Sleep, sedentary behavior, and physical activity: A compositional data analysis of 24-hour movement behaviors and executive function in preschool children

Ling Wang; Wanhong Luo; Yong Liu

doi:10.1371/journal.pone.0333563

Abstract

Objective

This study aims to examine the relationship between 24-h movement behaviors and executive function in preschool children.

Methods

This cross-sectional study included 266 preschool children (mean age: 3–6 years). Physical activity and sedentary behavior were measured using an accelerometer (ActiGraph GT3X-BT), sleep duration was assessed using sleep logs, and executive function was evaluated using the Early Years Toolbox. Compositional data analysis was then applied to examine the associations among these variables.

Results

(1) The relative distribution of 24-h movement behaviors was significantly associated with inhibitory control, cognitive flexibility, and working memory (all p < 0.001), with a model explanatory power > 10%. The explanatory power was the highest for inhibitory control (16.3%). (2) After adjusting for other movement behaviors, sedentary behavior was negatively associated with inhibitory control [γ₁² = –0.11 (−0.17, −0.05), p < 0.001], cognitive flexibility [γ₁² = –1.45 (−2.29, −0.61), p = 0.001], and working memory [γ₁² = –0.58 (−0.97, −0.19), p = 0.004]. In contrast, sleep was positively associated with cognitive flexibility [γ₁¹ = 1.60 (0.26, 2.94), p = 0.020]. (3) When 15 min/day of sedentary behavior was isotemporally substituted with sleep, inhibitory control scores increased by 0.003 (0.0004, 0.006), and cognitive flexibility scores increased by 0.064 (0.021,0.106); conversely, replacing sleep with sedentary behavior resulted in a significant decline.

Conclusion

Reallocating time from sedentary behavior to sleep was positively correlated with executive function, particularly cognitive flexibility. However, most other isotemporal substitution pathways did not show statistically significant effects, providing an important basis for future research to focus on specific behavioral reorganization strategies.

Citation: Wang L, Luo W, Liu Y (2026) Sleep, sedentary behavior, and physical activity: A compositional data analysis of 24-hour movement behaviors and executive function in preschool children. PLoS One 21(5): e0333563. https://doi.org/10.1371/journal.pone.0333563

Editor: Heather Macdonald, UBC: The University of British Columbia, CANADA

Received: September 15, 2025; Accepted: April 14, 2026; Published: May 8, 2026

Copyright: © 2026 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: This research was supported by the Humanities and Social Sciences Youth Project (Grant No. 23YJC890004) of the Ministry of Education of the People’s Republic of China, and the Hunan Province Philosophy and Social Sciences General Project (Grant No. 22JD080).

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: MBs, Movement Behaviors; PA, Physical Activity; LPA, Light-Intensity Physical Activity; MVPA, Moderate-to-Vigorous Physical Activity; SB, Sedentary Behavior; EF, Executive Function; BMI, Body Mass Index

1. Introduction

Executive function (EF) is a foundational construct rooted in neuropsychology, first identified by observing patients who sustained frontal lobe injuries during wartime, manifesting pronounced impairments in self-control and behavioral execution, hence the coining of the term “EF” [1]. Despite extensive study, the academic community has not reached consensus on a uniform definition of EF. The concept is often distinguished along two dimensions: in its broad sense, EF encompasses the coordinated orchestration of multiple cognitive processes during information processing, whereas in its narrow sense, it typically refers to inhibitory control [2]. EF, or cognitive control, is a conscious, top-down neurocognitive mechanism that encompasses three core components: cognitive flexibility, inhibitory control, and working memory. These components facilitate goal-directed regulation of thoughts, actions, and emotions [3]. This tripartite framework has achieved widespread acceptance; therefore, this study operationalizes EF in three sub-functions: cognitive flexibility, inhibitory control, and working memory. EF, a high-level cognitive capability, develops rapidly during early childhood [4].

