Evidence on the positive associations of cardiorespiratory fitness (CRF) and motor competence (MC) and the negative associations of overweight and obesity with cognition and academic achievement in children is emerging (1,2). However, the evidence is mainly based on the results of cross-sectional studies that suggest fairly consistent positive associations of CRF and MC and negative associations of overweight and obesity with cognition and academic achievement in children and adolescents (1–3).
Some studies have demonstrated positive associations of CRF and MC at baseline with cognition (4,5) and academic performance (6,7) after 1–2 yr of follow-up in children and adolescents. In longitudinal studies, children and adolescents with continuously higher levels of CRF have been found to have better cognition and academic performance than other children and adolescents over the 2-yr follow-up (8,9). Although the results of some longitudinal studies suggest that larger improvement in CRF is associated with greater improvement in academic performance (10), other studies have found only weak associations of changes in CRF with changes in cognition or academic performance in youth (8,9).
Overweight, obesity, and increased body fat percentage (BF%) measured by dual-energy x-ray absorptiometry (DXA) or MRI have been associated with decreased brain volumes and impaired cognition and academic achievement among children and adolescents in cross-sectional studies (11–13). Moreover, the results of some longitudinal studies (14,15), but not all of them (16,17), suggest that increased BF% and body mass index (BMI) are associated with impaired cognitive performance in school-age children. On the contrary, Bisset et al. (16) reported that underweight, but not overweight, was associated with impaired academic performance in children.
Evidence on the longitudinal associations of CRF, MC, and adiposity with cognition in children is sparse. Furthermore, few studies in population samples of children have used specific measures of CRF, MC, and adiposity in relation to cognition. We therefore investigated the associations of CRF assessed by maximal workload during a cycle ergometer exercise test, MC assessed by standardized motor ability tests, and BF% measured by DXA at baseline with the Raven’s Coloured Progressive Matrices (RCPM) score at the 2-yr follow-up and with changes in the RCPM score during the 2-yr follow-up in a population sample of children 6–9 yr of age at baseline. We also studied the associations of changes in CRF, MC, and BF% with changes in the RCPM over the 2-yr follow-up.
Study design and study population
The present data are from the Physical Activity and Nutrition in Children (PANIC) Study, which is an ongoing follow-up and physical activity and dietary intervention study in a population sample of children from the city of Kuopio, Finland. Altogether, 736 children 6–9 yr of age from primary schools of Kuopio were invited to participate in the baseline examination in 2007–2009. Altogether, 512 children (70% of those invited) participated in the baseline examinations and were divided in the intervention group (306 children) and the control group (200 children). We excluded six children from the study at baseline because of physical disabilities that could hamper participation in the intervention or no time or motivation to attend in the study. The participants did not differ in sex distribution, age, or BMI standard deviation score (BMI-SDS) from all children who started the first grade in 2007–2009 based on data from the standard school health examinations performed for all Finnish children before the first grade (data not shown). Altogether, 440 (87%) of all 506 children included in the intervention study also attended in the 2-yr follow-up examinations. Complete data on variables used in the analyses on the associations of CRF, MC, and BF% at baseline with cognition at the 2-yr follow-up were available for 371 children (188 boys and 183 girls). Complete data on variables used in the analyses on the associations of changes in CRF, MC, and BF% with changes in cognition during the 2-yr follow-up were available for 299 children (151 boys and 148 girls). Children who had complete baseline data for the analyses were faster in the 50-m shuttle run test than those who did not (P = 0.015). There were no differences in other characteristics between children with complete data and those without it. The study protocol was approved by the Research Ethics Committee of the Hospital District of Northern Savo. Both children and their parents gave their written informed consent.
Assessment of body size and composition
Body weight was measured twice the children after having fasted for 12 h, having emptied the bladder, and standing in light underwear by 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. Body height was measured three times the children standing in the Frankfurt plane without shoes using a wall-mounted stadiometer to an accuracy of 0.1 cm. The mean of the nearest two values was used in the analyses. BMI was calculated by dividing body weight (kg) by body height (m) squared. BMI-SDS was calculated based on the Finnish references (18). The prevalence of overweight and obesity was defined using the cutoff values provided by Cole et al. (19). Total fat mass, BF%, and lean body mass (LM) were measured by the Lunar® DXA device (GE Medical Systems, Madison, WI) using standardized protocols.
