Low levels of physical activity among children have raised concerns over the effects of a physically inactive lifestyle, not only on physical health but also on cognitive prerequisites of learning. This study examined how objectively measured and self-reported physical activity and sedentary behavior are associated with cognitive functions in school-aged children. The study population consisted of 224 children from five schools in the Jyväskylä school district in Finland (mean age 12.2 years; 56% girls), who participated in the study in the spring of 2011. Physical activity and sedentary time were measured objectively for seven consecutive days using the ActiGraph GT1M/GT3X accelerometer. Self-reported moderate to vigorous physical activity (MVPA) and screen time were evaluated with the questions used in the “WHO Health Behavior in School-aged Children” study. Cognitive functions including visual memory, executive functions and attention were evaluated with a computerized Cambridge Neuropsychological Test Automated Battery by using five different tests. Structural equation modeling was applied to examine how objectively measured and self-reported MVPA and sedentary behavior were associated with cognitive functions. High levels of objectively measured MVPA were associated with good performance in the reaction time test. High levels of objectively measured sedentary time were associated with good performance in the sustained attention test. Objectively measured MVPA and sedentary time were not associated with other measures of cognitive functions. High amount of self-reported computer/video game play was associated with weaker performance in working memory test, whereas high amount of computer use was associated with weaker performance in test measuring shifting and flexibility of attention. Self-reported physical activity and total screen time were not associated with any measures of cognitive functions. The results of the present study propose that physical activity may benefit attentional processes. However, excessive video game play and computer use may have unfavorable influence on cognitive functions.
Citation: Syväoja HJ, Tammelin TH, Ahonen T, Kankaanpää A, Kantomaa MT (2014) The Associations of Objectively Measured Physical Activity and Sedentary Time with Cognitive Functions in School-Aged Children. PLoS ONE 9(7): e103559. doi:10.1371/journal.pone.0103559
Editor: Yoko Hoshi, Tokyo Metropolitan Institute of Medical Science, Japan
Received: January 15, 2014; Accepted: July 2, 2014; Published: July 25, 2014
Copyright: © 2014 Syväoja et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was funded by Finnish Ministry of Education and Culture (101/627/2010, http://www.minedu.fi/OPM/?lang=en) and the Academy of Finland (grant 273971). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
In past decades, our lifestyles have become increasingly inactive ; only one-third of children are sufficiently active according to current physical activity recommendations . Low levels of physical activity have raised concerns over the effects of a physically inactive lifestyle on children’s physical health and, recently, also on children’s learning, especially on cognitive prerequisites of learning .
Previous studies have shown that physical activity enhances neurocognitive function and protects against neurodegenerative diseases in elderly –. During past few years, physical activity has been linked to enhanced cognition also in children. The meta-analytic study of Sibley & Etnier , showed the significant overall positive association between physical activity and cognition in children. In addition, in the study of Ruiz et al.  leisure time physical activity was associated with better cognitive performance in adolescents. Moreover, Ardoy et al.  reported that children participating in high intensity physical education intervention improved their cognitive performance compared to control children.
Regular physical activity has been especially linked to executive functions . Executive functions (also called executive control/cognitive control) are the collection of higher-order cognitive processes controlling goal-directed actions. Core executive functions are inhibitory control (including selective attention and the inhibition of inappropriate or interfering responses), working memory and mental flexibility . School-based interventions has shown that increased physical activity may improve children’s inhibitory control , planning ability  and working memory performance , . In addition, in recent studies, children with high aerobic fitness have demonstrated better inhibitory control – as well as better working memory performance  than less-fit children.
However, evidence of the favorable effects of physical activity on cognitive functions in healthy children and adolescents is still somewhat inconsistent – and based on scarce research data . Significantly, physical fitness has often been used as a proxy indicator of regular physical activity, without direct measurement of physical activity levels. This is particularly important, because in childhood, habitual physical activity is rarely intensive and lengthy enough to enhance aerobic fitness, and therefore, the relationship between physical activity and fitness may not be meaningful . Moreover, few studies have measured a broad range of cognitive functions, highlighting the need for new studies to clarify the benefits of physical activity on different dimensions of cognitive functions.
Besides physical activity, sedentary behavior and excessive media use may be associated with cognitive function in children and youth. Extensive screen time has been linked to elevated risk of attention and learning difficulties – and decreased verbal memory performance . However, in other recent studies, screen time had no association with visuospatial cognition , and even had a positive association with enhanced attentional skills  and higher-developed language skills  in children and adolescents. Diverging research results indicates that the association of sedentary behavior and cognition is more complicated than previously believed and needs clarification.
To our knowledge, no previous studies have examined the associations of objectively measured overall physical activity and sedentary time on cognitive functions in children. However, as the rates of childhood physical inactivity are increasing worldwide, it is important to better understand the potential effects of lack of physical activity and excessive sedentary time on cognitive prerequisites of learning. The purpose of this study was to examine how objectively measured and self-reported physical activity are associated with cognitive functions in school-aged children. In addition, this study aimed to determine how objectively measured sedentary time and self-reported screen time are associated with children’s cognitive functions. We hypothesized that physical activity is positively, and both sedentary time and screen time are inversely, associated with cognitive functions.