Extensive evidence has underscored the lasting impact of early childhood EF on various later-life outcomes. Early EF development correlates strongly with school readiness [5] and academic performance [6], as well as with adolescent behavioral patterns and broader adult outcomes, including health, socioeconomic status, and quality of life [7]. EF demonstrates considerable plasticity, especially during early childhood [8]. Emerging research points to 24-h movement behaviors (MBs), including physical activity (PA), sedentary behavior (SB),and sleep, as potentially critical influences on EF development [9,10]. Furthermore, MBs have been linked to diverse cognitive and psychological outcomes [11].

However, most extant studies have focused on the effects of individual behaviors in isolation, such as sleep [12], SB [13], and PA [14,15], without adequately addressing how these behaviors interact or compose across an entire day. This fragmented approach limits our understanding of how integrated daily patterns of MB influence EF. Accordingly, there is a growing recognition of the need for a holistic perspective that considers the combined effects of PA, SB, and sleep to inform more effective intervention strategies.

Recent studies using different methods have addressed this gap. McNeill et al. (2020) investigated the association between adherence to the 24-hour movement guidelines and both EF and psychosocial health in preschool children (mean age: 4.2 ± 0.6 years) [10]. Their findings regarding specific EF components were mixed. Specifically, while adherence was positively correlated with children’s cognitive flexibility and working memory in cross-sectional analyses, no significant longitudinal predictive effect of baseline adherence on these skills was observed. A Canadian study found that meeting both sleep and PA guidelines in early childhood (3–5 years) was related to overall developmental benefits and response inhibitory control, though SB showed no direct effect [16]. Lau et al. (2024) applied compositional and isotemporal reallocation analyses to assess the associations between MBs and EF in preschool children (3.8 ± 0.6 years), identifying significant links and demonstrating the practical value of reallocating time among behaviors for improving EF outcomes [17]. A more recent study further revealed that reallocating time from sleep or SB to moderate-to-vigorous PA (MVPA) can enhance cognitive flexibility in preschool children (3–5 years), reinforcing the importance of time-use intensity in EF development [18].

Nevertheless, existing findings remain inconsistent, partly due to variations in measurement tools, participant characteristics, and analytical approaches [19]. To address these gaps, this study sought to extend the current body of knowledge by systematically investigating the associations between 24-h MBs and objectively assessed EF subdomains in preschool children. This study employed compositional data analysis and isotemporal substitution modeling to quantify the potential effects of reallocating time among different MBs on EF outcomes. We hypothesized that reallocating time from SB to sleep or MVPA would be associated with more favorable EF scores. The anticipated results may advance theoretical understanding and offer an empirical foundation for developing integrated interventions to promote EF in early childhood.

2. Materials and methods

This study is a cross-sectional observational study designed to explore the associations between isotemporal substitution of 24-hour MBs and EF in preschool children. All study procedures, including participant recruitment, informed consent, testing protocols, and safety contingency plans, were reviewed and approved by the Human Research Ethics Committee of East China Normal University (Approval No. HR 342–2024). Prior to data collection, written informed consent was obtained from a parent or guardian of each participating child.

2.1. Participants

From September 9, 2024, to January 24, 2025, a total of 371 preschool children meeting the broad eligibility criteria (aged 3–6 years) were initially contacted via convenience sampling from one public and two private kindergartens in Changsha City. The study’s inclusion criteria required children to be enrolled in and expected to remain at the participating kindergartens throughout the data collection period, without major health conditions that could significantly impact PA or cognitive function, and to have written informed consent provided by a parent or legal guardian. To ensure the feasibility and validity of the EF assessments, recruitment was specifically targeted at children in middle and senior classes (approximately 4–6 years old). Children in junior classes (typically around 3–4 years old) were excluded based on pilot testing and related literature [9], which indicated that the assessment battery would be excessively time-consuming and overly difficult for this younger age group, potentially compromising data validity and child compliance. Following this focused recruitment approach, written informed consent was obtained from parents of 319 children.

2.2. Methods

2.2.1. 24-h MB Monitoring.

A triaxial accelerometer (ActiGraph GT3X-BT, Pensacola, FL, United States) was used to obtain objective measures of PA and SB. This device provides estimates of light-intensity PA (LPA) and MVPA. Children were asked to wear the accelerometer on the right hip at the level of the iliac crest for seven consecutive days (five weekdays and two weekend days). Before data collection, parents and teachers received detailed instructions, highlighting that the device should be worn continuously, except during sleep or water-based activities.

