A randomized controlled design investigating the effects of classroom-based physical activity on children’s fluid intelligence and achievement

Abstract

Existing literature shows promising effects of physical activity on children’s cognitive outcomes. This study assessed via a randomized, controlled design whether additional curricular physical activity during the school day resulted in gains for children’s fluid intelligence and standardized achievement outcomes. Participants were children (N = 460) from four urban schools in the Southeast United States. Schools were randomly assigned to treatment and control conditions. Treatment schools received additional physical activity breaks throughout the school day while control schools maintained a typical schedule without curricular activity breaks. Results from the one-year study show positive effects for children’s mathematics and reading achievement but no differences across treatment and control groups for children’s fluid intelligence scores. Implications for school psychologists in promoting physical activity breaks on a systems-wide level are discussed.

Keywords

Achievement fluid intelligence overweight physical activity school health

Physical activity is widely touted as promoting a healthy mind and body as it is associated with numerous health benefits. The Centers for Disease Control and Prevention (CDC) cites the benefits of physical activity including weight control, reducing the risk of cardiovascular maladies, reducing the risk of diabetes, and improving mental health (US Department of Health and Human Services, 2008). The incidence of overweight or obese status in children has nearly tripled in the past three decades (Ogden, Carroll, Kit, & Flegal, 2012). As of 2010, approximately one-third of youth were either overweight or obese (Ogden et al., 2012). Even among children as young as preschool age, rates of obesity are escalating in several states and the effects can be long-term. Preschoolers who were classified as overweight or obese were five times more likely than typical-weight preschoolers to be overweight or obese as adults (CDC, 2012b). Obese children have a greater likelihood of having pre-diabetes, cardiovascular disease, sleep apnea, social-psychological problems, and many long-term risks as well (CDC, 2014). Due to escalating rates in childhood obesity, professionals have begun to target youth in examining physical activity effects, and have found that these benefits apply not only to adults, but to children and adolescents as well (Ogden et al., 2012).

Although there are multiple causes of being overweight or obese, one such contributor is sedentary behavior (Davis et al., 2007). Though the United States Department of Health and Human Service recommends 60 minutes of daily exercise for children and adolescents, a CDC (2012a) report found that less than one-third of adolescents met the recommended number of minutes in the previous seven days. The same CDC report also found that the 14% of adolescents surveyed in the United States had not met the 60-minute recommendation on any of the previous 7 days. Internationally, levels of physical activity appear to paint a similar picture as children are not accumulating the suggested amounts of physical activity (Beets, Bornstein, Beighle, Cardinal, & Morgan, 2010). A review of youth physical activity as measured by pedometers found that boys and girls from European and Western Pacific regions of the world demonstrated significantly more physical activity than those from the United States and Canada (Beets et al., 2010). However, globally, 80.3% of youth are achieving fewer than 60 minutes of moderate to vigorous physical activity per day (Hallal et al., 2012). Studies documenting the worldwide decline in cardiovascular fitness levels (Tomkinson & Olds, 2007) and increases in youth overweight status (Janssen et al., 2005) are consistent with these outcomes. Physical activity is the fourth leading risk factor for death worldwide (World Health Organization, 2014). These findings give reason to speculate that a lack of physical activity by children is a significant contributor to the obesity trends. Given the considerable short-term and long-term risks associated with childhood obesity and being overweight, physical activity for children should be considered a central component to intervention efforts.

In addition to the physiological benefits of activity, a host of studies also suggest that physical activity may exert a positive effect on children’s cognitive functioning, brain physiology, and academic achievement (Fedewa & Ahn, 2011). Preliminary findings suggest that physical activity (namely cardiovascular or aerobic exercise) benefits children’s executive control task performance, memory performance, and attention (Tomporowski, Davis, Miller, & Naglieri, 2008; Voelcker-Rehage & Niemann, 2013; Voss, Nagamatsu, Liu-Ambrose, & Kramer, 2011). Two recent meta-analyses examining the effect of physical activity on children’s cognitions have also found moderate therapeutic effects of physical activity (e.g. resistance training, motor skills training, or aerobic training) on children’s cognitive functioning (e.g. perceptual reasoning, general intelligence, mathematics and verbal tests; Fedewa & Ahn, 2011; Sibley & Etnier, 2003). With electromagnetic imaging, researchers have found that these changes in cognitive functioning are observed alongside direct changes in brain structures and physiology, including increased volume of the basal ganglia and hippocampus, increases in volume and ‘better maintenance of white matter’, and changes in neuroelectric activity—all changes occurring when children engage in bouts of physical activity (Hillman, Erickson, & Kramer, 2008; Hillman, Kamijo, & Scudder, 2011; Voelcker-Rehage & Niemann, 2013).

