Abstract
Background
Early childhood is a critical period for children's growth and development. Given that over 30% of Thai children under age five experience developmental delays, it is important to examine their movement behavior: physical activity, sedentary behavior, and sleep. These three components are important factors for optimal development. This study assessed 24-h movement behaviors of Thai preschoolers, determined the proportion meeting WHO guidelines, and explored how demographic factors differentiate these behaviors.
Methods
Data from the Sunrise Thailand Dataset 2023, comprising 518 children aged 3-4 years (50.5% boys and 49.5% girls), were analyzed. Physical activity, sedentary behavior, and sleep were measured using ActiGraph GT3X + accelerometers. Children's adherence to WHO guidelines was evaluated using descriptive statistics. Independent-samples t-tests and one-way ANOVAs examined differences across demographic subgroups. Multiple linear regression models identified predictors of movement behaviors.
Results
Only 26.3% of Thai preschoolers met recommended physical activity levels, 49.8% adhered to sleep duration guidelines, and 68.5% followed sedentary behavior limits. Just 7.5% met all three recommendations simultaneously. Regression analyses revealed socioeconomic factors as the strongest predictors: children from low-income households and those with parents in agriculture or government sectors showed significantly reduced sleep duration and increased sedentary time. Single-parent families demonstrated the lowest overall adherence.
Conclusions
The majority of Thai preschoolers failed to meet WHO guidelines, with particularly low compliance in physical activity. These results emphasize the need for developing public health strategies to improve movement behavior, especially in vulnerable populations such as single-parent families and rural communities. Policies should focus on increasing access to safe play environments, promoting active lifestyles, and providing targeted support to caregivers to help children meet these critical health recommendations.
Keywords: Early years, Children, Growth and development, Health promotion, Well-being
1. Introduction
Within the 24-h movement behavior framework, physical activity, sedentary behavior, and sleep form an interrelated continuum that supports healthy growth and development during early childhood. The early years of a child's life, especially before the age of five, are crucial, as this period lays the foundation for lifelong health and development.1,2 In Thailand, 30% of children under five are developmentally delayed.3 Although recent national data suggest some improvement in developmental outcomes, with 77.2% of children aged 0–2 years and 63.2% of those aged 3–5 years meeting developmental milestones, these figures remain below the national target of 85%.4
Adequate movement behaviors are associated with better physical fitness, cognitive function, and socio-emotional development during early childhood.5, 6, 7 Reflecting the interrelated nature of these behaviors, the World Health Organization (WHO) recommends that children aged 3–4 years accumulate at least 180 min of daily physical activity, including 60 min of moderate-to-vigorous-intensity activity, limit sedentary screen time to no more than 1 h per day, and obtain 10–13 h of quality sleep.8
Movement behaviors during early childhood are shaped by family, socioeconomic, and environmental contexts. Parents and caregivers influence children's routines through opportunities for active play, regulation of screen exposure, and sleep scheduling.9,10 In Thailand and other Asian settings, family and caregiving arrangements may further shape children's daily movement routines, particularly where grandparents or extended family members contribute to childcare. In addition, BMI status has been associated with physical inactivity, sedentary behavior, and poorer sleep outcomes among young children.5,6
Previous studies have shown that boys generally accumulate higher levels of moderate-to-vigorous physical activity than girls, while children from socioeconomically disadvantaged households often have fewer opportunities for healthy movement behaviors.9, 10, 11 Evidence from Asian and low- and middle-income settings points in a similar direction. Studies from Ethiopia, Bangladesh, and Japan, together with cross-national SUNRISE work, have reported low adherence to 24-h movement guidelines and variation by sex, residence, and socioeconomic circumstances.12, 13, 14
However, representative data within Thailand remain limited, particularly for children under five years. Existing national surveillance mainly focuses on school-aged children and adolescents, leaving a substantial gap in information on movement behaviors during early childhood. As a result, little is known about how Thai preschoolers accumulate physical activity, sedentary behavior, and sleep throughout the 24-h cycle, or how these behaviors differ by demographic and family characteristics.
This study examined 24-h movement behaviors among a nationally representative sample of Thai preschoolers. Specifically, the study aimed to: (1) investigate levels and patterns of physical activity, sedentary behavior, and sleep among Thai preschoolers; (2) determine the proportion of children meeting WHO 24-h movement guidelines; and (3) examine demographic and family-related correlates of these movement behaviors. Based on previous literature, we hypothesized that boys would demonstrate higher physical activity levels than girls and that movement behaviors would vary significantly according to area of residence, socioeconomic status, and family characteristics.