Assessment of CRF and MC
CRF was assessed using a maximal exercise test with an electromagnetically braked Ergoselect 200 K® cycle ergometer (Ergoline, Bitz, Germany) (20). The exercise test protocol included a 3-min warm-up period at 5 W, a 1-min steady-state period at 20 W, an exercise period with a workload increase of 1 W every 6 s until exhaustion, and a 4-min cooldown period at 5 W. The exercise test was considered maximal if the reason for terminating the test indicated maximal effort and maximal cardiopulmonary capacity. CRF was defined as the maximal workload at the end of the exercise test per kilogram of LM.
Speed and agility were assessed by the 50-m shuttle run test (21). The children were asked to run 5 m from a starting line to another line as fast as possible, to turn on the line, to run back to the starting line, and to continue until five shuttles were completed. The test score was the running time in seconds, with a longer time indicating a poorer performance.
Static balance was assessed by the modified flamingo balance test (22). The children were asked to stand barefoot on one self-chosen leg with eyes closed for 30 s. The test score was the number of floor touches with a free foot or eye openings during 30 s, a higher number 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 (23). The children were asked to pick up small wooden cubes (2.5 cm per side) one by one with the dominant hand from one side of a wooden box (53.7 × 25.4 × 8 cm) and to move as many cubes as possible to the other side of the box during 60 s and to repeat the same task with the nondominant hand. The test score was the total number of cubes moved to the other side of the box during 120 s, and a smaller number of cubes moved, indicating poorer manual dexterity.
We computed the MC score as the sum of sex-specific z-scores for the test scores of the 50-m shuttle run test (inverse), the modified flamingo balance test (inverse), and the box and block test as described previously (6,22). A higher score indicates better MC.
Assessment of cognition
Nonverbal reasoning was assessed using the Raven Coloured Progressive Matrices (RCPM) (24) that was administered by trained researchers at the Institute of Biomedicine, University of Eastern Finland. The RCPM has been suggested to represent higher-order executive functioning that involves all core components of it: inhibition, working memory, and mental flexibility (25). The RCPM requires the ability to find similarities, differences, and discrete patterns and does not depend on acquired knowledge or language skills (24). The RCPM includes three sets of 12 items. Each test page includes a large item or a pattern of items and six small items. The child was asked to select the correct small item, which completes the large item or the set of items. The RCPM score was the number of correct answers, ranging from 0 to 36.
The parents were asked to report in a questionnaire their annual household income (≤€30,000, €30,001–60,000, and ≥€60,001) and their highest completed or ongoing educational degrees (vocational school or less, polytechnic, and university). 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 Malina et al. (26). The boys were defined as having entered clinical puberty if their testicular volume assessed by an orchidometer was ≥4 mL (stage ≥2). The girls were defined having entered clinical puberty if their breast development had started (stage ≥2). Maturity offset as a measure of age from peak height velocity was computed using a sex-specific formula described by Moore et al. (27).
We performed all statistical analyses using the SPSS Statistics (Version 23.0; IBM Corp., Armonk, NY). All statistical analyses were run separately for boys and girls because we observed that sex partly modified the associations between the MC at baseline and the RCPM score at baseline (P = 0.004 for interaction) and 2-yr follow-up (P = 0.082 for interaction) and the association between the CRF at baseline and the RCPM at the 2-yr follow-up (P = 0.077 for interaction). Basic characteristics between boys and girls were compared using the Student t-test for normally distributed continuous variables, the Mann–Whitney U test for skewed continuous variables, and the chi-square test for dichotomous variables. We analyzed the associations of CRF, the measures of MC, and the BF% at baseline with the RCPM score at the 2-yr follow-up using linear regression analyses adjusted for age and study group. These observational data were adjusted for the study group because the PANIC study is originally a controlled physical activity and dietary intervention study (28). Although we did not observe a statistically significant effect of the intervention on the RCPM score, the current data were adjusted for the study group to account of the residual confounding. These data were additionally adjusted for the RCPM score at baseline. The associations of changes in CRF, MC, and BF% with changes in the RCPM score were analyzed using linear regression analyses adjusted for age and study group. These data were further adjusted for the RCPM score and CRF, MC, or BF% at baseline. Differences in the RCPM score over the 2-yr follow-up between the thirds of CRF, MC, and BF% were investigated using repeated-measures ANCOVA adjusted for age and study group. After age and study group adjustments, all data were additionally adjusted for pubertal status, maturity offset, parental education, or household income at baseline, which were entered one by one into the model.
