dx.doi.org/10.1016/j.ypmed.2011.01.024

    http://www.ncbi.nlm.nih.gov/pubmed/21281669

    http://dx.doi.org/10.1123/pes.2013-0125

    http://www.ncbi.nlm.nih.gov/pubmed/24722921

    http://dx.doi.org/10.3389/fnhum.2013.00072

    http://dx.doi.org/10.3389/fnhum.2013.00072

    http://www.ncbi.nlm.nih.gov/pubmed/23487583

    http://dx.doi.org/10.1542/peds.2013-3219

    http://www.ncbi.nlm.nih.gov/pubmed/25266425

    http://dx.doi.org/10.1111/j.1467-7687.2011.01054.x

    http://www.ncbi.nlm.nih.gov/pubmed/21884320

    http://dx.doi.org/10.1016/j.ypmed.2004.09.034

    http://www.ncbi.nlm.nih.gov/pubmed/15850863

    http://dx.doi.org/10.1016/j.jpeds.2010.01.019

    http://dx.doi.org/10.1016/j.jpeds.2010.01.019

    http://www.ncbi.nlm.nih.gov/pubmed/20304415

    http://dx.doi.org/10.1146/annurev.neuro.24.1.167

    http://www.ncbi.nlm.nih.gov/pubmed/11283309

    http://dx.doi.org/10.1073/pnas.0402680101

    http://www.ncbi.nlm.nih.gov/pubmed/15148381

    http://dx.doi.org/10.1038/373109a0

    http://www.ncbi.nlm.nih.gov/pubmed/7816089

    http://dx.doi.org/10.1002/(SICI)1097-4547(19991001)58:1<76::AID-JNR8>3.0.CO;2–0

    http://www.ncbi.nlm.nih.gov/pubmed/10491573

    http://dx.doi.org/10.1073/pnas.96.23.13427

    http://dx.doi.org/10.1073/pnas.96.23.13427

    http://www.ncbi.nlm.nih.gov/pubmed/10557337

    http://dx.doi.org/10.1016/j.tics.2011.12.010

    http://www.ncbi.nlm.nih.gov/pubmed/22245618

    http://dx.doi.org/10.1016/0013-4694(91)90062-9

    http://www.ncbi.nlm.nih.gov/pubmed/1712280

    http://dx.doi.org/10.1111/j.1467-9280.1993.tb00586.x

    http://dx.doi.org/10.1037/a0014437

    http://www.ncbi.nlm.nih.gov/pubmed/19209995

    21. Pontifex MB, Raine LB, Johnson CR, Chaddock L, Voss MW, Cohen NJ, et al. Cardiorespiratory fitness
    and the flexible modulation of cognitive control in preadolescent children. J Cogn Neurosci. 2011;
    23:1332–45. Epub 2010/06/05. doi: 10.1162/jocn.2010.21528 PMID: 20521857.

    22. Carter CS, Braver TS, Barch DM, Botvinick MM, Noll D, Cohen JD. Anterior cingulate cortex, error
    detection, and the online monitoring of performance. Science. 1998; 280:747–9. Epub 1998/05/23. doi:
    10.1126/science.280.5364.747 PMID: 9563953.

    23. Dehaene S, Posner MI, Tucker DM. Localization of a neural system for error detection and compensa-
    tion. Psychol Sci. 1994; 5:303–5. doi: 10.1111/j.1467-9280.1994.tb00630.x

    24. Miltner WH, Lemke U, Weiss T, Holroyd C, Scheffers MK, Coles MG. Implementation of error-process-
    ing in the human anterior cingulate cortex: A source analysis of the magnetic equivalent of the error-
    related negativity. Biol Psychol. 2003; 64:157–66. Epub 2003/11/07. doi: 10.1016/S0301-0511(03)
    00107-8 PMID: 14602360.

    25. Kerns JG, Cohen JD, MacDonald AW 3rd, Cho RY, Stenger VA, Carter CS. Anterior cingulate conflict
    monitoring and adjustments in control. Science. 2004; 303:1023–6. Epub 2004/02/14. doi: 10.1126/
    science.1089910 PMID: 14963333.

    26. Voss MW, Chaddock L, Kim JS, Vanpatter M, Pontifex MB, Raine LB, et al. Aerobic fitness is associ-
    ated with greater efficiency of the network underlying cognitive control in preadolescent children. Neu-
    roscience. 2011; 199:166–76. Epub 2011/10/27. doi: 10.1016/j.neuroscience.2011.10.009 PMID:
    22027235.

    27. Berchicci M, Pontifex MB, Drollette ES, Pesce C, Hillman CH, Di Russo F. From cognitive motor prepa-
    ration to visual processing: The benefits of childhood fitness to brain health. Neuroscience. 2015;
    298:211–9. doi: 10.1016/j.neuroscience.2015.04.028 PMID: 25907444.

    28. Chatham CH, Frank MJ, Munakata Y. Pupillometric and behavioral markers of a developmental shift in
    the temporal dynamics of cognitive control. Proc Natl Acad Sci U S A. 2009; 106:5529–33. doi: 10.
    1073/pnas.0810002106 PMID: 19321427.

    29. Andrews-Hanna JR, Mackiewicz Seghete KL, Claus ED, Burgess GC, Ruzic L, Banich MT. Cognitive
    control in adolescence: Neural underpinnings and relation to self-report behaviors. PLoS One. 2011; 6:
    e21598. doi: 10.1371/journal.pone.0021598 PMID: 21738725.

    30. De Pisapia N, Braver TS. A model of dual control mechanisms through anterior cingulate and prefrontal
    cortex interactions. Neurocomputing. 2006; 69:1322–6. doi: 10.1016/j.neucom.2005.12.100

    31. Burgess GC, Braver TS. Neural mechanisms of interference control in working memory: Effects of inter-
    ference expectancy and fluid intelligence. PLoS One. 2010; 5:e12861. doi: 10.1371/journal.pone.
    0012861 PMID: 20877464.

    32. Rabbitt PM. Errors and error correction in choice-response tasks. J Exp Psychol. 1966; 71:264–72. doi:
    10.1037/h0022853 PMID: 5948188.

    33. Kamijo K, Pontifex MB, Khan NA, Raine LB, Scudder MR, Drollette ES, et al. The negative association
    of childhood obesity to cognitive control of action monitoring. Cereb Cortex. 2014; 24:654–62. doi: 10.
    1093/cercor/bhs349 PMID: 23146965.

    34. Japanese Ministry of Education, Culture, Sports, Science and Technology; 1981–2002. Report of the
    1980–2001 National Growth Survey of normal Japanese children, 5–17 years of age (in Japanese).
    Tokyo.

    35. Olvet DM, Hajcak G. The stability of error-related brain activity with increasing trials. Psychophysiology.
    2009; 46:957–61. doi: 10.1111/j.1469-8986.2009.00848.x PMID: 19558398.

    36. Pontifex MB, Scudder MR, Brown ML, O’Leary KC, Wu CT, Themanson JR, et al. On the number of tri-
    als necessary for stabilization of error-related brain activity across the life span. Psychophysiology.
    2010; 47:767–73. Epub 2010/03/17. doi: 10.1111/j.1469-8986.2010.00974.x PMID: 20230502.

    37. DuPaul GJ, Power TJ, Anastopoulos AD, Reid R. ADHD Rating Scale—IV: Checklists, norms, and clin-
    ical interpretation. New York: Guilford Press; 1998.

    38. Thomas S, Reading J, Shephard RJ. Revision of the Physical Activity Readiness Questionnaire (PAR-
    Q). Can J Sport Sci. 1992; 17:338–45. PMID: 1330274.

    39. Mezzacappa E. Alerting, orienting, and executive attention: Developmental properties and sociodemo-
    graphic correlates in an epidemiological sample of young, urban children. Child Dev. 2004; 75:1373–
    86. Epub 2004/09/17. doi: 10.1111/j.1467-8624.2004.00746.x PMID: 15369520.

    40. Freitas D, Maia J, Beunen G, Claessens A, Thomis M, Marques A, et al. Socio-economic status,
    growth, physical activity and fitness: The Madeira growth study. Ann Hum Biol. 2007; 34:107–22. doi:
    10.1080/03014460601080983 PMID: 17536760.

    41. Chatrian GE, Lettich E, Nelson PL. Ten percent electrode system for topographic studies of spontane-
    ous and evoked EEG activity. Am J EEG Technol. 1985; 25:83–92.

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 12 / 13

    http://dx.doi.org/10.1162/jocn.2010.21528

    http://www.ncbi.nlm.nih.gov/pubmed/20521857

    http://dx.doi.org/10.1126/science.280.5364.747

    http://www.ncbi.nlm.nih.gov/pubmed/9563953

    http://dx.doi.org/10.1111/j.1467-9280.1994.tb00630.x

    http://dx.doi.org/10.1016/S0301-0511(03)00107-8

    http://dx.doi.org/10.1016/S0301-0511(03)00107-8

    http://www.ncbi.nlm.nih.gov/pubmed/14602360

    http://dx.doi.org/10.1126/science.1089910

    http://dx.doi.org/10.1126/science.1089910

    http://www.ncbi.nlm.nih.gov/pubmed/14963333

    http://dx.doi.org/10.1016/j.neuroscience.2011.10.009

    http://www.ncbi.nlm.nih.gov/pubmed/22027235

    http://dx.doi.org/10.1016/j.neuroscience.2015.04.028

    http://www.ncbi.nlm.nih.gov/pubmed/25907444

    http://dx.doi.org/10.1073/pnas.0810002106

    http://dx.doi.org/10.1073/pnas.0810002106

    http://www.ncbi.nlm.nih.gov/pubmed/19321427

    http://dx.doi.org/10.1371/journal.pone.0021598

    http://www.ncbi.nlm.nih.gov/pubmed/21738725

    http://dx.doi.org/10.1016/j.neucom.2005.12.100

    http://dx.doi.org/10.1371/journal.pone.0012861

    http://dx.doi.org/10.1371/journal.pone.0012861

    http://www.ncbi.nlm.nih.gov/pubmed/20877464

    http://dx.doi.org/10.1037/h0022853

    http://www.ncbi.nlm.nih.gov/pubmed/5948188

    http://dx.doi.org/10.1093/cercor/bhs349

    http://dx.doi.org/10.1093/cercor/bhs349

    http://www.ncbi.nlm.nih.gov/pubmed/23146965

    http://dx.doi.org/10.1111/j.1469-8986.2009.00848.x

    http://www.ncbi.nlm.nih.gov/pubmed/19558398

    http://dx.doi.org/10.1111/j.1469-8986.2010.00974.x

    http://www.ncbi.nlm.nih.gov/pubmed/20230502

    http://www.ncbi.nlm.nih.gov/pubmed/1330274

    http://dx.doi.org/10.1111/j.1467-8624.2004.00746.x

    http://www.ncbi.nlm.nih.gov/pubmed/15369520

    http://dx.doi.org/10.1080/03014460601080983

    http://www.ncbi.nlm.nih.gov/pubmed/17536760

    42. Gratton G, Coles MG, Donchin E. A new method for off-line removal of ocular artifact. Electroencepha-
    logr Clin Neurophysiol. 1983; 55:468–84. doi: 10.1016/0013-4694(83)90135-9 PMID: 6187540.

    43. Ford JM. Schizophrenia: The broken P300 and beyond. Psychophysiology. 1999; 36:667–82. doi: 10.
    1111/1469-8986.3660667 PMID: 10554581.

    44. Leger LA, Mercier D, Gadoury C, Lambert J. The multistage 20 metre shuttle run test for aerobic fitness.
    J Sports Sci. 1988; 6:93–101. doi: 10.1080/02640418808729800 PMID: 3184250.

    45. Japanese Ministry of Education, Culture, Sports, Science and Technology. Report of the 2011 School
    Health Examination Survey (in Japanese); 2012. Database: [Internet]. Accessed: http://www.mext.go.
    jp/b_menu/toukei/chousa05/hoken/1268826.

    46. Botvinick M, Nystrom LE, Fissell K, Carter CS, Cohen JD. Conflict monitoring versus selection-for-
    action in anterior cingulate cortex. Nature. 1999; 402:179–81. Epub 2000/01/26. doi: 10.1038/46035
    PMID: 10647008.

    47. Loveless NE, Sanford AJ. Slow potential correlates of preparatory set. Biol Psychol. 1974; 1:303–14.
    doi: 10.1016/0301-0511(74)90005-2 PMID: 4425714.

    48. Weerts TC, Lang PJ. The effects of eye fixation and stimulus and response location on the contingent
    negative variation (CNV). Biol Psychol. 1973; 1:1–19. doi: 10.1016/0301-0511(73)90010-0 PMID:
    4804295.

    49. Speer NK, Jacoby LL, Braver TS. Strategy-dependent changes in memory: effects on behavior and
    brain activity. Cognitive, Affective and Behavioral Neuroscience. 2003; 3:155–67. doi: 10.3758/CABN.
    3.3.155 PMID: 14672153.

    50. Shibasaki H, Hallett M. What is the Bereitschaftspotential? Clin Neurophysiol. 2006; 117:2341–56. doi:
    10.1016/j.clinph.2006.04.025 PMID: 16876476.

    51. Berchicci M, Lucci G, Pesce C, Spinelli D, Di Russo F. Prefrontal hyperactivity in older people during
    motor planning. Neuroimage. 2012; 62:1750–60. doi: 10.1016/j.neuroimage.2012.06.031 PMID:
    22732557.

    Fitness and Action Monitoring Strategy

    PLOS ONE | DOI:10.1371/journal.pone.0150691 March 3, 2016 13 / 13

    http://dx.doi.org/10.1016/0013-4694(83)90135-9

    http://www.ncbi.nlm.nih.gov/pubmed/6187540

    http://dx.doi.org/10.1111/1469-8986.3660667

    http://dx.doi.org/10.1111/1469-8986.3660667

    http://www.ncbi.nlm.nih.gov/pubmed/10554581

    http://dx.doi.org/10.1080/02640418808729800

    http://www.ncbi.nlm.nih.gov/pubmed/3184250

    http://www.mext.go.jp/b_menu/toukei/chousa05/hoken/1268826

    http://www.mext.go.jp/b_menu/toukei/chousa05/hoken/1268826

    http://dx.doi.org/10.1038/46035

    http://www.ncbi.nlm.nih.gov/pubmed/10647008

    http://dx.doi.org/10.1016/0301-0511(74)90005-2

    http://www.ncbi.nlm.nih.gov/pubmed/4425714

    http://dx.doi.org/10.1016/0301-0511(73)90010-0

    http://www.ncbi.nlm.nih.gov/pubmed/4804295

    http://dx.doi.org/10.3758/CABN.3.3.155

    http://dx.doi.org/10.3758/CABN.3.3.155

    http://www.ncbi.nlm.nih.gov/pubmed/14672153

    http://dx.doi.org/10.1016/j.clinph.2006.04.025

    http://www.ncbi.nlm.nih.gov/pubmed/16876476

    http://dx.doi.org/10.1016/j.neuroimage.2012.06.031

    http://www.ncbi.nlm.nih.gov/pubmed/22732557

    Copyright of PLoS ONE is the property of Public Library of Science and its content may not
    be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s
    express written permission. However, users may print, download, or email articles for
    individual use.

    138

    The authors are with the Dept. of Kinesiology and Community
    Health, University of Illinois at Urbana-Champaign, Urbana,
    IL. Address author correspondence to Naiman A. Khan at
    nakhan2@illinois.edu.

    Pediatric Exercise Science, 2014, 26, 138-146
    http://dx.doi.org/10.1123/pes.2013-0125
    © 2014 Human Kinetics, Inc.

    The Relation of Childhood Physical Activity and Aerobic
    Fitness to Brain Function and Cognition: A Review

    Naiman A. Khan and Charles H. Hillman
    University of Illinois at Urbana-Champaign

    Physical inactivity has been shown to increase the risk for several chronic diseases across the lifespan. How-
    ever, the impact of physical activity and aerobic fitness on childhood cognitive and brain health has only
    recently gained attention. The purposes of this article are to: 1) highlight the recent emphasis for increasing
    physical activity and aerobic fitness in children’s lives for cognitive and brain health; 2) present aspects of
    brain development and cognitive function that are susceptible to physical activity intervention; 3) review neu-
    roimaging studies examining the cross-sectional and experimental relationships between aerobic fitness and
    executive control function; and 4) make recommendations for future research. Given that the human brain is
    not fully developed until the third decade of life, preadolescence is characterized by changes in brain structure
    and function underlying aspects of cognition including executive control and relational memory. Achieving
    adequate physical activity and maintaining aerobic fitness in childhood may be a critical guideline to follow
    for physical as well as cognitive and brain health.

    Keywords: executive function, relational memory, pediatrics

    Regular physical activity has been shown to be pro-
    tective against the development of several diseases includ-
    ing obesity, cardiovascular disease, certain cancers, and
    Type II diabetes (73). Given that these diseases have also
    been associated with reduced cognitive and brain health
    among older adults (21,35), physical activity is suggested
    to indirectly improve cognition and brain health by
    attenuating the risk for disease. However, research from
    rodent models demonstrates that physical activity is a
    potent stimulator of processes underlying neurogenesis,
    synaptogenesis, as well as brain vasculature (53,72). In
    addition, physical activity training has been shown to
    counter age-related hippocampal tissue loss and improve
    spatial memory function among older adults (31). Taken
    together, the findings from both rodent and older human
    studies suggest that physical activity may indirectly or
    directly modulate cognitive function and brain health.

    Converging lines of research indicate that regular
    physical activity and enhanced aerobic fitness may
    improve cognitive function and brain health in childhood
    as well. Higher-fit preadolescent children exhibit greater
    attention (42), faster information processing speed (43),
    and achieve higher scores on standardized achievement
    tests (11,27), relative to their lower-fit counterparts.

    These benefits were highlighted by a recent Institute
    of Medicine (48) committee charged with examining
    the status of physical activity and physical education in
    schools, how physical activity and fitness affect health
    outcomes, and ways to help schools get students to
    become more active. While acknowledging the fiscal and
    policy challenges involved, the final committee report
    recognized that attaining over 60 min of moderate to
    vigorous physical activity (MVPA) during the school
    day is necessary for optimal learning in the classroom.
    To represent the full scope of the positive contribution of
    regular physical activity to overall health and function,
    a team of kinesiologists validated the Human Capital
    Model (HCM) of physical activity (3). The HCM is sup-
    ported by a growing community of public, private, and
    civil sector organizations. It considers physical activity
    an investment and consolidates the evidence for physi-
    cal activity benefits into six domains including physical,
    emotional, individual, social, intellectual, and financial.
    Taken together, the Institute of Medicine report and the
    HCM place an emphasis on childhood health and provide
    a platform for implementing physical education and other
    physical activity opportunities in schools along with
    a holistic conceptual model that incorporates physical
    activity benefits for cognitive function and brain health.

    However, much remains to be learned regarding
    the influence of physical activity on specific cognitive
    processes and their neural substrates. Knowledge from
    the developmental literature is largely based on observa-
    tional/cross-sectional studies. Thus, information on the

    Official Journal of NASPEM and the
    European Group of PWP

    www.PES-Journal.com
    REVIEWS

    Brain and Cognition 139

    efficacy of improving physical activity and/or aerobic
    fitness for cognitive function and brain health in child-
    hood remains limited. It is of particular importance to
    examine how the protracted development of specific
    brain structures provides opportunities for environmental
    modulation by health behaviors including physical activ-
    ity. Keeping this in mind, our laboratory and colleagues
    have focused efforts on examining physical activity
    effects on the cognitive processes of executive control
    and relational/associative memory because the key neural
    structures subserving these processes—the prefrontal
    cortex and hippocampus—continue to develop through-
    out childhood. Furthermore, these cognitive processes
    and their neural substrates provide the foundation for
    successful learning and scholastic achievement, thereby
    influencing overall health and well-being throughout life.

    In this article, we review the brain developmental
    trajectory and evaluate observational and intervention
    studies examining relationships between physical activity
    and fitness with cognitive performance and brain health
    in childhood.

    Brain Development
    The human brain undergoes a fourfold increase in volume
    from birth to adolescence resulting in an adult brain that
    is highly structured and functionally specialized (49).
    Gestation represents a period of rapid brain development
    involving several synchronized processes including neu-
    rogenesis, migration, programmed cell death, myelina-
    tion, and synaptogenesis (56). In addition, sulci and gyri
    formation is nearly complete by birth (57) and by 2 years
    the brain achieves 80% of its adult weight (25).

    Despite the fact that the brain achieves 95% of its
    maximum size by 6 years, the processes underlying
    functional connectivity—including competitive elimina-
    tion of synapses, myelination, and dendritic and axonal
    arborization—continues throughout life (56). The early
    rapid increase in synaptic density is followed by a period
    during which synaptic connections that are not used are
    eliminated or pruned (58). This elimination increases both
    computational capacity and speed of information process-
    ing and serves as a functional mechanism for plasticity,
    which supports the hypothesis that the immature brain is
    sculpted to fit the individual’s environment (2). Further,
    synaptic pruning occurs at varying velocities in different
    parts of the brain with sensory regions—such as the visual
    cortex—achieving maturity by 7 years while the middle
    frontal gyrus—a region involved in executive function—
    not maturing until 20 years (47). One of the implications
    of this hierarchical growth model is that development of
    executive control—which consists of inhibition (resisting
    distractions or habits to maintain focus), working memory
    (mentally holding and manipulating information), and
    cognitive flexibility (multitasking)—is guided by the late
    maturation of the prefrontal cortex (10). Furthermore,
    protracted myelination throughout the cortices supports
    the position that childhood and adolescence are periods

    of modification in connectivity between distant brain
    circuitries as well as prefrontal specialization (38).

    In addition to modifications in connectivity, different
    regions of the cortices display varying growth trajecto-
    ries. Gray matter volume, which consists of neuronal
    cell bodies, dendrites, and unmyelinated axons, peaks
    between 10 and 12 years in the frontal and parietal lobes
    while temporal lobe gray matter volume does not peak
    until 16–17 years of age (37). Indeed, the dorsolateral
    prefrontal cortex—a cortical area subserving control of
    impulses, judgment, and decision-making—reaches adult
    levels of cortical thickness last (56).

    The relatively delayed rate of maturation of the
    human brain, compared with other mammals, may pro-
    vide opportunity for postnatal environmental modulation.
    The discovery that the dentate gyrus of the hippocampus
    in the adult brain continues to undergo neurogenesis—
    previously assumed to be complete at birth—may provide
    additional support to this theory (1). Thus, the protracted
    development of the prefrontal cortex and neurogenic
    capacity of the dentate gyrus offer the possibility of excit-
    ing mechanisms by which physical activity may affect
    cognitive function and brain health. Rodent models have
    been particularly useful in examining the role of physical
    activity in neurogenesis, and older human studies have
    provided further empirical support for this relationship
    (19,20,31).

    Mechanisms Underlying Physical
    Activity-Brain Relationships

    Although brain development is complete by the third
    decade of life, it is now well accepted that that the adult
    human brain has the capacity to form new neurons
    throughout life. The two brain regions that exhibit adult
    neurogenesis are the subventricular zone of the lateral
    ventricle and the dentate gyrus in the hippocampus
    (59). Evidence from rodent studies has revealed that
    several factors affect neurogenesis including stress,
    aging, environmental enrichment, and physical activity
    (40,50,54,68). However, physical activity has been identi-
    fied as a critical neurogenic component of environmental
    enrichment (29,67). Indeed, wheel running in rodents
    enhances performance on hippocampal-dependent tasks
    including spatial memory and novel object recognition
    (61,69). Subsequent studies established that the neu-
    rogenic effects of exercise are localized to the dentate
    gyrus of the hippocampus and not the subventricular
    zone/olfactory bulb; thus, providing a model to explain
    the enhanced hippocampal function observed following
    exercise (6). This is further supported by the observation
    that long-term potentiation (LTP)—a persistent increase
    in synaptic strength that may underpin certain forms of
    learning or memory—is enhanced in the dentate gyrus
    of running mice compared with controls (72). However,
    the granule cells in the dentate gyrus can be influenced
    by a variety of factors including neurotransmitters, neural
    peptides, and growth factors.