Raw data were processed using ActiLife software. Non-wear time was determined using the Choi algorithm [20], and data were considered valid if children had at least three days of recording, including two weekdays and one weekend day, with a minimum daily wear time of 480 min [19]. The epoch length was set as 15 s. Activity intensity cutoff points followed the thresholds proposed by Chang & Wang (2021): SB = 0–116 counts/15 s, LPA = 117–551 counts/15 s, Moderate-Intensity PA = 552–997 counts/15 s, and Vigorous-Intensity PA ≥ 998 counts/15 s. These criteria have been validated in Chinese preschoolers aged three to six years [21].

Sleep duration was determined using an online collaborative sleep log jointly completed by the parents, head teachers, and classroom teachers. The log captured the children’s bedtimes, wake-up times, and nap onset and offset. Adapted from Zhenya and Ling (2023), this log displayed strong agreement with ActiGraph wGT3X-BT sleep estimates derived from the Tudor-Locke algorithm (r = 0.972, p < 0.001) [22].

2.2.2. EF Assessment.

EF was assessed using the Early Years Toolbox on an iPad. The Early Years Toolbox is a validated and user-friendly tool that supports offline multi-device use and employs a gamified design to enhance children’s engagement [23]. Assessments were conducted in a quiet, well-lit room, with each child accompanied by a trained assessor who provided standardized instructions.

The Early Years Toolbox included three core tasks. (1) Card sorting: This task evaluates cognitive flexibility. Children were required to sort cards based on color or shape, with the sorting rule changing partway throughout the task. The score is based on the number of correct responses following the rule switches. (2) Go/No-Go: This task measures inhibitory control. Children were instructed to respond (“Go”) when a fish appeared and withhold response (“No-Go”) when a shark appeared. The probabilities of the fish and shark stimuli were 80% and 20%, respectively. The inhibitory control score was calculated as the product of the accuracy in Go and No-Go trials. (3) Mr. Ant: This task assesses working memory. Children had to remember the locations of the stickers on an ant’s body and identify them after a delay. The task included eight difficulty levels, with points awarded for each completed level. The task was terminated after three consecutive failures. All three tasks demonstrated good criterion-related validity and were significantly correlated with the NIH Toolbox measures of working memory (r = 0.46), inhibitory control (r = 0.40), and cognitive flexibility (r = 0.45; all p < 0.001) [23]. Assessment Duration: A pilot study indicated that completing the three core tasks of the Early Years Toolbox (inhibitory control, working memory, and cognitive flexibility) took approximately 40–45 minutes per child. To minimize the impact of child fatigue or inattention on data quality, all assessments were conducted one-on-one by trained evaluators in a quiet room, and the following standardized measures were implemented: (a) scheduling assessments during the morning hours when children were at their optimal mental state; (b) providing brief rest intervals between each task; and (c) evaluators using standardized encouraging language to maintain children’s engagement. These measures effectively ensured the completeness and reliability of the data [9].

2.2.3. Covariates.

Covariates included sex, age, and body mass index (BMI) of children. Sex and age information were obtained from class rosters. Height and weight were measured by kindergarten staff using standardized equipment (Jianmin brand) one week before testing. BMI was calculated using the following formula: BMI = weight (kg)/height (m²). The child’s body mass index was included as a covariate based on existing literature indicating its associations with both MBs [24] and EF [25] development. Controlling for BMI in the statistical analysis aimed to reduce its potential confounding effect on the relationship between the core variables, thereby improving the accuracy of the model estimates.

2.3. Data analysis

Data processing and statistical analyses were conducted as follows. First, based on the wearing requirement, theoretical sleep duration was defined as 1440 min minus wear time. Data with a difference exceeding 10% between log-reported and theoretical sleep duration were then excluded, following the method of Weijs et al. [26]. Final sleep duration was determined as follows: if log-reported sleep duration plus wear time was ≥ 1440 min, sleep duration was defined as the theoretical value; otherwise, the log-reported value was adopted, and the remaining time was proportionally allocated to each intensity of PA [27].

