Effects of Physical Exercise on Executive Functions
in Children, Adolescents and Young Adults: A
Multilevel Meta-Analysis
Mara Wierstra, 10005676
Supervisors: Dr. Joyce Weeland, Prof. Dr. Geertjan Overbeek Master Thesis Research Master Child Development and Education Number of words (without tables, figures and appendices): 6,842 Number of words abstract: 249
2 Abstract
Physical exercise does not only have positive effects on physical and mental health but also on executive functions. Therefore, it is alarming that only a minority of children engage in one-hour physical exercise each day, as recommended by the WHO (2015). To date, one meta-analysis reported on the beneficial effects of acute physical exercise (i.e., a single exercise bout) on executive functions in children, adolescents and young adults (Verburgh et al., 2013). However, an update is justified by the recent growth of research in this area, allowing to test the stability of prior results and investigate moderators, such as the diagnosis of ADHD. A systematic search of the databases Medline, PsycINFO, SPORTDiscus, Embase and Google Scholar (2011 – 2016) yielded 45 studies consisting of 49 samples, 1,915
participants (Mage = 16.44; SD = 5.86; 41% female) and 138 effect sizes. Acute physical
exercise had a small, but significant effect (Cohen’s d = 0.16, 95% CI [0.07, 0.25]) on executive functions. The multilevel approach showed significant variation in effect sizes between samples, test scores, and around the estimated population effect. However, specified moderators (i.e., length and type of exercise condition, age group, diagnosis of ADHD, type of test score and type of executive function) could not explain this significant variation in effect sizes. Future research should investigate moderators, such as fitness of the participants and exercise intensity. Overall, the present study suggests that acute physical exercise may be a useful tool to enhance executive functions in children, adolescents and young adults.
3 Acknowledgement
Hereby I would like to expand on my thesis process. First, I have studied the literature on physical exercise and executive functions resulting in a research proposal. Then, with the help of dr. Janneke Staaks, I have written a search strategy. Subsequently, I have performed the data collection, coding of the articles, and analyses. Throughout the process of stating research questions, collecting data, coding the articles, doing the analyses and writing the results, dr. Joyce Weeland and Prof. dr. Geertjan Overbeek provided me with extensive and valuable feedback. Although my thesis process has not always been flawless, I would like to thank dr. Joyce Weeland and Prof. dr. Geertjan Overbeek for providing me with the
opportunity to learn doing research. My supervisors allowed me to develop many research skills for improving the process and the content of my thesis, for which I am very thankful. Finally, I would like to thank the authors who have shared their data with me.
4 Effects of Physical Exercise on Executive Functions in Children, Adolescents and
Young Adults: A Multilevel Meta-Analysis
Worldwide, only a minority of children engage in one hour moderate-to-vigorous physical exercise each day, as has been recommended by the World Health Organization (2015). This is alarming, since physical exercise does not only have positive effects on physical and mental health (Chaput et al., 2008; Janssen & LeBlanc, 2010; Josefsson, Lindwall, & Archer, 2014), but also on executive functions (Barenberg, Berse, & Dutke, 2011; Colcombe & Kramer, 2003; Verburgh, Königs, Scherder, & Oosterlaan, 2013). The term executive functions refers to meta-cognitive functions managing basic cognitive processes, regulating emotions and attention necessary for purposeful behavior (Etnier & Chang, 2009). That is, the ability to direct behavior consciously to an intended goal, without being led by automatic responses (Diamond, 2013). Executive functions are related to a large variety of positive outcomes, such as academic achievement (Best, Miller, & Naglieri, 2011; Biederman et al., 2004; St Clair-Thompson & Gathercole, 2006), job success (Bailey, 2007), and lower rates of social inappropriateness, depression, risk-taking behavior and problem gambling (Best, Miller, & Jones, 2009). Given the relevance of executive functions in diverse contexts, efforts to enhance their effectiveness are warranted.
For a better understanding of the effects of acute physical exercise (i.e., a single short bout of physical exercise) on executive functions, it is important to get more insight into the physiological processes that underlie these changes. In the literature, several physiological hypotheses have been raised to explain the beneficial effects of acute physical exercise on executive functions, among which increases in Brain-Derived Neurotropic Factor (BDNF) and increased activation of brain areas that are associated with executive functioning. In the first hypothesis, acute physical exercise increases BDNF which supports the survival and differentiation of neurons in the developing brain, and is associated with improved cognitive
5 functioning (Barenberg, Berse, & Dutke, 2011; Best, 2010). For instance, one study (Griffin et al., 2011), found elevated levels of BDNF after a short bout of ergometer cycling in young adult males, which was associated with improvements in one executive function task, but not in another task. Further, another study (Tsai et al., 2014a) reported elevated levels of BDNF in young adult males after 30 minutes of treadmill walking, but these changes were not correlated with behavioral measures of executive functioning. In the second hypothesis, acute physical exercise activates brain areas that are associated with executive functioning (Byun et al., 2014). In agreement with this hypothesis, several studies found enhanced activation of brain areas that are associated with executive functions, such as the left dorsolateral
prefrontal cortex and the right middle prefrontal gyrus after ergometer cycling at both light and moderate intensity (Byun et al., 2014; Li et al., 2014). However, the activation of these brain areas was not consistently associated with improvements in behavioral measures of executive functions (Byun et al., 2014; Li et al., 2014). In conclusion, both hypotheses are partially supported by empirical findings, but do not consistently establish evidence for associated improvement in executive functions.