    140 Khan and Hillman

    Although several neurotrophins are involved in the
    maintenance of neural function and plasticity, brain-
    derived neurotrophic factor (BDNF) is proposed to be
    one of the key mediators of exercise effects on the brain
    (22). BDNF is expressed in several tissues including the
    brain, muscle, and adipose tissue and plays an important
    role in various aspects of developmental and adult brain
    plasticity, including proliferation, differentiation, and
    survival of neurons (46). Studies in animals have shown
    that exercise-induced increases in BDNF mRNA are
    specific to the dentate gyrus (34), an area vital for learn-
    ing and memory. Among humans, circulating BDNF has
    been related to hippocampal volume, with aerobic fitness
    related to the upregulation of BDNF serum levels and
    greater hippocampal volume among older adults (31,55).
    Although differences in hippocampal volume as a func-
    tion of fitness have been recently observed in children
    as well (discussed below), additional studies are needed
    to determine exercise effects on neurochemical factors
    that mediate the effects of physical activity on cognition
    in childhood. A complete review of possible mechanisms
    for the effects of physical activity on brain and cognition
    is beyond the scope of this article, but several informative
    reviews on the topic exist in the literature (39,70).

    Aerobic Fitness and
    Hippocampal-Dependent Memory

    The hippocampus is essential for relational/associative
    memory, which refers to the ability to form and use rep-
    resentations among the constituent elements of an event
    or scene (18,23). This form of memory is particularly
    important because it is critical for binding arbitrary
    associations between pieces of information and their
    flexible expression (30), thus representing a continuous
    cognitive process that is integral to learning in school
    and everyday life. Therefore, elucidating whether factors
    such as physical activity or fitness can enhance relational
    memory could be crucial for achieving optimal learning
    and cognitive development, potentially influencing life
    outcomes beyond childhood.

    Given that wheel running increases neurogenesis in
    the hippocampus and enhances hippocampal-dependent
    memory in rodents (51), the impact of physical activity
    and aerobic fitness on hippocampal structure and function
    has received much attention in recent years. Among older
    adults, Erikson et al. (32) found that higher aerobic fitness
    was associated with increased hippocampal volumes,
    which translated to superior performance on a spatial
    memory task. Recent evidence has emerged indicating
    that differences in hippocampal structure and function
    may extend to children as well.

    Specifically, Chaddock et al. (12) examined differ-
    ences in hippocampal volumes and performance on a
    relational memory task between higher- (≥70th VO2max
    percentile) and lower- (≤30th VO2max percentile) fit 9- to
    10-year-olds. Higher-fit children exhibited larger bilat-
    eral hippocampal volumes and greater accuracy on the

    relational memory task. Furthermore, the hippocampal
    volume mediated the positive association observed
    between fitness and relational memory performance.
    No differences were observed—across fitness levels—
    for item memory performance and nucleus accumbens
    volume (assessed as a control region), demonstrating
    the selectivity of fitness to specific aspects of memory
    and their neural substrates. In a subsequent study, the
    behavioral findings were extended to a different rela-
    tional memory task among a unique sample, as selective
    associations with fitness were only observed for the rela-
    tional task (15). To test whether the provision of physi-
    cal activity would enhance relational memory, Monti et
    al. (60) used eye-movements—an implicit measure of
    hippocampal activity—to demonstrate that 8- to 9-year-
    olds who participated in a 9-month physical activity
    intervention showed eye movement patterns indicative
    of superior relational memory without any difference in
    the item condition (an aspect of memory subserved by
    regions outside the hippocampus). Although pretest eye-
    movement data were not collected, the observation that
    there was a significant group difference on eye-movement
    measures specific to the relational memory condition at
    posttest provided additional support for the selective link
    between the hippocampus, relational memory, and fitness.

    Aerobic Fitness and Executive
    Control: Evidence From Structural

    and Functional MRI
    Unlike the study of physical activity and aerobic fitness
    effects on hippocampal plasticity—which has predomi-
    nantly been studied using animal models—the relation-
    ship between physical activity and aerobic fitness and the
    prefrontal cortex has been illuminated by several studies
    among humans. Of particular interest has been the influ-
    ence of fitness on executive control; goal-directed cogni-
    tive processes underlying perception, memory, and action.
    Indeed, much of the currently available evidence suggests
    disproportionately larger effects of fitness on executive
    control processes—relative to controlled, visuospatial,
    and speed tasks—among adult populations (21,45).

    Subcortical structures subserving the fronto-striatal
    connection are also suggested to modulate efficient
    recruitment of executive control and these structures may
    be influenced by fitness. Chaddock et al. (13) investigated
    the relationships between aerobic fitness, performance on
    a modified flanker task, and volume of the basal ganglia,
    a group of structures located at the base of the forebrain
    and implicated in action selection and execution (41).
    The flanker task is an executive control task that specifi-
    cally requires variable amounts of inhibitory control. It
    consists of arrays of congruent and incongruent stimuli,
    with the instruction to respond to the directionality of
    the central (i.e., target) stimulus and ignore flanking
    stimuli. Higher-fit children exhibited larger volumes of
    dorsal striatum (eg, left caudate nucleus, bilateral puta-
    men, globus pallidus), which was negatively associated

    Brain and Cognition 141

    with behavioral interference attributed to the incongruent
    flanking stimuli, providing a behavioral sequelae for the
    observed structural differences between groups. Indeed,
    lower-fit children exhibited over a twofold higher inter-
    ference effect indicating less efficient management of
    conflicting cues compared with higher-fit children.

    Beyond MRI measures of brain structure, the avail-
    ability of functional MRI (fMRI) allows for a proxy
    measure (eg, blood flow) of underlying brain activation
    during task performance, thereby detecting regional
    locations and networks that are associated with higher
    aerobic fitness. Although research is limited, a handful
    of studies have used fMRI to assess how physical activ-
    ity or aerobic fitness changes patterns of brain activation
    (14,17,24,52,71). Specifically, Chaddock et al. (14) com-
    pared performance on early and later blocks of a flanker
    task between higher and lower fit 9- and 10-year-olds
    using fMRI. During congruent trials requiring lower
    amounts of executive control, both groups showed greater
    activation in the prefrontal and parietal cortex (eg., left
    middle frontal gyrus, supplementary motor area, anterior
    cingulate cortex [ACC], left superior parietal lobe) during
    the early blocks when the paradigm was more novel, fol-
    lowed by a decrease in activity during the later blocks.
    However, during incongruent trials requiring the upregu-
    lation of executive control, higher-fit children maintained
    accuracy across blocks and exhibited increased activa-
    tion in the left middle frontal gyrus, right middle frontal
    gyrus, supplementary motor area, ACC, and the left
    superior parietal lobe during the early task block and
    reduced activity in the later block. In contrast, the lower-
    fit children declined in accuracy across blocks without
    displaying any changes in brain activity. These findings
    are consistent with studies that have used event-related
    brain potentials (ERPs, discussed later) to indicate that
    higher-fit children may have greater ability to upregulate
    neural processes involved in executive control to meet
    task demands and maintain performance.

    Differences in underlying brain activity have also
    been found as a function of physical activity interven-
    tions. Davis et al. (24) tested the effect of 3 months of
    physical activity training on executive function in over-
    weight children using cognitive assessments, achieve-
    ment measures, and fMRI. Participants were randomized
    into high (40 min) and low-dose (20-min) exercise and
    control groups. Exercise selectively enhanced executive
    control tasks and math achievement in a dose-dependent
    manner. The improvement in math achievement was
    particularly important because no educational instruction
    was given. Relative to the fMRI data, the exercise group
    (high and low-dose exercise groups were collapsed for
    fMRI analyses) exhibited increased bilateral prefrontal
    cortex activity and decreased activity in the posterior
    parietal cortex. Similarly, Chaddock et al. (17) observed
    differences in brain activity between children participat-
    ing in a 9-month physical activity intervention, relative
    to children assigned to a wait-list control. In addition to
    improved performance on an attentional task of inhibi-
    tory control, intervention participants exhibited decreased

    activation in the right prefrontal cortex while the control
    group showed no changes in brain function from baseline
    to posttest. Furthermore, posttest brain activity among
    the intervention group showed similar anterior frontal
    brain patterns and incongruent accuracy performance to
    a group of college-aged adults while the control group
    children failed to show such a pattern.

    A third physical activity intervention study exam-
    ined changes in accuracy and brain function among
    8- to 11-year-old overweight children using flanker and
    antisaccade tasks (52). Relative to the control group,
    intervention participants exhibited decreased activity in
    several areas subserving the antisaccade performance,
    including the precentral gyrus and posterior parietal
    cortex. However, increased activity was observed in
    regions involved in flanker accuracy including the ACC
    and superior frontal gyrus. This observation for ACC
    activation was in contrast with Chaddock et al. (17) who
    did not observe any changes in ACC activation using the
    flanker task. Nevertheless, evidence from fMRI studies
    suggests that that physical activity may influence the
    modulation of neural circuitry supporting executive
    control in prepubertal children, but also suggests that
    further work is needed to better determine the differential
    patterns of activation across various cognitive tasks.

    Aerobic Fitness and Executive
    Control: Evidence From
    Neuroelectric Studies

    In addition to fMRI, other neuroimaging tools have been
    used to study the relation of physical activity to brain
    function. Findings from our laboratory have indicated
    that fitness has a positive relationship with performance
    on cognitive tasks requiring variable levels of executive
    control in children (7). Several subsequent studies have
    assessed ERPs during stimulus engagement and response
    production to determine which aspects of cognition are
    influenced by fitness. ERPs refer to patterns of neuroelec-
    tric activation that occur in response to, or in preparation
    for, a stimulus or response. Although ERPs have low
    spatial resolution, they have high temporal resolution;
    therefore, reflecting specific neural operations that occur
    between stimulus encoding and response execution.
    The P3 (P300 or P3b) is a positive-going component
    observed in the stimulus-locked ERP waveform that
    has captured considerable attention in the literature and
    is believed to represent the updating of memory once
    sensory information has been analyzed (26). P3 ampli-
    tude and latency are thought to be directly proportional
    to the amount of attentional resources allocated during
    stimulus engagement and information processing speed
    during stimulus evaluation, respectively (28,63). Thus,
    this endogenous component provides rich information
    regarding brain function underlying behavior within the
    stimulus environment.

    Several studies in our laboratory have demonstrated
    differences in the P3 component between higher- and

    142 Khan and Hillman

    lower-fit children. Hillman et al. (42) assessed neuroelec-
    tric differences between higher and lower-fit 9- to 10-year-
    olds on a modified flanker task. Higher-fit children not
    only out-performed lower-fit children, they also exhibited
    larger P3 amplitude, indicating greater attentional resource
    allocation during stimulus evaluation. Interestingly though,
    the findings were observed across both flanker condi-
    tions requiring variable (ie, lower and higher) amounts
    of inhibition. In a subsequent study among another group
    of children (mean = 10.1 years) the flanker task was
    manipulated by adding an incompatible stimulus-response
    condition during which participants were instructed to
    press a button that opposed the direction of the central
    stimulus (64), necessitating greater inhibitory control
    and cognitive flexibility. Higher-fit children maintained
    their response accuracy (which was higher than lower-fit
    children) regardless of the stimulus-response compatibility
    condition. However, lower-fit children exhibited reduced
    accuracy with increases in task difficulty. Inspection of the
    underlying ERPs showed that higher-fit children exhibited
    larger P3 amplitude relative to lower-fit children, but also
    for incompatible compared with compatible conditions;
    modulation that was not evidenced in the lower-fit group.
    Furthermore, higher-fit children had shorter P3 latencies
    than their lower-fit counterparts. These results indicated
    that higher-fit children have greater attentional resource
    allocation and enhanced cognitive flexibility during tasks
    that modulate cognitive control demands. Given that cog-
    nitive processing speed was also faster, the data suggest
    that higher amounts of fitness are related to better acqui-
    sition of information within the stimulus environment. In
    summary, our initial findings suggested that fitness had a
    general relationship with cognition across tasks requiring
    variable amounts of executive control. Subsequent studies
    in our laboratory modified the flanker paradigm to extend
    the literature further by demonstrating selective effects of
    fitness on inhibitory control, suggesting a need for sensitive
    assays to bore out the selective or disproportionate nature
    of the fitness-cognition relationship.

    In addition to improved cognitive function during
    stimulus acquisition, higher-fit children also exhibit
    enhanced action monitoring during response execution;
    another important aspect of executive control. To carry
    out goal-directed behavior, individuals must continuously
    monitor interactions between intended and executed
    actions. The error-related negatively (ERN)—localized
    to the dorsal portion of the ACC—is a negative-going
    component observed during a response and is suggested
    to index the action monitoring system (33,36). Findings
    from both Hillman et al. (42) and Pontifex et al. (64)
    indicated that when instructed to respond as quickly
    as possible, higher-fit children exhibited smaller ERN
    amplitudes and higher response accuracy following com-
    mission errors, relative to their lower-fit counterparts.
    Interpreting these findings in the context of the conflict
    monitoring theory—which posits that the ACC detects
    response conflicts and transmits signals to several regions
    of the brain, including the dorsolateral prefrontal cortex,
    which in turn regulate executive control in support of
    ongoing environmental interaction (5,9)—suggests

    that higher-fit children exhibit lower response conflict
    during task execution. Furthermore, similar to findings
    for the P3, Pontifex et al. (64) observed that the ERN
    was significantly larger for the incompatible condition
    compared with the compatible condition of a modified
    flanker task among the higher-fit children; an effect that
    was not observed among lower-fit children. Although
    the ERN in the incompatible condition did not differ
    across fitness groups, the lower-fit group did not exhibit
    significant changes in ERN. Therefore, higher-fit children
    not only exhibit less conflict, but they also appear to flex-
    ibly modulate action monitoring processes depending
    on executive control demands to optimize behavioral
    interactions within the task environment (44).

    Therefore, cross-sectional studies indicate that
    higher-fit children allocate greater attentional resources
    during stimulus engagement (P3 amplitude) and have
    greater capability to flexibly regulate executive control,
    relative to their lower-fit counterparts. This difference in
    strategy may account for some of the variability observed
    in task performance across fitness levels. Furthermore,
    a recently concluded randomized controlled trial in our
    laboratory demonstrated that 8- and 9-year-olds receiv-
    ing 9 months of physical activity (5 days/week) exhib-
    ited greater improvements in attentional inhibition and
    cognitive flexibility coupled with increased attentional
    resources (eg, increased P3 amplitude) during tasks
    requiring the upregulation of attentional inhibition and
    cognitive flexibility; an effect not observed for the waitlist
    control (Hillman et al., submitted for publication).

    Considering both the hippocampal and prefrontal
    cortex literature, fitness has selectively positive effects on
    volumes of particular structures that subserve relational
    memory and executive control. In addition, the positive
    associations between fitness and brain structure were
    only found for performance on hippocampal-dependent/
    relational memory tasks and not item memory tasks,
    further reinforcing the fitness-brain relationship to the
    hippocampus. These findings are consistent with those
    from animal studies indicating that the hippocampus
    is positively influenced by physical activity. Emerg-
    ing evidence from neuroimaging studies suggests that
    higher-fit children demonstrate differential patterns in
    brain activity while performing executive control tasks,
    relative to their lower-fit counterparts. Furthermore, the
    provision of physical activity appears to alter efficiency
    and flexible modulation of neural circuitry that supports
    executive control in children.

    Translational Applications of
    Physical Activity to Academics,
    Learning and Real-World Tasks

    The relationship between physical activity, fitness, and
    academic achievement has received attention in recent
    years due to the increased prevalence of children who
    are lower-fit and overweight. Several publications on the
    topic indicate that regular physical activity and higher
    levels of aerobic fitness have weak but positive effects

    Brain and Cognition 143

    on academic achievement (4,11). Given that much of this
    evidence is based on cross-sectional studies (however, see
    Donnelly et al. (27) for an exception), the magnitude of
    the effect of physical activity on academic achievement
    remains an issue of debate. However, there is no empirical
    data in the literature that suggests that increasing time
    spent in physical activity in schools has a detrimental
    effect on academic achievement.

    In addition, the literature thus far has focused pre-
    dominantly on the ability to perform cognitive tasks.
    That is, human physical activity studies have focused on
    differences in task performance as a function of fitness or
    engagement in a physical activity program, rather than the
    ability to acquire or learn new information per se. Demon-
    strating that learning is positively influenced by physical
    activity or aerobic fitness would provide an important
    link between physical activity and cognitive and brain
    health that is directed toward the everyday acquisition
    of information with implications for lifelong cognitive
    wellbeing. A recent study in our laboratory examined
    the influence of fitness on learning and memory among
    9- to 10-year-olds (65). Higher- and lower-fit children
    performed a task requiring them to learn the names of
    specific regions on a fictitious map, under a condition
    in which they only studied the maps and names versus
    a condition in which they were tested during studying
    process. Testing during studying has been previously
    shown to enhance accuracy for retrieval and assists learn-
    ing through the provision of a strategy (8). The retention
    day occurred one day after initial learning and involved
    two different recall conditions: free recall and cued recall.
    No differences in performance at initial learning were
    observed between higher-fit and lower-fit participants.
    However, during the retention session higher-fit children
    outperformed lower-fit children, particularly when the
    material was learned without an explicit strategy (ie, the
    study only condition). Therefore, fitness appears to be
    associated with enhanced learning, particularly during
    conditions in which learning is more challenging. Given
    the greater retrieval among higher-fit children and the
    lack of interaction between fitness and the cued condi-
    tion, the beneficial influence of fitness may primarily
    occur via enhancements during initial encoding for novel
    information, rather than through the enhancement of
    retrieval processes. These findings extend the literature
    by showing that differences in learning are evident among
    prepubertal children as a function of fitness.

    A last example of how fitness influences aspects of
    children’s everyday life examined the role of fitness in
    successful street crossing among 8- to 10-year-olds (16).
    Higher- and lower-fit children were asked to navigate
    trafficked roads—while walking on a manually driven
    treadmill –within a modeled virtual environment. Child
    pedestrians crossed the street while undistracted, listening
    to music, or conversing on a hands-free cellular phone.
    There was an interaction between fitness and street cross-
    ing condition such that higher-fit children maintained
    street crossing rates across all conditions, regardless of
    the amount of distraction. In contrast, lower-fit children
    exhibited decreases in street crossing success rates when

    on the phone, compared with the undistracted and music
    condition. Therefore, higher levels of aerobic fitness may
    attenuate impairment typically observed with multitask-
    ing during street crossing. These findings are particularly
    important considering that pedestrian accidents are the
    second leading cause of injury and mortality in children
    between the ages of 5 and 14 in the U.S. (66).

    Research Needs

    Additional longitudinal studies are needed to better exam-
    ine the association between changes in fitness, physical
    activity, brain, and cognition. It should be noted that
    neuroimaging evidence is limited in its generalizability
    because of the typically lower sample sizes and variability
    in subject characteristics. Therefore, additional random-
    ized controlled trials are needed to elucidate how changes
    in physical activity and fitness predict changes in brain
    structure and function in children. Further research is
    needed to determine to what extent genetics, motivation,
    personality characteristics, nutrition, and intellectual
    stimulation play roles in mediating the fitness-brain
    relationship observed in cross-sectional studies. Critically
    missing from the literature is the examination of the rela-
    tionship between chronic physical activity, independent
    of aerobic fitness, and cognitive function in childhood.
    Thus, future studies should objectively assess physical
    activity in addition to aerobic fitness. Given that much
    of the current knowledge is based on studies among nor-
    mally developing and healthy children, additional studies
    among children with autism, ADHD, and other disorders
    are needed to enhance the generalizability of findings.

    General Conclusions
    Evidence from rodent models strongly indicates that
    physical activity is a potent stimulator of molecular and
    cellular components underlying brain structure and func-
    tion. Furthermore, studies in humans suggest that physical
    activity may be protective against age-related brain tissue
    loss and may be positively associated with brain health
    and cognitive function in children as well. Childhood
    presents a critical period in brain growth characterized by
    prolonged maturation of structures involved in executive
    function and relational memory as well as fine-tuning of
    the brain circuitry intended to support operations of the
    adult brain (58). Therefore, this protracted development
    may provide opportunity for physical activity to optimize
    cognitive function and brain health during childhood.
    However, longitudinal studies are needed to examine
    how changes in physical activity affect the physical and
    functional developmental trajectory of the brain.

    Considering the rise in physical inactivity and obe-
    sity in the U.S. and other industrialized nations, the results
    from the studies reviewed in this article have significant
    public health and educational implications (62). Although
    additional experimental studies are warranted, the current
    evidence points to the benefits of physical activity and
    aerobic fitness for cognitive and brain health in childhood.

    144 Khan and Hillman

    References
    1. Altman J, Das GD. Post-natal origin of microneurones in

    the rat brain. Nature. 1965; 207(5000):953–956. PubMed
    doi:10.1038/207953a0

    2. Andersen SL. Trajectories of brain development: point
    of vulnerability or window of opportunity? Neurosci
    Biobehav Rev. 2003; 27(1–2):3–18. PubMed doi:10.1016/
    S0149-7634(03)00005-8

    3. Bailey R, Hillman C, Arent S, Petitpas A. Physical activity:
    an underestimated investment in human capital? J Phys
    Act Health. 2013; 10(3):289–308. PubMed

    4. Biddle SJ, Asare M. Physical activity and mental health
    in children and adolescents: a review of reviews. Br J
    Sports Med. 2011; 45(11):886–895. PubMed doi:10.1136/
    bjsports-2011-090185

    5. Botvinick MM, Braver TS, Barch DM, Carter CS, Cohen
    JD. Conflict monitoring and cognitive control. Psychol
    Rev. 2001; 108(3):624–652. PubMed doi:10.1037/0033-
    295X.108.3.624

    6. Brown J, Cooper‐Kuhn CM, Kempermann G, et al.
    Enriched environment and physical activity stimulate
    hippocampal but not olfactory bulb neurogenesis. Eur J
    Neurosci. 2003; 17(10):2042–2046. PubMed doi:10.1046/
    j.1460-9568.2003.02647.x

    7. Buck SM, Hillman CH, Castelli DM. The relation of
    aerobic fitness to stroop task performance in preadolescent
    children. Med Sci Sports Exerc. 2008; 40(1):166–172.
    PubMed doi:10.1249/mss.0b013e318159b035

    8. Carpenter SK, Pashler H. Testing beyond words: using tests
    to enhance visuospatial map learning. Psychon Bull Rev.
    2007; 14(3):474–478. PubMed doi:10.3758/BF03194092

    9. Carter CS, Van Veen V. Anterior cingulate cortex and
    conflict detection: an update of theory and data. Cogn
    Affect Behav Neurosci. 2007; 7(4):367–379. PubMed
    doi:10.3758/CABN.7.4.367

    10. Casey BJ, Giedd JN, Thomas KM. Structural and func-
    tional brain development and its relation to cognitive
    development. Biol Psychol. 2000; 54(1–3):241–257.
    PubMed doi:10.1016/S0301-0511(00)00058-2

    11. Castelli DM, Hillman CH, Buck SM, Erwin HE. Physical
    fitness and academic achievement in third-and fifth-grade
    students. J Sport Exerc Psychol. 2007; 29(2):239–252.
    PubMed

    12. Chaddock L, Erickson KI, Prakash RS, et al. A neuro-
    imaging investigation of the association between aerobic
    fitness, hippocampal volume, and memory performance in
    preadolescent children. Brain Res. 2010; 1358:172–183.
    PubMed doi:10.1016/j.brainres.2010.08.049

    13. Chaddock L, Erickson KI, Prakash RS, et al. Basal ganglia
    volume is associated with aerobic fitness in preadolescent
    children. Dev Neurosci. 2010; 32(3):249–256. PubMed
    doi:10.1159/000316648

    14. Chaddock L, Erickson KI, Prakash RS, et al. A functional
    MRI investigation of the association between childhood
    aerobic fitness and neurocognitive control. Biol Psychol.
    2012; 89(1):260–268. PubMed doi:10.1016/j.biopsy-
    cho.2011.10.017

    15. Chaddock L, Hillman CH, Buck SM, Cohen NJ. Aerobic
    fitness and executive control of relational memory in pread-
    olescent children. Med Sci Sports Exerc. 2011; 43(2):344–
    349. PubMed doi:10.1249/MSS.0b013e3181e9af48

    16. Chaddock L, Neider MB, Lutz A, Hillman CH, Kramer
    AF. Role of childhood aerobic fitness in successful street
    crossing. Med Sci Sports Exerc. 2012; 44(4):749–753.
    PubMed doi:10.1249/MSS.0b013e31823a90cb

    17. Chaddock-Heyman L, Erickson KI, Voss MW, et al. The
    effects of physical activity on functional MRI activation
    associated with cognitive control in children: A random-
    ized controlled intervention. Front. Hum. Neurosci. 2013;
    7:72. PubMed doi:10.3389/fnhum.2013.00072

    18. Cohen NJ, Eichenbaum H. Memory, Amnesia, and the
    Hippocampal System. Cambridge, MA: MIT Press, 1995.

    19. Colcombe SJ, Erickson KI, Scalf PE, et al. Aerobic exer-
    cise training increases brain volume in aging humans. J
    Gerontol A Biol Sci Med Sci. 2006; 61(11):1166–1170.
    PubMed doi:10.1093/gerona/61.11.1166

    20. Colcombe SJ, Kramer AF, Erickson KI, et al. Cardiovas-
    cular fitness, cortical plasticity, and aging. Proc Natl Acad
    Sci USA. 2004; 101(9):3316–3321. PubMed doi:10.1073/
    pnas.0400266101

    21. Colcombe S, Kramer AF. Fitness effects on the cognitive
    function of older adults: a meta-analytic study. Psychol Sci.
    2003; 14(2):125–130. PubMed doi:10.1111/1467-9280.
    t01-1-01430

    22. Cotman CW, Berchtold NC, Christie L. Exercise builds
    brain health: key roles of growth factor cascades and
    inflammation. Trends Neurosci. 2007; 30(9):464–472.
    PubMed doi:10.1016/j.tins.2007.06.011

    23. Davachi L. Item, context and relational episodic encoding
    in humans. Curr Opin Neurobiol. 2006; 16(6):693–700.
    PubMed doi:10.1016/j.conb.2006.10.012

    24. Davis CL, Tomporowski PD, McDowell JE, et al. Exercise
    improves executive function and achievement and alters
    brain activation in overweight children: a randomized, con-
    trolled trial. Health Psychol. 2011; 30(1):91–98. PubMed
    doi:10.1037/a0021766

    25. Dekaban AS, Sadowsky D. Changes in brain weights
    during the span of human life: relation of brain weights
    to body heights and body weights. Ann Neurol. 1978;
    4(4):345–356. PubMed doi:10.1002/ana.410040410

    26. Donchin E. Surprise!… surprise? Psychophysiology. 1981;
    18(5):493–513. PubMed doi:10.1111/j.1469-8986.1981.
    tb01815.x

    27. Donnelly JE, Greene JL, Gibson CA, et al. Physical
    Activity Across the Curriculum (PAAC): a randomized
    controlled trial to promote physical activity and diminish
    overweight and obesity in elementary school children.
    Prev Med. 2009; 49(4):336–341. PubMed doi:10.1016/j.
    ypmed.2009.07.022

    28. Duncan-Johnson CC. P300 latency: a new metric of infor-
    mation processing. Psychophysiology. 1981; 18(3):207–
    215. PubMed

    29. Ehninger D, Kempermann G. Regional effects of wheel
    running and environmental enrichment on cell genesis
    and microglia proliferation in the adult murine neocortex.