The study was approved by the Ethics Committee of the University of Jyväskylä, and followed the principles of the Declaration of Helsinki and the Finnish legislation. Participation in the study was voluntary, and all participants had the right to drop out of the study at any time without a specific reason. Only children with a fully completed consent form (Certificate of Consent signed by a parent/guardian and the child) on the day of the first measurements were included in the study.
During spring 2011, 475 fifth and sixth graders were invited to participate in the study. 277 children (participation rate 58%) from five schools in the Jyväskylä school district in Central Finland participated in the study. 230 of the 277 children were selected to participate in cognitive tests according to successful objective measurement of physical activity. If a child’s measurement did not succeed because of technical problems or the child did not remember to wear the accelerometer, they were not invited to the cognitive tests. Seven children (3 boys, 4 girls) were excluded from analysis because, according to their parents’ survey, they had physical disabilities, chronic diseases or severe learning disabilities. The final sample size used in the analyses was 224.
Cognitive functions (Table 1) were assessed using the Neuropsychological Test Automated Battery (CANTAB) (a PaceBlade Slimbook P110 tablet PC with a 12-inch touch-screen monitor and Windows XP Professional operating system, CANTABeclipse version 3). The test battery was run individually in a silent location with the guidance of trained research assistants and in accordance with the standard instructions. The execution required about 45 minutes for each individual.
Visual memory was assessed with a Pattern Recognition Memory (PRM) test. PRM measures recognition memory of visual patterns in a two-choice forced discrimination paradigm. In this test, children had to remember the presented geometric patterns and discriminate them from the novel patterns. The score of the task is the number of correct responses.
Executive functions were assessed with Spatial Span (SSP) and Intra-Extra Dimensional Set Shift (IED) tests. SSP measures the length of the visuospatial working memory span based on the Corsi blocks task . In this test, specified number of white boxes changed their color one by one and children had to reproduce the same sequence by touching the boxes in the same order the boxes changed their color. The score of the task is the maximum number of items that the child can successfully remember in the correct order. IED is based on the Wisconsin Card Sorting test  and measures sifting and flexibility of attention. Specifically, it measures the ability to maintain attention to different stimuli within a relevant dimension, and shift attention to a previously irrelevant dimension. There are nine stages with increasing difficulty in this task. The children were instructed to choose one of the two different dimensions: one is correct, and the other is incorrect. According to immediate feedback, they were expected to choose the correct pattern and learn the rule. The score in this task is based on the number of stages completed.
The tests assessing attention were Reaction Time (RTI) and Rapid Visual Information Processing (RVP). RTI measures children’s reaction time and speed of response to a visual target. In the unpredictable five-choice condition, a yellow spot appeared randomly in one of the five circles on the screen. Children were instructed to hold down the press pad button until they saw the yellow spot and then touch the middle of the correct circle as quickly as possible. The total score of this task is the sum of reaction time (ms) and movement time (ms). RVP is similar to the Continuous Performance Task measuring sustained attention. In this test, children had to touch the press pad button every time they discriminate the target sequence (digits 3, 5, and 7) from the digits appearing in a pseudo-random order at the rate of 100 digits per minute. The score of this task is RVP A’, which measures the child’s skill at detecting target sequences (3, 5, 7) from a pseudo-random sequence of numbers (range 0.00 to 1.00; bad to good).
Internal reliability, assessed with Cronbach’s alpha reliability coefficients, was 0.49 for the RVP A’, 0.65 for the PRM number of correct responses, 0.66 for the RTI five-choice reaction time and 0.87 for the RTI five-choice movement time. For hampering tests (SSP and IED), Cronbach’s alpha could not be determined.
Objectively measured physical activity and sedentary time
The ActiGraph GT1M/GT3X accelerometers with vertical axel were used to measure children’s moderate to vigorous physical activity (MVPA) and sedentary time. The accelerometer was worn on the right hip with an elastic waistband during waking hours for seven consecutive days. During bathing, swimming, and other water activities, the monitor was requested to be removed because it was not water-resistant. The ActiLife accelerometer software (ActiLife version 5; http://support.theactigraph.com/dl/ActiLife-software) was used to collect the data. Epoch length was 10 seconds and non-wearing time 30 minutes. Customized software was used for data reduction and analysis. A cut-off value of 2,296 counts per minute was used for MVPA , and 100 counts per minute for sedentary time. Children were included in the analysis if they had valid data for at least 500 minutes per day on two weekdays and on one weekend day. In order to compare children, who had worn the accelerometers for different amounts of time per day, objectively measured sedentary time was expressed as percentage of daily registration time.
Self-reported physical activity and screen time
Physical activity and screen time were assessed with a self-reported questionnaire used earlier in the WHO Health Behavior in School-aged Children (HBSC) study . Self-reported MVPA was measured with the following question: “Over the past 7 days, on how many days were you physically active for a total of at least 60 minutes per day?” The response categories were as follows: 0 days, 1 day, 2 days, … 7 days. There was a short description about what kind of physical activity should be taken into account when answering the question: “In the next question, physical activity is defined as any activity that increases your heart rate and makes you get out of breath some of the time.” Examples included running, walking quickly, rollerblading, biking, dancing, skateboarding, swimming, snowboarding, cross-country skiing, soccer, basketball, and Finnish baseball. Test-retest agreement for self-reported MVPA has been very good (ICC = 0.82) , . Self-reported screen time was evaluated with the question: “About how many hours a day do you usually a) watch television (including videos), b) play computer or video games, or c) use a computer (for purposes other than playing games, for example, emailing, chatting, or surfing the Internet or doing homework) in your free time?” The response options were as follows: not at all, about half an hour per day, about an hour a day, about two hours per day, … about five hours per day or more. Children responded separately for both weekdays and weekends. Test-retest agreement for watching television (ICC = 0.72–0.74) and for playing computer or video games (ICC = 0.54–0.69) has been substantial, and fair to moderate (ICC = 0.33–0.50) for using the computer . Total daily screen time averages were calculated by adding these three questions, including weekdays and weekends, together.