Schools serve as an ideal venue for implementing physical activity interventions as children spend the majority of their waking hours in the classroom. However, some consistent issues seem to arise with school-based physical activity both in research and in practice. From a research standpoint, many studies are generally wrought with methodological flaws that potentially influence findings. Many of the physical activity studies conducted with children are cross-sectional or correlational in design, presenting difficulties in interpreting causality and controlling for confounding variables (Fedewa & Ahn, 2011; Rasberry et al., 2011). From an applied perspective, several barriers to implementing physical activity in the schools have been cited in the literature. These barriers include budget constraints, the need to meet federal and state testing mandates causing curricular changes, perceived time-constraints by school personnel, and personnel lacking the expertise to establish a physical activity program (Fedewa, Candelaria, Erwin, & Clark, 2013). Thus, despite promising research demonstrating that physical activity may enhance cognitive functioning and achievement in children, both methodological and practice barriers must be considered when implementing a physical intervention in the schools.

The purpose of the current study is in part to build upon previous research suggesting a positive influence of classroom-based physical activity on fluid intelligence and areas of academic achievement in elementary aged children (Reed et al., 2010). This study will use the same fluid intelligence definition as the previous study as a measure of the ‘ability to reason quickly and abstractly’ (Reed et al., 2010, p. 344). Despite promising results from several studies, inconsistent findings and a limited literature base impact the clarity of the relationship between physical activity and its influence on fluid intelligence and academic achievement in children. This study further explores this relationship through a randomized controlled design that accounts for potential confounding variables when assessing the relationship between physical activity and children’s cognitions (i.e. fluid intelligence and academic achievement). Additionally, given the lack of integrity monitoring with physical activity interventions conducted in schools, the second purpose of this study is to examine the results of an intervention where teachers have been trained and fidelity of the intervention is consistently monitored throughout the academic year.

Method

Participants

Children in the third to fifth grades from four urban schools participated in the current study. Of the four schools, two were randomly assigned to the experimental group and two were assigned to the control group. In the experimental group, there were a total of 15 classrooms comprised of 431 children. The control group consisted of 23 classrooms with a total of 486 children. Of these children, 156 from the experimental group obtained parental consent (36%), whereas 304 from the control group had parental consent (63%). Parental consent allowed the researchers to collect activity, achievement and fluid intelligence data on the children. However, all children in the treatment classrooms participated in the physical activity breaks.

Procedure

Prior to the start of the intervention, the university and school district Institutional Review Boards approved the study protocol, as did the principals from all four elementary schools. Consent forms were sent home with the children from the four schools at the beginning of the school year (M = 50% return rate). No data were collected from children without parental consent, although the children would participate in the physical activity intervention if they were part of the classrooms in the experimental group. Alphanumeric identification codes were used in the database to protect the confidentiality of participants.

All of the experimental classrooms integrated physical activity into their core academic curricula for 20 minutes daily, five days a week using a set of standardized movement cards (Pangrazi, Beighle, & Pangrazi, 2009). These cards consisted of aerobic-based activities that were developmentally-appropriate (i.e. the activities were unique for upper or lower elementary school-aged youth) and lasted approximately 5 minutes in duration. Some examples of the activities included having children do jumping jacks with mathematical facts, or finding different decks of cards that are spread around the classroom (i.e. the teacher calls out a letter, color, or number and students move around the room trying to find the designated card). The physical activity intervention spanned eight months from September 2012 through April 2013. Fidelity was monitored via use of daily physical activity logs emailed to researchers and via drop-in observation visits by student researchers. No additional supplies or equipment were required for the physical activity intervention and all activity was conducted in the classroom setting. The control classrooms went about business as usual during the eight months with no additional physical activity.

Physical activity measures

Participants wore a pedometer (Walk4Life, LS 2500, Plainfield, IL) during three different time periods of the school year (fall, winter, and spring). Each of those time periods consisted of five consecutive school days. This exceeds monitor period recommendations of four consecutive days for this age range of children (Vincent & Pangrazi, 2002). The pedometer brand and model used for the present study is considered an accurate and appropriate pedometer for research with children (Beets, Patton, & Edwards, 2005).

To avoid reactivity with the pedometers, researchers presented participants the opportunity to hold the pedometer, open and close it, and practice applying and removing it from their waistband prior to data collection. Participants also did a walk test in which they took 50 steps. Those whose pedometer step count was more than 10% off were assisted in finding a more accurate placement to ensure valid data. On the first day of the study, each participant was assigned a pedometer marked with his or her name to be used for the duration of the study. Students wore pedometers for the entire school day and placed the pedometer in the designated case as they left the classroom for the day. Researchers recorded pedometer steps and activity onto a recording sheet each afternoon and reset them for the following day.

