1. No category

Physical Activity, Sedentary Behavior,
Physical Performance, Adiposity, and
Academic Achievement in Primary-School
Children
EERO ANTERO HAAPALA
Physical Activity, Sedentary Behavior,
Physical Performance, Adiposity, and
Academic Achievement in Primary-School
Children
To be presented by permission of the Faculty of Health Sciences, University of Eastern Finland for
public examination in building Seminarium of the University of Jyväskylä, Auditorium S212,
Jyväskylä, on Friday, February 20th 2015, at 12 noon
Publications of the University of Eastern Finland
Dissertations in Health Sciences
Number 266
Faculty of Health Sciences, School of Medicine, Institute of Biomedicine, University of Eastern Finland
Faculty of Education, Department of Teacher Education, University of Jyväskylä
Faculty of Social Sciences, Department of Psychology, University of Jyväskylä
2015
Jyväskylän yliopistopaino
Jyväskylä, 2015
Series Editors:
Professor Veli-Matti Kosma, M.D., Ph.D.
Institute of Clinical Medicine, Pathology
Faculty of Health Sciences
Professor Hannele Turunen, Ph.D.
Department of Nursing Science
Faculty of Health Sciences
Professor Olli Gröhn, Ph.D.
A.I. Virtanen Institute for Molecular Sciences
Faculty of Health Sciences
Professor Kai Kaarniranta, M.D., Ph.D.
Institute of Clinical Medicine, Ophthalmology
Faculty of Health Sciences
Lecturer Veli-Pekka Ranta, Ph.D. (pharmacy)
School of Pharmacy
Faculty of Health Sciences
Distributor:
University of Eastern Finland
Kuopio Campus Library
P.O.Box 1627
FI-70211 Kuopio, Finland
http://www.uef.fi/kirjasto
ISBN (print): 978-952-61-1688-4
ISBN (pdf): 978-952-61-1689-1
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
III
Author’s address:
Institute of Biomedicine/Physiology, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND
Supervisors:
Professor Timo Lakka, M.D., Ph.D.
Institute of Biomedicine/Physiology, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND
Professor Anna-Maija Poikkeus, Ph.D.
Department of Teacher Education, Faculty of Education
University of Jyväskylä
JYVÄSKYLÄ
FINLAND
Professor Paavo Leppänen, Ph.D.
Department of Psychology, Faculty of Social Sciences
University of Jyväskylä
JYVÄSKYLÄ
FINLAND
Virpi Lindi, Ph.D.
Institute of Biomedicine/Physiology, School of Medicine
University of Eastern Finland
KUOPIO
FINLAND
Reviewers:
Professor Charles H. Hillman, Ph.D.
Department of Kinesiology and Community Health
University of Illinois at Urbana-Champaign
URBANA-CHAMPAIGN
UNITED STATES OF AMERICA
Adjunct Professor Katja Borodulin, Ph.D.
Department of Chronic Disease Prevention
National Institute for Health and Welfare
HELSINKI
FINLAND
Opponent:
Associate Professor Darla Castelli, Ph.D.
Department of Kinesiology and Health Education
University of Texas at Austin
AUSTIN
UNITED STATES OF AMERICA
IV
V
Haapala, Eero Antero
Physical Activity, Sedentary Behavior, Physical Performance, Adiposity, and Academic Achievement in PrimarySchool Children
University of Eastern Finland, Faculty of Health Sciences
Publications of the University of Eastern Finland. Dissertations in Health Sciences Number 266. 2015. 68 p.
ISBN (print): 978-952-61-1688-4
ISBN (pdf): 978-952-61-1689-1
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
ABSTRACT
Physically active lifestyles have become increasingly rare in developed countries, while
endurance and neuromuscular performance have declined and the prevalence of overweight
among children and youth has increased in recent decades. Sedentary lifestyles may not only
be associated with increased cardiometabolic risk but also less optimal circumstances for
cognitive development and academic achievement, though the evidence is still limited. This
thesis investigated the associations of physical activity, sedentary behavior, cardiorespiratory
fitness, and motor performance with reading and arithmetic skills in children. The associations
of adiposity with cardiorespiratory fitness and neuromuscular performance were also studied.
This thesis is based on the data from the Physical Activity and Nutrition in Children
(PANIC) Study and the First Steps Study and a review of earlier studies on the associations of
cardiorespiratory fitness and motor performance with cognition and academic achievement.
The PANIC Study is an ongoing exercise and diet intervention study in a population sample of
children (N = 512 at baseline). The First Step Study was a longitudinal study following up the
learning paths and motivation of a large sample of children (N = 2000) during their early
school years (2006–2011). Physical activity, sedentary behavior, cardiorespiratory fitness,
neuromuscular performance including motor performance and adiposity, were assessed in the
PANIC Study in Grade 1 when the children were 6–8-years of age. Reading and arithmetic
skills were assessed in the First Steps Study in Grades 1–3. Altogether 207 children
participated in both studies. There were 167 to 186 participants in the original investigations of
Study II and III. The study investigating the associations of adiposity with cardiorespiratory
fitness and neuromuscular performance included 403 participants (Study IV).
Poorer motor performance was associated with both poorer academic achievement and the
prerequisites of learning such as working memory and executive control. Engaging in physical
activities during recess and in sports was related to better academic achievement in children.
Higher physical activity level, reading and writing for leisure, and computer use and
videogame playing were associated with better academic achievement in boys but these
associations were partly explained by motor performance, cardiorespiratory fitness and
adiposity. In girls, physical activity was inversely associated with reading and arithmetic skills
but these associations were attenuated after adjustment for adiposity. Higher adiposity was
related to a lower neuromuscular performance in both boys and girls and cardiorespiratory
fitness as expressed as maximal workload adjusted for lean body mass in boys.
The results of this thesis suggest that poor motor performance and lack of physical activity
worsen academic achievement, and that increased adiposity decrease physical performance.
National Library of Medicine Classification: QT 260, QT 256, QT 250, WD 210, WS 105.5.M5
Medical Subject Headings: Exercise; Leisure Activities; Motor Activity; Physical Fitness; Sports; Educational
Status;
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VII
Haapala, Eero Antero
Physical Activity, Sedentary Behavior, Physical Performance, Adiposity, and Academic Achievement in PrimarySchool Children
Itä-Suomen yliopisto, terveystieteiden tiedekunta
Publications of the University of Eastern Finland. Dissertations in Health Sciences Numero 266. 2015. 68 s.
ISBN (print): 978-952-61-1688-4
ISBN (pdf): 978-952-61-1689-1
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
TIIVISTELMÄ
Lasten liikunta on vähentynyt ja kestävyyskunto sekä neuromuskulaarinen suorituskyky ovat
heikentyneet. Myös ylipaino on lisääntynyt kuluneiden vuosikymmenten aikana. Liikkumaton
elämäntapa lisää riskiä moniin kroonisiin sairauksiin, mutta jo lapsilla se voi häiritä
oppimiseen ja tarkkaavaisuuteen yhteydessä olevaa hermostollista kehitystä sekä toimintaa ja
siten heikentää koulumenestystä. Tutkimusnäyttö on kuitenkin puutteellista. Tässä
tutkimuksessa tarkasteltiin liikunnan ja fyysisesti passiivisen ajan, kestävyyskunnon ja
motorisen suorituskyvyn yhteyksiä lukutaitoon ja matemaattiseen osaamiseen sekä tutkittiin
kehon rasvakudoksen määrän yhteyksiä kestävyyskuntoon ja neuromuskulaariseen
suorituskykyyn alakouluikäisillä lapsilla.
Tämä väitöstutkimus perustuu Lasten liikunta ja ravitsemus- (PANIC) sekä Alkuportaattutkimusten aineistoihin sekä edeltävään tutkimukseen perustuvaan kirjallisuuskatsaukseen.
PANIC-tutkimus on edelleen jatkuva liikunta ja ravitsemus -interventiotutkimus
väestöpohjaisessa lapsiotoksessa (N=512). Alkuportaat-seurannan ensimmäinen alakouluikään
keskittyvä vaihe toteutettiin vuosina 2006–2011 suuressa väestöotoksessa (N=2000). Liikuntaaktiivisuus ja fyysisesti passiivinen aika, kestävyyskunto, neuromuskulaarinen suorituskyky
sisältäen motorisen suorituskyvyn ja rasvakudoksen määrä mitattiin PANIC-tutkimuksessa
ensimmäisellä luokalla lasten ollessa 6–8-vuotiaita. Lukutaitoa ja matemaattista osaamista
arvioitiin Alkuportaat-tutkimuksessa 1–3-luokilla. Yhteensä 207 lasta osallistui sekä PANICettä Alkuportaat-tutkimukseen. Osatutkimuksiin osallistuneiden lasten määrä vaihteli välillä
167–403.
Heikompi motorinen suorituskyky oli yhteydessä heikompiin oppimistuloksiin sekä
kognitiivisiin toimintoihin kuten työmuistiin ja inhibitioon. Välituntiliikunta sekä
urheiluseuran harjoituksiin osallistuminen olivat yhteydessä parempiin oppimistuloksiin.
Lisäksi pojilla runsaampi liikunta, lukeminen ja kirjoittaminen, sekä tietokoneen käyttö ja
videopelien pelaaminen olivat yhteydessä parempiin oppimistuloksiin, mutta nämä yhteydet
selittyivät osin motorisella suorituskyvyllä, kestävyyskunnolla ja rasvakudoksen määrällä.
Tytöillä liikunta oli käänteisesti yhteydessä oppimistuloksiin, mutta nämä yhteydet selittyivät
suurimmaksi osaksi rasvakudoksen määrällä. Rasvakudoksen määrä oli käänteisesti
yhteydessä neuromuskulaariseen suorituskykyyn pojilla ja tytöillä sekä kestävyyskunnon
mittarina käytettyyn rasvattomalla massalla vakioituun maksimityötehoon pojilla.
Tulokset viittaavat siihen, että heikko motorinen suorituskyky ja vähäinen liikunta saattavat
altistaa heikommille oppimistuloksille ja että liiallinen rasvakudoksen määrä heikentää
neuromuskulaarista ja motorista suorituskykyä.
Luokitus: QT 260, QT 256, QT 250, WD 210, WS 105.5.M5
Yleinen Suomalainen asiasanasto: liikunta; fyysinen
oppimistulokset; ylipaino; lapset; seurantatutkimus
aktiivisuus;
fyysinen
kunto;
opintomenestys;
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Acknowledgements
This study was carried out in the Faculty of Health Sciences, School of Medicine, Institute of
Biomedicine/Physiology and in the Graduate School of the Clinical Research, the University of
Eastern Finland, during the years 2011–2015.
I express my deepest gratitude to my supervisors who had a great influence on work and my
growth as a researcher. Their contribution to my scholarship cannot be measured by any
available measure. First of all, my principal supervisor, Professor Timo Lakka, M.D., Ph.D.,
made it possible for me to join in the PANIC Study team. I thank him wholeheartedly for his
indefatigable guidance, effort, encouragement, and trusting me during this period. He taught
me that “good is not enough; we must do better than that”. Professor Anna-Maija Poikkeus,
Ph.D., my supervisor, made it possible for me to delve into the data of the First Step Study. She
was an irreplaceable repository of information regarding the study of school readiness and she
gave me important lessons on how to write scientific papers. Virpi Lindi Ph.D., my supervisor,
was an essential part of my supervisory team. I thank her for the continuous support, patience
when I was impatient, great attitude, and positivity towards my endless problems and
questions. I would like to thank Professor Paavo H.T. Leppänen, Ph.D., for sharing his
knowledge of brain functioning and his important suggestions on all my original articles. Last
but not least on my team, Katriina Kukkonen-Harjula, M.D. Ph.D., spent countless hours
reading my manuscripts, giving invaluable comments to improve my writing, and sharing her
expertise in obesity and physical activity. I could not hope for better guidance and supervision
than my wonderful team has had given. I had the opportunity to learn from the best. I was
honored to work with you all.
I sincerely thank the pre-examiners of my thesis, Professor Charles H. Hillman and Docent
Katja Borodulin. Your careful work, constructive comments, and helpful tips significantly
improved the thesis.
I also thank the people who led the Physical Activity and Nutrition in Children Study and the
First Steps Study. Their work was the foundation of my thesis. I would especially like to thank
my co-authors Tuomo Tompuri, M.D., M.Sc., Eeva-Kaarina Lampinen, M.Sc., Juuso Väistö,
M.Sc., Niina Lintu, M.Sc. and David Laaksonen, M.D., Ph.D., for their help in writing the
manuscripts. I express special gratitude for and to my co-author Arja Sääkslahti, Ph.D., for her
encouraging and tireless support. I also owe a dept of thanks to Marja-Leena Lamidi,
statistician, for her knowledge and support on the data analyses.
I express my gratitude to Helena Viholainen, Ph.D., for her valuable comments and help and
to Veijo Turpeinen, Ph.D., for his positive and genuine interest in my research.
This work would not have been possible without financial support from the Juho Vaino
Foundation, the Päivikki and Sakari Sohlberg Foundation, and the Foundation for Pediatric
Research.
X
Finally, I dedicate my thesis to my lovely daughter Verna and my beloved wife Henna (and to
“Masuvauva”, who was not born when this was written). Verna, we spent immeasurable
hours in sandpits, play grounds and woods, and there you let me air my ideas about research
while playing with you. Henna, you have never understood my passion for research, but
regardless of that you let me spend an extensive amount of time writing, reading, and talking
about my work. I love you.
Jyväskylä, January 2015
Eero Haapala
XI
List of the original publications
This dissertation is based on the following original publications:
I
Haapala E A. Cardiorespiratory fitness and motor skills in relation to cognition and
academic performance in children – A review. Journal of Human Kinetics 36: 55–68,
2013.
II
Haapala E A, Poikkeus A-M, Kukkonen-Harjula K, Tompuri T, Leppänen P H T,
Lindi V, Lakka T A. Associations of motor and cardiovascular performance with
academic skills in children. Medicine & Science in Sports & Exercise 46, 1016–1024,
2014.
III
Haapala E A, Poikkeus A-M, Kukkonen-Harjula K, Tompuri T, Väistö J, Lintu N,
Laaksonen D E, Lindi V, Lakka T A. Associations of physical activity and sedentary
behavior with academic skills – A follow-up study among primary school children
PLoS ONE 10;9:e107031, 2014.
IV
Haapala E A, Väistö J, Lintu N, Tompuri T, Lampinen E-K, Sääkslahti A, Lindi V,
Lakka T A. Associations of adiposity assessed by dual-energy X-ray absorptiometry
with cardiovascular and neuromuscular performance in 6–8-year old children – The
PANIC Study. Submitted.
The publications were adapted with the permission of the copyright owners.
XII
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Contents
1 INTRODUCTION ................................................................................................................................ 1
2 REVIEW OF THE LITERATURE ..................................................................................................... 2
2.1 Physical activity among children .................................................................................2
2.1.1 Definitions of physical activity ..............................................................................2
2.1.2 Assessment of physical activity .............................................................................2
2.1.3 Levels of physical activity among children..........................................................3
2.2 Sedentary behavior among children............................................................................4
2.2.1 Definitions of sedentary behavior .........................................................................4
2.2.2 Assessment of sedentary behavior ........................................................................4
2.2.3 Levels of sedentary behavior among children ....................................................4
2.3 Cardiorespiratory fitness among children ..................................................................5
2.3.1 Definition of cardiorespiratory fitness ..................................................................5
2.3.2 Assessment of cardiorespiratory fitness ...............................................................5
2.3.3 Levels of cardiorespiratory fitness among children ...........................................6
2.4 Neuromuscular performance among children ..........................................................6
2.4.1 Definitions of neuromuscular performance .........................................................6
2.4.2 Assessment of neuromuscular performance .......................................................7
2.4.3 Levels of neuromuscular performance among children ....................................7
2.5 Adiposity among children ............................................................................................7
2.5.1 Definitions of adiposity ...........................................................................................7
2.5.2 Assessment of adiposity .........................................................................................8
2.5.3 Prevalence of overweight among children ..........................................................8
2.6 Cognition and academic achievement among children ...........................................9
2.6.1 Definitions of cognition and academic achievement ..........................................9
2.6.2 Assessment of cognition and academic achievement ........................................9
2.6.3 Development of cognition ....................................................................................10
2.7 Physical activity, cognition, and academic achievement among children ...........11
2.8 Sedentary behavior, cognition, and academic achievement among children .....12
2.9 Cardiorespiratory fitness, cognition, and academic achievement among
children ................................................................................................................................13
2.10 Neuromuscular performance, cognition, and academic achievement among
children ................................................................................................................................13
2.11 Mechanisms underlying the associations of physical activity and physical
performance with cognition ..............................................................................................16
2.11.1 Mechanisms underlying the associations of aerobic exercise with cognition16
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2.11.2 Mechanisms underlying the associations of coordinative exercise with
cognition .......................................................................................................................... 17
2.11.3 Mechanisms underlying the associations of combined aerobic and
coordinative exercise with cognition ........................................................................... 18
2.11.4 Mechanisms underlying the associations of cardiorespiratory fitness with
cognition .......................................................................................................................... 18
2.11.5 Mechanisms underlying the associations of neuromuscular and motor
performance with cognition .......................................................................................... 19
2.11.6 Psychosocial factors underlying the associations of physical activity and
physical performance with cognition .......................................................................... 19
2.11.7 Mechanisms linking sedentary behavior to cognition ................................... 19
3 AIMS OF THE STUDY ......................................................................................................................20
4 STUDY POPULATIONS AND METHODS ..................................................................................21
4.1 Study populations ........................................................................................................ 21
4.2 assessments ................................................................................................................... 24
4.2.1 Physical activity and sedentary behavior .......................................................... 24
4.2.2 Physical performance ............................................................................................ 24
4.2.3 Body composition and anthropometrics ............................................................ 26
4.2.4 Academic achievement ......................................................................................... 26
4.2.5 Other assessments ................................................................................................. 26
4.3 statistical methods ........................................................................................................ 27
5 RESULTS ..............................................................................................................................................29
5.1 Narrative review of cardiorespiratory fitness, motor performance, cognition,
and academic achievement (Study I) .............................................................................. 29
5.2 Motor performance, cardiorespiratory fitness, and academic achievement
during first three school years (Study II) ........................................................................ 29
5.2.1 Associations of motor performance and cardiorespiratory fitness in Grade 1
with reading and arithmetic skills in Grades 1–3 ...................................................... 29
5.2.2 Differences in reading and arithmetic skills among children in the lowest,
middle, and highest third of motor performance ...................................................... 30
5.3 Physical activity, sedentary behavior, and academic achievement during first
three school years (Study III) ............................................................................................ 31
5.3.1 Physical activity and academic achievement among all children .................. 31
5.3.2 Physical activity and academic achievement among boys .............................. 32
5.3.3 Physical activity and academic achievement among girls .............................. 32
5.3.4 Sedentary behavior and academic achievement among all children............. 34
5.3.5 Sedentary behavior and academic achievement among boys ........................ 34
5.3.6 Sedentary behavior and academic achievement among girls ......................... 34
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5.4 Adiposity, neuromuscular performance, and cardiorespiratory fitness (Study
IV) .........................................................................................................................................35
5.4.1 Associations of adiposity with cardiorespiratory fitness and neuromuscular
performance .....................................................................................................................35
5.4.2 Cardiorespiratory fitness and neuromuscular performance children in
different body fat percentage categories .....................................................................36
6 DISCUSSION ..................................................................................................................................... 38
6.1 Methodological aspects and limitations ....................................................................38
6.1.1 Methodological issues in Study I .........................................................................38
6.1.2 Study population and design in Studies II, III, and IV ....................................38
6.1.3 Assessment of physical activity ...........................................................................39
6.1.4 Assessment of sedentary behavior ......................................................................39
6.1.5 Assessment of cardiorespiratory fitness .............................................................39
6.1.6 Assessment of neuromuscular performance .....................................................40
6.1.7 Assessment of body composition and anthropometrics ..................................40
6.1.8 Assessment of academic achievement ................................................................40
6.1.9 Assessment of confounding factors ....................................................................41
6.2 Physical activity, sedentary behavior, physical performance, adiposity, and
academic achievement .......................................................................................................41
6.3 The interwoven relationships between physical activity, physical performance,
cogntion, and academic achievement ..............................................................................42
6.3.1 Cardiorespiratory fitness, cognition, and academic achievement: Seeking
meaning in measurements.............................................................................................44
6.3.2 Adiposity and physical performance: Their interrelationships and relevance
for academic achievement .............................................................................................45
7 CONCLUSIONS................................................................................................................................. 47
8 IMPLICATIONS AND FUTURE DIRECTIONS ......................................................................... 48
9 REFERENCES ..................................................................................................................................... 49
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Abbreviations
ANCOVA
Analysis of covariance
BDNF
Brain-derived neurotropic factor
BMI
Body mass index
CRF
Cardiorespiratory fitness
DXA
Dual-energy X-ray absorptiometry
HSBC
Health Behaviour in School-Aged Children survey
IGF-1
Insulin-like growth factor-1
IQ
Intelligence quotient
MET
Metabolic equivalent
PWC170
Physical work capacity at the heart rate of 170
SRT
Shuttle run test
TV
Television
VEGF
Vascular endothelial growth factor
VO2max
Maximal oxygen consumption
VO2peak
Peak oxygen consumption
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1
1 Introduction
“Mens sana in corpora sano – a sound mind in a healthy body”
-Juvenalis
“The running man” has been the gold standard phenotype of homo sapiens for thousands of
years (1). Physical activity was an essential and universal part of our lifestyle no more than 100
years ago, but the need for physical work has decreased dramatically since then. Changes in
lifestyle have led to physical inactivity and the concomitant development of health problems
causing economic losses (1). Physical inactivity causes 6–10% of major non-communicable
diseases, such as coronary heart disease, type 2 diabetes mellitus and various cancers. Physical
inactivity has also been estimated to cause about 9% of premature deaths (2).
Less than half of children and adolescents undertake the recommended 60 minutes of
moderate to vigorous physical activity daily (3,4). Moreover, levels of physically active
commuting have declined over the years (5), whereas the use of electronic media and other
modes of screen time have increased among children (3,6). The evidence also suggests that
“the running man” is much more an exception than a rule among the children and adolescents
of this millennium. Children and adolescents have poorer endurance performance than was
true a few decades ago (7–9). Motor performance also shows declining trends in children and
adolescents (10,11). Finally, the prevalence of overweight and obesity has increased
substantially during the past three decades (11–13).
Studies among animals suggest that physical inactivity worsens neural survival, synaptic
plasticity, and learning and memory (1,14). Studies among humans suggest that physical
inactivity and poor physical performance increase the risk of cognitive decline and dementia
among older adults (15). There is also some evidence that physical inactivity is associated with
impaired cognition in children (16). Physical inactivity may, therefore, have far-reaching
effects on children´s lives in later years. If physical inactivity and poor physical performance
are associated with less than optimal cognitive function and academic development, they may
also be associated with poorer education levels leading to lower socioeconomic attainment and
various physical and psychosocial problems.
The objective of this doctoral thesis was to investigate the associations of physical activity,
sedentary behavior, cardiorespiratory fitness and motor performance with reading and
arithmetic skills and the relation of adiposity to cardiorespiratory fitness and neuromuscular
performance in children.
2
2 Review of the Literature
2.1 PHYSICAL ACTIVITY AMONG CHILDREN
2.1.1 Definitions of physical activity
Physical activity refers to any bodily movement that increases energy expenditure and can be
roughly categorized as exercise and non-exercise physical activity (17). Exercise is planned and
structured physical activity, such as sports, with an objective to improve physical
performance, whereas non-exercise physical activity includes for example physically active
transportation, physically active play and housekeeping chores (17). Total physical activity
volume is the product of intensity, frequency and duration of a single session of physical
activity (18). The intensity of physical activity can be divided into light, moderate and vigorous
based on its energy demands (19); the intensities are usually expressed using metabolic
equivalent (MET) values (19). MET is an indicator of energy expenditure, with one MET
representing an energy cost when sitting at rest and corresponding to oxygen uptake of
approximately 3.5 mL·kg-1·min-1 (20). However, conversion of resting metabolic rate to MET
values may lead to 10–15% overestimation of the resting metabolic rate in adults (21), while
children often expend more energy on a given task than adults so MET values underestimate
energy expenditure among children (22). Based on adult data, Ainsworth et al. (19) consider
< 3 METs as light intensity, 3–6 METs as moderate intensity and > 6 METs as vigorous
intensity.
Children are not considered endurance animals due to the features of their habitual physical
activity (23). Children´s physical activity is often intermittent and includes short bouts of
vigorous physical activity followed by periods of low intensity activity or rest (24). Moreover,
the type of physical activity may also differ between boys and girls; boys tend to participate in
physically active play, such as rough and tumble play and play fighting, whereas girls tend to
engage in activities where the physical dimension is not emphasized (25).