The parent or the child’s main caregiver filled in a questionnaire including the mother and father’s education, family income, marital status, and children’s learning difficulties and need for remedial education. The highest level of parental education, which was calculated from the mother’s and father’s education, was categorized as tertiary level education (1) and basic or upper secondary education (0). Marital status of the main caregiver was categorized as married or cohabiting (1) and divorced or single/widow (0). Children’s learning difficulties and need for remedial education were categorizes as yes (1) and no or don’t know (0).
The SPSS 19.0 for Windows statistical package (SPSS (2010) IBM SPSS Statistics 19 Core System User’s Guide (SPSS Inc., Chicago, IL)) and the Mplus statistical package (Version 7; Muthèn & Muthèn, 1998–2012)  were used for the statistical analyses. Pearson’s correlation coefficients were calculated to estimate preliminary associations between objectively and subjectively measured physical activity, sedentary behavior, cognitive tests and potential confounders. Structural equation modeling was applied to examine physical activity and sedentary time in association with cognitive function. Structural equation modeling was used because it enables to estimate the measurement errors away and, therefore, increases the reliability of cognitive tests. Single scores of every problem or level of the tests were applied instead of the total score. Item parcels  were constructed to combine the large number of these single scores for each latent factor. These parcels were then used as indicators for latent variables. Multiple logistic regression was used to examine physical activity and sedentary time in association with cognitive function, when the outcome was dichotomous. Full information maximum likelihood (FIML) estimation with robust standard errors (MLR) was used under the assumption of data missing at random. Gender, the highest level of parental education and child’s need for remedial education were chosen to represent different aspects of potential confounders and were added to the main analysis. In order to avoid multicollinearity, highly correlated objectively measured MVPA and sedentary time were added to the model using a Cholesky factoring of the predictors . The Satorra–Bentler scaled χ2-test, the comparative fit index (CFI), the Tucker–Lewis Index (TLI), the root mean square error of approximation (RMSEA) and the standardized root-mean-square residual (SRMR) were used to evaluate the goodness-of-fit of the models. The model fits the data well if the p-value for the χ2-test is non-significant, CFI and TLI values are close to 0.95, the RMSEA value is below 0.06 and the SRMR value is below 0.08 .
The mean age of the children was 12.2 years and 56% of the children were girls (Table 2). 71% of children’s mothers and 56% of children’s fathers had tertiary level education, and 76% of parents were married or cohabiting. 5% of children had a diagnosed learning difficulty and 13% of children needed remedial education, according to their parent’s reports.
Based on objective physical activity measurements, children had, on average, 58 minutes of MVPA per day, with no statistically significant gender difference (Table 2). However, girls spent more of their waking hours sedentary than boys (Table 2). Based on self-reports, children reported at least 60 minutes of MVPA a day for 5 days per week on average, with no significant difference between boys and girls (Table 2). Boys reported more total screen time than girls, especially they spent more time playing computer or videogames than girls (Table 2). In cognitive tests, boys were faster than girls in the RTI test, but no significant gender differences were observed in performance in the PRM, SSP, RVP or IED tests.
For structural equation modeling, three subscales of RTI were formed from 15 individual patterns of the RTI, and each subscale divided by 10. Each subscale loaded on the hypothesized factor. The model for the associations of objectively measured MVPA, sedentary time (SED) and performance in the Reaction Time (RTI) test is presented in Figure 1. The goodness-of-fit statistics of the model were good (χ2 (10) = 14.52 p = 0.763, CFI = 0.979, TLI = 0.948, RMSEA = 0.045, SRMR = 0.018). Objectively measured MVPA was negatively associated with the RTI five-choice test score (ms), whereas objectively measured sedentary time was not associated with the RTI five-choice test score after adjusting for gender, the highest level of parental education and child’s need for remedial education (Table 3).
This figure presents the estimation results of the model for the associations of objectively measured moderate to vigorous physical activity (MVPA), sedentary time (SED) and the Reaction Time (RTI) test. Standardized parameter estimates and standard errors are presented. For structural equation modeling, three subscales of the RTI (RTI 1, RTI 2, RTI 3) (divided by 10) were formed from 15 patterns of RTI. The RTI test result is in milliseconds, where faster time indicates better performance. Confounding factors, gender (female), the highest level of parental education (tertiary level) and child’s need for remedial education (yes) were taken into account. Highly correlated objectively measured MVPA and sedentary time were added to the model as latent variables.
The RVP A’ (multiplied by 10) scores of the three blocks of the test were used as indicator variables in the structural equation modeling. Each block loaded on the hypothesized factor. The model for the associations of objectively measured MVPA, sedentary time (SED) and performance in the Rapid Visual Information Processing (RVP) test is presented in Figure 2. The goodness-of-fit statistics of the model were good (χ2 (10) = 9.45 p = 0.490, CFI = 1.000, TLI = 1.010, RMSEA = 0.000, SRMR = 0.028). Objectively measured sedentary time was positively associated with RVPA’, whereas objectively measured MVPA was not associated with RVPA’ after adjusting for gender, the highest level of parental education and child’s need for remedial education (Table 3).