It was anticipated by the researchers that very little seasonal effect would take place between fall, winter, and spring seasons because the data were collected during the school day only. Previous research has indicated that, due to the somewhat structured nature of the school day, children’s school day physical activity does not fluctuate much due to seasonality (Beighle, Erwin, Morgan, & Alderman, 2012). Analysis comparing pedometer steps across seasons showed that there was, in fact, a seasonality effect for the children in this study for both control (F_{(1.50, 442.15)} = 61.64, p < 0.01, $η_{p}^{2} = 0.17$ ) and treatment groups (F_{(1.61, 208.11)} = 16.66, p < 0.01, $η_{p}^{2} = 0.11$ ). Further analyses indicated a significant decrease in pedometer steps during the winter (M = 3579.49, SD = 1498.59, N = 296) when compared to the fall (M = 4207.26, SD = 1650.75, N = 296) in the control group (p < 0.01), yet a significant increase in pedometer steps during the winter (M = 3606.43, SD = 1694.58, N = 130) when compared to the fall (M = 3032.84, SD = 1235.79, N = 130) in the treatment group. Then, in both groups, significant increases in pedometer steps from winter to spring were found (M_diff = 1801.77, SE = 179.49, p < 0.01 for control group; M_diff = 770.39, SE = 283.82, p = 0.02 for treatment group).

Fluid intelligence

Established as a psychometrically sound instrument, the Standard Progressive Matrices (SPM) test has been used to measure both adult and child fluid intelligence since the 1980s (Raven, Raven, & Court, 1998). Fluid intelligence is a construct that measures an individual’s ability to solve problems and to think logically in novel situations. Fluid intelligence does not depend on an individual’s acquired knowledge through experience and is thus thought to be more generalizable to an individual’s performance across situations.

The Raven’s SPM consists of five sets of diagrammatic figures (A, B, C, D, & E) that become incrementally harder as an individual completes each set. The sets are each comprised of 12 figures, totaling 60 problems. Each figure is missing a part, wherein the correct response is provided among a list of options. The test has been used in over 2,000 published studies and has shown robust internal consistency and retest reliability (Raven et al., 1998).

The SPM can be administered in both an individual and a group setting. For the purposes of the present study, the SPM was group administered by classroom. Thus, no more than 25 students were assessed at a time, and the test took approximately 45 minutes. The administration took place two weeks prior to the start of the intervention and two weeks after the intervention ended in the spring of 2013. Most students were present during the pre- and post-test assessment (98.3% during the pre-test and 97.2% during the post-test).

Academic achievement

Students’ achievement levels in mathematics and reading were assessed with a national standardized test (Measures of Academic Progress [MAP]; Northwest Evaluation Association, 2014). Students took the test prior to the beginning of the intervention (fall 2012) and after the intervention (spring 2013). Scores were provided in percentiles (1% to 99%). Although standardized data were collected from all four schools, two of the schools (one control and one treatment) made a decision at the end of the study to not release student test scores due to privacy concerns. Thus, the two schools opted to provide classroom-based performance measures in percentile scores for students in mathematics and reading in lieu of the standardized test scores.

Teacher training

The teachers in the experimental group (N = 15) received two separate training sessions related to the integration of the physical activity break cards. These sessions were led by the lead researchers and went over the physical activity break logs and logistics on how to use the movement cards in the classroom. A full year membership to a local gym was offered as a lottery prize to one of the experimental group teachers for participating in the study and implementing the intervention for the full academic year.

Data analysis

The current study sought to examine whether additional physical activity breaks resulted in higher levels of fluid intelligence and achievement scores in reading and mathematics. In order to address this issue, a series of a two-level hierarchical linear model (HLM; Raudenbush & Bryk, 2002), wherein students (Level-1) were treated as being nested within schools (Level-2), at which the randomization of groups occurred, were performed on each outcome (i.e. level of fluid intelligence, reading achievement, and mathematics achievement). For each outcome measured in the spring (POSTTEST), a sequential HLM model-building process was employed using the HLM 6.06 software (Raudenbush, Bryk, & Condon, 2004).

The sequential model-building process consisted of the following three phases: (a) a fully unconditional two-level model, in which no Level-1 and Level-2 predictors were included so that the intraclass correlation coefficient (ICC)—which quantifies the amount of variability in POSTTEST attributed to the teacher-level—was computed; (b) a Level-1 model predicting POSTTEST as a function of outcome measured in the fall (PRETEST), which was centered around a grand mean; (c) a model for predicting the Level-1 intercept and slope of PRETEST by an un-centered grouping variable (GROUP: 1 = treatment, 0 = control) at Level-2, wherein mean POSTTEST scores for students whose PRETEST is at the mean was compared between the treatment and control groups.

