Step to it – can physical activity improve kids’ cognition? A six-month longitudinal study

Step to it – can physical activity improve kids’ cognition? 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A six-month longitudinal study Adrian McPherson, Scott Duncan, Lisa MacKay, Jule Kunkel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5425164/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Identifying the relationships among physical activity, cognition, and academic performance in children is important for targeted public health and education initiatives. However, the majority of research has been cross-sectional in nature; we have a limited understanding of the causal direction of these associations. Therefore, aim of this study was to utilise longitudinal data to explore causal relationships among physical activity, cognition and academic performance in elementary school children. Methods Data were sourced from 675 New Zealand children aged 5–11 years. Weekday home, weekday school, and weekend physical activity was measured by multiple pedometer step readings, cognition by four measures from the CNS Vital Signs assessment, and academic performance from the New Zealand Ministry of Education Assessment Tools for Teaching and Learning (asTTle) reading and maths scores. Measures were taken at baseline, two months, and six-month intervals. Data were analysed for 632 students identified with data for at least half of the 27 variables. A generalised linear mixed model was used to investigate changes in physical activity, cognition and academic performance over those three time periods while adjusting for gender, school, age, and socioeconomic status. Results No significant relationships were identified between physical activity and three of the cognitive domains. However, significant, positive relationships were observed between physical activity change at two-months and (1) composite memory change at six-months, (2) maths proficiency change at two-months, and (3) math proficiency change at six-months. Regression coefficients suggest that a child who doubles step count - a 100% increase in PA - will affect a 3.7% improvement in maths proficiency after two months, and after six months affect a 2.6% improvement in maths proficiency and a 4.7% improvement in composite memory. Conclusions This six-month longitudinal analysis identified that an increase physical activity led to small but significant improvements in composite memory and maths proficiency. The small associations suggest that substantial improvements in PA would be required to generate meaningful improvements in cognition and academic achievement. However, timeframes longer than six-months are recommended to identify long-term changes. Physical activity cognition academic performance school children causation Figures Figure 1 Background Walking is one of the easiest, accessible forms of exercise. Almost obsessively, people monitor step counts aiming for a magical 10,000 steps a day for fitness. And with the growing body of research showing high levels of physical activity (PA) have been linked with cognitive benefits [ 1 – 9 ], can the simple act of walking and counting steps to promote PA have positive academic performance and learning outcomes for children? Exercise has exciting potential for improving both physical and cognitive abilities for children, but there is limited information on how the relationship between PA and cognition interacts, particularly causation. Cross-sectional studies show consistent relationships between PA and cognition [ 2 , 10 – 12 ]. Positive relationships have also been identified between PA and academic performance in school settings [ 11 , 13 – 16 ]. Our previous investigation on this subject group used structured equation modelling to demonstrate PA has independent relationships with both cognition and academic performance but could not ascribe causation [ 16 ]. Do smart children exercise, or does exercise make children smarter? To determine causation, changes in subjects’ performance and abilities need to be measured over time. Findings of four key longitudinal studies are detailed below. The Vanves study was completed in 1950, Paris, France [ 17 ]. Academic instruction was reduced by 26%, with a range of interventions added including PA in afternoons, but children were calmer, more attentive, and school results were comparable to other schools. The Trois Rivieres study analysed the effect of one hour extra PE for students taught by a specialist PE teacher over a six year period [ 18 ]. The control group received 13–15% more academic instruction than the experiment group. In the first year, the control group had higher average grades, but in Grades 2–6, the experiment group had higher grades, significantly in years 2, 3, 5, and 6 [ 18 ]. A secondary analysis of the ‘The Early Childhood Longitudinal Study (ECLS), Kindergarten Class of 1998 to 1999’ comparing children in low, medium and high activity groups also found girls in the high activity group had a small benefit in mathematics and reading, but there was no positive or negative association for boys [ 19 ]. In the last longitudinal study considered, Physical Activity Across the Curriculum (PAAC) was a 3-year cluster randomized controlled trial in 24 elementary schools in Kansas, USA, with a primary focus of decreasing BMI and improving physical health of students and a secondary aim to assess changes in academic achievement [ 3 ]. The experiment classes engaged in 90 minutes additional PA per week and were found to score significantly better than the control group for reading, writing, mathematics and oral language skills [ 3 ]. In further studies, Haapala et al studied 635 children aged 11–13 years, and found positive baseline correlations between Moderate to Vigorous Physical Activity (MVPA) and grade point average (GPA) [ 20 ]. MVPA was assessed by self-reported responses to the question, “Over the past 7 days, on how many days were you physically active for a total of at least 60 minutes per day?” Students were assessed almost two years later, when it was found MVPA was associated with better GPA in boys, but not girls. That finding differs with studies showing cognitive improvement with increased PA. The authors state contrasts may be due to their study controlling for academic achievement at baseline which is the strongest predictor of academic achievement at follow up [ 20 ]. That may be correct, but our previous study found the relationship between PA and academic performance independent of the child’s cognitive ability [ 16 ]. The lack of relationship between MVPA and GPA identified by the authors may be due to the poor reliability of self-reported data and the timeframe between the analyses. Also, although measures were taken at two timepoints, there were no analyses of subjects’ change over time, so this was effectively two cross-sectional studies and not a longitudinal study. In a study of 902 Danish school aged 7–12 years over a three-year period, academic performance was measured in maths and Danish, and PA was measured using an accelerometer for at least four full days at four time points [ 21 ]. Interestingly, they found both MVPA and sedentary time were directly associated with academic performance. It was theorised that sedentary time was associated with study time for this population. Wickel measured MVPA of 1364 children at baseline then again six years later [ 22 ]. At the six-year point, children’s cognition was also measured. Because there were no baseline cognitive measures, the study could only identify cross-sectional relationships between MVPA and cognition at the second timepoint. Against the theory PA improves cognition, the Wickel found sedentary time was positively associated with executive function and increases in PA were inversely related with executive function. However, the lack of baseline cognitive data and six-year period between MVPA assessments impact the ability to draw long-term conclusions. Hence, the author also acknowledges a gap in the knowledge base about the associations among PA, sedentary time and cognition in scientific literature. Petrigna et al, completed a systematic review to see if learning through movement improves academic performance in primary children [ 23 ]. They identified 54 articles finding a range of simple PAs had positive associations with maths, attention, and other academic scores. However, all of the interventions considered were integrated as part of the overall classroom curriculum making it difficult to ascribe relationships, and there were no longitudinal studies so it was not possible to identify causation. A thorough meta-analyses specifically investigating the relationship between PA and cognition in children was completed by Donnelly et al [ 24 ] in 2016. The authors started from 6,237 articles but using the 27 point Downs and Black checklist that considers methodology rigor [ 25 ], only identified 137 articles suitable to consider [ 24 ]. The Downs and Black checklist considers methodological strengths including validity characteristics, clarity of hypothesis and outcome measure details, participant compliance, and study power [ 25 ]. The review found PA has a positive influence on cognitive function as well as brain structure and function but noted limitations on conclusions due to weaknesses including a lack of information about estimates of random variability in the outcome data, statistical power not being stated, larger sample sizes needed, and lack of randomised controlled trials [ 24 ]. They only identified two longitudinal studies that were robust enough to be considered in their review [ 24 ]. Particularly, they advise more research is necessary to establish causality, to determine mechanisms, and to investigate long-term effects [ 24 ]. It is essential to consider the differences of measures and methodology to understand the PA, cognition, academic performance relationship. For example, a 2020 meta-analysis compared the effects on cognition of closed skill exercise (CSE) such as running and swimming with open skilled exercise (OSE) where environment and movement needs to be continually adapted such as basketball and soccer on children and adults [ 12 ]. Cross-sectional studies found OSE was superior to CSE with a small effect regarding cognitive performance, inhibition and cognitive flexibility. Among the four intervention studies examined, no significant differences were observed between OSE and CSE. Findings only partially supported the hypothesis that OSE is superior to CSE in terms of executive function. Further to the CSE OSE debate, an Italian study compared the effect of 30 minute bouts of CSE with OSE on short-term-memory on 125 children aged 7–10 years [ 26 ]. OSE had positive effects on STM for the full sample and of CSE had positive effects on STM for children 9–10 years, but not for children aged 7–8 years. These longitudinal studies show PA likely has causal links with cognition, but each study has shortcomings that limit conclusions on causation. It is difficult to generalise the Vanves findings because experimental sample was small, it is not clear how the control group was matched in terms of size and SES, and the treatment included more than just PA [ 17 ]. The ECLS study found differences between boys and girls [ 19 ], but none of the other studies consider gender effects. SES is recognised as one of the main influences on children’s academic success [ 13 , 27 , 28 ], but none of the papers adjust for SES. Therefore, the aim of this study was to explore causal relationships between PA, cognition, and academic performance over a six-month longitudinal period for primary school children aged 7–10 years. Methods Participants A total of 675 participants (326 male, 349 female) were part of an eight-week randomised controlled trial: Healthy Homework was a curriculum-based, classwork and homework schedule designed to promote PA and healthy eating [ 29 ]. Full details of the Healthy Homework programme are described in its pilot study [ 30 ]. All measurements were taken at baseline, immediately post-intervention, and six-month post-intervention. The study comprised eight control and eight experimental schools. For the purposes of this study, data from both groups were used for analysis – see later under ‘Statistical Analysis’ for rationale and statistical consideration. Eligibility criteria for the schools were as follows: a school with more than 100 students, location