Effects of a Physical Activity Intervention on Movement Behaviour Patterns and Body Mass Index: A Compositional Data Analysis Approach

Effects of a Physical Activity Intervention on Movement Behaviour Patterns and Body Mass Index: A Compositional Data Analysis Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of a Physical Activity Intervention on Movement Behaviour Patterns and Body Mass Index: A Compositional Data Analysis Approach Hanif Abdul Rahman, Songdhasn Chinapong, Korntawat Klinchan, Vimolmas Tansathitaya, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7200565/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 Objectives Childhood physical inactivity is a significant public health concern, exacerbated by contemporary sedentary lifestyles. School-based interventions, including physical education (PE), and targeted PE teacher training programs, have been introduced to address this trend by promoting increased movements. However, the impact of these programs on the complete range of movement behaviors is still not well understood. This study assessed the effects of the Promoting Physical Activity through Physical Education (PEPA) training program on the movement behavior composition of Thai primary school children aged 10–12 years. Methods A longitudinal study was conducted with 1,343 participants from North and East Thailand, with assessments at baseline and 14 weeks. Compositional data analysis and linear mixed models were employed to determine changes in movement behaviour patterns, both in varying adiposity levels and as a result of the intervention. Results Intervention group exhibited a clustering towards higher moderate-to-vigorous and light PA compared to controls. Additionally, non-overweight children experienced greater improvements in moderate-to-vigorous PA, while overweight children showed reductions in sedentary behaviour. Linear mixed models confirm these shifts, demonstrating a significant decrease in the active-to-passive movement ratio at 14 weeks, highlighting the intervention’s role in fostering more active behaviours. Conclusion The PEPA training program effectively reshapes movement behaviour composition among school children by increasing active behaviours and decreasing sedentary time. Despite the concern regarding potential sleep reduction, these promising results support comprehensive, school-based strategies to drive sustainable PA promotion. Further research is warranted to refine these interventions for long-term success. physical activity school-based intervention physical education movement behaviour composition compositional data analysis body mass index Figures Figure 1 Figure 2 WHAT IS ALREADY KNOWN ON THIS TOPIC School-based physical activity interventions have the potential to improve children's movement behaviours. However, their impact on the overall composition of these behaviours—including moderate-to-vigorous physical activity, sedentary behaviour, and sleep—using compositional data analysis remains largely unknown. WHAT THIS STUDY ADDS Using compositional data analysis, this 14-week physical education program led to modest increases in moderate-to-vigorous physical activity and reductions in sedentary time. Interestingly, all children showed increases in sedentary behaviour and decreases in sleep. The study also found that movement composition patterns varied by body mass index, emphasizing the need for tailored programs for overweight and obese children. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY While the physical activity intervention programs implemented through physical education classes demonstrated effectiveness, stakeholders may need to incorporate parental and community engagement, and policy changes to better address sedentary behaviour and sleep reduction. Targeted strategies addressing barriers such as self-efficacy and access to PA opportunities are crucial for overweight and obese children, who show different movement composition patterns. Future interventions should adopt a holistic approach that prioritizes adequate sleep, given its importance for health and well-being. BACKGROUND Physical activity (PA) interventions have been widely recognized for their potential to improve movement behaviours, particularly among children and adolescents ( 1 , 2 ). School-based PA interventions, including structured physical education (PE) and targeted activity initiatives, have demonstrated effectiveness in increasing moderate-to-vigorous physical activity (MVPA) and decreasing sedentary time ( 3 ). These programs often incorporate components in PE classes such as teacher-led activities, structured play, and active classroom strategies, aiming to integrate movement seamlessly into the school days ( 3 ). Furthermore, physical education teacher training programs are crucial, equipping educators with the necessary skills to effectively promote and implement physical activity (PA) in schools ( 3 – 5 ). This suggests that such initiatives can enhance motor skills, increase overall PA, and improve academic performance in children and adolescents( 4 ). A key challenge in PA interventions is their impacts on the overall movement behaviour composition, which includes MVPA, light PA, sedentary behaviour (SB), and sleep. Research has indicated that interventions promoting one behaviour may inadvertently displace another, underscoring the importance of a balanced approach ( 6 ). While increasing MVPA is beneficial, it is crucial to ensure that it does not come at the cost of essential behaviours like sleep or light activity, which also play significant roles in health outcomes ( 6 , 7 ). Moreover, the effects of PA interventions can vary depending on individual factors such as age, gender, and body mass index (BMI) ( 8 ). Overweight and obese children, for example, often face additional obstacles to participating in physical activity (PA), such as lower self-efficacy, physical discomfort, and environmental limitations. Therefore, it is essential to understand how interventions affect movement behaviours in different BMI groups to design inclusive and effective PA promotion programs ( 8 ). Despite the well-documented benefits of PA interventions, there remain critical gaps in the literature regarding their long-term impact on movement behaviour composition. Most studies focus on short-term changes in MVPA, with limited examination of how these interventions affect the full spectrum of daily movement behaviours, including sleep and sedentary time ( 9 – 11 ). Furthermore, despite the widespread implementation of school-based interventions, their effectiveness across diverse populations, especially among overweight and obese children, remains largely unexamined ( 8 ). A significant limitation lies in the reliance on traditional analytical methods that often overlook the interconnectedness of movement behaviours. These methods fail to fully capture the trade-offs where an increase in one behaviour leads to a decrease in another. In contrast, compositional data analysis (CoDA) offers a more sophisticated approach by treating movement behaviours as parts of a finite 24-hour day, providing a clearer understanding of how time is allocated across various activities. There is a scarcity of research, especially longitudinal studies using Compositional Data Analysis (CoDA), examining how school-based interventions reshape the overall composition of movement behaviours. Addressing this gap, the present study aimed to evaluate the longitudinal impact of the Promoting Physical Activity through Physical Education (PEPA) program on the movement behaviour composition of Thai primary school children (aged 10–12). Delivered within PE classes, the study specifically sought to determine if the PEPA intervention resulted in meaningful shifts in the relative proportions of time dedicated to MVPA, Light PA, sedentary behaviour, and sleep, and whether these effects differed across BMI categories. The application of CoDA enabled an analysis of the interactions between these behaviours and an assessment of the intervention's effectiveness in fostering beneficial changes to daily time-use patterns. METHODS Study design and participants This study employed a longitudinal school-based design to evaluate the compositional structure of movement behaviours across different BMI categories. A total of 1,343 participants consisting of primary school students aged 10–12 years, were drawn from eight schools in the North and the East of Thailand with data collected at baseline (Week 0) and follow up (Week 14). All selected schools were involved in the PEPA training program as the intervention of this current study. Further details of the intervention are available, as referenced in Amornsriwatanakul et al. (2025) ( 12 ). Data from this PEPA study was used to evaluate how time spent in SB, sleep, light PA, and MVPA varied across BMI categories and whether the intervention influenced movement behaviour composition over time. Ethical approval was obtained from Mahidol University Central Institutional Review Board (COA No. MU-CIRB 2023/144.2609). Parents provided written informed consent, and students provided written informed assent for participation in the study. Data were collected during November 2023 to January 2024. Instruments Physical activity Children's in-school PA was measured using the Feelfit®, a hip-worn, tri-axial accelerometer developed by Mahidol University's Faculty of Engineering. This device provides accurate data on energy expenditure, steps, distance, and PA intensity, using metabolic equivalent (MET) classifications for children and youth. The Feelfit® demonstrates high accuracy, exceeding 80% for calorie estimation and 90% for activity classification, compared to commercial monitors. Further details on the device's algorithms can be found in Arnin et al. (2014) ( 13 ). Children wore the device on their right hip, secured with an elastic belt, during school hours for at least 7 hours (7:00 hrs.