In each model described above, the fixed-effect coefficient and heterogeneity in each coefficient (i.e. variance component of the random-effect coefficient) were examined as suggested by Raudenbush and Bryk (2002) at the significance level (α) of 0.05. Initially, all the random-effects were allowed to vary. Then, if the variance of the qth random effect was found not to be significantly different from zero under the chi-squared distribution (i.e. no significant variance in the associated qth coefficient), the relevant random-effect was fixed in the subsequent models. The uncombined equations below summarize the a priori model tested during the last phase of the model-building process:

Level-1 model:

{POSTTEST}_{ij} = β_{0 j} + β_{1 j} * ({PRETEST}_{ij} - \bar{{PRETEST}_{ij}}) + r_{ij} .

Level-2 model:

β_{oj} = γ_{00} + γ_{01} * {GROUP}_{j} + u_{oj} β_{1 j} = γ_{10} + γ_{11} * {GROUP}_{j} + u_{1 j} .

In particular, the significance of the fixed-effect coefficient related to GROUP, $γ_{01}$ , addresses the main research question posited in the model, which addresses the mean difference in the POSTTEST scores between treatment and control groups, whose PRETEST score is at the mean.

Results

Descriptive statistics

A total of 460 students with parental consent in grades 3–5 (Level-1 unit: n_student = 304 in control classrooms from two schools and n_student = 156 in treatment classrooms from two schools) were taught by 38 individual teachers (level-2 unit: n_teacher = 24 in control classrooms from two schools and n_teacher = 14 in treatment classrooms from two schools). A Level-1 classroom size per individual teacher varied from 4 to 24 (M = 12.67, SD = 6.26) in control classrooms, while it ranged from 7 to 19 (M = 11.14, SD = 3.55) in treatment classrooms.

Students in the treatment classrooms showed an increase in mean fluid intelligence scores from fall (M = 35.25, SD = 7.83, n_student = 155) to spring (M = 38.64, SD = 7.08, n_student = 154). In addition, both mathematics and reading achievement increased from fall (M = 66.44, SD = 30.11, n_student = 153 for mathematics achievement; M = 64.01, SD = 32.83, n_student = 153 for reading achievement) to spring (M = 72.49, SD = 28.11, n_student = 153 for mathematics achievement; M = 70.57, SD = 30.41, n_student = 153 for reading achievement). In control classrooms, students showed an increase in mean fluid intelligence scores from fall (M = 37.40, SD = 9.67, n_student = 297) to spring (M = 39.90, SD = 8.49, n_student = 293). However, in the control group classrooms, both mathematics and reading achievement slightly decreased from fall (M = 70.05, SD = 20.95, n_student = 209 for mathematics achievement; M = 71.08, SD = 18.95, n_student = 203 for reading achievement) to spring (M = 69.99, SD = 20.22, n_student = 276 for mathematics achievement; M = 70.29, SD = 20.39, n_student = 276 for reading achievement).

Effects on fluid intelligence

An unconditional two-level model was first performed on fluid intelligence measured in spring so as to estimate the amount of variability between teachers in the outcome of fluid intelligence. The estimated Level-1 ( ${\overset{\land}{σ}}^{2}$ ) and Level-2 ( ${\overset{\land}{τ}}_{00}$ ) variances were 49.55 and 13.68, respectively, resulting in the estimated intra-class correlation (ICC) of 0.22 (13.68/[49.55 + 13.68] = 0.22). An ICC of 0.22 indicates that approximately 22% of the total variance in students’ fluid intelligence score exists due to differences in Level-2 (i.e. the classroom teacher). In addition, the Level-2 random-effect (τ₀₀) was also found to be significant (χ²(37) = 173.07, p < 0.01), indicating that a significant variability exists in student’s fluid intelligence scores measured in spring due to differences at Level-2 of the teacher.

The following two two-level HLM models were tested: 1) A model predicting fluid intelligence scores measured in spring (POSTTEST) using the fluid intelligence scores measured in Fall (PRETEST), and 2) A model predicting the Level-1 intercept and slope related to PRETEST using GROUP (intervention versus control classrooms). The first model indicated that students’ spring fluid intelligence scores were significantly related to their fluid intelligence scores measured in the fall ( ${\overset{\land}{γ}}_{01}$ = 0.68, SE = 0.04, t(37) = 16.26, p < 0.01), indicating a significant increase in students’ mean fluid intelligence scores from fall to spring. The residual variance of the POSTTEST means between teachers ( ${\overset{\land}{τ}}_{00}$ ) was 0.60, which was not statistically significant ( $χ^{2}$ (37) = 50.59, p = 0.07). However, the residual variance of the slope related to the PRETEST ( ${\overset{\land}{τ}}_{01}$ ) was 0.033, which was statistically significant ( $χ^{2}$ (37) = 74.56, p < 0.01), inferring a significant variation in the relationship between the fluid intelligence PRETEST and POSTTEST.