This figure presents the estimation results of the model for the associations of objectively measured MVPA, sedentary time (SED) and the Rapid Visual Information Processing (RVP) test. Standardized parameter estimates and standard errors are presented. The RVP (multiplied by 10) of the three blocks of the test (RVP 1, RVP 2, RVP 3) were used as indicator variables in the structural equation modeling. The scale for the RVP test result (A’) is 0.00–1.00, where 0.00 indicates a poor result and 1.00 a good result. Confounding factors, gender (female), the highest level of parental education (tertiary level) and child’s need for remedial education (yes) were taken into account. Highly correlated objectively measured MVPA and sedentary time were added to the model as latent variables.
Self-reported playing of computer/video games was negatively associated with SSP span length after adjusting for gender, the highest level of parental education and child’s need for remedial education (Table 3). The model was fully saturated. Self-reported use of computer for other purposes than playing was negatively associated with IED number of children who completed the test (Table 4).
According to the results of this study, a high level of objectively measured MVPA was associated with good performance in the reaction time test (RTI), which measures children’s reaction time and the speed of response to a visual target. In addition, a high level of objectively measured sedentary time was associated with good performance in the sustained attention test (RVP). However, objectively measured physical activity or sedentary time were not associated with any other assessments of cognitive functions. High amount of self-reported playing of computer or video games was associated with weaker performance in Spatial Span (SSP) test measuring visuospatial working memory. Moreover, self-reported use of computer for other purposes than playing was negatively associated with performance in Intra-Extra Dimensional Set Shift (IED) test, which measures sifting and flexibility of attention. Self-reported physical activity, total screen time or television viewing had no association with any of the cognitive tests measuring visual memory, executive functions or attention.
Physical activity and cognitive functions
In our study, a high level of objectively measured physical activity was associated with better performance in attentional reaction time test. Recent study of Spitzer and Hollmann  supports our finding by reporting that implementation of physical activity had positive effects on children’s attention. In addition, recent intervention study of Chaddock-Heyman et al.  showed that physical activity enhances children’s performance in inhibitory control task requiring selective attention and inhibition of interfering responses (e.g. the Eriksen flanker task; ). Similarly, according to Castelli et al.  engagement in vigorous physical activity was positively associated with performance in inhibitory control task. However, in the studies of Fisher et al. , Davis et al.  and Puder et al.  physical activity intervention had no effect on children’s attentional processes. Previous studies have also reported that physically fit children outperform their less fit peers inhibitory control task , , , but also no differences in inhibitory control performance between physically fit and less fit children , .
Physical activity may enhance attentional processes and other cognitive functions through different mechanisms. It has been suggested that physical activity may improve brain volume in regions supporting executive functions , ; produce specific changes in the activity patterns in the brains , ; increase the levels of brain-derived neurotrophic factor (BDNF) ,  and enhance cerebrovascular function ; all of which may mediate the effects of physical activity on cognition. In addition, motor function has shown to be closely connected to children’s cognitive and academic skills and development , , and may be an important factor driving the effects of physical inactivity on cognitive prerequisites of learning . Furthermore, obesity has been related to poorer academic  and cognitive performance  and may, thereby, be one factor mediating the association of physical activity and cognition. Moreover, participation in physical activities is often a social phenomenon offering opportunities for interaction with other children and adults, and this interaction may also have a significant impact on children’s cognitive development and learning. However, this has rarely been taken into account in research . Finally, physical activity may facilitate cognitive function through the cognitive demands inherent in the structure of goal-directed and engaging exercise .
In the present study, neither objectively measured nor self-reported physical activity was associated with visual memory, working memory, sifting and flexibility of attention or sustained attention performance. These results are in line with studies showing that physical activity is not necessary associated with all domains of cognitive functions –, , . These diverging results indicate the importance of future studies to define these associations. Some of the cognitive tests used in the present study were not able to differentiate healthy 12-year-old children. Neuropsychological test batteries have originally been developed to detect neurocognitive deficits, and due to that, the tests may be too easy for healthy children. In the present study, particularly in the tests of visual memory (PRM), sifting and flexibility of attention (IED) and sustained attention (RVP), children, on average, achieved very high results, which may partly explain the lack of association between physical activity and cognitive test results. Additionally, Stroth et al.  speculated that one reason behind the result of aerobic fitness not being associated with children’s performance in cognitive control tasks may be the ceiling effect.
In previous studies, aerobic fitness has often been used as a proxy measure of regular physical activity, which might contribute to diverging results: physical activity is behavior, which increases energy expenditure and occurs within a cultural context, while physical fitness is an adaptive state of the human body, affected by heritable and environmental factors and physical activity. Especially in childhood, both the levels of physical activity and physical fitness may vary independently of each other due to growth, maturation and aging , . In addition, aerobic fitness measures are often confounded by adiposity and obesity in childhood , which are also potential factors attenuating cognitive and academic performance . Moreover, the definitions, patterns and measurements of cognitive functions and physical activity have varied across different studies. Therefore, it is difficult to directly compare and summarize the results of earlier studies and determine the specific benefits regular physical activity may have on cognitive functions.