–14:00 hrs.). Data were collected on two school days, one with PE class and the other without the PE class. Further details about data collection procedure can be found elsewhere ( 12 ). Sleep time Data on sleep time were collected using the Students' Opinions about Physical Education Class (SOAPE) questionnaire ( 12 ). Sleep time was self-reported by children as part of sociodemographic information by asking what time children went to bed last night and got up in the next morning. Data management Movement behaviours PA data collected by the Feelfit® were presented as time use (minutes) on SB, light PA, and moderate, vigorous, and very vigorous PA. MVPA was derived as the sum of moderate to very vigorous PA. Total sleep time was calculated in minutes from the time children went to bed and got up. All movement behaviours were scaled to reflect a 24-hour composition, ensuring that the sum of all behaviours equalled 1,440 minutes per day. BMI categorization Sample BMI was scaled as Z-scores to enable standardized comparisons across age groups. Z-scores were computed using the formula: $$\:BM{I}_{Z}=\frac{BMI-\text{mean}\left(BMI\right)}{\text{SD}\left(BMI\right)}$$ BMI categories were defined as follows: ‘underweight’ ( 1 to 2 standard deviation), and ‘overweight/obese’ (> 2 standard deviation) ( 14 ). Underweight and normal weight were combined due to small sample size in underweight and labelled as ‘non-overweight’. Statistical analysis All statistical procedures were conducted using R (R Development Core Team, Vienna, Austria), the packages Compositions ( 15 ), and robCompositions ( 16 ). Firstly , descriptive statistics were computed for baseline characteristics and each movement behaviour between intervention and control group. Next , ternary plots were generated to visualize the compositional differences of a three-dimensional component from all possible combinations of the movement behaviour (i.e., MVPA-light PA-Sedentary, Sleep-light PA-Sedentary, MVPA-Sleep-light PA, and MVPA-Sleep-Sedentary) at baseline and 14-week follow up, simultaneously. The 95% confidence interval of the plots reflected the regions for control and intervention groups where overlapping areas indicated region of similarity. The center of the plot indicated no change in the movement behaviours. Thirdly , a compositional log-ratio approach was used to analyse the relative proportions of sedentary time, sleep, light PA, and MVPA across BMI categories. Group differences were visualized using bar plots, stratified by school type (Intervention vs. Control) and time point (Baseline vs. Post-Intervention). Bar plots were generated to illustrate the relative distribution of movement behaviours across BMI categories. To ensure accurate interpretation, the log-ratio values were retained as both positive and negative values, preserving the full range. Separate bars were used for each movement behaviour to avoid misinterpretation of stacked compositions. Patterns were assigned to each behaviour category to enhance clarity in distinguishing trends. Finally, Given the movement behaviours form a time-constrained composition (i.e., a 24-hour sum constraint), CoDA was applied to account for co-dependency between behaviours. The cantered log-ratio (clr) transformation was used to normalize the data, ensuring that results reflected relative differences rather than absolute minutes. The clr transformation was applied to the geometric mean \(\:g\left(X\right)\) of each movement \(\:{x}_{i}\) behaviour using the formula: $$\:\varvec{c}\varvec{l}\varvec{r}\left({\varvec{x}}_{\varvec{i}}\right)=\mathbf{l}\mathbf{n}\left(\frac{{\varvec{x}}_{\varvec{i}}}{\varvec{g}\left(\varvec{X}\right)}\right)$$ The isometric log-ratio coordinates were used in the analysis to compare: 1) active (light PA and MVPA) and passive (Sedentary and sleep) coordinates, 2) Light PA and MVPA coordinates, and 3) Sedentary and sleep coordinates. The differences in each of the three comparisons were then compared between the groups using linear mixed models with random effects to account for heterogeneity. RESULTS Baseline characteristics of the samples Baseline characteristics of the intervention (n = 651) and control (n = 692) groups are presented in Table 1 . Samples in both groups were age-matched (control: 11.4 ± 0.6 years; intervention: 11.3 ± 0.7 years) and had balanced gender distribution (approximately 50% each). BMI classifications showed modest differences, with the control group having more normal weight children (61.2% vs. 55.9%) and fewer underweight children (10.2% vs. 13.6%) compared to the intervention group. The intervention group had slightly higher prevalence of obesity (18.6% vs. 16.7%) and overweight (12.0% vs. 11.9%) classifications. Table 1 Baseline characteristics of the intervention and the control groups Control ( n = 692) Intervention ( n = 651) Mean (SD) Mean (SD) Age 11.4 (0.6) 11.3 (0.7) n (%) n (%) Gender Female 353 (51.0%) 313 (48.1%) Male 339 (49.0%) 338 (51.9%) BMI classification Underweight 70 (10.2%) 84 (13.6%) Normal 421 (61.2%) 346 (55.9%) Obese 115 (16.7%) 115 (18.6%) Overweight 82 (11.9%) 74 (12.0%) Mean time-use of movement behaviours by groups Table 2 presents normalized (scaled) mean daily time allocations (in minutes) across four movement behaviour categories for intervention and control groups at baseline and 14-week follow-up. Both groups exhibited notable shifts in their 24-hour movement profiles over the study period. The control group demonstrated substantial increases in light PA (from 112.2 to 149.3 minutes/day, + 33.1%) and moderate increases in MVPA (from 41.9 to 48.1 minutes/day, + 14.8%). The intervention group also showed modest increases in both light PA (from 110.6 to 117.4 minutes/day, + 6.2%) and MVPA (from 44.8 to 47.5 minutes/day, + 6.0%). Both groups increased sedentary time, with a slightly larger increase in the intervention group (+ 99.1 minutes vs. +79.0 minutes in the control group). Most notably, both groups demonstrated substantial reductions in sleep duration, with the control group decreasing from 778.7 to 656.4 minutes/day (-15.7%) and the intervention group from 798.5 to 689.9 minutes/day (-13.6%). Table 2 Mean time-use (in minutes) of movement behaviours in the intervention and the control groups at baseline (Week 0) and follow up (Week 14). Control Intervention Time point Light PA MVPA Sedentary Sleep Light PA MVPA Sedentary Sleep Week 0 112.2 41.9 507.2 778.7 110.6 44.8 486.2 798.5 Week 14 149.3 48.1 586.2 656.4 117.4 47.5 585.3 689.9 Note: The means are scaled to sum up to 1440 min (24 h). Light PA: Light physical activity, MVPA: moderate-to-vigorous physical activity The compositional differences of the four-behaviour composition Figure 1 displays the compositional differences in the four-behaviour composition between baseline and 14 weeks, along with 95% confidence regions for the control (red dots and line) and intervention (blue dots and line) groups. Figure 1A depicts that the intervention group generally engaged in higher MVPA compared to the control group. The control group had a wider spread in SB, while the intervention group clustered toward higher light PA and MVPA. The convex hulls suggest a significant difference in overall activity distribution between the groups. Figure 1B shows that the distribution of SB, light physical activity, and sleep differs between the intervention and control groups. The intervention group appeared to shift towards increased light PA, with a corresponding decrease in sedentary time. Moderate variability within the confidence intervals suggests some overlap in activity compositions between participants in both groups. Figure 1C highlights that when comparing light PA, sleep, and MVPA, the intervention group trended towards increased MVPA and light PA, whereas the control group exhibited greater variability in sleep duration. This suggested that intervention participants might be replacing sleep or light PA with more MVPA, potentially a key outcome of the intervention. Figure 1D shows the trade-off between MVPA and sedentary time. The intervention group tended to show higher MVPA and lower SB, while the control group displayed a more sedentary pattern. The confidence intervals reinforce the intervention’s impact on reducing sedentary time in favour of more active behaviours. Overall, the ternary plots highlighted significant compositional differences between the control and intervention groups. The intervention effectively shifted participants toward higher MVPA and light PA, and reduced sedentary time. Sleep patterns showed greater variability, suggesting individual differences in how participants balanced activity and rest. Differences in movement composition across BMI Categories Figure 2 presents the log-ratio transformed composition of SB, sleep, light PA, and MVPA across BMI categories. Higher observed MVPA log-ratios among non-overweight participants indicated greater engagement in MVPA. This trend of greater MVPA levels in lower BMI categories might signal better weight regulation and metabolic health. However, those in non-overweight group also demonstrated higher SB log-ratios, suggesting more prolonged sitting time compared to other BMI categories. Additionally, light PA levels were generally lower in the overweight/obese group and higher in the non-overweight group. Furthermore, sleep composition varied slightly across BMI groups, but no clear pattern emerged between BMI and sleep log-ratios. Intervention effects on movement behaviours Figure 2 also shows the data stratified by school type (Intervention vs. Control) and time point (Week 0 to Week 14) to assess the intervention's impact on movement behaviours. The intervention group demonstrated a more positive shift in MVPA log-ratios over time, particularly among the non-overweight and at risk of overweight groups. Among intervention participants, SB log-ratios also declined, particularly in the at risk of overweight group. The control group did not show a similar reduction in SB log-ratios. At baseline, both groups exhibited similar movement behaviour compositions. Over time, the intervention group showed higher MVPA log-ratios and lower SB log-ratios whereas the control group maintained a similar composition, with less MVPA change over time. Ratio of changes in movement behaviours over time between groups of activities Table 3 shows the linear mixed model with random effects computed to further explore the ratio of changes in balance coordinates (movement behaviours) over time between groups in three possible comparisons including the ratio of active (light PA and