2.1.2 Assessment of physical activity
Physical activity can be assessed using both subjective and objective methods (18). Most
questionnaires used among children and adolescents assess specific types of physical activity
and their duration and frequency (22). The weakness of questionnaires is that it is difficult to
recall past physical activity accurately and therefore questionnaires tend to over- or
underestimate the duration and intensity of physical activity but questionnaires are able to
categorize individuals in rank order according to their physical activity level (22,26). Young
children can rarely accurately report their physical activity and therefore questionnaires
administered by parents or teachers are used (22). However, estimating children´s physical
activity is difficult for adults (18). Self-reports are also prone to desirability bias (18).
Nevertheless, questionnaires can be used to assess long-term habitual physical activity easily
and inexpensively (22). Objective methods, such as pedometers, accelerometers, heart rate
monitoring and muscle activity measurements or combinations thereof, involve the
measurement of physiological or biomechanical parameters during physical activity (18,27).
Accelerometers have become the most commonly used method to assess habitual physical
activity objectively in children and adolescents (22), but they assess only upright physical
activity that induce acceleration of the body in one or more directions, such as running and
3
walking (22,28). Accelerometers do not assess physical activity that does not induce high
accelerations such as bicycling or swimming, and do not assess postures, quality of movement,
or the activity setting (22,28). Thus, although objective methods are useful in the assessment of
physical activity, questionnaires are currently the only method to assess the wider range of
physical activity, such as ball games and gymnastics, and the easiest way to assess the activity
setting (18,22). With children, objective methods have the drawbacks that assessing physical
activity alters physical activity levels (the Hawthorne effect), the epoch length set as short as
possible because of the nature of children´s physical activity, and the relatively long (4–9 days)
wear time of the device to provide accurate estimates (18).
2.1.3 Levels of physical activity among children
Dollman and colleagues (5) suggested that there is no evidence for the decline in total physical
activity among children and youth during the past 40 years. Nevertheless, they found a
consistent decline in physically active transportation during the past 30 to 40 years (5). Van der
Ploeg et al. (29) showed a 25% decline in the prevalence of physically active school
transportation and a threefold increase in motorized school transportation between 1971 and
2003 among children 5–9 years of age.
The physical activity recommendations for school-aged youth suggest that children and
adolescents should accumulate at least 60 minutes of moderate-to-vigorous physical activity
daily (30–32). These recommendations are mainly based on the data from self-reports. Using a
data from self-reports, it has been found that 90% of Finnish boys and girls aged seven years
and 30% of boys and 20% of girls aged 12 years achieve the recommended levels of moderateto-vigorous physical activity (33). Recent accelerometer data from Canada and Finland
suggest that 17–78% of children aged 6–19 years achieve the recommended amount of
moderate-to-vigorous physical activity (3,34). However, these estimates were based on
accelerometer measurements for three days, and the proportion of Canadian children
accumulating at least 60 minutes of moderate-to-vigorous physical activity for six days a week
was only 9% in boys and 4% in girls (3). Moreover, Verloigne et al. (35) investigated
objectively-measured physical activity levels among 686 children aged 10–12 years from
Switzerland, the Netherlands, Greece, Hungary and Belgium. They found that girls and boys
had on average 32 and 43 minutes of daily moderate-to-vigorous physical activity,
respectively, and that 13% of girls and 17% of boys met the physical activity recommendations
of at least 60 minutes of daily moderate-to-vigorous physical activity (35).
Boys tend to be more physically active than girls (34,36,37). Väistö et al. (36) found in a
population sample of Finnish children 6–8 years of age using data from a questionnaire
administered by parents, that boys averaged 118 minutes and girls 104 minutes of total
physical activity daily. In the Health Behaviour in School-Aged Children (HBSC) survey (37),
38% of Finnish boys aged 11 years reported that they had at least one hour of moderate-tovigorous physical activity daily, whereas 25% of girls reported reaching the same threshold.
Similar differences in physical activity levels between boys and girls were observable in most
countries participating in the HBSC survey (37). Moreover, levels of physical activity decline
with increasing age. Cross-sectional HBSC data show that the proportion of Finnish children
reporting at least 60 minutes of daily moderate-to-vigorous physical activity decreases from
25–38% to 10–17% between ages 11 and 15 years (37). Similarly, objectively-measured daily
moderate-to-vigorous physical activity has been reported to decline gradually with increasing
age; a cross-sectional comparison among Finnish children shows 25 minutes less moderate-tovigorous physical activity a day in Grade 7–8 students compared to Grade 1–2 students (34).
Finally, a follow-up study among Swedish and Estonian youth showed a nearly 30 minute
decline in daily moderate-to-vigorous physical activity during a 6–10 year follow-up (38).
4
2.2 SEDENTARY BEHAVIOR AMONG CHILDREN
2.2.1 Definitions of sedentary behavior
Sedentary behavior refers to “any waking behaviors, characterized by an energy expenditure less
than 1.5 METs while in a sitting or reclining posture” (39). Relatively weak associations have been
reported for sedentary behavior and daily moderate-to-vigorous physical activity suggesting
that they are separate entities (39). Moreover, sedentary behavior should not be confused with
light physical activity or insufficient physical activity (40). Having low-intensity physical
activity is not the same as sedentary behavior, because accumulation of low-intensity bouts of
physical activity during a day substantially increases energy expenditure (40). In addition,
people engaging in 60 minutes of daily moderate-to-vigorous physical activity may be
categorized as physically active although the rest of their day may be sedentary. Those
without daily moderate-to-vigorous physical activity but engaging in low intensity physical
activity, may be categorized as insufficiently physically active although their total energy
expenditure are higher than those people categorized as physically active (40). The accurate
definition and segregation of sedentary behavior and low-intensity physical activity is
important because there is some evidence that low-intensity physical activity improves
glucose metabolism and reduces the risk of developing obesity, whereas sedentary time has
unfavorable metabolic effects (40). Finally, a number of physical activities performed by
children, such as climbing and balancing, that improve motor skills are often classified as lowintensity physical activities (41).
2.2.2 Assessment of sedentary behavior
Most commonly, sedentary behavior has been assessed using questionnaires that addressed
primarily the time spent in screen-based sedentary behavior such as watching TV or using a
computer (42). Moreover, accelerometers have been used to assess total sedentary time (42). A
cut-off point of < 100 counts per minute has been used to define sedentary time in children and
adolescents (42). The use of questionnaires in conjunction with objective measures of sedentary
behavior may have additional value because different types of sedentary behavior may be
differently related to various outcome measures such as cardiometabolic risk factors (36),
adiposity, or mental health (43).
2.2.3 Levels of sedentary behavior among children
There is insufficient data on the secular trends of sedentary behavior in children (5).
Nevertheless, there is some evidence that occupational and housework energy expenditure
have declined remarkably from 1960 to 2000 due to a substantial increase in sedentary time in
adults (44,45).
Tremblay et al. (43) proposed that children and adolescents should not watch TV more than
two hours per day to prevent unfavorable changes in body composition, cardiometabolic
health and mental health. A recent Finnish study (36) reported that time spent in screen-based
sedentary behavior averaged 1.7 hours per day in 6- to 8-year-old boys and girls. In addition,
5–80% of 7–15-year old Finnish children and adolescents reported that they had at least two
hours screen-based sedentary behavior per day (33,37). Other studies using accelerometer data
suggest that Finnish primary school children in Grades 1–2 spend five hours per day being
sedentary (34) and 6- to 10-year-old Canadian children have seven hours of sedentary behavior
daily (3).
Sedentary time appears to increase with age, though there are no differences in the
proportions of children and adolescents reporting at least two hours TV watching per day
between ages 11 and 15 (37). Nevertheless, based on accelerometer measurements, Colley et al.
5
(3) reported approximately a two-hour increase in daily sedentary time from the ages of 6–10
years to the ages of 15–19 years. Similarly, Tammelin and colleagues (34) found a three-hour
increase in daily sedentary time from Grades 1–2 to Grades 7–8. Ortega et al. (38) showed a
three-hour increase in objectively-measured sedentary time from the age of 9 to the age of 15
years in their 6–10 year follow-up. However, they also found that sedentary time did not
increase substantially between 15 and 25 years of age.
2.3 CARDIORESPIRATORY FITNESS AMONG CHILDREN
2.3.1 Definition of cardiorespiratory fitness
Cardiorespiratory fitness is defined as the capacity of the cardiopulmonary and vascular
systems to deliver oxygen to the exercising skeletal muscles, and the oxidative mechanisms of
those muscles to utilize oxygen in energy generation (46,47). Maximal oxygen consumption
(VO2max) is considered the gold standard for measuring cardiorespiratory fitness (47). Oxygen
consumption (VO2) is a product of cardiac output (i.e. heart rate x stroke volume) and arteriovenous oxygen difference (47). VO2 increases along with exercise intensity until reaching a
plateau, which is used as the criterion of achieving VO2max in adults (47). However, the plateau
of VO2 is rarely seen in children (48,49). Among children, therefore, the highest achieved or
peak VO2 (VO2peak) observed during a maximal exercise test until voluntary exhaustion can be
used as the measure of maximal cardiorespiratory capacity (47,48,50). Children who achieve
the plateau of VO2 have not been reported to have higher VO2, minute ventilation, respiratory
exchange ratio, or blood lactate levels than children who do not reach the VO2 plateau (50).
2.3.2 Assessment of cardiorespiratory fitness
VO2peak is assessed using either treadmill or cycle ergometer in the laboratory during an
incremental exercise test until volitional exhaustion (46). However, in many epidemiological
studies cardiorespiratory fitness has been assessed using field tests, such as the distance run
test or the 20-meter endurance shuttle run test (51–54). VO2peak has been shown to explain at
most half of distance run and the endurance shuttle run test performance suggesting that
physical performance in those tests has a relatively low dependence on cardiorespiratory
capacity (7,55). For example, running efficiency, anaerobic capacity, VO 2 kinetics, maximal
running speed, environmental conditions, test familiarization, attention spans, motor
performance, self-efficacy, motivation, and adiposity have all been shown to influence
performance in field tests (7,49,55). Therefore, it has been suggested that performance results
in field tests should not be termed cardiorespiratory fitness but rather endurance fitness or
endurance performance (23). Moreover, body mass and stature are directly related to VO2peak in
children and adolescents (49,56). Therefore, VO2peak scaled by body mass is used to control for
body size and is expressed as mL·kg-1·min-1. However, this traditional method of expressing
VO2peak or VO2max is problematic, because it involves adiposity (56–58) and thus reflects not
only cardiorespiratory fitness but also adiposity. Log-linear scaling of VO2peak or VO2max has
been proposed to solve problems related to scaling by body weight (48). More recently, VO2peak
or VO2max divided by lean body mass, assessed by magnetic resonance imaging, dual-energy Xray absorptiometry (DXA), or bioelectrical impedance, has been suggested as the gold
standard for measuring cardiorespiratory fitness, because the amount of lean body mass
resembles that of the skeletal muscle that is the metabolically-active tissue during exercise
(23,58).
6
2.3.3 Levels of cardiorespiratory fitness among children
Endurance performance in field tests has declined all over the world, including Finland,
among children and adolescents (7,8). For example, 13- to 18-year-old Finnish adolescent
showed a poorer endurance performance in 2001 than in 1976 (9). Moreover, male Finnish 20
year-olds ran over 300 meters less in a 12-minute running test in 2004 than their counterparts
in 1979 (59). Nevertheless, there is no evidence that children´s VO2peak would have declined in
the period spanning from 1930 to 2000 (55,60). In conjunction with decreased endurance
performance, the prevalence of overweight and obesity has increased markedly among
children and adolescents (13,61–63). Moreover, adiposity has been shown to account for 15–
45% of variance in endurance performance in field tests, while change in adiposity has been
found to account for 30–60% of change in endurance performance in field tests over several
decades (64). However, the residual variance remains unexplained. The remaining variability
may be due to decreased motor performance, psychosocial factors, or children’s unfamiliarity
to vigorous endurance exercise (64).
In children and adolescents, cardiorespiratory fitness has been observed to improve
approximately 5% in response to vigorous physical exercise (65) but it remains largely affected
by genetic factors, growth and maturation (66). VO2peak (L/min-1) increases nearly linearly with
age during childhood and shows a plateau in at around age 14 in girls and at around age 16 in
boys (49). This increase in VO2peak (L/min-1) is attributable to growth of the heart, lungs, and
skeletal muscle mass (23). Moreover, boys tend to have 12% higher VO2peak (L/min-1) than girls
at the age of 10 years (48). Among 16-year-olds, boys already have 37% higher VO2peak (L/min-1)
than girls (48). VO2peak scaled by body mass remains fairly stable in boys across the age range 8
to 16 years whereas it declines gradually after age of eight among girls (49). The higher VO2peak
among boys than girls may be due to differences in body composition, hemoglobin
concentration, maximal stroke volume and arterio-venous oxygen difference (48).
2.4 NEUROMUSCULAR PERFORMANCE AMONG CHILDREN
2.4.1 Definitions of neuromuscular performance
Neuromuscular performance refers to coordinated activity of skeletal muscle under the control
of the nervous system (67). Neuromuscular performance can be broadly defined as an ability
to carry out the activities of daily living in a controlled manner and without excessive fatigue
(20). Neuromuscular performance can be divided into muscular endurance, muscular strength
and motor performance (17,20). Muscular endurance refers to the ability of skeletal muscles to
overcome external force for many repetitions under a submaximal load. Muscular strength
refers to the maximal amount of force that skeletal muscle can generate (17). Motor
performance includes a number of fundamental movement skills and attributes such as agility,
balance, speed and coordination (17). Motor performance can also be divided into movement
skills such as agility, hopping, galloping and running, and to object-control skills such as
manual dexterity, throwing and kicking (68). Motor performance creates a basis for more
complex and sport-specific movements (68). Motor performance does not develop
automatically but needs to be rehearsed and practiced (68–70). Neuromuscular, especially
motor, performance is an important determinant of physical activity engagement (71), and
practicing motor performance has been proposed as the main goal of physical activity in
children under ten years of age (30).
7
2.4.2 Assessment of neuromuscular performance
Muscular strength and endurance can be assessed using dynamometer or force-plate
laboratory tests and functional neuromuscular field tests (72). Commonly used field tests for
muscle strength, such as the standing long jump (73), are not only tests for explosive strength
of the lower limbs but also for motor performance. Motor performance can be assessed using a
specific test for a specific component of motor performance. However, a number of tests, such
as agility running tests, also include other components of motor performance such as speed,
coordination and balance. There are at least two possible ways to analyze motor performance
tests. The more common is to use the product of movement, such as running time, to measure
motor performance, though it is also possible to investigate the quality of movement, or how
specific movements are performed.
2.4.3 Levels of neuromuscular performance among children
Some evidence suggests negative secular trends from 1980 to 2006 in speed and agility,
flexibility, dynamic trunk strength and upper body strength, but a positive secular trend in
explosive lower limb strength among children and adolescents (10). Tomkinson and Olds (55)
found a slight improvement in speed and power test performance from 1958 to 2003.
The development of neuromuscular performance begins in utero and muscular strength and
motor performance improve gradually with increasing age (74,75). Muscle strength increases
nearly linearly from early childhood until reaching a plateau at the onset of puberty in boys
and at the end of the puberty in girls (75). Moreover, peak strength velocity has been shown to
be highest a year after peak height velocity showing the importance of maturation in the
development of muscle strength (75). The increase in muscle strength has been linked to neural
and somatic maturation, increased circulating testosterone and growth hormone
concentrations, which increase lean body mass particularly in boys reaching puberty (75).
There are no significant sex differences in muscle strength before the age of 14 years, but after
that, males outperform females in muscle strength tests (75). The reason for the evolving
differences at the age of 14 years has been linked to the timing of peak height velocity. Peak
height velocity occurs at the age of 8–14 years in girls and at the age of 10–16 in boys. Because
of advanced maturation in girls compared to boys, girls experience also peak strength velocity
earlier than boys.
The improvement in motor performance with increasing age is partly due to neural
maturation (66,74), but even children 14 months old learn to adapt their locomotion for
changing terrain (66,74). Basic movements such as grasping, turning, crawling and other forms
of locomotion evolve during infancy and by two years of age the child can perform basic
jumping and running (66,74). At the age of 6–7 years, all fundamental skills can be performed
in rudimentary fashion (66,74). Finally, boys tend to have better motor performance in tests
involving throwing, running and jumping whereas girls outperform boys in balance, flexibility
and fine motor skill tests (66,74).
2.5 ADIPOSITY AMONG CHILDREN
2.5.1 Definitions of adiposity
Adiposity is defined as an accumulation of extra adipose tissue and is often defined as the
proportion of adipose tissue in body mass or body fat percentage (76). Fat deposits are found
in subcutaneous adipose tissue, around and in internal organs, and between and within
skeletal muscle fibers (76).
8
2.5.2 Assessment of adiposity
Because of the costs and special requirements of the assessment of body fat percentage,
indirect methods for body fat assessment have often been used. For example, body mass index
(BMI) is a widely-used surrogate measure of adiposity in conjunction with cut-off points for
underweight, normal weight, overweight and obesity (77,78).
Adiposity can be assessed using anthropometrics, such as BMI, waist and hip
circumferences, skinfold thicknesses and hydrodensitometry, plethysmography, and more
recently by bioimpedance analysis, DXA, and magnetic resonance imaging (72,79). BMI is
calculated as body weight in kg divided by stature in m2. BMI and the cut-off points for
underweight, normal weight, overweight, and obesity (77,80) are the most commonly used
parameters of adiposity. However, BMI is far from optimal surrogate measure of body fatness
(76). Especially in children BMI is not sensitive to body fat because it includes not only fat
mass but also lean body mass (66,72,76). Among adults, BMI < 18.5 is considered as
underweight, 18.5 to 24.9 normal weight, 25.0 to 29.9 overweight and ≥ 30 obesity (76).
However, absolute BMI values are not used among children because changes in BMI during
growth are normal; therefore age and sex-specific BMI-percentiles derived from nationally
representative samples are commonly used (76,80). Because body fat distribution has been
recognized as an important determinant of cardiometabolic health, waist circumference has
been adopted to assess abdominal fatness. Abdominal adiposity has been more strongly
related to cardiometabolic risk than fat accumulated in hips or thighs among adults (72).
Moreover, skinfold measurements have been used to estimate total body fat from
subcutaneous fat using either three (chest, abdomen, thigh or chest, triceps, subscapular) or
seven (chest, midaxillary, triceps, subscapular, abdomen, suprailiac, thigh) measurement sites
(72), although they have limited ability to estimate body fat percentage (72). Densitometry has
been used as a gold standard for the assessment of adiposity; it is based on the estimation of
whole-body density using the ratio of body mass to body volume (72). Bioimpedance analyses
are based on the resistance to a small electrical current trough the body´s water pool (79); it has
the advantages of being portable, low-cost and easy to use. Bioimpedance also has a relatively
good agreement with DXA (79,81), which has been cited as a criterion reference method in the
assessment of adiposity together with densitometry, although DXA has been reported to
differentiate lean and fat tissue better than densitometry (72,76,78,79). DXA is based on the
attenuation of two X-ray beams (76). Magnetic resonance imaging is one of the most accurate
methods to assess body composition and is based on the interaction between protons present
in all biological tissues and magnetic fields generated by the magnetic resonance imaging
device (76,79). Magnetic resonance imaging can be used to quantify the distribution of adipose
tissue into visceral, subcutaneous, and intramuscular fat depots (79). The disadvantages of
DXA, hydrodensitometry and magnetic resonance imaging are their relatively high cost,
limited applicability in routine assessments, and the need for qualified personnel (72,79).
2.5.3 Prevalence of overweight among children
Pediatric overweight and obesity have become a global burden (12,82). Approximately 30% of
American children and adolescents are overweight (12,37). In Europe, the prevalence of
overweight varies from approximately 10% in the Netherlands and Switzerland to 30–40% in
Greece, Italy, and Spain (37,83). In Finland, it has been estimated that 10% of boys and 18% of
girls aged 5 years and 20% of children aged 7–13 years are overweight or obese (33,63).
Moreover, a recent study in a population sample of Finnish children aged seven years reported
that 11% of boys and 15% of girls were overweight or obese (84). The prevalence of overweight
and obesity among 12-year-old boys and girls has been estimated to be 19–24% and 13–19%,
respectively (13,63). The increase of the prevalence of overweight is caused by a sustained
9
imbalance between energy intake and energy expenditure. There is inconsistent evidence
about what drives increased adiposity among children (85), though eating patterns have
changed to favor increased body fatness such as skipping meals, a high consumption of lowquality food, snacks, candy, and sugar-sweetened beverages (84). The increased time spent in
sedentary behavior, particularly when it is screen-based, has also been linked to increased
levels of adiposity in children (86). Moreover, physical activity behavior has been suggested to
be at least as important determinant of weight gain as dietary factors (85). In addition to
physical activity, sedentary behavior and dietary factors, poor sleeping patterns, poor prenatal
growth environment and psychosocial stress may have contributed to increased fatness (87).
2.6 COGNITION AND ACADEMIC ACHIEVEMENT AMONG CHILDREN
2.6.1 Definitions of cognition and academic achievement
Cognition refers to neural processes that support the flexible use of information to execute
goal-directed behavior (88). Executive functions, which are an essential part of cognition, are
particularly important in everyday life; they are essential for controlling emotional impulses,
self-control, remembering what happened in the past and solving everyday problems (89).
Executive functions also predict academic achievement in children better than intelligence
quotient (IQ) (90). Executive functions are defined as top-down mental processes needed for
example for concentration and paying attention and inhibiting automatic responses when they
are ill-advised (89). Executive functions include three core components: inhibition, working
memory and mental flexibility (89). It has been suggested that there are also higher-order
executive functions, such as reasoning, that are built upon these three core components (89).
Inhibitory control (inhibition) refers to the ability to control attention, behavior and
emotions to override internal predispositions or external stimuli (89). Working memory refers
to the ability to keep information in mind and to manipulate it (89). Working memory is
distinct construct from short-term memory, which includes keeping information in mind
without manipulating it (89). Mental flexibility is the ability to adjust to changing demands
and changing one´s approach if an earlier approach was not successful (89). The core
components of executive functions interact with each other (89). Academic achievement
broadly refers to a child´s success and performance in school.
2.6.2 Assessment of cognition and academic achievement
There are any number of test batteries and single tests to assess different aspects of cognition
(89,91–93). Common test batteries, such as the Wechsler Intelligence Scale for Children (WISC)
(91) and the Developmental Neuropsychological Assessment (NEPSY) (92) include tests for
different aspects of cognition including working memory, mental flexibility, attention,
visuospatial performance, and language skills, along with the possibility of calculating total
scores such as IQ. In this chapter, commonly used test to assess inhibitory control, working
memory, mental flexibility, and higher order executive functions in children are reviewed. In
addition to these tests, there are many other tests that can be used to assess executive functions
in children.
The Stroop task and the flanker task are commonly used to assess inhibitory control in
children (89). In the Stroop task, the congruent trial that requires the child to read the word
“green” printed in green does not require extensive amounts of inhibition. In contrast,
incongruent trials that require the child to read the word “green” printed in red require
inhibition of the visual cue (i.e. name the color instead of reading the word) compared than the
congruent trial (89). The flanker task requires the child to identify as quickly and accurately as
10
possible the direction of a centrally-positioned arrow amid either congruent (e.g. < < < < <) or
incongruent (e.g. < < > > >) flanking conditions. The demands of inhibitory control can be
further increased using incompatible response conditions. That is, the child is instructed to
answer the opposite direction from the direction that the centrally presented arrow is pointing
(89).
The tests based on the self-pointing task and the n-back task are commonly used to assess
working memory (89). In the self-pointing task, the child is asked to point at 3–12 items on the
computer screen in any order. The screen is refreshed and the same items are relocated on the
screen. Then the child is asked to point out the items in the same order; the test is based on the
child´s encoding of item features, not locations (89). In the n-back task the assessor reads the
names of items such as numbers, animals, or alphabets to the child in random order, and the
child has to remember the item that was read 1–5 iterations previously (89). Working memory
has been assessed using variations of the digit span and Corsi block tests, but Diamond (89)
argues that they do not assess working memory because they do not involve manipulating
information.
Mental flexibility, meanwhile, can be assessed by using design fluency, semantic fluency,
verbal fluency, task-switching and set-shifting tasks, among other tasks (89). These tests
require creativity and flexibility to create new answers and solutions for the task at hand. The
Wisconsin Card Sorting Test is one of the most commonly-used task-switching tests. The cards
in the test can be sorted by color, number, or shape. In the task, the child has to deduce how
the cards should be sorted and flexibly change the sorting criterion whenever needed (89).
One example of higher-order executive function tests is Raven´s Coloured Progressive
Matrices (93), which has been used to assess non-verbal reasoning. It calls on the ability to find
similarities, differences, and discrete patterns and does not depend on acquired knowledge or
language skills. Each test page includes a large item or pattern of items and six small items.
The child is required to select the correct small item to complete the large item or set of items.
Academic achievement can be measured by grade point averages or results on various
achievement tests. In addition to plain academic knowledge, grade point averages usually
contain information about class attendance or classroom behavior, whereas standardized tests
rely on academic knowledge. Additionally, academic achievement can be assessed using
specific tests to assess reading or arithmetic skills, such as reading speed, reading fluency,
reading comprehension, and the ability to solve arithmetic problems.