    Brain and Cognition 145

    Cereb Cortex. 2003; 13(8):845–851. PubMed doi:10.1093/
    cercor/13.8.845

    30. Eichenbaum H, Cohen NJ. From Conditioning to Con-
    scious Recollection: Memory Systems of the Brain. Oxford:
    Oxford University Press, 2001.

    31. Erickson KI, Voss MW, Prakash RS, et al. Exercise training
    increases size of hippocampus and improves memory. Proc
    Natl Acad Sci USA. 2011; 108(7):3017–3022. PubMed
    doi:10.1073/pnas.1015950108

    32. Erickson KI, Prakash RS, Voss MW, et al. Aerobic fit-
    ness is associated with hippocampal volume in elderly
    humans. Hippocampus. 2009; 19(10):1030–1039. PubMed
    doi:10.1002/hipo.20547

    33. Falkenstein M, Hohnsbein J, Hoormann J, Blanke L.
    Effects of crossmodal divided attention on late ERP com-
    ponents. II: error processing in choice reaction tasks. Elec-
    troencephalogr Clin Neurophysiol. 1991; 78(6):447–455.
    PubMed doi:10.1016/0013-4694(91)90062-9

    34. Farmer J, Zhao Xv, Van Praag H, Wodtke K, Gage F,
    Christie B. Effects of voluntary exercise on synaptic
    plasticity and gene expression in the dentate gyrus of
    adult male sprague–dawley rats in vivo. Neuroscience.
    2004; 124(1):71–79. PubMed doi:10.1016/j.neurosci-
    ence.2003.09.029

    35. Fillit HM, Butler RN, O’Connell AW, et al. Achieving and
    maintaining cognitive vitality with aging. Mayo Clin Proc.
    2002; 77(7):681–696. PubMed doi:10.4065/77.7.681

    36. Gehring WJ, Goss B, Coles MGH, Meyer DE, Donchin E. A
    neural system for error detection and compensation. Psychol
    Sci. 1993; 4(6):385–390. doi:10.1111/j.1467-9280.1993.
    tb00586.x

    37. Giedd JN, Blumenthal J, Jeffries NO, et al. Brain develop-
    ment during childhood and adolescence: a longitudinal
    MRI study. Nat Neurosci. 1999; 2(10):861–863. PubMed
    doi:10.1038/13158

    38. Goldman-Rakic PS. Topography of cognition: parallel
    distributed networks in primate association cortex. Annu
    Rev Neurosci. 1988; 11(1):137–156. PubMed doi:10.1146/
    annurev.ne.11.030188.001033

    39. Gomez-Pinilla F, Hillman C. The influence of exercise on
    cognitive abilities. Compr Physiol. 2013; 3(1):403–428.
    PubMed

    40. Gould E, Woolley C, McEwen B. Short-term glucocorticoid
    manipulations affect neuronal morphology and survival in
    the adult dentate gyrus. Neuroscience. 1990; 37(2):367–
    375. PubMed doi:10.1016/0306-4522(90)90407-U

    41. Graybiel AM. The basal ganglia: learning new tricks and
    loving it. Curr Opin Neurobiol. 2005; 15(6):638–644.
    PubMed doi:10.1016/j.conb.2005.10.006

    42. Hillman CH, Buck SM, Themanson JR, Pontifex MB,
    Castelli DM. Aerobic fitness and cognitive development:
    event-related brain potential and task performance indices
    of executive control in preadolescent children. Dev Psychol.
    2009; 45(1):114–129. PubMed doi:10.1037/a0014437

    43. Hillman CH, Castelli DM, Buck SM. Aerobic fitness and
    neurocognitive function in healthy preadolescent children.
    Med Sci Sports Exerc. 2005; 37(11):1967–1974. PubMed
    doi:10.1249/01.mss.0000176680.79702.ce

    44. Hillman CH, Kamijo K, Scudder M. A review of chronic
    and acute physical activity participation on neuroelectric
    measures of brain health and cognition during child-
    hood. Prev Med. 2011; 52(Suppl 1):S21–S28. PubMed
    doi:10.1016/j.ypmed.2011.01.024

    45. Hillman CH, Kramer AF, Belopolsky AV, Smith DP. A
    cross-sectional examination of age and physical activ-
    ity on performance and event-related brain potentials in
    a task switching paradigm. Int J Psychophysiol. 2006;
    59(1):30–39. PubMed doi:10.1016/j.ijpsycho.2005.04.009

    46. Huang T, Larsen KT, Ried-Larsen M, Møller NC, Andersen
    L. The effects of physical activity and exercise on brain-
    derived neurotrophic factor in healthy humans: a review.
    Scand J Med Sci Sports. 2014; 24(1):1–10. PubMed

    47. Huttenlocher PR, Dabholkar AS. Regional differences in
    synaptogenesis in human cerebral cortex. J Comp Neurol.
    1997; 387(2):167–178. PubMed doi:10.1002/(SICI)1096-
    9861(19971020)387:2<167::AID-CNE1>3.0.CO;2-Z

    48. Institute of Medicine (IOM). Educating the Student Body:
    Taking Physical Activity and Physical Education to School.
    Washington, DC: The National Academies Press, 2013.

    49. Johnson MH. Functional brain development in humans.
    Nat Rev Neurosci. 2001; 2(7):475–483. PubMed
    doi:10.1038/35081509

    50. Kempermann G, Kuhn HG, Gage FH. More hippocam-
    pal neurons in adult mice living in an enriched envi-
    ronment. Nature. 1997; 386(6624):493–495. PubMed
    doi:10.1038/386493a0

    51. Kobilo T, Liu Q, Gandhi K, Mughal M, Shaham Y, van
    Praag H. Running is the neurogenic and neurotrophic
    stimulus in environmental enrichment. Learn Mem. 2011;
    18(9):605–609. PubMed doi:10.1101/lm.2283011

    52. Krafft CE, Schwarz NF, Chi L, et al. An 8-month random-
    ized controlled exercise trial alters brain activation during
    cognitive tasks in overweight children. Obesity (Silver
    Spring). 2014; 22(1):232–242. PubMed

    53. Kramer AF, Erickson KI, Prakash R, Voss M. Risk Reduc-
    tion Factors for Alzheimer’s Disease and Cognitive
    Decline in Older Adults: Physical Activity. Preventing
    Alzheimer’s Disease and Cognitive Decline Program and
    Abstracts, 2010, pp. 65–69.

    54. Kuhn HG, Dickinson-Anson H, Gage FH. Neurogenesis
    in the dentate gyrus of the adult rat: age-related decrease
    of neuronal progenitor proliferation. J Neurosci. 1996;
    16(6):2027–2033. PubMed

    55. Laske C, Banschbach S, Stransky E, et al. Exercise-induced
    normalization of decreased BDNF serum concentration
    in elderly women with remitted major depression. Int J
    Neuropsychopharmacol. 2010; 13(5):595–602. PubMed
    doi:10.1017/S1461145709991234

    56. Lenroot RK. Brain development in children and ado-
    lescents: insights from anatomical magnetic resonance
    imaging. Neurosci Biobehav Rev. 2006; 30(6):718–729.
    PubMed doi:10.1016/j.neubiorev.2006.06.001

    57. Levine D, Barnes PD. Cortical maturation in normal and
    abnormal fetuses as assessed with prenatal MR imaging.
    Radiology. 1999; 210(3):751–758. PubMed doi:10.1148/
    radiology.210.3.r99mr47751

    146 Khan and Hillman

    58. Luna B. Developmental changes in cognitive control
    through adolescence. Adv Child Dev Behav. 2009; 37:233–
    278. PubMed doi:10.1016/S0065-2407(09)03706-9

    59. Ming GL, Song H. Adult neurogenesis in the mam-
    malian central nervous system. Annu Rev Neurosci.
    2005; 28:223–250. PubMed doi:10.1146/annurev.
    neuro.28.051804.101459

    60. Monti JM, Hillman CH, Cohen NJ. Aerobic fitness
    enhances relational memory in preadolescent children: the
    FITKids randomized control trial. Hippocampus. 2012;
    22(9):1876–1882. PubMed doi:10.1002/hipo.22023

    61. O’Callaghan RM, Ohle R, Kelly ÁM. The effects of
    forced exercise on hippocampal plasticity in the rat: a
    comparison of LTP, spatial-and non-spatial learning. Behav
    Brain Res. 2007; 176(2):362–366. PubMed doi:10.1016/j.
    bbr.2006.10.018

    62. Olshansky SJ, Passaro DJ, Hershow RC, et al. A poten-
    tial decline in life expectancy in the United States in the
    21st century. N Engl J Med. 2005; 352(11):1138–1145.
    PubMed doi:10.1056/NEJMsr043743

    63. Polich J. Updating P300: an integrative theory of P3a
    and P3b. Clin Neurophysiol. 2007; 118(10):2128–2148.
    PubMed doi:10.1016/j.clinph.2007.04.019

    64. Pontifex MB, Raine LB, Johnson CR, et al. Cardiore-
    spiratory fitness and the flexible modulation of cognitive
    control in preadolescent children. J Cogn Neurosci. 2011;
    23(6):1332–1345. PubMed doi:10.1162/jocn.2010.21528

    65. Raine LB, Lee HK, Saliba BJ, Chaddock-Heyman LC,
    Hillman CH, Kramer AF. The influence of childhood
    aerobic fitness on learning and memory. PLoS ONE. 2013;
    8(9):e72666. PubMed doi:10.1371/journal.pone.0072666

    66. Safe Kids USA [Internet]. Latest trends in child pedestrian
    safety: a five year review. 2007 [Accessed 2013 Aug 2].

    Available from: http://www.safekids.org/research-report/
    latest-trends-child-pedestrian-safety-five-year-review-
    october-2007.

    67. van Praag H, Christie BR, Sejnowski TJ, Gage FH.
    Running enhances neurogenesis, learning, and long-
    term potentiation in mice. Proc Natl Acad Sci USA.
    1999; 96(23):13427–13431. PubMed doi:10.1073/
    pnas.96.23.13427

    68. van Praag H, Kempermann G, Gage FH. Running increases
    cell proliferation and neurogenesis in the adult mouse
    dentate gyrus. Nat Neurosci. 1999; 2(3):266–270. PubMed
    doi:10.1038/6368

    69. van Praag H, Shubert T, Zhao C, Gage FH. Exercise
    enhances learning and hippocampal neurogenesis in aged
    mice. J Neurosci. 2005; 25(38):8680–8685. PubMed
    doi:10.1523/JNEUROSCI.1731-05.2005

    70. Vivar C, Potter MC, van Praag H. All about running:
    synaptic plasticity, growth factors and adult hippocampal
    neurogenesis. In: Curr Top Behav Neurosci. Springer,
    2013, pp. 189–210.

    71. Voss MW, Chaddock L, Kim JS, et al. Aerobic fitness
    is associated with greater efficiency of the network
    underlying cognitive control in preadolescent children.
    Neuroscience. 2011; 199:166–176. PubMed doi:10.1016/j.
    neuroscience.2011.10.009

    72. Wang Z, Van Praag H. Exercise and the brain: neurogenesis,
    synaptic plasticity, spine density, and angiogenesis. In:
    Functional Neuroimaging in Exercise and Sport Sciences.
    Springer, 2012, pp. 3–24.

    73. Warburton DE, Nicol CW, Bredin SS. Health benefits of
    physical activity: the evidence. CMAJ. 2006; 174(6):801–
    809. PubMed doi:10.1503/cmaj.051351

    Copyright of Pediatric Exercise Science is the property of Human Kinetics Publishers, Inc.
    and its content may not be copied or emailed to multiple sites or posted to a listserv without
    the copyright holder’s express written permission. However, users may print, download, or
    email articles for individual use.

    BRIEF REPORT

  • Exercise and Fitness Modulate Cognitive Function in Older Adults
  • Chien-Heng Chu
    National Taiwan Sport University

    Ai-Guo Chen
    Yangzhou University

    Tsung-Min Hung
    National Taiwan Normal University

    Chun-Chih Wang and Yu-Kai Chang
    National Taiwan Sport University

    This study investigated the effects of acute exercise on cognitive function and the modulatory role of
    fitness in the relationship between exercise and cognition. Forty-six healthy older adults, categorized into
    higher or lower fitness groups, completed the Stroop test after both 30 min of aerobic exercise and a
    reading control with a counterbalanced order. Our findings demonstrated that acute exercise leads to
    general improvements in 2 types of cognitive functions and to specific improvements in executive
    function. Additionally, older adults with initially higher fitness levels experienced greater beneficial
    effects from acute exercise.

    Keywords: aerobic exercise, cognition, executive function, inhibition, Stroop test

    The population over 60 years old has rapidly grown and
    changed the worldwide demographic landscape (Gorman, 2002).
    This aging population not only experiences the deterioration of
    physical functions but also suffers from declining brain and cog-
    nitive functions. Indeed, normal aging is associated with brain
    volume atrophy of approximately 15% to 25% (Jernigan et al.,
    2001) and with it the degradation of cognitive processes, including
    memory, reasoning, and information processing speed (Salthouse,
    2004). The influence of acute exercise, defined as a single bout of
    exercise, on cognitive performance has received substantial atten-
    tion within younger populations, demonstrating positive changes
    with small to moderate effects on various types of cognitive
    performance (Chang & Etnier, 2015; Chang, Labban, Gapin, &
    Etnier, 2012; Chu, Alderman, Wei, & Chang, 2015; Lambourne &
    Tomporowski, 2010; McMorris, Sproule, Turner, & Hale, 2011).
    However, examination of whether the positive effects of acute

    exercise extend to older adults has been limited, with ambiguous
    findings.

    Pesce and Audiffren (2011) found that switch performance
    improved following acute exercise at moderate intensity in both
    younger and older adult groups. Given that switching is one of the
    primary executive function aspects, these results suggested that the
    beneficial effects of acute exercise could extend to higher order
    cognitive function, regardless of age. In contrast, research that
    used a similar paradigm (i.e., Alternate Uses test) found partially
    conflicting findings wherein the positive effects of acute exercise
    in older adults only partially benefited switching (Netz, Tomer,
    Axelrad, Argov, & Inbar, 2007). Another study found changes in
    only basic cognition levels in older adults following acute exercise
    (i.e., Stroop color condition) and failed to demonstrate an effect on
    the inhibition- and interference-related executive function aspects
    (i.e., Stroop inhibition and interference conditions; Barella, Etnier,
    & Chang, 2010). Notably, these studies measured different cogni-
    tive functions, implying that the cognition type plays a moderating
    role in the relationship between acute exercise and cognition.
    Indeed, Etnier and Chang (2009) proposed that acute exercise
    effects might differ depending on the specific type of cognitive
    function and further studies are required that utilize assessments
    that not only are widely used but also posit multiple cognition
    subtypes with similar features, such as the Stroop test. Therefore,
    future research should examine the effects of acute exercise on
    different cognitive functions derived from similar task character-
    istics to explore these relationships.

    Another potential moderator that must be considered is the
    participant’s cardiovascular fitness status (Brisswalter, Collardeau,
    & René, 2002; Chang et al., 2012). Longitudinal studies have
    indicated that, along with the positive association between cardio-
    vascular fitness and cognitive function (Etgen et al., 2010), exer-

    Chien-Heng Chu, Graduate Institute of Athletics and Coaching Science,
    National Taiwan Sport University, Taoyuan, Taiwan, Republic of China;
    Ai-Guo Chen, College of Physical Education, Yangzhou University, Ji-
    angsu, People’s Republic of China; Tsung-Min Hung, Department of
    Physical Education, National Taiwan Normal University, Taipei, Taiwan,
    Republic of China; Chun-Chih Wang and Yu-Kai Chang, Graduate Insti-
    tute of Athletics and Coaching Science, National Taiwan Sport Universit

    y.

    This research was supported by a portion of Grants NSC 101-2628-H-
    179-002 and NSC 102-2420-H-179-001-MY3 from the Ministry of Sci-
    ence and Technology, Taiwan, to Yu-Kai Chang.

    Correspondence concerning this article should be addressed to Yu-
    Kai Chang, Graduate Institute of Athletics and Coaching Science,
    National Taiwan Sport University, No. 250, Wenhua 1st Road, Guishan
    Township, Taoyuan County 333, Taiwan, Republic of China. E-mail:
    yukaichangnew@gmail.com

    T
    hi

    s
    do

    cu
    m

    en
    t

    is
    co

    py
    ri

    gh
    te

    d
    by

    th
    e

    A
    m

    er
    ic

    an
    P

    sy
    ch

    ol
    og

    ic
    al

    A
    ss

    oc
    ia

    ti
    on

    or
    on

    e
    of

    it
    s

    al
    li

    ed
    pu

    bl
    is

    he
    rs

    .
    T

    hi
    s

    ar
    ti

    cl
    e

    is
    in

    te
    nd

    ed
    so

    le
    ly

    fo
    r

    th
    e

    pe
    rs

    on
    al

    us
    e

    of
    th

    e
    in

    di
    vi

    du
    al

    us
    er

    an
    d

    is
    no

    t
    to

    be
    di

    ss
    em

    in
    at

    ed
    br

    oa
    dl

    y.

    Psychology and Aging © 2015 American Psychological Association
    2015, Vol. 30, No. 4,

    842

    – 848 0882-7974/15/$12.00 http://dx.doi.org/10.1037/pag0000047

    842

    mailto:yukaichangnew@gmail.com

    http://dx.doi.org/10.1037/pag0000047

    cise interventions that induced fitness improved specific cognitive
    function types (Angevaren, Aufdemkampe, Verhaar, Aleman, &
    Vanhees, 2008; Smith et al., 2010). These superior cognitive-
    related foundations associated with high cardiovascular fitness
    may result in larger improvements following acute exercise. A
    meta-analysis conducted by Chang et al. (2012) indicated that
    individuals with higher fitness levels received the largest acute
    exercise benefit effects compared with individuals with low or
    moderate fitness levels, both immediately and following a delay
    after exercise. Notably, this viewpoint is primarily based upon
    studies performed with younger adults. Furthermore, only a few
    studies have investigated these differences among individuals with
    different fitness levels (individuals with high fitness levels, but not
    moderate or low fitness, were typically evaluated; Chang, Chi, et
    al., 2014; Chang et al., 2012).

    Recently, the modulatory role of fitness in acute exercise and
    cognition in older adults has been preliminarily explored. Pesce,
    Cereatti, Forte, Crova, and Casella (2011) indicated that road
    cyclists had better visual attention control and performance in
    attentional tasks involving executive control compared with a
    sedentary group during an acute bout of aerobic exercise. Similar
    fitness-moderated effects of acute exercise on cognitive flexibility
    were observed (Netz, Argov, & Inbar, 2009). However, these
    studies focused on highly trained individuals and cognitive per-
    formance assessed during acute exercise or utilized assessments
    that examined a single construct.

    Whether the effects of acute exercise have general or specific
    effects on the different cognition types and whether fitness status
    moderates the magnitude of favorable acute exercise effects on
    these cognitive performances, particularly in older adults, remain
    undetermined. The present study examined the effects of acute
    exercise on two types of cognitive processes derived from the
    Stroop test (i.e., Stroop congruent and incongruent conditions),
    where the Stroop incongruent condition is believed to engage a
    greater amount of executive control than does the Stroop congru-
    ent condition, which reflects more-basic information processing
    (e.g., perceptual-motor level; Liotti, Woldorff, Perez, & Mayberg,
    2000; Miyake et al., 2000; West & Alain, 1999). Additionally, the
    acute effects on these cognitive functions were compared between
    older adults with higher and lower fitness levels to explore the
    modulatory role of fitness. Acute effects were expected to induce
    favorable effects on multiple cognition types, and older adults with
    higher fitness were expected to receive larger acute exercise ben-
    efits than were older adults with lower fitness.

    Method

    Participants

    Seventy healthy older adults, ages 60 to 70 years, were
    initially recruited in Taoyuan County, Taiwan. The participants
    were screened using physical activity readiness and health
    screening questionnaires and were required to meet the follow-
    ing criteria: (a) right-hand dominant, (b) no history of neuro-
    logical or major psychiatric disorders, (c) normal or corrected-
    to-normal vision, and (d) no color-blindness to minimize the
    confounders between acute exercise and cognition. Eligible
    participants completed the Digit Span test (Wechsler, 1997).
    Then, the participants were categorized into a higher or a lower

    fitness group on the basis of a VO2peak that fell above or below
    the 55th percentile (�35.0 ml/kg/min for men and �29.4 ml/
    kg/min for women; American College of Sports Medicine,
    2013), resulting in 46 participants, with 22 in the higher fitness
    group and 24 in the lower fitness group. This study was ap-
    proved by the university Institutional Review Board, and all
    participants provided informed consent.

    Cardiovascular Fitness Test

    Cardiovascular fitness was assessed via a submaximal exercise
    test according to the YMCA cycle ergometry protocol (Golding,
    1989). The protocol was appropriate for adults with a Class A risk
    stratification (Fletcher et al., 2001). The YMCA protocol includes
    two to four consecutive 3-min circuits, which have specific work-
    loads designed to raise the steady-state heart rate between 110
    beats/min and 85% of the age-predicted maximal heart rate (e.g.,
    220-age). To begin, the participant rode a cycle ergometer (Er-
    goselect 100/200, Ergoline GmbH, Germany) with a workload of
    150 kpm/min (25 W) and a 50-rpm pedaling rate. The average
    heart rate during the last 15–30 s of the final second and third
    minutes determined the subsequent workloads (e.g., 750 kpm/min,
    600 kpm/min, or 300 kpm/min). When the target steady-state heart
    rate was observed for two consecutive circuits, the VO2peak was
    calculated on the basis of the slope regarding heart rates, workload,
    and body mass.