MVPA) vs. passive (sedentary and sleep), the ratio of light PA vs. MVPA, and the ratio of sedentary and sleep. Model 1 examined factors influencing the balance between active and passive movement behaviours. There was a significant decrease in the active-to-passive movement ratio at week 14 compared to baseline (β = -44.21, 95% CI [-68.18, -20.24], p = 0.000), indicating participants generally shifted toward more passive movement behaviours over time. Model 2 examines factors influencing the balance between light PA and MVPA. No statistically significant effects were observed for any variables in this model. Model 3 examined factors influencing the balance between SB and sleep. A significant decrease in the sedentary-to-sleep ratio at week 14 was observed (β = -118.25, 95% CI [-161.07, -75.43], p = 0.000), indicating participants shifted toward relatively more sleep over time. Overweight participants showed significantly less SB relative to sleep compared to normal weight participants (β = -44.27, 95% CI [-83.08, -5.46], p = 0.026). Table 3 Results of linear mixed models of the three balance coordinates vs. the covariates. β 95% CI Lower 95%CI Upper P -value Model 1: Active vs. Passive Movement Behaviour Gender (Ref: Male) 7.22 -7.25 21.69 0.328 Age 3.89 -7.11 14.88 0.489 BMI (Ref: Obese) 10.03 -10.01 30.08 0.327 BMI (Ref: Overweight) -1.32 -23.04 20.41 0.906 BMI (Ref: Underweight) -0.87 -22.11 20.37 0.936 Group (Ref: Intervention) -1.05 -17.40 15.30 0.900 Time point (Ref: Week 14) -44.21 -68.18 -20.24 0.000 Group * Time point 15.79 -20.08 51.65 0.389 Model 2: Light PA vs. MVPA Gender (Ref: Male) -2.61 -9.79 4.56 0.476 Age -1.05 -6.51 4.40 0.705 BMI (Ref: Obese) -1.48 -11.42 8.47 0.771 BMI (Ref: Overweight) -1.30 -12.07 9.48 0.814 BMI (Ref: Underweight) -1.34 -11.88 9.20 0.803 Group (Ref: Intervention) 2.86 -5.55 11.27 1.000 Time point (Ref: Week 14) 6.67 -5.22 18.56 0.272 Group * Time point -2.64 -20.43 15.15 0.772 Model 3: Sedentary vs. Sleep Gender (Ref: Male) -4.86 -30.70 20.99 0.713 Age -2.40 -22.03 17.24 0.811 BMI (Ref: Obese) 32.06 -3.75 67.87 0.080 BMI (Ref: Overweight) -44.27 -83.08 -5.46 0.026 BMI (Ref: Underweight) -23.86 -61.81 14.09 0.218 Group (Ref: Intervention) 19.62 -30.08 69.32 0.439 Time point (Ref: Week 14) -118.25 -161.07 -75.43 0.000 Group * Time point 28.69 -35.38 92.76 0.380 Note: Group: intervention and control groups; Time point: change from baseline to week 14; Light PA: light physical activity, MVPA: moderate-to-vigorous physical activity, Ref: Reference group. DISCUSSION Understanding the effects of PA interventions on movement behaviours is crucial for developing effective strategies to improve children's health. This study evaluated the effects of the PEPA training program on movement behaviour composition over 14 weeks and discovered several interesting findings. Effects of the intervention on movement behaviour patterns The ternary plots indicate that the intervention group generally engaged more in MVPA compared to the control group, which aligns with previous findings that structured interventions can promote higher PA levels ( 17 , 18 ). However, both groups showed increased SB over time, suggesting that external factors like academic demands (data collection at week 14 was near final exams) or screen time may have outweighed the intervention's effects ( 19 , 20 ). The convex hulls of the ternary plots further highlight the degree of variation in movement behaviours within each group. The control group showed a wider spread of SB, while the intervention group clustered more closely around higher MVPA and light PA. This suggested that the intervention was associated with slightly higher engagement in MVPA and lower SB indicating a stabilizing effect on promoting active behaviours. ( 2 , 21 ). Additionally, examination of the balance between light PA and MVPA suggests that while MVPA increased slightly in both groups, the proportional relationship between Light PA and MVPA remained stable. One possible explanation is that light PA did not serve as a major compensatory behaviour for MVPA; instead, sedentary time may have displaced both forms of activity to a greater extent ( 22 , 23 ). This suggested that compensatory reductions in light PA following increase in MVPA may result from difficulty that inactive children encountered when engaging in unfamiliar or new activities of moderate-to-vigorous intensity ( 23 ). Similarly, recent studies for associations with light PA using CODA and isotemporal substitutions also showed inconsistent evidence that were adverse ( 7 , 24 ), null ( 25 ), mixed ( 1 ), and favourable ( 26 ) outcomes. Perhaps due to methodological inconsistencies such as measurement issues and confounding factors ( 22 ). Furthermore, the results of linear mixed models examining the relative balance of movement behaviours over time reiterated the findings that there was a significant decrease in the active-to-passive movement ratio at the 14-week follow-up. One possible explanation may be linked to sleep reduction ( 11 , 26 ) and behavioural sustainability issues ( 9 ). This suggested that reduced sleep was likely caused by fatigue from elevated MVPA resulted from the invention, and perhaps increased productive sedentary time (i.e. final exam preparation). Additionally, the intervention might have yet fostered long-term habitual change, as PA was primarily structured in the intervention and not reinforced beyond school hours. Without sustained engagement strategies, adolescents may have reverted to passive behaviours, particularly in modern lifestyle that increases screen and sitting-down time. Further interventions emphasizing habitual formation and consistent reinforcement should be explored to maintain activity levels over time. BMI-related differences in movement behaviour composition The compositional log-ratio models examining sedentary-to-sleep balance showed a significant decrease in sedentary-to-sleep ratio over time. This indicated that participants, on average, spent relatively more time sedentary compared to sleeping. Overweight children had significantly lower sedentary-to-sleep ratios compared to their normal-weight peers, suggesting that they might sleep more but still engaged in prolonged sedentary periods when awake. This was perhaps due to higher metabolic strain and fatigue, requiring additional rest to compensate for reduced physical endurance ( 27 , 28 ). However, their waking hours remained largely sedentary suggesting lifestyle habits, such as increased screen time and passive recreation contributing to prolonged inactivity ( 19 ). As a result, extended sleep might not indicate better rest but rather poor sleep quality and excessive daytime tiredness, further reinforcing a cycle of SB and inactivity. These findings reinforced the need for targeted strategies to improve sleep and reduce sedentary time, particularly among at-risk children. Additionally, the overall trend of sleep reduction suggested a shift away from rest toward more wakeful behaviours. Given the known associations between insufficient sleep and obesity risk in children ( 29 ), this decline in sleep is concerning and warrants further investigation with holistic approach that integrates PA promotion with strategies to reduce SB and protect sleep duration. Conversely, the data indicate that non-overweight children exhibited higher MVPA log-ratios, reinforcing the notion that children with healthier weight status are more likely to engage in PA. This finding aligns with previous research that links SB to increased adiposity and metabolic risk ( 8 , 10 ). Light PA was generally lower in overweight/obese children, indicating potential substitution effects where sedentary time displaced light PA opportunities. The intervention showed some positive effects in increasing MVPA log-ratios among the non-overweight and at-risk-for-overweight groups, while SB log-ratios declined particularly among those at risk for overweight, further suggesting that external factors influenced the overall trends. Future programs should tailor PA promotion strategies to address barriers faced by overweight/obese children, such as motivation, self-efficacy, and access to movement-friendly environments. Implications for future physical activity interventions The findings of this study highlighted several key implications for designing more effective PA interventions. Firstly, while school-based interventions like PEPA can lead to modest increases in MVPA, they may be insufficient to counteract broader lifestyle factors that contribute to SB and sleep reduction. Future interventions may consider integrating additional components, such as parental involvement, community engagement, and policy-driven changes in school schedules to prioritize movement-friendly environments. Secondly, targeted strategies are needed to support overweight and obese children, who exhibited distinct movement behaviour composition patterns compared to their non-overweight peers. Interventions should be designed to address barriers, such as self-efficacy, social support, and environmental accessibility to PA opportunities. Finally, the substantial reduction in sleep observed in both groups underscores the need for a more holistic approach to movement behaviour interventions. Ensuring adequate sleep duration should be a key consideration in future programs, as sleep plays a critical role in metabolic health, cognitive function, and overall well-being ( 30 ). Strengths and limitations This study’s strengths include its longitudinal design, use of compositional data analysis to capture the interdependent nature of movement behaviours, and a large, diverse sample of Thai primary school children. The inclusion of both intervention and control groups enhances the robustness of the findings. However, limitations include reliance on self-reported sleep data and the short 14-week follow-up, which may not capture long-term intervention effects. Additionally, the study’s focus on school hours may overlook out-of-school activity patterns. Future research should address these limitations to provide a more comprehensive understanding of intervention impacts. CONCLUSION This study offers valuable insights into how a 14-week, school-based physical activity (PA) intervention, implemented during PE