2.6.3 Development of cognition
Executive functions improve with increasing age during childhood and adolescence, reaching
their peak in early adulthood (89). For example, children under nine years of age show
particular deficiencies in inhibition compared with older children and young adults (89,94).
Even one-year-old children can manipulate information within working memory but the
capacity to manipulate many items is lower than in older children (89). Moreover, some
evidence suggests a U-shaped association between mental flexibility and age among 7- to 82year-old individuals (95). Mental flexibility improves gradually until age 20 years, stays
relatively stable during adulthood, and begins to decline at age 60 (95).
11
2.7 PHYSICAL ACTIVITY, COGNITION, AND ACADEMIC ACHIEVEMENT
AMONG CHILDREN
Physical activity may improve the cognitive functions underlying academic achievement in
children, though a meta-analysis of 44 studies showed that higher levels of physical activity
had a weak association with better cognitive functions among children (96). Physical activity
has been found to have pronounced effects on executive functions (97). Recently, a
randomized controlled trial showed that overweight children who exercised 40 minutes five
times a week for 13 weeks had better executive functions and mathematical performance than
those who did not exercise (98). Hillman and associates (99–103) conducted a randomized
controlled trial study called FITKids to investigate whether physical exercise improves
cognitive functions in 9-year-old children. Children in the exercise group engaged in two-hour
exercise lessons after every school day for nine months and children in the control group
continued their normal after-school activities. Children in the exercise group showed
improvements in working memory (99), inhibitory control (100,103), and mental flexibility
(103) whereas such improvements were not observed among children in the control group.
Children in the exercise group also had better eye-movement patterns during a relational
memory task than children in the control group, indicating more efficient hippocampal
encoding (102). However, the researchers found no differences on the relational memory test
between the two groups of children (102). Nevertheless, eye-movement patterns have been
found to predict hippocampal encoding even in the absence of behavioral improvements (104).
Moreover, Castelli et al. (101) showed that children in the exercise group who had a mean
heart rate above 80% of the maximal heart rate during the exercise sessions had better
inhibitory control, task switching and visual attention than children in the exercise group who
had a lower mean heart rate during the exercise sessions.
The results of a recent systematic review suggested that physical activity has beneficial
effects on academic achievement in children and adolescents (105). However, the results of the
intervention studies were equivocal. For example, Ahamed and associates found no
differences in academic achievement between children who participated in a classroom based
exercise intervention for 16 months and children who followed the usual practice (106).
Another study found no effect of a 14-week physical activity intervention on academic
achievement in 10-year-old children (107). Similarly, one study found no clear and
unambiguous effects of exercise on academic achievement during a two-year physical
education intervention (108). However, Donnelly and associates (109) did find improved
reading, spelling, and mathematical achievement among children who participated in 90
minutes of physical activity per school week for three years. Moreover, the results of a nineyear intervention study suggested that grade point average improved more among Swedish
children and adolescents engaging in daily physical education than among their peers who
engaged in only two weekly physical education lessons (110).
Cross-sectional studies have also provided some evidence that higher levels of self-reported
physical activity are associated with better academic achievement in children and adolescents
(111–115). However, a recent study found no relationship between objectively measured
physical activity and such achievement (116), though another showed that higher levels of
objectively measured physical activity at the age of 11 were related to better academic
achievement at ages of 11, 13, and 16. Syväoja and co-workers meanwhile, observed no
association between objectively-measured physical activity and academic achievement in 12year-old Finnish children (117), but they did find that higher levels of self-reported physical
activity were associated with better academic achievement in these children. The researchers
speculated that physical activity derived from accelerometers and questionnaires represent
12
different contexts and entities that may be differentially associated with academic
achievement.
Although a meta-analysis showed that neither the type (i.e. sports, extracurricular exercise,
unstructured physical activity) nor the mode (i.e. aerobic, coordinative, strength training) of
physical activity has no effect on the gains in cognitive performance (96), some studies suggest
that different types and modes of physical activity are more strongly associated with cognitive
functions and academic achievement than others. Higher levels of physically active school
transportation (112) and engagement in sports (113) have been linked to better cognitive
functions independent of total physical activity. Moreover, Pesce and her colleagues (118)
found that children improved their working memory after aerobic circuit training and team
games, but that the improvement was larger after engagement in team games. Similarly,
Budde and coworkers (119) showed that cognitive performance improved after acute exercise,
but that coordinative exercise had a stronger effect on attention than conventional physical
education. Thus, cognitively and motorically challenging activities may be more effective in
improving cognitive performance than simple aerobic exercise of similar intensity.
In summary, the available evidence suggests that higher levels of physical activity may be
beneficial for cognition and academic achievement in children, but the results of earlier studies
are far from unequivocal. Therefore, more studies on the associations of different types (recess,
school transportation, sports) and different modes (coordinative vs. endurance vs. a
combination of the two) of physical activity with cognition and academic achievement are
clearly warranted.
2.8 SEDENTARY BEHAVIOR, COGNITION, AND ACADEMIC ACHIEVEMENT
AMONG CHILDREN
Screen-based sedentary behavior has been especially linked to cognition and academic
achievement in children (43), but the nature of these associations has varied from study to
study. A longer time spent in video game playing has been associated with improved
processing speed (120) whereas higher levels of objectively-measured sedentary time have
been related to better sustained attention (121). However, higher levels of video game playing
have also been linked to a poorer working memory in children (121). Moreover, children who
spend a lot of time using computers have been reported to have worse mental flexibility than
children with less computer use (121).
Higher levels of TV watching may be detrimental to academic achievement in children and
adolescents (43,122), but the evidence is not consistent (123). The results of studies suggest that
educational TV programs enhance academic achievement, whereas non-educational content
may have adverse effects on school performance in children and adolescents (124,125) and
having a TV set in the bedroom has been linked to poorer academic achievement in children
(123). Access to a home computer and a longer time spent using it may improve academic
achievement (123) but a high amount of video game playing may worsen academic
achievement in children (126). One study did find an inverse relationship between screenbased sedentary time and academic achievement, but no association between objectively
measured total sedentary time and academic achievement in Finnish children 12 years of age
(117).
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2.9 CARDIORESPIRATORY FITNESS, COGNITION, AND ACADEMIC
ACHIEVEMENT AMONG CHILDREN
High levels of cardiorespiratory fitness and endurance performance have been linked to better
executive functions and academic achievement in children (127). A summary of these studies
is provided in Table 1. These results are supported by an extensive literature that suggests that
better cardiorespiratory fitness is not only associated with better cognitive functions at
baseline but also preserved cognitive functions and brain structures during follow-up among
older adults (15,128,129). However, almost all previous studies investigating the associations
between cardiorespiratory fitness and academic achievement in children have used field
fitness tests. Therefore, the evidence on the associations of VO2max with academic achievement
is limited. Children with higher endurance performance, as assessed by the field tests, often
have better motor performance and less adiposity than children with lower endurance
performance (130). Moreover, both motor performance and adiposity are characteristics that
have been linked to academic achievement in children (131–133). Most studies that have found
that children with higher VO2max have better executive functions than children with lower
VO2max have compared children falling into the top and bottom 30th percentile of VO2max (134–
136), thus ignoring the 40% between the two. The evidence from studies investigating the
associations of cardiorespiratory fitness as a continuous variable with cognition have yielded
equivocal results (137). Moreover, because previous studies have used fitness field tests or
measures of cardiorespiratory fitness scaled by body mass, their results are partly confounded
by motor performance, adiposity, and other factors. Thus, the question of any associations of
cardiorespiratory fitness with cognition and academic achievement remains unresolved.
2.10 NEUROMUSCULAR PERFORMANCE, COGNITION, AND ACADEMIC
ACHIEVEMENT AMONG CHILDREN
The results of a systematic review (138) suggested that there is inconsistent evidence on the
associations of muscular strength and endurance with academic achievement in children and
adolescents. Motor performance and cognitive development have been found to be tightly
interrelated (139), a relationship that may have its origin in infancy, where more developed
motor performance enables an infant to crawl, climb, and eventually walk, thus exploring and
learning the world (140). As cognitive functions mature, they in turn enable more sophisticated
use of motor performance (139,140). This two-way relationship increases interaction with other
people and may therefore improve language skills and subsequent academic skills such as
reading (140). Better motor performance is also associated with better academic achievement
in children with learning difficulties (141). Motor performance, cardiorespiratory fitness and
adiposity are interrelated (71,130) but there are no systematic evaluations of their independent
associations with cognition and academic achievement among children.
14
Table 1. Associations of cardiorespiratory fitness with academic achievement
Reference and
Participants
Assessment of
country
cardiorespiratory fitness
Castelli et al.,
259 children (9.5 years
20m SRT
USA (142)
old, 51% boys)
Assessment of Academic
Achievement
ISAT: multiple choice and extended
questions of reading and arithmetic
Main Results
Better 20m SRT performance was
related to better reading and arithmetic
achievement
312 children (6th–8 th
grades, 12.1 years old,
52% boys)
20m SRT
Grades (on a scale 1–5) in English,
math, science, world studies; Terra
Nova (reading/language arts, math,
science and social studies).
20m SRT performance was positively
correlated with grades and Terra Nova
scores
Coe et al., USA
(144)
1,701 students from
grades 3, 6, and 9 (47–
55% boys)
20m SRT (except for Grade 3)
MEAP (Michigan Merit Exam): math,
English, social studies
20m SRT was not related to academic
achievement
Davis et al., USA
(145)
170 overweight and
sedentary children (7–11
years old, 44% boys)
Modified Balke protocol until
voluntary exhaustion, VO2peak
(mL·kg-1·min-1) and exercise time
on treadmill
Cognitive assessment system: planning,
attention, simultaneous, successive;
Woodcock-Johnson III (broad reading
and broad math)
VO2peak and treadmill time were
positively correlated with planning and
attention and reading and math scores
Dwyer et al.,
Australia (146)
7,961 children
15 years old)
Lean body mass proportional
physical work capacity at heart
rate of 170 and 1.6-km run (time)
Representative (principal) reported
school achievement (poor, below
average, average, above average,
excellent).
CRF assessed by cycle ergometer was
not associated with academic
achievement; 1.6-km run time was
inversely associated with academic
achievement
Eveland-Sayers
et al., USA (147)
134 children (8–11 years
old, 54% boys)
1-mile run (time)
Terra Nova, reading/language and
mathematics
1-mile run time was inversely
associated with mathematics scores
Kantomaa et al.,
Finland (148)
8,061 children (8 years
old at baseline, age at
follow-up 15–16 years,
51% boys)
Submaximal cycle ergometer test
at age 16 and estimated VO2peak
(mL·kg-1·min-1)
Grade point average
CRF was not associated with grade
point average
Kwak et al.,
Sweden (115)
232 adolescents (16 year
old, 48% girls)
Maximal cycle ergometer test and
CRF was defined as maximal
workload in Watts
The sum of individual marks
CRF was positively associated with
academic achievement in boys, but not
in girls
van der Niet et
al., the
Netherlands
(149)
243 children (7–12 years
old, 55% boys)
20m SRT
Standardized achievement tests in
reading, arithmetic and spelling
Better 20m SRT performance was
related to better reading, arithmetic
and spelling achievement
Pindus et al.,
England (150)
1,977 children (9–11
years old, 41% boys)
Physical work capacity at a heart
rate of 170 relative to body mass
Total marks achieved in English and
mathematics.
Endurance performance was not
associated with academic achievement
(7–
Table 1 to be continued
14
Coe et al., USA
(143)
15
Reference
and country
Rauner et al.,
USA (151)
Participants
Assessment of cardiorespiratory
fitness
20m SRT, children were divided to those
achieving and not achieving HFZ
Assessment of Academic
Achievement
Nebraska state accountability for
mathematics and reading (pass or
not pass)
Main Results
Roberts et
al., USA
(152)
1,989 students (5th,
7th, 9th grade, 51%
boys)
1-mile run (continuous and achieving
HFZ)
California Achievement test v6 and
California Standard test for
reading/language and
mathematics
1-mile run time was inversely associated with
academic achievement
Scudder et
al., USA
(153)
46 children (10
years old, 48%
boys)
Indirect calorimetry using modified
Balke protocol until voluntary
exhaustion. VO2max (mL·kg-1·min-1).
Children divided into lower (<30th %)
and higher fitness (>70th %)
WRAT 3: reading, spelling and
arithmetics; sentence processing
task for congruent, semantic and
syntactic sentences, event-related
potential
HF children had better reading and spelling
achievement than LF children; HF children
also had faster reaction times and response
accuracy in all sentence processing
conditions; LF children had a larger N400
latency and a lower N400 amplitude
Santiago et
al. USA (154)
155 children (9–13
years old, 54%
boys)
20m SRT
Reading and mathematics grades
20m SRT performance was positively
associated with mathematics performance in
girls; no associations of 20m SRT with
academic achievement in boys
van Dusen et
al. , USA
(155)
254,743 children
and adolescents
(grades 3–11, 49%
boys)
20m SRT, 1-mile run (quintiles).
Texas Assessment of Knowledge
(reading and mathematics)
Endurance performance was directly related to
reading and mathematics achievement with
stronger association in girls than boys
Welk et al.,
USA (156)
2,157 children and
adolescents
(Grades 3–12)
20m SRT, 1-mile-run. Children were
divided into achieving or not achieving
HFZ
Texas Assessment of Knowledge
and Skill (% achieving standards)
Endurance performance was positively
associated with academic achievement. The
association was weaker in elementary
students than middle school students
Wittberg et
al., USA
(157)
1,725 children (5th–
7th grade, 50%
boys)
Baseline and follow-up two years later.
20m SRT, 1-mile-run. Divided into
achieving or not achieving HFZ
WESTEST (mathematics, science,
social studies, reading and
language)
Children who stayed in the HFZ had better
academic achievement than students who
were not in HFZ at baseline and follow-up.
Wittberg et
al., USA
(158)
1,740 children (9–
13 year old, 51%
boys)
20m SRT, 1-mile-run
WESTEST (mathematics, science,
social studies, reading and
language)
In boys, 1-mile run time was inversely
correlated with academic achievement; in
girls, 20m SRT performance was positively
correlated with academic achievement
11,743 children
(from kindergarten
to 8th grade, 51%
boys)
Students who achieved HFZ were more likely
to pass mathematics and reading tests than
children not achieving HFZ
15
Table 1 continues.
20m SRT = 20 meter endurance shuttle run test; HF = higher fit; HFZ = healthy fitness zone; LF = lower fit; WRAT3 = Wide-range achievement test
version 3.
16
2.11 MECHANISMS UNDERLYING THE ASSOCIATIONS OF PHYSICAL
ACTIVITY AND PHYSICAL PERFORMANCE WITH COGNITION
Most evidence for the mechanisms mediating the associations of physical activity,
cardiorespiratory fitness, and neuromuscular performance with cognition has derived from
animal studies or studies among older adults. In older adults, low levels of physical activity
have been associated with a lower cerebrovascular reserve (159) and increased brain
atrophy (160). However, these degenerative functional and structural brain changes are not
plausible mechanisms in children undergoing rapid growth and maturation. Possible
biological mechanisms for the associations of aerobic physical activity, cardiorespiratory
fitness, and motor performance with cognition in children are summarized in Figure 1.
Although resistance exercise may improve cognition, there are no studies on the biological
mechanisms for the associations of resistance exercise with cognition among children (129).
Figure 1. Summary of factors mediating the associations of physical activity, cardiorespiratory
fitness, and neuromuscular performance with cognition and academic achievement
2.11.1 Mechanisms underlying the associations of aerobic exercise with cognition
There are three candidate growth factors, namely brain-derived neurotropic factor (BDNF),
insulin-like growth factor-1 (IGF-1), and vascular endothelial growth factor (VEGF) that
have been linked to cognitive enhancement in response to aerobic exercise (161). Plasma
and serum BDNF concentrations have been found to increase after acute aerobic exercise
(162,163). Aerobic exercise training may also increase circulating BDNF concentrations
(1,162,163). In contrast to the results of intervention studies, cross-sectional studies have
shown inverse associations of aerobic physical activity with circulating BDNF
concentrations (162). BDNF may improve neurogenesis, survival of newborn neurons, and
learning and memory (1,164,165). Similarly, aerobic exercise has been found to increase
circulating IGF-1 and VEGF concentrations (161,166). It has been suggested that exerciseinduced improvement in learning and memory is mediated by BDNF and IGF-1 whereas
exercise-induced increases in IGF-1 and VEGF enhance angiogenesis and neurogenesis in
17
the hippocampus (161). IGF-1 has also been found to increase BDNF signaling after
exercise, suggesting their interactive role (161). Moreover, blocking VEGF has been shown
to inhibit exercise-induced neurogenesis (165). BDNF is also a strong candidate for
mediating the associations of short-term aerobic exercise training with memory and
hippocampal plasticity by activating signaling pathways related to synaptogenesis and by
increasing concentration of synaptic proteins such as synapthophysin and synapsin
(14,128,161,167). BDNF has also been observed to regulate long-term potentiation,
neuroelectric correlate of learning and memory, in the hippocampus (15,161,168), and has
been shown to increase neurotransmitter release (1,168).
Aerobic exercise has been observed to increase angiogenesis in various brain structures
such as the cerebellum and the hippocampus (128,169). Increased angiogenesis has been
found to be regulated by IGF-1 and VEGF (128,169). Aerobic exercise has also been found to
increase blood flow and blood volume in the dentate gyrus of the hippocampus and to
improve hippocampus dependent learning (161,168); increased blood volume has been
linked to increased neurogenesis in the dentate gyrus (168). In one study, aerobic exercise
training increased hippocampal volume that was directly related to spatial working
memory (168). Change in circulating BDNF was directly related to change in hippocampal
volume in the intervention group but not in the control group (168). In addition, moderate
intensity aerobic exercise for six months increased white and gray matter volumes in the
frontal and temporal lobes in older adults (170). Some studies have also shown increased
dendritic spine density in the hippocampus after aerobic exercise intervention (171). In
addition, physical activity may improve functional connectivity between brain regions
(172).
Voss and colleagues (173) showed that a one-year aerobic training intervention did not
alter white matter integrity, memory, or executive functioning in older adults. However,
they showed that change in VO2max in response to the intervention was directly associated
with white matter integrity in the intervention group, but not in the control group. Aerobic
exercise training has also been associated with higher gray matter volume in frontal and
temporal lobes including hippocampus in adults (172).
Neuroelectric indices of neural functioning have been under extensive research in recent
years. The event-related potential component P3 has relevance not only for cognitive
functions but also for academic achievement (174). Hillman and associates (174) showed
that larger P3 amplitude, which indicates the attentional resources allocated to the task at
hand, during the go-nogo task was related to better academic achievement in children,
suggesting that P3 may serve as a marker of academic readiness in children. Additionally,
acute and chronic aerobic exercise training have been associated with increased P3
amplitude during cognitive tasks in children and adults (168). This phenomenon is
especially apparent when cognitive demands are higher (175). Moreover, one study showed
that neurocognitive and neuroelectric responses to acute exercise are particularly evident
among children with poor baseline cognitive control (176). Acute moderate-intensity
aerobic exercise has been observed to increase P3 amplitude, cognitive control and
academic achievement among children with attention deficit hyperactivity disorder (177).
Higher levels of physical activity have also been associated with reduced error-related
negativity indicating enhanced top-down control of action monitoring (168).
2.11.2 Mechanisms underlying the associations of coordinative exercise with cognition
Relatively little is known about the functional and structural changes of the brain after
coordinative exercise with cognitive parameters (172), though some evidence has been
derived from studies among older adults and animals, and from motor learning studies
(172). In diabetic rodents, housing in enriched environments that provide opportunities for
motor training and play but no access to a running wheel, has been found to enhance cell
proliferation and increase dendritic spine density and even vascularization in the dentate
gyrus of the hippocampus (169,178). An enriched environment including ladders and
18
ropes, rearranged twice a week, increased angiogenesis in the frontal cortex and
hippocampus in rats (179). In contrast, aerobic exercise has been found to increase capillary
density and motor training has been found to increase the number of synapses (169).
Moreover, performing skillful movements and motor learning both require the activation of
the cerebellum, basal ganglia and dorsolateral prefrontal cortex, which are linked to
executive functions and learning and may thus improve cognitive functions (97,139).
Draganski et al. (180) showed an increased gray matter volume after a three-month
juggling intervention whereas no changes were observed in the control group. Twelvemonth coordinative training designed to improve fine- and cross-motor coordination, but
not cardiorespiratory fitness, was also found to improve flanker task performance
accompanied by enhanced processing as quantified by functional magnetic resonance
imaging (172). There is also some evidence that motor training increases functional
connectivity and gray matter volume in frontal, temporal, occipital and parietal cortices
(172) and increases circulating BDNF concentration (171).
2.11.3 Mechanisms underlying the associations of combined aerobic and coordinative
exercise with cognition
Most intervention studies among children and adolescents investigating the effects of
physical activity on cognition or academic achievement have used training protocols
including aerobic, strength, and coordinative exercises including various games (98–
100,107). An eight-week soccer training study including aerobic and motor skill exercises
increased P3 amplitude and shortened P3 latency and showed an improvement in the
flanker task accuracy and reaction time (181). Similarly, a nine-month exercise intervention
including aerobic and motor components improved neural activation measured using
functional magnetic resonance imaging and neurocognitive performance in the flanker task
among children (100). Moreover, a nine-month exercise intervention improved not only
working memory but also cognitive preparation towards task indicated by a larger initial
contingent negative variation, an event-related potential component linked to task
preparation, among children (99). A recent study also showed improved white matter
integrity after an eight-month physical activity intervention in children (182).
Although aerobic exercise training has been found to increase cell proliferation in the
hippocampus in rodents, the newborn cells will die without adequate learning
opportunities (183,184), so an enriched environment is needed to improve the survival of
those newborn cells in the hippocampus (183). Although it is clear that children´s physical
activities are variable and multifaceted, there is limited evidence on the independent
associations of cardiorespiratory fitness and motor performance with cognition and
academic achievement in children.
2.11.4 Mechanisms underlying the associations of cardiorespiratory fitness with
cognition
Children with better cardiorespiratory fitness have been found to have larger basal ganglia
volumes, particularly dorsal striatum volumes, than children with poorer fitness (16).
Subsequently, a larger dorsal striatum has been related to better inhibition among children
as measured by the flanker task (16). One study among older adults showed that the
association between cardiorespiratory fitness and task-switching performance was partially
mediated by the volume of the caudate nucleus (185). Cardiorespiratory fitness was directly
related to hippocampal volume that mediated the association between cardiorespiratory
fitness and relational memory among children (135). Finally, higher cardiorespiratory
fitness has been associated with enhanced neuroelectric cognitive processing during
cognitive tasks in children (168).
N-acetylaspartate, a marker of neural vitality, has been found to mediate the association
between cardiorespiratory fitness and working memory among older adults (186). Gonzales
et al. (187) found a direct association of cardiorespiratory fitness with N-
19
acetylaspartate/creatine ratio in frontal and occipitoparietal gray matter in adults.
Moreover, functional magnetic resonance imaging studies suggest that higher
cardiorespiratory fitness is associated with more efficient activation of brain areas involved
in cognitive control and action monitoring in older adults (168,188). Cardiorespiratory
fitness has also been found to be inversely associated with circulating BDNF in adults (162).
In children, lower white matter integrity has been linked to poorer cognitive control in the
flanker task (189). Some evidence indicates that cardiorespiratory fitness is directly related
to white matter integrity in older adults (190).
Regardless of the impressive amount of literature concerning the associations of
cardiorespiratory fitness with brain function, some confounding factors have not usually
been considered. As noted above, the conventional scaling of VO2max using body mass is
confounded by adiposity (23,49,57,58). For example, a higher BMI has been linked to lower
gray matter volumes (191), white matter integrity (192), and N-acetylaspartate
concentration (193) in adults. Metabolic syndrome which has been strongly and inversely
associated with VO2max divided by body mass but not with VO2max adjusted for body fat and
lean body mass (194), has been associated with poorer cognition, smaller hippocampal
volumes, and lower white matter integrity among adolescents (195).
2.11.5 Mechanisms underlying the associations of neuromuscular and motor
performance with cognition
Voelcker-Rehage and colleagues (196) found a direct association of motor performance with
cognitive control, working memory and perceptual speed among older adults. Better motor
performance was also associated with more efficient cognitive processing as determined by
functional magnetic resonance imaging in the frontal, parietal, and occipital areas among
older adults (196).
2.11.6 Psychosocial factors underlying the associations of physical activity and physical
performance with cognition
The beneficial effects of physical activity on cognition and academic achievement among
children may be partly explained by improved self-esteem, self-worth and self-efficacy
(197). Physical activity may also improve cognitive and academic functions among children
by improving psychosocial factors or even their lived experiences of the school atmosphere,
for example by reducing teasing related to overweight (197). However, information on
these issues is limited (197).