Basic characteristics at baseline
Boys were taller and heavier and had lower maturity offset and BF% than girls (Table 1). Boys also had a higher CRF and a shorter 50-m shuttle run test duration and moved less cubes in 2 min in the box and block test compared with girls.
Associations of CRF, MC, and BF% at baseline with RCPM score at the 2-yr follow-up
In boys, a higher CRF at baseline was associated with a lower RCPM score at the 2-yr follow-up after adjustment for age and the study group (Table 2). The inverse association between CRF at baseline and RCPM score at the 2-yr follow-up in boys was no longer statistically significant after further adjustment for the RCPM score at baseline (β = −0.100, 95% confidence interval [CI] = −0.222 to 0.022, P = 0.108). Moreover, the associations of the MC score (β = −0.002, 95% CI = −0.131 to 0.127, P = 0.972), the time spent in the 50-m shuttle run test (β = 0.019, 95% CI = −0.103 to 0.141, P = 0.760), the number of cubes moved in the box and block test (β = −0.045, 95% CI = −0.169 to 0.079, P = 0.478), the errors in the flamingo balance test (β = −0.057, 95% CI = −0.178 to 0.064, P = 0.352), and the BF% (β = 0.058, 95% CI = −0.059 to 0.176, P = 0.328) with the RCPM score were further attenuated after adjustment for the RCPM score at baseline. Adjustment for pubertal status, maturity offset, parental education, or household income had no effect on these associations.
In girls, the CRF, the MC score, the time spent in the 50-m shuttle run test, the number of cubes moved in the box and block test, the errors in the flamingo balance test, or the BF% at baseline was not associated with the RCPM score at the 2-yr follow-up (Table 2). Further adjustment for the RCPM score, pubertal status, maturity offset, parental education, or household income at baseline had no effect on these associations.
Associations of CRF, MC, and BF% at baseline with changes in RCPM score during the 2-yr follow-up
In boys, a higher MC score (β = −0.161, 95% CI = −0.314 to −0.009, P = 0.039), a shorter 50-m shuttle run test duration (β = 0.152, 95% CI = 0.007–0.296, P = 0.040), and a larger number of cubes moved in the box and block test (β = −0.161, 95% CI = −0.309 to −0.013, P = 0.033) at baseline were associated with a smaller increase in the RCPM score during the 2-yr follow-up after adjustment for age and study group. None of these associations remained statistically significant after further adjustment for the RCPM score at baseline (data not shown).
In girls, the CRF, the MC score, the 50-m shuttle run test duration, the number of cubes moved in the box and block test, or the BF% at baseline was not associated with the change in RCPM score during the 2-yr follow-up.
Associations of changes in CRF, MC, and BF% with changes in RCPM score during the 2-yr follow-up
Changes in the CRF, the MC score, the 50-m shuttle run test duration, the number of cubes moved in the box and block test, the errors in the flamingo balance test, and the BF% were not associated with changes in the RCPM score in boys or girls after adjustment for age and study group. These associations remained similar after further adjustment for the RCPM score and the corresponding measure of CRF, MC, and BF% at baseline.