Our finding that only objectively measured physical activity was associated with attentional reaction time may reflect the difference between objective and subjective measurements of physical activity. Accelerometer-measured MVPA mainly illustrates cardiovascular activity with increased heart rate and respiratory frequency, while self-reported physical activity may represent different constructs and contexts. Self-reported physical activity may include skill-specific types of physical activities, which require balance and agility but hardly accumulate activity counts . Our results may indicate that moderate to vigorous intensity exercise that increases cardiovascular function has benefits on attentional processes.
Sedentary behavior and cognitive functions
Previous studies have largely suggested that screen-based sedentary behaviors have an unfavorable effect on children’s cognition, especially on attention and learning difficulties –. The results of the present study supports the previous results by showing a negative association between self-reported computer/video game playing and visuospatial working memory as well as negative association between self-reported use computer and shifting and flexibility of attention. Drowak et al. , reported also declines in verbal memory performance after computer game exposure. However, some previous studies have reported that screen-based sedentary behavior, especially videogames, is linked to enhanced cognitive skills –. According to Dye and Matthew , children who used to play video games had faster reaction times in attention control tests (ANT) without a notable loss in accuracy compared to non-players, indicating that action game players made faster correct responses to targets and had more resources to process distractions. Bittman et al.  reported that computer use was associated with higher-developed language skills.
In the present study, some children spent excessive amounts of time in front of the screens on their free time: one fifth of the children reported having screen time about 5 or more hours per day. Excessive amounts of screen time may displace activities involving learning opportunities and increase children’s impulsive behavior, and eventually decrease academic skills .
Disadvantages, but also benefits of screen-based sedentary behavior on cognitive functions may be explained by the content of the screen time: not all screen time has an equal role in benefitting or impairing children’s cognitive skills and learning , . For example, Ennemoser & Schneider  reported that educational program viewing was positively, but entertainment program viewing negatively, correlated with reading speed and comprehension in children. In the study of Feng et al. , action game training in young adults improved performance and attenuated the gender differences favoring males in spatial attention test, while control subjects who played a non-action game showed no improvements. According to Kuhn et al. , video game playing may induce structural brain plasticity in the areas important to spatial navigation, strategic planning, working memory and motor performance. On the other hand, in the study of Drowak et al. , interactive computer game play resulted significant declines in verbal memory performance and slow wave sleep, which is important for memory consolidation, whereas viewing exciting films had no effects. Finally, it should be kept in mind that computer-based cognitive assessments may require similar cognitive skills as video and computer games, which would favor children who play a lot of video and computer games.
Most of the previous studies have used self-reported methods assessing sedentary behavior. In the present study, objectively measured sedentary time was positively associated with sustained attention; children who spent more time being sedentary achieved higher scores in sustained attention test. Objectively measured sedentary time is a summary measure of all kinds of sedentary behaviors, including various activities such as screen time, reading, doing homework, interaction with friends, et cetera. During some sedentary activities, such as reading and doing homework, sustained attention and distraction exclusion are needed, which may explain objectively measured sedentary time being associated with good performance in sustained attention test.
In the present study, however, objectively measured sedentary time was not associated with visual memory, working memory, set-sifting/mental flexibility or attentional reaction time. In addition, total screen time or television viewing were not associated with cognition. Neither did Ferguson et al.  observe any association between video game playing and visuospatial cognition. Moreover, in two recent studies , , television viewing was not associated with cognitive and language skills after adjusting for the parent’s role in monitoring and involvement in the child’s media use.
Strength and limitations of the study
To our knowledge, this was the first study examining the association of objectively measured overall physical activity and sedentary time with cognitive functions in children. Conclusions regarding causality of the observed associations cannot be drawn due to the cross-sectional design. In addition, the content of sedentary time and screen-based sedentary behavior was not assessed, which limits the interpretation of the results concerning sedentary behavior. Moreover, some cognitive tests used in the present study may have been too easy for 12-year-old healthy children, and did not optimally discriminate children’s performance. Furthermore, pubertal timing, motor skills and fitness were not assessed, which limits the interpretation of the results.
Studies with longitudinal designs or randomized controlled trials are needed to clarify the effects of total physical activity and sedentary behavior on cognitive prerequisites of learning. Future studies should assess what kind of physical activity or sedentary behavior affects specific kinds of cognition, as well as the mechanisms behind these associations. In the future, social interaction and context-related factors should be considered to be taken into account. In addition, the cognitive tests should be chosen so as to measure a wide range of cognitive performance.
In this study, objectively measured physical activity and sedentary time were positively associated with attentional processes, but not with other domains of cognitive functions. Self-reported computer/video game play was negatively associated with visuospatial working memory, whereas computer use was negatively associated with shifting and flexibility of attention. Self-reported physical activity and total screen time were not associated with any of the cognitive tests measuring visual memory, executive functions or attention in children. The results of the present study propose that physical activity may benefit attentional processes. However, excessive video game play and computer use may have unfavorable influence on cognitive functions.
Conceived and designed the experiments: HJS THT TA MTK. Performed the experiments: HJS THT MTK. Analyzed the data: HJS THT AK MTK. Contributed reagents/materials/analysis tools: HJS THT TA AK MTK. Wrote the paper: HJS. Gave critical input on all versions of the manuscript: HJS THT TA AK MTK. Approved the final version of the manuscript: HJS THT TA AK MTK.