2.11.7 Mechanisms linking sedentary behavior to cognition
Evidence on mechanisms underlying the associations of sedentary behavior with cognition
and academic achievement is limited. Video game playing has been found to increase gray
matter volume in the hippocampus, prefrontal cortex and the cerebellum in young adults
(198). Excess TV-watching is most commonly associated with impaired cognition and
academic achievement (43), though the unfavorable effects of watching TV may be partly
due to snacking at the same time. A high-fat diet has been observed to decrease circulating
BDNF concentration and high-fat snacks may therefore impair cognitive performance (1).
However, there is no direct evidence that the association between watching TV and
cognition would be mediated by fat intake or other dietary factors. The time replacing
hypothesis may also partly explain the relationship of TV watching to cognition and
academic achievement. That is, time spend watching TV takes time from other activities
that may support cognition and academic achievement such as reading or doing homework.
20
3 Aims of the Study
The aims of this doctoral thesis were to provide new scientific information about the
associations of physical activity, sedentary behavior, cardiorespiratory fitness, and motor
performance with reading and arithmetic skills and about the relationships of adiposity to
cardiorespiratory fitness and neuromuscular performance among children.
The specific research questions were:
1.
2.
3.
4.
What is the evidence for the associations of cardiorespiratory fitness and
neuromuscular performance with cognition and academic achievement in
children? (I)
Are cardiorespiratory fitness and neuromuscular performance associated with
reading and arithmetic skills in children? (II)
Are physical activity, sedentary behavior and different types thereof related to
reading and arithmetic skills in children? (III)
Is adiposity associated with cardiorespiratory fitness and neuromuscular
performance in children? (IV)
21
4 Study Populations and Methods
4.1 STUDY POPULATIONS
Data for the present analyses were derived from the Physical Activity and Nutrition in
Children (PANIC) Study (84) and the First Steps Study (199), two independent studies that
are being conducted simultaneously among primary school children in the City of Kuopio,
Finland. The PANIC Study is a controlled physical activity and diet intervention study in a
population sample of primary school children. Altogether 736 children 6–8 years of age
who were in Grade 1 in 2007–2009 were invited to participate in the baseline examinations
in that timeframe. Of the 736 invited children, 512 (264 boys, 248 girls) participated. The
First Steps Study was a 5-year follow-up study conducted in 2006–2011 of a populationbased sample of 2000 children from four municipalities in different parts of Finland
(including approximately 700 children from the City of Kuopio). Of these 700 children from
the City of Kuopio, 207 (117 boys, 90 girls) participated simultaneously in both the PANIC
Study and the First Steps Study.
An exercise specialist or a nutritionist instructed the parents to fill out the PANIC
questionnaires that included items on several topics, including physical activity, parent’s
education, and parent’s stature. A physician, a research nurse, or a nutritionist of the
PANIC study assessed stature and weight, pubertal status, cardiorespiratory fitness, and
motor performance. A research nurse assessed body fat mass, body fat percentage, and lean
mass at the Department of Clinical Physiology and Nuclear Medicine in Kuopio University
Hospital. Trained university students with a bachelor’s degree assessed the reading and
arithmetic skills and the risk of reading disability under supervision of a research
coordinator in the First Steps study. The characteristics of Studies I, II, III, and IV are
presented in Table 2 and the characteristics of the participants in Studies II, III, and IV are
presented in Table 3.
Table 2. Characteristics of Studies I, II, III and IV
Study Setting
Explanatory variables
I
Narrative review of 23
Cardiorespiratory fitness, motor
cross-sectional studies
performance
II
Prospective study in 99
Cardiorespiratory fitness, speed
boys and 75 girls
and agility, static balance,
manual dexterity, overall motor
performance in Grade 1
III
Prospective study in 107
Physical activity, sedentary
boys and 79 girls
behavior and their components
in Grade 1
IV
Cross-sectional study in
Body fat percentage in Grade 1
202 boys and 201 girls
Outcome variables
Inhibition, memory, academic
achievement
Reading fluency, reading
comprehension and arithmetic skills
in Grades 1, 2, and 3
Reading fluency, reading
comprehension and arithmetic skills
in Grades 1, 2, and 3
Cardiorespiratory fitness, speed and
agility, maximal running speed, hand
grip strength, abdominal strength and
endurance, static balance, manual
dexterity, hamstring and lower back
flexibility in Grade 1
The PANIC Study protocol was approved by the Research Ethics Committee of the
Hospital District of Northern Savo, Kuopio, and the First Steps Study protocol was
approved by the Research Ethics Committee of the University of Jyväskylä. All
participating children and their parents provided written informed consent.
22
Table 3. Characteristics of participants in Studies II, III and IV
Study II
Study III
Study IV
All
Boys
Girls
All
Boys
Girls
All
Boys
Girls
174
99
75
186
107
79
403
202
201
Age (years)
7.7 (0.4)
7.7 (0.4)
7.6 (0.3)
7.7 (0.4)
7.7 (0.4)
7.6 (0.3)*
7.6 (0.4)
7.6 (0.4)
7.6 (0.4)
Stature (cm)
129 (6)
130 (6)
128 (5)
129 (6)
130 (6)
128 (6)**
Weight (kg)
27.4 (5.4)
27.7 (5.6)
27.0 (5.1)
26.9 (5.1)
27.3 (5.1)
26.6 (5.1)
Lean body mass (kg)
21.0 (2.4)
21.8 (2.3)
19.9 (2.1)***
20.2 (8.9)
18.0 (8.6)
23.1 (8.6)***
16.12
14.1
18.7
74.5 (3.5)
72.5 (2.7)
77.0 (2.8)***
N
Fat mass (kg)
Body fat percentage
Prevalence of overweight (%)
Current height as a percentage of predicted adult height
18.4 (11.8)1
74.6 (3.6)
14.9 (11.2)
72.4 (2.6)
20.9 (13.7)***
10.9
72.2 (2.6)
76.9 (3.0)***
2.5
1.5
3.5
109 (41)
116 (43)
103 (37)**
102 (53)
115 (55)
91 (48)***
4.7
3.1
6.8
4.9
4.8
5.2
17.5
8.1
13.6
17.9
7.7*
20.8
41.0
38.2
66.7
23.5
35.7
40.8
67.7
17.3
48.0
34.7
65.3
20.0
39.5
40.5
68.8
23.6
33.0
43.4
69.2
15.2
48.1
36.7
68.4
105 (40)
115 (43)
92.5 (29.8)**
105 (40)
112 (43)
95.4 (32.2)*
13.6 (12.7)
14.3 (12.9)
12.6 (12.6)
8.4 (11.5)
8.6 (11.4)
8.0 (11.7)
Organized sports (min·d-1)
Organized exercise other than sports
5.2 (5.9)
5.7 (6.3)
4.6 (5.4)
Unsupervised physical activity (min·d-1)
44.2 (30.6)
51.1 (32.0)
35.0 (26.1)***
Physically active school transportation (min·d-1)
18.7 (15.1)
17.6 (15.7)
20.2 (14.1)
Physical activity during recess (min·d-1)
22.0 (6.1)
23.0 (6.0)
20.8 (5.9)*
215 (110)
206 (102)
227 (119)
106 (54)
111 (60)
99.5 (45)
68.9 (32.6)
66.0 (31.9)
74.1 (32.9)
34.2 (33.8)
42.0 (37.4)
23.5 (24.5)***
Total sedentary behavior
(min·d-1)
(min·d-1)
Screen-based sedentary behavior (min·d-1)
Watching TV and videos
(min·d-1)
Using computer and playing video games (min·d-1)
Using mobile phone and playing mobile games (min·d-1)
3.3 (8.4)
4.3 (11.3)
1.9 (5.7)
Sedentary behavior related to academic skills (min·d-1)
21.4 (38.6)
22.9 (38.6)
21.4 (36.4)
Sedentary behavior related to music (min·d-1)
16.0 (23.0)
11.9 (16.3)
21.6 (28.9)*
Sedentary behavior related to arts, crafts and games (min·d-1)
46.1 (47.7)
38.6 (45.7)
60.0 (42.9)***
22
16.8
74.5 (3.7)
13.5
Supervised exercise
6.4 (3.5)***
22.5 (7.8)***
77.2 (2.7)***
Children at risk of reading disability (%)
Parental education (%)
Vocational school or less
Polytechnic
University degree
Children in intervention group of PANIC study (%)5
(min·d-1)
5.2 (3.6)
17.5 (8.1)
13.93
Children entered clinical puberty (%)4
Total physical activity (min·d-1)
5.8 (3.6)
20.0 (8.3)
Table 3 to be continued
23
Table 3 continues
Study II
Study III
Study IV
All
Boys
Girls
All
Boys
Girls
All
Boys
Girls
174
99
75
186
107
79
403
202
201
11.9 (26.9)
11.7 (28.0)
12.2 (25.7)
3.6 (14.5)
3.7 (0.5)
3.4 (0.4)***
3.6 (0.5)
3.7 (0.5)
3.4 (0.4)***
75.9 (14.9)
81.7 (14.4)
70.0 (12.9)***
Time in 50-m shuttle test (s)
24.1 (2.1)
23.7 (2.1)
24.7 (2.0)**
24.1 (2.1)
23.8 (2.2)
24.3 (2.1)**
Number of errors in the modified flamingo balance test
3.5 (2.6)
3.6 (2.7)
3.3 (2.4)
3.8 (2.8)
4.4 (3.1)
3.2 (2.3)***
Number of cubes moved in two minutes in box and block in two minutes
100 (15)
98.8 (15)
103 (14)
102 (13)
99.1 (13)
104 (13)***
N
Sitting and lying to rest
(min·d-1)
Maximal workload (W·kg-1 of lean body mass)
Maximal workload (W)
Time 15-meter sprint test (s)
3.1 (0.2)
3.1 (0.2)
3.2 (0.2)***
Pressing power in the hand grip strength test (kilopascals)
47.1 (8.3)
48.7 (8.1)
45.4 (8.2)***
Distance jumped in standing long jump test (cm)
125 (16)
129 (16)
120 (16)***
Number of repetitions in sit up test in 30 seconds
10.6 (4.8)
10.7 (4.9)
10.6 (4.8)
–3.1 (7.7)
–5.8 (7.2)
0.4 (7.2)***
Distance reached in sit-and-reach test (cm)
Overall motor performance
0.1 (1.9)
0.0 (1.9)
0.1 (2.0)
0.1 (2.0)
0.1 (2.0)
18.8 (9.0)
17.8 (9.6)
20.2 (8.0)*
18.8 (9.2)
18.2 (9.2)
19.6 (8.1)
23
0.1 (1.9)
Academic achievement in Grade 1
Reading fluency (score)
Reading comprehension(score)
5.0 (3.4)
4.7 (3.5)
5.3 (3.3)
4.9 (3.3)
4.8 (3.4)
5.2 (3.2)
Arithmetic skills (score)
10.3 (4.1)
10.7 (4.3)
9.8 (3.9)
10.4 (4.2)
10.6 (4.3)
10.1 (3.9)
25.1 (8.0)
24.6 (8.7)
25.7 (7.0)
25.2 (8.2)
24.9 (9.1)
25.6 (6.9)
Academic achievement in Grade 2
Reading fluency
Reading comprehension
8.0 (2.9)
7.6 (3.0)
8.5 (2.6)
7.8 (3.0)
7.2 (3.2)
8.5 (2.6)**
Arithmetic skills
15.5 (5.0)
15.6 (5.4)
15.4 (4.4)
15.6 (5.2)
15.4 (5.6)
15.8 (4.5)
36.6 (8.5)
35.3 (8.7)
38.3 (8.1)*
36.6 (8.7)
35.5 (8.9)
38.1 (8.3)*
Academic achievement in Grade 3
Reading fluency
Reading comprehension
9.0 (2.0)
8.8 (2.2)
9.3 (1.6)
8.9 (2.2)
8.6 (2.4)
9.2 (1.7)
Arithmetic skills
19.6 (4.4)
19.7 (4.2)
19.5 (4.8)
19.7 (4.6)
19.8 (4.5)
19.7 (4.8)
Data are presented as means (standard deviations), medians (interquartile ranges or percentages. *P < 0.05, **P < 0.01, ***P < 0.001 between boys
and girls within the study. 1Medians; 2Prevalence of overweight was defined by the cut-offs in the International Obesity Task Force (77); 3Prevalence of
overweight was defined by national cut-offs (80); 4Boys: testicular volume >3 ml, Girls: breast development had started (66).
24
4.2 ASSESSMENTS
Physical activity, sedentary behavior, cardiorespiratory fitness, neuromuscular
performance, adiposity, maturation, and family background were assessed in the PANIC
Study in Grade 1 and academic achievement were assessed in the First Steps Study in
Grades 1, 2, and 3.
4.2.1 Physical activity and sedentary behavior
Physical activity and sedentary behavior were assessed by the PANIC Physical Activity
Questionnaire administered by the parents with the children (36). The types of physical
activities included supervised exercise (organized sports and organized exercise other than
sports), unsupervised physical activity, physically active school transportation (mainly
walking or bicycling), physical activity during recess and physical education. In Finnish
schools, there are 90 minutes of compulsory physical education per week in Grade 1, so
physical education was not used as one type of physical activity. The frequency and
duration of a single session of each type of physical activity were queried. The amount of
each physical activity type was calculated by multiplying the frequency of the activity with
the duration of a session and expressed in minutes per day. Total physical activity was
calculated by adding the amounts of each type of physical activity including physical
education and expressed in minutes per day.
The types of sedentary behavior during leisure time included screen-based sedentary
behavior (watching TV and videos, using computer and playing video games, using a
mobile phone and playing mobile games), sedentary behavior related to music (listening to
music, playing music), sedentary behavior related to academic skills (reading, writing),
sedentary behavior related to arts, crafts, and games (drawing, doing arts and crafts,
playing board and card games), and sitting and lying in order to rest. Times spent in each
sedentary behavior were queried separately for weekdays and weekends and were
expressed in minutes per day. The amount of total sedentary behavior was calculated by
adding the times spent in each sedentary behavior and expressed in minutes per day,
weighted by the number of weekdays and weekend days.
The PANIC Physical Activity Questionnaire was validated using the Actiheart monitor
(Actiheart, CamNtech, Cambridge, UK) combining heart rate and accelerometer
measurements in a subsample of 38 children examined at baseline of the PANIC Study (36).
Total physical activity measured by the questionnaire correlated positively with total
physical activity measured by the Actiheart monitor (r = 0.37, P = 0.033).
4.2.2 Physical performance
Cardiorespiratory fitness was assessed using a maximal exercise test with an
electromagnetically-braked cycle ergometer (Ergoselect 200 K; Ergoline, Bitz, Germany).
The exercise test protocol included a 3-minute warm-up period at 5 watts, a 1-minute
steady-state period at 20 watts, an exercise period with a workload increase of 1 watt every
6 seconds until exhaustion and a 4-minute cooling-down period at 5 watts (200,201). The
exercise test was considered maximal if it continued until voluntary exhaustion. Absolute
maximal workload in watts at the end of the exercise test and maximal workload per
kilograms of lean body mass were used as measures of cardiorespiratory fitness.
Speed and agility were assessed by the 50-meter shuttle run test (73). Children were
asked to run five meters from a starting line to another line as fast as possible, to turn on
25
the line, to run back to the starting line, and to repeat until five repetitions were completed.
The test score was the running time in seconds, with a longer time indicating a poorer
performance.
Maximal running speed was assessed by the 15-meter sprint test. The children were
asked to run 18 meters as fast as possible five times at 50-second intervals. After a recovery
period of four minutes, the children were again asked to run 18 meters at 50-second
intervals. Time spent in each run was measured using two photo cells (ALGE-TIMING
GmbH, Lustenau, Austria). The first photocell was placed at 2.5 meters from the start that
allowed the children to speed up before the timer started, while the second was placed 0.5
meters before the finish line. Maximal running speed was defined as time taken in the
fastest 15-meter run.
Hand grip strength was assessed by the Martin vigorimeter (Martin, Tuttlingen,
Germany). The children were asked to keep the elbow close to the body, their arm flexed at
90° and to press a rubber bulb maximally three times each with their right and left hand.
The mean of the best trial of each hand was used in the analyses and was expressed in
kilopascals.
Lower limb explosive strength was assessed by the standing long jump test (73).The
children were asked to place their feet next to each other, jump as far as possible and land
on both feet. The test score was the best result of three attempts in centimeters.
The sit-up test was used to assess abdominal muscle strength and endurance (73). The
children were asked to lie down with knees flexed at 90°, feet on the ground, and arms
behind the neck. The children were told to perform as many sit-ups as possible in 30
seconds with their elbows touching their knees and the assistant holding their feet on the
floor. The test score was the number of technically correct sit-ups completed in 30 seconds.
Static balance was assessed by the modified flamingo balance test. The children were
asked to stand barefoot on one leg with eyes closed for 30 seconds. The test score was the
number of floor touches with a free foot or eye openings during 30 seconds, higher
numbers of floor touches and eye openings indicating poorer static balance.
Manual dexterity and upper limb movement speed were assessed by the box and block
test (202). The children were asked to pick up 150 small wooden cubes (2.5cm/side) one by
one with the dominant hand from one side of a wooden box (53.7cm x 25.4cm x 8cm), and
to move as many cubes as possible to the other side of the box over 60 seconds and to
repeat the same task with the non-dominant hand. The test score was the number of cubes
moved to the other side of the box, with smaller number of cubes moved indicating poorer
manual dexterity.
Lower back and hamstring muscle flexibility was assessed by the sit-and-reach test (73).
The children were asked to sit down with their heels 25 centimeters apart at the zero line. A
measuring stick was placed to –38 centimeters from the zero line. The children were asked
to reach slowly forward as far as possible while keeping the hands parallel and to repeat
the same task three times. The test score was the longest distance in centimeters reached
with the fingertips from the starting line of –38 centimeters, with a smaller distance reached
indicating a poorer lower back and hamstring flexibility.
The overall motor performance was calculated as the sum of a reversed shuttle run test
Z-score, a reversed flamingo balance test z-score and a box and block test z-score. The zscores were computed using the current study sample. A lower score indicated poorer
overall motor performance.
26
4.2.3 Body composition and anthropometrics
Body fat mass, body fat percentage and lean body mass were measured after emptying the
bladder, in a supine position and light clothing and after removing all metal objects, by a
Lunar® dual energy X-ray absorptiometry (DXA) device (Lunar Prodigy Advance; GE
Medical Systems, Madison, WI, USA) (81). In Study IV, body fat percentage was
categorized as < 10% body fat, 10–24.9% body fat and ≥ 25% body fat in boys, and as < 15%
body fat, 15–29.9% body fat, and ≥ 30% body fat in girls (203). Body weight was measured
twice after overnight fasting, after emptying the bladder, and standing in light underwear
using a calibrated InBody® 720 bioelectrical impedance device (Biospace, Seoul, South
Korea) to an accuracy of 0.1 kg. The mean of these two values was used in the analyses.
Stature was measured three times in the Frankfurt plane without shoes using a wallmounted stadiometer to accuracy of 0.1 cm. BMI was calculated as body mass (kg) divided
by stature (m) squared. The BMI-standard deviation score (BMI-SDS) was calculated based
on Finnish references values (80). The prevalence of underweight, normal weight,
overweight and obesity was defined using the national reference values provided by Saari
and colleagues (80) and cut-offs from the International Obesity Task Force (IOTF) (77).
4.2.4 Academic achievement
Academic achievement was assessed during Grades 1, 2 and 3. Reading fluency was
assessed using a group-administered speeded subtest of the nationally normed reading
achievement test battery (ALLU) (204). The test score was the number of correct answers,
ranging from 0 to 80, during a two-minute time limit for items that involved identifying the
correct word from four phonologically similar alternatives linked to an adjoining picture.
This procedure required either using whole-word recognition based on the orthographic
structure of the words or phonological decoding of all letters of the word.
Reading comprehension was assessed with a group-administered subtest from the ALLU
battery (204). After reading a short text, children were asked to answer to 12 multiple choice
questions including facts, causal relationships, interpretations, or conclusions drawn from
the text. The test score was the number of correct answers, ranging from 0 to 12, during the
30-minute test period with children allowed to refer to the original text.
Arithmetic skills were assessed using a basic arithmetic test (205) with a set of addition
and subtraction tasks presented visually. Children were asked to perform as many
calculations as they could. The test score was the number of correct answers ranging from 0
to 28 within the 3-minute time limit.
4.2.5 Other assessments
Parents were asked to report their completed or ongoing educational degrees (vocational
school or less, polytechnic or university) in a questionnaire. The degree of the more
educated parent was used in the analyses.
The research physician assessed pubertal status using the five-stage scale described by
Tanner (206). The boys were defined as having entered clinical puberty, if their testicular
volume assessed by an orchidometer was > 3 ml (Stage ≥ 2). The girls were defined as
having entered puberty if their breast development had started (Stage ≥ 2). Because only a
few children had entered clinical puberty, current height as a percentage of predicted adult
height was also used as a measure of maturity. The boys’ predicted adult height (cm) was
calculated as follows: the mean of the height of Finnish men (178.6 cm) + [the standard
deviation of the height of Finnish men (6.0 cm) x the deviation of the child’s predicted adult
height from the average of the predicted adult height of Finnish children]. The girl’s
27
predicted adult height was calculated as follows: the mean of the height of Finnish women
(165.3 cm) + [the standard deviation of the height of Finnish women (5.4 cm) x the deviation
of the child’s predicted adult height from the average of the predicted adult height of
Finnish children]. The deviation of the child’s predicted adult height from the average of
the predicted adult height of Finnish children was calculated according to the national
guidelines as follows: (the arithmetic mean of the father’s and mother’s height-171)/10.
The risk of reading disabilities was determined based on children’s scores in
kindergarten-age assessments of letter knowledge, phonemic awareness and rapid
automatized naming, and parental self-report of reading difficulties, meaning parents
indicated on a questionnaire that they had had “mild” or “severe” problems in reading at
school-entering age, indicating family risk (207). The children were defined as being at risk
of reading disabilities if their score was at or below the 15th percentile (about one SD below
mean of the sample of approximately 2000 children in the follow-up) in at least two of the
three skills or in one skill with the presence of family risk.
4.3 STATISTICAL METHODS
Statistical analyses were performed using SPSS statistical software versions 19 and 21 (IBM
Corp., Armonk, NY, USA). The Student’s t-test, the Mann-Whitney U-test, and the Chisquare test were used to compare the means, medians and percentages of the variables
between the sexes and between the children in the final study samples and the rest of
children from the original PANIC Study and First Steps Study (Studies II, III and IV). The
PANIC study group (intervention vs. control) was used as a covariate in Study II and III,
because the intervention in The PANIC Study began before the assessment of reading and
arithmetic skills in The First Steps Study.
Study II. The associations of the measures of cardiorespiratory fitness and motor
performance in Grade 1 with reading and arithmetic skills in Grades 1–3 were examined
using multivariate linear regression analyses. Age, sex, parental education, and the PANIC
study group were forced into the linear regression models; maximal workload per lean
body mass, 50-meter shuttle run time, errors in the modified flamingo balance test, and
cubes moved in box and block test or overall motor performance were entered stepwise
into the models. If the associations were statistically significant with these adjustments, the
data were additionally adjusted for body fat percentage, physical activity, clinical puberty,
current height as a percentage of predicted adult height and the risk of reading disabilities.
The differences in academic skills in Grades 1–3 between children in the sex-specific thirds
of cardiorespiratory fitness and the measures of motor performance in Grade 1 were
investigated using analyses of covariance (ANCOVA) with repeated measures. These
analyses were performed combining data on boys and girls because of a limited statistical
power to analyze differences among the thirds separately in boys and girls. Adjustments
for multiple comparisons were made using the Sidak correction. The data for the ANCOVA
analyses were adjusted for age, parental education and the PANIC study group. If the
associations were statistically significant with these adjustments, the data were additionally
adjusted for other measures of cardiorespiratory fitness and motor performance, body fat
percentage, physical activity, clinical puberty, current height as a percentage of predicted
adult height, and the risk of reading disabilities.
28
Study III. The question of whether different types of physical activity and sedentary
behavior during Grade 1 as continuous variables were independently associated with
reading and arithmetic skills in Grades 1, 2 and 3 was examined using multivariate linear
regression analyses. In the first step, age, sex, parental education, and the PANIC study
group (intervention vs. control) were forced as basic covariates, and different types of
physical activity and sedentary behavior were entered one by one into the models. If the
associations of different types of physical activity and sedentary behavior with reading and
arithmetic skills were statistically significant after adjustment for the basic covariates,
clinical puberty, current height as a percentage of predicted adult height, body fat
percentage, cardiorespiratory fitness, overall motor performance, the risk of reading
disability or the different types of physical activity or sedentary behavior were forced one
by one as additional covariates into the models. Variables that were strongly correlated
were not forced into the same models to avoid collinearity problems.
ANCOVA with repeated measures was used to investigate whether there were
differences in reading and arithmetic skills in Grades 1–3 between children in the lower and
upper halves of different types of physical activity or sedentary behavior in Grade 1, using
sex-specific medians as cut-offs. However, organized sports were categorized as those who
participated in organized sports and those who did not, because a relatively small number
of children participated in organized sports. The ANCOVA data were adjusted using the
same principle as the linear regression analysis data.