In the final HLM model, no significant mean difference in fluid intelligence scores measured in the Spring (POSTTEST) between treatment and control groups ( ${\overset{\land}{γ}}_{01}$ = 0.62, SE = 0.55, t(36) = 1.214, p = 0.26) were found after controlling for fall fluid intelligence scores (PRETTEST). In addition, the change in students’ fluid intelligence scores from fall to spring was not significantly different between treatment and control classrooms ( ${\overset{\land}{γ}}_{11}$ = 0.01, SE = 0.07, t(36) = 0.23, p = 0.82). Further, the residual variance of the POSTTEST means between teachers ( ${\overset{\land}{τ}}_{00}$ ) was 0.55, which was not statistically significant ( $χ^{2}$ (36) = 49.22, p = 0.07). Last, the residual variance of the slope related to the PRETEST ( ${\overset{\land}{τ}}_{01}$ ) was 0.032 and was statistically significant ( $χ^{2}$ (36) = 74.28, p < 0.01), inferring an unexplained variation in the slope of the PRETEST.

Effects on mathematics achievement

An unconditional two-level model was first performed on spring mathematics achievement measured to estimate the amount of variability between teachers. The estimated Level-1 ( ${\overset{\land}{σ}}^{2}$ ) and Level-2 ( ${\overset{\land}{τ}}_{00}$ ) variances were 177.09 and 378.09, respectively, leading to the estimated intra-class correlation (ICC) of 0.68. An ICC of 0.68 represents approximately 68% of the total variance in students’ mathematics achievement due to differences in Level-2 (i.e. the classroom teacher). In addition, the Level-2 random-effect ( ${\overset{\land}{τ}}_{00}$ ) was found to be statistically significant (χ²(37) = 925.85, p < 0.01), indicating that a significant variability exists in students’ mathematics achievement due to differences at Level-2.

The following two two-level HLM models were tested: 1) A model predicting mathematics achievement measured in the spring (POSTTEST) using one measured in the fall (PRETEST), and 2) A model predicting the Level-1 intercept and slope related to the PRETEST using GROUP as the predictor variable (intervention versus control classrooms). The first model showed that students’ spring mathematics achievement was significantly related to their fall mathematics achievement ( ${\overset{\land}{γ}}_{01}$ = 0.82, SE = 0.03, t(34) = 12.47, p < 0.01), indicating a significant increase in students’ mean mathematics achievement scores from fall to spring. The residual variance of the POSTTEST means was statistically significant at 32.58 ( $χ^{2}$ (33) = 192.28, p < 0.01). In addition, the residual variance of the slope related to PRETEST ( ${\overset{\land}{τ}}_{01}$ ) was 0.01, which was also statistically significant ( $χ^{2}$ (33) = 65.61, p < 0.01), inferring a significant variation remained after explaining spring mathematics achievement using baseline or fall mathematics achievement.

In the subsequent two-level HLM model, no significant mean differences in spring mathematics achievement were found between treatment and control groups ( ${\overset{\land}{γ}}_{01}$ = −0.10, SE = 2.25, t(33) = −0.05, p = 0.96) after controlling for fall mathematics achievement. However, the change in students’ mathematics achievement from fall to spring was significantly different between the treatment and control groups ( ${\overset{\land}{γ}}_{11}$ = 0.13, SE = 0.06, t(33) = 2.17, p = 0.04). In particular, as shown in Figure 1, the change in students’ mathematics achievement from fall to spring was significantly larger in the treatment groups when compared to the control groups. The random coefficient related to the POSTTEST means ( ${\overset{\land}{τ}}_{00}$ ) was statistically significant at 35.47 ( $χ^{2}$ (32) = 192.85, p < 0.01). In addition, the residual variance of the slope related to the PRETEST ( ${\overset{\land}{τ}}_{01}$ ) was 0.008, which was statistically significant ( $χ^{2}$ (32) = 55.76, p = 0.006), inferring an unexplained variation left in the slope of students’ fall mathematics achievement.

Figure 1.

Change in mathematics achievement by intervention groups.

Effects on reading achievement

An unconditional two-level model was first performed on reading achievement so as to estimate the amount of variability between teachers in the outcome. The estimated Level-1 ( ${\overset{\land}{σ}}^{2}$ ) and Level-2 ( ${\overset{\land}{τ}}_{00}$ ) variances were 209.51 and 392.29, respectively, which in turn resulted in the estimated intra-class correlation (ICC) of 0.65. An ICC of 0.65 represents that about 65% of the total variance in students’ reading achievement score exists due to differences in Level-2 (i.e. classroom teachers). A Level-2 random-effect ( ${\overset{\land}{τ}}_{00}$ ) was found to be statistically significant (χ²(37) = 825.51, p < 0.01), indicating that a significant variability exists in students’ reading achievement due to differences at Level-2.