Although the underlying physiological mechanisms are yet to be unraveled, ample evidence indicates beneficial effects of acute physical exercise on executive functions. A pioneering meta-analysis by Colcombe and Kramer (2003) showed that both acute and chronic physical exercise (i.e., multiple sessions of physical exercise) had a moderate effect on executive functions in a population of older adults. Since then, research on this topic has broadened its focus to younger populations. For instance, both a short bout of moderate ergometer cycling and treadmill walking were found to increase executive functions in preadolescent children and young adults (Hillman et al., 2009; Yanagisawa et al., 2010). Accordingly, a recent meta-analysis reported a moderate effect for acute physical exercise on executive functions in children, adolescents and young adults (Verburgh et al., 2013). In spite
6 of the established positive effects, the main unsolved question remains the source of
significant heterogeneity reported in the prior meta-analysis (Verburgh et al., 2013). This indicates that unknown factors moderate the effects of acute physical exercise on executive functions. Moreover, Verburgh and colleagues (2013) could not identify variables that explained the differences in effect size between studies. To wit, both length of the exercise condition and age group (i.e. children, adolescents and young adults) did not moderate the effects of acute physical exercise on executive functions. Yet, the non-significant moderator analysis of age group might be explained by the fact that the majority of studies was
performed on young adults (k = 14) and only a few on children (k = 2) and adolescents (k = 3), possibly causing low power to detect a moderator effect. Altogether, the literature repeatedly found evidence for the beneficial effects of acute physical exercise on executive functions, but lags behind in providing moderator variables that explain differences in effects.
Yet, it is important to identify moderator variables, since they might increase knowledge for whom and when physical exercise enhances executive functioning. For instance, it is helpful to determine participant characteristics that are particularly sensitive to the effects of acute physical exercise on executive functions for effective implementation into educational settings. We suggest that acute physical exercise is particularly effective for individuals with Attention Deficit Hyperactivity Disorder (ADHD). This hypothesis is grounded in two lines of reasoning. First, BDNF, which is thought to improve executive functions, is reduced in individuals with ADHD (Tsai, 2007). At the same time, BDNF is increased by the intervention of regular physical exercise (Archer & Kostrzewa, 2012), which might make individuals with ADHD particularly susceptible to the positive effects of
physical exercise on executive functions. Second, the “cognitive reserve hypothesis” states that physical exercise is more beneficial for individuals with challenged cognitive reserves (Diamond, 2013; Gapin, Labban, & Etnier, 2011), such as individuals with ADHD, who are
7 characterized by executive function deficits (Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Indeed, one meta-analysis found that individuals with ADHD showed impaired performance on executive function tasks relative to individuals without ADHD (Willcut et al., 2005). Altogether, these findings suggest that the diagnosis of ADHD might moderate the effects of acute physical exercise on executive functions.
The effects of physical exercise on executive functions might not only be moderated by sample characteristics, such as the diagnosis of ADHD, but also by the type of executive function. In order to understand the possible moderating role of type of executive function, it is important to briefly expand on the core executive functions of shifting, working memory and inhibition. First, shifting refers to the ability to shift between multiple tasks or mental sets in the presence of irrelevant stimuli (Miyake, 2000). For instance, in education this ability is used during the transitions from breaks to learning tasks. Second, working memory involves the ability to store (e.g., teacher’s instructions) and manipulate information in working
memory without the help of external cues (Best & Miller, 2010; Garon et al., 2008; Miyake et al., 2000). Finally, inhibition refers to the ability to consciously inhibit “dominant, automatic or pre-potent responses” (Miyake et al., 2000, p.57). For example, children inhibit the
tendency to talk during instructions. Although the executive functions of shifting, working memory and inhibition are interrelated, it is important to treat them as separate categories, as they show differential developmental trajectories (Best & Miller, 2010; Garon et al., 2008; Huizinga, van Dolan, & van der Molen, 2006). That is, inhibition seems to develop most vigorously in childhood, whereas shifting and working memory show a more gradual improvement into adolescence (Best & Miller, 2010). Following these results, it is hypothesized that age group and type of executive function moderate the effects of acute physical exercise on executive functions. For instance, Best (2010) states that inhibition
8 might be particularly sensitive to the effects of acute physical exercise during its critical phase in childhood, but less during adolescence.