class, affected children's movement behaviors. Although the intervention was linked to slight increases in MVPA and reductions in SB, a concerning trend emerged in both groups: sedentary time increased and sleep duration decreased over the period. Furthermore, BMI-related differences in movement behaviors highlight the need for targeted interventions addressing the specific needs of children with overweight and obesity. These findings underscore that promoting PA, reducing sedentary time, and ensuring adequate sleep requires a comprehensive, multi-component approach for school-aged children. Future research should explore the long-term effects of such interventions and identify strategies to enhance their impact across diverse populations. Declarations Acknowledgement The authors gratefully acknowledge the partnership and advice of the Physical Education and Sport Science Network for Physical Activity throughout this study. They also thank the principals of the participating schools for their hospitality and access to facilities. Special thanks are due to the physical education teachers for their dedication and active involvement. The authors appreciate the parents' understanding and permission for their children to participate. This study would not have been possible without the participation of the students. Contributors HR: conceptualisation, methodology, data curation, writing–original draft preparation. SC: methodology, investigation, writing. MC: methodology, writing. KK: investigation, data curation. VT: writing. AA: conceptualisation, methodology, data curation, investigation, resources, writing–original draft preparation, supervision. Funding This research was supported by Thai Health Promotion Foundation (Ref: 66-00382). The Foundation had no role in the study's design, conduct, or interpretation of findings. Competing interests The authors declare they have no competing interests. Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research. Patient consent for publication Not applicable. Ethics statement Patient consent for publication Not applicable. Ethics approval This study involves human participants and ethical approval was provided by Mahidol University Central Institutional Review Board (COA No. MU-CIRB 2023/144.2609). Parents provided written informed consent, and students provided written informed assent before participating in the study. Provenance and peer review Not commissioned; externally peer reviewed. Data availability statement The data that support the findings of this study are available from the corresponding author (AA), upon reasonable request. Equity, diversity, and inclusion (EDI) statement This study aligns with BJSM commitment to advancing equity, diversity, and inclusion in sports and exercise medicine. 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International Journal of Clinical and Health Psychology. 2015;15(2):113–20. Telford DM, Meiring RM, Gusso S. Moving beyond moderate-to-vigorous physical activity: the role of light physical activity during adolescence. Front Sports Act Living. 2023;5:1282482. Bourdier P, Simon C, Bessesen DH, Blanc S, Bergouignan A. The role of physical activity in the regulation of body weight: The overlooked contribution of light physical activity and sedentary behaviors. Obesity Reviews. 2023;24(2):e13528. Dalene KE, Anderssen SA, Andersen LB, Steene-Johannessen J, Ekelund U, Hansen BH, et al. Cross‐sectional and prospective associations between physical activity, body mass index and waist circumference in children and adolescents. Obes Sci Pract. 2017;3(3):249–57. Loprinzi PD, Cardinal BJ, Lee H, Tudor-Locke C. Markers of adiposity among children and adolescents: implications of the isotemporal substitution paradigm with sedentary behavior and physical activity patterns. J Diabetes Metab Disord. 2015;14:1–14. del Pozo-Cruz B, Gant N, del Pozo-Cruz J, Maddison R. Relationships between sleep duration, physical activity and body mass index in young New Zealanders: An isotemporal substitution analysis. PLoS One. 2017;12(9):e0184472. Oukheda M, Bouaouda K, Mohtadi K, Lebrazi H, Derouiche A, Kettani A, et al. Association between nutritional status, body composition, and fitness level of adolescents in physical education in Casablanca, Morocco. Front Nutr. 2023;10:1268369. Kehar M, Huerta-Saenz L, Strain J, Kawesa S, Yaraskavitch J, Stine J, et al. Challenges in Promoting Physical Activity for Managing MASLD in Canadian Children: Insights and Barriers. Dig Dis Sci. 2025;1–7. Almulla AA, Zoubeidi T. Association of overweight, obesity and insufficient sleep duration and related lifestyle factors among school children and adolescents. Int J Adolesc Med Health. 2022;34(2):31–40. Schlieber M, Han J. The role of sleep in young children’s development: a review. J Genet Psychol. 2021;182(4):205–17. Additional Declarations No competing interests reported. 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-7200565","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533455845,"identity":"75b3ca3d-e157-4c1c-b3cd-881feb809e6b","order_by":0,"name":"Hanif Abdul 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09:21:52","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120162,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7200565/v1/acc911d595ae021794d6dc3c.html"},{"id":94945155,"identity":"75273c91-1e0f-4ade-baaf-9b5a21bdf1c3","added_by":"auto","created_at":"2025-11-02 09:21:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":203060,"visible":true,"origin":"","legend":"\u003cp\u003eTernary plots of the compositions of the compositional differences between the baseline and 14-week time point and the 95% confidence regions for the control and the intervention group participants.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7200565/v1/c1286bc1c26ab59a7676fbaa.png"},{"id":94945157,"identity":"1b4325d5-287e-46a0-8e42-dd0a6fbad49e","added_by":"auto","created_at":"2025-11-02 09:21:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":238411,"visible":true,"origin":"","legend":"\u003cp\u003eCompositional geometric mean bar plots comparing non-overweight, at-risk of overweight, and overweight/obese subgroups for sedentary, sleep, light PA, and MVPA by groups (Intervention and Control) and time point (Week 0 to Week 14).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7200565/v1/54c0976f05d1522385dd0c85.png"},{"id":96071244,"identity":"7b946c2d-1a20-4c80-89da-3b922da7b921","added_by":"auto","created_at":"2025-11-17 09:54:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1472130,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7200565/v1/8ed639bb-597f-42ea-84e6-5a61376f196d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of a Physical Activity Intervention on Movement Behaviour Patterns and Body Mass Index: A Compositional Data Analysis Approach","fulltext":[{"header":"WHAT IS ALREADY KNOWN ON THIS TOPIC","content":"\u003cp\u003eSchool-based physical activity interventions have the potential to improve children's movement behaviours. However, their impact on the overall composition of these behaviours—including moderate-to-vigorous physical activity, sedentary behaviour, and sleep—using compositional data analysis remains largely unknown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWHAT THIS STUDY ADDS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing compositional data analysis, this 14-week physical education program led to modest increases in moderate-to-vigorous physical activity and reductions in sedentary time. Interestingly, all children showed increases in sedentary behaviour and decreases in sleep. The study also found that movement composition patterns varied by body mass index, emphasizing the need for tailored programs for overweight and obese children.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile the physical activity intervention programs implemented through physical education classes demonstrated effectiveness, stakeholders may need to incorporate parental and community engagement, and policy changes to better address sedentary behaviour and sleep reduction. \u0026nbsp;Targeted strategies addressing barriers such as self-efficacy and access to PA opportunities are crucial for overweight and obese children, who show different movement composition patterns. Future interventions should adopt a holistic approach that prioritizes adequate sleep, given its importance for health and well-being.\u003c/p\u003e"},{"header":"BACKGROUND","content":"\u003cp\u003ePhysical activity (PA) interventions have been widely recognized for their potential to improve movement behaviours, particularly among children and adolescents (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). School-based PA interventions, including structured physical education (PE) and targeted activity initiatives, have demonstrated effectiveness in increasing moderate-to-vigorous physical activity (MVPA) and decreasing sedentary time (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These programs often incorporate components in PE classes such as teacher-led activities, structured play, and active classroom strategies, aiming to integrate movement seamlessly into the school days (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Furthermore, physical education teacher training programs are crucial, equipping educators with the necessary skills to effectively promote and implement physical activity (PA) in schools (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This suggests that such initiatives can enhance motor skills, increase overall PA, and improve academic performance in children and adolescents(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA key challenge in PA interventions is their impacts on the overall movement behaviour composition, which includes MVPA, light PA, sedentary behaviour (SB), and sleep. Research has indicated that interventions promoting one behaviour may inadvertently displace another, underscoring the importance of a balanced approach (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). While increasing MVPA is beneficial, it is crucial to ensure that it does not come at the cost of essential behaviours like sleep or light activity, which also play significant roles in health outcomes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Moreover, the