The following two two-level HLM models were tested: 1) A model predicting reading achievement measured in the spring using reading achievement scores measured in the fall, and 2) A model predicting Level-1 intercept and slope related to baseline reading achievement using GROUP (intervention versus control classrooms) as the predictor. The first model shows that students’ spring reading achievement was significantly related to their fall reading achievement ( ${\overset{\land}{γ}}_{01}$ = 0.85, SE = 0.03, t(33) = 32.77, p < 0.01), indicating a significant increase in students’ mean reading achievement scores from fall to spring. The residual variance of the POSTTEST means was 10.41, which was statistically significant ( $χ^{2}$ (33) = 118.93, p < 0.01). In addition, the residual variance of the slope related to baseline reading achievement ( ${\overset{\land}{τ}}_{01}$ ) was 0.003, which was also statistically significant ( $χ^{2}$ (33) = 72.23, p < 0.01), inferring a significant variation left after explaining spring reading achievement using reading achievement.

In the subsequent two-level HLM model, no significant mean differences were found in the spring mean reading achievement scores between treatment and control groups ( ${\overset{\land}{γ}}_{01}$ = 1.71, SE = 1.42, t(32) = 1.21, p = 0.24) after controlling for fall reading achievement. In addition, the change in students’ reading achievement scores from fall to spring were not significantly different between treatment and control classrooms ( ${\overset{\land}{γ}}_{11}$ = 0.10, SE = 0.06, t(32) = 1.69, p = 0.10). The random coefficient related to mean reading achievement measured in the spring ( ${\overset{\land}{τ}}_{00}$ ) was 15.67, which was statistically significant ( $χ^{2}$ (32) = 118.97, p < 0.01). In addition, the residual variance of the slope related to the PRETEST ( ${\overset{\land}{τ}}_{01}$ ) was 0.007, was statistically significant ( $χ^{2}$ (32) = 69.53, p < 0.01), meaning there was unexplained variation left in the slope of baseline reading achievement.

Relationship between physical activity and fluid intelligence

On the two-level HLM model described above (A two-level HLM model predicting Level-1 intercept and slope related to baseline fluid intelligence using GROUP [intervention versus control classrooms] as the predictor), the following two Level-1 grand-mean centered predictors––steps per day and teacher reported activity in minutes per day––were added to examine the effect of physical activity on children’s fluid intelligence. However, neither of the physical activity variables was found to be significantly related to children’s mean fluid intelligence scores after controlling for baseline fluid intelligence ( ${\overset{\land}{γ}}_{20}$ = 0.002, SE = 0.003, t(19) = 1.21, p = 0.43 for steps per day; ${\overset{\land}{γ}}_{30}$ = −0.21, SE = 0.22, t(19) = −0.92, p = 0.37 for teachers’ reported activity in minutes per day). Further, the change in students’ fluid intelligence scores from fall to spring were not significantly different between treatment and control classrooms when the physical activity variables were used to explain the variance between intervention and control ( ${\overset{\land}{γ}}_{21}$ = −0.003, SE = 0.003, t(19) = −1.16, p = 0.26 for steps per day; ${\overset{\land}{γ}}_{31}$ = 0.16, SE = 0.32, t(19) = 0.52, p = 0.61 for teachers’ reported activity in minutes per day).

Relationship between physical activity and achievement

On the two-level HLM model described above (A two-level HLM model predicting Level-1 intercept and slope related to baseline achievement using GROUP [intervention versus control classrooms] as the predictor), the following two Level-1 grand-mean centered predictors––steps per day and teacher reported activity in minutes per day––were added to examine the effect of physical activity on each achievement outcome.

Effect on mathematics achievement

Neither of the physical activity variables was found to be significantly related to fall mathematics achievement after controlling for children’s baseline achievement score ( ${\overset{\land}{γ}}_{20}$ = −0.002, SE = 0.005, t(210) = −0.30, p = 0.76 for steps per day; ${\overset{\land}{γ}}_{30}$ = 0.46, SE = 0.45, t(210) = 1.01, p = 0.31 for teachers’ reported activity in minutes per day). Note that the random-effects associated with physical activity variables were fixed because it was found not significantly vary. Further, the change in students’ mathematics achievement scores from fall to spring were not significantly different between treatment and control classrooms when using the two physical activity variables to explain the variance between intervention and control classrooms ( ${\overset{\land}{γ}}_{21}$ = 0.005, SE = 0.005, t(210) = −0.30, p = 0.76 for steps per day; ${\overset{\land}{γ}}_{31}$ = −0.66, SE = 0.58, t(210) = −1.12, p = 0.26 for teachers’ reported activity in minutes per day).