Differences in RCPM score over the 2-yr follow-up in thirds of CRF, MC, and BF% at baseline
Boys in the highest third (mean difference 2.5, 95% CI for mean difference = 0.66–4.33, P = 0.004) and the middle third (mean difference 2.1, 95% CI for mean difference = 0.39–3.82, P = 0.010) of the MC score at baseline had a higher RCPM score than boys in the lowest third over the 2-yr follow-up after adjustment for age and study group (Fig. 1). There were no differences in the RCPM score among the thirds of CRF, the 50-m shuttle run test duration, the number of cubes moved in the box and block test, the errors in the flamingo balance test, or the BF% in boys. Further adjustment for pubertal status, maturity offset, parental education, or household income at baseline had no effect on these differences.
There were no differences in the RCPM score among the thirds of CRF, the MC score, the 50-m shuttle run test duration, the number of cubes moved in the box and bloc test, the errors in the balance test, or the BF% in girls.
Associations of RCPM score at baseline with RCPM score at the 2-yr follow-up and changes in RCPM score, CRF, MC, and adiposity during the 2-yr follow-up
The RCPM score at baseline was positively related to the RCPM score at the 2-yr follow-up in boys (β = 0.601, 95% CI = 0.482–0.720, P < 0.001) and girls (β = 0.552, 95% CI = 0.426–0.679, P < 0.001). The RCPM score at baseline was inversely associated with the change in the RCPM score during the 2-yr follow-up in boys (β = −0.720, 95% CI = −0.832 to −0.608, P < 0.001) and girls (β = −0.714, 95% CI = −0.848 to −0.634, P < 0.001). In boys, a higher RCPM score at baseline was also related to a smaller decrease in the 50-m shuttle run test duration after adjustment for age and study group (β = −0.165, 95% CI = −0.329 to 0.000, P = 0.049). Further adjustment for pubertal status, maturity offset, parental education, or household income had no effect on these associations.
We found that a lower MC score at baseline was associated with poorer cognition at the 2-yr follow-up in boys and that boys in the lowest third of the MC score at baseline had poorer cognition over the 2-yr follow-up than boys in other thirds. However, CRF or adiposity was not associated with cognition in boys and CRF, MC, or adiposity was not related to cognition in girls. We also observed that cognitive performance at baseline was a strong predictor of cognition 2 yr later in boys and girls and that cognition at baseline partly explained the longitudinal associations of CRF, MC, and adiposity with cognition in boys.
Our findings along with the limited evidence from previous longitudinal studies (8,9,29) suggest that changes in CRF and MC have weak and inconsistent associations with changes in cognition and academic achievement in children. One study among children 11–14 yr of age at baseline (30) reported that improvement in the 20-m endurance shuttle run test performance during the 3-yr follow-up period was related to better academic achievement at follow-up. However, these data were not adjusted for academic achievement at baseline, and therefore it is not known whether improved CRF was associated with improved academic achievement. We observed a positive association of MC at baseline with the RCPM score at the 2-yr follow-up but a negative association with changes in the RCPM score over the 2-yr follow-up. However, these associations were explained by the RCPM score at baseline. These results suggest that cognition and academic performance at baseline may be stronger determinants of subsequent cognitive and academic performance than changes in CRF and MC among children.
We observed that boys in the lowest third of the MC score at baseline had consistently poorer cognition over the 2-yr follow-up. We have previously reported that children with lower MC at baseline had poorer reading and arithmetic skills in grades 1–3 (6). Furthermore, children with lower CRF and poorer cognitive performance at baseline have been found to exhibit larger gains in cognitive performance over the 1-yr follow-up than children with higher CRF and better cognitive performance at baseline (4). These partly contrasting findings between cross-sectional and longitudinal studies maybe related to the regression toward the mean phenomenon during growth and maturation. This means that boys with poorer cognitive performance at baseline may have reached other boys at the 2-yr follow-up by change or because of improvement in cognitive ability related to normal growth and maturation.