within Auckland or Dunedin cities, and a contributing, full primary, or composite structure that included at least one class each of students in school years 3–5. A total of 16 primary schools from Auckland (n = 10) and Dunedin (n = 6) were selected to participate in the study. Socioeconomic decile ratings of participating schools ranged from 3 to 10 (median [IQR] = 8 [ 6 , 9 ]). Decile is a New Zealand Ministry of Education (MoE) socioeconomic rating system for school funding based on SES with 1 being low and 10 being high. Decile is a rating of the whole school, and not specific to individual students. It is common to have children from a range of SES within one school, with socioeconomic decile being an average representation of the school’s surrounding area. Students were selected to participate from one Year 3, one Year 4, and one Year 5 class from each school; simple random sampling was used in instances where there were two or more classes per year. All children in each participating class were invited to take part in the evaluation (i.e., no formal inclusion or exclusion criteria). Written parental consent and assent was obtained for children to participate in the study. Ethical approval was obtained from the Auckland University of Technology Ethics Committee (10/159). The Healthy Homework study only measured changes in PA and diet and found the programme was successful resulted in substantial and consistent increases in PA and had limited effects on body size and fruit consumption [ 29 ]. That original study obtained data on academic performance and cognitive ability, but those were never analysed. This second analysis investigates longitudinal changes and relationships between PA, cognition and academic performance. Measures PA was assessed using sealed NL-1000 pedometers (New Lifestyles Inc, Lee’s Summit, MO) over five consecutive days (three weekdays, two weekend days). Research has established the validity of these NL-1000 pedometers for measuring steps in children [ 31 ]. NL-1000 pedometers have a multiday memory that automatically categorizes data according to the day of the week which enables step count for weekdays and weekends to be collected [ 32 ]. Pedometers were used to gain three measures of PA: average weekday steps at home, average weekday steps at school, and average steps at weekend. Image 1. NL-1000 pedometer used in the study to record children’s steps. The cognitive abilities of children were measured using CNS Vital Signs (CNSVS): a standardised cognitive screen assessment suitable for participants aged 7–90 years [ 33 ]. CNSVS is a web-based assessment battery with seven tests that are scored individually and combined to give scores in nine different areas. Four of the nine CNSVS domains were considered for this study: Composite Memory (recognize, remember, and retrieve words and geometric figures), Executive Function (recognize rules, categories, and manage or navigate rapid decision making), Psychomotor Speed (perceive, attend, respond to complex visual-perceptual information and perform simple fine motor coordination), and Reaction Time (react, in milliseconds, to a simple and increasingly complex direction set) [ 34 ]. The other domains could not be used because of the difficulty in administering the Complex Attention Test, and the four remaining domains used combinations of the same base assessment. Academic performance was measured using the New Zealand Ministry of Education electronic Assessment Tools for Teaching and Learning (e-asTTle). The e-asTTle assessments have more than 2,000 curriculum-based assessment items standardised on over 50,000 students covering curriculum levels 2—4 to assess student’s achievement and progress in reading, writing and mathematics and the New Zealand native language Māori equivalents of panui, tuhituhi, and pangarau [ 35 – 38 ]. Measures are norm-referenced and used to evaluate children’s progress through the school year [ 36 ]. Teachers create their own multi-choice assessment as the e-asTTle software generates a test that selects the best set of items meeting the teacher’s content and difficulty constraints [ 37 ]. For the purpose of this research, a research team conducted both the reading and maths assessments, which were done using pen and paper with a time limit. Researchers marked total scores (0–12), and results were entered into a computer by research assistants. Testing was completed within 10 minutes. The e-asTTle software converts raw scores into measures that align with a child’s curricular needs [ 37 ]. Raw scores were sufficient for the current analyses because they give a measure of academic performance for students in relation to peers of the same school year. Demographic information was obtained from the school records and included gender, age, school, ethnicity and decile. Study Protocol Two pedometers were assigned to each child: one clearly labelled ‘School’ and the other ‘Home’. The ‘School’ pedometer was worn during school hours, while the ‘Home’ pedometer was left inside; a collection tray in the classroom. At the end of the school day, each child placed their ‘School’ pedometer in the tray and attached their ‘Home’ pedometer. Parents were given instructions how to attach the ‘Home’ pedometer to the child when he/she got up in the morning and take it off when before going to bed at night. Upon arrival at school the next day, the teacher reminded the children to switch over their pedometers again. Pedometers were issued to children and height and weight measures taken on one a separate day within a month by trained researchers. For each school, CNSVS and e-asTTle baseline measures were collected by a team of researchers on one day. CNSVS assessment was completed before the e-asTTle test, with at least 30 minutes between the two. The CNSVS assessment was conducted in groups using school computer facilities or libraries and assisted by at least three researchers. Group sizes and types of computers depended on the facilities and computers provided by the school. Researchers introduced the test beforehand while each instruction for each test appeared on the screen before each test started. CNSVS was introduced for the research purposes and not part of routine school assessment practice. Thus, as it is not part of the students’ normal education practice and procedures, they may have struggled with it being an unfamiliar task and not necessarily had difficulty with the cognitive demands and content. Researchers were available for the children in case they did not understand the instructions or if children clicked it away too quickly. The e-asTTle assessments were introduced and explained by the researchers. While the attitude questions were read out by the researchers, waiting for all children to go through them and ensuring that they understand them, the reading test was then conducted before the math test with a time limit of ten minutes. Students are used to e-asTTle assessments through the year as part of their normal school routines. Statistical Analysis All variables were checked for normality, skewness and outliers. Three students were identified to have special needs and removed from the analysis because the cognitive and academic measures are not specific enough to cater for their needs and abilities. The distribution of the CNSVS composite memory item was skewed positively, but that reflects what is to be expected in the general population thus data were not transformed [ 33 ]. The other CNSVS measures were normally distributed. The two asTTle variables were normally distributed with no problematic outliers. One problematic outlier was identified with weekday steps which was clearly a data entry mistake. As all other variables were appropriate for the subject, the weekday steps value was removed and a new value was imputed later as part of the missing values analysis (MVA). The final analyses used the total weekly steps, which was gained by the formula: (mean weekday steps home x 5) + (mean weekday steps school x 5) + (mean weekend steps x 2). The extent of missing values was assessed on the full study cohort. To minimize loss of data, subjects with data for at least half of variables included in the final model were retained. Our study sample was reduced from 675 to 632. Details of missing data from the 675 and 632 subjects is included in Appendix 1. An MVA was completed on the three pedometer step readings. Data were not found to be Missing Completely at Random (MCAR; Little's MCAR test: Chi-Square = 684.058, DF = 595, Sig. = 0.007). The researchers inspected the data visually and could not see any patterns for missing IV data, so data was presumed to be Missing at Random (MAR). Expectation Maximisation (EM) was then used to impute missing values for the IVs. An MVA was then completed on the four CNSVS measures and two asTTle measures. The data were not found to be MCAR (Little's MCAR test: Chi-Square = 1358.221, DF = 1172, Sig. = 0.000). Based on inspection of missing data patterns, data are assumed to be MAR. Missing data for CNSVS and asTTle measures is due to a child not being present in class when the test was being taken. EM was then used to impute missing values. In a detailed study, Dong and Peng found as long as data are MAR, EM data imputation produced statistically significant results to p < .001 when removing 20%, 40% and 60% data from a complete dataset of 432 subjects [ 39 ]. This present study did not evaluate differences between experiment and control groups. Whilst the intervention of the experiment group aimed to improve diet and PA, there were no interventions directed at cognition and academic performance. This present study sought to investigate changes over time in the new areas of cognition and academic performance, and their relationship to PA. The original study followed RCT methodology including randomly assigning schools to experiment and control group. Thus, adjusting school effects in our analyses account in for any potential effect of both school clusters and the RCT intervention impact on the experiment group. Furthermore, adjusting for school effects also addresses any possible actual or placebo benefit caused differences subjects’ performance. Changes in the total weekly PA, the six cognitive domains, and the two academic outcomes were analysed over the two-month and six-month periods using generalised linear mixed models (GLMMs). GLMM was the most appropriate methodology for analysis because we measured change in the population as a whole, and did not analyse differences between groups. Physical activity change over two months was compared with cognitive and academic change over both two and six months (12 models in total). The GLMM analysis adjusted for fixed (age, gender, decile) and random (subjects nested in schools) effects. Although decile is different from SES as stated in methods, adjusting for decile has the same effect as adjusting for SES. All analyses were completed using IBM SPSS 24 (Armonk, NY: IBM Corp). Anthropometric data of subjects was obtained and used in the initial analyses, but analyses showed no difference in findings with or without that data. The final analyses did not include anthropometric data to increase model parsimony. Results Assumptions Demographic data for the full 632 students from this analysis (48.7% male) aged 5.2–10.8 years residing in New Zealand were available for analyses (Table 1 ). There was an even spread of children across the three school years (3: 32.3%, 4: 34%, 5: 33.7%). The majority of students were of New Zealand European ethnicity (69.9%). Students were from schools of predominantly high socioeconomic decile. Tables 2 and 3 show the with mean, median, SD and inter-quartile range for step counts and cognitive/academic data (respectively) for the 632 students considered in this analysis, with EM imputations for missing data. Table 2 and Fig. 1 show total average steps for students was consistent for the three timepoints and that weekend day average steps were lower than weekday average steps. Table 1 Sociodemographic characteristics of the study sample. Age Male Female Total N M + SD Min + Max N M + SD Min + Max N M + SD Min + Max School Year 3 98 7.74, ± 0.56 6.65, 9.21 106 7.72, ± 0.67 6.48, 9.25 204 7.73, ± 0.62 6.48, 9.25 School Year 4 104 8.70, ± 0.60 7.60, 9.86 111 8.72, ± 0.66 5.21, 9.89 215 8.71, ± 0.64 5.22, 9.89 School Year 5 106 9.64, ± 0.52 8.11, 10.84 107 9.74, ± 0.65 6.88, 10.8 213 9.69, ± 0.59 6.88, 10.8 Total 308 8.72, ± 