- 1. Pate RR, Mitchell JA, Byun W, Dowda M (2011) Sedentary behaviour in youth. Br J Sports Med 45: 906–913 doi: 10.1136/906 bjsports-2011-090192.
- 2. Ekelund U, Tomkinson G, Armstrong N (2011) What proportion of youth are physically active? Measurement issues, levels and recent time trends. Br J Sports Med 45: 859–865 doi: 10.1136/bjsports-2011-090190.
- 3. Hillman CH, Kamijo K, Scudder M (2011) A review of chronic and acute physical activity participation on neuroelectric measures of brain health and cognition during childhood. Prev Med 52: S21–S28 doi: 10.1016/j.ypmed.2011.01.024.
- 4. Erickson KI, Voss MW, Prakash RS, Basak C, Szabo A, et al. (2011) Exercise training increases size of hippocampus and improves memory. Proc Natl Acad Sci U S A 108: 3017–3022 doi: 10.1073/pnas.1015950108.
- 5. Lautenschlager NT, Cox KL, Flicker L, Foster JK, van Bockxmeer FM, et al. (2008) Effect of physical activity on cognitive function in older adults at risk for alzheimer disease: A randomized trial. JAMA 300: 1027–1037 doi: 10.1001/jama.300.9.1027.
- 6. Kramer AF, Erickson KI (2007) Effects of physical activity on cognition, well-being, and brain: Human interventions. Alzheimers Dement 3: S45–S51 doi: 10.1016/j.jalz.2007.01.008.
- 7. Sibley BA, Etnier JL (2003) The relationship between physical activity and cognition in children: A meta-analysis. Pediatr Exerc Sci 15: 243–256.
- 8. Ruiz JR, Ortega FB, Castillo R, Martín-Matillas M, Kwak L, et al. (2010) Physical activity, fitness, weight status, and cognitive performance in adolescents. J Pediatr 157: 917–922 doi: 10.1016/j.jpeds.2010.06.026.
- 9. Ardoy D, Fernández-Rodríguez J, Jiménez-Pavón D, Castillo R, Ruiz J, et al. (2014) A physical education trial improves adolescents’ cognitive performance and academic achievement: The EDUFIT study. Scand J Med Sci Sports 24: e52–e61 doi: 10.1111/sms.12093.
- 10. Guiney H, Machado L (2013) Benefits of regular aerobic exercise for executive functioning in healthy populations. Psychon Bull Rev 20: 73–86 doi: 10.3758/s13423-012-0345-4.
- 11. Diamond A (2013) Executive functions. Annu Rev Psychol 64: 135–168 doi: 10.1146/annurev-psych-113011-143750.
- 12. Chaddock-Heyman L, Erickson KI, Voss MW, Knecht AM, Pontifex MB, et al.. (2013) The effects of physical activity on functional MRI activation associated with cognitive control in children: A randomized controlled intervention. Front Hum Neurosci 7. doi: 10.3389/fnhum.2013.00072.
- 13. Davis C, Tomporowski P, McDowell J, Austin B, Miller P, et al. (2011) Exercise improves executive function and achievement and alters brain activation in overweight children: A randomized, controlled trial. Health Psychol 30: 91–98 doi: 10.1037/a0021766.
- 14. Fisher A, Boyle J, Paton J, Tomporowski P, Watson C, et al. (2011) Effects of a physical education intervention on cognitive function in young children: Randomized controlled pilot study. BMC Pediatr 11: 97 doi: 10.1186/1471-2431-1-97.
- 15. Kamijo K, Pontifex M, O’Leary K, Scudder M, Wu C, et al.. (2011) The effects of an afterschool physical activity program on working memory in preadolescent children. Dev Sci: 1–13. doi: 10.1111/j.1467-7687.2011.01054.x.
- 16. Chaddock L, Hillman CH, Pontifex MB, Johnson CR, Raine LB, et al. (2012) Childhood aerobic fitness predicts cognitive performance one year later. J Sports Sci 30: 421–430 doi: 10.1080/02640414.2011.647706.
- 17. Chaddock L, Erickson KI, Prakash RS, Voss MW, VanPatter M, et al. (2012) A functional MRI investigation of the association between childhood aerobic fitness and neurocognitive control. Biol Psychol 89: 260–268 doi: 10.1016/j.biopsycho.2011.10.017.
- 18. Pontifex MB, Raine LB, Johnson CR, Chaddock L, Voss MW, et al. (2011) Cardiorespiratory fitness and the flexible modulation of cognitive control in preadolescent children. J Cogn Neurosci 23: 1332–1345 doi: 10.1162/jocn.2010.21528.
- 19. Chaddock L, Erickson K, Prakash R, Kim J, Voss M, et al. (2010) A neuroimaging investigation of the association between aerobic fitness, hippocampal volume, and memory performance in preadolescent children. Brain Res 1358: 172–183 doi: 10.1016/j.brainres.2010.08.049.
- 20. Puder J, Marques-Vidal P, Schindler C, Zahner L, Niederer I, et al. (2011) Effect of multidimensional lifestyle intervention on fitness and adiposity in predominantly migrant preschool children (ballabeina): Cluster randomised controlled trial. BMJ 343: d6195 doi: 10.1136/bmj.d6195.
- 21. Reed JA, Maslow AL, Long S, Hughey M (2012) Examining the impact of 45 minutes of daily physical education on cognitive ability, fitness performance, and body composition of African American youth. J Phys Act Health 10: 185–197.