It is also possible that the development of MC and cognitive skills may have a more interwoven relationship in early childhood than in middle and late childhood or in adolescence (3,31). Therefore, early developing children may have better MC and cognitive skills than late developing children, which results in cross-sectional differences in cognition between children with different levels of MC (3,22). This hypothesis is partly supported by our findings that a higher RCPM score at baseline was associated with a smaller change in RCPM score and smaller improvement in the 50-m shuttle run test duration among boys during the 2-yr follow-up. We have previously reported that boys with a lower RCPM score at baseline also had a poorer 50-m shuttle run test performance at baseline than other boys (22), and in the present study, we showed that these boys improved their RCPM score and the 50-m shuttle run test performance more than other boys over the 2-yr follow-up. These results suggest that differences in cognition related to MC in cross-sectional studies may reflect differences in the rate and stage of neuromuscular and cognitive development. Another explanation for these observations may be that opportunities for MC enhancing physical activity during early childhood may also improve cognition (31).
We found that adiposity was not associated with cognition in children. This is in contrast to previous findings demonstrating a negative association of BF% measured by either DXA or MRI with cognition in children (12,14,32). Although impaired cognition has been linked to larger gains in adiposity among children and adults suggesting a bidirectional association between adiposity and cognition (33), we found no association of cognition at baseline with change in BF% during the 2-yr follow-up. One reason for these contrasting observations may be that the aspects of cognition used in previous studies, such as inhibition, working memory, and executive functions, may be more sensitive to changes in adiposity and vice versa than the RCPM score.
We found a weak if any association of CRF, MC, or adiposity with cognition in girls. This observation is in line with the results of previous studies suggesting that CRF and MC are more strongly related to cognition and academic achievement in boys than in girls (6,22,34). The reason for these findings is not known, but it may relate to a more rapid maturation process in girls than in boys. Furthermore, there is some evidence that the size of the brain areas linked to CRF, MC, and cognition, such as basal ganglia, peaks earlier in girls than in boys (35,36). Therefore, it is possible that the magnitude of the associations of CRF, MC, and adiposity with cognition is different in girls and boys and among age and developmental groups (3).
In contrast to the positive association between CRF and cognition observed in most previous cross-sectional studies (1,29), we found a negative relationship of CRF at baseline with cognition at the 2-yr follow-up, although this association was attenuated after controlling for cognition at baseline. Our observations are in line with the results of other studies on weak associations of CRF, assessed by cycle ergometer exercise tests, with cognition and academic achievement among children (6,22,37,38). Most previous studies on the associations of CRF with cognition have used either indirect field measures of CRF, such as the 20-m shuttle run test, or have compared cognitive functions between children with very high and very low maximal oxygen uptake (1,29). Field measures of CRF and maximal oxygen uptake normalized for body mass reflect not only CRF but also adiposity and MC (39). By contrast, a cycle ergometer exercise test is relatively independent of MC (40) and does not require supporting body mass. Furthermore, we normalized CRF for LM that has been recommended to take body size and composition into account in the assessment of CRF (39). Another reason maybe that hippocampal-dependent memory and inhibition are more sensitive to changes in CRF (1) than nonverbal reasoning. Finally, some longitudinal evidence suggests that childhood cognition is a stronger determinant of adulthood cognition than adulthood CRF, suggesting that the link between cognition and CRF is neuroselective rather than neuroprotective (41).
The strengths of our study include the longitudinal study design, the large population sample of boys and girls, and the valid and comprehensive assessments of CRF, MC, adiposity, and cognition. We were also able to control the data for important confounding factors such as pubertal status and socioeconomic positioning. A weakness of the study is that we used only one measure of cognition instead of a more comprehensive testing of different components of cognition. Furthermore, this observational study was concentrated on the associations of CRF, MC, and BF% with cognition, and therefore these results cannot be used to draw causal conclusions on the effects of physical activity and dietary intervention on cognition.
In conclusion, we observed that boys in the lowest third of MC score at baseline had poorer cognition over the 2-yr follow-up than those in the middle and highest thirds of MC score at baseline. However, we found no evidence for the associations of changes in CRF, MC, or adiposity with changes in cognition during the 2-yr follow-up among boys. In girls, CRF, MC, and adiposity exhibited weak if any relationships to cognition. Our results suggest that cross-sectional differences in cognition among boys with different levels of MC persist over the 2-yr follow-up. More longitudinal studies starting from infancy are warranted to investigate the trajectories of cognitive development related to CRF, MC, and adiposity in children.