0.96 6.65, 10.8 324 8.73, ± 1.05 5.22, 10.8 632 8.73, ± 1.00 5.22, 10.8 Ethnicity Male Female TOTAL Māori 19 (6.2%) 25 (7.7%) 44 (7%) Pacific Island 12 (3.9%) 11 (3.4%) 23 (3.6%) Asian 34 (11.0%) 70 (21.6%) 104 (16.5%) Other 9 (2.9%) 12 (3.7%) 21 (3.3%) NZ European 234 (76%) 206 (63.6%) 440 (69.6%) Total 308 324 632 Decile Decile 3 23 (7.5%) 20 (6.2%) 43 (6.8%) Decile 4 9 (2.9%) 11 (3.4%) 20 (3.2%) Decile 5 13 (4.2%) 38 (11.7%) 51 (8.1%) Decile 6 45 (14.6%) 52 (16%) 97 (15.3%) Decile 7 46 (14.9%) 48 (14.8%) 94 (14.9%) Decile 8 60 (19.5%) 47 (14.5%) 107 (16.9%) Decile 9 47 (15.3%) 40 (12.3%) 87 (13.8%) Decile 10 65 (21.1%) 68 (21%) 133 (21%) Total 308 324 632 School year Year 3 98 (31.8%) 106 (32.7%) 204 (32.3%) Year 4 104 (33.8%) 111 (34.3%) 215 (34%) Year 5 106 (34.4%) 107 (33%) 213 (33.7%) Total 308 324 632 Table 2 Descriptive statistics of the three step count measurements. Baseline Mean Median SD 25th %ile 50th %ile 75th %ile Weekday (home) 5118 4995 2027 3896 4996 6054 Weekday (school) 5447 5219 2118 3988 5219 6410 Weekend day 7507 7235 3448 5337 7235 8785 Daily average* 9691 9490 2914 7728 9490 11211 Two-months Mean Median SD 25th %ile 50th %ile 75th %ile Weekday (home) 5288 5214 2193 3972 5214 6288 Weekday (school) 5944 5796 2397 4400 5796 5796 Weekend day 8279 7899 3433 6244 7899 9774 Daily average 10388 10121 3139 8264 10121 12195 Six-months Mean Median SD 25th %ile 50th %ile 75th %ile Weekday (home) 5235 5214 2179 3833 5214 6081 Weekday (school) 5997 5796 2190 4734 5796 6891 Weekend day 7909 7899 3225 6107 7899 9200 Daily average 10283 10121 3028 8357 10121 11971 Table 3 Descriptive statistics of the dependent variables of four cognitive domains and two academic domains for 632 students considered in final analyses including EM imputed data for missing values. Baseline Mean Median SD 25th %ile 50th %ile 75th %ile Composite Memory 73 76 27.6 54 76 95 Executive Functioning 95 96 15.3 84 96 105 Psychomotor Speed 93.9 94.9 13 85 94.9 102 Reaction Time 95.6 95.5 18.9 83 95.5 107 Reading Proficiency 5.08 5.83 3.03 2 5.83 8 Maths Proficiency 6.06 6 3.2 3.57 4 6 Two-months Mean Median SD 25th %ile 50th %ile 75th %ile Composite Memory 75.7 78 25.9 58 78 95 Executive Functioning 103 102 16 92 102 114 Psychomotor Speed 97.3 97 13.6 89 97 106 Reaction Time 96.6 96 18.2 86 96 108 Reading Proficiency 6.14 6 2.59 4 6 8 Maths Proficiency 6.56 6.66 2.87 4 6.66 9 Six-months Mean Median SD 25th %ile 50th %ile 75th %ile Composite Memory 79 79 22.7 67 79 95 Executive Functioning 104 103 15 96 103 115 Psychomotor Speed 97.4 96.2 12.3 89.5 96.1 105 Reaction Time 94.2 95.5 16.6 84 95.4 104 Reading Proficiency 6.34 6 2.42 5 6 8 Maths Proficiency 7.39 7 2.38 6 7 9 Table 4 shows the mean change for cognitive and academic domains at the two-month and six-month intervals from the 12 generalised mixed models, adjusted for 2-month physical activity change, age, sex, socioeconomic status (decile), and school clustering. The β coefficient indicates the percentage change to each domain associated with a 1% increase in PA at two-months. Significant, positive relationships were observed between PA change and composite memory change at six-months (0.021), and nearing significance for change in composite memory at two-months (0.051). PA change and maths proficiency change were significant at two-months (0.019) and six-months. at six-months (0.034). No other associations were significant. Table 4 Associations between changes in physical activity at two-months with changes in cognitive/academic outcomes at two-months and six-months. Domain Mean % change (LCL, UCL) β (LCL, UCL) P Composite Memory change at 2 months 19.5 (14.0, 25.4) 0.048 (0.000, 0.097) 0.051 at 6 months 29.7 (22.9, 36.8) 0.047 (0.007, 0.087) 0.021 Reaction Time change at 2 months 3.55 (1.68, 5.58) -0.009 (-0.157, 0.139) 0.904 at 6 months 1.73 (-0.114, 3.72) -0.028 (-0.176, 0.121) 0.714 Psychomotor Speed change at 2 months 4.68 (3.62, 5.77) -0.013 (-0.260, 0.234) 0.917 at 6 months 5.10 (3.83, 6.37) -0.012 (-0.240, 0.216) 0.921 Executive Function change at 2 months 9.31 (7.92, 10.5) 0.123 (-0.083, 0.330) 0.242 at 6 months 11.0 (9.60, 12.5) 0.138 (-0.055, 0.331) 0.162 Reading Proficiency change at 2 months 72.9 (63.1, 84.0) 0.011 (-0.014, 0.037) 0.385 at 6 months 84.8 (72.2, 98.0) 0.010 (-0.014, 0.034) 0.402 Maths Proficiency change at 2 months 41.5 (32.9, 50.5) 0.037 (0.006, 0.068) 0.019 at 6 months 70.3 (58.5, 82.4) 0.026 (0.002, 0.050) 0.034 β = standardised coefficient; LCL = lower 95% confidence limit, UCL = upper 95% confidence limit using bias-corrected bootstrapping. Discussion To our knowledge, this is the first study investigating the effects of changes in PA on change in cognitive ability and academic performance in school children over a six-month period. Our findings suggest that small gains in specific areas of cognition and academic function – namely composite memory and maths – can be obtained with increased PA. That is acknowledged by Ziv et al, who say findings regarding the effects of exercise on cognitive performance are also usually modest [ 40 ]. Although the gains indicated are small, success is a series of small victories, and they could represent a meaningful impact for children and their learning. For example, a 1% increase in PA after two months was associated with a 0.037% increase in maths proficiency, and after six months was associated with a 0.047% increase in composite memory and 0.026% increase for maths proficiency. Thus, if students doubled their PA (100% increase) with a simple, closed skill exercise activity as walking, that would theoretically affect a 3.7% or 2.6% increase in maths proficiency and a 4.7% improvement in composite memory. The present results concur with other studies that have found increased PA is associated with improvement in executive function, memory and maths [ 14 , 41 ]. Other longitudinal studies have indicated that PA has a positive impact on maths and reading scores [ 3 , 19 ]. Importantly, the present analyses adjusted for potential confounding factors such as gender [ 19 ], age [ 10 , 11 ], and the impact of SES through the socioeconomic decile differences between schools [ 13 , 27 , 28 ]. This study found that the significant relationships PA had on composite memory and maths proficiency is independent of such confounding factors. Studies have highlighted possible methodological flaws that may bias research to support the PA-cognition relationship [ 40 ] [ 42 , 43 ]. For example, randomised control trials (RCT) may have a tendency to have higher performing subjects in the experiment group and pretest and post-test equivalence need to be verified [ 42 ]. As our study used data from control and experiment groups from a RCT trial and adjusted for differences between subjects and schools, neither of these were an issue. Ciria et al, say there is a preference for RCT studies which are seen as a gold-standard to ascertain causal links, but other sources of empirical evidence, such as observational or epidemiological studies, should also be considered in their ability to determine causation [ 43 ]. Our study is an observational longitudinal study with rigorous methodology that identified small but valid changes inferring causation. Ziv et al note with the placebo effect, it is important control and experiment groups have similar expectations of input [ 40 ]. Our experiment and control groups had similar input with all students given pedometers to motivate them. Again, to counter such a bias, our study pooled data and adjusted from both groups, thus negating such concerns. Publication bias finds studies with large and positive changes are more prevalent [ 43 ], whereas the effects of PA on cognitive performance are usually modest [ 40 ]: "We believe this exponential accumulation of low-quality evidence has led to stagnation rather than advance in the field hindering the discernment of the real existing effect." [ 43 ] Our study did not identify any significant relationships between increased PA and the three other cognitive tested or reading proficiency. Most other longitudinal studies that investigated the relationship between PA and cognition analyse change over periods longer than six-months [ 3 , 18 , 19 , 44 ]. It is possible that two-months and six-months were not a long enough time span to notice gradual cognitive changes. In addition, the measures used in this study have potential limitations. Pedometers give a valid and reliable indicator of overall volume of physical activity and have been used widely among student populations [ 31 ], but do not consider the intensity of the steps or time of day. High intensity aerobic activity and activity immediately prior cognitive assessment have been linked to greater cognitive function and academic performance [ 2 , 4 , 11 ]. Furthermore, pedometers do not monitor other aspects of fitness that have been linked to cognitive function such as acute effects of activity, cardiorespiratory fitness, resistance exercise, or combinations of open skill exercise and activity [ 11 , 13 , 15 ]. Similarly, the two e-asTTle measures are well researched and robust, but additional school-based assessments such as writing could provide greater insights to children’s academic performance. The CNSVS measures used in this study give a good insight to cognitive function, but as a screen assessment CNSVS may not have been sensitive enough to detect changes in some areas. Thus, a significant relationship was only identified in composite memory. Students were not familiar with the CNSVS assessment and thus results may reflect this unfamiliarity rather than difficulty with the cognitive demands and content. A last possible limitation is use of school decile as a measure of SES. It would have been better to have the SES for each individual child and not the school as a whole. Lastly, the lack of relationships may simply mean there were no relationships with increased PA and those domains with this population in this study. Identifying causation is one of the key questions in the PA/cognition field. Are smart children active or does being active make children smart? The results of this study provide some evidence that the more a child increases PA, the greater the improvement in memory and maths after six months. To examine the relationships further, future studies could consider wider ranges of PA, robust paediatric cognitive assessment, alternative academic performance measures. Further, as this study demonstrated small cognitive and academic gains over a short period, future studies should be completed over longer timeframes which will give greater opportunity to identify how changes in PA can make larger quantifiable changes in cognition and academic performance. Conclusions This six-month longitudinal study provides some support for the theory that increased PA improves cognition and academic performance in children. The analysis identified after adjustment for age, sex, socioeconomic status, and school clustering, increased PA was associated with small but significant improvements in composite memory and maths but not for executive function, psychomotor speed, reaction time, or reading proficiency. While this reinforces that PA may have a role to play in children’s learning, the relatively small magnitude of the associations suggests that substantial improvements in PA would be required to generate meaningful improvements in cognition and academic achievement. Further research into the long-term effects of PA on brain function would provide additional information regarding the potential benefits of increasing PA in school children. Abbreviations CSE: Closed skill exercise OSE: Open skill exercise CNSVS: CNS Vital Signs GLMM: General Linear Mixed Model MAR: Missing at random MCAR: Missing completely at random MNAR: Missing not at random MVA: Missing values analysis MVPA: Moderate to Vigorous Physical Activity PA: Physical Activity PE: Physical education RCT: Randomized control trial SES: Socio-economic status Declarations Acknowledgements The authors would like to thank all participating schools, children, and parents for contributing their time and effort to this study. I would also like to thank my son, Campbell McPherson, for his assistance in the data preparation. Funding This study was funded by a project grant from the Health Research Council of New Zealand (10/207). The funder had no involvement in the design of the study, the collection, analysis or interpretation of the data, or in writing the manuscript. Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to confidentiality but are available from the corresponding author on reasonable request. Authors’ contributions AM Study design, analysis and manuscript preparation. SD Study design, data curation, and manuscript review. LM Contribution to analysis and manuscript review. JK Study design, data collection, and manuscript review. All authors read and approved the final manuscript. Ethics approval and consent to participate Written informed parental consent and personal assent was obtained from each participant for the collection and use of the data in future publication. Ethical approval for the study was obtained from the Auckland University of Technology Ethics Committee (10/159). Consent for publication Not applicable. 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Black, The Feasibility of Creating a Checklist for the Assessment of the Methodological Quality Both of Randomised and Non-Randomised Studies of Health Care Interventions . 1998, British Medical Association. p. 377. Giovanni Ottoboni, A.C., Alessia Tessari, The Effect of Structured Exercise on Short-Term Memory Subsystems: New Insight on Training Activities. International Journal of Environmental Research and Public Health, 2021. 18 (14): p. 1-10. Trudeau, F. and R.J. Shephard, Physical education, school physical activity, school sports and academic performance . 2008. Florence, M.D., M. Asbridge, and P.J. Veugelers, Diet Quality and Academic Performance. Journal of School Health, 2008. 78 (4): p. 209-215. Duncan, S.J., et al., Healthy Homework: A Physical Activity and Nutrition Intervention for Children Research Project Full Application (GA210F) . 2010, Auckland University of Technology: Auckland: New Zealand. Duncan, S.J., et al., Efficacy of a compulsory homework programme for increasing physical activity and healthy eating in children: the healthy homework pilot study. International Journal of Behavioral and Nutrition and Physical Activity, 2011. 8 (127). Duncan, S., et al., Effects of age, walking speed, and body composition on pedometer accuracy in children. Research quarterly for exercise and sport, 2007. 78 (5): p. 420-428. Duncan, E.K., J.S. Duncan, and G. Schofield, Pedometer-determined physical activity and active transport in girls. International Journal of Behavioral Nutrition & Physical Activity, 2008. 5 : p. 1. Gualtieri, T. and L.G. Johnson, Reliability and validity of a computerized neurocognitive test battery, CNS Vital Signs. Archives of Clinical Neuropsychology, , 2006. 21 (7): p. 623-643. CNS Vital Signs - Clinical Practice Test Domains . 2018 [cited 2018 2018, June 18]; Available from: http://www.cnsvs.com/ClinicalPractice.html. Ministry of Education. e-asTTle, www.e-asttle.tki.org.nz , New Zealand. 2016. Hattie, J.A.C., et al. Validation Evidence of asTTle Reading Assessment Results: Norms and Criteria. asTTle Tech. Rep. 22. 2003. Hattie, J.A.C., G.T.L. Brown, and P.J. Keegan, A National Teacher-Managed, Curriculum-Based Assessment System. International Journal of Learning, 2003. 10 : p. 771-778. Lavery, L. and G.T.L. Brown, Overall Summary of Teacher Feedback from the Calibrations and Trials of the asTTle Reading, Writing, and Mathematics Assessments Technical Report 33, University of Auckland, 2002: p. 1-7. Dong, Y. and C.Y.J. Peng, Principled missing data methods for researchers . Ziv, G.L., O.; Netz, Y., Selecting an appropriate control group for studying the effects of exercise on cognitive performance. Psychology of Sport and Exercise, 2024. 72 . Hollar, D., et al., Effect of a two-year obesity prevention intervention on percentile changes in body mass index and academic performance in low-income elementary school children. American Journal of Public Health, 2010. 100 (4): p. 646-653. Liu, S.L., J.C.; Tenenbaum, G., Does Exercise Improve Cognitive Performance: A Conservative Message from Lord's Paradox. Frontiers in Psychology, 2016. 7 . Ciria, L.F.R.-C., R; Vadillo, M.A.; Holgado, D.; Luque-Casado, A.; Perakakis, P.; Sanabria, D., An umbrella review of randommized control trials on the effects of physical exercise on cognition. Nature Human Behaviour, 2023. 7 (6). Dwyer, T., et al., An investigation of the effects of daily physical activity on the health of primary school students in South Australia. International Journal of Epidemiology, 1983. 12 (3): p. 308-313. Image 1 Image 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Image1.png Image 1. NL-1000 pedometer used in the study to record children’s steps. Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5425164","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383648028,"identity":"66cd4f41-7986-4387-a42d-6b33853e74f4","order_by":0,"name":"Adrian McPherson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACfigtByIOPCBGi2QDhDYGa0kgRovBAQidCNZKlBaGa4efbvhQUZc+P+zwQ6AtdnK6DQR0MM5OM7s548zh3I230wyAWpKNzQ4Q0MIsncN2m7ftQO7G2QkgLQcStxHSwgbRUpduODv9A3FaeCBamBPkpXOItEVCGuIXww3SOQUHEgyI8Iv97eRnN4AhJi8/O33zhw8VdnIEtcABJIIMiFUOAvINpKgeBaNgFIyCEQUAcU5HEJbpqHYAAAAASUVORK5CYII=","orcid":"","institution":"Auckland University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Adrian","middleName":"","lastName":"McPherson","suffix":""},{"id":383648029,"identity":"9a2b5a54-eb26-443f-ae80-2f3db4a7e2fa","order_by":1,"name":"Scott Duncan","email":"","orcid":"","institution":"Auckland University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Scott","middleName":"","lastName":"Duncan","suffix":""},{"id":383648030,"identity":"64859dd7-77ed-4d42-a653-9ce01d1945f4","order_by":2,"name":"Lisa MacKay","email":"","orcid":"","institution":"Auckland University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"MacKay","suffix":""},{"id":383648031,"identity":"548546ef-682c-442c-9698-eaedf36b2770","order_by":3,"name":"Jule Kunkel","email":"","orcid":"","institution":"Auckland University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jule","middleName":"","lastName":"Kunkel","suffix":""}],"badges":[],"createdAt":"2024-11-10 09:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5425164/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5425164/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71605858,"identity":"cbdedff4-7595-49c6-96a5-7b9c6e9f89b3","added_by":"auto","created_at":"2024-12-17 06:11:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":185207,"visible":true,"origin":"","legend":"\u003cp\u003eGraph of average daily steps at three timepoints in study.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5425164/v1/64a1bef0426423b596322b6e.png"},{"id":73134226,"identity":"ac3a96f5-911d-407c-930c-90bf6cd30341","added_by":"auto","created_at":"2025-01-07 05:38:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1070855,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5425164/v1/7f0942e9-a631-445e-9fd4-7525298a3996.pdf"},{"id":71605856,"identity":"ce9df43a-e1fe-4852-ad40-64859f4cf124","added_by":"auto","created_at":"2024-12-17 06:11:55","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":284758,"visible":true,"origin":"","legend":"\u003cp\u003eImage 1. NL-1000 pedometer used in the study to record children’s steps.\u003c/p\u003e","description":"","filename":"Image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5425164/v1/76f79c26e0c01d45095659db.png"},{"id":71605857,"identity":"53130f30-47c7-4b9e-8429-783337ec7b44","added_by":"auto","created_at":"2024-12-17 06:11:55","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":54192,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5425164/v1/59724b9ebe4abaa3d502f8cb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Step to it – can physical activity improve kids’ cognition? A six-month longitudinal study","fulltext":[{"header":"Background","content":"\u003cp\u003eWalking is one of the easiest, accessible forms of exercise. Almost obsessively, people monitor step counts aiming for a magical 10,000 steps a day for fitness. And with the growing body of research showing high levels of physical activity (PA) have been linked with cognitive benefits [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], can the simple act of walking and counting steps to promote PA have positive academic performance and learning outcomes for children?\u003c/p\u003e \u003cp\u003eExercise has exciting potential for improving both physical and cognitive abilities for children, but there is limited information on how the relationship between PA and cognition interacts, particularly causation. Cross-sectional studies show consistent relationships between PA and cognition [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Positive relationships have also been identified between PA and academic performance in school settings [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our previous investigation on this subject group used structured equation modelling to demonstrate PA has independent relationships with both cognition and academic performance but could not ascribe causation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Do smart children exercise, or does exercise make children smarter? To determine causation, changes in subjects\u0026rsquo; performance and abilities need to be measured over time.\u003c/p\u003e \u003cp\u003eFindings of four key longitudinal studies are detailed below. The Vanves study was completed in 1950, Paris, France [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Academic instruction was reduced by 26%, with a range of interventions added including PA in afternoons, but children were calmer, more attentive, and school results were comparable to other schools. The Trois Rivieres study analysed the effect of one hour extra PE for students taught by a specialist PE teacher over a six year period [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The control group received 13\u0026ndash;15% more academic instruction than the experiment group. In the first year, the control group had higher average grades, but in Grades 2\u0026ndash;6, the experiment group had higher grades, significantly in years 2, 3, 5, and 6 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A secondary analysis of the \u0026lsquo;The Early Childhood Longitudinal Study (ECLS), Kindergarten Class of 1998 to 1999\u0026rsquo; comparing children in low, medium and high activity groups also found girls in the high activity group had a small benefit in mathematics and reading, but there was no positive or negative association for boys [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the last longitudinal study considered, Physical Activity Across the Curriculum (PAAC) was a 3-year cluster randomized controlled trial in 24 elementary schools in Kansas, USA, with a primary focus of decreasing BMI and improving physical health of students and a secondary aim to assess changes in academic achievement [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The experiment classes engaged in 90 minutes additional PA per week and were found to score significantly better than the control group for reading, writing, mathematics and oral language skills [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn further studies, Haapala et al studied 635 children aged 11\u0026ndash;13 years, and found positive baseline correlations between Moderate to Vigorous Physical Activity (MVPA) and grade point average (GPA) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. MVPA was assessed by self-reported responses to the question, \u0026ldquo;Over the past 7 days, on how many days were you physically active for a total of at least 60 minutes per day?\u0026rdquo; Students were assessed almost two years later, when it was found MVPA was associated with better GPA in boys, but not girls. That finding differs with studies showing cognitive improvement with increased PA. The authors state contrasts may be due to their study controlling for academic achievement at baseline which is the strongest predictor of academic achievement at follow up [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. That may be correct, but our previous study found the relationship between PA and academic performance independent of the child\u0026rsquo;s cognitive ability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The lack of relationship between MVPA and GPA identified by the authors may be due to the poor reliability of self-reported data and the timeframe between the analyses. Also, although measures were taken at two timepoints, there were no analyses of subjects\u0026rsquo; change over time, so this was effectively two cross-sectional studies and not a longitudinal study.