Yet, to value the importance of investigating physical exercise as a means of improving executive functions, both the evaluations of current executive functions training programs and the inherent advantages of physical exercise should be taken in mind. First, effective training programs that are designed for this matter are currently lacking for clinical (Rapport, Orban, Kofler, & Friedman, 2013) and non-clinical (Melby-Lervåg & Hulme, 2013) populations. For instance, one meta-analysis (Rapport et al., 2013) found no effect of multiple sessions of cognitive training (with an average total of 734 minutes) on untrained tasks measuring executive functions that were trained in the program, in individuals with ADHD. Another meta-analysis (Melby-Lervåg & Hulme, 2013) reported that working memory training had large immediate effects on working memory, but no transfer or long term effects. These findings support the need for alternative interventions aimed at improving executive functions, such as short bouts of physical exercise. Moreover, implementing short bouts of physical exercise to, for example, school or work days, has the additional advantage of other health benefits associated with physical exercise (Penedo & Dahn, 2005; Shoup et al., 2008; Strong et al., 2008), such as lower rates of depression (Janssen & LeBlanc, 2010) and obesity (Chaput et al., 2008). In sum, physical exercise does not only potentially improve executive functions, but simultaneously has inherent advantages over current training
programs.
In conclusion, the present study aims to update the meta-analysis by Verburgh and colleagues (2013) by investigating the effects of acute physical exercise on executive
functions in children, adolescents and young adults. This will be done by pooling randomized controlled trials (RCT) and within-subject cross-over designs on this topic. The reason for this update is twofold. First, the recent growth of research in this area allows to test the
9 stability of prior results and conduct meaningful subgroup analyses that might identify
moderator variables. The identification of moderator variables might be useful in identifying those children for whom acute physical exercise is most beneficial for executive functions. Therefore, we will explore whether age group, length of the exercise condition and diagnosis of ADHD moderate the effects of acute physical exercise on executive functions. Second, the use of a multilevel approach enables to include all effects within a study, which gives a more realistic estimate of the effects of acute physical exercise on executive functions relative to the previous analysis that only included one effect for each study. Based on the meta-analysis by Verburgh and colleagues (2013), we expect that acute physical exercise has a moderate effect on executive functions. Moreover, we expect that acute physical exercise will yield larger effects in individuals with ADHD relative to individuals without ADHD.
Method Data Collection
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist (PRISMA; Moher, Liberati, Tetzlaff, & Altman, 2009) was used to guide the data collection, data extraction and reporting of the current meta-analysis, as can be seen in appendix A. In order to assesses the effects of acute physical exercise on executive functions in children, adolescents and young adults, we tried to retrieve all articles that reported on this topic. The data collection was performed in six steps. Figure 1 summarizes the described search process. First, a search strategy that consisted of words that refer to physical activity (i.e., aerobic*, “physical activit*” and treadmill*), executive functions (i.e., “cognitive ability”, “executive process*” and “executive function*”) and the population of children, adolescents and young adults (i.e., child*, adolesc* and “young adult”) was created. Examples of tasks that measure executive functions were derived from scientific literature on executive functions (Best &
10 Miller, 2010; Etnier & Chang, 2009) and added to the search syntax. The full search string is given in appendix B. Second, the search syntax was run in the following electronic databases Medline, PsycINFO, SPORTDiscus and Embase from 2011 up to may, 4th, 2016. For Google Scholar, we screened the first 200 results on title (on May 17th, 2016), which yielded 58 articles. Together, the literature search yielded 4,052 articles, which were saved in Refworks. Third, we removed additional duplicates and excluded articles based on title and abstract. Fourth, full-text versions of the remaining articles were read to check whether the inclusion criteria, as described below, were met. In case information was missing, either the first author of the study or the author whose contact information was given on the article was contacted, which was executed in 32 cases and yielded 19 studies. In case of non-response a follow-up e-mail was sent. Reasons for excluding articles were listed hierarchically, as described in appendix C. That is, it was first investigated whether the first inclusion criterion was met (i.e., full-text is available in English), then the second (i.e., article is published in English) and so on. Only the highest ranked reason was mentioned in the flow-chart. Fifth, the 14 articles included in the meta-analysis of Verburgh and colleagues (2013) were read full-text, which yielded six new articles. Finally, we emailed the authors of the included studies for
recommendations on additional (unpublished) studies, which yielded one new article. In total, we read the full-text versions of 181 articles of which 45 met inclusion criteria.
Inclusion Criteria
Both published and unpublished articles (e.g., dissertations) were included in the analysis. We used the following criteria to select studies for inclusion in the meta-analysis:
1. The maximum mean age of the participants in the study was 30. The maximum age range of the participants was 35.
11 2. The participants in the study had to undergo a maximum of one bout of whole-body
physical exercise (opposite to, for instance, chewing gum).