Second, descriptive statistics and independent-samples t-tests were employed to examine differences in EF and its subcomponents across demographic variables. Children’s weight status was classified based on the World Health Organization’s definition of overweight for children under five years old [28].

Third, compositional data analysis was applied to describe the composition of 24-h MBs, including measures of central tendency (compositional mean) and dispersion (log-ratio variance). Since compositional data must undergo log-ratio transformation for analysis, and log-ratios require strictly non-zero observations, the zero values in this study were processed using a log-normal mixed model validated by scholars [29].

Fourth, MBs data were transformed using the isometric log-ratio transformation to investigate the associations between different MBs and EF. Regression models were established as follows: E(Y|Z) = β₀ + β₁z₁ + β₂z₂ + β₃z₃ + … + β(d-1)z(d-1) + Covariates; where Y represents EF or its subcomponents, d is the number of MB components, z_i are the isometric log ratio transformed variables, and β_i are the corresponding regression coefficients. The β values and their 95% confidence intervals are reported as the primary effect sizes in this study. The covariates included sex, age, and BMI.

Finally, R software (version 4.3.1) was used to analyze the isometric substitution effects of reallocating time between different MBs on EF, following the approach proposed by Dumuid et al. [30,31]. Following methodological approaches used in prior studies [9,19], an isotemporal substitution of 15 minutes per day was applied to analyze the relationship between the reallocation of time among different MBs and EF.

3. Results

3.1. Sample characteristics

Of the initial cohort, 266 participants provided valid data across all 24-hour MBs assessment and EF evaluations, constituting the final analytical sample. Attrition occurred primarily at two stages: 52 children were excluded due to parental lack of time or interest during the consent process, and a further 53 children were excluded during data collection—31 for incomplete device wear or logs, and 22 for insufficient data validity (e.g., inadequate accelerometer wear days).

The demographic characteristics of the final sample are summarized in Table 1. In brief, the sample was balanced in sex (approximately 50% boys and 50% girls) and age. Slightly more children attended middle classes than senior classes. The prevalence of overweight exceeded the national average for children under six years reported in the Report on Nutrition and Chronic Disease Status of Chinese Residents (2020) (6.8%). Nearly all children were primarily cared for by parents, with grandparents or other caregivers representing a small proportion (Table 1).

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Table 1. Demographic characteristics of the participants.

https://doi.org/10.1371/journal.pone.0333563.t001

3.2. Basic characteristics of EF in preschool children

Girls exhibited significantly higher inhibitory control than boys (t = –3.43, p = 0.001), with no significant differences in cognitive flexibility (t = 0.78, p = 0.434) or working memory (t = –0.85, p = 0.395).

Regarding age, preschool children aged three–four years performed significantly lower than those aged five–six years across all three EF dimensions, inhibitory control (t = –2.43, p = 0.016), cognitive flexibility (t = –3.05, p = 0.003), and working memory (t = –3.19, p = 0.002), indicating that EF improves with age.

No significant differences were observed in overall EF or any of its subcomponents between overweight and non-overweight children (all p > 0.05) (Table 2).

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Table 2. EF status of preschool children.

https://doi.org/10.1371/journal.pone.0333563.t002

3.2. Characteristics of 24-h MBs in preschool children

The average composition of 24-h MBs among preschool children is presented in Table 3. Sleep accounted for 42.11% of the day, SB for 44.10%, LPA for 10.84%, and MVPA for 2.94% (Table 3).

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Table 3. Compositional descriptive statistics of 24-h MBs: central tendency and dispersion measures.

https://doi.org/10.1371/journal.pone.0333563.t003

The variation matrix analysis indicated that the log-ratio variance between SB and MVPA was the highest (0.38), suggesting a relatively high variability and low dependency between these two behaviors. Changes in MVPA time were more likely to be compensated by changes in LPA (log-ratio variance = 0.14). However, alterations in sleep time were likely replaced by SB (log-ratio variance = 0.08) and LPA (log-ratio variance = 0.05).