effects of PA interventions can vary depending on individual factors such as age, gender, and body mass index (BMI) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Overweight and obese children, for example, often face additional obstacles to participating in physical activity (PA), such as lower self-efficacy, physical discomfort, and environmental limitations. Therefore, it is essential to understand how interventions affect movement behaviours in different BMI groups to design inclusive and effective PA promotion programs (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Despite the well-documented benefits of PA interventions, there remain critical gaps in the literature regarding their long-term impact on movement behaviour composition. Most studies focus on short-term changes in MVPA, with limited examination of how these interventions affect the full spectrum of daily movement behaviours, including sleep and sedentary time (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Furthermore, despite the widespread implementation of school-based interventions, their effectiveness across diverse populations, especially among overweight and obese children, remains largely unexamined (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). A significant limitation lies in the reliance on traditional analytical methods that often overlook the interconnectedness of movement behaviours. These methods fail to fully capture the trade-offs where an increase in one behaviour leads to a decrease in another. In contrast, compositional data analysis (CoDA) offers a more sophisticated approach by treating movement behaviours as parts of a finite 24-hour day, providing a clearer understanding of how time is allocated across various activities.\u003c/p\u003e\u003cp\u003eThere is a scarcity of research, especially longitudinal studies using Compositional Data Analysis (CoDA), examining how school-based interventions reshape the overall composition of movement behaviours. Addressing this gap, the present study aimed to evaluate the longitudinal impact of the Promoting Physical Activity through Physical Education (PEPA) program on the movement behaviour composition of Thai primary school children (aged 10–12). Delivered within PE classes, the study specifically sought to determine if the PEPA intervention resulted in meaningful shifts in the relative proportions of time dedicated to MVPA, Light PA, sedentary behaviour, and sleep, and whether these effects differed across BMI categories. The application of CoDA enabled an analysis of the interactions between these behaviours and an assessment of the intervention's effectiveness in fostering beneficial changes to daily time-use patterns.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eStudy design and participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study employed a longitudinal school-based design to evaluate the compositional structure of movement behaviours across different BMI categories. A total of 1,343 participants consisting of primary school students aged 10–12 years, were drawn from eight schools in the North and the East of Thailand with data collected at baseline (Week 0) and follow up (Week 14). All selected schools were involved in the PEPA training program as the intervention of this current study. Further details of the intervention are available, as referenced in Amornsriwatanakul et al. (2025) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Data from this PEPA study was used to evaluate how time spent in SB, sleep, light PA, and MVPA varied across BMI categories and whether the intervention influenced movement behaviour composition over time. Ethical approval was obtained from Mahidol University Central Institutional Review Board (COA No. MU-CIRB 2023/144.2609). Parents provided written informed consent, and students provided written informed assent for participation in the study. Data were collected during November 2023 to January 2024.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInstruments\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eChildren's in-school PA was measured using the Feelfit®, a hip-worn, tri-axial accelerometer developed by Mahidol University's Faculty of Engineering. This device provides accurate data on energy expenditure, steps, distance, and PA intensity, using metabolic equivalent (MET) classifications for children and youth. The Feelfit® demonstrates high accuracy, exceeding 80% for calorie estimation and 90% for activity classification, compared to commercial monitors. Further details on the device's algorithms can be found in Arnin et al. (2014) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Children wore the device on their right hip, secured with an elastic belt, during school hours for at least 7 hours (7:00 hrs.–14:00 hrs.). Data were collected on two school days, one with PE class and the other without the PE class. Further details about data collection procedure can be found elsewhere (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSleep time\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData on sleep time were collected using the Students' Opinions about Physical Education Class (SOAPE) questionnaire (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Sleep time was self-reported by children as part of sociodemographic information by asking what time children went to bed last night and got up in the next morning.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData management\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMovement behaviours\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePA data collected by the Feelfit® were presented as time use (minutes) on SB, light PA, and moderate, vigorous, and very vigorous PA. MVPA was derived as the sum of moderate to very vigorous PA. Total sleep time was calculated in minutes from the time children went to bed and got up. All movement behaviours were scaled to reflect a 24-hour composition, ensuring that the sum of all behaviours equalled 1,440 minutes per day.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBMI categorization\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSample BMI was scaled as Z-scores to enable standardized comparisons across age groups. Z-scores were computed using the formula:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:BM{I}_{Z}=\\frac{BMI-\\text{mean}\\left(BMI\\right)}{\\text{SD}\\left(BMI\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eBMI categories were defined as follows: ‘underweight’ (\u0026lt; -2.0 standard deviation), ‘healthy weight’ (-2 to 1 standard deviation), ‘at risk for overweight’ (\u0026gt; 1 to 2 standard deviation), and ‘overweight/obese’ (\u0026gt; 2 standard deviation) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Underweight and normal weight were combined due to small sample size in underweight and labelled as ‘non-overweight’.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical procedures were conducted using R (R Development Core Team, Vienna, Austria), the packages Compositions (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), and robCompositions (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). \u003cem\u003eFirstly\u003c/em\u003e, descriptive statistics were computed for baseline characteristics and each movement behaviour between intervention and control group. \u003cem\u003eNext\u003c/em\u003e, ternary plots were generated to visualize the compositional differences of a three-dimensional component from all possible combinations of the movement behaviour (i.e., MVPA-light PA-Sedentary, Sleep-light PA-Sedentary, MVPA-Sleep-light PA, and MVPA-Sleep-Sedentary) at baseline and 14-week follow up, simultaneously. The 95% confidence interval of the plots reflected the regions for control and intervention groups where overlapping areas indicated region of similarity. The center of the plot indicated no change in the movement behaviours. \u003cem\u003eThirdly\u003c/em\u003e, a compositional log-ratio approach was used to analyse the relative proportions of sedentary time, sleep, light PA, and MVPA across BMI categories. Group differences were visualized using bar plots, stratified by school type (Intervention vs. Control) and time point (Baseline vs. Post-Intervention). Bar plots were generated to illustrate the relative distribution of movement behaviours across BMI categories. To ensure accurate interpretation, the log-ratio values were retained as both positive and negative values, preserving the full range. Separate bars were used for each movement behaviour to avoid misinterpretation of stacked compositions. Patterns were assigned to each behaviour category to enhance clarity in distinguishing trends. Finally, Given the movement behaviours form a time-constrained composition (i.e., a 24-hour sum constraint), CoDA was applied to account for co-dependency between behaviours. The cantered log-ratio (clr) transformation was used to normalize the data, ensuring that results reflected relative differences rather than absolute minutes. The clr transformation was applied to the geometric mean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:g\\left(X\\right)\\)\u003c/span\u003e\u003c/span\u003e of each movement \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}\\)\u003c/span\u003e\u003c/span\u003e behaviour using the formula:\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{c}\\varvec{l}\\varvec{r}\\left({\\varvec{x}}_{\\varvec{i}}\\right)=\\mathbf{l}\\mathbf{n}\\left(\\frac{{\\varvec{x}}_{\\varvec{i}}}{\\varvec{g}\\left(\\varvec{X}\\right)}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eThe isometric log-ratio coordinates were used in the analysis to compare: 1) active (light PA and MVPA) and passive (Sedentary and sleep) coordinates, 2) Light PA and MVPA coordinates, and 3) Sedentary and sleep coordinates. The differences in each of the three comparisons were then compared between the groups using linear mixed models with random effects to account for heterogeneity.