    The Stroop test

    The Stroop test (Stroop, 1935) is a widely used neuropsycho-
    logical assessment recommended for adaptation in exercise–
    cognition research. The computerized Stroop test consists of two
    types of conditions— congruent and incongruent—and was pre-
    sented using Stim2 (Neurosoft Labs, Inc., Sterling, VA). In the
    congruent condition, Chinese words (i.e., 紅 [red], 藍 [blue], and
    綠 [green]) were presented in the same color as the meaning of the
    words. In the incongruent condition, the name of the color word
    was printed in a different font color. Each stimulus word was
    presented in equal proportions in the congruent (i.e., 33.3% each
    for red, blue, and green words) and incongruent (e.g., the word
    “red” printed in either blue or green color) conditions to minimize
    specific word facilitation. Each block had 60 target stimuli con-
    sisting of 38 congruent and 22 incongruent stimuli with mixed
    presentation. Each 2-cm stimulus was displayed in the center of a
    21-in. (53.3 cm) computer screen. Each trial began with the pre-
    sentation of a fixed cross for 500 ms. Then, either a target-
    congruent or -incongruent stimulus was presented for 506 ms; the
    interval between the fixed cross and the target stimulus was 383,
    583, or 783 ms in a random order to minimize anticipation.
    Participants were instructed to respond as quickly and accurately
    as possible to the color of the presented stimulus by pressing their
    thumb on one of three buttons on a response pane. Each trial was
    completed once the target stimulus response was made within
    1,000 ms. Response time and accuracy were identified as primary
    indices. Each participant was required to complete six blocks with
    a 2-min rest between each block, resulting in a total testing period
    of approximately 25 min.

    T
    hi
    s
    do
    cu
    m
    en
    t
    is
    co
    py
    ri
    gh
    te
    d
    by
    th
    e
    A
    m
    er
    ic
    an
    P
    sy
    ch
    ol
    og
    ic
    al
    A
    ss
    oc
    ia
    ti
    on
    or
    on
    e
    of
    it
    s
    al
    li
    ed
    pu
    bl
    is
    he
    rs
    .
    T
    hi
    s
    ar
    ti
    cl
    e
    is
    in
    te
    nd
    ed
    so
    le
    ly
    fo
    r
    th
    e
    pe
    rs
    on
    al
    us
    e
    of
    th
    e
    in
    di
    vi
    du
    al
    us
    er
    an
    d
    is
    no
    t
    to
    be
    di
    ss
    em
    in
    at
    ed
    br
    oa
    dl
    y.

    843ACUTE EXERCISE, FITNESS, COGNITION, OLDER ADULT

    Experimental Procedures

    The participants attended the laboratory individually on three
    separate days, with at least a 3-day interval between each day
    and within a 2-week period. On Day 1, participants meeting the
    inclusion criteria were fit with a Polar HR monitor (Sport Tester
    PE 3000, Kempele, Finland) and completed a submaximal
    exercise test with a YMCA cycle ergometry protocol. The
    participants were then assigned to either the high- or low-fitness
    group on the basis of their VO2peak (categorized as good or
    poor, respectively).

    The experimental conditions for Days 2 and 3 (i.e., exercise
    and control days) were performed in a counterbalanced order to
    control for potential practice and order effects. Each participant
    was tested at a similar time of day on the 2 days to control
    confounding due to time of testing (Hasher, Chung, May, &
    Foong, 2002). On the exercise day, the resting heart rate (HR)
    was measured by having the participants sit quietly in a chair
    for 10 –15 min. Then, participants completed practice trials to
    become familiarized with the test procedure; once an 85%
    correct rate was achieved, they started the experimental trials.
    Next, participants were instructed to complete a modified acute
    cycling ergometer protocol on the basis of Chang et al. (2011).
    The protocol consisted of three stages: a 5-min warm-up, a
    20-min primary exercising stage at 65% heart rate reserve
    (HRR; the difference between maximal and resting heart rates),
    and a 5-min cool-down. The peddling rate was set at 70 rpm,
    and the workload began with 15 W and then increased or
    decreased gradually until a steady state at the required HR was
    reached. The participants performed the Stroop test within 5
    min of exercise cessation. On the control day, participants
    completed procedures similar to those on the exercise day,
    except that participants read a book related to exercise and
    cognition during treatment. The control condition was intended
    to maintain a low arousal level compared to the exercise con-
    dition.

    A Polar HR monitor and the Rating of Perceived Exertion (RPE)
    scale (Borg, 1982) were used to objectively and subjectively
    confirm the intensity manipulation, respectively. The RPE scale
    ranges from 6 (no exertion at all) to 20 (maximum exertion). The
    experimental session lasted approximately one and a half hours
    each day. Participants were informed about the purpose of the
    study and compensated with US$15 after completing the overall
    experimental session.

    Statistical Analyses

    This study was a randomized control group posttest design. A
    mixed three-way analysis of variance (ANOVA), with a between-
    subjects (i.e., group: lower vs. higher fitness) and two within-
    subject (i.e., treatment: control vs. exercise; Stroop condition:
    congruent vs. incongruent) were used to analyze response time and
    accuracy. Multiple comparisons were performed using t tests with
    Bonferroni adjustments when appropriate. The effect size of the
    partial eta-square was reported for significant effects derived from
    the ANOVA. An alpha of 0.05 was set as significant for all
    analyses.

    Results

    Participant Characteristics and Exercise
    Intensity Check

    Higher scores in the higher fitness group were observed for only
    fitness-related variables (see Table 1). The HR values (beats per
    minute [bpm]) for the lower and higher fitness groups during the
    primary exercise were 124.8 � 7.2 bpm and 119.5 � 8.8 bpm,
    respectively, representing 60% to 65% of HRR. Along with the
    RPE range of 12 to 14, these values suggest that the exercise
    intensity was appropriate.

    Stroop Test Performances

    A preliminary analysis was conducted to test the effects of
    session order. Neither a main effect of session order nor any
    interaction with session order was observed for any dependent
    variable, Fs(1, 21) � 1.81, p � .19.

    A main effect of the treatment condition revealed a shorter
    response time for the exercise compared with that for the control
    condition, F(1, 44) � 169.75, p � .001, partial �2 � 0.79, and a
    main effect of the Stroop condition revealed a longer response time
    for the incongruent compared with the congruent condition,(F(1,
    44) � 123.91, p � .001, partial �2 � 0.73 (see Table 2).

    An interaction between the treatment and fitness was observed,
    F(1, 44) � 10.17, p � .03, partial �2 � 0.18. The follow-up
    comparisons revealed that the exercise condition had a shorter
    response time compared to that of the control condition in both the
    higher (p � .001) and lower (p � .001) fitness groups. Addition-
    ally, the higher fitness group demonstrated a shorter response time
    relative to the lower fitness group in the exercise condition (p �
    .04) but not the control condition (see Figure 1a).

    An interaction between treatment and Stroop condition was also
    observed, F(1, 44) � 10.17, p � .001, partial �2 � 0.24. The
    follow-up comparisons revealed that the response time for the
    incongruent condition was longer than that for the congruent
    condition in both the exercise (632 � 102 vs. 587 � 83, respec-
    tively, p � .001) and control (695 � 99 vs. 632 � 82, respectively,

    Table 1
    Participant Demographics for the Higher and Lower
    Fitness Groups

    Variable

    Higher
    fitnessa

    Lower
    fitnessb

    p ESM SD M SD

    Age (years) 63.8 2.3 64.9 4.0 .29
    Education (years) 9.3 3.5 10.0 4.1 .57
    Height (cm) 161.7 8.6 158.2 6.7 .13
    Weight (kg) 63.8 8.6 61.8 9.4 .45
    BMI (kg.m�2) 24.2 2.5 24.3 3.1 .95
    Digit Span Forward 11.5 2.4 11.2 2.5 .69
    Digit Span Backward 6.0 2.4 6.7 2.4 .32
    VO2peak (mL.kg

    �1.min�1) 36.0 1.2 23.5 2.8 .01 5.80�

    Resting heart rate (bpm) 65.5 8.7 70.0 6.6 .05 0.58�

    Note. ES � effect size with the value of Cohen’s d; BMI � body mass
    index; bpm � beats per minute.
    a Sample size � 22 (12 female). b Sample size � 24 (10 female).
    � p � .05.

    T
    hi
    s
    do
    cu
    m
    en
    t
    is
    co
    py
    ri
    gh
    te
    d
    by
    th
    e
    A
    m
    er
    ic
    an
    P
    sy
    ch
    ol
    og
    ic
    al
    A
    ss
    oc
    ia
    ti
    on
    or
    on
    e
    of
    it
    s
    al
    li
    ed
    pu
    bl
    is
    he
    rs
    .
    T
    hi
    s
    ar
    ti
    cl
    e
    is
    in
    te
    nd
    ed
    so
    le
    ly
    fo
    r
    th
    e
    pe
    rs
    on
    al
    us
    e
    of
    th
    e
    in
    di
    vi
    du
    al
    us
    er
    an
    d
    is
    no
    t
    to
    be
    di
    ss
    em
    in
    at
    ed
    br
    oa
    dl
    y.

    844 CHU, CHEN, HUNG, WANG, AND CHANG

    p � .001) conditions. The exercise condition resulted in shorter
    response times compared to those for the control condition with
    both the congruent and incongruent conditions (p � .001). An
    additional paired t test revealed a smaller difference between the
    congruent and incongruent conditions in the exercise condition
    relative to the control condition, t(45) � 3.83, p � .001 (see Figure
    1b). No three-way interaction was observed.

    Regarding accuracy, main effects of treatment and Stroop con-
    dition were revealed. Higher accuracy for the exercise condition
    compared to the control condition, F(1, 44) � 5.12, p � .03,
    partial �2 � 0.11, and lower accuracy for the incongruent condi-
    tion compared to the congruent condition, F � 28.23, p � .001,
    partial �2 � 0.41, were observed. Neither the main effect of fitness
    nor any interaction was significant.

    Discussion

    This study investigated how cardiovascular fitness moderates
    two types of cognitive function assessed by the Stroop test, fol-
    lowing an acute bout of moderate aerobic exercise in an older
    population. Although participants had a shorter response time and
    an increased accuracy rate in both conditions of the Stroop test
    following exercise, reflecting general improvements, acute exer-
    cise led to additional benefits for executive function by demon-
    strating a smaller difference between the congruent and incongru-

    ent conditions after acute exercise compared to results for the
    control condition. Moreover, older adults with a higher fitness
    level performed significantly better following acute exercise than
    did those with a lower fitness level, suggesting that the level of
    fitness modulates the relationship between acute exercise and
    cognition. Thus, older adults with a higher fitness level received
    disproportionally more benefits from acute exercise than did those
    with a lower fitness level.

    The longer response time and lower accuracy rate in the Stroop
    incongruent condition relative to the Stroop congruent condition,
    regardless of treatment conditions, demonstrate the typical Stroop
    effect (Cohen, Dunbar, & McClelland, 1990). Specifically, com-
    pared with the congruent condition, in which colors of the char-
    acters were named in the absence of interference (i.e., presenting
    automatic activation), greater attentional demand was required to
    resolve the conflicts between the stimulus meaning and color in the
    incongruent condition to inhibit the automatic nature of word-
    reading tendency (Cohen et al., 1990; Milham et al., 2002). Fur-
    thermore, the initiating response to inhibit the bias toward word
    reading is also believed to reflect an inhibitory aspect of executive
    function (Bugg, Jacoby, & Toth, 2008; Nigg, 2000).

    Acute exercise not only reduced the response times for both
    Stroop test conditions but also diminished the interference, sug-
    gesting that acute exercise led to both general and specific im-
    provements in cognitive functions. Our findings that these im-
    provements are associated with acute exercise agree with the
    findings of many previous studies and confirm that an acute bout
    of moderate exercise increases cognitive performance requiring
    different amounts of executive control (Chang, Tsai, Huang,
    Wang, & Chu, 2014; Hyodo et al., 2012; Sibley, Etnier, & Le
    Masurier, 2006; Tam, 2013; Yanagisawa et al., 2010). For exam-
    ple, Tam (2013) reported that, compared with a response time
    reduction of 10.2% in the congruent condition, a 20.6% reduction
    was found in the incongruent condition after acute exercise.
    Chang, Tsai, et al. (2014) also reported that acute exercise im-
    proved performances in five conditions of the Stroop test (i.e.,
    Stroop congruent, word, neutral, square, and incongruent), in
    which the largest increase was observed in the incongruent con-
    dition.

    Table 2
    Stroop Test Performances of Fitness Groups and
    Treatment Conditions

    Variable

    Higher fitness Lower fitness

    Control Exercise Control Exercise

    M SD M SD M SD M SD

    Response time (ms)
    Congruent 622 67 567 61 642 96 608 99
    Incongruent 679 86 602 83 711 109 664 110

    Accuracy rate (%)
    Congruent 92 7 94 5 94 7 95 7
    Incongruent 80 15 84 21 86 9 93 6

    Figure 1. (a) The response time of the Stroop test is a function of treatment condition and fitness. (b) Stroop
    differences during the congruent and incongruent conditions between the exercise and control conditions. Error
    bars represent standard error of the means. � Represents a significant difference between treatments. # Represents
    a significant difference between fitness group (p � .05).

    T
    hi
    s
    do
    cu
    m
    en
    t
    is
    co
    py
    ri
    gh
    te
    d
    by
    th
    e
    A
    m
    er
    ic
    an
    P
    sy
    ch
    ol
    og
    ic
    al
    A
    ss
    oc
    ia
    ti
    on
    or
    on
    e
    of
    it
    s
    al
    li
    ed
    pu
    bl
    is
    he
    rs
    .
    T
    hi
    s
    ar
    ti
    cl
    e
    is
    in
    te
    nd
    ed
    so
    le
    ly
    fo
    r
    th
    e
    pe
    rs
    on
    al
    us
    e
    of
    th
    e
    in
    di
    vi
    du
    al
    us
    er
    an
    d
    is
    no
    t
    to
    be
    di
    ss
    em
    in
    at
    ed
    br
    oa
    dl
    y.

    845ACUTE EXERCISE, FITNESS, COGNITION, OLDER ADULT

    General improvement may be attributed to exercise-induced
    increases in cerebral blood flow (Heo et al., 2010; Ide & Secher,
    2000). That is, brain neuronal activity and metabolism increase
    during exercise (Ide & Secher, 2000), resulting in elevated cerebral
    blood flow (Ogoh & Ainslie, 2009). In contrast, specific improve-
    ments may be interpreted by neuroelectric studies. Acute exercise
    enlarges the P3 amplitude only in tasks reflecting executive func-
    tion (Chu et al., 2015; Hillman, Snook, & Jerome, 2003; Kamijo,
    Nishihira, Higashiura, & Kuroiwa, 2007). These findings from
    neuroelectric perspectives suggest that acute exercise may benefit
    cognition through increased attentional resource allocation for
    tasks requiring greater executive control processes. Taken to-
    gether, increased cerebral blood flow and attention alterations
    suggest possible general and specific functional roles in exercise-
    induced cognitive enhancement.

    Another novel finding from the current study was an interaction
    between treatment and fitness level, namely, the more-fit older
    adults had superior improvements in the Stroop test than did their
    less-fit counterparts. This finding is consistent with a previous
    meta-analysis (Chang et al., 2012) and extends the current knowl-
    edge regarding older adults with extreme higher fitness status (e.g.,
    highly trained) to those with moderate to high fitness status (Netz
    et al., 2009; Pesce & Audiffren, 2011). Although the underlying
    mechanisms remain unknown, potential interpretations based on
    studies indirectly examining this issue have been proposed. Older
    adults with higher fitness may maintain better brain structures and
    functions, providing the foundation for superior benefits from
    acute exercise. Studies associated with structural and functional
    magnetic resonance imaging (MRI) have indicated that older
    adults with higher fitness or long-term exercise training demon-
    strate larger volumes of several brain regions that are the core of
    cognitive functions, such as white and gray matter in the ventro-
    lateral and dorsolateral prefrontal cortexes (Colcombe et al., 2003)
    and the hippocampus (Erickson et al., 2009), as well as greater
    activations in similar brain regions during cognitive task perfor-
    mance (Colcombe et al., 2004). Using an electroencephalogram,
    Hogan et al. (2013) found that adolescents with high fitness levels
    experienced greater lower upper alpha and beta coherence after
    acute exercise, whereas no beneficial acute effect was observed for
    those with lower fitness levels, implying that the individuals with
    higher fitness posited better cortical efficiency.

    The present study was restricted by several factors. First, a
    causal relationship between fitness and cognitive performance
    could not be established because of the cross-sectional design.
    Additionally, the Stroop test reflected only the interference aspect
    of inhibition rather than inhibition related to motor suppression
    (Aron et al., 2007). Therefore, caution should be taken with the
    generalization of the results. Moreover, Boot, Simons, Stothart,
    and Stutts (2013) indicated that different expectancy track benefit
    performances were observed when conducting computer-based
    games, reflecting that the treatment effect may be confounded by
    expectancy individual posited. Expectancy has yet to be consid-
    ered in acute exercise– cognition studies, and future research that
    considers this confounder is suggested. The disproportionate num-
    ber of congruent and incongruent trials may also lead to potential
    bias regarding inhibitory processes. Specifically, more incongruent
    trials than congruent trials may increase the Stroop effect. Al-
    though such bias was limited in the present study because the
    number of trials was constant across groups and conditions, the

    percentage of each trial type is worth considering in future study
    designs.

    In conclusion, acute exercise leads to general and specific im-
    provements for two types of cognitive functions derived from the
    Stroop test, and the beneficial effects of acute exercise are greater
    for older adults with higher fitness. These findings are important
    for older adults and suggest that performing a single bout of
    exercise can improve cognitive performance. These results also
    indicate that good fitness levels can maximize these beneficial
    cognitive effects (i.e., processing speed of cognitive performance)
    induced by acute exercise.

    References

    American College of Sports Medicine. (2013). ACSM’s guidelines for
    exercise testing and prescription (9th ed.). New York, NY: Lippincott
    Williams and Wilkins.

    Angevaren, M., Aufdemkampe, G., Verhaar, H. J., Aleman, A., & Van-
    hees, L. (2008). Physical activity and enhanced fitness to improve
    cognitive function in older people without known cognitive impairment.
    Cochrane Database of Systematic Reviews, 2008(3): CD005381.

    Aron, A. R., Durston, S., Eagle, D. M., Logan, G. D., Stinear, C. M., &
    Stuphorn, V. (2007). Converging evidence for a fronto-basal-ganglia
    network for inhibitory control of action and cognition. Journal of Neu-
    roscience, 27, 11860 –11864. http://dx.doi.org/10.1523/JNEUROSCI
    .3644-07.2007

    Barella, L. A., Etnier, J. L., & Chang, Y.-K. (2010). The immediate and
    delayed effects of an acute bout of exercise on cognitive performance of
    healthy older adults. Journal of Aging and Physical Activity, 18, 87–98.

    Boot, W. R., Simons, D. J., Stothart, C., & Stutts, C. (2013). The pervasive
    problem with placebos in psychology: Why active control groups are not
    sufficient to rule out placebo effects. Perspectives on Psychological
    Science, 8, 445– 454. http://dx.doi.org/10.1177/1745691613491271

    Borg, G. A. (1982). Psychophysical bases of perceived exertion. Medicine
    and Science in Sports and Exercise, 14, 377–381. http://dx.doi.org/10
    .1249/00005768-198205000-00012

    Brisswalter, J., Collardeau, M., & René, A. (2002). Effects of acute
    physical exercise characteristics on cognitive performance. Sports Med-
    icine (Auckland, NZ), 32, 555–566. http://dx.doi.org/10.2165/00007256-
    200232090-00002

    Bugg, J. M., Jacoby, L. L., & Toth, J. P. (2008). Multiple levels of control
    in the Stroop task. Memory & Cognition, 36, 1484 –1494. http://dx.doi
    .org/10.3758/MC.36.8.1484

    Chang, Y.-K., Chi, L., Etnier, J. L., Wang, C.-C., Chu, C.-H., & Zhou, C.
    (2014). Effect of acute aerobic exercise on cognitive performance: Role
    of cardiovascular fitness. Psychology of Sport and Exercise, 15, 464 –
    470. http://dx.doi.org/10.1016/j.psychsport.2014.04.007

    Chang, Y.-K., & Etnier, J. L. (2015). Acute exercise and cognitive func-
    tion: Emerging research issues. Journal of Sport and Health Science, 4,
    1–3. http://dx.doi.org/10.1016/j.jshs.2014.12.001

    Chang, Y.-K., Labban, J. D., Gapin, J. I., & Etnier, J. L. (2012). The effects
    of acute exercise on cognitive performance: A meta-analysis. Brain
    Research, 1453, 87–101. http://dx.doi.org/10.1016/j.brainres.2012.02
    .068

    Chang, Y.-K., Tsai, C.-L., Huang, C.-C., Wang, C.-C., & Chu, I.-H.
    (2014). Effects of acute resistance exercise on cognition in late middle-
    aged adults: General or specific cognitive improvement? Journal of
    Science and Medicine in Sport, 17, 51–55. http://dx.doi.org/10.1016/j
    .jsams.2013.02.007

    Chang, Y.-K., Tsai, C.-L., Hung, T.-M., So, E. C., Chen, F.-T., & Etnier,
    J. L. (2011). Effects of acute exercise on executive function: A study
    with a Tower of London task. Journal of Sport & Exercise Psychology,
    33, 847– 865.

    T
    hi
    s
    do
    cu
    m
    en
    t
    is
    co
    py
    ri
    gh
    te
    d
    by
    th
    e
    A
    m
    er
    ic
    an
    P
    sy
    ch
    ol
    og
    ic
    al
    A
    ss
    oc
    ia
    ti
    on
    or
    on
    e
    of
    it
    s
    al
    li
    ed
    pu
    bl
    is
    he
    rs
    .
    T
    hi
    s
    ar
    ti
    cl
    e
    is
    in
    te
    nd
    ed
    so
    le
    ly
    fo
    r
    th
    e
    pe
    rs
    on
    al
    us
    e
    of
    th
    e
    in
    di
    vi
    du
    al
    us
    er
    an
    d
    is
    no
    t
    to
    be
    di
    ss
    em
    in
    at
    ed
    br
    oa
    dl
    y.

    846 CHU, CHEN, HUNG, WANG, AND CHANG

    http://dx.doi.org/10.1523/JNEUROSCI.3644-07.2007

    http://dx.doi.org/10.1523/JNEUROSCI.3644-07.2007

    http://dx.doi.org/10.1177/1745691613491271

    http://dx.doi.org/10.1249/00005768-198205000-00012

    http://dx.doi.org/10.1249/00005768-198205000-00012

    http://dx.doi.org/10.2165/00007256-200232090-00002

    http://dx.doi.org/10.2165/00007256-200232090-00002

    http://dx.doi.org/10.3758/MC.36.8.1484

    http://dx.doi.org/10.3758/MC.36.8.1484

    http://dx.doi.org/10.1016/j.psychsport.2014.04.007

    http://dx.doi.org/10.1016/j.jshs.2014.12.001

    http://dx.doi.org/10.1016/j.brainres.2012.02.068

    http://dx.doi.org/10.1016/j.brainres.2012.02.068

    http://dx.doi.org/10.1016/j.jsams.2013.02.007

    http://dx.doi.org/10.1016/j.jsams.2013.02.007

    Chu, C.-H., Alderman, B. L., Wei, G.-X., & Chang, Y.-K. (2015). Effects
    of acute aerobic exercise on motor response inhibition: An ERP study
    using the stop-signal task. Journal of Sport and Health Science, 4,
    73– 81. http://dx.doi.org/10.1016/j.jshs.2014.12.002

    Cohen, J. D., Dunbar, K., & McClelland, J. L. (1990). On the control of
    automatic processes: A parallel distributed processing account of the
    Stroop effect. Psychological Review, 97, 332–361. http://dx.doi.org/10
    .1037/0033-295X.97.3.332

    Colcombe, S. J., Erickson, K. I., Raz, N., Webb, A. G., Cohen, N. J.,
    McAuley, E., & Kramer, A. F. (2003). Aerobic fitness reduces brain
    tissue loss in aging humans. Journals of Gerontology Series A: Biolog-
    ical Sciences and Medical Sciences, 58, M176 –M180. http://dx.doi.org/
    10.1093/gerona/58.2.M176

    Colcombe, S. J., Kramer, A. F., Erickson, K. I., Scalf, P., McAuley, E.,
    Cohen, N. J., . . . Elavsky, S. (2004). Cardiovascular fitness, cortical
    plasticity, and aging. PNAS Proceedings of the National Academy of
    Sciences of the United States of America, 101, 3316 –3321. http://dx.doi
    .org/10.1073/pnas.0400266101

    Erickson, K. I., Prakash, R. S., Voss, M. W., Chaddock, L., Hu, L., Morris,
    K. S., . . . Kramer, A. F. (2009). Aerobic fitness is associated with
    hippocampal volume in elderly humans. Hippocampus, 19, 1030 –1039.
    http://dx.doi.org/10.1002/hipo.20547

    Etgen, T., Sander, D., Huntgeburth, U., Poppert, H., Förstl, H., & Bickel,
    H. (2010). Physical activity and incident cognitive impairment in elderly
    persons: The INVADE study. Archives of Internal Medicine, 170, 186 –
    193. http://dx.doi.org/10.1001/archinternmed.2009.498

    Etnier, J. L., & Chang, Y.-K. (2009). The effect of physical activity on
    executive function: A brief commentary on definitions, measurement
    issues, and the current state of the literature. Journal of Sport & Exercise
    Psychology, 31, 469 – 483.