\u003c/p\u003e \u003cp\u003eIn a study of 902 Danish school aged 7\u0026ndash;12 years over a three-year period, academic performance was measured in maths and Danish, and PA was measured using an accelerometer for at least four full days at four time points [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Interestingly, they found both MVPA and sedentary time were directly associated with academic performance. It was theorised that sedentary time was associated with study time for this population. Wickel measured MVPA of 1364 children at baseline then again six years later [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. At the six-year point, children\u0026rsquo;s cognition was also measured. Because there were no baseline cognitive measures, the study could only identify cross-sectional relationships between MVPA and cognition at the second timepoint. Against the theory PA improves cognition, the Wickel found sedentary time was positively associated with executive function and increases in PA were inversely related with executive function. However, the lack of baseline cognitive data and six-year period between MVPA assessments impact the ability to draw long-term conclusions. Hence, the author also acknowledges a gap in the knowledge base about the associations among PA, sedentary time and cognition in scientific literature.\u003c/p\u003e \u003cp\u003ePetrigna et al, completed a systematic review to see if learning through movement improves academic performance in primary children [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. They identified 54 articles finding a range of simple PAs had positive associations with maths, attention, and other academic scores. However, all of the interventions considered were integrated as part of the overall classroom curriculum making it difficult to ascribe relationships, and there were no longitudinal studies so it was not possible to identify causation.\u003c/p\u003e \u003cp\u003eA thorough meta-analyses specifically investigating the relationship between PA and cognition in children was completed by Donnelly et al [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] in 2016. The authors started from 6,237 articles but using the 27 point Downs and Black checklist that considers methodology rigor [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], only identified 137 articles suitable to consider [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The Downs and Black checklist considers methodological strengths including validity characteristics, clarity of hypothesis and outcome measure details, participant compliance, and study power [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The review found PA has a positive influence on cognitive function as well as brain structure and function but noted limitations on conclusions due to weaknesses including a lack of information about estimates of random variability in the outcome data, statistical power not being stated, larger sample sizes needed, and lack of randomised controlled trials [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. They only identified two longitudinal studies that were robust enough to be considered in their review [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Particularly, they advise more research is necessary to establish causality, to determine mechanisms, and to investigate long-term effects [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is essential to consider the differences of measures and methodology to understand the PA, cognition, academic performance relationship. For example, a 2020 meta-analysis compared the effects on cognition of closed skill exercise (CSE) such as running and swimming with open skilled exercise (OSE) where environment and movement needs to be continually adapted such as basketball and soccer on children and adults [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Cross-sectional studies found OSE was superior to CSE with a small effect regarding cognitive performance, inhibition and cognitive flexibility. Among the four intervention studies examined, no significant differences were observed between OSE and CSE. Findings only partially supported the hypothesis that OSE is superior to CSE in terms of executive function. Further to the CSE OSE debate, an Italian study compared the effect of 30 minute bouts of CSE with OSE on short-term-memory on 125 children aged 7\u0026ndash;10 years [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. OSE had positive effects on STM for the full sample and of CSE had positive effects on STM for children 9\u0026ndash;10 years, but not for children aged 7\u0026ndash;8 years.\u003c/p\u003e \u003cp\u003eThese longitudinal studies show PA likely has causal links with cognition, but each study has shortcomings that limit conclusions on causation. It is difficult to generalise the Vanves findings because experimental sample was small, it is not clear how the control group was matched in terms of size and SES, and the treatment included more than just PA [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The ECLS study found differences between boys and girls [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], but none of the other studies consider gender effects. SES is recognised as one of the main influences on children\u0026rsquo;s academic success [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], but none of the papers adjust for SES.\u003c/p\u003e \u003cp\u003eTherefore, the aim of this study was to explore causal relationships between PA, cognition, and academic performance over a six-month longitudinal period for primary school children aged 7\u0026ndash;10 years.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 675 participants (326 male, 349 female) were part of an eight-week randomised controlled trial: \u003cem\u003eHealthy Homework\u003c/em\u003e was a curriculum-based, classwork and homework schedule designed to promote PA and healthy eating [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Full details of the Healthy Homework programme are described in its pilot study [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. All measurements were taken at baseline, immediately post-intervention, and six-month post-intervention. The study comprised eight control and eight experimental schools. For the purposes of this study, data from both groups were used for analysis \u0026ndash; see later under \u0026lsquo;Statistical Analysis\u0026rsquo; for rationale and statistical consideration. Eligibility criteria for the schools were as follows: a school with more than 100 students, location within Auckland or Dunedin cities, and a contributing, full primary, or composite structure that included at least one class each of students in school years 3\u0026ndash;5. A total of 16 primary schools from Auckland (n\u0026thinsp;=\u0026thinsp;10) and Dunedin (n\u0026thinsp;=\u0026thinsp;6) were selected to participate in the study. Socioeconomic decile ratings of participating schools ranged from 3 to 10 (median [IQR]\u0026thinsp;=\u0026thinsp;8 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]). Decile is a New Zealand Ministry of Education (MoE) socioeconomic rating system for school funding based on SES with 1 being low and 10 being high. Decile is a rating of the whole school, and not specific to individual students. It is common to have children from a range of SES within one school, with socioeconomic decile being an average representation of the school\u0026rsquo;s surrounding area. Students were selected to participate from one Year 3, one Year 4, and one Year 5 class from each school; simple random sampling was used in instances where there were two or more classes per year. All children in each participating class were invited to take part in the evaluation (i.e., no formal inclusion or exclusion criteria). Written parental consent and assent was obtained for children to participate in the study. Ethical approval was obtained from the Auckland University of Technology Ethics Committee (10/159). The Healthy Homework study only measured changes in PA and diet and found the programme was successful resulted in substantial and consistent increases in PA and had limited effects on body size and fruit consumption [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. That original study obtained data on academic performance and cognitive ability, but those were never analysed. This second analysis investigates longitudinal changes and relationships between PA, cognition and academic performance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003ePA was assessed using sealed NL-1000 pedometers (New Lifestyles Inc, Lee\u0026rsquo;s Summit, MO) over five consecutive days (three weekdays, two weekend days). Research has established the validity of these NL-1000 pedometers for measuring steps in children [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. NL-1000 pedometers have a multiday memory that automatically categorizes data according to the day of the week which enables step count for weekdays and weekends to be collected [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Pedometers were used to gain three measures of PA: average weekday steps at home, average weekday steps at school, and average steps at weekend.\u003c/p\u003e \u003cp\u003eImage 1. NL-1000 pedometer used in the study to record children\u0026rsquo;s steps.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe cognitive abilities of children were measured using CNS Vital Signs (CNSVS): a standardised cognitive screen assessment suitable for participants aged 7\u0026ndash;90 years [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. CNSVS is a web-based assessment battery with seven tests that are scored individually and combined to give scores in nine different areas. Four of the nine CNSVS domains were considered for this study: Composite Memory (recognize, remember, and retrieve words and geometric figures), Executive Function (recognize rules, categories, and manage or navigate rapid decision making), Psychomotor Speed (perceive, attend, respond to complex visual-perceptual information and perform simple fine motor coordination), and Reaction Time (react, in milliseconds, to a simple and increasingly complex direction set) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The other domains could not be used because of the difficulty in administering the Complex Attention Test, and the four remaining domains used combinations of the same base assessment.\u003c/p\u003e \u003cp\u003eAcademic performance was measured using the New Zealand Ministry of Education electronic Assessment Tools for Teaching and Learning (e-asTTle). The e-asTTle assessments have more than 2,000 curriculum-based assessment items standardised on over 50,000 students covering curriculum levels 2\u0026mdash;4 to assess student\u0026rsquo;s achievement and progress in reading, writing and mathematics and the New Zealand native language Māori equivalents of panui, tuhituhi, and pangarau [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Measures are norm-referenced and used to evaluate children\u0026rsquo;s progress through the school year [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Teachers create their own multi-choice assessment as the e-asTTle software generates a test that selects the best set of items meeting the teacher\u0026rsquo;s content and difficulty constraints [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. For the purpose of this research, a research team conducted both the reading and maths assessments, which were done using pen and paper with a time limit. Researchers marked total scores (0\u0026ndash;12), and results were entered into a computer by research assistants. Testing was completed within 10 minutes. The e-asTTle software converts raw scores into measures that align with a child\u0026rsquo;s curricular needs [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Raw scores were sufficient for the current analyses because they give a measure of academic performance for students in relation to peers of the same school year. Demographic information was obtained from the school records and included gender, age, school, ethnicity and decile.