3. The study consisted of tasks that measured executive functions (i.e., shifting, working memory and inhibition) through the use of behavioral measures.
4. The study used either an RCT or a within-subjects cross-over design.
5. Posttest results were reported with sufficient detail to allow the calculation of an effect size, or additional information was made available by the authors.
6. The diagnosis of ADHD was either given by a psychiatrist or pediatrician or the child should was diagnosed with ADHD through the use of the DSM-IV/5 or the ICD-10. 7. Articles were published in English.
Data Extraction
Coding was done by the first author with the help of a coding schedule. First, data necessary for effect size calculation was extracted (i.e., means, standard deviations or standard errors, and sample sizes). Next, methodological characteristics, intervention
characteristics, and participant characteristics were coded. Age of the participants was coded as both a categorical variable, reflecting the groups of children (0-12), adolescents (13-17) and young adults (18-35), as well as a continuous variable. The presence of the clinical diagnosis of ADHD was coded as a dichotomous variable. Type of exercise was coded as a categorical variable, according to the following categories treadmill walking, ergometer cycling, exergaming and a rest category that included among others resistance exercise and a gymnastic lesson. Length of the exercise condition was coded as a continuous variable, reflecting the number of minutes the exercise condition lasted (including possible warming-up and cooling-down). In case the length of the exercise condition differed between
12 the categories of shifting, working memory, inhibition and a fourth category that included tests that tapped several executive functions (e.g., the Wisconsin Card Sorting Task). Cognitive tasks were listed in a self-developed table and built on literature, in order to simplify the process of categorizing. Moreover, disagreement in the literature was solved by discussion. In the extraction of executive function tasks, we preferred interference scores, which is the difference in reaction time or accuracy between a incongruent trial and a
congruent trial, and reflect the shift cost or the loss of time caused by inhibition (e.g., Stroop, 1935). However, if these were not reported, we included incongruent trials. In the case executive functioning was measured several times after the physical exercise, we selected the posttest results that were closest to the end of the exercise condition. Moreover, in coding the outcome variables, we differentiated between reaction time, accuracy and a complex score (conform Barenberg, Berse, & Dutke, 2011). Furthermore, in case the original study made a for the present meta-analysis non-relevant distinction between samples (e.g., fit vs. non-fit individuals) or physical activities (e.g., ergometer cycling at 20% vs. 80% VO2 max), we
pooled the means and standard deviations of groups in order to create a single effect size. The pooled standard deviation was calculated according to the following formula
√(𝑁1−1)𝑆𝐷12+(𝑁2−1)𝑆𝐷22+𝑁1+𝑁2𝑁1𝑁2(𝑀12+𝑀22−2𝑀1𝑀2)
𝑁1+𝑁2−1 (Higgins & Green, 2008).
Effect Size Calculation
The standardized mean difference effect size statistic was calculated for randomized control trials by the use of Cohen’s d (Durlak, 2009; Lipsey & Wilson, 2001). In case means and standard deviations were missing, confidence intervals and standard errors were used to calculate the effect size. Separate effect sizes were calculated for different populations (i.e., individuals with and without ADHD), different executive function tasks (e.g., Stroop Color-
13 Word task and the Flanker task) and different outcome measures (i.e., reaction time,
accuracy, or other). Because we included studies that used different tests to assess executive functions, we expected substantially heterogeneity in effect sizes among studies. Therefore, a random effects model was used to pool the effect sizes.
Statistical Analyses
We used a multilevel approach using the “metaphor”-package of R (Wibbelink & Assink, 2015) to examine whether the clustering of test scores (level 2) would account for a substantial portion of the variance in executive function tests (level 3), within executive functions (level 4) above and beyond variation between samples (level 5) (van den Noortgate, López-López, Marín-Martínez, & Sánchez-Meca, 2012; Wibbelink & Assink, 2015). The Restricted Maximum Likelihood estimation method (Wibbelink & Assink, 2015) was conducted for the estimation of the parameters, because it takes into account both bias and efficiency (Viechtbauer, 2005). First, an overall effect size was estimated. One cross-over study (O’Leary, Pontifex, Scudder, Brown, & Hillman, 2011) consisting of a single sample that performed both ergometer cycling and treadmill walking was included twice in the analysis. Therefore, we repeated the analysis with and without the additional sample resulting in no differences, on the basis of which we concluded to leave the additional sample in the analysis. Second, log-likelihood ratio tests were conducted to compare model fit for nested models. It was examined whether models that take into account variance between test scores (level 2) within tests (level 3), within executive functions (level 4) and across samples (level 5) have a better model fit than a model that only considers variance around population
estimates (level 1). Third, the degree of heterogeneity between samples was tested in order to determine the variability across samples. First, the Q-statistic was calculated, which tests
14 whether the observed effect sizes are significantly more different from each other than would be expected due to chance (Ioannidis, Patsopoulos, & Evangelou, 2007). A significant Q-statistic implies significant heterogeneity between samples. Second, in order to assess the degree of heterogeneity between samples, the I2-statistic was calculated (Higgins, Thompson, Deeks, & Altman, 2003). Values of I2 between 1-25%, 26-74%, and 75-100% indicate low, moderate and high heterogeneity, respectively (Higgins et al., 2003). In order to gain a better insight into the spread of the data, a forest plot was displayed with the effect estimates and 95% confidence intervals (Figure 2). Then, we included the moderators in the model (i.e., age group, length of the exercise condition and diagnosis of ADHD) on those levels that
produced significant heterogeneity. Finally, it was investigated post-hoc whether type of exercise condition (i.e., treadmill walking, ergometer cycling, exergaming and a rest category), year of publication and type of test score (i.e., reaction time, accuracy and other) moderated the effects of acute physical exercise on executive functions. For the moderator analysis of type of test score, we combined test scores within one test that belonged to the same category (e.g., reaction time of trial 1 and 2) in order to perform a moderator analysis at the test score level.