- 22. Stroth S, Kubesch S, Dieterle K, Ruchsow M, Heim R, et al. (2009) Physical fitness, but not acute exercise modulates event-related potential indices for executive control in healthy adolescents. Brain Res 1269: 114–124 doi:10.1016/j.brainres.2009.02.073.
- 23. Armstrong N, Tomkinson G, Ekelund U (2011) Aerobic fitness and its relationship to sport, exercise training and habitual physical activity during youth. Br J Sports Med 45: 849–858 doi: 10.1136/bjsports-2011-090200.
- 24. Johnson JG, Cohen P, Kasen S, Brook JS (2007) Extensive television viewing and the development of attention and learning difficulties during adolescence. Arch Pediatr Adolesc Med 161: 480–486. doi: 10.1001/archpedi.161.5.480
- 25. Landhuis CE, Poulton R, Welch D, Hancox RJ (2007) Does childhood television viewing lead to attention problems in adolescence? Results from a prospective longitudinal study. Pediatrics 120: 532–537 doi: 10.1542/peds.2007-0978.
- 26. Swing EL, Gentile DA, Anderson CA, Walsh DA (2010) Television and video game exposure and the development of attention problems. Pediatrics 126: 214–221 doi: 10.1542/peds.2009-1508.
- 27. Weis R, Cerankosky BC (2010) Effects of video-game ownership on young boys’ academic and behavioral functioning: A randomized, controlled study. Psychol Sci 21: 463–470 doi: 10.1177/0956797610362670.
- 28. Dworak M, Schierl T, Bruns T, Strüder HK (2007) Impact of singular excessive computer game and television exposure on sleep patterns and memory performance of school-aged children. Pediatrics 120: 978–985 doi: 10.1542/peds.2007-0476.
- 29. Ferguson CJ, Garza A, Jerabeck J, Ramos R, Galindo M (2012) Not worth the fuss after all? Cross-sectional and prospective data on violent video game influences on aggression, visuospatial cognition and mathematics ability in a sample of youth. J Youth Adolesc 42: 109–122 doi: 10.1007/s10964-012-9803-6.
- 30. Dye MW, Green CS, Bavelier D (2009) Increasing speed of processing with action video games. Curr Dir Psychol Sci 18: 321–326 doi: 10.1016/j.neuropsychologia.2009.02.002.
- 31. Bittman M, Rutherford L, Brown J, Unsworth L (2011) Digital natives? New and old media and children’s outcomes. AJE 55: 161–175. doi: 10.1177/000494411105500206
- 32. Milner B (1971) Interhemispheric differences in the localization of psychological processes in man. Br Med Bull 27: 272–277.
- 33. Mishkin M, Appenzeller T (1987) The anatomy of memory. Sci Am 256: 80–89 doi: 10.1038/scientificamerican0687-80.
- 34. Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG (2006) Calibration of two objective measures of physical activity for children. J Sports Sci 24: 1557–1565 doi: 10.1080/02640410802334196.
- 35. Currie C, Zanotti C, Morgan A, Currie D, De Looze M, et al.. (2012) 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. Health policy for children and adolescents. Available: http://www.euro.who.int/__data/assets/pdf_file/0003/163857/Social-determinants-of-health-and-well-being-among-young-people.pdf. Assessed 2014 May 6.
- 36. Booth M, Okely A, Chey T, Bauman A (2001) The reliability and validity of the physical activity questions in the WHO health behaviour in schoolchildren (HBSC) survey: A population study. Br J Sports Med 35: 263–267 doi: 10.1136/bjsm.35.4.263.
- 37. Liu Y, Wang M, Tynjälä J, Lv Y, Villberg J, et al. (2010) Test-retest reliability of selected items of health behaviour in school-aged children (HBSC) survey questionnaire in Beijing, china. BMC Med Res Methodol 10: 73 doi: 10.1186/1471-2288-10-73.
- 38. Muthén L, Muthén B, editors (1998–2012) Mplus User’s guide. Seventh edition. Los Angeles: CA: Muthén & Muthén.
- 39. Little TD, Cunningham WA, Shahar G, Widaman KF (2002) To parcel or not to parcel: Exploring the question, weighing the merits. Struct Equ Modeling 9: 151–173 doi:_10.1207/S15328007SEM0902_1.
- 40. de Jong PF (1999) Hierarchical regression analysis in structural equation modeling. Struct Equ Modeling 6: 198–211 doi: 10.1080/10705519909540128.
- 41. Hu L, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling 6: 1–55 doi: 10.1080/10705519909540118.
- 42. Spitzer US, Hollmann W (2013) Experimental observations of the effects of physical exercise on attention, academic and prosocial performance in school settings. Trends Neurosci Educ 2: 1–6 doi: 10.1016/j.tine.2013.03.002.
- 43. Eriksen BA, Eriksen CW (1974) Effects of noise letters upon the identification of a target letter in a nonsearch task. Percept Psychophys 16: 143–149. doi: 10.3758/bf03203267
- 44. Castelli D, Hillman C, Hirsch J, Hirsch A, Drollette E (2011) FIT kids: Time in target heart zone and cognitive performance. Prev Med 52: S55–S59 doi: 10.1016/j.ypmed.2011.01.019.