\u003c/p\u003e\n\u003ch3\u003eStudy Protocol\u003c/h3\u003e\n\u003cp\u003eTwo pedometers were assigned to each child: one clearly labelled \u0026lsquo;School\u0026rsquo; and the other \u0026lsquo;Home\u0026rsquo;. The \u0026lsquo;School\u0026rsquo; pedometer was worn during school hours, while the \u0026lsquo;Home\u0026rsquo; pedometer was left inside; a collection tray in the classroom. At the end of the school day, each child placed their \u0026lsquo;School\u0026rsquo; pedometer in the tray and attached their \u0026lsquo;Home\u0026rsquo; pedometer. Parents were given instructions how to attach the \u0026lsquo;Home\u0026rsquo; pedometer to the child when he/she got up in the morning and take it off when before going to bed at night. Upon arrival at school the next day, the teacher reminded the children to switch over their pedometers again. Pedometers were issued to children and height and weight measures taken on one a separate day within a month by trained researchers. For each school, CNSVS and e-asTTle baseline measures were collected by a team of researchers on one day. CNSVS assessment was completed before the e-asTTle test, with at least 30 minutes between the two. The CNSVS assessment was conducted in groups using school computer facilities or libraries and assisted by at least three researchers. Group sizes and types of computers depended on the facilities and computers provided by the school. Researchers introduced the test beforehand while each instruction for each test appeared on the screen before each test started. CNSVS was introduced for the research purposes and not part of routine school assessment practice. Thus, as it is not part of the students\u0026rsquo; normal education practice and procedures, they may have struggled with it being an unfamiliar task and not necessarily had difficulty with the cognitive demands and content. Researchers were available for the children in case they did not understand the instructions or if children clicked it away too quickly. The e-asTTle assessments were introduced and explained by the researchers. While the attitude questions were read out by the researchers, waiting for all children to go through them and ensuring that they understand them, the reading test was then conducted before the math test with a time limit of ten minutes. Students are used to e-asTTle assessments through the year as part of their normal school routines.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll variables were checked for normality, skewness and outliers. Three students were identified to have special needs and removed from the analysis because the cognitive and academic measures are not specific enough to cater for their needs and abilities. The distribution of the CNSVS composite memory item was skewed positively, but that reflects what is to be expected in the general population thus data were not transformed [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The other CNSVS measures were normally distributed. The two asTTle variables were normally distributed with no problematic outliers. One problematic outlier was identified with weekday steps which was clearly a data entry mistake. As all other variables were appropriate for the subject, the weekday steps value was removed and a new value was imputed later as part of the missing values analysis (MVA). The final analyses used the total weekly steps, which was gained by the formula: (mean weekday steps home x 5) + (mean weekday steps school x 5) + (mean weekend steps x 2).\u003c/p\u003e \u003cp\u003eThe extent of missing values was assessed on the full study cohort. To minimize loss of data, subjects with data for at least half of variables included in the final model were retained. Our study sample was reduced from 675 to 632. Details of missing data from the 675 and 632 subjects is included in Appendix 1. An MVA was completed on the three pedometer step readings. Data were not found to be Missing Completely at Random (MCAR; Little's MCAR test: Chi-Square\u0026thinsp;=\u0026thinsp;684.058, DF\u0026thinsp;=\u0026thinsp;595, Sig. = 0.007). The researchers inspected the data visually and could not see any patterns for missing IV data, so data was presumed to be Missing at Random (MAR). Expectation Maximisation (EM) was then used to impute missing values for the IVs. An MVA was then completed on the four CNSVS measures and two asTTle measures. The data were not found to be MCAR (Little's MCAR test: Chi-Square\u0026thinsp;=\u0026thinsp;1358.221, DF\u0026thinsp;=\u0026thinsp;1172, Sig. = 0.000). Based on inspection of missing data patterns, data are assumed to be MAR. Missing data for CNSVS and asTTle measures is due to a child not being present in class when the test was being taken. EM was then used to impute missing values. In a detailed study, Dong and Peng found as long as data are MAR, EM data imputation produced statistically significant results to p\u0026thinsp;\u0026lt;\u0026thinsp;.001 when removing 20%, 40% and 60% data from a complete dataset of 432 subjects [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis present study did not evaluate differences between experiment and control groups. Whilst the intervention of the experiment group aimed to improve diet and PA, there were no interventions directed at cognition and academic performance. This present study sought to investigate changes over time in the new areas of cognition and academic performance, and their relationship to PA. The original study followed RCT methodology including randomly assigning schools to experiment and control group. Thus, adjusting school effects in our analyses account in for any potential effect of both school clusters and the RCT intervention impact on the experiment group. Furthermore, adjusting for school effects also addresses any possible actual or placebo benefit caused differences subjects\u0026rsquo; performance.\u003c/p\u003e \u003cp\u003eChanges in the total weekly PA, the six cognitive domains, and the two academic outcomes were analysed over the two-month and six-month periods using generalised linear mixed models (GLMMs). GLMM was the most appropriate methodology for analysis because we measured change in the population as a whole, and did not analyse differences between groups. Physical activity change over two months was compared with cognitive and academic change over both two and six months (12 models in total). The GLMM analysis adjusted for fixed (age, gender, decile) and random (subjects nested in schools) effects. Although decile is different from SES as stated in methods, adjusting for decile has the same effect as adjusting for SES. All analyses were completed using IBM SPSS 24 (Armonk, NY: IBM Corp). Anthropometric data of subjects was obtained and used in the initial analyses, but analyses showed no difference in findings with or without that data. The final analyses did not include anthropometric data to increase model parsimony.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAssumptions\u003c/h2\u003e \u003cp\u003eDemographic data for the full 632 students from this analysis (48.7% male) aged 5.2\u0026ndash;10.8 years residing in New Zealand were available for analyses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There was an even spread of children across the three school years (3: 32.3%, 4: 34%, 5: 33.7%). The majority of students were of New Zealand European ethnicity (69.9%). Students were from schools of predominantly high socioeconomic decile. Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show the with mean, median, SD and inter-quartile range for step counts and cognitive/academic data (respectively) for the 632 students considered in this analysis, with EM imputations for missing data. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show total average steps for students was consistent for the three timepoints and that weekend day average steps were lower than weekday average steps.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of the study sample.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u0026thinsp;+\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u0026thinsp;+\u0026thinsp;Max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eM\u0026thinsp;+\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u0026thinsp;+\u0026thinsp;Max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eM\u0026thinsp;+\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMin\u0026thinsp;+\u0026thinsp;Max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool Year 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.74, \u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.65, 9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.72, \u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.48, 9.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.73, \u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.48, 9.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool Year 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.70, \u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.60, 9.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.72, \u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.21, 9.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.71, \u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.22, 9.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool Year 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.64, \u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.11, 10.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.74, \u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.88, 10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.69, \u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.88, 10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.72, \u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.65, 10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.73, \u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.22, 10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.73, \u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.22, 10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMāori\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e19 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e25 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e44 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePacific Island\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e12 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e11 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e23 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e34 (11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e70 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e104 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e9 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e12 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e21 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNZ European\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e234 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e206 (63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e440 (69.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003e308\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003e324\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e632\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eDecile\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDecile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e23 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e20 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e43 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDecile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e9 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e11 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e20 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDecile 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e13 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e38 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e51 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDecile 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e45 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e52 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e97 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDecile 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e46 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e48 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e94 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDecile 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e60 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e47 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e107 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDecile 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e47 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e40 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e87 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDecile 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e65 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e68 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e133 (21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e308\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e324\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e632\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eSchool year\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYear 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e98 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e106 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e204 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYear 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e104 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e111 (34.