    Fletcher, G. F., Balady, G. J., Amsterdam, E. A., Chaitman, B., Eckel, R.,
    Fleg, J., . . . Bazzarre, T. (2001). Exercise standards for testing and
    training: A statement for healthcare professionals from the American
    Heart Association. Circulation, 104, 1694 –1740. http://dx.doi.org/10
    .1161/hc3901.095960

    Golding, L. A. (1989). YMCA fitness testing and assessment manual.
    Champaign, IL: Human Kinetics.

    Gorman, M. (2002). Global ageing—The non-governmental organization
    role in the developing world. International Journal of Epidemiology, 31,
    782–785. http://dx.doi.org/10.1093/ije/31.4.782

    Hasher, L., Chung, C., May, C. P., & Foong, N. (2002). Age, time of
    testing, and proactive interference. Canadian Journal of Experimental
    Psychology, 56, 200 –207. http://dx.doi.org/10.1037/h0087397

    Heo, S., Prakash, R. S., Voss, M. W., Erickson, K. I., Ouyang, C., Sutton,
    B. P., & Kramer, A. F. (2010). Resting hippocampal blood flow, spatial
    memory and aging. Brain Research, 1315, 119 –127. http://dx.doi.org/
    10.1016/j.brainres.2009.12.020

    Hillman, C. H., Snook, E. M., & Jerome, G. J. (2003). Acute cardiovas-
    cular exercise and executive control function. International Journal of
    Psychophysiology, 48, 307–314. http://dx.doi.org/10.1016/S0167-
    8760(03)00080-1

    Hogan, M., Kiefer, M., Kubesch, S., Collins, P., Kilmartin, L., & Brosnan,
    M. (2013). The interactive effects of physical fitness and acute aerobic
    exercise on electrophysiological coherence and cognitive performance in
    adolescents. Experimental Brain Research, 229, 85–96. http://dx.doi
    .org/10.1007/s00221-013-3595-0

    Hyodo, K., Dan, I., Suwabe, K., Kyutoku, Y., Yamada, Y., Akahori, M.,
    . . . Soya, H. (2012). Acute moderate exercise enhances compensatory
    brain activation in older adults. Neurobiology of Aging, 33, 2621–2632.
    http://dx.doi.org/10.1016/j.neurobiolaging.2011.12.022

    Ide, K., & Secher, N. H. (2000). Cerebral blood flow and metabolism
    during exercise. Progress in Neurobiology, 61, 397– 414. http://dx.doi
    .org/10.1016/S0301-0082(99)00057-X

    Jernigan, T. L., Archibald, S. L., Fennema-Notestine, C., Gamst, A. C.,
    Stout, J. C., Bonner, J., & Hesselink, J. R. (2001). Effects of age on
    tissues and regions of the cerebrum and cerebellum. Neurobiology of
    Aging, 22, 581–594. http://dx.doi.org/10.1016/S0197-4580(01)00217-2

    Kamijo, K., Nishihira, Y., Higashiura, T., & Kuroiwa, K. (2007). The
    interactive effect of exercise intensity and task difficulty on human
    cognitive processing. International Journal of Psychophysiology, 65,
    114 –121. http://dx.doi.org/10.1016/j.ijpsycho.2007.04.001

    Lambourne, K., & Tomporowski, P. (2010). The effect of exercise-induced
    arousal on cognitive task performance: A meta-regression analysis.
    Brain Research, 1341, 12–24. http://dx.doi.org/10.1016/j.brainres.2010
    .03.091

    Liotti, M., Woldorff, M. G., Perez, R., & Mayberg, H. S. (2000). An ERP
    study of the temporal course of the Stroop color-word interference
    effect. Neuropsychologia, 38, 701–711.

    McMorris, T., Sproule, J., Turner, A., & Hale, B. J. (2011). Acute,
    intermediate intensity exercise, and speed and accuracy in working
    memory tasks: A meta-analytical comparison of effects. Physiology &
    Behavior, 102, 421– 428. http://dx.doi.org/10.1016/j.physbeh.2010.12
    .007

    Milham, M. P., Erickson, K. I., Banich, M. T., Kramer, A. F., Webb, A.,
    Wszalek, T., & Cohen, N. J. (2002). Attentional control in the aging
    brain: Insights from an fMRI study of the Stroop task. Brain and
    Cognition, 49, 277–296. http://dx.doi.org/10.1006/brcg.2001.1501

    Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A.,
    & Wager, T. D. (2000). The unity and diversity of executive functions
    and their contributions to complex “frontal lobe” tasks: A latent variable
    analysis. Cognitive Psychology, 41, 49 –100. http://dx.doi.org/10.1006/
    cogp.1999.0734

    Netz, Y., Argov, E., & Inbar, O. (2009). Fitness’s moderation of the
    facilitative effect of acute exercise on cognitive flexibility in older
    women. Journal of Aging and Physical Activity, 17, 154 –166.

    Netz, Y., Tomer, R., Axelrad, S., Argov, E., & Inbar, O. (2007). The effect
    of a single aerobic training session on cognitive flexibility in late
    middle-aged adults. International Journal of Sports Medicine, 28, 82–
    87. http://dx.doi.org/10.1055/s-2006-924027

    Nigg, J. T. (2000). On inhibition/disinhibition in developmental psycho-
    pathology: Views from cognitive and personality psychology and a
    working inhibition taxonomy. Psychological Bulletin, 126, 220 –246.
    http://dx.doi.org/10.1037/0033-2909.126.2.220

    Ogoh, S., & Ainslie, P. N. (2009). Cerebral blood flow during exercise:
    Mechanisms of regulation. Journal of Applied Physiology, 107, 1370 –
    1380. http://dx.doi.org/10.1152/japplphysiol.00573.2009

    Pesce, C., & Audiffren, M. (2011). Does acute exercise switch off switch
    costs? A study with younger and older athletes. Journal of Sport &
    Exercise Psychology, 33, 609 – 626.

    Pesce, C., Cereatti, L., Forte, R., Crova, C., & Casella, R. (2011). Acute
    and chronic exercise effects on attentional control in older road cyclists.
    Gerontology, 57, 121–128. http://dx.doi.org/10.1159/000314685

    Salthouse, T. A. (2004). What and when of cognitive aging. Current
    Directions in Psychological Science, 14, 140 –144.

    Sibley, B. A., Etnier, J. L., & Le Masurier, G. C. (2006). Effects of an acute
    bout of exercise on cognitive aspects of Stroop performance. Journal of
    Sport & Exercise Psychology, 28, 285–299.

    Smith, P. J., Blumenthal, J. A., Hoffman, B. M., Cooper, H., Strauman,
    T. A., Welsh-Bohmer, K., . . . Sherwood, A. (2010). Aerobic exercise
    and neurocognitive performance: A meta-analytic review of randomized
    controlled trials. Psychosomatic Medicine, 72, 239 –252. http://dx.doi
    .org/10.1097/PSY.0b013e3181d14633

    Stroop, J. R. (1935). Studies of interference in serial verbal reactions.
    Journal of Experimental Psychology, 18, 643– 662. http://dx.doi.org/10
    .1037/h0054651

    Tam, N. D. (2013). Improvement of processing speed in executive function
    immediately following an increase in cardiovascular activity. Cardio-

    T
    hi
    s
    do
    cu
    m
    en
    t
    is
    co
    py
    ri
    gh
    te
    d
    by
    th
    e
    A
    m
    er
    ic
    an
    P
    sy
    ch
    ol
    og
    ic
    al
    A
    ss
    oc
    ia
    ti
    on
    or
    on
    e
    of
    it
    s
    al
    li
    ed
    pu
    bl
    is
    he
    rs
    .
    T
    hi
    s
    ar
    ti
    cl
    e
    is
    in
    te
    nd
    ed
    so
    le
    ly
    fo
    r
    th
    e
    pe
    rs
    on
    al
    us
    e
    of
    th
    e
    in
    di
    vi
    du
    al
    us
    er
    an
    d
    is
    no
    t
    to
    be
    di
    ss
    em
    in
    at
    ed
    br
    oa
    dl
    y.

    847ACUTE EXERCISE, FITNESS, COGNITION, OLDER ADULT

    http://dx.doi.org/10.1016/j.jshs.2014.12.002

    http://dx.doi.org/10.1037/0033-295X.97.3.332

    http://dx.doi.org/10.1037/0033-295X.97.3.332

    http://dx.doi.org/10.1093/gerona/58.2.M176

    http://dx.doi.org/10.1093/gerona/58.2.M176

    http://dx.doi.org/10.1073/pnas.0400266101

    http://dx.doi.org/10.1073/pnas.0400266101

    http://dx.doi.org/10.1002/hipo.20547

    http://dx.doi.org/10.1001/archinternmed.2009.498

    http://dx.doi.org/10.1161/hc3901.095960

    http://dx.doi.org/10.1161/hc3901.095960

    http://dx.doi.org/10.1093/ije/31.4.782

    http://dx.doi.org/10.1037/h0087397

    http://dx.doi.org/10.1016/j.brainres.2009.12.020

    http://dx.doi.org/10.1016/j.brainres.2009.12.020

    http://dx.doi.org/10.1016/S0167-8760%2803%2900080-1

    http://dx.doi.org/10.1016/S0167-8760%2803%2900080-1

    http://dx.doi.org/10.1007/s00221-013-3595-0

    http://dx.doi.org/10.1007/s00221-013-3595-0

    http://dx.doi.org/10.1016/j.neurobiolaging.2011.12.022

    http://dx.doi.org/10.1016/S0301-0082%2899%2900057-X

    http://dx.doi.org/10.1016/S0301-0082%2899%2900057-X

    http://dx.doi.org/10.1016/S0197-4580%2801%2900217-2

    http://dx.doi.org/10.1016/j.ijpsycho.2007.04.001

    http://dx.doi.org/10.1016/j.brainres.2010.03.091

    http://dx.doi.org/10.1016/j.brainres.2010.03.091

    http://dx.doi.org/10.1016/j.physbeh.2010.12.007

    http://dx.doi.org/10.1016/j.physbeh.2010.12.007

    http://dx.doi.org/10.1006/brcg.2001.1501

    http://dx.doi.org/10.1006/cogp.1999.0734

    http://dx.doi.org/10.1006/cogp.1999.0734

    http://dx.doi.org/10.1055/s-2006-924027

    http://dx.doi.org/10.1037/0033-2909.126.2.220

    http://dx.doi.org/10.1152/japplphysiol.00573.2009

    http://dx.doi.org/10.1159/000314685

    http://dx.doi.org/10.1097/PSY.0b013e3181d14633

    http://dx.doi.org/10.1097/PSY.0b013e3181d14633

    http://dx.doi.org/10.1037/h0054651

    http://dx.doi.org/10.1037/h0054651

    vascular Psychiatry and Neurology, 2013: 212767. http://dx.doi.org/10
    .1155/2013/212767

    Wechsler, D. (1997). WAIS-III: Administration and scoring manual. San
    Antonio, TX: Psychological Corporation.

    West, R., & Alain, C. (1999). Event-related neural activity associated with
    the Stroop task. Cognitive Brain Research, 8, 157–164. http://dx.doi.org/
    10.1016/S0926-6410(99)00017-8

    Yanagisawa, H., Dan, I., Tsuzuki, D., Kato, M., Okamoto, M., Kyutoku,
    Y., & Soya, H. (2010). Acute moderate exercise elicits increased dor-

    solateral prefrontal activation and improves cognitive performance with
    Stroop test. NeuroImage, 50, 1702–1710. http://dx.doi.org/10.1016/j
    .neuroimage.2009.12.023

    Received September 22, 2014
    Revision received July 5, 2015

    Accepted July 8, 2015 �

    Members of Underrepresented Groups:
    Reviewers for Journal Manuscripts Wanted

    If you are interested in reviewing manuscripts for APA journals, the APA Publications and
    Communications Board would like to invite your participation. Manuscript reviewers are vital to the
    publications process. As a reviewer, you would gain valuable experience in publishing. The P&C
    Board is particularly interested in encouraging members of underrepresented groups to participate
    more in this process.

    If you are interested in reviewing manuscripts, please write APA Journals at Reviewers@apa.org.
    Please note the following important points:

    • To be selected as a reviewer, you must have published articles in peer-reviewed journals. The
    experience of publishing provides a reviewer with the basis for preparing a thorough, objective
    review.

    • To be selected, it is critical to be a regular reader of the five to six empirical journals that are most
    central to the area or journal for which you would like to review. Current knowledge of recently
    published research provides a reviewer with the knowledge base to evaluate a new submission
    within the context of existing research.

    • To select the appropriate reviewers for each manuscript, the editor needs detailed information.
    Please include with your letter your vita. In the letter, please identify which APA journal(s) you
    are interested in, and describe your area of expertise. Be as specific as possible. For example,
    “social psychology” is not sufficient—you would need to specify “social cognition” or “attitude
    change” as well.

    • Reviewing a manuscript takes time (1– 4 hours per manuscript reviewed). If you are selected to
    review a manuscript, be prepared to invest the necessary time to evaluate the manuscript
    thoroughly.

    APA now has an online video course that provides guidance in reviewing manuscripts. To learn
    more about the course and to access the video, visit http://www.apa.org/pubs/authors/review-
    manuscript-ce-video.aspx.

    T
    hi
    s
    do
    cu
    m
    en
    t
    is
    co
    py
    ri
    gh
    te
    d
    by
    th
    e
    A
    m
    er
    ic
    an
    P
    sy
    ch
    ol
    og
    ic
    al
    A
    ss
    oc
    ia
    ti
    on
    or
    on
    e
    of
    it
    s
    al
    li
    ed
    pu
    bl
    is
    he
    rs
    .
    T
    hi
    s
    ar
    ti
    cl
    e
    is
    in
    te
    nd
    ed
    so
    le
    ly
    fo
    r
    th
    e
    pe
    rs
    on
    al
    us
    e
    of
    th
    e
    in
    di
    vi
    du
    al
    us
    er
    an
    d
    is
    no
    t
    to
    be
    di
    ss
    em
    in
    at
    ed
    br
    oa
    dl
    y.

    848 CHU, CHEN, HUNG, WANG, AND CHANG

    http://dx.doi.org/10.1155/2013/212767

    http://dx.doi.org/10.1155/2013/212767

    http://dx.doi.org/10.1016/S0926-6410%2899%2900017-8

    http://dx.doi.org/10.1016/S0926-6410%2899%2900017-8

    http://dx.doi.org/10.1016/j.neuroimage.2009.12.023

    http://dx.doi.org/10.1016/j.neuroimage.2009.12.023

      Exercise and Fitness Modulate Cognitive Function in Older Adults
      Method
      Participants
      Cardiovascular Fitness Test
      The Stroop test
      Experimental Procedures
      Statistical Analyses
      Results
      Participant Characteristics and Exercise Intensity Check
      Stroop Test Performances
      Discussion
      References

    ORIGINAL PAPER

    The Effect of a Community-Based

    Exercise Program

    on Inflammation, Metabolic Risk, and Fitness Levels
    Among Persons Living with HIV/AIDS

    Stacy E. Cutrono1,2 • John E. Lewis3 • Arlette Perry1 • Joseph Signorile1 •

    Eduard Tiozzo3 • Kevin A. Jacobs1

    Published online: 25 November 201

    5

    � Springer Science+Business Media New York 20

    15

    Abstract The human immunodeficiency virus (HIV)

    pandemic remains a top national health priority. Chronic

    inflammation may be a critical component in the disease

    course of HIV as C-reactive protein (CRP) is elevated and

    associated with increased mortality. This study examined

    the effect of 3 months of combined aerobic and resistance

    exercise training among a diverse cohort of HIV-infected

    men and women. The fixed effect of time for CRP was

    found to be non-significant (F[1,57.3] = 1.7, p = 0.19).

    There was a significant fixed effect for time for upper body

    (F[1,51.6] = 18.1, p \ 0.05) and lower body strength
    (F[1,48.0] = 15.7, p \ 0.05) and significant declines in
    diastolic blood pressure (p = 0.002) and waist circumfer-

    ence (p = 0.027). Though levels of CRP were not impac-

    ted after 3 months training, participants demonstrated a

    significant increase in muscular strength as well as bene-

    ficial changes in metabolic risk factors. Future studies

    should focus on determining the optimal exercise inter-

    vention length and mode to reduce inflammation among

    individuals living with HIV.

    Keywords Human immunodeficiency virus � Aerobic
    exercise � Resistance training � C-reactive protein �
    Inflammation � Metabolic risk

    Introduction

    Globally, the rate of new human immunodeficiency virus

    (HIV) infections has fallen by 33 % since 2001 [1], but has

    held steady in the United States (U.S.) at an estimated

    50,000 new cases per year [2]. As such, the HIV/acquired

    immune deficiency syndrome (AIDS) pandemic continues

    to affect millions worldwide and remains a top health

    priority in the U.S. Recent reports indicate that the state of

    Florida has one of the highest rates of newly reported HIV

    infections and newly reported AIDS cases in the country

    [3]. Furthermore, the burden of HIV/AIDS continues to

    disproportionately affect individuals of minority race/eth-

    nicity, such as African Americans and Hispanics who

    represent 44 and 20 % of new HIV infections, respectively,

    as well as individuals with lower socioeconomic status

    (SES) and reduced access to quality health care [2].

    The use of combination antiretroviral therapy (ART) has

    significantly reduced the risk of mortality and morbidity in

    persons living with HIV (PLWH) since its introduction in

    the mid-1990s [4–7]. However, the extensive use of ART

    has given rise to serious and adverse side effects including

    hyperlipidemia, insulin resistance, and lipodystrophy thus

    increasing the risk for non-AIDS events such as cardio-

    vascular disease and the development of metabolic syn-

    drome (MetS) [8, 9]. The pathogenic mechanism for

    metabolic changes secondary to combination ART have yet

    to be fully elucidated, however, current investigations

    indicate a greater risk of negative side effects are associ-

    ated with use of drug combinations containing protease

    & Stacy E. Cutrono
    scutrono@med.miami.edu

    1
    Department of Kinesiology and Sports Sciences, School of

    Education and Human Development, University of Miami,

    Coral Gables, FL, USA

    2
    Sylvester Comprehensive Cancer, University of Miami,

    Miller School of Medicine, 1475 NW 12th Avenue, Suite

    C-021, Miami, FL 33136, USA

    3
    Department of Psychiatry & Behavioral Sciences, University

    of Miami, Miller School of Medicine, Miami, FL, USA

    123

    AIDS Behav (2016) 20:1123–1131

    DOI 10.1007/s10461-015-1245-1

    http://crossmark.crossref.org/dialog/?doi=10.1007/s10461-015-1245-1&domain=pdf

    http://crossmark.crossref.org/dialog/?doi=10.1007/s10461-015-1245-1&domain=pdf

    inhibitors or nucleoside reverse transcriptase inhibitors

    [10]. The risks associated with widespread and prolonged

    use of ART may be managed through effective lifestyle

    interventions incorporating exercise and weight

    management.

    Current research suggests that chronic inflammation

    may be a critical component in the course of disease states.

    The American Heart Association and the Centers for Dis-

    ease Control and Prevention support the use of C-reactive

    protein (CRP), an acute, non-specific inflammatory bio-

    marker, as an independent predictor of increased coronary

    risk and recommends using 3.0 mg/L as the minimum

    threshold for high risk classification [11]. In healthy young

    adults the median level of CRP is 0.8 mg/L [12]. However,

    among PLWH, CRP levels are elevated [9, 13] with

    reported ranges of 1.94–4.80 mg/L [14–16] and are asso-

    ciated with opportunistic infections, progression to AIDS,

    and mortality. Individuals enrolled in the Multicenter AIDS

    Cohort Study with CRP levels B1.2 mg/L were found to

    have a 47 % reduction in time to AIDS progression com-

    pared to those with [2.3 mg/L [17]. Individuals in the
    Strategies for Management of Anti-Retroviral Therapy trial

    with CRP levels C5 mg/L had 7.6-fold higher odds of

    developing an opportunistic infection than those with

    CRP B 1.0 mg/L [18]. Thus, interventions that reduce

    CRP levels may improve the cardiovascular risk profiles

    and disease prognosis among PLWH.

    The physiological and psychological benefits of regular

    exercise are numerous and well established. The available

    literature supports the therapeutic use of aerobic and

    resistance exercise for improving health and fitness out-

    comes among PLWH [19, 20]. For this reason, the Amer-

    ican College of Sports Medicine (ACSM) recommends that

    PLWH engage in a regular exercise program consisting of

    aerobic exercise and resistance exercise on most days of

    the week [21]. A reduction in systemic inflammation may

    be one of the mechanisms driving the protective effects of

    regular exercise for chronic disease risk [22], though the

    specific mechanisms by which exercise training may

    reduce systemic inflammation has not yet been established.

    Recent research examining the effects of exercise inter-

    ventions on circulating inflammatory biomarkers has pro-

    duced inconsistent results. The third National Health and

    Nutrition Examination Survey found that 21 % of seden-

    tary individuals had elevated CRP levels compared to 13 %

    of moderately active individuals [23]. Several other studies

    have reported significant declines in CRP levels after aer-

    obic exercise interventions among older individuals [24],

    obese women [25] and breast cancer survivors [22]. Yet, a

    recent meta-analysis of randomized controlled trials

    reported a non-significant decrease in CRP levels among

    subjects in aerobic exercise interventions [26]. The pro-

    inflammatory changes secondary to treatment with ART

    are accepted as a necessary risk in an effort to reduce

    progression to AIDS and AIDS mortality, yet inflammatory

    changes measured by elevated CRP increase the risk of

    non-AIDS events, cardiovascular mortality, as well as

    progression to AIDS. Interventions with potential to man-

    age treatment side effects and reduce inflammation are

    necessary among PLWH. The effect of exercise on CRP

    levels has not been well examined among PLWH, how-

    ever, given the severity of treatment side effects its

    potential beneficial impact warrants further investigation.

    The purpose of this study was to determine the effect of

    combined aerobic and resistance exercise training

    (CARET) on inflammation, metabolic risk profile, and

    aerobic and muscular fitness among PLWH after 3 months

    of training using data collected from the Healthy Living for

    Better Days program. We hypothesized that 3 months of

    CARET would significantly improve aerobic and muscular

    fitness, and metabolic risk profile and to a lesser extent

    systemic

    inflammation.

    Methods

    Study Design

    The Healthy Living for Better Days was a 12-month,

    community exercise program conducted by research staff

    at the University of Miami to improve the health of low

    SES individuals with HIV residing in Miami-Dade. This

    study specifically analyzed baseline and 3-month data.

    Program outcome variables measured at baseline and 3

    months included: (1) physical characteristics (body weight,

    body mass index, waist and hip circumferences, blood

    pressure), (2) non-lipid blood markers (high sensitivity

    CRP, fasting blood glucose, and insulin), (3) blood lipid

    profile (total cholesterol, low-density lipoprotein choles-

    terol, high-density lipoprotein cholesterol, and total

    triglycerides), and (4) physical fitness variables (estimated

    VO2max and one-repetition maximum for upper and lower

    body strength).

    Participants

    Ninety male and female participants were enrolled in

    Healthy Living for Better Days through referrals from the

    Adult HIV clinic at the University of Miami/Jackson

    Health System and other local HIV clinics. Program eli-

    gibility criteria included: [1] confirmed HIV infection as

    established by external laboratory reports, [2] men or

    women C18 years of age, [3] currently receiving

    antiretroviral treatment, and [4] ability to attend weekly

    exercise sessions at the UHealth Fitness and Wellness

    Center. Program exclusion criteria included any medical

    1124 AIDS Behav (2016) 20:1123–1131

    123

    condition or situation for which unsupervised exercise

    would be contraindicated. The Institutional Review Board

    of the University of Miami approved Healthy Living for

    Better Days and all participants gave written informed

    consent.