The authors are grateful to all the children and their parents for participating in the PANIC study. The authors are also indebted to the members of the PANIC research team for their skilful contribution in performing the study. The PANIC study has financially been supported by the Ministry of Education and Culture of Finland, the Research Committee of the Kuopio University Hospital Catchment Area (State Research Funding), the Finnish Innovation Fund Sitra, the Social Insurance Institution of Finland, the Finnish Cultural Foundation, the Foundation for Paediatric Research, the Diabetes Research Foundation in Finland, the Finnish Foundation for Cardiovascular Research, the Juho Vainio Foundation, the Paavo Nurmi Foundation, the Yrjö Jahnsson Foundation, and the city of Kuopio. Moreover, the PhD students and postdoctoral researchers of the PANIC study have been supported by the Program for Clinical Research and Program for Health Sciences of Doctoral School of University of Eastern Finland, the Finnish Doctoral Programs in Public Health, the Päivikki and Sakari Sohlberg Foundation, the Paulo Foundation, the Jalmari and Rauha Ahokas Foundation, the Aarne and Aili Turunen Foundation, the Finnish Medical Foundation, the Jenny and Antti Wihuri Foundation, the Kuopio Naturalists’ Society, the Olvi Foundation, the Aino Eerola and Orion Trusts of Finnish Medical Foundation, the Foundation for Diabetes Research, and the city of Kuopio. The work of Dr. Haapala was part of the University of Jyväskylä profiling area of multidisciplinary brain research funded by the Academy of Finland. The sponsors had no role in designing the study; the collection, analysis, or interpretation of the data; the writing of the report; or the decision to submit the manuscript for publication. The authors declare that there are no conflicts of interest.
The results of the present study do not constitute endorsement by the American College of Sports Medicine. The authors declare that the results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.
1. Donnelly JE, Hillman CH, Castelli D, et al. Physical activity, fitness, cognitive function, and academic achievement in children
: a systematic review. Med Sci Sports Exerc
2. Marques A, Santos DA, Hillman CH, Sardinha LB. How does academic achievement relate to cardiorespiratory fitness
, self-reported physical activity and objectively reported physical activity: a systematic review in children
and adolescents aged 6–18 years. Br J Sports Med
3. van der Fels IM, Te Wierike SC, Hartman E, Elferink-Gemser MT, Smith J, Visscher C. The relationship between motor skills
and cognitive skills in 4–16 year old typically developing children
: a systematic review. J Sci Med Sport
4. Chaddock L, Hillman CH, Pontifex MB, Johnson CR, Raine LB, Kramer AF. Childhood aerobic fitness predicts cognitive performance one year later. J Sports Sci
5. Niederer I, Kriemler S, Gut J, et al. Relationship of aerobic fitness and motor skills
with memory and attention in preschoolers (Ballabeina): a cross-sectional and longitudinal study. BMC Pediatr
6. Haapala EA, Poikkeus A-M, Tompuri T, et al. Associations of motor and cardiovascular performance with academic skills in children
. Med Sci Sports Exerc
7. Suchert V, Hanewinkel R, Isensee B. Longitudinal relationships of fitness, physical activity, and weight status with academic achievement in adolescents. J Sch Health
8. 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
9. London RA, Castrechini S. A longitudinal examination of the link between youth
physical fitness and academic achievement. J Sch Health
10. Raine LB, Biggan JR, Baym CL, Saliba BJ, Cohen NJ, Hillman CH. Adolescent changes in aerobic fitness are related to changes in academic achievement. Pediatr Exerc Sci
11. Kamijo K, Khan NA, Pontifex MB, et al. The relation of adiposity to cognitive control and scholastic achievement in preadolescent children
12. 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(1 Suppl):65–9.
13. Caird J, Kavanagh J, Oliver K, et al. Childhood Obesity and Educational Attainment: A Systematic Review
. London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London; 2011.