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of the samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics of the intervention (n\u0026thinsp;=\u0026thinsp;651) and control (n\u0026thinsp;=\u0026thinsp;692) groups are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Samples in both groups were age-matched (control: 11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 years; intervention: 11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 years) and had balanced gender distribution (approximately 50% each). BMI classifications showed modest differences, with the control group having more normal weight children (61.2% vs. 55.9%) and fewer underweight children (10.2% vs. 13.6%) compared to the intervention group. The intervention group had slightly higher prevalence of obesity (18.6% vs. 16.7%) and overweight (12.0% vs. 11.9%) classifications.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of the intervention and the control groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;692)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntervention (\u003cem\u003en\u0026thinsp;=\u003c/em\u003e\u0026thinsp;651)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.4 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.3 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e \u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e \u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e353 (51.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e313 (48.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e339 (49.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338 (51.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI classification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e421 (61.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346 (55.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115 (18.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eMean time-use of movement behaviours by groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents normalized (scaled) mean daily time allocations (in minutes) across four movement behaviour categories for intervention and control groups at baseline and 14-week follow-up. Both groups exhibited notable shifts in their 24-hour movement profiles over the study period. The control group demonstrated substantial increases in light PA (from 112.2 to 149.3 minutes/day, +\u0026thinsp;33.1%) and moderate increases in MVPA (from 41.9 to 48.1 minutes/day, +\u0026thinsp;14.8%). The intervention group also showed modest increases in both light PA (from 110.6 to 117.4 minutes/day, +\u0026thinsp;6.2%) and MVPA (from 44.8 to 47.5 minutes/day, +\u0026thinsp;6.0%).\u003c/p\u003e\n\u003cp\u003eBoth groups increased sedentary time, with a slightly larger increase in the intervention group (+\u0026thinsp;99.1 minutes vs. +79.0 minutes in the control group). Most notably, both groups demonstrated substantial reductions in sleep duration, with the control group decreasing from 778.7 to 656.4 minutes/day (-15.7%) and the intervention group from 798.5 to 689.9 minutes/day (-13.6%).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMean time-use (in minutes) of movement behaviours in the intervention and the control groups at baseline (Week 0) and follow up (Week 14).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eIntervention\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime point\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLight PA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMVPA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSedentary\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSleep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLight PA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMVPA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSedentary\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSleep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeek 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e507.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e778.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e486.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e798.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeek 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e586.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e656.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e585.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e689.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eNote: The means are scaled to sum up to 1440 min (24 h). Light PA: Light physical activity, MVPA: moderate-to-vigorous physical activity\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eThe compositional differences of the four-behaviour composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 displays the compositional differences in the four-behaviour composition between baseline and 14 weeks, along with 95% confidence regions for the control (red dots and line) and intervention (blue dots and line) groups. Figure\u0026nbsp;1A depicts that the intervention group generally engaged in higher MVPA compared to the control group. The control group had a wider spread in SB, while the intervention group clustered toward higher light PA and MVPA. The convex hulls suggest a significant difference in overall activity distribution between the groups. Figure\u0026nbsp;1B shows that the distribution of SB, light physical activity, and sleep differs between the intervention and control groups. The intervention group appeared to shift towards increased light PA, with a corresponding decrease in sedentary time. Moderate variability within the confidence intervals suggests some overlap in activity compositions between participants in both groups. Figure\u0026nbsp;1C highlights that when comparing light PA, sleep, and MVPA, the intervention group trended towards increased MVPA and light PA, whereas the control group exhibited greater variability in sleep duration. This suggested that intervention participants might be replacing sleep or light PA with more MVPA, potentially a key outcome of the intervention. Figure\u0026nbsp;1D shows the trade-off between MVPA and sedentary time. The intervention group tended to show higher MVPA and lower SB, while the control group displayed a more sedentary pattern. The confidence intervals reinforce the intervention\u0026rsquo;s impact on reducing sedentary time in favour of more active behaviours.\u003c/p\u003e\n\u003cp\u003eOverall, the ternary plots highlighted significant compositional differences between the control and intervention groups. The intervention effectively shifted participants toward higher MVPA and light PA, and reduced sedentary time. Sleep patterns showed greater variability, suggesting individual differences in how participants balanced activity and rest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferences in movement composition across BMI Categories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the log-ratio transformed composition of SB, sleep, light PA, and MVPA across BMI categories. Higher observed MVPA log-ratios among non-overweight participants indicated greater engagement in MVPA. This trend of greater MVPA levels in lower BMI categories might signal better weight regulation and metabolic health. However, those in non-overweight group also demonstrated higher SB log-ratios, suggesting more prolonged sitting time compared to other BMI categories. Additionally, light PA levels were generally lower in the overweight/obese group and higher in the non-overweight group. Furthermore, sleep composition varied slightly across BMI groups, but no clear pattern emerged between BMI and sleep log-ratios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntervention effects on movement behaviours\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e also shows the data stratified by school type (Intervention vs. Control) and time point (Week 0 to Week 14) to assess the intervention\u0026apos;s impact on movement behaviours. The intervention group demonstrated a more positive shift in MVPA log-ratios over time, particularly among the non-overweight and at risk of overweight groups. Among intervention participants, SB log-ratios also declined, particularly in the at risk of overweight group. The control group did not show a similar reduction in SB log-ratios. At baseline, both groups exhibited similar movement behaviour compositions. Over time, the intervention group showed higher MVPA log-ratios and lower SB log-ratios whereas the control group maintained a similar composition, with less MVPA change over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRatio of changes in movement behaviours over time between groups of activities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the linear mixed model with random effects computed to further explore the ratio of changes in balance coordinates (movement behaviours) over time between groups in three possible comparisons including the ratio of active (light PA and MVPA) vs. passive (sedentary and sleep), the ratio of light PA vs. MVPA, and the ratio of sedentary and sleep. \u003cem\u003eModel 1\u003c/em\u003e examined factors influencing the balance between active and passive movement behaviours. There was a significant decrease in the active-to-passive movement ratio at week 14 compared to baseline (\u0026beta; = -44.21, 95% CI [-68.18, -20.24], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), indicating participants generally shifted toward more passive movement behaviours over time. \u003cem\u003eModel 2\u003c/em\u003e examines factors influencing the balance between light PA and MVPA. No statistically significant effects were observed for any variables in this model. \u003cem\u003eModel 3\u003c/em\u003e examined factors influencing the balance between SB and sleep. A significant decrease in the sedentary-to-sleep ratio at week 14 was observed (\u0026beta; = -118.25, 95% CI [-161.07, -75.43], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000), indicating participants shifted toward relatively more sleep over time. Overweight participants showed significantly less SB relative to sleep compared to normal weight participants (\u0026beta; = -44.27, 95% CI [-83.08, -5.46], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of linear mixed models of the three balance coordinates vs. the covariates.