    Exercise Program

    All exercise sessions for Healthy Living for Better Days

    were held at the UHealth Fitness and Wellness Center at

    the University of Miami Medical campus. Each participant

    was required to swipe an electronic badge to gain admit-

    tance to the wellness center allowing attendance to be

    recorded and tracked electronically. Participants were

    encouraged to attend the supervised exercise sessions held

    four times a week, but were also given open access to the

    wellness center. Study personnel directed each supervised

    session and were available to advise participants on their

    exercise intensity and progression. Each supervised exer-

    cise session was 40–60 min in length and consisted of at

    least 30 min of aerobic exercise completed on a treadmill,

    elliptical machine, or stationary bike and resistance exer-

    cises completed on stacked weight machines (bench press,

    shoulder press, biceps curl, triceps extension, leg extension,

    leg curls, leg press, squat, lateral raises, lat pull downs,

    back extension, and abdominal crunches). Aerobic exercise

    was performed at 60–80 % of each individual’s age-pre-

    dicted maximum heart rate (HRmax). The duration of

    exercise sessions progressed from 40 to 60 min over the

    first 2 weeks of the program. Two to four sets of 8 to 15

    repetitions were performed for each upper and lower body

    exercise.

    Physical Characteristics

    Research staff used standard techniques to obtain anthro-

    pometric measurements. Weight and height were recorded

    to the nearest 0.1 kg and 0.1 cm, respectively, to calculate

    body mass index (BMI). Waist circumference was mea-

    sured in inches at the narrowest portion between the lowest

    rib and the iliac crest. Systolic blood pressure (SBP) and

    diastolic blood pressure (DBP) were measured by use of

    the automatic oscillometric device (Omron HEM-712CN2,

    Omron Healthcare, Inc., Bannockburn, Illinois).

    Blood Sampling and Analyses

    Blood samples were drawn from participants in the

    morning in a fasted condition and processed by the Dia-

    betes Research Institute Clinical Laboratory. Chemistry

    and immunoassays were performed by automated analyzer

    (Roche Cobas-6000; Roche Diagnostics, Indianapolis, IN)

    utilizing the manufacturer’s reagents and following the

    manufacturer’s instructions. High sensitivity CRP (hsCRP)

    was quantified in serum by a high sensitivity latex-particle

    enhanced immunoturbidimetric assay with a detection limit

    of 0.1 mg/L with an intra- and inter-assay coefficients of

    variation (CV) of 1.1 and 2.2 %, respectively. Fasting

    glucose (FG) was measured by the hexokinase method with

    intra- and inter-assay CVs of 1.9 and 2.7 %, respectively.

    Total cholesterol and triglycerides were determined in

    serum or plasma by enzymatic, colorimetric assay with

    intra- and inter-assay CVs are 0.7 and 1.8 %, respectively

    for total cholesterol and 0.9 and 2.3 %, respectively for

    triglycerides. High density lipoprotein cholesterol (HDL-

    C) was measured using a third generation homogenous

    enzymatic colorimetric assay with intra- and inter-assay

    CVs of 0.6 and 1.9 %, respectively. Low density lipopro-

    tein cholesterol (LDL-C) was calculated using the Friede-

    wald equation.

    Physical Fitness

    Cardiorespiratory fitness was measured using a Rockport

    One-Mile Fitness Walking Test [21], which has been val-

    idated in healthy adults aged 30–69 years [27] and been

    used in other clinical populations [28]. The test was

    modified for use indoors with participants performing the

    one-mile walk on a treadmill rather than on an outdoor

    track. Participants were instructed to walk for one mile on

    the treadmill as quickly as possible and were allowed to

    modify speed at their discretion throughout the test. Heart

    rate was measured for 10 s immediately upon completion

    by palpating the radial artery. Age, gender, body weight,

    and walk time were also recorded and used in a regression

    equation to predict maximal oxygen consumption

    (VO2max).

    Muscular strength was measured using the ACSM pro-

    tocol for one-repetition maximum (1-RM) testing [21].

    Program participants completed a maximum of four trials

    of 10, 8, 6, and 3 repetitions with rest periods between 2

    and 4 min between trials. The initial weight was selected

    within the subject’s perceived capacity (50–70 % of

    capacity) and resistance was progressively increased until

    the participants reached their maximum. The final maxi-

    mum weight lifted successfully one time for bench press

    and leg press was recorded as the 1-RM.

    Metabolic syndrome was defined using ATPIII criteria

    [29]. Three or more criteria had to be met to be classified as

    having MetS: (1) high fasting serum triglycerides(C150 mg/

    dL), (2) abnormal waist circumference ([102 cm for men
    and[88 cm for women), (3) low HDL-C level (\40 mg/dL
    for men and\50 mg/dL for women), (4) high blood pressure
    (BP) (C130/85 mmHg), and (5) high FG level (C110 mg/

    dL). Participants who self-reported being diagnosed with

    diabetes or who were receiving treatment for diabetes were

    AIDS Behav (2016) 20:1123–1131 11

    25

    123

    classified as having a high FG level. The same criteria were

    used for high BP.

    Statistical Analysis

    Statistical analyses were performed with the Statistical

    Package for Social Sciences (SPSS) version 22 for Win-

    dows (IBM Inc., Chicago, IL, USA). Statistical analyses

    included descriptive statistics and frequencies for each

    variable. Linear Mixed Modeling (LMM) was used to

    assess the fixed effect of time on changes in the outcome

    variables (hsCRP, estimated VO2max, 1-RM bench press,

    and 1-RM leg press) from baseline to 3-months follow

    up. The significance level of all analyses was a \ 0.05.
    LMM with heterogeneous compound symmetry covariance

    allowed us to account for missing values, subject attrition,

    inter-correlated responses between time points, and non-

    constant variability. Changes in hsCRP from baseline to

    3-months follow up were further examined controlling for

    potential confounders, specifically body mass index, waist

    circumference, aerobic fitness, and individuals with

    hsCRP [ 3 mg/L classified as high risk at baseline. Paired
    t tests were used to assess changes in metabolic risk factors

    (BP, BMI, FG, HDL-C, LDL-C, waist circumference and

    triglycerides) from baseline to 3-months follow up. Chi

    square analysis was used to assess the change in MetS

    prevalence from baseline to 3-months follow up.

    Given that the exercise program consisted of four ses-

    sions per week, participants were stratified into exercise

    compliance groups based on average exercise sessions

    attended as follows: (a) Non-compliant (average of \19/
    week for 3 months), (b) Somewhat compliant (average

    1–29/week for 3 months), and (c) Compliant (C29/week

    for 3 months), where compliant individuals completed at

    least 50 % of the prescribed exercise. Comparisons

    between groups from baseline to 3 months were analyzed

    using LMM for outcome variables.

    Results

    Demographic data are presented in Table 1. Ninety PLWH

    were enrolled in Healthy Living for Better Days. The

    majority of participants were women (53.9 %), Black/

    African American (65.2 %), and unemployed or disabled at

    the time of participation (83.1 %). Nearly one-third of

    participants were classified as having MetS at baseline.

    Fifty-five percent of total participants were non-compliant

    (49/89), 20.2 % were somewhat compliant (18/89), and

    24.7 % were compliant (22/89) with the prescribed exer-

    cise. After 3 months participation in Healthy Living for

    Better Days, nearly one-quarter (24.7 %) of our partici-

    pants were meeting physical activity recommendations

    defined as a combination of moderate- and vigorous-in-

    tensity aerobic exercise at least 75 min/week and resistance

    training twice per week.

    The fixed effect of time for hsCRP was found to be non-

    significant (F[1,57.3] = 1.7, p = 0.19) (Fig. 1). Mean

    hsCRP at baseline was 5.75 ± 7.62 mg/L (median 2.30)

    and 7.54 ± 14.19 mg/L (median 2.95) at 3-months follow

    up. Comparing hsCRP across categories of exercise com-

    pliance groups (see Fig. 1) revealed non-significant fixed

    effects for time (F[1,55.5] = 2.4, p = 0.13), exercise

    compliance (F[2,62.1] = 0.06, p = 0.94) and exercise

    compliance 9 time (F[2,55.5] = 0.99, p = 0.38). When

    examining the effect of the exercise intervention on

    changes in hsCRP from baseline to 3-months follow up

    only among individuals classified as high risk

    (hsCRP [ 3 mg/dL) at baseline, the fixed effect for time
    was still found to be non-significant (F[1,30.3] = 0.2

    0

    p = 0.657). Comparing hsCRP levels by gender group

    revealed a significant fixed effect for gender

    (F[1,68.6] = 4.08, p \ 0.05]), with women displaying an
    overall higher mean hsCRP (8.50 ± 12.69 mg/L) than men

    (4.46 ± 13.65 mg/L). The fixed effect for time

    (F[1,57.4] = 1.75, p = 0.19]) and gender 9 time was non-

    significant (F[1,57.4] = 0.06, p = 0.80]). The fixed effect

    of time on changes in CRP from baseline to 3-months

    follow up controlling for the use of protease inhibitors

    (F[1,55.6] = 1.7, p = 0.200), BMI (F[1,53.2] = 1.7,

    p = 0.199), aerobic fitness (F[1,50.1] = 1.1, p = 0.304),

    and sleep duration (F[1,56.1] = 2.4, p = 0.129) was found

    to be non-significant.

    Changes in participant’s metabolic risk profile can be

    found in Table 2. Diastolic BP (t(52) = 3.247, 95 % CI

    1.55–6.58, p = 0.002) and waist circumference

    (t(58) = 2.268, 95 % CI 0.06–1.02, p = 0.027) signifi-

    cantly decreased from baseline to 3 months. There were no

    significant changes from baseline to 3 months for body

    weight (t(58) = 0.405, 95 % CI -1.24 to 1.86, p = 0.687),

    SBP (t(52) = 1.796, 95 % CI -0.41 to 7.31, p = 0.078),

    BMI (t(58) = 0.196, 95 % CI -0.24 to 0.29, p = 0.845),

    triglycerides (t(61) = 0.806, 95 % CI -9.69 to 22.79,

    p = 0.423), total cholesterol (t(61) = 0.065, 95 % CI

    -7.22 to 7.70, p = 0.948), HDL-C (t(61) = 1.875, 95 %

    CI -0.17 to 5.37, p = 0.066), VLDL-C (t(61) = 0.845,

    95 % CI -1.87 to 4.62, p = 0.401), LDL-C

    (t(61) = -1.186, 95 % CI -10.01 to 2.55, p = 0.240), or

    FG (t(61) = 1.226, 95 % CI -3.49 to 14.56, p = 0.225).

    There was a non-significant decline in individuals with

    MetS from baseline to 3 months (32 vs. 19 %, v2(1,
    Nbaseline = 89, N3months = 63) = 3.43, p = 0.06).

    The fixed effect of time for changes in VO2max was

    found to be non-significant (F[1,36.3] = 3.5, p = 0.07)

    (Table 3). For upper body 1-RM, a significant fixed

    effect was found for time (F[1,51.6] = 18.1, p \ 0.05)

    1126 AIDS Behav (2016) 20:1123–1131

    123

    and the parameter estimate between baseline and 3

    months follow up was also significant (t[51.6] = -4.3,

    p \ 0.05). Likewise, for lower body 1-RM a significant
    fixed effect was found for time (F[1,48.0] = 15.7,

    p \ 0.05) and the parameter estimate between baseline
    and 3 month follow up was also significant

    (t[48.0] = -4.0, p \ 0.05).

    Discussion

    Among our participants, changes in hsCRP were not

    impacted by 3 months of CARET, even among individuals

    with high hsCRP levels at baseline. The number of indi-

    viduals classified as having MetS declined from baseline to

    3-months, however these results were found to be non-

    Table 1 Demographic and
    baseline population

    characteristics by gender

    Overall (n = 89) Men (n = 41) Women (n = 48)

    Age (years) 48 ± 7 48.7 ± 7 47.8 ± 7.6

    Body mass index (kg/m
    2
    ) 31.2 ± 7.8 28.7 ± 5.2 33.4 ± 8.9

    Duration of HIV (years) 17.6 ± 12.7 15.3 ± 7.6 19.5 ± 15.7

    Ethnic, n (%)

    Non-Hispanic White 9 (10.1) 7 (17.1) 2 (4.2)

    African-American 58 (65.2) 21 (51.2) 37 (77.1)

    Hispanic 20 (22.5) 12 (29.3) 8 (16.7)

    Current smoker, n (%) 32 (36.0) 16 (39.0) 16 (33.3)

    Antiretroviral therapy, n (%)

    Protease inhibitors 46 (51.7) 22 (53.7) 24 (50.0)

    Non-protease inhibitors 36 (40.4) 16 (39.0) 20 (41.7)

    Employment, n (%)

    Unemployed 74 (83.1) 30 (73.2) 44 (91.7)

    Employed (part or full time) 14 (15.7) 11 (26.8) 3 (6.3)

    Yearly household income, n (%)

    \$5000 27 (30.3) 11 (26.8) 16 (33.3)
    $5000–$14,999 38 (42.7) 20 (48.7) 18 (37.6)

    $15,000–$39,999 12 (13.4) 6 (14.7) 6 (12.5)

    Data are mean ± SD or n (%)

    0
    5

    10

    15

    20

    25

    30

    35

    40

    Baseline 3-Months

    M
    ea

    n
    CR

    P
    (m

    g/
    L)

    Non-compliant Somewhat Compliant Compliant

    Fig. 1 Changes in levels of
    C-reactive protein across

    exercise compliance groups.

    Data are mean ± SD. Non-

    compliant, average exercise

    session of \19/week for
    3 months; Somewhat

    Compliant, average exercise

    session of 1–29/week for

    3 months; Compliant, average

    exercise session of C29/week

    for 3 months; hsCRP, high

    sensitivity C-reactive protein

    AIDS Behav (2016) 20:1123–1131 1127

    123

    significant. Participants did significantly increase muscular

    strength of the upper and lower body and displayed a trend

    for improved aerobic capacity.

    Disappointingly, a majority of participants (55 %) were

    not compliant with the prescribed exercise regime. Previ-

    ous literature has documented barriers and challenges to

    appointment adherence or research participation among

    PLWH [31, 32]. Though few studies have specifically

    assessed challenges to participating in exercise programs,

    there have been reports of moderate withdrawals (range

    3–44 %) and low compliance (range 24–82 %) in other

    exercise interventions [19]. Macarthur et al. [33], reported

    transportation and difficulty exercising as challenges to

    completing exercise testing and training. Similarly, Neidig

    et al. [34], reported changes in employment, unreliable

    transportation, and family responsibilities as contributors to

    withdrawal from an aerobic exercise trial. Nevertheless,

    Healthy Living for Better Days was designed as a com-

    munity-based exercise program in an effort to expand

    access to a variety of participants. The program was well

    received by most participants (data not reported); however,

    the low compliance highlights the challenge of engaging

    this population in exercise programs.

    Mean levels of hsCRP were elevated at baseline

    (5.75 ± 0.82 mg/L, Fig. 1) and 40 % of our participants

    had hsCRP values that would be classified as high coronary

    risk ([3 mg/L) under AHA and CDC guidelines [11].
    Median levels of hsCRP at baseline and 3-months follow

    up were greater than values previously reported in the lit-

    erature among PLWH (1.20–2.83 mg/L) [14, 17]. Elevated

    CRP has been associated with metabolic risk factors such

    as obesity, hypertension, and dyslipidemia [35]. Among

    our participants BMI and waist circumference were ele-

    vated at baseline and additionally a few of our participants

    displayed very high CRP values perhaps reflective of the

    disease course of the HIV infection. Thus, it is possible that

    our cohort had more severe inflammation than the general

    population of PLWH. Women in our sample were found to

    have significantly higher hsCRP levels than men. This is

    consistent with data from the third National Health and

    Nutrition Examination Survey that found that the odds of

    having elevated CRP levels is twofold higher among

    women than men [35].

    Participants showed a trend for an increase in VO2max of

    2.2 mL/kg/min with 3 months of training (Table 3).

    Although this trend was not significant, our results were

    consistent with previously reported changes in VO2max
    (range ?2.6 to ?4.7 mL/kg/min) after 3 months of training

    among PLWH [8, 36]. Exercise adherence did not appear

    to be a contributing factor as even the compliant cohort of

    subjects showed no significant improvement in hsCRP. In

    contrast to our 3-month program consisting of CARET,

    Lindegaard et al. [37] found that hsCRP levels declined in

    a small sample (n = 18) of HIV-positive men who per-

    formed 35 min of endurance training 39/week for

    16 weeks (baseline hsCRP, 2.42 mg/L [1.01–5.80],

    16-week hsCRP, 1.82 mg/L [0.76–4.36]; p \ 0.0001).
    However, the effect of exercise on inflammatory

    biomarkers was not the primary variable studied by Lin-

    degaard et al. [37]. Nonetheless, we cannot rule out the

    possibility that a longer intervention or higher sustained

    intensity of aerobic exercise is needed to impact systemic

    inflammation.

    Table 2 Changes in metabolic risk profile after 3-months of CARET

    Baseline 3 months p value

    Total body weight (lbs) 191.9 ± 46.5 191.6 ± 46.6 0.687

    Systolic BP (mmHg) 127 ± 12 124 ± 11 0.078

    Diastolic BP (mmHg) 82 ± 9 78 ± 8 0.002*

    Body mass index (kg/m
    2
    ) 30.7 ± 7.4 30.7 ± 7.4 0.845

    Waist circumference (inch) 41.2 ± 7.0 40.7 ± 7.4 0.027*

    Total triglycerides (mg/dL) 125.4 ± 72.6 118.8 ± 55.7 0.423

    Total cholesterol (mg/dL) 186.7 ± 35.0 186.5 ± 42.0 0.948

    HDL cholesterol (mg/dL) 52.3 ± 16.1 49.7 ± 13.7 0.066

    VLDL cholesterol (mg/dL) 25.1 ± 14.5 23.7.1 ± 11.2 0.401

    LDL cholesterol (mg/dL) 109.4 ± 30.2 113.1 ± 38.1 0.240

    Fasting glucose (mg/dL) 95.7 ± 35.1 90.2 ± 16.5 0.225

    Data are mean ± SD

    CARET combined aerobic and resistance exercise training, BP blood

    pressure, VLDL very low density lipoprotein, LDL low density

    lipoprotein, HDL high density lipoprotein

    * Significant difference from baseline to 3 months (p \ 0.05, paired
    t test)

    Table 3 Changes in
    cardiorespiratory fitness and

    muscular strength

    Baseline 3 months Statistic

    VO2max (mL/kg/min) 27.2 ± 8.9 29.4 ± 10.0 F[1,36.3] = 3.5, p = 0.07

    Upper body 1-RM (lbs) 114 ± 51 125 ± 46 F[1,51.6] = 18.1, *p \ 0.001
    Lower body 1-RM (lbs) 225 ± 81 250 ± 96 F[1,48.0] = 15.7, *p \ 0.001

    Data are mean ± SD

    VO2max maximal volume of oxygen consumed, 1-RM one-repetition maximum

    * Significant fixed effect on time (p \ 0.05)

    1128 AIDS Behav (2016) 20:1123–1131

    123

    Participants significantly increased both upper and lower

    body strength after 3 months of CARET as has been shown

    in other studies that involved resistance training among

    PLWH [20, 38, 39]. The improvements in strength in our

    study, however, were not associated with a significant

    improvement in hsCRP levels from baseline to the 3-month

    follow up (Fig. 1). Likewise, Lindegaard et al., [37] found

    that despite a 30 % improvement in strength after

    16 weeks of resistance training, there was no significant

    change in CRP levels (baseline CRP: 1.54 mg/L

    [1.0–2.37], 16-week CRP: 1.65 mg/L [1.07–2.54];

    p = 0.44) among HIV-positive men. Similarly, among

    older adults assigned to 10-months of strength and flexi-

    bility training serum CRP levels were not improved com-

    pared to those in an aerobic exercise arm [24]. These

    results may indicate that aerobic exercise rather than

    resistance training may be the primary mode of exercise by

    which systemic inflammation is impacted.

    It has been suggested that reduction in systemic

    inflammation may be the mechanism driving the pro-

    tective effects of regular physical activity and exercise

    for chronic disease risk [22]. Yet, a meta-analysis by

    Kelley et al., [26] of randomized controlled trials among

    adult subjects reported a non-significant 3 % decrease in

    CRP levels in aerobic exercise interventions ranging

    from 8 weeks to 6 years. On the other hand, CRP levels

    were reported to significantly decline in aerobic exercise

    trials among older adults [24] and postmenopausal obese

    women [25] after 10- and 12-months of training,

    respectively. Thus, greater gains in aerobic fitness as

    measured by VO2max may be necessary to affect hsCRP

    levels in PLWH. Future studies examining the role of

    exercise interventions on systemic inflammation should

    incorporate randomization to an aerobic-only comparison

    arm.

    The presence of one or more cardiovascular risk fac-

    tors, specifically those which contribute to the classifica-

    tion of MetS, are associated with a pro-inflammatory

    state. Data from the third National Health and Nutrition

    Examination Survey indicated that the presence of at least

    one abnormal cardiovascular risk factor was associated

    with a threefold higher prevalence of elevated CRP [35].

    Among PLWH metabolic changes secondary to the

    extensive use of ART, such as insulin resistance, adiposity

    and poor lipid profiles, has contributed to an elevated

    cardiovascular risk profile [40–42]. In our cohort nearly

    one-third of participants had MetS at baseline. After 3

    months of CARET we observed a significant decrease in

    DBP and waist circumference. Loss of abdominal sub-

    cutaneous fat and therefore reduced central obesity could

    have been indicative of HIV-associated lipoatrophy [43].

    Further investigation of body composition and body fat

    distribution may be warranted to fully understand the

    relationship between metabolic risk factors and inflam-

    mation. Though not significant, we observed a trend

    towards decreased SBP, total triglycerides, FG, and the

    number of individuals with MetS. We did not observe

    changes in total body weight or BMI. Several studies have

    reported a rapid progression to MetS after initiation of

    ART [44–47]. Given the deleterious impact of ART on

    metabolic risk factors and the rate of change reported in

    previous studies among PLWH, it appears that our pro-

    gram may have been effective in delaying a worsening of

    these side effects. A longer intervention, in combination

    with changes in total body weight, may be necessary to

    reduce metabolic risk factors and potentially impact

    hsCRP levels.

    A strength of the Health Living for Better Days pro-

    gram was its enrollment of a sample of participants

    comprised mainly of minorities ([85 %). African Amer-
    icans and Hispanics continue to bear a disproportionate

    burden of new HIV cases despite only representing 13.1

    and 16.9 % of the general U.S. population, respectively

    [30]. For this reason our results may be more generaliz-

    able to the larger population of PLWH. A majority of

    participants were of low SES as evidenced by high rates

    of unemployment (83.1 %) and low household income

    (73 %), defined as earning less than $15,000 annually.

    Over half of the sample was comprised of women, who

    according to the CDC accounted for one in four new HIV

    infections in 2010 [2]. Our program included a compre-

    hensive assessment of fitness and obtained extensive

    information on metabolic risk factors. Furthermore, while

    several other protocols have utilized a 6–8 h fast for

    blood sampling, we utilized a 12-h fast, thus avoiding

    exaggerated triglycerides and eliminating serum chy-

    lomicrons which could overestimate triglyceride and

    MetS rates.

    In addition to the strengths of our study, several limi-

    tations were present. Our study design was not a ran-

    domized controlled trial and did not include a non-

    exercise control arm, though all analyses were conducted

    as intent-to-treat. We did not obtain HIV ribonucleic acid

    (RNA) levels or information pertaining to secondary

    infections, which limits our ability to examine the effect

    of CARET on systemic inflammation independent of

    changes in the disease course. Similarly, while we did

    ascertain the type of ART used among our participants we

    did not obtain the total duration of exposure to combi-

    nation ART. Extended exposure to combination ART

    regimes has been found to increase the risk of myocardial

    infarction [48, 49] and thus has the potential to impact

    both metabolic risk profile and

    systemic inflammation.

    Further, we cannot rule out the impact of diet and overall

    nutritional status on cardiovascular risk reduction and

    systemic inflammation.

    AIDS Behav (2016) 20:1123–1131 1129

    123

    Conclusion

    In conclusion, our study enrolled a diverse cohort of HIV-

    infected individuals receiving ART, who were of pre-

    dominantly low SES. Overall mean levels of hsCRP were

    elevated in our cohort of HIV-infected individuals receiv-

    ing ART. Though levels of hsCRP were not impacted after

    3 months of CARET, participants demonstrated a signifi-

    cant increase in upper and lower body muscular strength as

    well as beneficial changes in waist circumference and

    diastolic blood pressure. Although low compliance to the

    prescribed exercise limits our ability to make definitive

    conclusions about the impact of exercise on systemic

    inflammation, it is possible that a longer intervention or

    greater intensity is necessary to induce improvements in

    aerobic fitness and metabolic profiles and thus impact

    systemic inflammation. Future studies should focus on

    effective ways of engaging and retaining HIV-infected

    minorities in structured exercise interventions and dis-

    cerning the optimal dose and duration of exercise to reduce

    inflammation.