Publication Bias
The presence of a publication bias was checked in three ways. First, publication bias was graphically examined using a funnel plot, which is helpful for detecting
underrepresentation of small studies and insignificant or small effect sizes (Lipsey & Wilson, 2001). Second, Egger’s test was performed in order to test a potential publication bias (Egger, Smith, Schneider, & Minder, 1997). Third, Duval and Tweedie’s (2000) trim and fill
15 the funnel plot. Together, these steps help us to assess the probability that our results are distorted by the tendency to publish significant effect sizes.
Results Descriptive Statistics
The present meta-analysis included a total of 45 articles consisting of 138 effect sizes within 49 samples (k) making up 1,915 participants of whom 41% female (see Table 1 for study characteristics). The weighted mean age of the participants was 16.44 years (SD = 5.86; range: 4.12-25.95). Of the included samples, 14consisted of children (0-12 years), four of adolescents (13-17 years) and 31of young adults (aged 18-35). In the majority of the studies, the exercise condition consisted of treadmill walking (k = 13), ergometer cycling (k = 25) or exergaming (k = 4). The average length of the exercise condition was 25.39 minutes (SD = 12.97, range: 5-60). In the majority of studies, the control condition did not consist of an activity (k = 18). If the control condition involved a sedentary activity, it was mostly watching a video (k = 15) or reading a book (k = 6).
Of the included studies, 28 were within-subjects studies with a cross-over design and 17 were randomized controlled trials. All cross-over studies reported that they
counterbalanced the order in which the participants underwent the exercise and the control condition. Also, 17 studies reported the number of participants that dropped out after randomization, which ranged between 0 and 32% (Mdrop-out = 11%).The majority of studies
performed the Stroop Color-Word task (k = 20), the Flanker task (k = 13) and the Trail Making Task (k = 6) or derivatives of them, such as the Stroop Day/Grass. Five studies were conducted on children with ADHD consisting of 154 children of whom 8% female with a weighted mean age of 10.23 years (SD = 0.42, range: 9.42-10.47) and a mean exercise length of 25.00 minutes (SD = 11.18, range: 5-30). The majority of studies performed on children
16 with ADHD consisted of treadmill walking (k = 3). The control condition mostly consisted of video watching (k = 4).
Overall Effect Size
Results of the present meta-analysis showed that the pooled effect of acute physical exercise on executive functions was small but significant (d = 0.16, SE = .05, t(138) = 3.48, p < .001, 95% CI [0.07, 0.25]). This indicates that acute physical exercise has a small but significant positive effect on executive functions. The Q-statistic was significant, Q(138) = 289.53, p<.001, which indicates significant heterogeneity. The associated I2 was 52% which is in the moderate range. This means that 52% of the variability in effect estimates is due to heterogeneity rather than sampling error. In other words, other factors influence the
differences in effect sizes rather than chance or physical exercise. In order to identify sources of heterogeneity, we took a closer look at the distribution of variance across levels. The level-2 variance (i.e., the variation in effect sizes across test scores) was 0.03. The model with level-2 variance fitted the data significantly better than a model with level-2 variance
constrained to zero, Δχ2 (1) = 5.92, p = .007. This means that differences between test scores accounted for a significant proportion of variance in effect sizes. Level-3 variance (i.e., variation in the effect sizes across executive function tests) was .01. Adding level-3 variance to the model did not improve model fit, Δχ2 (1) = 0.36, p = .274. Level-4 variance (i.e., variation in the effect sizes between executive function) was .00, which also did not significantly improve model fit, Δχ2 (1) = 0.00, p = .500. Finally, level-5 variance (i.e., variation in effect sizes across samples) was .04 which was significant. Constraining level-5 variance to zero significantly deteriorated model fit, Δχ2 (1) = 5.36, p = .010. The percentages of explained variances for the different levels were 45%, 22%, 7%, 0% and 26%, for level 1, 2, 3, 4 and 5, respectively. This indicates that the majority of variance was explained by the
17 first, second and fifth level, meaning variation in effect sizes around the population mean, between test scores and between samples. Therefore, we conducted moderator analyses at the fifth level in order to investigate what factors cause a high degree of variation in effect sizes by comparing the effects of age group (i.e., children, adolescents and young adults), diagnosis of ADHD and length of the exercise condition.