3.3. Associations Between 24-h MBs and EF

The associations between 24-h MBs and EF are presented in Table 4. The relative composition of 24-h MBs was significantly associated with inhibitory control, cognitive flexibility, and working memory (all p < 0.001), with model R² values > 10% for each EF dimension. The highest model explanatory power was observed for inhibitory control (R² = 16.3%). After controlling for other MBs, SB was negatively associated with inhibitory control [γ₁² = –0.11 (−0.17, −0.05), p < 0.001], cognitive flexibility [γ₁² = –1.45 (−2.29, −0.61), p = 0.001], and working memory [γ₁² = –0.58 (−0.97, −0.19), p = 0.004]. In contrast, sleep was positively associated with cognitive flexibility [γ₁¹ = 1.60 (0.26, 2.94), p = 0.020; Table 4]

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Table 4. Associations between 24-h MBs and EF.

https://doi.org/10.1371/journal.pone.0333563.t004

3.4. Changes in EF Following 15-min/day Isotemporal Substitution of 24-h MBs

It is worth noting that among the various isotemporal substitution pathways examined, only a few reached statistical significance, although some significant findings were also observed. The results indicated that replacing SB with sleep 15 min/day increased the EF scores of preschool children: inhibitory control increased by 0.003 (0.0004, 0.006), and cognitive flexibility increased by 0.064 (0.021, 0.106). Conversely, reallocating time in the opposite direction resulted in significant decreases in the EF scores (Table 5).

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Table 5. Effects of 15-min daily time reallocation between 24-h MBs on EF in preschool children.

https://doi.org/10.1371/journal.pone.0333563.t005

4. Discussion

This study employed compositional data analysis to systematically investigate the relationship between the overall composition of 24-hour MBs and objectively assessed EF in preschool children. The main findings include: first, the overall pattern of 24-hour time composition was significantly associated with all three core subcomponents of EF (inhibitory control, cognitive flexibility, and working memory), highlighting the importance of analyzing these behaviors as an integrated system; second, isotemporal substitution model analysis revealed that reallocating 15 minutes per day from SB to sleep was significantly associated with improvements in inhibitory control and cognitive flexibility scores. This finding provides methodological support for the “time reallocation” hypothesis and offers specific quantitative guidance for behavioral interventions. Notably, the majority of time reallocation pathways in the model did not reach statistical significance (Table 5), which may suggest that EF in generally healthy preschool children is relatively insensitive to minor adjustments in daily activity time, or that detecting such effects requires a larger sample size or longer-term observation.

Specifically regarding various types of behaviors, this study found that sleep and SB are key behaviors closely linked to EF in preschool children. This aligns with existing evidence: prior systematic reviews have reported a modest positive correlation between sleep and EF [32], a finding corroborated by our results which indicate that, after controlling for other behaviors, sleep remained positively associated with cognitive flexibility. Notably, substituting 15 minutes per day of sedentary time with sleep was associated with significant improvements in both inhibitory control and cognitive flexibility, thereby reinforcing the potential benefits of this specific behavioral reallocation.

The effects of SB warrant further investigation. The existing literature presents some inconsistencies on this issue. While a review of 16 studies indicated that seven reported negative associations between SB and EF and two reported positive associations [33], the findings of the current study are clear and align with the predominant trend in the literature: SB demonstrated a significant negative association with all three dimensions of EF. This consistency underscores the potential detrimental role of excessive sedentary time in early cognitive development. From a neurobiological perspective, SB may impair white matter development, which is crucial for EF [34]. Given that myelination and neural signal conduction efficiency are influenced by environmental factors [35], prolonged SB may negatively impact EF development.

However, we must interpret the associations of SB with caution. Different types of sedentary activities may show divergent associations with EF; engagement in cognitively stimulating activities (for instance, puzzles or problem-solving games) has been associated with better EF [36], whereas time spent in passive screen-based activities has been associated with poorer outcomes [33]. Neuroimaging studies have demonstrated that excessive screen time is associated with reduced frontal gray matter volume and decreased white matter anisotropy [34,37], and the frontal lobe is a key brain region for cognitive flexibility [13]. Overall, research on the neural mechanisms linking SB, especially cognitive flexibility and EF, remains limited and warrants further investigation [13,33].