Because sex modified some associations of physical activity and sedentary behavior with
academic skills, multivariate linear regression analyses and the ANCOVAs was also
conducted separately among boys and girls.
Study IV. The associations of body fat percentage with cardiorespiratory fitness and
neuromuscular performance were analyzed using multivariate linear regression analyses.
Age and sex were entered first into the model, followed by body fat percentage. The mean
values of cardiorespiratory fitness and neuromuscular performance among children with
different levels of body fat percentage were compared using ANCOVA with Sidak
correction for multiple comparisons. The associations of body fat percentage with maximal
workload during exercise test were additionally adjusted for lean body mass as suggested
(23). A similar approach was used in the analyses regarding pressing power in the hand
grip strength test. If the associations were statistically significant after adjustment for age
and sex, the data were additionally adjusted for physical activity, screen-based sedentary
behavior, and current height as a percentage of predicted adult height, or clinical puberty.
29
5 Results
5.1 NARRATIVE REVIEW OF CARDIORESPIRATORY FITNESS, MOTOR
PERFORMANCE, COGNITION, AND ACADEMIC ACHIEVEMENT (STUDY I)
The results of this narrative review indicated that children with higher levels of
cardiorespiratory fitness (VO2max) outperformed their less fit peers in tasks that require
variable amounts of cognitive control, attention, and cognitive flexibility (Tables 1 and 2,
Study I). Better endurance performance in field tests was related to a better academic
achievement as well (Table 3, Study I).
Better cardiorespiratory fitness (VO2max) was also associated with larger basal ganglia and
hippocampal volumes based on magnetic resonance imaging scans (Tables 1 and 2, Study
I), and children with better cardiorespiratory fitness exhibited more efficient cognitive
processes indicated by event-related potential and functional magnetic resonance imaging
recordings (Tables 1 and 2, Study I). The most convincing evidence suggested that children
with better cardiorespiratory fitness exhibited larger event-related potential component P3.
Moreover, some studies indicated that more fit children had better action monitoring and
anticipatory attention, reflected by reduced error-related negativity and increased initial
contingence negative variation, than less fit children.
Children with better motor performance had better cognitive control, working memory
and academic achievement (Tables 1, 2 and 3, Study I). There was also some evidence
suggesting that better motor performance predicted cognitive functions in subsequent
years.
The results of intervention studies suggest that aerobic exercise and motor skill training
improve cognitive functions and academic achievement. However, these studies are few in
number and the results are inconclusive.
5.2 MOTOR PERFORMANCE, CARDIORESPIRATORY FITNESS, AND
ACADEMIC ACHIEVEMENT DURING FIRST THREE SCHOOL YEARS
(STUDY II)
5.2.1 Associations of motor performance and cardiorespiratory fitness in Grade 1 with
reading and arithmetic skills in Grades 1–3
A poorer time in the 50-meter shuttle run test in Grade 1 was related to worse reading
fluency in Grades 1–3, worse reading comprehension in Grade 3, and worse arithmetic
skills in Grades 1-3 (Table 4). A larger number of errors in the modified flamingo balance
test were associated with worse reading fluency in Grade 3, with worse reading
comprehension in Grade 1, and with worse arithmetic skills in Grade 3. A smaller number
of cubes moved during the box and block test in Grade 1 was related to worse reading
fluency in Grade 1–2 and to worse arithmetic skills in Grade 2 (Table 4). Poorer overall
motor performance in Grade 1 was associated with worse reading fluency, reading
comprehension, and arithmetic skills in Grades 1-3 (Table 4). The associations of motor
30
performance with academic skills were generally stronger in boys than in girls (Table 4).
Cardiorespiratory fitness was not related to reading and arithmetic skills.
Table 4. The associations of motor performance and cardiorespiratory fitness in Grade 1 with
reading and arithmetic skills in Grades 1–3
Reading fluency
Boys
Girls
(N =
(N =
99)
75)
Grade 1
–0.21*
–0.35†
–0.01
–0.05
–0.01
–0.06
Reading comprehension
All
Boys
Girls
(N =
(N =
(N =
174)
99)
75)
Grade 1
–0.11
–0.29**
0.14
–0.16*
–0.20*
0.02
0.18*
0.28**
0.14
0.12
0.15
0.12
0.14
0.23*
0.13
0.28†
–0.01
0.40†
0.04
0.11
0.06
0.22**
0.09
0.39†
0.16
–0.01
–0.02
0.31†
–0.02
0.41†
0.00
0.18
0.08
Time in 50-m shuttle run test (s)
Errors in modified flamingo balance test
in 30 seconds
Number of cubes moved in box and
block test in 2 minutes
Overall motor performance
Maximal workload/lean body mass
–0.16*
–0.12
Grade 2
–0.29**
–0.07
0.07
–0.08
–0.13
–0.14
Grade 2
–0.25*
–0.12
0.02
–0.08
–0.38†
–0.11
Grade 2
–0.40†
–0.10
–0.25*
–0.13
0.20*
0.23*
0.26*
0.10
0.07
0.20
0.18*
0.21*
0.19
0.30†
0.03
0.38†
0.12
0.19
–0.10
0.19*
0.06
0.25*
0.13
0.11
–0.06
0.38†
0.01
0.44†
0.02
0.32**
0.13
Time in 50-m shuttle run test (s)
Errors in modified flamingo balance test
in 30 seconds
Number of cubes moved in box and
block test in 2 minutes
Overall motor performance
Maximal workload/lean body mass
–0.31†
–0.16*
Grade 3
–0.39†
–0.17
–0.27*
–0.12
–0.18*
–0.09
Grade 3
–0.19
–0.12
–0.10
–0.04
–0.20*
–0.17*
0.08
0.16
0.09
0.12
0.23*
0.01
0.09
0.17
0.12
0.35†
–0.12
0.41†
–0.04
0.28*
–0.14
0.22**
–0.04
0.31**
–0.06
0.07
0.13
0.28†
0.04
0.39†
0.04
0.17
0.13
All
(N =
174)
Time in 50-m shuttle run test (s)
Errors in modified flamingo balance test
in 30 seconds
Number of cubes moved in box and
block test in 2 minutes
Overall motor performance
Maximal workload/lean body mass
Arithmetic skills
All
Boys
Girls
(N =
(N =
(N =
174)
99)
75)
Grade 1
–0.31†
–0.35†
–0.15
–0.11
–0.08
–0.10
Grade 3
–0.33**
–0.19
–0.10
–0.14
Data are standardized regression coefficients from multivariate linear regression analyses adjusted for age,
sex, parental education, and the PANIC Study group (intervention vs. control). Overall motor performance
was calculated by z-score of reversed 50-meter shuttle run, reversed modified flamingo balance, and box
and block test performance. Analyses of overall motor performance were conducted separately from other
measures of motor performance. There were 167 children (96 boys, 71 girls) in Grade 3. *P < 0.05, ** P
< 0.01, †P < 0.001
5.2.2 Differences in reading and arithmetic skills among children in the lowest, middle,
and highest third of motor performance
Children in the third with the poorest time in the shuttle run test had consistently lower
reading fluency (F(2,161) = 4.68, P = 0.011) and arithmetic test (F(2,161) = 6.50, P = 0.002)
scores than those in the other thirds (Figure 2A). Children in the lowest third of cubes
moved in the box and block test had consistently poorer reading fluency (F(2,161) = 3.86, P
= 0.023) and arithmetic skills (F(2,161) = 3.88, P = 0.023) in grades 1–3 than other children
(Figure 2C). There were no statistically significant differences in reading and arithmetic
skills among children in the thirds of modified flamingo balance (Figure 2B) or
cardiorespiratory fitness (data not shown).
31
A)
Speed and agility
B) Static balance
C) Manual dexterity
Figure 2. Differences in reading and arithmetic skills in Grades 1–3 among children in the sexspecific thirds of the 50-meter shuttle run test time (speed and agility), errors in the modified
flamingo balance test (static balance) and cubes moved in the box and block test (manual
dexterity) in Grade 1 in a study sample of 167 children. The estimates are from the analyses of
covariance with repeated measures. Data were adjusted for age, parental education, and the
PANIC study group (intervention vs. control). HP = High performance, MP = moderate
performance, LP = Low performance.
5.3 PHYSICAL ACTIVITY, SEDENTARY BEHAVIOR, AND ACADEMIC
ACHIEVEMENT DURING FIRST THREE SCHOOL YEARS (STUDY III)
5.3.1 Physical activity and academic achievement among all children
Among all children, physical activity during recess in Grade 1 was directly associated with
reading fluency in Grades 1–2 after adjustment for age, sex, parental education, and the
PANIC study group (Table 5). Children in the upper half (≥ median of 20 min/d) of physical
activity during recess in Grade 1 had better reading fluency in Grades 1–3 than those in the
lower half after these adjustments (P = 0.015). Further adjustments for clinical puberty,
current height as a percentage of predicted adult height, body fat percentage,
cardiorespiratory fitness, overall motor performance, the risk of reading disability or the
different types of physical activity or sedentary behavior had no effect on these associations
or differences (data not shown).
32
Organized sports in Grade 1 as a continuous variable was not associated with academic
achievement in Grades 1-3 after adjustment for age, sex, parental education, and the PANIC
study group (Table 5). However, children who participated in any organized sports in
Grade 1 had better arithmetic skills in Grades 1–3 than those who did not engage in
organized sports after these adjustments (P = 0.015). Further adjustments had no effect on
these differences (data not shown).
Physically active school transportation in Grade 1 as a continuous variable was not
associated with academic skills in Grades 1-3 after adjustment for age, sex, parental
education, and the PANIC study group (Table 5). However, children in the upper half
(≥median of 14 min/d) of physically active school transportation in Grade 1 had a better
reading fluency in Grades 1–3 than those in the lower half after these adjustments (P =
0.038). Further adjustments had no effect on these differences (data not shown).
5.3.2 Physical activity and academic achievement among boys
Total physical activity in Grade 1 was directly associated with reading fluency in Grades 1–
2 after adjustment for age, parental education, and the PANIC study group (Table 5). The
relationship between total physical activity in Grade 1 and reading fluency in Grade 2 was
slightly weakened after adjustment for body fat percentage (β = 0.19, P = 0.059),
cardiorespiratory fitness (β = 0.19, P = 0.072), or motor performance (β = 0.19, P = 0.049).
Boys in the upper half of total physical activity (≥ median of 110 min/d) in Grade 1 had
better reading fluency and reading comprehension in Grades 1–3 than the boys in the lower
half of total physical activity after adjustment for age, parental education, and the PANIC
study group (Figure 3A). These differences were no longer statistically significant after
further adjustment for body fat percentage, cardiorespiratory fitness, or motor performance
(data not shown). The associations of total physical activity in Grade 1 with reading fluency
and reading comprehension in Grades 1–3 were no longer statistically significant after
adjustment for physically active school transportation (data not shown).
Physically active school transportation in Grade 1 was directly related to reading fluency
in Grades 1–3 and with reading comprehension in Grade 1 after adjustment for age,
parental education, and the PANIC study group (Table 5). Further adjustments for clinical
puberty, current height as a percentage of predicted adult height, body fat percentage,
cardiorespiratory fitness, overall motor performance, the risk of reading disability or the
different types of physical activity or sedentary behavior had no effect on these associations
(data not shown). Boys in the upper half (≥ median of 14 min/d) of physically active school
transportation in Grade 1 had better reading fluency and reading comprehension in Grades
1–3 than boys in the lower half after adjustment for age, parental education, and the PANIC
study group (Figure 3B). These differences were no longer statistically significant after
further adjustment for motor performance (data not shown). Additional adjustments had
no effect on these differences (data not shown).
5.3.3 Physical activity and academic achievement among girls
Total physical activity in Grade 1 was inversely associated with reading fluency in Grade 2
and arithmetic skills in Grade 1 after adjustment for age, parental education and the PANIC
study group (Table 5). Further adjustment for body fat percentage weakened the association
between total physical activity in Grade 1 and arithmetic skills in Grade 1 (β = –0.19, P =
0.096). Girls in the upper half (≥ median of 97 min/d) of total physical activity in Grade 1
had worse reading comprehension and worse arithmetic skills in Grades 1-3 than girls in
the lower half after adjustment for age, parental education, and the PANIC study group
33
(Figure 3A). These differences were no longer statistically significant after additional
adjustment for body fat percentage or motor performance (data not shown). Further
adjustments had no effect on these associations (data not shown). Total physical activity in
Grade 1 was inversely associated with reading fluency in Grades 1-3 among girls whose
parents had no university degree, but was directly related to reading fluency among girls
whose parents had a university degree (P = 0.009 for interaction). Parental education
similarly modified the associations of total physical activity with arithmetic skills in Grades
1-3 among girls (P = 0.002 for interaction).
Unsupervised physical activity in Grade 1 was inversely related to reading fluency in
Grade 2 after adjustment for age, parental education and the PANIC study group (Table 5).
This association was slightly weakened after further adjustment for body fat percentage (β =
–0.20, P = 0.077).
Organized exercise other than sports in Grade 1 was inversely related to arithmetic skills
in Grade 3 (Table 5). Physically active school transportation in Grade 1 was inversely
associated with reading comprehension in Grade 3 after adjustment for age, parental
education, and the PANIC study group (Table 5). Further adjustments had no effect on
these associations (data not shown).
Table 5. The associations of different types of physical activity in Grade 1 with reading and
arithmetic skills in Grades 1–3
Total physical activity (min·d-1)
Supervised exercise (min·d-1)
Organized sports (min·d-1)
Organized exercise other than sports (min·d-1)
Unsupervised physical activity (min/d)
Physically active school transportation (min·d-1)
Physical activity during recess (min·d-1)
Reading fluency
All
Boys
Grade 1
0.11
0.23*
0.08
0.16
0.05
0.09
0.09
0.18
0.02
0.10
0.12
0.26**
0.19**
0.14
–0.17
–0.08
–0.03
–0.13
–0.14
–0.13
0.22
Reading comprehension
All
Boys
Girls
Grade 1
0.06
0.16
–0.12
0.06
0.08
0.01
0.00
0.04
–0.08
0.13
0.09
0.18
-0.03
0.05
–0.11
0.14
0.25**
–0.08
0.03
0.02
0.06
Arithmetic skills
All
Boys
Girls
Grade 1
–0.00 0.08
–0.25*
0.08
0.14
–0.05
0.07
0.09
0.03
0.04
0.14
–0.17
–0.03 0.04
–0.19
–0.07 0.01
–0.21
0.14
0.12
0.13
Total physical activity (min·d-1)
Supervised exercise (min·d-1)
Organized sports (min·d-1)
Organized exercise other than sports (min·d-1)
Unsupervised physical activity (min·d-1)
Physically active school transportation (min·d-1)
Physical activity during recess (min·d-1)
Grade 2
0.08
0.04
–0.02
0.09
–0.00
0.13
0.17*
0.22*
0.10
0.04
0.14
0.11
0.26**
0.18
–0.28*
–0.09
–0.08
–0.05
–0.23*
–0.16
0.13
Grade 2
0.07
0.06
0.02
0.09
0.01
0.06
0.13
0.12
0.07
0.05
0.06
0.03
0.16
0.14
–0.02
–0.04
–0.10
0.13
0.07
–0.21
0.18
Grade 2
0.05
0.15
0.10
0.17
0.11
0.12
0.02
0.15
–0.01 0.05
0.04
0.12
0.11
0.14
–0.17
–0.04
0.06
–0.22
–0.12
–0.14
0.09
Total physical activity (min·d-1)
Supervised exercise (min·d-1)
Organized sports (min·d-1)
Organized exercise other than sports (min·d-1)
Unsupervised physical activity (min·d-1)
Physically active school transportation (min·d-1)
Physical activity during recess (min·d-1)
Grade 3
0.06
0.04
0.02
0.04
0.01
0.08
0.14
0.14
0.05
–0.01
0.13
0.04
0.22*
0.11
–0.12
0.01
0.05
–0.10
–0.06
–0.19
0.10
Grade 3
–0.03
–0.00
–0.01
0.01
–0.06
0.03
0.09
0.05
–0.01
–0.04
0.04
–0.03
0.15
0.15
–0.13
0.03
0.03
–0.00
–0.06
–0.24*
0.09
Grade 3
–0.03 0.04
0.04
0.03
0.07
–0.03
–0.04 0.13
–0.07 –0.01
–0.03 0.09
0.12
0.07
–0.20
0.03
0.17
–0.29**
–0.18
–0.20
0.22
Girls
Data are standardized regression coefficients from multivariate linear regression analyses
adjusted for age, sex, parental education, and the PANIC study group. *P < 0.05, **P < 0.01, †P
< 0.001
34
5.3.4 Sedentary behavior and academic achievement among all children
Sedentary behavior related to academic skills was directly related to reading fluency in
Grades 1–3 after adjustment for age, sex, parental education and the PANIC study group.
Children in the upper half (≥ median of 23 min/d in boys and 21 min/d in girls) of sedentary
behavior related to academic skills in Grade 1 had a better reading fluency in Grades 1–3
than children in the lower half (P < 0.001). Further adjustments had no effect on these
associations (data not shown).
Sedentary behavior related to music and sedentary behavior related to arts, crafts and
games were inversely associated with arithmetic skills in Grade 3 after adjustment for age,
sex, parental education, and the PANIC Study group (Table 3, Study III). The association of
sedentary behavior related to music with arithmetic skills in Grade 3 was no longer
statistically significant after further adjustment for motor performance (data not shown).
The association of sedentary behavior related to arts, crafts, and games with arithmetic
skills in Grade 3 was no longer statistically significant after additional adjustment for
cardiorespiratory fitness (data not shown).
5.3.5 Sedentary behavior and academic achievement among boys
In boys, total sedentary behavior in Grade 1 was directly associated with reading fluency in
Grades 1 and 3 after adjustment for age, parental education, and the PANIC study group.
However, these relationships were no longer statistically significant after further
adjustment for sedentary behavior related to academic skills (data not shown). Additional
adjustments had no effect on these associations (data not shown).
Sedentary behavior related to academic skills was directly associated with reading
fluency in Grades 1–3 and with reading comprehension in Grade 1 after adjustment for age,
parental education, and the PANIC study group. Boys in the upper half of sedentary
behavior related to academic skills (≥ median of 23 min/d) in Grade 1 had better reading
fluency and arithmetic skills in Grades 1-3 than those in the lower half after these
adjustments (Figure 2, B and C, Study III). Further adjustments had no effect on these
associations and differences (data not shown).
Using a computer and playing video games in Grade 1 as a continuous variable were not
associated with academic skills after adjustment for age, parental education and the PANIC
study group (Table 3). However, boys in the upper half (≥ median of 39 min/d) of computer
use and video game playing in Grade 1 had better arithmetic skills in Grades 1-3 than those
were in the lower half after these adjustments (Figure 2, A in Study III). These differences
were no longer statistically significant after further adjustment for motor performance (data
not shown). Sitting and lying in order to rest was directly related to reading comprehension
in Grade 1 after adjustment for age, parental education, and the PANIC study group.
Further adjustment had no effect on these associations (data not shown).
5.3.6 Sedentary behavior and academic achievement among girls
Total sedentary behavior and sedentary behavior related to music were inversely associated
with arithmetic skills in Grades 2–3 after adjustment for age, parental education, and the
PANIC study group. Sedentary behavior related to arts, crafts, and games was also
inversely associated with arithmetic skills in Grade 3. Further adjustments had no effect on
these associations.
35
Figure 3. The differences in reading and arithmetic skills between children with lower and higher
levels of total physical activity and physically active school transportation. The differences in
academic skills in Grades 1–3 between children with lower and higher levels of total physical
activity (A) and physically active school transportation (B) in Grade 1 among 107 boys and 79
girls. The data were adjusted for age, parental education, and the PANIC study group
(intervention vs. control) from the analyses of covariance with repeated measures. Error bars
represent standard error of the mean.
5.4 ADIPOSITY, NEUROMUSCULAR PERFORMANCE, AND
CARDIORESPIRATORY FITNESS (STUDY IV)
5.4.1 Associations of adiposity with cardiorespiratory fitness and neuromuscular
performance
Higher body fat percentage was associated with a higher maximal workload in all children
and in girls but not boys after basic adjustments (Table 6). However, after further
adjustment for lean body mass, higher body fat percentage was related to lower maximal
workload in all children and in boys but not girls. This relationship was no longer
statistically significant after additional adjustment for total physical activity in all children
(β = –0.040, P = 0.256) or in boys (β = –0.083, P = 0.137).
36
Higher body fat percentage was associated with longer times in the 50-meter shuttle
and 15-meter sprint test, shorter standing long jump, and fewer sit-ups completed in 30
seconds in all children and in boys and girls after basic adjustments. Higher body fat
percentage was related to higher absolute pressing power in the hand grip strength test
in all children and in girls but not in boys (Table 6). This association was no longer
statistically significant after additional adjustment for the child´s current height as a
percentage of predicted adult height in all children (β = 0.055, P = 0.301) or in girls (β =
0.073, P = 0.325). Further adjustment for lean body mass also attenuated the association
between body fat percentage and pressing power in all children and in girls (Table 6).
Higher body fat percentage was associated with a higher number of errors in the
modified flamingo balance test and a smaller number of cubes moved in the box and
block test in all children but not in boys or girls after basic adjustments. Other
adjustments had no effect on these relationships (data not shown).
5.4.2 Cardiorespiratory fitness and neuromuscular performance children in different
body fat percentage categories
Table 6. Associations of body fat percentage with cardiorespiratory fitness and
neuromuscular performance
All
N = 403
Maximal workload (W)
Maximal workload (W) adjusted for lean mass
Time in 50-meter shuttle run test (s)
Time in 15-meter sprint test (s)
Pressing power (kPa)
Pressing power (kPa) adjusted for lean mass
Distance jumped in standing long jump test
Number of repetitions in sit-ups in 30 seconds
Number of errors in modified flamingo balance in 30 seconds
0.111
-0.101
0.274
0.446
0.131
-0.002
-0.443
-0.255
0.105
Boys
N = 202
0.016
0.007
<0.001
<0.001
0.010
0.964
<0.001
<0.001
0.040
0.074
-0.175
0.308
0.480
0.100
-0.040
-0.505
-0.226
0.131
Girls
N = 201
0.280
0.002
<0.001
<0.001
0.147
0.558
<0.001
0.001
0.061
0.163
-0.032
0.220
0.388
0.161
0.042
-0.374
-0.263
0.103
0.016
0.564
0.002
<0.001
0.024
0.552
<0.001
<0.001
0.153
Data are standardized regression coefficients from multivariate linear regression analyses
adjusted for age and sex.
Lean mass adjusted maximal workload decreased with increasing body fat percentage
expressed in three groups in all children and in boys (Figure 4). However, these group
differences were no longer statistically significant after further adjustment for total
physical activity among all children (P for main effect = 0.246) or among boys (P for
main effect = 0.311). Performance in the shuttle run test, the 15-meter sprint test, the
standing long jump test and the sit-up test decreased with increasing body fat
percentage, expressed in three groups, in all children and in boys and girls (Figure 6).
37
Figure 4. Body fat percentage was categorized according to Williams et al. (203). Error bars
represent 95% confidence intervals. BF = body fat, LM = lean mass, SLJ = standing long jump,
W=watts
38
6 Discussion
6.1 METHODOLOGICAL ASPECTS AND LIMITATIONS
6.1.1 Methodological issues in Study I
The narrative review of the associations of cardiorespiratory fitness and motor performance
with cognition and academic achievement in children indicated that poorer
cardiorespiratory fitness and motor performance were related to poorer cognitive function
and academic achievement (Study I). As the review was not systematic, but rather
narrative, some relevant studies may not have been included. None of the studies included
in the review provided evidence for the inverse association of cardiorespiratory fitness or
motor performance with measures of cognition among children. However, it is possible
that such studies exist but were not found during the article-searching process.
6.1.2 Study population and design in Studies II, III, and IV
The PANIC Study is a large controlled physical activity and diet intervention study in a
representative population based sample of 512 children from the city of Kuopio in Eastern
Finland. The First Steps Study is a large follow-up study of 2000 children from four
municipalities in different parts of Finland. Altogether 207 children participated in the
PANIC Study and the First Steps Study. Altogether 174 children had complete data in
Study II and 186 children had complete data in Study III. These children did not differ
significantly in socioeconomic background or anthropometric measures from those who
were not in these study samples. However, boys who were included in Study II had better
balance, poorer cardiorespiratory fitness, and poorer reading comprehension in Grades 1
and 2 than other boys. More boys included in Study III had entered clinical puberty than
other boys. Girls included in Studies II and III had poorer cardiorespiratory fitness and
lower levels of physical activity than other girls. Girls included in Study III also had lower
levels of sedentary behavior related to academic skills, higher levels of computer use, and
sitting and lying in order to rest than other girls. These modest differences may hinder the
generalizability of the results.
Follow-up data on motor performance, cardiorespiratory fitness, physical activity or
sedentary behavior were not available in Study II or III. Therefore, it is not possible to
draw conclusions as to whether changes in these parameters during follow-up had an effect
on the development of reading and arithmetic skills. Changes in motor performance,
cardiorespiratory fitness, physical activity and sedentary behavior could be a source of
residual confounding. However, short-term tracking of physical performance, physical
activity, and sedentary behavior is moderate (208–211), suggesting that such confounding is
not a major problem in these analyses. Similarly, the cross-sectional design does not allow
conclusions about the time order of the association between adiposity and physical
performance or about any causal relationship between them (Study IV).