Moderator Analyses
We conducted moderator analyses in order to investigate whether the specified moderators of age group, length of the exercise condition and diagnosis of ADHD accounted for the significant heterogeneity at the sample level. Our results show that none of the
suggested moderators significantly influence the effects of acute physical exercise on executive functions. First, neither age group of the participants nor the continuous age variable moderated the effect of acute physical exercise on executive functions, F(2,135) = 0.66, p = .516 and F(1, 132) = 0.48, p = .490, respectively. This means that the effect of acute physical exercise on executive functions is small but significant for all age groups. Second, also length of the exercise condition did not moderate the effects of acute physical exercise on executive functions, F(1,122) = 0.80, p = .373. Finally, the diagnosis of ADHD did not moderate the effects of acute physical exercise on executive functions, F(1,136) = 0.24, p = .625. This means that effects of acute physical exercise on executive functions did not differ between individuals with and without ADHD.
Subsequently, we conducted post-hoc moderator analyses, because our specified moderators failed to explain variation in the effects of acute physical exercise on executive functions. On the sample level, we investigated whether type of exercise condition (i.e., treadmill walking, ergometer cycling, exergaming or other), length of the exercise condition and publication year moderated the effect of acute physical exercise on executive functions.
18 On the test score level, we explored whether type of test score (i.e., reaction time, accuracy and other) moderated the effects of acute physical exercise on executive functions. First, type of exercise condition did not moderate the effects of acute physical exercise on executive functions indicating that effects are found regardless the type of exercise, F(3,134) = 1.20, p = .351. Second, also publication year did not account for the significant heterogeneity at the sample level, F(1, 136) = 0.00, p = .935. This means that older studies do not report higher effect sizes than more recently published studies. Finally, at the test score level it appeared that type of test score did not moderate the effects of acute physical exercise on executive functions, F(2, 135) = 0.97, p = .383. Together, it can be concluded that none of our post-hoc moderators could account for the significant variation in effect sizes at the sample and test score level.
Publication Bias
Publication bias was checked in order to assess the tendency to publish significant effect sizes. This was done by means of a funnel plot, Egger’s test (1997) and Duval and Tweedie’s trim-and-fill procedure (2000). First, visual inspection of the funnel plot did not indicate a publication bias, since the effect sizes were roughly equally spread across both triangles. Accordingly, the Egger’s test was not significant (z = -1.02, p = .307), which means that the funnel plot was not significantly asymmetric. Further, Duval and Tweedie’s trim-and-fill procedure (2000) revealed that positive effect sizes should be imputed in order to
symmetrize the funnel plot, because the majority of effect sizes was negative (see Figure 3). Altogether, all checks argue against the presence of a publication bias.
19 Discussion
The purpose of this meta-analysis was to investigate the effects of acute physical exercise on executive functions in children, adolescents and young adults. Therefore, we pooled the results of 49 samples (k) consisting of 1,915 participants producing 138 effect sizes. Results of our multilevel meta-analysis show that acute physical exercise has a small, but significant positive effect (d = 0.16, 95% CI = [0.07, 0.25]) on executive functions in children, adolescents and young adults. These results were found regardless of the length and type of the exercise condition (i.e., treadmill walking, ergometer cycling, exergaming and other), and across executive functions (i.e., shifting, working memory, inhibition and other). Moreover, the results held across groups of youth with and without ADHD.
The pooled effect size differed from the previous meta-analysis by Verburgh and colleagues (2013), who found a moderate effect (d = 0.52, 95% CI [0.29, 0.76]) of acute physical exercise on executive functions in the same age range. There are, however, several methodological differences that might explain the discrepant results, namely the exclusion of baseline scores, the multilevel approach and the addition of a large number of studies. First, the present meta-analysis excluded studies that used baseline data as control condition, because individuals tend to improve on executive function tasks with practice (Barenberg, Berse, & Dutke, 2015; Berse et al., 2015). To be exact, this means that the previous meta-analysis (Verburgh et al., 2013) also included studies in which the posttest scores of the experimental condition were compared with the baseline scores of the control condition. This implies that positive effect sizes may not only reflect beneficial effects of physical exercise, but also of practice. Second, the use of a multilevel approach enabled us to include several tasks for each study. In comparison, the previous meta-analysis (Verburgh et al., 2013) included the effect of one randomly chosen executive function task for each study. However, it is important to include all tasks within a study, because non-significant results are often
20 described as being “non-significant”, which reduces the chance of inclusion in the meta-analysis (Hoyle, Harris, & Judd, 2002, p.496). Third, the impressive growth in research in the last five years enabled us to include a large number of additional samples (i.e., 49 vs. 19 samples in the Verburgh et al. study), because of which the effects of acute physical exercise could be estimated more precisely (Hoyle et al., 2002, p.209). This is especially important in the present research area in which small sample sizes with associated wide confidence intervals are the norm. Altogether, this suggests that the present meta-analysis gives a more realistic estimate of the effects of acute physical exercise on executive functions compared to the previous meta-analysis (Verburgh et al., 2013).