Unlike SB and sleep, the relationship between PA and EF presents a more complex picture. Unlike the strong “PA-EF” associations observed in some studies [9,10], the present study found relatively limited contributions from PA variables, a discrepancy that may be related to differences in sample characteristics or measurement contexts. The current empirical evidence regarding the association between PA and EF has not yet reached a consensus. For example, some studies have shown a significant positive correlation between moderate-to-vigorous PA and inhibitory control [9], while other research has found a significant positive correlation between vigorous intensity PA and cognitive flexibility [15]. Still other studies have reported no significant association between PA and inhibitory control or cognitive flexibility, only a negative correlation with working memory, and have emphasized that such associations may be limited by specific environments and types of activities [14]. Notably, the heterogeneity in findings may also stem from methodological diversity. For instance, a study employing multivariate pattern analysis revealed highly complex nonlinear relationships between PA intensity and EF [38], some of which were even difficult to explain directly using existing theories. In contrast, compositional data analysis offers a novel and complementary research perspective for understanding these complex associations by focusing on the relative proportions and structural balance of time allocation.

The value of this study is primarily reflected in two aspects. At the measurement level, compared to our team’s previous reliance on parent-reported behavioral assessments [19], this study utilized standardized cognitive tasks administered via tablets for direct measurement. This approach avoids the subjective bias inherent in third-party reports and provides a more precise reflection of real-time performance in core cognitive components. At the methodological level, the adoption of the compositional data analysis framework addresses the multicollinearity issues among behavioral variables commonly encountered in traditional regression analysis [39]. This allows for more rigorous statistical support for composite health recommendations such as “reducing SB and increasing sleep.”

This study has several limitations. First, there is a limitation in the study design. This study employed a cross-sectional design, which precludes causal inference between variables. Although we hypothesized that MBs may influence the development of EF, the reverse explanation—that children with better EF may tend to choose healthier activity patterns—is equally plausible. Second, there are limitations in the measurement methods. The measurement of sleep duration relied primarily on parent-reported logs. Although preliminary validation showed a high correlation with device-monitored data [22], recall bias cannot be completely ruled out. Third, there is a limitation in sample representativeness. The study sample was recruited through convenience sampling from kindergartens in eastern Chinese cities. The generalizability of the findings to rural areas, children from different cultural backgrounds, or varying socioeconomic backgrounds requires further verification. Fourth, there was inadequate control for covariates. Due to parental concerns about privacy, key socioeconomic indicators such as household monthly income had a high missing data rate (up to 19.6%), which may have limited our ability to control for potential confounding effects of socioeconomic status..

5. Conclusions

The present study demonstrates that the 24-h MBs composition significantly predicts EF in preschool children, with sleep showing a positive association and SB a negative association. Reallocating time from SB to sleep is associated with improved cognitive flexibility. These findings have important practical implications: given that preschool children’s daily movement patterns are highly modifiable, interventions targeting sleep enhancement may yield tangible cognitive benefits. Notably, the lack of significant effects for most other substitution pathways suggests that sleep may play a unique and non-substitutable role in preschool EF. Future research should employ longitudinal and experimental designs to establish causality, investigate the neurobiological mechanisms linking sleep to EF in young children, and examine whether modest increases in sleep duration produce measurable cognitive improvements.

Supporting information

S1 File. Article Data.

This is the supporting data for the manuscript.

https://doi.org/10.1371/journal.pone.0333563.s001

(XLS)

Acknowledgments

Thank you for the strong support and assistance of the Three Education Series Kindergartens and Ronsheng Huayu City Kindergarten.

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Figures

Abstract

Objective

Methods

Results

Conclusion

1. Introduction

2. Materials and methods

2.1. Participants

2.2. Methods

2.2.1. 24-h MB Monitoring.

2.2.2. EF Assessment.

2.2.3. Covariates.

2.3. Data analysis

3. Results

3.1. Sample characteristics

3.2. Basic characteristics of EF in preschool children

3.2. Characteristics of 24-h MBs in preschool children

3.3. Associations Between 24-h MBs and EF

3.4. Changes in EF Following 15-min/day Isotemporal Substitution of 24-h MBs

4. Discussion

5. Conclusions

Supporting information

S1 File. Article Data.

Acknowledgments

References