Effect on reading achievement

Neither of the physical activity variables was found to be significantly related to fall mean reading scores after controlling for baseline reading ( ${\overset{\land}{γ}}_{20}$ = 0.004, SE = 0.008, t(210) = 0.52, p = 0.61 for steps per day; ${\overset{\land}{γ}}_{30}$ = −0.79, SE = 0.71, t(210) = −1.13, p = 0.26 for teachers’ reported activity in minutes per day). Note that the random-effects associated with physical activity variables were fixed because they did not significantly vary. Last, the change in students’ reading scores from fall to spring were not significantly different between treatment and control classrooms when using the two physical activity variables to account for the variance between intervention and control classrooms ( ${\overset{\land}{γ}}_{21}$ = 0.003, SE = 0.01, t(210) = 0.25, p = 0.80 for steps per day; ${\overset{\land}{γ}}_{31}$ = −0.19, SE = 0.91, t(210) = −0.21, p = 0.84 for teachers’ reported activity in minutes per day).

Discussion

Physical activity breaks did not result in significant increases in children’s fluid intelligence scores, although both the treatment and control classrooms saw slight increases in their scores from pre- to post-test. These findings are in contrast to one prior study that also investigated the influence of physical activity on children’s fluid intelligence. Reed and colleagues (2010) found that 90 minutes per week of integrated movement in the classroom significantly enhanced fluid intelligence as measured by the SPM fluid intelligence measures. Several factors potentially explain the divergence across study findings. The World Health Organization (2014) recommends 60 minutes of moderate to vigorous physical activity daily to improve one’s health concerning cardiorespiratory fitness, bone health, reduced risk of hypertension, and weight control. Physical activity breaks are in part intended to meet the 60 minute goal; however, many questions still surround the most beneficial amount, duration, and type of physical activity integrated into the school day for school-age children (Janssen & LeBlanc, 2010), particularly as it relates to improvements in fluid intelligence. There is evidence for the effectiveness of fundamental skills (i.e. running, hopping, and jumping) integrated for 30 minutes, three days a week (Reed et al., 2010); however, the present study’s use of standard movement cards 20 minutes, five days a week is at best promising as it relates to benefits to fluid intelligence. The incorporation of moderate to vigorous exercise and aerobic activities are often supported for their physical health and academic benefits (Floriani & Kennedy, 2008; Torrijos-Niño et al., 2014). Differences in the type of physical activity offered in these two studies could explain the divergent results. Furthermore, the differences in the length of interventions might also have an indirect effect on the results due to a possible loss of fidelity in teacher-led physical activity intervention over an eight-month period compared to a three-month period used in Reed’s study; however, the current study’s large sample (N = 486) might also lend itself to a more accurate finding than the previous study in this field (N = 155). In addition, the use of hierarchical linear modeling allows for the consideration of the hierarchical social structure of the schools involved in the study, which is unlike the data analysis utilized by Reed and colleagues (2010) that did not take into account these nested variables.

Results from the present study also indicate treatment classrooms experienced larger gains in mathematics achievement, but no differences were found due to the physical activity intervention in reading achievement. Interestingly, although not significant, control classrooms saw a slight decrease in mathematics and reading achievement scores while treatment classrooms experienced gains. Such findings possibly support an argument for the need for physical activity breaks as they (a) did not detrimentally influence children’s achievement and, instead, (b) may have enhanced achievement. Similar positive trends in physical fitness and academic skills have consistently been found when students are given physical activity breaks (Kubesch et al., 2009; Mahar, 2011; Mahar et al., 2006; Wadsworth, Robinson, Beckham, & Webster, 2012). Furthermore, a review of 50 studies examining the effectiveness of school-based physical activity on academic performance and achievement found that approximately half of the studies produced positive associations while half resulted in no effects, but no studies showed negative effects (CDC, 2010). Of those studies focusing specifically on classroom physical activities, eight of nine studies found positive associations on indicators such as academic achievement with no studies finding negative associations (CDC, 2010).

However, under further consideration, there may be other factors explaining the positive slope across mathematics and reading achievement in the treatment group given that the two physical activity variables examined in the present study did not explain the significant difference between treatment and control groups in the achievement outcomes. The two measurements used for physical activity (i.e. pedometer steps and teacher reported minutes in physical activity) did not explain a significant amount of the variance found in children’s fluid intelligence and achievement outcomes. As mentioned previously, a potential limitation could have been the fidelity of the teacher logs as several (N = 5) teachers neglected to complete their physical activity logs and some (N = 7) were not completing their logs consistently. The pedometer data collection procedures might have also been an additional limitation as it only provided a snapshot of physical activity given that these data were collected at three points throughout the year. More research on the effects of physical activity on fluid intelligence and academic achievement is needed utilizing triaxial, or three dimensional, accelerometers that have shown higher accuracy for physical activity measurement (Jakicic et al., 1999; Puyua, Adolph, Vohra, Zakeri, & Butte, 2004; Sirard & Pate, 2001), especially for moderate to vigorous activity as compared to walking-only interventions (Kinnunen et al., 2011). Some researchers have also recommended a combined approach to physical activity assessment for larger studies with the resources available to use the stronger construct validity of accelerometers and self-reports of activity (Harris et al., 2009). The analysis of accelerometer and Global Positioning System (GPS) data has also been utilized as an effective means to track where students are most active (Dessing et al., 2013). Future research might also benefit from more frequent data collection throughout the school year. Reed and colleagues (2010) also explored the influence of BMI and classifications using Fitnessgram’s Healthy Fitness Zone measure of fitness performance that was not a focus of the current study, but should be considered in future studies considering that Reed et al. (2010) found that students not reaching the Healthy Fitness Zone indicators showed lower SPM Fluid Intelligence scores.