    Acknowledgments This material is based on work supported by
    AstraZeneca HealthCare Foundation’s Connections for Cardiovas-

    cular HealthSM (CCH) program. The CCH program funds charita-

    ble work, not research that addresses cardiovascular health issues

    within the United States and its territories. Any opinions, findings,

    and conclusions or recommendations expressed in this material are

    those of the authors and have not been reviewed for approval by the

    AstraZeneca HealthCare Foundation.

    References

    1. Joint United Nations Programme on HIV/AIDS. UNAIDS Report

    on the Global AIDS Epidemic. 2013.

    2. Centers for Disease Control and Prevention. HIV in the United

    States: at a glance. http://www.cdc.gov/hiv/statistics/basics/ata

    glance.html#ref1. Accessed 15 Nov 2013.

    3. Florida Health. Epidemiology of HIV Infection and trends in

    Florida reported through 2012. 2013.

    4. Collaboration Antiretroviral Therapy Cohort. Causes of death in

    HIV-1-infected patients treated with antiretroviral therapy,

    1996–2006: collaborative analysis of 13 HIV cohort studies. Clin

    Infect Dis. 2010;50(10):1387–96.

    5. Darbyshire J. Therapeutic interventions in HIV infection—a

    critical view. Trop Med Int Health. 2000;5(7):A26–31.

    6. Gebhardt M, Rickenbach M, Egger M. Impact of antiretroviral

    combination therapies on AIDS surveillance reports in Switzer-

    land. Swiss HIV Cohort Study AIDS. 1998;12(10):1195–201.

    7. Murphy EL, Collier AC, Kalish LA, et al. Highly active

    antiretroviral therapy decreases mortality and morbidity in

    patients with advanced HIV disease. Ann Intern Med.

    2001;135(1):17–26.

    8. Tiozzo E, Jayaweera D, Rodriguez A, et al. Short-term combined

    exercise training improves the health of HIV-infected patients.

    J AIDS HIV Res. 2013;5(3):80–9.

    9. Lichtfuss GF, Hoy J, Rajasuriar R, Kramski M, Crowe SM,

    Lewin SR. Biomarkers of immune dysfunction following

    combination antiretroviral therapy for HIV infection. Biomark

    Med. 2011;5(2):171–86.

    10. Leow MK, Addy CL, Mantzoros CS. Clinical review 159: human

    immunodeficiency virus/highly active antiretroviral therapy-as-

    sociated metabolic syndrome: clinical presentation, pathophysi-

    ology, and therapeutic strategies. J Clin Endocrinol Metab.

    2003;88(5):1961–76.

    11. Pearson TA, Mensah GA, Alexander RW, et al. Markers of

    inflammation and cardiovascular disease: application to clinical

    and public health practice: a statement for healthcare profes-

    sionals from the Centers for Disease Control and Prevention and

    the American Heart Association. Circulation. 2003;

    107(3):499–511.

    12. Pepys MB, Hirschfield GM. C-reactive protein: a critical update.

    J Clin Invest. 2003;111(12):1805–12.

    13. Nixon DE, Landay AL. Biomarkers of immune dysfunction in

    HIV. Curr Opin HIV AIDS. 2010;5(6):498–503.

    14. Neuhaus J, Jacobs DR Jr, Baker JV, et al. Markers of inflam-

    mation, coagulation, and renal function are elevated in adults

    with HIV infection. J Infect Dis. 2010;201(12):1788–95.

    15. Baker J, Ayenew W, Quick H, et al. High-density lipoprotein

    particles and markers of inflammation and thrombotic activity in

    patients with untreated HIV infection. J Infect Dis. 2010;

    201(2):285–92.

    16. Boulware DR, Hullsiek KH, Puronen CE, et al. Higher levels of

    CRP, D-dimer, IL-6, and hyaluronic acid before initiation of

    antiretroviral therapy (ART) are associated with increased risk of

    AIDS or death. J Infect Dis. 2011;203(11):1637–46.

    17. Lau B, Sharrett AR, Kingsley LA, et al. C-reactive protein is a

    marker for human immunodeficiency virus disease progression.

    Arch Intern Med. 2006;166(1):64–70.

    18. Rodger AJ, Fox Z, Lundgren JD, et al. Activation and coagulation

    biomarkers are independent predictors of the development of

    opportunistic disease in patients with HIV infection. J Infect Dis.

    2009;200(6):973–83.

    19. O’Brien K, Nixon S, Tynan AM, Glazier R. Aerobic exercise

    interventions for adults living with HIV/AIDS. Cochrane Data-

    base Syst Rev. 2010. doi:10.1002/14651858.CD001796.pub3.

    20. O’Brien K, Tynan AM, Nixon S, Glazier RH. Effects of pro-

    gressive resistive exercise in adults living with HIV/AIDS: sys-

    tematic review and meta-analysis of randomized trials. AIDS

    Care. 2008;20(6):631–53.

    21. ACSM. ACSM’s guidelines for exercise testing and prescription.

    9th ed. Baltimore: Wolters Kluwer|Lippincott Williams &

    Wilkins; 2009.

    22. Arikawa AY, Thomas W, Schmitz KH, Kurzer MS. Sixteen

    weeks of exercise reduces C-reactive protein levels in young

    women. Med Sci Sports Exerc. 2011;43(6):1002–9.

    23. Ford ES. Does exercise reduce inflammation? Physical activity

    and C-reactive protein among U.S. adults. Epidemiology.

    2002;13(5):561–8.

    24. Kohut ML, McCann DA, Russell DW, et al. Aerobic exercise, but

    not flexibility/resistance exercise, reduces serum IL-18, CRP, and

    IL-6 independent of beta-blockers, BMI, and psychosocial factors

    in older adults. Brain Behav Immun. 2006;20(3):201–9.

    25. Campbell PT, Campbell KL, Wener MH, et al. A yearlong

    exercise intervention decreases CRP among obese post-

    menopausal women. Med Sci Sports Exerc. 2009;41(8):1533–9.

    26. Kelley GA, Kelley KS. Effects of aerobic exercise on C-reactive

    protein, body composition, and maximum oxygen consumption in

    adults: a meta-analysis of randomized controlled trials. Metabo-

    lism. 2006;55(11):1500–7.

    27. Kline GM, Porcari JP, Hintermeister R, et al. Estimation of

    VO2max from a one-mile track walk, gender, age, and body

    weight. Med Sci Sports Exerc. 1987;19(3):253–9.

    1130 AIDS Behav (2016) 20:1123–1131

    123

    http://www.cdc.gov/hiv/statistics/basics/ataglance.html%23ref1

    http://www.cdc.gov/hiv/statistics/basics/ataglance.html%23ref1

    http://dx.doi.org/10.1002/14651858.CD001796.pub3

    28. Maruf FA, Akinpelu AO, Salako BL. Self-reported quality of life

    before and after aerobic exercise training in individuals with

    hypertension: a randomised-controlled trial. Appl Psychol Health

    Well Being. 2013;5(2):209–24.

    29. Program National Cholesterol Education. (NCEP) Expert Panel

    on Detection, Evaluation, and Treatment of High Blood

    Cholesterol in Adults (Adult Treatment Panel III). Third Report

    of the National Cholesterol Education Program (NCEP) Expert

    Panel on Detection, Evaluation, and Treatment of High Blood

    Cholesterol in Adults (Adult Treatment Panel III) final report.

    Circulation. 2002;106(25):3143–421.

    30. United States Census Bureau. State and county QuickFacts.

    http://quickfacts.census.gov/qfd/states/00000.html. Accessed 18

    May 2014.

    31. Meredith K, Delaney J, Horgan M, Fisher E Jr, Fraser V. A

    survey of women with HIV about their expectations for care.

    AIDS Care. 1997;9(5):513–22.

    32. Kelly PJ, Cordell JR. Recruitment of women into research stud-

    ies: a nursing perspective. Clin Nurse Spec. 1996;10(1):25–8.

    33. MacArthur RD, Levine SD, Birk TJ. Supervised exercise training

    improves cardiopulmonary fitness in HIV-infected persons. Med

    Sci Sports Exerc. 1993;25(6):684–8.

    34. Neidig JL, Smith BA, Brashers DE. Aerobic exercise training for

    depressive symptom management in adults living with HIV

    infection. J Assoc Nurses AIDS Care. 2003;14(2):30–40.

    35. Miller M, Zhan M, Havas S. High attributable risk of elevated

    C-reactive protein level to conventional coronary heart disease

    risk factors: the Third National Health and Nutrition Examination

    Survey. Arch Intern Med. 2005;165(18):2063–8.

    36. Smith BA, Neidig JL, Nickel JT, Mitchell GL, Para MF, Fass RJ.

    Aerobic exercise: effects on parameters related to fatigue, dysp-

    nea, weight and body composition in HIV-infected adults. AIDS.

    2001;15(6):693–701.

    37. Lindegaard B, Hansen T, Hvid T, et al. The effect of strength and

    endurance training on insulin sensitivity and fat distribution in

    human immunodeficiency virus-infected patients with lipodys-

    trophy. J Clin Endocrinol Metab. 2008;93(10):3860–9.

    38. Gomes Neto M, Ogalha C, Andrade AM, Brites C. A systematic

    review of effects of concurrent strength and endurance training on

    the health-related quality of life and cardiopulmonary status in

    patients with HIV/AIDS. Biomed Res Int. 2013;2013:319524.

    39. Hand GA, Lyerly GW, Jaggers JR, Dudgeon WD. Impact of

    aerobic and resistance exercise on the health of HIV-infected

    persons. Am J Lifestyle Med. 2009;3(6):489–99.

    40. DAD Study Group, Friis-Moller N, Reiss P, et al. Class of

    antiretroviral drugs and the risk of myocardial infarction. N Engl

    J Med. 2007;356(17):1723–35.

    41. Fontas E, van Leth F, Sabin CA, et al. Lipid profiles in HIV-

    infected patients receiving combination antiretroviral therapy: are

    different antiretroviral drugs associated with different lipid pro-

    files? J Infect Dis. 2004;189(6):1056–74.

    42. Borato DC, Parabocz GC, Ribas SR, et al. Changes of metabolic

    and inflammatory markers in HIV infection: glucose, lipids,

    serum Hs-CRP and myeloperoxidase. Metabolism. 2012;61(10):

    1353–60.

    43. Tiozzo E, Konefal J, Adwan S, et al. A cross-sectional assessment

    of metabolic syndrome in HIV-infected people of low socio-

    economic status receiving antiretroviral therapy. Diabetol Metab

    Syndr. 2015;7:15. doi:10.1186/s13098-015-0008-5.

    44. Jacobson DL, Tang AM, Spiegelman D, et al. Incidence of

    metabolic syndrome in a cohort of HIV-infected adults and

    prevalence relative to the US population (National Health and

    Nutrition Examination Survey). J Acquir Immune Defic Syndr.

    2006;43(4):458–66.

    45. Wand H, Calmy A, Carey DL, et al. Metabolic syndrome, car-

    diovascular disease and type 2 diabetes mellitus after initiation of

    antiretroviral therapy in HIV infection. AIDS. 2007;21(18):

    2445–53.

    46. Palacios R, Santos J, Gonzalez M, Ruiz J, Marquez M. Incidence

    and prevalence of the metabolic syndrome in a cohort of naive

    HIV-infected patients: prospective analysis at 48 weeks of highly

    active antiretroviral therapy. Int J STD AIDS. 2007;18(3):184–7.

    47. Worm SW, Friis-Moller N, Bruyand M, et al. High prevalence of

    the metabolic syndrome in HIV-infected patients: impact of dif-

    ferent definitions of the metabolic syndrome. AIDS. 2010;24(3):

    427–35.

    48. Warnke D, Barreto J, Temesgen Z. Antiretroviral drugs. J Clin

    Pharmacol. 2007;47(12):1570–9.

    49. Friis-Moller N, Sabin CA, Weber R, et al. Combination

    antiretroviral therapy and the risk of myocardial infarction.

    N Engl J Med. 2003;349(21):1993–2003.

    AIDS Behav (2016) 20:1123–1131 1131

    123

    http://quickfacts.census.gov/qfd/states/00000.html

    http://dx.doi.org/10.1186/s13098-015-0008-5

    AIDS & Behavior is a copyright of Springer, 2016. All Rights Reserved.

    • The Effect of a Community-Based Exercise Program on Inflammation, Metabolic Risk, and Fitness Levels Among Persons Living with HIV/AIDS
    • Abstract
      Introduction
      Methods
      Study Design
      Participants
      Exercise Program
      Physical Characteristics
      Blood Sampling and Analyses
      Physical Fitness
      Statistical Analysis
      Results
      Discussion
      Conclusion
      Acknowledgments
      References

    Multiple Sclerosis Journal
    2014, Vol. 20(3) 382 –390
    © The Author(s) 2013
    Reprints and permissions:
    sagepub.co.uk/journalsPermissions.nav
    DOI: 10.1177/1352458513507358
    msj.sagepub.com

    MULTIPLE
    SCLEROSIS MSJ
    JOURNAL

    Introduction

    Currently approved disease-modifying therapies (DMTs)
    for multiple sclerosis (MS) have limited impact on the neu-
    rodegenerative component of the disease. Therapeutic
    effects on cognition, which might be the best clinical cor-
    relate of widespread neurodegeneration, are modest at
    best.1, 2 One review shows that attempts to develop pharma-
    cological therapies for cognitive impairment have been
    unsuccessful in MS.3 Evidence for the effectiveness of
    rehabilitation strategies to ameliorate cognitive deficits in
    MS is limited, as well.4 Therefore, therapeutic approaches
    that target neuroprotective mechanisms and may improve
    cognition and motor function are urgently needed.5

    Intriguingly, exercise is shown to promote neuroregen-
    eration and plasticity and to improve learning and memory,
    in rodents.6 Several randomized controlled trials (RCTs) of

    Effects of exercise on fitness and cognition
    in progressive MS: a randomized,
    controlled pilot trial

    S Briken1,2, SM Gold1, S Patra3, E Vettorazzi4, D Harbs3,
    A Tallner5, G Ketels6, KH Schulz3,7 and C Heesen1,2

    Abstract
    Background: Exercise may have beneficial effects on both well-being and walking ability in multiple sclerosis (MS). Exer-
    cise is shown to be neuroprotective in rodents and may also enhance cognitive function in humans. It may, therefore, be
    particularly useful for MS patients with pronounced neurodegeneration.
    Objective: To investigate the potential of standardized exercise as a therapeutic intervention for progressive MS, in a
    randomized-controlled pilot trial.
    Methods: Patients with progressive MS and moderate disability (Expanded Disability Status Scale (EDSS) of 4–6) were
    randomized to one of three exercise interventions (arm ergometry, rowing, bicycle ergometry) for 8–10 weeks or a
    waitlist control group. We analyzed the drop-out rate as a measure of feasibility. The primary endpoint of the study was
    aerobic fitness. Secondary endpoints were walking ability, cognitive function as measured by a neuropsychological test
    battery, depression and fatigue.
    Results: A total of 42 patients completed the trial (10.6% drop-out rate). Significant improvements were seen in aero-
    bic fitness. In addition, exercise improved walking ability, depressive symptoms, fatigue and several domains of cognitive
    function.
    Conclusion: This study indicated that aerobic training is feasible and could be beneficial for patients with progressive
    MS. Larger exercise studies are needed to confirm the effect on cognition.
    Trial Registration: ISRCTN (trial number 76467492) http://isrctn.org

    Keywords
    Aerobic exercise, clinical trial, cognition, depression, fatigue, fitness, motor function, multiple sclerosis, progressive
    multiple sclerosis, rehabilitation, walking ability

    Date received: 15 July 2013; accepted: 8 September 2013

    1 Institute for Neuroimmunology and Clinical Multiple Sclerosis Research
    (inims), University Hospital Eppendorf, Hamburg, Germany.

    2 Department of Neurology, University Hospital Eppendorf, Hamburg,
    Germany.

    3 Competence Center for Sports and Exercise Medicine (Athleticum),
    University Hospital Eppendorf, Hamburg, Germany.

    4 Department of Medical Biometry and Epidemiology, University Medical
    Center Hamburg-Eppendorf, Germany.

    5 Institute of Sport Science, University of Erlangen-Nürnberg, Germany.
    6 Department of Physiotherapy, University Medical Center Hamburg-
    Eppendorf, Germany.

    7 Institute for Medical Psychology, Hamburg, Germany.
    Authors Briken, Gold, Schulz and Heesen contributed equally.

    Corresponding author:
    Christoph Heesen, Institute for Neuroimmunology and Clinical MS
    Research (inims), University Medical Center Eppendorf, Martinistrasse,
    Hamburg, Germany.
    Email: heesen@uke.de

    507358MSJ20310.1177/1352458513507358Multiple Sclerosis JournalBriken et al.
    2013

    Research Paper

    Briken et al. 383

    aerobic exercise in both young healthy and aging adults
    demonstrate improved cognitive function.7 Exercise may
    therefore have therapeutic potential for improving cogni-
    tive function in MS, but empirical evidence from rand-
    omized controlled trials is lacking.8

    Since the first RCT of exercise in MS by Petajan et al.9,
    both improved quality of life and walking ability in MS
    after exercise training were confirmed by several RCTs.10,
    11 In addition to improving well-being, recent experimental
    evidence suggests that exercise might directly affect pathol-
    ogy, by showing neuronal protection during experimental
    autoimmune encephalomyelitis (EAE).12

    Exercise therapy might be a particularly useful approach
    for MS patients with progressive disease, as treatment
    options are very limited.13 On the other hand, the more
    advanced motor impairment in secondary progressive MS
    (SPMS) might interfere with the ability of the patients to
    perform aerobic exercise training programs. Therefore,
    there is a need to rigorously test the feasibility and effec-
    tiveness of exercise in progressive MS.

    In the present pilot-RCT, we aimed to compare three
    endurance-training interventions in progressive MS
    patients. We expected exercise to improve their physical
    fitness, walking ability and cognitive function. Based on a
    meta-analysis in non-MS populations7, we expected
    improved performance in learning/memory, attention and
    executive function.

    Materials and methods

    Study design, overview and patient recruitment

    Our study was a RCT of three different exercise training
    tools (arm ergometry, rowing and bicycle ergometry) and a
    waitlist control group with progressive MS patients with
    moderate disability (see Supplemental Figure). The training
    programs consisted of 8–10 weeks of standardized exercise
    with 2–3 sessions per week. This time frame was chosen as a
    pragmatic approach, to also allow short interruptions of the
    training with the aim of obtaining about 20 training sessions.
    The training program was tailored to the individual level of
    fitness of the participants, as determined by standard ergom-
    etry at baseline. The feasibility measure of the study was the
    percentage of subjects completing the trial.

    Our main hypothesis was that the training program
    would increase fitness, as measured by increased peak oxy-
    gen consumption (VO2), and would improve walking abil-
    ity, as measured by gait tests. The primary endpoint was
    aerobic fitness (as defined by peak oxygen consumption
    during an exhaustion test). Secondary endpoints were:
    walking ability (as defined by the 6-Minute Walk Test
    (6MWT)), cognitive function (as measured by a neuropsy-
    chological battery), depressive symptoms and fatigue. All
    endpoints were assessed at baseline, as well as at the end of
    the 8–10 week intervention (unblinded to the group).
    Patients were recruited through the MS outpatient clinic at

    the University Medical Center Hamburg Eppendorf, as
    well as through advertisements on the website of the
    German MS Society and leaflets left in neurologists’
    offices. We also contacted patients from our database who
    had agreed to be informed about new studies.

    Standard protocol approvals and patient consent

    The trial was approved by the ethics committee of the
    Chamber of Physicians, City of Hamburg, Germany
    (Registration Number PV3689). Participants provided
    written informed consent prior to enrollment.

    Participant inclusion and exclusion criteria

    Patients had to meet diagnostic criteria for clinically defi-
    nite MS14 with a secondary-progressive disease course15
    and moderate disability (EDSS 4–6). To enhance recruit-
    ment, we later also allowed patients with primary-
    progressive MS (PPMS) to enter the trial, as long as they
    met the EDSS inclusion criterion.

    Patients were excluded if they had any medical contrain-
    dications for exercise therapy (cardiovascular or major
    orthopedic disease, general medical contraindications for
    increased aerobic activity) as assessed by self-report using
    the revised Physical Activity Readiness Questionnaire
    (rPAR-Q).16 We excluded patients if they had started immu-
    nomodulatory therapy within the last 6 months, undergone
    steroid therapy within the last 4 weeks, documented
    relapses within the last 12 months, abnormal liver or kidney
    function, immunodeficiency, diagnosis of other serious
    medical illnesses, or if they had severe developmental, psy-
    chiatric and neurological disorders other than MS.
    Eligibility was determined by an experienced senior neu-
    rologist. As no previous studies were available to power the
    exercise trial, its sample size was based on patient availa-
    bility, with a recruitment goal of n = 40 (at least n = 10 in
    each group).

    Randomization

    Patients were consecutively randomized to one of the four
    groups, using an automated biased coin algorithm.17 This
    approach used the a priori defined variables age, sex, EDSS
    and previous group size. To ensure a concealed allocation,
    we performed randomization after determining eligibility.

    Individually-tailored training intervention

    The training schedule for each participant was tailored to
    the individual results from a bicycle ergometry perfor-
    mance test that assessed aerobic fitness levels.18 Since MS
    patients might not be able to reach their maximum perfor-
    mance in a stepwise exhaustion test, we used a submaximal
    performance index (aerobic threshold (AT)) for the adjust-
    ment of the training intervention.

    384 Multiple Sclerosis Journal 20(3)

    The training was then performed with the modalities the
    participants were randomized to. We used the following
    equipment for training: First Degree Fitness® E-920 Upper
    Body arm ergometer, the Ergofit® 3000 bicycle ergometer
    and the Waterrower®.

    We determined the training levels using a performance
    test (ramp test) on the specific selected training tool (see
    Supplemental Figure). We used the performance in Watts
    (W) recorded at the individual AT as an anchor for the fol-
    lowing training intensity categories: AT, 120% of AT and
    130% of AT. The length of each training interval and the
    target performance were steadily increased with every
    training session (including regeneration sessions), accord-
    ing to a predefined training plan. The performance as well
    as subjective work load ratings (Borg scale) were recorded
    during each training session.19 The length of the training
    sessions steadily increased from 15 to 45 minutes (see
    Supplemental Table 1). All training sessions were per-
    formed at the Department of Physiotherapy, University
    Medical Center Hamburg-Eppendorf, under the supervi-
    sion of a licensed physiotherapist.

    Outcome measures

    Aerobic fitness. Participants started cycling at 25 W and
    resistance was steadily increased with an incline of 12.5 W/
    min. We obtained VO2 and heart rate continuously and
    recorded maximum power. We measured lactate every 2
    minutes. As some patients (n = 14) were unable to perform
    this standard ergometry, for these participants, an easier
    protocol was used starting at 8W with incremental increases
    of 8W/min. These tests were conducted at the Competence
    Center for Sports and Exercise Medicine in Hamburg, Ger-
    many by an experienced sports scientist.

    Motor function. We assessed walking ability before and
    after the training program, using the 6MWT.20

    Neuropsychological function. Cognitive impairment in MS
    affects domains including attention, processing speed,
    long-term memory and executive function.21 Therefore, we
    administered a battery of standardized neuropsychological
    tests that covered these domains.

    The “Symbol Digit Modalities Test” (SDMT)22 was
    utilized to measure processing speed. The “Verbal
    Learning and Memory Test” (VLMT) was used to evalu-
    ate declarative memory and learning abilities.23 Here, a
    word list of 15 unrelated words is presented 5 times. The
    test provides a measure of learning (sum of correctly
    recalled words during the 5 trials), plus delayed recall
    after 30 minutes. To assess attention, we used the “alert-
    ness” and “shift of attention” subtests of the computerized
    “Test Battery of Attention” (TAP).24 The “alertness” TAP
    subtest consists of a simple reaction time test, with and
    without cue (for “tonic alertness” and “phasic alertness”,
    respectively). In the TAP subtest “shift of attention”, a

    Posner paradigm with valid and invalid cues is used: After
    valid cues, the stimulus is presented in the area, as indi-
    cated by the arrow. After invalid cues, the stimulus is pre-
    sented on the side opposite to that indicated by the arrow.
    We quantified executive function with subtest 3 of the
    “Achievement Testing System” (Leistungsprüfsystem
    (LPS)).25 In this test, subjects select a symbol that does
    not fit a sequence, i.e. they identify the rule behind the
    sequence. The LPS was employed as a measure of logical
    reasoning. In addition, we assessed verbal fluency using
    the “Regensburg Verbal Fluency Test” (RWT), specifi-
    cally subtest “letters G-R”.26 In a previous study, we dem-
    onstrated that the VLMT, RWT and TAP are sensitive for
    detecting cognitive impairment in MS.27

    Patient-based outcome measures. We assessed depression
    using the self-reported version of the 30-item “Inventory of
    Depressive Symptoms” (IDS-SR30).