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI Lower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI Upper\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1: Active vs. Passive Movement Behaviour\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Ref: Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (Ref: Obese)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (Ref: Overweight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (Ref: Underweight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup (Ref: Intervention)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime point (Ref: Week 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-44.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-68.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup * Time point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2: Light PA vs. MVPA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Ref: Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (Ref: Obese)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (Ref: Overweight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (Ref: Underweight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup (Ref: Intervention)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime point (Ref: Week 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup * Time point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3: Sedentary vs. Sleep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Ref: Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-30.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-22.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (Ref: Obese)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (Ref: Overweight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-44.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-83.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (Ref: Underweight)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-23.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-61.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup (Ref: Intervention)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-30.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime point (Ref: Week 14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-118.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-161.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-75.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup * Time point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-35.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: Group: intervention and control groups; Time point: change from baseline to week 14; Light PA: light physical activity, MVPA: moderate-to-vigorous physical activity, Ref: Reference group.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eUnderstanding the effects of PA interventions on movement behaviours is crucial for developing effective strategies to improve children's health. This study evaluated the effects of the PEPA training program on movement behaviour composition over 14 weeks and discovered several interesting findings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEffects of the intervention on movement behaviour patterns\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe ternary plots indicate that the intervention group generally engaged more in MVPA compared to the control group, which aligns with previous findings that structured interventions can promote higher PA levels (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, both groups showed increased SB over time, suggesting that external factors like academic demands (data collection at week 14 was near final exams) or screen time may have outweighed the intervention's effects (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The convex hulls of the ternary plots further highlight the degree of variation in movement behaviours within each group. The control group showed a wider spread of SB, while the intervention group clustered more closely around higher MVPA and light PA. This suggested that the intervention was associated with slightly higher engagement in MVPA and lower SB indicating a stabilizing effect on promoting active behaviours.\u003c/p\u003e\u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Additionally, examination of the balance between light PA and MVPA suggests that while MVPA increased slightly in both groups, the proportional relationship between Light PA and MVPA remained stable. One possible explanation is that light PA did not serve as a major compensatory behaviour for MVPA; instead, sedentary time may have displaced both forms of activity to a greater extent (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This suggested that compensatory reductions in light PA following increase in MVPA may result from difficulty that inactive children encountered when engaging in unfamiliar or new activities of moderate-to-vigorous intensity (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Similarly, recent studies for associations with light PA using CODA and isotemporal substitutions also showed inconsistent evidence that were adverse (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), null (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), mixed (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), and favourable (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) outcomes. Perhaps due to methodological inconsistencies such as measurement issues and confounding factors (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, the results of linear mixed models examining the relative balance of movement behaviours over time reiterated the findings that there was a significant decrease in the active-to-passive movement ratio at the 14-week follow-up. One possible explanation may be linked to sleep reduction (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and behavioural sustainability issues (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This suggested that reduced sleep was likely caused by fatigue from elevated MVPA resulted from the invention, and perhaps increased productive sedentary time (i.e. final exam preparation). Additionally, the intervention might have yet fostered long-term habitual change, as PA was primarily structured in the intervention and not reinforced beyond school hours. Without sustained engagement strategies, adolescents may have reverted to passive behaviours, particularly in modern lifestyle that increases screen and sitting-down time. Further interventions emphasizing habitual formation and consistent reinforcement should be explored to maintain activity levels over time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBMI-related differences in movement behaviour composition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe compositional log-ratio models examining sedentary-to-sleep balance showed a significant decrease in sedentary-to-sleep ratio over time. This indicated that participants, on average, spent relatively more time sedentary compared to sleeping. Overweight children had significantly lower sedentary-to-sleep ratios compared to their normal-weight peers, suggesting that they might sleep more but still engaged in prolonged sedentary periods when awake. This was perhaps due to higher metabolic strain and fatigue, requiring additional rest to compensate for reduced physical endurance (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). However, their waking hours remained largely sedentary suggesting lifestyle habits, such as increased screen time and passive recreation contributing to prolonged inactivity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). As a result, extended sleep might not indicate better rest but rather poor sleep quality and excessive daytime tiredness, further reinforcing a cycle of SB and inactivity. These findings reinforced the need for targeted strategies to improve sleep and reduce sedentary time, particularly among at-risk children. Additionally, the overall trend of sleep reduction suggested a shift away from rest toward more wakeful behaviours. Given the known associations between insufficient sleep and obesity risk in children (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), this decline in sleep is concerning and warrants further investigation with holistic approach that integrates PA promotion with strategies to reduce SB and protect sleep duration.\u003c/p\u003e\u003cp\u003eConversely, the data indicate that non-overweight children exhibited higher MVPA log-ratios, reinforcing the notion that children with healthier weight status are more likely to engage in PA. This finding aligns with previous research that links SB to increased adiposity and metabolic risk (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Light PA was generally lower in overweight/obese children, indicating potential substitution effects where sedentary time displaced light PA opportunities. The intervention showed some positive effects in increasing MVPA log-ratios among the non-overweight and at-risk-for-overweight groups, while SB log-ratios declined particularly among those at risk for overweight, further suggesting that external factors influenced the overall trends. Future programs should tailor PA promotion strategies to address barriers faced by overweight/obese children, such as motivation, self-efficacy, and access to movement-friendly environments.