39
6.1.3 Assessment of physical activity
Total physical activity measured by the PANIC Physical Activity Questionnaire had a
statistically significant positive correlation with total physical activity measured by the
Actiheart monitor (r = 0.37, P = 0.033) (36). However, the assessment of physical activity
using questionnaires is affected by cognitive development, the ability to accurately recall
past physical activity, and the pressure for socially-accepted answers (18,22). Self-reported
questionnaires also tend to overestimate the true duration and intensity of physical activity
and the correlation between self-reported and objectively measured physical activity is low
to moderate (18,22).
Regardless of these limitations, physical activity questionnaires are the only feasible
method for assessing the setting and type of physical activity (22). Moreover, some physical
activities that improve neuromuscular performance and substantially increase energy
expenditure above the resting rate in children, such as balancing (including skateboarding
and cycling) and climbing activities, are classified as low-intensity physical activity
assessed by accelerometers (41). Such physical activities as tree climbing require variable
amounts of muscular strength and various motor skills but do not cause acceleration that
could be recorded by accelerometers. The setting, type, and quality (endurance vs. skill) of
physical activity affect the associations between physical activity and academic
achievement (97,113,118), and some recent studies have not found a relationship between
objectively-measured physical activity and academic achievement among children
(116,117).
6.1.4 Assessment of sedentary behavior
It is difficult to assess different types of sedentary behavior objectively, whereas
questionnaires are useful for that purpose. Sedentary behaviors such as reading, using
computer and watching TV may have opposite effects on children´s learning. Some studies
have shown no association between objectively-assessed sedentary time and academic
achievement in children (117). Therefore, assessing sedentary behavior by a questionnaire
when investigating the relationships of different types of sedentary behavior to cognition
and academic achievement, was acceptable and reasonable in this study.
6.1.5 Assessment of cardiorespiratory fitness
Cardiorespiratory fitness was assessed using a maximal cycle ergometer exercise test and
was defined as maximal workload per lean body mass (Study II and Study III) or maximal
workload adjusted for lean body mass (Study IV). A limitation of the study is that
respiratory gas analysis was not performed during the exercise test to obtain VO2peak.
However, maximal workload is a good surrogate measure of VO2max in children (212), and it
was possible to use maximal workload divided by lean body mass as a measure of
cardiorespiratory fitness. The conventional scaling of cardiorespiratory fitness by body
mass is confounded by adiposity that places overweight children in a disadvantageous
position in terms of cardiopulmonary capacity compared with normal-weight children
(47,49,57,58). Therefore, measures of cardiorespiratory fitness are recommended to be
scaled allometrically or divided by lean body mass to obtain measures of cardiorespiratory
fitness (47,58) that better reflect the capacity of cardiovascular, pulmonary, and skeletal
muscle systems to deliver and use oxygen for energy production (47). Moreover, cycle
ergometer exercise tests have been described as relatively free of children´s motor
competence (213) whereas inter-individual differences in O2 uptake during walking or
running do not necessarily reflect differences in mechanical efficiency at the cellular level,
40
but rather differences in the economy of locomotion (214). For example, Chia et al. (215)
demonstrated that children with developmental coordination disorder operated at a higher
heart rate and at a higher percentage of their VO2peak than normally-developing children at
any given treadmill speed.
6.1.6 Assessment of neuromuscular performance
The field tests used in Studies II and IV have been found to be reliable measures of
neuromuscular performance (55,202,216,217). Nevertheless, the findings of the specific
component of neuromuscular performance reported in Studies II and IV should be
interpreted cautiously because performance in field tests rely on physiological (e.g. energy
metabolism or muscle cell type), psychological (e.g. motivation or self-efficacy), and
behavioral (e.g. habituation to exertion or physical activity level) factors as well as
fundamental movement skill proficiency like how skillfully a child can jump, run, or make
a pivot turn (55,66). For example, although standing long jump and sprint running tests
have been considered to reflect explosive muscular power and maximal intensity anaerobic
power, respectively (55), they also represent both movement skills and adiposity (Study
IV) and motivation (55). Moreover, a poorer 50-meter shuttle run time has been reported to
be associated with less sit-ups, shorter standing long-jump, and worse 20-meter endurance
shuttle run performance (149) and increased body fat percentage, as seen in Study IV.
It would have been valuable to measure different object control skills, such as throwing
and kicking, to obtain more accurate information about children´s motor performance. In
addition to static balance test, dynamic balance tests could have added meaningful
information about balancing ability.
6.1.7 Assessment of body composition and anthropometrics
Body fat mass, body fat percentage, and lean body mass were assessed using DXA, a
criterion reference for body composition assessment (78), by qualified and experienced
research nurses in standard circumstances. DXA measurements are highly reproducible in
children and adults (218–221). Total body fat percentage derived from DXA has a high
agreement with total body fat percentage measured by magnetic resonance imaging and
four-component underwater weighing in children and adolescents (222,223). Limitations
related to DXA have been associated with changes in devices and algorithms that may limit
the comparability between body composition measures derived from DXA devices from
different manufacturers (224). In the present study, the same DXA device was used in all
assessments. Body weight and stature were measured by two qualified and experienced
research nurses and reproducibility of these measures was very high in the baseline of the
PANIC Study (data not shown). Children were categorized into underweight, normal
weight and overweight using two different references for cut-offs (77,80) (Studies II and
IV), which both of which have been accepted for assessing weight status in pediatric
populations.
6.1.8 Assessment of academic achievement
Reading fluency and reading comprehension were assessed using the ALLU test battery
(204,207). The ALLU test battery is nationally normed using nearly 13,000 children from 651
different primary schools. Reading fluency, reading comprehension, and arithmetic skill
tests used in Studies II and III have been used in large follow-up studies on the
development and determinants of reading and arithmetic skills in children (225–227).
Reading skills in the ALLU test battery have been validated against reading skills evaluated
41
by the children´s classroom teachers. Reading fluency and reading comprehension tests in
Grade 1 had relatively strong correlations (r ≈ 0.50, P < 0.001) with reading skills rated by
teachers in Grade 1 (228). Reading skills in Grades 1–3 assessed by ALLU tests have been
reported to have a moderate to high Kuder-Richardson reliability coefficient (> 80) that
suggests good internal consistency (228).
The assessment of reading and arithmetic skills restricts the conclusions to only these
factors. Therefore, the associations of cardiorespiratory fitness, motor performance,
physical activity, and sedentary behavior with other skills such as writing and achievement
in school subjects remains to be determined in Finnish primary school children in Grades
1–3.
6.1.9 Assessment of confounding factors
The assessment of risk for reading disabilities was based on children´s own performance in
tests that have been shown to predict reading skills as well as parental self-reports of
reading difficulties to obtain possible inherited risk for reading disabilities. These tests have
been suggested by the meta-analyses and dyslexia follow-up studies indicating their
importance for the development of reading skills (207). Sexual maturation related to
puberty was assessed during medical examination using procedures described by Tanner
(206), which are widely used in research and clinical practice. There may be large
individual variation in biological maturation among prepubertal children examined in the
present study. Therefore, current height as a percentage of the predicted adult height was
used as an alternative measure of maturation. The formula included self-reported height of
the mother and father, which is a source of measurement error. However, there is evidence
that height is systematically overestimated so the error in the estimation of height may not
be a major problem (229). It would be also optimal to use standard radiographs to assess
children´s skeletal age, a gold standard method for assessing young children´s biological
maturity (66).
6.2 PHYSICAL ACTIVITY, SEDENTARY BEHAVIOR, PHYSICAL
PERFORMANCE, ADIPOSITY, AND ACADEMIC ACHIEVEMENT
The findings of the narrative review of earlier studies (Study I) showed that children with
poorer cardiorespiratory fitness and motor performance have worse cognitive performance
and poorer academic achievement than their peers with better cardiorespiratory fitness and
motor performance. The results of Study II based on data from the PANIC Study and the
First Steps Study confirmed that motor performance is associated with reading and
arithmetic skills among children. However, the associations of motor performance with
academic skills were stronger in boys than in girls. Moreover, cardiorespiratory fitness was
not related to reading or arithmetic skills among children, which is inconsistent with the
results of some earlier studies (Study I). Study III showed that higher levels of physical
activity and especially physically active school transportation were related to better reading
skills in boys, whereas physical activity had weak inverse associations with academic skills
in girls. However, the relationship between physical activity and academic skills was
modified by parental education in girls. More physically active girls from less-educated
families had poorer academic skills than less active girls. In contrast, higher levels of
physical activity among girls from better-educated families were related to better academic
achievement. Study III also showed that physical activity during recess was associated
42
with improved academic achievement among children. In boys, more time spent in
sedentary behavior related to academic skill was directly related to reading and arithmetic
skills and more time spent using computer and playing video games was associated with
better arithmetic skills. However, sedentary behaviors had weak if any associations with
academic skills in girls.
Study IV showed inverse associations of adiposity with physical performance in tests
that require moving body mass. Moreover, there were weak, yet statistically significant,
inverse associations of body fat percentage with manual dexterity and static balance.
Notably, cardiorespiratory fitness, as expressed by maximal workload adjusted for lean
body mass, had an inverse association with body fat percentage in boys but no such
association was found in girls.
6.3 THE INTERWOVEN RELATIONSHIPS BETWEEN PHYSICAL ACTIVITY,
PHYSICAL PERFORMANCE, COGNITION, AND ACADEMIC ACHIEVEMENT
IN CHILDREN
Studies I and II demonstrated strong direct relationships of motor performance to
cognition and academic achievement. However, in Study II, the associations between
motor performance and academic skills were evident in boys, while the corresponding
associations were weaker in girls. These results are consistent with the results showing
tightly-interwoven associations between motor and cognitive development and with the
results showing that many brain areas are involved in both motor control and cognitive
processes (139). Moreover, Study II is one of the first studies showing the direct
associations of motor performance with academic achievement independent of
cardiorespiratory fitness and adiposity.
The development of motor performance is partly regulated by genetic factors (66).
However, physical activity and informal active play form the foundation for the normal
development of motor performance during childhood (68–71). Stodden et al. (71) provided
a model suggesting that the link between motor performance and physical activity is
bidirectional and depends on age. Good motor performance during infancy and early
childhood enables engagement in various physical activity including walking, running and
climbing; afterwards, practice due to physical activity refines and improves motor
performance. Thus, the relationship linking motor performance to cognition and
subsequent academic achievement may have its origin in early childhood. Iverson (140)
suggested that the development of motor performance enables the infant to engage in
various postures and activities providing opportunities to interact with the environment
(other people, objects, etc.) and rehearse language and cognitive skills.
Motor performance may reflect adaptation to chronic physical activity (68–71). Physical
activity and especially aerobic exercise have been linked to increased peripheral BDNF
concentration, which has been associated with synaptic plasticity, learning, and memory
(161,162,164). Moreover, some evidence suggests that motor training without pronounced
cardiorespiratory stimulus also increases circulating BDNF concentration and may thereby
improve cognitive functions (171). The latter observation is notable, because children do not
naturally engage in long-term aerobic exercise but rather high-intensity intermittent activity
bouts accompanied by motorically-challenging tasks (23,24,230). In any case, physical
activity itself may be goal-oriented activity and require cooperation, planning, and
adapting behavior and thus, improving cognitive functions (97). Activities, such as games
43
and active play, also involve complex, controlled, and adaptive movements and activate
prefrontal circuits that may improve inhibitory control and working memory (97). Brain
areas such as the cerebellum, basal ganglia, and the dorsolateral prefrontal cortex are
involved in motor control and motor learning, are also important for cognition (97,139).
Complex movements cause formation of new neurons or neurogenesis in the hippocampus,
the cerebellum, and cerebral cortex (97). The combination of higher-intensity aerobic
exercise and coordinative exercises may have stronger effects on brain structure and
function than either may have independently (184). In school-age children, poor motor
performance has been associated with worse peer relations (231), which may partly mediate
the associations of motor performance with academic achievement. Thus, it is possible and
plausible that not only brain changes related to physical activity but also psychosocial
factors associated with physical activity and motor performance improve cognitive
functions and academic achievement in children.
Study III suggested that physical activities during recess and sports team participation
are beneficial for reading and arithmetic skills. It is the first study on the associations of
recess physical activity with measures of academic achievement among children. Previous
studies have shown an improvement in attention, concentration, and on-task behavior after
recess physical activity among children (114,232). These changes in cognitive function and
classroom behavior may explain the associations of recess physical activity with academic
achievement in the present study. The associations of sports team participation and
academic achievement have been reported in previous studies (233). Sport team
participation may have even stronger associations with prerequisites of learning than total
physical activity (234). The association of sports participation with academic achievement
may be partly explained by the enhanced brain structures and functions caused by
increased circulating BDNF, IFG-1, and VEGF concentration (161). Finally, the associations
of recess physical activity and sports participation with academic achievement may be
mediated by psychosocial factors such as social competence or self-regulation (235,236).
The results of Study III showed that physical activity relates differently to reading and
arithmetic skills in the first three school years of boys and girls. These results are surprising,
because it is generally believed that physical activity has beneficial effects on cognition and
academic achievement regardless of sex (96,105,175). Moreover, one study showed that
physical activity had stronger associations with academic achievement in female
adolescents than male adolescents (115). It is not known why the associations of physical
activity with academic achievement were direct in boys whereas the corresponding
associations were inverse in girls. However, it could be that physical activity plays a larger
role in boys´ lives as they are more physically active than girls (36,237). Boys´ physical
activity may be more cognitively challenging in nature, such as tag and team games than
physical activity among girls (230). The findings of the Study III also suggest that the
associations of physical activity with academic achievement are largely modified by
adiposity in girls. In boys, the associations between total physical activity and academic
achievement disappeared when physical performance and adiposity were taken into
account. Boys with better physical performance and lower levels of adiposity may be more
physically active than other boys. According to these results, physical activity, physical
performance, and adiposity appear to have multidimensional and complex associations
with academic achievement in children. The results of Study III also suggest that higher
levels of physically active school transportation are largely independently associated with
better academic achievement in boys. These results are unique because no previous studies
have investigated the associations of physically active school transportation with measures
44
of academic achievement. However, Martinez-Gomez et al. (112) showed that active
commuting to and from school was related to better verbal, numeric, and reasoning ability
as well as better overall cognitive performance in female adolescents but not in male
adolescents. One mechanism explaining these results may be that physically active school
transportation reduces perceived and cardiovascular stress reactions during a cognitive
task (238). Another explanation may be that acute physical activity before academic lessons
in school improves attention and concentration as well as time in-task during these lessons
(232).
The results of Study III also suggest that parental education, a proxy for socioeconomic
status, modified the associations of physical activity with academic achievement in girls.
Among girls of parents with less than university degree physical activity was inversely
associated with reading skills, whereas the opposite was found among girls of parents with
university degrees. This was also surprising, because the evidence suggests that physical
activity also has beneficial effects on cognition and academic achievement among lower
socioeconomic groups (239). Some of these differences may be related to sociocultural
factors and school systems. Finland has a high-quality, free, and equal educational system
as well as high achievement in the Programme for International Student Assessment tests
(240). The school system provides equal opportunities for education for all children
regardless of socioeconomic status. Peer attitudes, parental beliefs, and guidance may thus
have a pronounced role in these associations. Better-educated parents may promote both
the physical activity engagement of their children and their academic achievement by
reading with them and helping with their homework. In contrast, although merely
speculative, girls of lesser-educated parents may emphasize academic development over
the physical activity participation.
6.3.1 Cardiorespiratory fitness, cognition, and academic achievement: Seeking meaning
in measurements
The associations of cardiorespiratory fitness with cognition and academic achievement
were investigated in Studies I and II. In contrast to most prior studies as detailed in Study
I, cardiorespiratory fitness was not related to academic achievement in Study II.
One explanation for not finding such a relationship may be that cardiorespiratory fitness
assessed by cycle ergometer is not sensitive to differences in academic achievement in
children. There are studies showing that cardiorespiratory fitness assessed by treadmill
tests and endurance performance assessed by the 1.6-mile running test or the 20-meter
shuttle run test are directly related to cognition and academic achievement
(134,135,142,146,241). In contrast, other studies have found only weak if any associations of
cardiorespiratory fitness assessed using cycle ergometer with cognition and academic
achievement (115,146,148,150,242,243). Cycle ergometer is an optimal way to measure
mechanical efficiency whereas measuring efficiency and movement economy is hard to
assess in other activities. Inter-individual differences in oxygen consumption during
walking or running do not necessarily reflect differences in mechanical efficiency at the
cellular level, but rather differences in economy of locomotion (244).
Cycle ergometer tests have also been found to be relatively free of motor competence
(213). Chia et al. (215) demonstrated that children with developmental coordination
disorder operated at a higher heart rate and at a higher percentage of their VO 2peak than
normally-developing children at any given treadmill speed. Moreover, children with
developmental coordination disorder had also higher blood lactate levels than normallydeveloping children (215).
45
Running
test
performance
is
affected
by
motor
performance
and
thus running efficiency and a number of factors such as anaerobic capacity, VO2 kinetics,
maximal running speed, environmental conditions, test familiarization, attention spans,
self-efficacy, and motivation have all been shown to contribute to physical performance in
field-based tests (47,55). One of the important confounding factors in the associations of
cardiorespiratory fitness with cognition and academic achievement is adiposity, which
affects not only on the interpretation of VO2peak but also the aforementioned confounding
factors, especially motor performance and movement economy. The common scaling of
VO2peak using body mass (i.e. expressing it as mL·kg-1·min-1) is affected by body mass
(23,47,57). In addition, VO2peak/max explains only a small part of performance in field-based
tests. For example, Rowland and associates (54) demonstrated that VO2max (mL·kg-1·min-1)
explained 28% of variances in 1.6km run test time while body fat accounted for 31%.
The consideration of factors other than cardiorespiratory fitness is of importance because
a number of factors that influence cardiorespiratory fitness test performance have been
associated with cognition and academic achievement. Of these, motor performance and
adiposity have strong associations with cognition and academic achievement (139,245,246).
Accordingly, the current evidence suggests that VO2peak/max is not the only or even the most
important factor contributing to cognitive function and academic achievement in children.
Rather, overall physical performance, including motor coordination, optimal fat mass, and
adequate cardiopulmonary function may be the strongest combination for improving
cognitive function and academic achievement in children.
6.3.2 Adiposity and physical performance: Their interrelationships and relevance for
academic achievement
Study IV was one of the first studies on the associations of body fat percentage assessed by
DXA with cardiorespiratory fitness and neuromuscular performance. The results of the
study suggest that body adiposity hampers performance in tests that require supporting
and moving body mass and in manual dexterity and balance tests. There were particularly
strong inverse associations of body fat percentage with a 15-meter sprint run and standing
long jump performance but somewhat weaker associations of body fat percentage with a
50-meter shuttle run and number of sit-ups.
Earlier studies have shown a decreased endurance performance assessed by field tests,
such as 20-meter endurance shuttle run and 1.6-mile distance run tests, with increasing BMI
in children and adolescents (51,53). However, these differences are largely dependent on
the differences in fat mass and not stroke volume or cardiac output (23,51). To avoid
confounding due to adiposity, measures of cardiorespiratory fitness divided by lean body
mass have been suggested as a gold standard for measuring cardiorespiratory fitness,
because the amount of lean mass is close to that of the skeletal muscle that is the
metabolically active tissue during exercise (23). Accordingly, Ekelund et al. (58) found no
differences in VO2max scaled by lean body mass between normal weight and obese
adolescents. However, Study IV was one of the first studies investigating the relationship
between body fat percentage assessed by DXA and cardiorespiratory fitness among
children. Body fat percentage had a weak inverse association with maximal workload using
continuous variables after adjustment for lean body mass. Additionally, boys with a body
fat of ≥25% had a much lower lean mass adjusted maximal workload than boys with a body
fat of <10%. Nevertheless, differences between boys with lower and higher levels of
adiposity were explained by physical activity. One reason for this finding may be that
46
children with a higher body fat percentage do not engage in the higher-intensity physical
activity that has been observed to improve cardiorespiratory fitness in children (65).
These results have also implications to the results of Studies I, II, and III. The results
reviewed in Study I showed that motor performance was directly related to inhibition,
short-term memory, and academic achievement. Among the research reviewed in Study I,
there was no systematic evaluation of the effects of adiposity on these associations. Thus, it
is possible that children with higher adiposity had worse motor performance than leaner
children. In Study II worse motor performance in Grade 1 was related to poorer reading
and arithmetic skills in Grades 1–3 independent of adiposity. However, it is possible that
academic achievement in subsequent years is not only associated with motor performance
but also physical activity and adiposity. For example, Kantomaa and associates (148) found
that motor problems at the age of eight were related to lower levels of physical activity and
a higher incidence of obesity at the age of 16. Moreover, adolescents with lower levels of
physical activity and who were obese had also poorer academic achievement than more
physically active and normal-weight adolescents. Accordingly, the relationships between
adiposity, motor performance, physical activity, and academic achievement are
multifaceted and complicated and need more research in cross-sectional and longitudinal
settings. Whereas better motor performance may drive for higher levels of physical activity
and lower levels of body adiposity, higher levels of body adiposity may decrease the levels
of physical activity and thereby hamper the development of motor performance (71). The
earlier this circle begins, the higher the possibility is that it will affect cognitive
development. Obese children will not move and explore their environments as much as
leaner children and may therefore have delays in physical and psychological development
(140).
47
7 Conclusions
Based on the results of Studies I and II it can be concluded that better motor performance
is associated with better cognitive functions and academic achievement in children. In
addition, Study I´s narrative review provides some evidence that better cardiorespiratory
fitness is beneficial for cognition and academic achievement. However, Study II did not
confirm these findings. Study III suggests that higher levels of physical activity during
recess are related to better reading skills and any sports engagement is associated with
better arithmetic skills in children. Furthermore, the results of Study III suggest that the
associations of physical activity and sedentary behavior with academic achievement are
different in boys than in girls with more physical activity improving academic achievement
in boys, whereas the associations seems to be more complicated in girls. Moreover,
sedentary behavior related to academic skills was associated with better reading and
arithmetic skills, particularly in boys. Computer use and video game playing was also
directly related to arithmetic skills in boys. However, the associations between physical
activity and academic achievement may partly be explained by adiposity, cardiorespiratory
fitness, and motor performance. The results of Study IV suggest that adiposity decreases
physical performance in tests requiring moving and supporting body mass. These results
also suggest that body fat percentage decreases manual dexterity and balance in children.
The results of Study IV further suggest that body fat percentage is inversely associated
with cardiorespiratory fitness in boys but that the association is not completely
independent of physical activity.
In summary, the findings of this thesis suggest that children with a higher motor
competence have better cognitive performance and academic achievement compared with
children with lower motor competence. Although motor performance should be seen as a
product of chronic physical activity engagement, measures of physical activity and motor
performance may have independent cross-sectional associations with academic
achievement. Moreover, increased body adiposity appears to be related to worsened
neuromuscular and motor performance and may have long-term adverse health and
academic consequences in children.
48
8 Implications and future directions
The results of this study show that better physical performance, physical activity and
certain sedentary behaviors are associated with better academic achievement particularly in
boys. These results highlight the need for appropriate physical activity and adequate motor
performance to improve academic achievement in children. Moreover, ways to increase
recess physical activity should be investigated and promoted (237). In addition, children
with poor motor competence in Grade 1 should be followed and interventions to improve
motor performance and reading and arithmetic skills should be started whenever
necessary.
The results of this study suggest that the relationships of physical activity, sedentary
behavior, cardiorespiratory fitness, and motor performance with academic achievement in
children are multidimensional and can never be fully separated. Thus, there is a need for
investigations that explore possible interactions of these constructs in relation to cognition
and academic achievement in children. Moreover, there is a lack of knowledge of possible
mediators for these associations. In future studies it is important to investigate plausible
biological and psychosocial mechanisms that could explain the associations of physical
activity, cardiorespiratory fitness, motor performance, and adiposity with cognition and
academic achievement. Further studies on the sex-differences on the associations of
physical activity, physical performance, and adiposity with cognition and academic
achievement are required. More research on the associations of cardiorespiratory fitness
and endurance performance with cognition and academic achievement, assessed by a
variety of different ergometers and field tests with adequate scaling, to get a full picture of
the determinants of brain functions. In addition, interventions with different settings and
cohort studies are amply warranted.
49
9 References
1.
Vaynman S, Gomez-pinilla F. Revenge of the “Sit” : How lifestyle impacts neuronal
and cognitive health through molecular systems that interface energy metabolism
with neuronal plasticity. J Neurosci Res 2006;715:699–715.
2.
Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical
inactivity on major non-communicable diseases worldwide: an analysis of burden of
disease and life expectancy. Lancet 2012;380:219–229.
3.
Colley R, Garriguet D, Janssen I, Craig C, Clarce J, Tremblay M. Physical activity of
Canadian children and youth: accelometer results from the 2007-2009 Health
Measures Survey. Health Rep 2011;22:15–22.