14. Raine LB, Khan NA, Drollette ES, Pontifex MB, Kramer AF, Hillman CH. Obesity
, visceral adipose tissue, and cognitive function in childhood. J Pediatr
15. Datar A, Sturm R. Childhood overweight and elementary school outcomes. Int J Obes (Lond)
16. Bisset S, Foumier M, Fournier M, Pagani L, Janosz M. Predicting academic and cognitive outcomes from weight status trajectories during childhood. Int J Obes (Lond)
17. Afzal AS, Gortmaker S. The relationship between obesity
and cognitive performance in children
: a longitudinal study. Child Obes
18. 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-for-age, weight-for-length/height, and body mass index-for-age. Ann Med
19. Cole T, Bellizzi M, Flegal K, Dietz W. Establishing a standard definition for child overweight and obesity
worldwide: international survey. BMJ
20. Lintu N, Tompuri T, Viitasalo A, et al. Cardiovascular fitness and haemodynamic responses to maximal cycle ergometer exercise test in children
6-8 years of age. J Sports Sci
21. European Council. EUROFIT: Handbook for the EUROFIT Tests of Physical Fitness
. Rome: Council of Europe; 1988. pp. 42–3, 56–7.
22. Haapala EA, Lintu N, Väistö J, et al. Associations of physical performance and adiposity with cognition in children
. Med Sci Sports Exerc
23. Jongbloed-Pereboom M, Nijhuis-van der Sanden MW, Steenbergen B. Norm scores of the box and block test for children
ages 3–10 years. Am J Occup Ther
24. Raven J, Raven J, Court J. Coloured Progressive Matrices. Manual for Raven’s Progressive Matrices and Vocabulary Scales
. London: Oxford Psychologist Press Ltd.; 1998.
25. Diamond A. Executive functions. Annu Rev Psychol
26. Malina RM, Bouchard C, Bar-Or O. Growth, Maturation, and Physical Activity
. 2nd ed. Champaign (IL): Human Kinetics; 2004. pp. 283–90, 350–7.
27. Moore SA, McKay HA, Macdonald H, et al. Enhancing a somatic maturity prediction model. Med Sci Sports Exerc
28. Viitasalo A, Eloranta A-M, Lintu N, et al. The effects of a 2-year individualized and family-based lifestyle intervention on physical activity, sedentary behavior and diet in children
. Prev Med
29. Santana CCA, Azevedo LB, Cattuzzo MT, Hill JO, Andrade LP, Prado WL. Physical fitness and academic performance in youth
: a systematic review. Scand J Med Sci Sports
30. Sardinha LB, Marques A, Minderico C, et al. Longitudinal relationship between cardiorespiratory fitness
and academic achievement. Med Sci Sports Exerc
31. Iverson JM. Developing language in a developing body: the relationship between motor development and language development. J Child Lang
32. Khan NA, Baym CL, Monti JM, et al. Central adiposity is negatively associated with hippocampal-dependent relational memory among overweight and obese children
. J Pediatr
33. Smith E, Hay P, Campbell L, Trollor JN. A review of the association between obesity
and cognitive function across the lifespan: implications for novel approaches to prevention and treatment. Obes Rev
34. Drollette ES, Scudder MR, Raine LB, et al. The sexual dimorphic association of cardiorespiratory fitness
to working memory in children
. Dev Sci
35. Lenroot RK, Giedd JN. Brain development in children
and adolescents: insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev
36. Chaddock L, Erickson KI, Prakash RS, et al. Basal ganglia volume is associated with aerobic fitness in preadolescent children
. Dev Neurosci
37. Dwyer T, James F, Blizzard L, Lazarus R, Dean K. Relation of academic performance to physical activity and fitness in children
. Pediatr Exerc Sci
38. Kantomaa MT, Stamatakis E, Kankaanpää 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
39. Rowland T. Oxygen uptake and endurance fitness in children
, revisited. Pediatr Exerc Sci
40. Kantomaa MT, Purtsi J, Taanila AM, 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
41. Belsky DW, Caspi A, Israel S, Blumenthal JA, Poulton R, Moffitt TE. Cardiorespiratory fitness
and cognitive function in midlife: neuroprotection or neuroselection? Ann Neurol