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e215 (34%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYear 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e106 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e107 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e213 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e308\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e324\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003e632\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of the three step count measurements.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25th %ile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50th %ile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75th %ile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday (home)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday (school)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily average*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTwo-months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e25th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e50th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e75th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday (home)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6288\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday (school)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9774\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSix-months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e25th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e50th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e75th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday (home)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekday (school)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeekend day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of the dependent variables of four cognitive domains and two academic domains for 632 students considered in final analyses including EM imputed data for missing values.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25th %ile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50th %ile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75th %ile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComposite Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive Functioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychomotor Speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReaction Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReading Proficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaths Proficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTwo-months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e25th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e50th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e75th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComposite Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive Functioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychomotor Speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReaction Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReading Proficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaths Proficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSix-months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e25th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e50th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e75th %ile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComposite Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive Functioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychomotor Speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReaction Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReading Proficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaths Proficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the mean change for cognitive and academic domains at the two-month and six-month intervals from the 12 generalised mixed models, adjusted for 2-month physical activity change, age, sex, socioeconomic status (decile), and school clustering. The \u003cem\u003eβ\u003c/em\u003e coefficient indicates the percentage change to each domain associated with a 1% increase in PA at two-months. Significant, positive relationships were observed between PA change and composite memory change at six-months (0.021), and nearing significance for change in composite memory at two-months (0.051). PA change and maths proficiency change were significant at two-months (0.019) and six-months. at six-months (0.034). No other associations were significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between changes in physical activity at two-months with changes in cognitive/academic outcomes at two-months and six-months.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDomain\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean % change (LCL, UCL)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e (LCL, UCL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComposite Memory change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 2 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.5 (14.0, 25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.048 (0.000, 0.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.7 (22.9, 36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047 (0.007, 0.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReaction Time change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 2 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.55 (1.68, 5.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.009 (-0.157, 0.139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73 (-0.114, 3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.028 (-0.176, 0.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychomotor Speed change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 2 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.68 (3.62, 5.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.013 (-0.260, 0.234)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.10 (3.83, 6.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.012 (-0.240, 0.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExecutive Function change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 2 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.31 (7.92, 10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.123 (-0.083, 0.330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0 (9.60, 12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.138 (-0.055, 0.331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReading Proficiency change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 2 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.9 (63.1, 84.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011 (-0.014, 0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.8 (72.2, 98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010 (-0.014, 0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMaths Proficiency change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 2 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.5 (32.9, 50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037 (0.006, 0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eat 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.3 (58.5, 82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026 (0.002, 0.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eβ\u0026thinsp;=\u0026thinsp;standardised coefficient; LCL\u0026thinsp;=\u0026thinsp;lower 95% confidence limit, UCL\u0026thinsp;=\u0026thinsp;upper 95% confidence limit using bias-corrected bootstrapping.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study investigating the effects of changes in PA on change in cognitive ability and academic performance in school children over a six-month period. Our findings suggest that small gains in specific areas of cognition and academic function \u0026ndash; namely composite memory and maths \u0026ndash; can be obtained with increased PA. That is acknowledged by Ziv et al, who say findings regarding the effects of exercise on cognitive performance are also usually modest [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Although the gains indicated are small, success is a series of small victories, and they could represent a meaningful impact for children and their learning. For example, a 1% increase in PA after two months was associated with a 0.037% increase in maths proficiency, and after six months was associated with a 0.047% increase in composite memory and 0.026% increase for maths proficiency. Thus, if students doubled their PA (100% increase) with a simple, closed skill exercise activity as walking, that would theoretically affect a 3.7% or 2.6% increase in maths proficiency and a 4.7% improvement in composite memory.\u003c/p\u003e \u003cp\u003eThe present results concur with other studies that have found increased PA is associated with improvement in executive function, memory and maths [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Other longitudinal studies have indicated that PA has a positive impact on maths and reading scores [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Importantly, the present analyses adjusted for potential confounding factors such as gender [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], age [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and the impact of SES through the socioeconomic decile differences between schools [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This study found that the significant relationships PA had on composite memory and maths proficiency is independent of such confounding factors.\u003c/p\u003e \u003cp\u003eStudies have highlighted possible methodological flaws that may bias research to support the PA-cognition relationship [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. For example, randomised control trials (RCT) may have a tendency to have higher performing subjects in the experiment group and pretest and post-test equivalence need to be verified [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. As our study used data from control and experiment groups from a RCT trial and adjusted for differences between subjects and schools, neither of these were an issue. Ciria et al, say there is a preference for RCT studies which are seen as a gold-standard to ascertain causal links, but other sources of empirical evidence, such as observational or epidemiological studies, should also be considered in their ability to determine causation [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our study is an observational longitudinal study with rigorous methodology that identified small but valid changes inferring causation. Ziv et al note with the placebo effect, it is important control and experiment groups have similar expectations of input [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Our experiment and control groups had similar input with all students given pedometers to motivate them. Again, to counter such a bias, our study pooled data and adjusted from both groups, thus negating such concerns. Publication bias finds studies with large and positive changes are more prevalent [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], whereas the effects of PA on cognitive performance are usually modest [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]: \"We believe this exponential accumulation of low-quality evidence has led to stagnation rather than advance in the field hindering the discernment of the real existing effect.