4.
Tammelin T, Laine K, Turpeinen S, editors. Oppilaiden fyysinen aktiivisuus.
Jyväskylä: Liikunnan ja kansanterveyden julkaisuja 272; 2013. pp. 25.
5.
Dollman J, Norton K, Norton L. Evidence for secular trends in children’s physical
activity behaviour. Br J Sports Med 2005;39:892–897.
6.
Suoninen A. Lasten mediabarometri 2012.
nuorisotutkimusverkosto; 2012.
7.
Tomkinson GR, Léger L a, Olds TS, Cazorla G. Secular trends in the performance of
children and adolescents (1980-2000): an analysis of 55 studies of the 20m shuttle run
test in 11 countries. Sports Med 2003;33:285–300.
8.
Tomkinson G, Olds T. Secular changes in pediatric aerobic fitness test performance:
The global picture. Med Sport Sci 2007;50:46–66.
9.
Huotari PRT, Nupponen H, Laakso L, Kujala UM. Secular trends in aerobic fitness
performance in 13-18-year-old adolescents from 1976 to 2001. Br J Sports Med
2010;44:968–972.
10.
Runhaar J, Collard DCM, Singh AS, Kemper HCG, van Mechelen W, Chinapaw M.
Motor fitness in Dutch youth: differences over a 26-year period (1980-2006). J Sci Med
Sport 2010;13:323–328.
11.
Matton L, Duvigneaud N. Secular trends in anthropometric characteristics, physical
fitness, physical activity, and biological maturation in Flemish adolescents between
1969 and 2005. Am J Hum Biol 2007;357:345–357.
12.
Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity and trends in body
mass index among US children and adolescents, 1999-2010. JAMA 2012;307:483–490.
Helsinki:
Nuorisotutkimusseura/
50
13.
Kautiainen S, Rimpelä A, Vikat A, Virtanen SM. Secular trends in overweight and
obesity among Finnish adolescents in 1977-1999. Int J Obes Relat Metab Disord
2002;26:544–552.
14.
Vaynman S, Gomez-Pinilla F. License to run: exercise impacts functional plasticity in
the intact and injured central nervous system by using neurotrophins. Neurorehabil
Neural Repair 2005;19:283–295.
15.
Kramer AF, Erickson KI, Colcombe SJ. Exercise, cognition, and the aging brain. J
Appl Physiol 2006;101:1237–1242.
16.
Chaddock L, Voss M, Kramer A. Physical activity and fitness effects on cognition and
brain health in children and older adults. Kinesiol Rev 2012;37–45.
17.
Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical
fitness: definitions and distinctions for health-related research. Public Health Rep
1985;100:126–31.
18.
Corder K, Ekelund U, Steele RM, Wareham NJ, Brage S. Assessment of physical
activity in youth. J Appl Physiol 2008;105:977–987.
19.
Ainsworth B, Haskell W, Whitt M, Irwin M, Swartz A, Strath S, et al. Compendium of
physical activities: an update of activity codes and MET intensities. Med Sci Sport
Exerc 2000;32(Suppl. 9):498–516.
20.
Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee I-M, et al.
American College of Sports Medicine position stand. Quantity and quality of exercise
for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor
fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports
Exerc 2011;43:1334–1359.
21.
McMurray RG, Soares J, Caspersen CJ, McCurdy T. Examining variations of resting
metabolic rate of adults: a public health perspective. Med Sci Sports Exerc
2014;46:1352–1358.
22.
Ekelund U, Tomkinson G, Armstrong N. What proportion of youth are physically
active? Measurement issues, levels and recent time trends. Br J Sports Med
2011;45:859–865.
23.
Rowland T. Oxygen uptake and endurance fitness in children, revisited. Pediatr
Exerc Sci 2013;25:508–514.
24.
Bailey R, Olson J, Pepper S, Porszasz J, Barstow T, Cooper D. The level and tempo of
children´s physical activities: an observational study. Med Sci Sport Exerc
1995;27:1033–1041.
25.
Pellegrini AD, Smith PK. Physical activity play : the nature and function of a
neglected aspect of play. Child Dev 1998;69:577–598.
51
26.
Strath SJ, Kaminsky LA, Ainsworth BE, Ekelund U, Freedson PS, Gary RA, et al.
Guide to the assessment of physical activity: Clinical and research applications: a
scientific statement from the American Heart Association. Circulation 2013;128:2259–
2279.
27.
Tikkanen O, Haakana P, Pesola A, Häkkinen K, Rantalainen T, Havu M, et al. Muscle
activity and inactivity periods during normal daily life. PLoS One 2013;8:e52228.
28.
Bussmann JBJ, van den Berg-Emons RJG. To total amount of activity….. and beyond:
perspectives on measuring physical behavior. Front Psychol 2013;4:463.
29.
Van der Ploeg HP, Merom D, Corpuz G, Bauman AE. Trends in Australian children
traveling to school 1971-2003: burning petrol or carbohydrates? Prev Med 2008;46:60–
62.
30.
Strong WB, Malina RM, Blimkie CJR, Daniels SR, Dishman RK, Gutin B, et al.
Evidence based physical activity for school-age youth. J Pediatr 2005;146:732–737.
31.
Tremblay MS, Warburton DER, Janssen I, Paterson DH, Latimer AE, Rhodes RE, et al.
New Canadian physical activity guidelines. Appl Physiol Nutr Metab 2011;36:36–46;
47–58.
32.
Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advi
sory Committee Report, 2008. Washington DC: U.S.; 2008.
33.
Kaikkonen R, Hakulinen-viitanen T, Markkula J, Ovaskainen M, Virtanen S,
Laatikainen T. Lasten ja lapsi- perheiden terveys- ja hyvinvointierot [Health and wellbeing inequalities among children and their families]. Helsinki: National Institute for
Health and Welfare (THL); 2012. pp. 128–134.
34.
Tammelin T, Aira A, Kulmala J, Kallio J, Kantomaa M, Valtonen M. Suomalaislasten
fyysinen aktiivisuus - tavoitteena vähemmän istumista ja enemmän liikuntaa
[Physical activity among Finnish children – less sedentary time and more physical
activity needed]. Suom Lääkäril 2014;69:1871–1876.
35.
Verloigne M, Van Lippevelde W, Maes L, Yildirim M, Chinapaw M, Manios Y, et al.
Levels of physical activity and sedentary time among 10- to 12-year-old boys and
girls across 5 European countries using accelerometers: an observational study within
the ENERGY-project. Int J Behav Nutr Phys Act 2012;9:34.
36.
Väistö J, Eloranta A-M, Viitasalo A, Tompuri T, Lintu N, Karjalainen P, et al. Physical
activity and sedentary behaviour in relation to cardiometabolic risk in children: crosssectional findings from the Physical Activity and Nutrition in Children (PANIC)
Study. Int J Behav Nutr Phys Act 2014;11:55.
37.
Social determinants of health and well-being among young people. Health behaviour
in school-aged children (HBSC) study. International report from the 2009/2010
survey. Copenhagen: WHO Regional Office for Europe; 2012. pp. 129–136.
52
38.
Ortega FB, Konstabel K, Pasquali E, Ruiz JR, Hurtig-Wennlöf A, Mäestu J, et al.
Objectively measured physical activity and sedentary time during childhood,
adolescence and young adulthood: a cohort study. PLoS One 2013;8:e60871.
39.
Sedentary Behaviour Researc Network. Letter to the Editor: Standardized use of the
terms “sedentary” and “sedentary behaviours.” Appl Physiol Nutr Metab
2012;37:540–542.
40.
Pate RR, Neill JRO, Lobelo F. The evolving definition of “Sedentary”. Exerc Sport Sci
Rev 2008;36:173–178.
41.
Laukkanen A, Finni T, Pesola A, Sääkslahti A. Reipas liikunta takaa lasten motoristen
perustaitojen kehityksen – mutta kevyttäkin tarvitaan! [Brisk physical activity
ensures the development of fundamental motor skills in children – but light is also
needed!]. Liikunta & Tiede 2013;35:47–52.
42.
Pate RR, Mitchell JA, Byun W, Dowda M. Sedentary behaviour in youth. Br J Sports
Med 2011;45:906–913.
43.
Tremblay MS, LeBlanc AG, Kho ME, Saunders TJ, Larouche R, Colley RC, et al.
Systematic review of sedentary behaviour and health indicators in school-aged
children and youth. Int J Behav Nutr Phys Act 2011;8:98.
44.
Archer E, Shook RP, Thomas DM, Church TS, Katzmarzyk PT, Hébert JR, et al. 45year trends in women’s use of time and household management energy expenditure.
PLoS One 2013;8:e56620.
45.
Church TS, Thomas DM, Tudor-Locke C, Katzmarzyk PT, Earnest CP, Rodarte RQ, et
al. Trends over 5 decades in U.S. occupation-related physical activity and their
associations with obesity. PLoS One 2011;6:e19657.
46.
Whaley M, Brubaker P, Otto R, editors. ACSM´s Guidelines for Exercise Testing and
Prescription. 7th ed. Baltimore: Lippincott, Williams & Wilkings; 2006. pp. 366.
47.
Armstrong N, Welsman J. Aerobic fitness: What are we measuring? Med Sport Sci.
2007;50:5–25.
48.
Armstrong N, Welsman J. Aerobic fitness. In: Armstrong N, van Mechelen W, editors.
Paediatric Exercise Science and Medicine. 2. ed. Oxford: Oxford University Press;
2008. pp. 97–108.
49.
Armstrong N, Welsman J. Assessment and interpretation of aerobic fitness in
children and adolescents. Exerc Sport Sci Rev 1994;22:435–476.
50.
Armstrong N, Welsman J, Winsley R. Is peak VO2 a maximal index of children’s
aerobic fitness? Int J Sports Med 1996;17:356–359.
53
51.
Artero EG, España-Romero V, Ortega FB, Jiménez-Pavón D, Ruiz JR, VicenteRodríguez G, et al. Health-related fitness in adolescents: underweight, and not only
overweight, as an influencing factor. The AVENA study. Scand J Med Sci Sport
2010;20:418–427.
52.
Moliner-Urdiales D, Ruiz JR, Ortega FB, Jiménez-Pavón D, Vicente-Rodriguez G,
Rey-López JP, et al. Secular trends in health-related physical fitness in Spanish
adolescents: the AVENA and HELENA studies. J Sci Med Sport 2010;13:584–588.
53.
Stigman S, Rintala P, Kukkonen-Harjula K, Kujala U, Rinne M, Fogelholm M. Eightyear-old children with high cardiorespiratory fitness have lower overall and
abdominal fatness. Int J Pediatr Obes 2009;4:98–105.
54.
Rowland T, Kline G, Goff D, Martel L, Ferrone L. One-mile run performance and
cardiovascular fitness in children. Arch Pediatr Adolesc Med 1999;153:845–849.
55.
Tomkinson GR, Olds T. Field tests of fitness. In: Armstrong N, van Mechelen W,
editors. Paediatric Exercise Science and Medicine. 2. ed. Oxford University Press;
2008. pp. 109–128.
56.
Welsman J, Armstrong N. Intepreting exercise performance data in relation to body
size. In: Armstrong N, van Mechelen W, editors. Paediatric Exercise Science and
Medicine. 2. ed. Oxfrord: Oxfrord University Press; 2008. pp. 13–22.
57.
Savonen K, Krachler B, Hassinen M, Komulainen P, Kiviniemi V, Lakka TA, et al. The
current standard measure of cardiorespiratory fitness introduces confounding by
body mass: the DR’s EXTRA study. Int J Obes 2012;36:1135–1140.
58.
Ekelund U, Franks PW, Wareham NJ, Aman J. Oxygen uptakes adjusted for body
composition in normal-weight and obese adolescents. Obes Res 2004;12:513–520.
59.
Santtila M, Kyröläinen H, Vasankari T, Tiainen S, Palvalin K, Häkkinen A, Häkkinen
K. Physical fitness profiles in young Finnish men during the years 1975-2004. Med Sci
Sports Exerc 2006;38:1990–1994.
60.
Eisenmann JC, Malina RM. Secular trend in peak oxygen consumption among United
States youth in the 20th century. Am J Hum Biol 2002;14:699–706.
61.
Vuorela N, Saha M-T, Salo MK. Toddlers get slimmer while adolescents get fatter –
BMI distribution in five birth cohorts from four decades in Finland. Acta Paediatr
2011;100:570–577.
62.
Vuorela N, Saha M-T, Salo MK. Change in prevalence of overweight and obesity in
Finnish children - comparison between 1974 and 2001. Acta Paediatr 2011;100:109–
115.
63.
Vuorela N, Saha M-T, Salo M. Prevalence of overweight and obesity in 5- and 12year-old Finnish children in 1986 and 2006. Acta Paediatr 2009;98:507–512.
54
64.
Olds TS, Ridley K, Tomkinson GR. Declines in aerobic fitness: are they only due to
increasing fatness? Med Sport Sci 2007;50:226–240.
65.
Armstrong N, Tomkinson G, Ekelund U. Aerobic fitness and its relationship to sport,
exercise training and habitual physical activity during youth. Br J Sports Med
2011;45:849–858.
66.
Malina RM, Bouchard C, Bar-Or O. Growth, Maturation, and Physical Activity. 2. ed.
Champaign: Human Kinetics; 2004. pp. 283–290, 350–357.
67.
Watkins J. Develompental biodynamics: the development of coordination. In:
Armstrong N, van Mechelen W, editors. Paediatric Exercise Science and Medicine. 2.
ed. Oxfrord: Oxfrord University Press; 2008. pp. 169–187.
68.
Logan SW, Robinson LE, Wilson AE, Lucas WA. Getting the fundamentals of
movement: a meta-analysis of the effectiveness of motor skill interventions in
children. Child Care Health Dev 2012;38:305–315.
69.
Riethmuller AM, Jones R, Okely AD. Efficacy of interventions to improve motor
development in young children: a systematic review. Pediatrics 2009;124(4):e782–792.
70.
Westendorp M, Houwen S, Hartman E, Mombarg R, Smith J, Visscher C. Effect of a
ball skill intervention on children’s ball skills and cognitive functions. Med Sci Sports
Exerc 2014;46:414–422.
71.
Stodden D, Goodway J, Langendorfer S, Roberton M, Rudisill M, Garcia C, et al. A
developmental perspective on the role of motor skill competence in physical activity:
an emergent relationship. Quest 2008;60:290–306.
72.
Pescatello L, Arena R, Riebe D, Thompson PD, editors. ACSM´s Guidelines for
Exercise Testing and Prescription. 9th ed. Baltimore: Lippincott, Williams & Wilkins;
2014. pp. 63–72, 94–104.
73.
European Council. EUROFIT: handbook for the EUROFIT tests of physical fitness.
Rome: Council of Europe; 1988. pp. 42–43, 56–57.
74.
Sugden D, Soucie H. Motor development. In: Armstrong N, van Mechelen W, editors.
Paediatric Exercise Science and Medicine. 2. ed. Oxfrord: Oxfrord University Press;
2008. pp. 188–189.
75.
De Ste Croix M. Muscle strength. In: Armstrong N, van Mechelen W, editors.
Paediatric Exercise Science and Medicine. 2. ed. Oxfrord: Oxfrord University Press;
2008. pp. 199–212.
76.
Cornier M-A, Després J-P, Davis N, Grossniklaus DA, Klein S, Lamarche B, et al.
Assessing adiposity: a scientific statement from the American Heart Association.
Circulation 2011;124:1996–2019.
55
77.
Cole T, Bellizzi M, Flegal K, Dietz W. Establishing a standard definition for child
overweight and obesity worldwide: international survey. BMJ 2000;320:1240–1243.
78.
Eisenmann JC, Heelan KA, Welk GJ. Assessing body composition among 3- to 8-yearold children: anthropometry, BIA, and DXA. Obes Res 2004;12:1633–1640.
79.
Yeong S, Gallagher D. Assessment methods in human body composition. Curr Opin
Clin Nutr Metab Care 2008;11:566–572.
80.
Saari A, Sankilampi U, Hannila M-L, Kiviniemi V, Kesseli K, Dunkel L. New Finnish
growth references for children and adolescents aged 0 to 20 years: Length/height-forage, weight-for-length/height, and body mass index-for-age. Ann Med
2011;43(3):235–248.
81.
Tompuri TT, Lakka TA, Hakulinen M, Lindi V, Laaksonen DE, Kilpeläinen TO, et al.
Assessment of body composition by dual-energy X-ray absorptiometry,
bioimpedance analysis and anthropometrics in children: the Physical Activity and
Nutrition in Children study. Clin Physiol Funct Imaging 2015;35(1):21–33.
82.
Haug E, Rasmussen M, Samdal O, Iannotti R, Kelly C, Vereecken C, et al. Overweight
in school-aged children and its relationship with democraphic and lifestyle factors:
Results from the WHO-Collaborative Health Behaviour in School-aged Children
(HBSC) Study. Int J Public Health 2010;54:167–179.
83.
Wijnhoven TM, van Raaij JM, Spinelli A, Starc G, Hassapidou M, Spiroski I, et al.
WHO European Childhood Obesity Surveillance Initiative: body mass index and
level of overweight among 6-9-year-old children from school year 2007/2008 to school
year 2009/2010. BMC Public Health 2014;14:806.
84.
Eloranta A-M, Lindi V, Schwab U, Tompuri T, Kiiskinen S, Lakka H-M, et al. Dietary
factors associated with overweight and body adiposity in Finnish children aged 6–8
years: the PANIC Study. Int J Obes 2012;36:950–955.
85.
Prentice AM, Jebb SA. Obesity in Britain: gluttony or sloth? BMJ. 1995;311:437–439.
86.
Herman KM, Sabiston CM, Mathieu M-E, Tremblay A, Paradis G. Sedentary behavior
in a cohort of 8- to 10-year-old children at elevated risk of obesity. Prev Med
2014;60:115–120.
87.
Eisenmann JC. Insight into the causes of the recent secular trend in pediatric obesity:
Common sense does not always prevail for complex, multi-factorial phenotypes. Prev
Med 2006;42:329–335.
88.
Wainwright PE, Colombo J. Nutrition and the development of cognitive functions:
interpretation of behavioral studies in animals and human infants. Am J Clin Nutr
2006;84:961–970.
89.
Diamond A. Executive functions. Annu Rev Psychol 2013;64:135–168.
56
90.
Best JR, Miller PH, Naglieri JA. Relations between executive function and academic
achievement from ages 5 to 17 in a large, representative national sample. Learn
Individ Differ 2011;21:327–336.
91.
Wechsler D. WISC-IV - Wechsler Intelligence Scale For Children - IV. Helsinki: NCS
Pearson, Ltd. Psykologien Kustannus Oy; 2010.
92.
Korkman M, Kirk U, Kemp S. NEPSY II - lasten neuropsykologinen tutkimus.
Helsinki: Psykologien Kustannus Oy; 2008.
93.
Raven J, Raven J, Court J. Coloured Progressive Matrices. Manual for Raven’s
Progressive Matrices and Vocabulary Scales. London: Oxford Psychologist Press Ltd.;
1998.
94.
Davidson M, Amso D, Anderson L, Diamond A. Development of cognitive control
and executive functions from 4 to 13 years: Evidence from manipulations of memory,
inhibition, and task switching. Neuropsychologia 2006;44:2037–2078.
95.
Cepeda N, Kramer A, Gonzales de Sather J. Changes in executive control across the
life span: examination of task-switching performance. Dev Psychol 2001;37:715–730.
96.
Sibley BA, Etnier JL. The Relationships between physical activity and cognition in
children : A meta-analysis. Pediatr Exerc Sci 2003;15:243–256.
97.
Best JR. Effects of physical activity on children’s executive function: Contributions of
experimental research on aerobic exercise. Dev Rev 2010;30:331–351.
98.
Davis CL, Tomporowski P, McDowell J, Austin B, Miller P, Yanasak N, et al. Exercise
improves executive function and achievement and alters brain activation in
overweight children: a randomized controlled trial. Health Psychol 2011;30:91–98.
99.
Kamijo K, Pontifex MB, O’Leary KC, Scudder MR, Wu C-T, Castelli DM, et al. The
effects of an afterschool physical activity program on working memory in
preadolescent children. Dev Sci 2011;14:1046–1058.
100. 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 randomized controlled intervention. Front Hum
Neurosci 2013;7:72.
101. Castelli DM, Hillman CH, Hirsch J, Hirsch A, Drollette E. FIT Kids: Time in target
heart zone and cognitive performance. Prev Med 2011;52:55–59.
102. Monti JM, Hillman CH, Cohen NJ. Aerobic fitness enhances relational memory in
preadolescent children: the FITKids randomized control trial. Hippocampus
2012;22:1876–1882.
57
103. 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:e1063–1071.
104. Hannula D, Ranganath C. The eyes have it: Hippocampal activity predicts expression
of memory in eye movements. Neuron 2010;63:592–599.
105. Singh A, Uijtdewilligen L, Twisk J, van Mechelen W, Chinapaw M. Physical activity
and performance at school. A systematic review of the literature including a
methodological quality assessment. Arch Pediatr Adolesc Med 2012;166:49–55.
106. Ahamed Y, Macdonald H, Reed K, Naylor P-J, Liu-Ambrose T, McKay H. Schoolbased physical activity does not compromise children’s academic performance. Med
Sci Sports Exerc 2007;39:371–376.
107. Dwyer T, Coonan W, Leitch D, Hetzel B, Baghurts R. An investigation of the effects of
daily physical activity on the health of primary school students in South Australia. Int
J Epidemiol 1983;12:308–312.
108. Sallis J, McKenzie T, Kolody B, Lewis M, Marshall S, Rosengard P. Effects of healthrelated physical education on academic achievement: project SPARK. Res Q Exerc
Sport 1999;70:127–134.
109. Donnelly JE, Greene JL, Gibson CA, Smith BK, Washburn RA, Sullivan DK, 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;49:336–341.
110. Ericsson I, Karlsson MK. Motor skills and school performance in children with daily
physical education in school - a 9-year intervention study. Scand J Med Sci Sports
2012;24:273–278.
111. Kantomaa MT, Tammelin TH, Demakakos P, Ebeling HE, Taanila AM. Physical
activity, emotional and behavioural problems, maternal education and self-reported
educational performance of adolescents. Health Educ Res 2010;25:368–379.
112. Martínez-Gómez D, Ruiz JR, Gómez-Martínez S, Chillón P, Rey-López JP, Díaz LE, et
al. Active commuting to school and cognitive performance in adolescents: the
AVENA study. Arch Pediatr Adolesc Med 2011;165:300–305.
113. Fox C, Barr-Anderson D, Neumar-Sztainer D, Wall M. Physical activity and sports
team participation: associations with academic outcomes in middle school and high
school students. J Sch Health 2010;80:31–37.
114. Centers for Disease Control and Prevention. The Association netween school-based
physical activity, including physical education, and academic performance. Atlanta,
GA; 2010. pp. 14–27.
58
115. Kwak L, Kremers SPJ, Bergman P, Ruiz JR, Rizzo NS, Sjöström M. Associations
between physical activity, fitness, and academic achievement. J Pediatr 2009;155:914–
918.
116. LeBlanc MM, Martin CK, Han H, Newton R, Sothern M, Webber LS, et al. Adiposity
and physical activity are not related to academic achievement in school-aged
children. J Dev Behav Pediatr 2012;33:486–494.
117. Syväoja HJ, Kantomaa MT, Ahonen T, Hakonen H, Kankaanpää A, Tammelin TH.
Physical activity, sedentary behavior, and academic performance in Finnish children.
Med Sci Sports Exerc 2013;45:2098–2104.
118. Pesce C, Crova C, Cereatti L, Casella R, Bellucci M. Physical activity and mental
performance in preadolescents: Effects of acute exercise on free-recall memory. Ment
Health Phys Act 2009;2:16–22.
119. Budde H, Voelcker-Rehage C, Pietrabyk-Kendziorra S, Ribeiro P, Tidow G. Acute
coordinative exercise improves attentional performance in adolescents. Neurosci Lett
2008;22:219–223.
120. Dye M, Green C, Bavelier D. Increasing speed of processing with action video games.
Curr Dir Psychol Sci 2009;18:321–216.
121. Syväoja HJ, Tammelin TH, Ahonen T, Kankaanpää A, Kantomaa MT. The
associations of objectively measured physical activity and sedentary time with
cognitive functions in school-aged children. PLoS One 2014;9:e103559.
122. Sharif I, Sargent JD. Association between television, movie, and video game exposure
and school performance. Pediatrics 2006;118:e1061–1070.
123. Borzekowski DLG, Robinson TN. The remote, the mouse, and the No. 2 pencil - The
household media environment and academic achievement among third grade
students. Arch Pediatr Adolesc Med 2005;159:607–613.
124. Ennemoser M, Schneider W. Relations of television viewing and reading: Findings
from a 4-year longitudinal study. J Educ Psychol 2007;99:349–368.
125. Kirkorian HL, Wartella EA, Anderson DR. Media and young children’s learning.
Futur Child 2008;18:39–61.