- 45. Hillman CH, Buck SM, Themanson JR, Pontifex MB, Castelli DM (2009) Aerobic fitness and cognitive development: Event-related brain potential and task performance indices of executive control in preadolescent children. Dev Psychol 45: 114–129 doi: 10.1037/a0014437.
- 46. Chaddock L, Erickson K, Prakash R, VanPatter M, Voss M, et al. (2010) Basal ganglia volume is associated with aerobic fitness in preadolescent children. Dev Neurosci 32: 249–256 doi: 10.1159/000316648.
- 47. Gomez-Pinilla F, Hillman C (2013) The influence of exercise on cognitive abilities. Compr Physiol 3: 403–428 doi: 10.1002/cphy.c110063.
- 48. Hopkins M, Davis F, Vantieghem M, Whalen P, Bucci D (2012) Differential effects of acute and regular physical exercise on cognition and affect. Neuroscience 215: 59–68 doi: 10.1016/j.neuroscience.2012.04.056.
- 49. Brown AD, McMorris CA, Longman RS, Leigh R, Hill MD, et al. (2010) Effects of cardiorespiratory fitness and cerebral blood flow on cognitive outcomes in older women. Neurobiol Aging 31: 2047–2057 doi:10.1016/j.neurobiolaging.2008.11.002.
- 50. Haapala EA, Poikkeus A, Tompuri T, Kukkonen-Harjula K, Leppänen P, et al.. (2013) Associations of motor and cardiovascular performance with academic skills in children. Med Sci Sports Exerc: In press. doi: 10.1249/MSS.0000000000000186.
- 51. Iverson JM (2010) Developing language in a developing body: The relationship between motor development and language development*. J Child Lang 37: 229 doi: 10.1017/S0305000909990432.
- 52. Kantomaa MT, Stamatakis E, Kankaanpää A, Kaakinen M, Rodriguez A, et al. (2013) Physical activity and obesity mediate the association between childhood motor function and adolescents’ academic achievement. PNAS 110: 1917–1922 doi:−10.1073/pnas.1214574110/−/DCSupplemental.
- 53. Burkhalter TM, Hillman CH (2011) A narrative review of physical activity, nutrition, and obesity to cognition and scholastic performance across the human lifespan. Adv Nutr 2: 201S–206S doi: 10.3945/an.111.000331.
- 54. Hillman CH, Erickson KI, Kramer AF (2008) Be smart, exercise your heart: Exercise effects on brain and cognition. Nat Rev Neurosci 9: 58–65 doi:10.1038/nrn2298.
- 55. Best JR (2010) Effects of physical activity on children’s executive function: Contributions of experimental research on aerobic exercise. Dev Rev 30: 331–351 doi: 10.1016/j.dr.2010.08.001.
- 56. Malina RM (1996) Tracking of physical activity and physical fitness across the lifespan. Res Q Exerc Sport 67: S48–S57. doi 10.1080/02701367.1996.10608853.
- 57. Rowland T (2013) Oxygen uptake and endurance fitness in children, revisited. Pediatr Exerc Sci 25: 508–514.
- 58. Syväoja HJ, Kantomaa MT, Ahonen T, Hakonen H, Kankaanpää A, et al. (2013) Physical activity, sedentary behavior, and academic performance in Finnish children. Med Sci Sports Exerc 45: 2098–2104 doi: 10.1249/MSS.0b013e318296d7b8.
- 59. Boot WR, Blakely DP, Simons DJ (2011) Do action video games improve perception and cognition? Frontiers in Psychology 2. doi: 10.3389/fpsyg.2011.00226.
- 60. Spence I, Feng J (2010) Video games and spatial cognition. Rev Gen Psychol 14: 92–104 doi: 10.1037/a0019491.
- 61. Granic I, Lobel A, Engels RC (2013) The benefits of playing video games. Am Psychol 69: 66–78 doi: 10.1037/a0034857.
- 62. Shin N (2004) Exploring pathways from television viewing to academic achievement in school age children. J Genet Psychol 165: 367–381 doi: 10.3200/GNTP.165.4.367-382.
- 63. Kirkorian HL, Wartella EA, Anderson DR (2008) Media and young children’s learning. Future Child 18: 39–61 doi: 10.1353/foc.0.0002.
- 64. Schmidt ME, Vandewater EA (2008) Media and attention, cognition, and school achievement. Future Child18: 63–85 doi: 10.1353/foc.0.0004.
- 65. Ennemoser M, Schneider W (2007) Relations of television viewing and reading: Findings from a 4-year longitudinal study. J Educ Psychol 99: 349–368 doi: 10.1037/0022-06126.96.36.1999.
- 66. Feng J, Spence I, Pratt J (2007) Playing an action video game reduces gender differences in spatial cognition. Psychol Sci 18: 850–855 doi: 10.1111/j.1467-9280.2007.01990.x.
- 67. Kühn S, Gleich T, Lorenz R, Lindenberger U, Gallinat J (2013) Playing super mario induces structural brain plasticity: Gray matter changes resulting from training with a commercial video game. Mol Psychiatry 19: 265–271 doi: 10.1038/mp.2013.120.
- 68. Munasib A, Bhattacharya S (2010) Is the ‘Idiot’s Box’raising idiocy? Early and middle childhood television watching and child cognitive outcome. Econ Educ Rev 29: 873–883 doi: 10.1016/j.econedurev.2010.03.005.