Implications for school psychologists

As previously recognized, there are many difficulties to implementing physical activity breaks into the school day, including budgetary limitations, perceived divergence from curricular mandates, school personnel reported time concerns, and a lack of physical activity program training (Fedewa et al., 2013). Professionals promoting and implementing integrated physical activity breaks at school must address these concerns through a preventative, systematic approach. School psychologists are in a prime position to deconstruct perceived barriers to implementing additional physical activity throughout the school day by establishing a framework for school-based physical activity and promoting the potential benefits to improving all students’ physical and mental health. In fact, second to physical educators, school psychologists may be the most appropriate facilitators and promoters with training in both assessment and intervention to establish a physical activity program, conceivably in a response-to-invention (RTI) format (Fedewa et al., 2013; Pyle et al., 2006). In an RTI approach, school psychologists can target additional physical activity at the universal, secondary, and tertiary level. At the universal level, all students would receive additional physical activity breaks similar to the design of the present study. For those students who may have additional attention or behavioral concerns, additional smaller group or more individualized physical activity interventions could be integrated into the child’s school day via the secondary or tertiary level (Fedewa et al., 2013).

The trend away from school-based physical activity in combination with the rise of overweight and obese youth around the world creates a serious global public health issue. School psychologists are in a position to influence behaviors by way of interventions that take into account the multitude of variables related to children’s needs and how physical activity interventions may promote positive behavior and achievement. The amount of physical activity youth participate in declines as they age (CDC, 2012a) and, globally, one in three youth do not reach recommended activity levels (WHO, 2014). Therefore, it is imperative that physical activity as a preventative method against obesity and related risks is a focus early on for students around the world. Based on the results of the present study, students’ achievement levels may be improved by the implementation of additional physical activity breaks. In the very least, breaks were not found to be detrimental to students’ achievement outcomes and might very well be the impetus behind the increased reading and mathematics scores noted in the treatment classrooms. Schools are in a unique position as they have access to the majority of children and adolescents in society; thus, providing physical activity breaks integrated into the school day could provide a wide-array of immediate and long-term physical and academic benefits at practically no cost. The WHO (2014b) suggests that low physical activity is not an individual problem, but a societal problem; thus, school-based physical activity breaks may help in addressing a growing problem on a global level because so many youth can be reached there. Going forward researchers have the responsibility to further investigate the benefits of physical activity breaks so that school psychologists have the evidence base to support physical activity programs in their schools. A system-wide change towards physical activity in the schools is fundamental for both the individual student’s and society’s public health.

Footnotes

Author biographies

Alicia Fedewa, PhD, NCSP, is an Associate Professor in the Educational, School, and Counseling Psychology department at the University of Kentucky in the United States. She is a Licensed Psychologist and Certified School Psychologist. Dr Fedewa has published articles and presented on the effects of curricular physical activity on children’s mental health and learning outcomes. She teaches courses in consultation, psychological services in schools, and ethical/legal issues in school-based practice.

Soyeon Ahn, PhD, is an Associate Professor of Research, Measurement, and Evaluation program at the University of Miami in the United States. Her research involves methodological issues in the use of advanced statistical techniques such as Meta-analysis, Structural Equation Modeling, and Hierarchical Linear Modeling. She has also worked on several grant projects as a statistical consultant and teaches courses in advanced quantitative methods.

Heather Erwin, PhD, is an Associate Professor in the Department of Kinesiology and Health Promotion at the University of Kentucky in the United States. Her research interests include physical activity promotion for youth, primarily in the school environment. She teaches courses on physical education methods, adapted physical education, physical education for the classroom teacher, and teaching effectiveness.

Matthew C. Davis, MS, is a doctoral student and Research Assistant in the School Psychology program at the University of Kentucky. He has Master’s Degrees in both Educational Psychology and School Psychology. Cody’s scholarly work has primarily included studies examining the effects of school-based physical activity on behavioral and academic outcomes. Most recently, he presented a study examining the influence of physical activity integrated into the school curriculum on fluid intelligence and academic achievement.

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