\" [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOur study did not identify any significant relationships between increased PA and the three other cognitive tested or reading proficiency. Most other longitudinal studies that investigated the relationship between PA and cognition analyse change over periods longer than six-months [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. It is possible that two-months and six-months were not a long enough time span to notice gradual cognitive changes. In addition, the measures used in this study have potential limitations. Pedometers give a valid and reliable indicator of overall volume of physical activity and have been used widely among student populations [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], but do not consider the intensity of the steps or time of day. High intensity aerobic activity and activity immediately prior cognitive assessment have been linked to greater cognitive function and academic performance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, pedometers do not monitor other aspects of fitness that have been linked to cognitive function such as acute effects of activity, cardiorespiratory fitness, resistance exercise, or combinations of open skill exercise and activity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similarly, the two e-asTTle measures are well researched and robust, but additional school-based assessments such as writing could provide greater insights to children\u0026rsquo;s academic performance. The CNSVS measures used in this study give a good insight to cognitive function, but as a screen assessment CNSVS may not have been sensitive enough to detect changes in some areas. Thus, a significant relationship was only identified in composite memory. Students were not familiar with the CNSVS assessment and thus results may reflect this unfamiliarity rather than difficulty with the cognitive demands and content. A last possible limitation is use of school decile as a measure of SES. It would have been better to have the SES for each individual child and not the school as a whole. Lastly, the lack of relationships may simply mean there were no relationships with increased PA and those domains with this population in this study.\u003c/p\u003e \u003cp\u003eIdentifying causation is one of the key questions in the PA/cognition field. Are smart children active or does being active make children smart? The results of this study provide some evidence that the more a child increases PA, the greater the improvement in memory and maths after six months. To examine the relationships further, future studies could consider wider ranges of PA, robust paediatric cognitive assessment, alternative academic performance measures. Further, as this study demonstrated small cognitive and academic gains over a short period, future studies should be completed over longer timeframes which will give greater opportunity to identify how changes in PA can make larger quantifiable changes in cognition and academic performance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis six-month longitudinal study provides some support for the theory that increased PA improves cognition and academic performance in children. The analysis identified after adjustment for age, sex, socioeconomic status, and school clustering, increased PA was associated with small but significant improvements in composite memory and maths but not for executive function, psychomotor speed, reaction time, or reading proficiency. While this reinforces that PA may have a role to play in children\u0026rsquo;s learning, the relatively small magnitude of the associations suggests that substantial improvements in PA would be required to generate meaningful improvements in cognition and academic achievement. Further research into the long-term effects of PA on brain function would provide additional information regarding the potential benefits of increasing PA in school children.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCSE: Closed skill exercise\u003c/p\u003e\n\u003cp\u003eOSE: Open skill exercise\u003c/p\u003e\n\u003cp\u003eCNSVS: CNS Vital Signs\u003c/p\u003e\n\u003cp\u003eGLMM: General Linear Mixed Model\u003c/p\u003e\n\u003cp\u003eMAR: Missing at random\u003c/p\u003e\n\u003cp\u003eMCAR: Missing completely at random\u003c/p\u003e\n\u003cp\u003eMNAR: Missing not at random\u003c/p\u003e\n\u003cp\u003eMVA: Missing values analysis\u003c/p\u003e\n\u003cp\u003eMVPA: Moderate to Vigorous Physical Activity\u003c/p\u003e\n\u003cp\u003ePA: Physical Activity\u003c/p\u003e\n\u003cp\u003ePE: Physical education\u003c/p\u003e\n\u003cp\u003eRCT: Randomized control trial\u003c/p\u003e\n\u003cp\u003eSES: Socio-economic status\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participating schools, children, and parents for contributing their time and effort to this study. I would also like to thank my son, Campbell McPherson, for his assistance in the data preparation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by a project grant from the Health Research Council of New Zealand (10/207). The funder had no involvement in the design of the study, the collection, analysis or interpretation of the data, or in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to confidentiality but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAM Study design, analysis and manuscript preparation.\u003c/p\u003e\n\u003cp\u003eSD Study design, data curation, and manuscript review.\u003c/p\u003e\n\u003cp\u003eLM Contribution to analysis and manuscript review.\u003c/p\u003e\n\u003cp\u003eJK Study design, data collection, and manuscript review.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed parental consent and personal assent was obtained from each participant for the collection and use of the data in future publication. Ethical approval for the study was obtained from the Auckland University of Technology Ethics Committee (10/159).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBest, J.R., \u003cem\u003eEffects of physical activity on children\u0026rsquo;s executive function: Contributions of experimental research on aerobic exercise.\u003c/em\u003e Developmental Review, 2010. \u003cstrong\u003e30\u003c/strong\u003e: p. 331-351.\u003c/li\u003e\n\u003cli\u003eChaddock, L., et al., \u003cem\u003eA Review of the Relation of Aerobic Fitness and Physical Activity to Brain Structure and Function in Children.\u003c/em\u003e Journal of the International Neuropsychological Society, 2011. \u003cstrong\u003e17\u003c/strong\u003e: p. 975-985.\u003c/li\u003e\n\u003cli\u003eDonnelly, J.E., et al., \u003cem\u003ePhysical Activity Across the Curriculum (PAAC): A randomized controlled trial to promote physical activity and diminish overweight and obesity in elementary school children.\u003c/em\u003e Preventive Medicine, 2009. \u003cstrong\u003e49\u003c/strong\u003e: p. 336-341.\u003c/li\u003e\n\u003cli\u003eHillman, C.H., K. 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Peng, \u003cem\u003ePrincipled missing data methods for researchers\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eZiv, G.L., O.; Netz, Y., \u003cem\u003eSelecting an appropriate control group for studying the effects of exercise on cognitive performance.\u003c/em\u003e Psychology of Sport and Exercise, 2024. \u003cstrong\u003e72\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eHollar, D., et al., \u003cem\u003eEffect of a two-year obesity prevention intervention on percentile changes in body mass index and academic performance in low-income elementary school children.\u003c/em\u003e American Journal of Public Health, 2010. \u003cstrong\u003e100\u003c/strong\u003e(4): p. 646-653.\u003c/li\u003e\n\u003cli\u003eLiu, S.L., J.C.; Tenenbaum, G., \u003cem\u003eDoes Exercise Improve Cognitive Performance: A Conservative Message from Lord\u0026apos;s Paradox.\u003c/em\u003e Frontiers in Psychology, 2016. \u003cstrong\u003e7\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eCiria, L.F.R.-C., R; Vadillo, M.A.; Holgado, D.; Luque-Casado, A.; Perakakis, P.; Sanabria, D., \u003cem\u003eAn umbrella review of randommized control trials on the effects of physical exercise on cognition.\u003c/em\u003e Nature Human Behaviour, 2023. \u003cstrong\u003e7\u003c/strong\u003e(6).\u003c/li\u003e\n\u003cli\u003eDwyer, T., et al., \u003cem\u003eAn investigation of the effects of daily physical activity on the health of primary school students in South Australia.\u003c/em\u003e International Journal of Epidemiology, 1983. \u003cstrong\u003e12\u003c/strong\u003e(3): p. 308-313.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Image 1","content":"\u003cp\u003eImage 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Physical activity, cognition, academic performance, school, children, causation","lastPublishedDoi":"10.21203/rs.3.rs-5425164/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5425164/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIdentifying the relationships among physical activity, cognition, and academic performance in children is important for targeted public health and education initiatives. However, the majority of research has been cross-sectional in nature; we have a limited understanding of the causal direction of these associations. Therefore, aim of this study was to utilise longitudinal data to explore causal relationships among physical activity, cognition and academic performance in elementary school children.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were sourced from 675 New Zealand children aged 5\u0026ndash;11 years. Weekday home, weekday school, and weekend physical activity was measured by multiple pedometer step readings, cognition by four measures from the CNS Vital Signs assessment, and academic performance from the New Zealand Ministry of Education Assessment Tools for Teaching and Learning (asTTle) reading and maths scores. Measures were taken at baseline, two months, and six-month intervals. Data were analysed for 632 students identified with data for at least half of the 27 variables. A generalised linear mixed model was used to investigate changes in physical activity, cognition and academic performance over those three time periods while adjusting for gender, school, age, and socioeconomic status.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNo significant relationships were identified between physical activity and three of the cognitive domains. However, significant, positive relationships were observed between physical activity change at two-months and (1) composite memory change at six-months, (2) maths proficiency change at two-months, and (3) math proficiency change at six-months. Regression coefficients suggest that a child who doubles step count - a 100% increase in PA - will affect a 3.7% improvement in maths proficiency after two months, and after six months affect a 2.6% improvement in maths proficiency and a 4.7% improvement in composite memory.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis six-month longitudinal analysis identified that an increase physical activity led to small but significant improvements in composite memory and maths proficiency. The small associations suggest that substantial improvements in PA would be required to generate meaningful improvements in cognition and academic achievement. However, timeframes longer than six-months are recommended to identify long-term changes.\u003c/p\u003e","manuscriptTitle":"Step to it – can physical activity improve kids’ cognition? A six-month longitudinal study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 06:11:50","doi":"10.21203/rs.3.rs-5425164/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c3a89ce-64db-4bba-b902-e06f1b3000b6","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-07T05:38:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-17 06:11:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5425164","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5425164","identity":"rs-5425164","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}