Despite their methodological differences, both meta-analyses have in common that the effect sizes reported in the included studies differ strongly in magnitude and direction. Our multilevel approach revealed that the majority of variation in effect sizes could be explained by differences between samples, between test scores within tests and around the estimated population effect. On the sample level, the differences in effect sizes could neither be explained by age group (i.e., children, adolescents and young adults) nor by length of the exercise condition. These results are in agreement with the previous meta-analysis (Verburgh et al., 2013). Further, it appeared that diagnosis of ADHD did not moderate the effects of acute physical exercise on executive functions. However, this finding should be interpreted with caution since only five studies included samples of children with ADHD. There are reasons to believe that differences between individuals with and without ADHD might first arise in adolescence and young adulthood when executive functions are further matured and education and society make a stronger appeal on executive functions (Best & Miller, 2010). Therefore, we believe that the effects of acute physical exercise on executive functions cannot be generalized to adolescent and young adults with ADHD. Therefore, further
21 research is required that investigate the differences in effects of acute physical exercise on executive functions in adolescents and young adults with and without ADHD.
Next, in order to unravel the variation in effect sizes at the test score and the sample level, additional moderator analyses were conducted post-hoc. On the test score level, it appeared that type of test score (i.e., reaction time, accuracy and other) did not moderate the effects of acute physical exercise on executive functions. Here we note that none of the included studies calculated a combined effect size of reaction time and accuracy, which is recommended for estimating the effects of acute physical exercise on executive functions more precisely (e.g., Pirrie & Lodewyk, 2012; Rattray & Smee, 2013). Namely, the combination of reaction time and accuracy at each age points to developmental processes, such as meta-cognition, strategy acquisition and efficient utilization (Best & Miller, 2011). Furthermore, on the sample level, it appeared post-hoc that type of exercise (i.e., treadmill walking, ergometer cycling, exergaming and other) did not moderate the effects of acute physical exercise on executive functions. This indicates that effects on executive functions are found regardless of the type of exercise, which simplifies implementation.
Regarding implementation of the results, the question arises whether a small effect of acute physical exercise on executive functions has significant meaning in educational
practice. Although the effect is statistically small, we do not consider it meaningless. For instance, one meta-analysis reported that executive function training for children with ADHD consisting of an average of 734 minutes did not influence executive functions (Rapport et al., 2013). In comparison, physical exercise of on average 25 minutes yielded a small, but
significant effect. Moreover, the possibility is left open that the effects might add up after multiple exercise sessions. Although the meta-analysis by Verburgh and colleagues (2013) did not find evidence for positive effects of chronic physical exercise on executive functions, it cannot be ruled out that chronic physical exercise positively affects executive functions.
22 For instance, the insignificant result might be caused by the inclusion of a small number of studies (k = 10), possibly causing low power to detect an effect. Furthermore, it is possible that both the participants in the experimental and the control condition reached a ceiling effect on the executive function tasks, because of which the prior meta-analysis (Verburgh et al., 2013) failed to find a significant effect. Altogether, these results suggest that the effects of acute physical exercise on executive functions are small, but meaningful. Furthermore, future research should investigate the effects of multiple sessions of physical exercise on executive functions.
Further, to estimate the value of acute physical exercise for improving executive functions, it is important to emphasize its possible advantages over traditional executive function training programs. First, Diamond (2013) states that a traditional executive function training program should continuously increase its complexity, since users will otherwise lose their interest or stop improving. In this respect, physical exercise has the advantage that it should not be continuously adjusted, which makes it cost-effective and more easy to implement. Second, to estimate the value of physical exercise as a means of improving executive functions, it should not be looked at the intervention in isolation. This means that an improvement in executive functions is only one positive consequence of physical exercise, next to, for example, preventing depression (Dinas, Koutedakis, & Flouris, 2011), which should also be taken into account when choosing an intervention. Third, physical exercise might address all subcomponents of executive functions (i.e., shifting, working memory and inhibition), as suggested by the present meta-analysis. This makes physical exercise
preferable over a training that only influences working memory (Melby-Lervåg & Hulme, 2013). Together, these results suggest that physical exercise as a means of improving executive functions has inherent advantages over traditional executive function programs,
23 which emphasizes the need for further research on the effects of acute physical exercise on executive functions.