    28 We measured fatigue
    with the “Modified Fatigue Impact Scale” (MFIS). 29

    Statistical analyses

    We tested the feasibility of the different exercise modali-
    ties by analyses of drop-out rates, using Fisher’s exact
    test. Based on visual inspection of Quantile-Quantile
    (Q-Q) plots, we conducted statistical analyses using para-
    metric tests. According to guidelines for statistical analy-
    sis of clinical trials, published by The European Agency
    for the Evaluation of Medicinal Products (CPMP/
    ICH/363/96 and CPMP/EWP/2863/99), we computed the
    primary statistical analysis for all outcomes using
    ANCOVA (Analysis of covariance) models adjusting for
    baseline measurement of the respective outcome variable,
    to evaluate treatment effects (measured as change from
    baseline). No other covariates were included in this pri-
    mary analysis. As recommended, this model also did not
    include treatment by covariate interactions. In the case of
    significant F values in the ANCOVA model, we conducted
    planned pairwise comparisons for each intervention group
    (bicycle, arm ergometry and rowing) compared to the
    waitlist control group.

    A total of 14 ANCOVAs were computed: One for the
    primary endpoint (VO2 peak), and one for each of the sec-
    ondary endpoints walking ability (6MWT), depressive
    symptoms (IDS) and fatigue (MFIS) – for the multidimen-
    sional assessment of cognitive function, we computed 10
    ANCOVAs, of which four assessed attention. In accord-
    ance with European Medicines Agency (EMA) guidelines,
    we also computed sensitivity analyses using ANCOVA
    models, adjusting for baseline as well as sex, age, EDSS
    and patient MS type (SPMS or PPMS), in addition to the
    primary analysis. For the ANCOVA models, we used avail-
    able data from all our subjects who completed the pre- and
    post-intervention assessments (n = 42).

    Finally, we also computed non-parametric intention-
    to-treat (ITT) analyses, using Kruskal-Wallis tests

    Briken et al. 385

    where the patients who dropped out were assigned the
    lowest rank. In the results section, we report p values
    from the primary analysis (ANCOVA, adjusted for
    baseline). The p values from sensitivity analyses can be
    found in Supplemental Table 2. Pre- and post mean
    scores, standard deviations as well as confidence inter-
    vals are indicated in Supplemental Table 3 and Spearman
    correlation coefficients of change scores are indicated
    in Supplemental Table 4. We performed statistical anal-
    yses and conducted 2-tailed testing, using the statistics
    package R 2.15.2.30 A value of p < .05 was considered statistically significant.

    Results

    Patient sample

    We sent out 423 letters advertising the study. Patients who
    indicated interest in participating (n = 80) were contacted
    by phone for further screening. We examined 50 MS
    patients in person at the MS clinic. After screening, 47
    patients met our inclusion criteria and were randomized to
    one of the four treatment arms: arm ergometry, rowing,
    bicycle ergometry, waitlist control (see flow chart in Figure
    1 and Table 1).

    Feasibility

    As a measure of feasibility, we analyzed MS patient drop-
    out rates in the four groups. Of the 47 study participants, 42
    finished the trial, while five dropped out. The drop-out rate
    did not differ between the groups (p = .892; Table 1), indi-
    cating that exercise intervention is feasible in progressive
    MS with moderate disability (EDSS 4–6). Reasons for not
    completing the trial included logistic and mobility difficul-
    ties (n = 3), fatigue (n = 1) and injury unrelated to the study
    (n = 1). Baseline characteristics for the four groups are
    shown in Table 1. On average, the subjects exercised 22 ses-
    sions. The average Borg rating during the sessions was 4.6.

    Effects of exercise on fitness

    Exercise induced significant improvements in aerobic fitness,
    as measured by VO2 peak during the bicycle ergometry test
    (Figure 2, p = .029). Only the bicycle ergometry group dif-
    fered significantly from the control group (p = .003).

    Effects of exercise on motor function

    Exercise significantly improved distance walked by
    patients during the 6MWT (Figure 3; p = .012). Significant

    Figure 1. Participant flow chart.

    386 Multiple Sclerosis Journal 20(3)

    improvements were seen for both the arm ergometry (p =
    .003) and bicycle ergometry groups (p = .005), in com-
    parison to the control group.

    Effects of exercise on neuropsychological function

    Exercise improved 4 out of 10 neuropsychological
    measures. Exercise significantly improved verbal
    learning, as measured by the VLMT (p = .011, Figure

    4(a)). Improvements were significant for the arm
    ergometry (p = .007), the rowing group (p = .001) and
    the bicycle group (p = .009), as compared to the waitlist
    control group. In addition, a significant effect was found
    for VLMT delayed recall (p = .002, Figure 4(b)). Again,
    all exercise groups showed significant improvement,
    when compared to the waitlist control group (arm
    ergometry p = .004; rowing p < .001; bicycle ergometry p < .001).

    Figure 2. Effects of standardized exercise therapy on
    aerobic fitness of MS patients. A significant training effect was
    observed in VO2 peak consumption during the step-wise bicycle
    ergometry exhaustion test, the primary endpoint of the study.
    Each dot represents one patient. Asterisks indicate significant
    pairwise comparisons (* < .05; ** < .01). ml: milliliter; min: minutes; MS: multiple sclerosis;VO2 peak: peak oxygen consumption.

    Figure 3. Effects of standardized exercise therapy on walking
    ability in MS patients. Exercise therapy significantly improved
    walking ability, as measured by the 6MWT. Each dot represents
    one patient. Asterisks indicate significant pairwise comparisons
    (* < .05; ** < .01). ergo: ergometry; MS: multiple sclerosis; 6MWT: 6-Minute Walking Test.

    Table 1. Clinical baseline characteristics and training intensity.

      Arm Rowing Bicycle Control

    Drop-outs (n, %) 2 (16.6%) 1 (8.3%) 1 (8.3%) 1 (10%)
    Subjects completing 10 11 11 10
    Age (years) 49.1±8.5 50.9±9.2 48.8±6.8 50.4±7.6
    Sex (m/f) 5/5 4/7 5/6 4/6
    Education (years) 13.7±2.7 12.8±3.7 13.2±3.3 12.9±3.7
    Disease duration (years) 17.1±7.2 14.1±6.1 13.3±5.4 18.9±9.8
    EDSS (score) 5.2±0.9 4.7±0.8 5.0±0.8 4.9±0.9
    MS type (SPMS/PPMS) 8/2 7 / 4 8/3 8/ 2
    Number of sessions 21.1±0.5 21.7±0.4 22.6±0.9 N/A
    Average Borg rating 4.3±0.5 5.3±0.3 4.3±0.1 N/A

    EDSS: Expanded Disability Status Scale, SPMS: secondary progressive multiple sclerosis, PPMS: primary progressive multiple sclerosis. Data given as
    mean+standard deviation.

    Briken et al. 387

    Significant group differences were observed for the
    TAP subtest “tonic alertness” (p < .001; Figure 4(c)). The bicycle ergometry group showed significant improve- ments, compared to waitlist controls (p = .005). Furthermore, we saw significant effects in the TAP sub- test “shift of attention” (valid cue p = .007; Figure 4(d)). Again, the arm ergometry (p = .026), as well as the bicy- cle ergometry group (p = .002), showed significant improvements, compared to waitlist controls. No effects

    of exercise were seen for the two other TAP subtests,
    RWT, LPS and SDMT.

    Effects of exercise on mood and fatigue

    At baseline, 23 patients (49%) had IDS scores of 18 or
    higher (indicating moderate to severe clinical depression).
    Moreover, 27 patients (64%) had a MFIS score of 38 or
    higher at baseline, indicative for substantial fatigue. Exercise

    Figure 4. Effects of standardized exercise therapy on neuropsychological function. (a) Exercise resulted in significantly
    more words remembered in the learning trials 1–5 of the VLMT. (b) Moreover, significant differences in delayed recall trial 7 were
    seen. (c) Exercise significantly improved mean reaction times in the TAP alertness and (d) TAP shift of attention subtests. Each dot
    represents one patient. Asterisks indicate significant pairwise comparisons (* < .05; ** < .01). TAP: Test Battery of Attention; VLMT: Verbal Learning of Memory Test.

    388 Multiple Sclerosis Journal 20(3)

    significantly decreased depressive symptoms as per IDS
    (p < .001), with arm ergometry (p = .001) and bicycle ergometry (p = .035) showing significant improvements, compared to the waitlist control group. Finally, exercise also significantly improved fatigue, as per MFIS total score (p = .019), but only the arm ergometry group was signifi- cantly better than waitlist control (p = .013).

    Associations between physical fitness,
    walking ability and cognition

    We saw significant, albeit small-to-moderate correlations
    between improvements in the VO2 peak and the VLMT, as well
    as walking ability (as measured by the 6MWT) and measures
    of attention, fatigue and depression (Supplementary Table 4).

    Discussion

    In this study, we obtained the first evidence for beneficial
    effects of exercise on physical measures (aerobic fitness
    and walking ability), as well as neuropsychiatric symptoms
    (cognitive impairment, depressive symptoms and fatigue)
    in progressive MS.

    In MS, exercise studies mostly focused on endpoints,
    such as walking ability10 and quality of life11. Given the
    high prevalence of neuropsychiatric symptoms such as
    depression, fatigue and cognitive impairment in MS31, our
    preliminary results obtained in this pilot study indicate
    that exercise might have therapeutic potential for these
    important symptom domains. Despite the evidence for the
    beneficial effects of exercise on brain function provided
    by animal models and RCTs in older healthy adults, only
    one study to date explored the effects of exercise on cog-
    nitive function in MS.32 This trial used a 6 months training
    intervention with aerobic exercise at a low intensity (2–3
    on the Borg scale), with one class per week plus home
    exercise, and found no significant effects on neuropsy-
    chological tests, compared to yoga delivered at the same
    frequency. In contrast, herein we report significant
    improvements in learning, memory and attention after
    exercise training at a higher intensity (mean Borg scaling
    4.6). Furthermore, for most of our endpoints, namely VO2
    peak, depression, fatigue and measures of attention, the
    strongest effects were seen in the bicycle group. Therefore,
    further studies are needed to determine optimal exercise
    intensity, as well as to verify the most effective training
    modalities required to gain beneficial effects on neuropsy-
    chiatric symptoms in MS.

    Importantly, we found better performance in aspects of
    verbal learning and delayed memory (VLMT), as well as
    alertness and shift of attention (TAP), but not in working
    memory (SDMT) or executive function (RWT and LPS).
    Memory and attention were also shown to be consistently
    improved by exercise training in a recent meta-analysis of
    RCTs in healthy aging adults.7 On the other hand, this meta-

    analysis also reported beneficial effects on executive function
    and processing speed, which we did not find in our sample.
    Whether this is due to chance, limited statistical power in our
    study or whether there might be domain-specific differences
    in the effects of exercise, depending on the patient population
    and exercise conditions, remains to be elucidated.

    Higher levels of fitness in MS patients are found to be
    correlated cross-sectionally with higher structural connec-
    tivity33 and higher gray matter density34, using neuroimag-
    ing. Together, these data suggested that physical fitness
    may be related to less severe CNS damage and higher struc-
    tural integrity of brain networks that are important for cog-
    nitive function, such as learning and memory in MS.

    Our study suggested that exercise may improve walk-
    ing ability in progressive MS. This corroborates and
    expands the evidence from one recent uncontrolled trial
    resulting in increased walking ability after 8 weeks of
    mixed aerobic, resistance, and balance training in pro-
    gressive patients with EDSS 4–6.35 Intriguingly, we found
    improved walking ability, not only in the bicycle ergom-
    etry group, but also for the arm ergometry training. The
    underlying mechanisms for improved walking ability
    after upper limb training in MS are unclear and warrant
    further investigation. One possibility is that the increased
    walking ability may be due to contributions of improved
    cardiorespiratory function toward walking, as was previ-
    ously shown for patients with peripheral arterial disease36,
    37; however, since in our study the arm ergometry group
    did not show significant improvement in the VO2 peak,
    this explanation seems less likely. Another possibility is
    that better core stability, through the training of abdomi-
    nal and back muscles by arm ergometry, could help to sta-
    bilize the body during movement, thereby improve
    walking ability. Alternatively, improved walking ability
    could be a non-specific result of the frequent trips to the
    training facility, which may have provided some walking
    training, but the rowing group had the same frequence of
    trips without these significant improvements. This should
    be investigated in future studies.

    Some other limitations of our study need to be consid-
    ered: First, the study sample was small and the findings,
    particularly on cognitive function, require replication in
    larger samples. The significant increase in fitness might
    have, at least in part, been due to the decreased VO2 peak
    in the control group; however, other outcomes remained
    stable (6MWT) or slightly improved in controls (VLMT).
    Therefore, we believe that the intervention effects are not
    in general based on a worsening of the control group.

    Secondly, our control condition consisted of a waitlist
    group, rather than a non-exercise control condition deliv-
    ered at the same frequency. Therefore, we cannot entirely
    rule out that some of the effects, particularly those seen
    in patient self-reporting measures such as the IDS and
    MFIS, may be contributed by non-specific factors, such as
    attention from the therapist or social support from other

    Briken et al. 389

    patients. However, the three different intervention groups
    received the same frequency of visits, yet they did not
    show the same pattern of therapeutic effects in the objec-
    tive tests of aerobic fitness, walking ability and neuropsy-
    chological function. This makes such non-specific effects
    unlikely to explain our findings.

    A related limitation might be that we had multiple sec-
    ondary endpoints, particularly in the neuropsychological
    domain. This was because of the lack of previous data
    regarding the question which MS-related cognitive impair-
    ments might be most likely to be affected by exercise.

    Our study should therefore be considered a pilot trial;
    however, the consistent pattern of improvements across
    endpoints (with strongest improvements always in the
    bicycle group) would argue against a chance finding
    based on multiple testing.

    While the short-term effects of our exercise training
    study are encouraging, it remains unknown whether these
    effects can be sustained over longer periods of time.
    Maintenance of exercise in MS remains a major issue38 and
    a better understanding of the barriers involved, as well as
    development of effective strategies to overcome these, are
    needed.39 Moreover, our findings were obtained in a sample
    of progressive MS patients with moderate disability (EDSS
    4–6). It therefore remains to be seen if our results can be
    extended to higher disability ranges.

    In summary, this trial provided the first evidence for ben-
    eficial effects of standardized exercise training on aerobic fit-
    ness, walking ability, cognitive function and neuropsychiatric
    symptoms in patients with progressive MS and moderate-to-
    advanced disability. Given the limited pharmacological treat-
    ment options for progressive MS, further investigation of
    exercise interventions in progressive MS is clearly warranted.

    Conflict of interest

    The authors declare that there are no conflicts of interest.

    Funding

    This work was supported by the “Bundesministerium für Bildung
    und Forschung” (within the Consortium “Neu2” for the develop-
    ment of new therapeutic approaches in MS) and a generous dona-
    tion from Birgit Nollmeyer. SM Gold is supported by the German
    Research Society (Heisenberg Fellowship number GO1357/4–1).

    References

    1. Comi G. Effects of disease modifying treatments on cogni-
    tive dysfunction in multiple sclerosis. Neurol Sci 2010; 31:
    S261–264.

    2. Amato MP, Langdon D, Montalban X, et al. Treatment of
    cognitive impairment in multiple sclerosis: Position paper. J
    Neurol 2012; 260(6):1452–1468.

    3. Benedict RH and Zivadinov R. Risk factors for and manage-
    ment of cognitive dysfunction in multiple sclerosis. Nat Rev
    Neurol 2011; 7: 332–342.

    4. Rosti-Otajarvi EM and Hamalainen PI. Neuropsychological
    rehabilitation for multiple sclerosis. Cochrane Database Syst
    Rev 2011: CD009131.

    5. Mullard A. Success of immunomodulators in MS shifts dis-
    covery focus to neuroprotection. Nat Rev Drug Discov 2011;
    10: 885–887.

    6. Cotman CW, Berchtold NC and Christie LA. Exercise builds
    brain health: Key roles of growth factor cascades and inflam-
    mation. Trends Neurosci 2007; 30: 464–472.

    7. Smith PJ, Blumenthal JA, Hoffman BM, et al. Aerobic exer-
    cise and neurocognitive performance: A meta-analytic review
    of randomized controlled trials. Psychosom Med 2010; 72:
    239–252.

    8. Motl RW and Pilutti LA. The benefits of exercise training in
    multiple sclerosis. Nat Rev Neurol 2012; 8(9):487–497.

    9. Petajan JH, Gappmaier E, White AT, et al. Impact of aerobic
    training on fitness and quality of life in multiple sclerosis. Ann
    Neurol 1996; 39: 432–441.

    10. Snook EM and Motl RW. Effect of exercise training on walk-
    ing mobility in multiple sclerosis: A meta-analysis. Neurore-
    habil Neural Repair 2009; 23: 108–116.

    11. Motl RW and Gosney JL. Effect of exercise training on qual-
    ity of life in multiple sclerosis: A meta-analysis. Mult Scler
    2008; 14: 129–135.

    12. Rossi S, Furlan R, De Chiara V, et al. Exercise attenuates the
    clinical, synaptic and dendritic abnormalities of experimen-
    tal autoimmune encephalomyelitis. Neurobiol Dis 2009; 36:
    51–59.

    13. Fox RJ, Thompson A, Baker D, et al. Setting a research agenda
    for progressive multiple sclerosis: The International Collabora-
    tive on Progressive MS. Mult Scler 2012; 18: 1534–1540.

    14. Polman CH, Reingold SC, Banwell B, et al. Diagnostic cri-
    teria for multiple sclerosis: 2010 Revisions to the McDonald
    criteria. Ann Neurol 2011; 69: 292–302.

    15. Lublin FD and Reingold SC. Defining the clinical course of
    multiple sclerosis: Results of an international survey. National
    Multiple Sclerosis Society (USA) Advisory Committee on
    Clinical Trials of New Agents in Multiple Sclerosis. Neurol-
    ogy 1996; 46: 907–911.

    16. Thomas S, Reading J and Shephard RJ. Revision of the Physi-
    cal Activity Readiness Questionnaire (PAR-Q). Can J Sport
    Sci 1992; 17: 338–345.

    17. Vickers AJ. How to randomize. J Soc Integr Oncol 2006; 4:
    194–198.

    18. Schulz KH, Gold SM, Witte J, et al. Impact of aerobic training
    on immune-endocrine parameters, neurotrophic factors, qual-
    ity of life and coordinative function in multiple sclerosis. J
    Neurol Sci 2004; 225: 11–18.

    19. Borg GA. Psychophysical bases of perceived exertion. Med
    Sci Sports Exerc 1982; 14: 377–381.

    20. Goldman MD, Marrie RA and Cohen JA. Evaluation of the
    six-minute walk in multiple sclerosis subjects and healthy
    controls. Mult Scler 2008; 14: 383–390.

    21. Chiaravalloti ND and DeLuca J. Cognitive impairment in
    multiple sclerosis. Lancet Neurol 2008; 7: 1139–1151.

    22. Smith A. The symbol digit modalities test. Learn Dis 1968; 3:
    83–91.

    23. Helmstaedter C, Lendt M and Lux S. Verbaler Lern- und
    Merkfähigkeitstest. Diagnostica 1999; 45: 205–211.

    390 Multiple Sclerosis Journal 20(3)

    24. Zimmermann P and Fimm B. Testbatterie zur aufmerksam-
    keitsprüfung. Würselen: Psytest, 1994.

    25. Horn W. L-P-S leistungsprüfsystem. Göttingen: Hogrefe,
    1983.

    26. Aschenbrenner S, Tucha O and Lange KW. Regensburger
    wortflüssigkeitstest. Göttingen: Hogrefe, 2000.

    27. Heesen C, Schulz KH, Fiehler J, et al. Correlates of cognitive
    dysfunction in multiple sclerosis. Brain Behav Immun 2010;
    24: 1148–1155.

    28. Trivedi MH, Rush AJ, Ibrahim HM, Carmody TJ, Biggs
    MM, Suppes T, Crismon ML, Shores-Wilson K, Toprac
    MG, Dennehy EB, Witte B, Kashner TM. The Inventory of
    Depressive Symptomatology, Clinician Rating (IDS-C) and
    Self-Report (IDS-SR), and the Quick Inventory of Depressive
    Symptomatology, Clinician Rating (QIDS-C) and Self-Report
    (QIDS-SR) in public sector patients with mood disorders: a
    psychometric evaluation. Psychol Med 2004;34(1):73–82.

    29. Flachenecker P, Kümpfel T, Kallmann B, Gottschalk M,
    Grauer O, Rieckmann P, Trenkwalder C, Toyka KV. Fatigue
    in multiple sclerosis: a comparison of different rating scales
    and correlation to clinical parameters. Mult Scler 2002;
    8(6):523–6

    30. R Core Team. R: A language and environment for statistical
    computing. Vienna: R Foundation for Statistical Computing,
    2012.

    31. Feinstein A, DeLuca J, Baune TB, et al. Cognitive and neuro-
    psychiatric disease manifestations in MS. Mult Scler Relat Dis
    2013; 2: 4–12.

    32. Oken BS, Kishiyama S, Zajdel D, et al. Randomized con-
    trolled trial of yoga and exercise in multiple sclerosis. Neurol-
    ogy 2004; 62: 2058–2064.

    33. Prakash RS, Patterson B, Janssen A, et al. Physical activity
    associated with increased resting-state functional connectiv-
    ity in multiple sclerosis. J Int Neuropsychol Soc 2011; 17:
    986–997.

    34. Prakash RS, Snook EM, Motl RW, et al. Aerobic fitness is
    associated with gray matter volume and white matter integrity
    in multiple sclerosis. Brain Res 2010; 1341: 41–51.

    35. Motl RW, Smith DC, Elliott J, et al. Combined training
    improves walking mobility in persons with significant dis-
    ability from multiple sclerosis: A pilot study. J Neurol Phys
    Ther 2012; 36: 32–37.

    36. Treat-Jacobson D, Bronas UG and Leon AS. Efficacy of arm-
    ergometry versus treadmill exercise training to improve walk-
    ing distance in patients with claudication. Vasc Med 2009; 14:
    203–213.

    37. Tew G, Nawaz S, Zwierska I, et al. Limb-specific and cross-
    transfer effects of arm-crank exercise training in patients with
    symptomatic peripheral arterial disease. Clin Sci 2009; 117:
    405–413.

    38. Vollmer TL, Benedict R, Bennett S, et al. Exercise as pre-
    scriptive therapy in multiple sclerosis. Int J MS Care 2012;
    14: 2–14.

    39. Asano M, Duquette P, Andersen R, et al. Exercise barriers and
    preferences among women and men with multiple sclerosis.
    Disabil Rehabil 2013; 35: 353–361.

    Copyright of Multiple Sclerosis Journal is the property of Sage Publications, Ltd. and its
    content may not be copied or emailed to multiple sites or posted to a listserv without the
    copyright holder’s express written permission. However, users may print, download, or email
    articles for individual use.

    Calculate your order
    Pages (275 words)
    Standard price: $0.00
    Client Reviews
    4.9
    Sitejabber
    4.6
    Trustpilot
    4.8
    Our Guarantees
    100% Confidentiality
    Information about customers is confidential and never disclosed to third parties.
    Original Writing
    We complete all papers from scratch. You can get a plagiarism report.
    Timely Delivery
    No missed deadlines – 97% of assignments are completed in time.
    Money Back
    If you're confident that a writer didn't follow your order details, ask for a refund.

    Calculate the price of your order

    You will get a personal manager and a discount.
    We'll send you the first draft for approval by at
    Total price:
    $0.00
    Power up Your Academic Success with the
    Team of Professionals. We’ve Got Your Back.
    Power up Your Study Success with Experts We’ve Got Your Back.

    Order your essay today and save 30% with the discount code ESSAYHELP