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplications for future physical activity interventions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe findings of this study highlighted several key implications for designing more effective PA interventions. Firstly, while school-based interventions like PEPA can lead to modest increases in MVPA, they may be insufficient to counteract broader lifestyle factors that contribute to SB and sleep reduction. Future interventions may consider integrating additional components, such as parental involvement, community engagement, and policy-driven changes in school schedules to prioritize movement-friendly environments. Secondly, targeted strategies are needed to support overweight and obese children, who exhibited distinct movement behaviour composition patterns compared to their non-overweight peers. Interventions should be designed to address barriers, such as self-efficacy, social support, and environmental accessibility to PA opportunities. Finally, the substantial reduction in sleep observed in both groups underscores the need for a more holistic approach to movement behaviour interventions. Ensuring adequate sleep duration should be a key consideration in future programs, as sleep plays a critical role in metabolic health, cognitive function, and overall well-being (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study\u0026rsquo;s strengths include its longitudinal design, use of compositional data analysis to capture the interdependent nature of movement behaviours, and a large, diverse sample of Thai primary school children. The inclusion of both intervention and control groups enhances the robustness of the findings. However, limitations include reliance on self-reported sleep data and the short 14-week follow-up, which may not capture long-term intervention effects. Additionally, the study\u0026rsquo;s focus on school hours may overlook out-of-school activity patterns. Future research should address these limitations to provide a more comprehensive understanding of intervention impacts.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study offers valuable insights into how a 14-week, school-based physical activity (PA) intervention, implemented during PE class, affected children's movement behaviors. Although the intervention was linked to slight increases in MVPA and reductions in SB, a concerning trend emerged in both groups: sedentary time increased and sleep duration decreased over the period. Furthermore, BMI-related differences in movement behaviors highlight the need for targeted interventions addressing the specific needs of children with overweight and obesity. These findings underscore that promoting PA, reducing sedentary time, and ensuring adequate sleep requires a comprehensive, multi-component approach for school-aged children. Future research should explore the long-term effects of such interventions and identify strategies to enhance their impact across diverse populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the partnership and advice of the Physical Education and Sport Science Network for Physical Activity throughout this study. They also thank the principals of the participating schools for their hospitality and access to facilities. Special thanks are due to the physical education teachers for their dedication and active involvement. The authors appreciate the parents' understanding and permission for their children to participate. This study would not have been possible without the participation of the students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHR: conceptualisation, methodology, data curation, writing–original draft preparation. SC: methodology, investigation, writing. MC: methodology, writing. KK: investigation, data curation. VT: writing. AA: conceptualisation, methodology, data curation, investigation, resources, writing–original draft preparation, supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Thai Health Promotion Foundation (Ref: 66-00382). The Foundation had no role in the study's design, conduct, or interpretation of findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and public involvement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatient consent for publication\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involves human participants and ethical approval was provided by Mahidol University Central Institutional Review Board (COA No. MU-CIRB 2023/144.2609). Parents provided written informed consent, and students provided written informed assent before participating in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProvenance and peer review\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot commissioned; externally peer reviewed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author (AA), upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEquity, diversity, and inclusion (EDI) statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aligns with BJSM commitment to advancing equity, diversity, and inclusion in sports and exercise medicine. We affirm that our research design, participant recruitment, and authorship practices were conducted with attention to inclusive representation.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eG\u0026aacute;ba A, Dygr\u0026yacute;n J, Štefelov\u0026aacute; N, Rub\u0026iacute;n L, Hron K, Jakubec L. Replacing school and out-of-school sedentary behaviors with physical activity and its associations with adiposity in children and adolescents: a compositional isotemporal substitution analysis. Environ Health Prev Med. 2021;26:1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDauenhauer B, Stoepker P. Physical education and physical activity within a whole school, whole community, whole child approach. J Phys Educ Recreat Dance. 2022;93(2):12\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWong LS, Gibson A, Farooq A, Reilly JJ. 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Front Sports Act Living. 2023;5:1282482.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBourdier P, Simon C, Bessesen DH, Blanc S, Bergouignan A. The role of physical activity in the regulation of body weight: The overlooked contribution of light physical activity and sedentary behaviors. Obesity Reviews. 2023;24(2):e13528.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDalene KE, Anderssen SA, Andersen LB, Steene-Johannessen J, Ekelund U, Hansen BH, et al. Cross‐sectional and prospective associations between physical activity, body mass index and waist circumference in children and adolescents. Obes Sci Pract. 2017;3(3):249\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoprinzi PD, Cardinal BJ, Lee H, Tudor-Locke C. Markers of adiposity among children and adolescents: implications of the isotemporal substitution paradigm with sedentary behavior and physical activity patterns. J Diabetes Metab Disord. 2015;14:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003edel Pozo-Cruz B, Gant N, del Pozo-Cruz J, Maddison R. Relationships between sleep duration, physical activity and body mass index in young New Zealanders: An isotemporal substitution analysis. PLoS One. 2017;12(9):e0184472.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOukheda M, Bouaouda K, Mohtadi K, Lebrazi H, Derouiche A, Kettani A, et al. Association between nutritional status, body composition, and fitness level of adolescents in physical education in Casablanca, Morocco. Front Nutr. 2023;10:1268369.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKehar M, Huerta-Saenz L, Strain J, Kawesa S, Yaraskavitch J, Stine J, et al. Challenges in Promoting Physical Activity for Managing MASLD in Canadian Children: Insights and Barriers. Dig Dis Sci. 2025;1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlmulla AA, Zoubeidi T. Association of overweight, obesity and insufficient sleep duration and related lifestyle factors among school children and adolescents. Int J Adolesc Med Health. 2022;34(2):31\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchlieber M, Han J. The role of sleep in young children\u0026rsquo;s development: a review. J Genet Psychol. 2021;182(4):205\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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, school-based intervention, physical education, movement behaviour composition, compositional data analysis, body mass index","lastPublishedDoi":"10.21203/rs.3.rs-7200565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7200565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eChildhood physical inactivity is a significant public health concern, exacerbated by contemporary sedentary lifestyles. School-based interventions, including physical education (PE), and targeted PE teacher training programs, have been introduced to address this trend by promoting increased movements. However, the impact of these programs on the complete range of movement behaviors is still not well understood. This study assessed the effects of the Promoting Physical Activity through Physical Education (PEPA) training program on the movement behavior composition of Thai primary school children aged 10\u0026ndash;12 years.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA longitudinal study was conducted with 1,343 participants from North and East Thailand, with assessments at baseline and 14 weeks. Compositional data analysis and linear mixed models were employed to determine changes in movement behaviour patterns, both in varying adiposity levels and as a result of the intervention.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIntervention group exhibited a clustering towards higher moderate-to-vigorous and light PA compared to controls. Additionally, non-overweight children experienced greater improvements in moderate-to-vigorous PA, while overweight children showed reductions in sedentary behaviour. Linear mixed models confirm these shifts, demonstrating a significant decrease in the active-to-passive movement ratio at 14 weeks, highlighting the intervention\u0026rsquo;s role in fostering more active behaviours.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe PEPA training program effectively reshapes movement behaviour composition among school children by increasing active behaviours and decreasing sedentary time. Despite the concern regarding potential sleep reduction, these promising results support comprehensive, school-based strategies to drive sustainable PA promotion. Further research is warranted to refine these interventions for long-term success.\u003c/p\u003e","manuscriptTitle":"Effects of a Physical Activity Intervention on Movement Behaviour Patterns and Body Mass Index: A Compositional Data Analysis Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-02 09:21:47","doi":"10.21203/rs.3.rs-7200565/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":"6bb2c1d7-cf35-4de8-a411-bab37746b4c0","owner":[],"postedDate":"November 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T09:53:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-02 09:21:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7200565","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7200565","identity":"rs-7200565","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}