126. Weis R, Cerankosky BC. Effects of video-game ownership on young boys’ academic
and behavioral functioning: a randomized, controlled study. Psychol Sci 2010;21:463–
470.
127. Hillman CH, Erickson KI, Kramer AF. Be smart, exerice your heart: exercise effects on
brain and cognition. Nat Rev Neurosci 2008;9:58–65.
59
128. Voss MW, Vivar C, Kramer AF, van Praag H. Bridging animal and human models of
exercise-induced brain plasticity. Trends Cogn Sci 2013;17:525–544.
129. Voss MW, Nagamatsu LS, Liu-Ambrose T, Kramer AF. Exercise, brain, and cognition
across the life span. J Appl Physiol 2011;111:1505–1513.
130. Lubans D, Morgan P, Cliff D, Barnet L, Okely A. Fundamental movement skills in
children and adolescents: review of associated health benefits. Sport Med
2010;40:1019–1035.
131. Reinert KRS, Po’e EK, Barkin SL. The relationship between executive function and
obesity in children and adolescents: a systematic literature review. J Obes
2013;2013:820956.
132. Rigoli D, Piek JP, Kane R, Whillier A, Baxter C, Wilson P. An 18-month follow-up
investigation of motor coordination and working memory in primary school children.
Hum Mov Sci 2013;32:1116–1126.
133. Wassenberg R, Feron FJM, Kessels AGH, Hendriksen JGM, Kalff AC, Kroes M, et al.
Relation between cognitive and motor performance in 5- to 6-year-old children:
results from a large-scale cross-sectional study. Child Dev 2005;76:1092–1103.
134. 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;23:1332–1345.
135. Chaddock L, Erickson KI, Prakash RS, Kim JS, Voss MW, Vanpatter M, et al. A
neuroimaging investigation of the association between aerobic fitness, hippocampal
volume, and memory performance in preadolescent children. Brain Res
2010;1358:172–183.
136. Chaddock L, Hillman CH, Pontifex MB, Johnson CR, Raine LB, Kramer AF.
Childhood aerobic fitness predicts cognitive performance one year later. J Sports Sci
2012;30:421–430.
137. Moore R, Wu C, Pontifex M, O´Leary K, Scudder MR, Raine LB, et al. Aerobic fitness
and intra-individual variability of neurocognition in preadolescent children. Brain
Cogn 2013;82:43–57.
138. Smith JJ, Eather N, Morgan PJ, Plotnikoff RC, Faigenbaum AD, Lubans DR. The
health benefits of muscular fitness for children and adolescents: A systematic review
and meta-analysis. Sports Med 2014;44:1209–1223.
139. Diamond A. Close interrelation of motor development and cognitive development
and of the cerebellum and prefrontal cortex. Child Dev 2000;71:44–56.
140. Iverson JM. Developing language in a developing body: the relationship between
motor development and language development. J Child Lang 2010;37(2):1–25.
60
141. Westendorp M, Hartman E, Houwen S, Smith J, Visscher C. The relationship between
gross motor skills and academic achievement in children with learning disabilities.
Res Dev Disabil 2011;32:2773–2779.
142. Castelli DM, Hillman CH, Buck SM, Erwin HE. Physical fitness and academic
achievement in third- and fifth-grade students. J Sport Exerc Psychol 2007;29:239–252.
143. Coe D, Pivarnik J, Womack C, Reeves M, Malina R. Health-related fitness and
academic achievement in middle school students. J Sports Med Phys Fitness
2012;52:654–660.
144. Coe DP, Peterson T, Blair C, Schutten MC, Peddie H. Physical fitness, academic
achievement, and socioeconomic status in school-aged youth. J Sch Health
2013;83:500–507.
145. Davis CL, Cooper S. Fitness, fatness, cognition, behavior, and academic achievement
among overweight children: do cross-sectional associations correspond to exercise
trial outcomes? Prev Med 2011;52:65–69.
146. Dwyer T, James F, Blizzard L, Lazarus R, Dean K. Relation of academic performance
to physical activity and fitness in children. Pediatr Exerc Sci 2001;13:225–237.
147. Eveland-Sayers BM, Farley RS, Fuller DK, Morgan DW, Caputo JL. Physical fitness
and academic achievement in elementary school children. J Phys Act Health
2009;6:99–104.
148. Kantomaa MT, Stamatakis E, Kankaanpää A, Kaakinen M, Rodriguez A, Taanila A, et
al. Physical activity and obesity mediate the association between childhood motor
function and adolescents’ academic achievement. Proc Natl Acad Sci U S A
2013;110:1917–1922.
149. van der Niet AG, Hartman E, Smith J, Visscher C. Modeling relationships between
physical fitness, executive functioning, and academic achievement in primary school
children. Psychol Sport Exerc 2014;15:319–325.
150. Pindus D, Bandelow S, Hogervorst E, Biddle S, Sherar L. Cardiorespiratory fitness
and academic achievement in a large cohort of British children: does selective
attention matter? Children & Exercise. 2013. pp. 139–143.
151. Rauner RR, Walters RW, Avery M, Wanser TJ. Evidence that aerobic fitness is more
salient than weight status in predicting standardized math and reading outcomes in
fourth- through eighth-grade students. J Pediatr 2013;163:344–348.
152. Roberts C, Freed B, McCarthy W. Low aerobic fitness and obesity are assiated with
lower standardized test scores in children. J Pediatr 2011;156(5):711–718.
61
153. Scudder MR, Federmeier KD, Raine LB, Direito A, Boyd JK, Hillman CH. The
association between aerobic fitness and language processing in children: Implications
for academic achievement. Brain Cogn 2014;87C:140–152.
154. Santiago JA, Roper A, Disch JG, Morales J. The relationship among aerobic capacity,
body composition , and academic achievement of fourth and fifth grade Hispanic
students. Phys Educ 2013;70:89–105.
155. VanDusen DP, Kelder SH, Kohl HW, Ranjit N, Perry CL. Associations of physical
fitness and academic performance among schoolchildren. J Sch Health 2011;81:733–
740.
156. Welk GJ, Jackson AW, Morrow JR, Haskell WH, Meredith MD, Cooper KH. The
association of health-related fitness with indicators of academic performance in Texas
schools. Res Q Exerc Sport 2010;81:16–23.
157. Wittberg RA, Northrup KL, Cottrell LA. Children ’ s aerobic fitness and academic
achievement : A longitudinal examination of students during their fifth and seventh
grade years. Am J Public Health 2012;102:2303–2308.
158. Wittberg R, Cottrell LA, Davis CL, Northrup KL. Aerobic fitness tresholds associated
with fifth grade academic achievement. Am J Heal Educ 2010;41:284–91.
159. Davenport MH, Hogan DB, Eskes G a, Longman RS, Poulin MJ. Cerebrovascular
reserve: the link between fitness and cognitive function? Exerc Sport Sci Rev
2012;40:153–158.
160. Yuki A, Lee S, Kim H, Kozakai R, Ando F, Shimokata H. Relationship between
physical activity and brain atrophy progression. Med Sci Sport Exerc 2012;44:2362–
2368.
161. Cotman CW, Berchtold NC, Christie L-A. Exercise builds brain health: key roles of
growth factor cascades and inflammation. Trends Neurosci 2007;30:464–472.
162. Huang T, Larsen KT, Ried-Larsen M, Møller NC, Andersen LB. The effects of physical
activity and exercise on brain-derived neurotrophic factor in healthy humans: A
review. Scand J Med Sci Sports 2013;24:1–10.
163. Knaepen K, Goekint M, Heyman EM, Meeusen R. Neuroplasticity - exercise-induced
response of peripheral brain-derived neurotrophic factor: a systematic review of
experimental studies in human subjects. Sports Med 2010;40:765–801.
164. Cotman CW, Berchtold NC. Exercise: a behavioral intervention to enhance brain
health and plasticity. TRENDS Neurosci 2002;25:295–301.
165. Kramer AF, Erickson KI. Capitalizing on cortical plasticity: influence of physical
activity on cognition and brain function. Trends Cogn Sci 2007;11:342–348.
62
166. Erickson KI, Colcombe SJ, Wadhwa R, Bherer L, Peterson MS, Scalf PE, et al.
Training-induced plasticity in older adults: effects of training on hemispheric
asymmetry. Neurobiol Aging 2007;28:272–283.
167. Vaynman S, Ying Z, Gomez-Pinilla F. Hippocampal BDNF mediates the efficacy of
exercise on synaptic plasticity and cognition. Eur J Neurosci 2004;20:2580–2590.
168. Gomez-pinilla F, Hillman C. The influence of exercise on cognitive abilities. Compr
Physiol 2013;3:403–428.
169. Kramer AF, Bherer L, Colcombe SJ, Dong W, Greenough WT. Environmental
influences on cognitive and brain plasticity during aging. J Gerontol A Biol Sci Med
Sci 2004;59:940–957.
170. Colcombe S, Erickson KI, Scalf P, Kim J, Prakash R, McAuley E, et al. Aerobic exercise
training increases brain volume in aging humans. J Gerontol Med Sci A 2006;61:1166–
1170.
171. Olson AK, Eadie BD, Ernst C, Christie BR. Environmental enrichment and voluntary
exercise massively increase neurogenesis in the adult hippocampus via dissociable
pathways. Hippocampus 2006;16:250–260.
172. Voelcker-Rehage C, Windisch C. Structural and functional brain changes related to
different types of physical activity across the life span. Neurosci Biobehav Rev
2013;37:2268–2295.
173. Voss MW, Heo S, Prakash RS, Erickson KI, Alves H, Chaddock L, et al. The influence
of aerobic fitness on cerebral white matter integrity and cognitive function in older
adults: Results of a one-year exercise intervention. Hum Brain Mapp 2012;34:2472–
2985.
174. Hillman CH, Pontifex MB, Motl RW, O’Leary KC, Johnson CR, Scudder MR, et al.
From ERPs to academics. Dev Cogn Neurosci 2012;2:90–98.
175. Hillman CH, Kamijo K, Scudder M. A review of chronic and acute physical activity
participation on neuroelectric measures of brain health and cognition during
childhood. Prev Med 2011;52:21–28.
176. Drollette ES, Scudder MR, Raine LB, Moore RD, Saliba BJ, Pontifex MB, et al. Acute
exercise facilitates brain function and cognition in children who need it most: An ERP
study of individual differences in inhibitory control capacity. Dev Cogn Neurosci
2014;7:53–64.
177. Pontifex MB, Saliba BJ, Raine LB, Picchietti DL, Hillman CH. Exercise improves
behavioral, neurocognitive, and scholastic performance in children with attentiondeficit/hyperactivity disorder. J Pediatr 2013;162:543–551.
63
178. Beauquis J, Roig P, De Nicola AF, Saravia F. Short-term environmental enrichment
enhances adult neurogenesis, vascular network and dendritic complexity in the
hippocampus of type 1 diabetic mice. PLoS One 2010;5:e13993.
179. Ekstrand J, Hellsten J, Tingström A. Environmental enrichment, exercise and
corticosterone affect endothelial cell proliferation in adult rat hippocampus and
prefrontal cortex. Neurosci Lett 2008;442:203–207.
180. Draganski B, Gaser C, Busch V. Neuroplasticity: changes in grey matter induced by
training. Nature 2004;427:311–312.
181. Chang Y-K, Tsai Y-J, Chen T-T, Hung T-M. The impacts of coordinative exercise on
executive function in kindergarten children: an ERP study. Exp Brain Res
2013;225:187–196.
182. Schaeffer DJ, Krafft CE, Schwarz NF, Chi L, Rodrigue AL, Pierce JE, et al. An 8-month
exercise intervention alters frontotemporal white matter integrity in overweight
children. Psychophysiology 2014;51:728–33
183. Thomas AG, Dennis A, Bandettini P a, Johansen-Berg H. The effects of aerobic
activity on brain structure. Front Psychol 2012;3:86.
184. Kempermann G, Fabel K, Ehninger D, Babu H, Leal-Galicia P, Garthe A, et al. Why
and how physical activity promotes experience-induced brain plasticity. Front
Neurosci 2010;4:189.
185. Verstynen TD, Lynch B, Miller DL, Voss MW, Prakash RS, Chaddock L, et al. Caudate
nucleus volume mediates the link between cardiorespiratory fitness and cognitive
flexibility in older adults. J Aging Res 2012;2012:939285.
186. Erickson KI, Weinstein AM, Sutton BP, Prakash RS, Voss MW, Chaddock L, et al.
Beyond vascularization: aerobic fitness is associated with N-acetylaspartate and
working memory. Brain Behav 2012;2:32–41.
187. Gonzales M, Tarumi T, Kaur S, Nualnim N. Aerobic fitness and the brain: Increased
N-acetyl-aspartate and choline concentrations in endurance-trained middle-aged
adults. Brain Topogr 2013;26:126–134.
188. Voss MW, Prakash RS, Erickson KI, Basak C, Chaddock L, Kim JS, et al. Plasticity of
brain networks in a randomized intervention trial of exercise training in older adults.
Front Aging Neurosci 2010;2:1–17.
189. Chaddock-Heyman L, Erickson KI, Voss MW, Powers JP, Knecht AM, Pontifex MB, et
al. White matter microstructure is associated with cognitive control in children. Biol
Psychol 2013;94:109–115.
64
190. Marks BL, Katz LM, Styner M, Smith JK. Aerobic fitness and obesity: relationship to
cerebral white matter integrity in the brain of active and sedentary older adults. Br J
Sports Med 2011;45:1208–1215.
191. Yokum S, Ng J, Stice E. Relation of regional gray and white matter volumes to current
BMI and future increases in BMI: a prospective MRI study. Int J Obes 2012;36:656–
664.
192. Verstynen T, Weinstein A, Schneider W, Jakicic J, Rofey D, Erickson KI. Increased
body mass index is associated with a global and distributed decrease in white matter
microstructural integrity. Psychosom Med 2012;74:682–690.
193. Gazdzinski S, Kornak J, Weiner MW, Meyerhoff J. Body mass index and magnetic
resonance markers of brain integrity in adults. Ann Neurol 2008;63:652–657.
194. Shaibi G, Cruz M, Ball G, Weigensberg M, Kobaissi H, Salem G, et al. Cardiovascular
fitness and the metabolic syndrome in overweight latino youths. Med Sci Sport Exerc
2005;37:922–928.
195. Yau PL, Castro MG, Tagani A, Tsui WH, Convit A. Obesity and metabolic syndrome
and functional and structural brain impairments in adolescence. Pediatrics
2012;130:e856–864.
196. Voelcker-Rehage C, Godde B, Staudinger UM. Physical and motor fitness are both
related to cognition in old age. Eur J Neurosci 2010;31:167–176.
197. Tomporowski PD, Lambourne K, Okumura MS. Physical activity interventions and
children’s mental function: an introduction and overview. Prev Med 2011;52:3–9.
198. Kühn S, Gleich T, Lorenz RC, Lindenberger U, Gallinat J. Playing Super Mario
induces structural brain plasticity: gray matter changes resulting from training with a
commercial video game. Mol Psychiatry 2014;19:265–271.
199. Nurmi J-E, Kiuru N, Lerkkanen M-K, Niemi P, Poikkeus A-M, Ahonen T, et al.
Teachers adapt their instruction in reading according to individual children’s literacy
skills. Learn Individ Differ 2013;23:72–79.
200. Veijalainen A, Tompuri T, Lakka H-M, Laitinen T, Lakka TA. Reproducibility of
pulse contour analysis in children before and after maximal exercise stress test: the
Physical Activity and Nutrition in Children (PANIC) study. Clin Physiol Funct
Imaging 2011;31:132–138.
201. Lintu N, Tompuri T, Viitasalo A, Soininen S, Savonen K, Lindi V, et al.
Cardiovascular fi tness and haemodynamic responses to maximal cycle ergometer
exercise test in children 6–8 years of age. J Sports Sci 2014;32:652–659.
65
202. Jongbloed-Pereboom M, Nijhuis-van der Sanden MWG, Steenbergen B. Norm scores
of the box and block test for children ages 3-10 years. Am J Occup Ther 2013;67:312–
318.
203. Williams DP, Going SB, Lohman TG, Harsha DW, Srinivasan SR, Webber LS, et al.
Body fatness and risk for elevated blood pressure, total cholesterol, and serum
lipoprotein ratios in children and adolescents. Am J Public Health 1992;82:358–363.
204. Lindeman J. ALLU-ala-asteen lukutesti [standardized reading test for comprehensive
school]. Turku: Center for Learning Research. University of Turku; 1998.
205. Räsänen P, Aunola K. Test of arithmetics. Unpublished test material developed in the
First Steps follow-up. Jyväskylä: University of Jyväskylä; 2007.
206. Tanner J. Growth at adolescence. Oxford: Blackwell; 1962.
207. Kiuru N, Aunola K, Torppa M, Lerkkanen M-K, Poikkeus A-M, Niemi P, et al. The
role of parenting styles and teacher interactional styles in children’s reading and
spelling development. J Sch Psychol 2012;50:799–823.
208. Telama R. Tracking of physical activity from childhood to adulthood: a review. Obes
Facts 2009;2:187–195.
209. Liu W, Zillifro T, Nichols R. Tracking of health-related physical fitness for middle
school boys and girls. Pediatr Exerc Sci 2013;24:549–562.
210. Souza M, Chaves R, Lopes VP, Malina R, Garganta R, Seabra A, et al. Motor
coordination, activity and fitness at 6 years relative to activity and fitness at 10 years
of age. J Physi Act Health 2013;11:1239–1247.
211. Telama R, Yang X, Leskinen E, Kankaanpää A, Hirvensalo M, Tammelin T, et al.
Tracking of physical activity from early childhood through youth into adulthood.
Med Sci Sports Exerc 2013;46:955–962.
212. Dencker M, Thorsson O, Karlsson MK, Lindén C, Wollmer P, Andersen LB. Maximal
oxygen uptake versus maximal power output in children. J Sports Sci 2008;26:1397–
1402.
213. Kantomaa MT, Purtsi J, Taanila AM, Remes J, Viholainen H, Rintala P, et al.
Suspected motor problems and low preference for active play in childhood are
associated with physical inactivity and low fitness in adolescence. PLoS One
2011;6:e14554.
214. Saunders P, Pyne D, Telford R, Hawley J. Factors affecting running economy in
trained distance runners. Sport Med 2004;34:465–85.
66
215. Chia LC, Reid SL, Licari MK, Guelfi KJ. A comparison of the oxygen cost and
physiological responses to running in children with and without Developmental
Coordination Disorder. Res Dev Disabil 2013;34:2098–2106.
216. Tsigilis N, Douda H, Tokmakidis SP. Test-retest reliability of the Eurofit test battery
administered to university students. Percept Mot Skills 2002;95:1295–1300.
217. Fjørtoft I, Pedersen AV, Sigmundsson H, Vereijken B. Measuring physical fitness in
children who are 5 to 12 years old with a test battery that is functional and easy to
administer. Phys Ther 2011;91:1087–1095.
218. Godang K, Qvigstad E, Voldner N, Isaksen GA, Frøslie KF, Nøtthellen J, et al.
Assessing body composition in healthy newborn infants: reliability of dual-energy xray absorptiometry. J Clin Densitom 2010;13:151–160.
219. Glickman SG, Marn CS, Supiano M a, Dengel DR. Validity and reliability of dualenergy X-ray absorptiometry for the assessment of abdominal adiposity. J Appl
Physiol 2004;97:509–514.
220. Gutin B, Litaker M, Islam S, Manos T, Smith C, Treiber F. Body-composition
measurement in 9-11-y-old children by dual-energy X-ray absorptiometry, skinfoldthickness measurements, and bioimpedance analysis. Am J Clin Nutr 1996;63:287–
292.
221. Figueroa-Colon R, Mayo MS, Treuth MS, Aldridge RA, Weinsier RL. Reproducibility
of dual-energy X-ray absorptiometry measurements in prepubertal girls. Obes Res
1998;6:262–267.
222. Karlsson A-K, Kullberg J, Stokland E, Allvin K, Gronowitz E, Svensson P-A, et al.
Measurements of total and regional body composition in preschool children: A
comparison of MRI, DXA, and anthropometric data. Obesity 2013;21:1018–1024.
223. Sopher A, Thornton J, Wang J, Pierson R, Heymsfield S, Horlick M. Measurement of
percentage of body fat in 411 children and adolescents: a comparison of dual-energy
X-ray absorptiometry with a four-compartment model. Pediatrics 2004;113:1285–1290.
224. Shypailo RJ, Butte NF, Ellis KJ. DXA: can it be used as a criterion reference for body
fat measurements in children? Obesity 2008;16:457–462.
225. Viljaranta J, Lerkkanen M-K, Poikkeus A-M, Aunola K, Nurmi J-E. Cross-lagged
relations between task motivation and performance in arithmetic and literacy in
kindergarten. Learn Instr 2009;19:335–344.
226. Lerkkanen M-J, Poikkeus A-M, Ahonen T, Siekkinen M, Niemi P, Nurmi J-E. Luku- ja
kirjoitustaidon kehitys sekä motivaatio esi- ja alkuopetusvuosina. Kasvatus
2010;41:116–128.
67
227. Ahtola A, Silinskas G, Poikonen P-L, Kontoniemi M, Niemi P, Nurmi J-E. Transition
to formal schooling: Do transition practices matter for academic performance? Early
Child Res Q 2011;26:295–302.
228. Lindeman J. Ala-asteen lukutesti. Tekniset tiedot. Turku, Finland: Åbo Akademis
förlag; 1998.
229. Connor Gorber S, Tremblay M, Moher D, Gorber B. A comparison of direct vs. selfreport measures for assessing height, weight and body mass index: a systematic
review. Obes Rev 2007;8:307–326.
230. Pellegrini AD, Kato K, Blatchford P, Baines E. A short-term longitudinal study of
children’s playground games across the first year of school : Implications for social
competence and adjustment to school. Am Educ Res J 2002;39:991–1015.
231. Livesey D, Lum Mow M, Toshack T, Zheng Y. The relationship between motor
performance and peer relations in 9- to 12-year-old children. Child Care Health Dev
2011;37:581–588.
232. Rasberry CN, Lee SM, Robin L, Laris B a, Russell L a, Coyle KK, et al. The association
between school-based physical activity, including physical education, and academic
performance: a systematic review of the literature. Prev Med 2011;52:10–20.
233. Trudeau F, Shepard R. Relationships of physical activity to brain health and the
academic performance of schoolchildren. Am J Lifestyle Med 2010;4:138–150.
234. Ruiz J, Ortega F, Castillo R, Martín-Matillas M, Kwak L, Vicente-Rodriguez G, et al.
Physical activity, fitness, weight status, and cognitive performance in adolescents. J
Pediatr 2010;157:917–922.
235. Murray R, Ramstetter C. The crucial role of recess in school. Pediatrics 2013;131:183–
188.
236. Jonker L, Elferink-Gemser MT, Toering TT, Lyons J, Visscher C. Academic
performance and self-regulatory skills in elite youth soccer players. J Sports Sci
2010;28:1605–1614.
237. Haapala HL, Hirvensalo MH, Laine K, Laakso L, Hakonen H, Lintunen T, et al.
Adolescents’ physical activity at recess and actions to promote a physically active
school day in four Finnish schools. Health Educ Res 2014;29:840–852.
238. Lambiase MJ, Barry HM, Roemmich JN. Effect of a simulated active commute to
school on cardiovascular stress reactivity. Med Sci Sports Exerc 2010;42:1609–1616.
239. Efrat M. The relationship between low-income and minority children’s physical
activity and academic-related outcomes: a review of the literature. Health Educ
Behav 2011;38:441–451.
68
240. Pearson. The learning curve. Lessons in country performance in education. 2012
REPORT. 2012.
241. Wu C-T, Pontifex MB, Raine LB, Chaddock L, Voss MW, Kramer AF, et al. Aerobic
fitness and response variability in preadolescent children performing a cognitive
control task. Neuropsychology 2011;25(3):333–341.
242. Stroth S, Kubesch S, Dieterle K, Ruchsow M, Heim R, Kiefer M. Physical fitness, but
not acute exercise modulates event-related potential indices for executive control in
healthy adolescents. Brain Res 2009;1269:114–1124.
243. Haapala EA, Tompuri T, Lintu N, Laitinen T, Lindi V, Lakka T. Associations of
Cardiovascular Fitness, Motor Performance and Adiposity with Cognition in
Children (Abstract). Med Sci Sport Exerc. Orlando, FL: Med Sci Sports Exerc vol 46:5
suppl; 2014;46(Suppl.):169.
244. Bar-Or O. Pediatric Sports Medicine for the Practitioner: From Physiologic Principles
to Clinical Applications. New York: Springer-Verlag; 1983. pp. 376.
245. Kamijo K, Pontifex MB, Khan NA, Raine LB, Scudder MR, Drollette ES, et al. The
association of childhood obesity to neuroelectric indices of inhibition.
Psychophysiology 2012;49:1361–1371.
246. Kamijo K, Khan NA, Pontifex MB, Scudder MR, Drollette ES, Raine LB, et al. The
relation of adiposity to cognitive control and scholastic achievement in preadolescent
children. Obesity 2012;20:2406–2411.