Several limitations of the present meta-analysis warrant mentioning. First, we did not control for pretest scores, which specifically could have biased the results of studies with small samples because of failures in the randomization process. For instance, an outlier in a small sample might more easily distort the study’s results relative to an outlier in a larger sample. These distorted results might have either diminished or exaggerated the effects of single studies. However, it is unlikely that this has affected the overall outcome, since diminished and exaggerated effects might cancel each other out. Second, studies that conducted the same tests for assessing executive functioning did not necessarily report the same test scores (e.g., one study reported the reaction time of Stroop Color-Word, whereas another reported the difference in reaction time between Stroop Color-Word and Stroop-Word). However, the variance in effect sizes between tests was not significant, which makes it improbable that the inclusion of different test scores affected the overall outcome.
Moreover, the inclusion of different types of test scores contributed to a more complete picture of the research field, which was the purpose of this meta-analysis. Third, the present meta-analysis only included posttests that were closest to the end of the exercise condition. However, for the incorporation of short bouts of physical exercise into educational settings, it would be informative to know how long the effect of acute physical exercise lasts. One study, for instance, found that the effect of 50-minute vigorous ergometer cycling on a variety of neuropsychological tests lasted for up to two hours after the end of the exercise condition (Basso, Shang, Elman, Karmouta, & Suzuki, 2015).
Fourth, the focus of the present meta-analysis was on lower-order executive functions, which makes generalization to higher-order executive functions such as reasoning, problem solving and planning impossible (Diamond, 2013). Similarly, little is known about whether
24 benefits of short bouts of physical exercise on executive functions are translated into
increased academic performance. In a large sample of children between 5 and 17 years a positive correlation was found between level of executive functioning and reading and math ability (Best, Miller, & Naglieri, 2011), which emphasizes the importance of investigating transfer effects to academic performance. Fifth, the majority of the included studies was conducted on college students, which can be considered a limitation. To wit, college students perform on a high academic level by definition and academic achievement is positively correlated with executive functions (Best & Miller, 2011). Simultaneously, executive function interventions are considered more effective for those who are most behind on executive functions (Diamond, 2013), which questions the generalizability of the results. Although this statement is contradicted by the non-significant moderator analysis of age group, in which the population of young adults almost entirely overlapped with college students, it cannot be ruled out that differences between college students and younger age groups exist. That is, the present meta-analysis only consisted of four studies that included adolescents, causing low power to detect a significant moderator effect, which increases the chance of a type II error (Hoyle, Harris, & Judd, 2002, p.460). Namely, concluding that age groups do not differ when in fact they do.
Finally, we would like to make suggestions for potential moderators in the effects of acute physical exercise on executive functions, namely fitness of the participants and exercise intensity. First, there are reasons to believe that the effects of acute physical exercise are larger for high-fit relative to low-fit individuals (Bullock & Giesbrecht, 2014; Hogan et al., 2013). This raises the question of whether children would become more susceptible to the effects of physical exercise on executive functions over time. The line of reasoning here is that repeated physical exercise possibly improves physical fitness (Kriemler et al., 2011), and high-fit individuals might benefit more from physical exercise (Diamond, 2013). Therefore, a
25 longitudinal intervention that investigates the effects of multiple sessions of physical exercise on executive functions over time, while considering the possible change in physical fitness would be suggested. Second, we suggest that exercise intensity might moderate the effects of acute physical exercise on executive functions. Currently, research has not yet found if high-intensity or moderate-high-intensity exercise is more beneficial for executive functions (Chang et al., 2012). For example, one study (Chang & Etnier, 2009) reported a quadratic relationship between exercise intensity and working memory performance in young adults, which means that moderate-intensity exercise was more beneficial for working memory performance than light- or high-intensity exercise. However, another study (Chen & Ringenbach, 2016) reported that physical exercise at high-intensity was most beneficial for executive functions. Moreover, there is no consensus about the operationalization of exercise intensity. For example, volumes of oxygen (VO2) and heart rate are used interchangeably (e.g., Chang &
Etnier, 2009; Chen & Ringenbach, 2016; Joyce, Graydon, McMorris, & Davranche, 2009), which complicates aggregating studies.
In sum, the present multilevel meta-analysis found a small but significant positive effect of acute physical exercise on the executive functions of children, adolescents and young adults. This effect was found across different types of executive functions (i.e., shifting, working memory, inhibition and other) regardless of length and type of physical exercise (i.e., treadmill walking, ergometer cycling, exergaming and other). Our results suggest that short bouts of physical exercise might be a useful tool to enhance executive functions in children, adolescents and young adults. Future research should demonstrate whether fitness of the participants and intensity of the exercise condition moderate the effects of acute physical exercise on executive functions – favorably in a longitudinal intervention.
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