2. Methods
2.1. Participants and study design
This study utilized data from the SUNRISE Thailand 2023 project, a national cross-sectional survey designed to examine 24-h movement behaviors among Thai children aged 3–4 years. Participants were selected using multistage probability random sampling from early childhood education centers across five regions of Thailand. The sampling process was designed to capture variation in geographic region, urban–rural setting, childcare context, gender, and age. Provinces, districts, and early childhood education centers were selected sequentially, and eligible children within participating centers were invited to participate after parents or caregivers provided informed consent. The final analytic sample consisted of 518 Thai preschoolers (mean age = 4.09 years, SD = .42), including 261 boys (50.5%) and 257 girls (49.5%).
Data collection was conducted by trained research staff following standardized SUNRISE protocols.15 Children's physical activity, sedentary behavior, and sleep were assessed using ActiGraph GT3X + accelerometers. Demographic and health characteristics, as well as socioeconomic indicators, were collected through face-to-face interviews with parents or caregivers. Socioeconomic status was assessed using household income and parental or caregiver occupation. Household income categories were based on monthly family income.
2.2. Measures
Physical activity, sedentary behavior, and sleep were measured objectively using the ActiGraph GT3× + accelerometer (ActiGraph, Pensacola, USA). The validity, reliability, and feasibility of ActiGraph/GT3X accelerometers have been supported in preschool-aged children, with accelerometry-based evidence also available among Asian young children of comparable age.16, 17, 18 Children were instructed to wear the device continuously, including during sleep but excluding water-based activities, for a minimum of 5 days to obtain at least three complete 24-h monitoring periods.15
Accelerometer data were processed and analyzed using an automated script created in R (version 4.2.1). The raw accelerometer data files (.gt3x) were first converted into counts per second format using the 'activityCounts' package.19
Sleep periods were identified using the decision-tree-based algorithm in the PhysActBedRest package,20 based on vertical axis accelerometer data. Previous validation studies among preschool-aged children demonstrated high sensitivity (.936), specificity (.970), and overall accuracy (.952) compared with visual identification of sleep periods. Sleep episodes occurring between 10:00 a.m. and 7:00 p.m. were classified as daytime naps.
Sedentary behavior was defined as <200 counts/15-sec, light-intensity physical activity (LPA) as 200-419 counts/15-sec, and moderate-to-vigorous physical activity (MVPA) as ≥420 counts/15-sec, based on validated cut-points developed by Pate et al.21,22. Only waking hours were included in sedentary behavior analyses. Valid wear days required at least 10 h of waking wear time and a minimum of 160 min of total sleep periods, with the first and last monitoring days excluded from analysis. 23, 24, 25
2.2.1. Meeting the 24-h movement guidelines
This study assessed children's movement behaviors in relation to the WHO 24-h movement guidelines for preschool-aged children (3–4 years).8 Children were classified as meeting the physical activity and sleep guidelines if they accumulated ≥180 min of total physical activity (TPA), including ≥60 min of MVPA, per day, and slept 10–13 h including naps. Sedentary behavior was operationalized using accelerometer-derived total waking sedentary time, with a pragmatic threshold of <8 h/day used to classify lower total sedentary exposure.16,26
2.2.2. Anthropometrics
Children's weight and height were measured to calculate body mass index (BMI) as weight in kilograms divided by height in meters squared. BMI was interpreted using age- and sex-specific percentile classifications commonly applied in pediatric populations to account for differences in growth patterns among children.27 Weight status categories included underweight, healthy weight, possible risk of overweight, overweight, and obesity, based on age- and sex-specific BMI percentile classifications. The additional possible risk of overweight category was retained in accordance with the original dataset classification structure.
2.3. Data management and analysis
This study employed a cross-sectional analytical design using baseline data collected from the SUNRISE Thailand 2023 project. At the initial stage, all variables were screened for completeness, consistency, and outliers. Approximate normality was assessed using descriptive statistics and visual inspection. Accelerometer data were processed according to the predefined protocols described above. These included criteria for valid wear time, sleep periods, and non-wear time. Missing data were minimal and were handled using listwise deletion in inferential and regression analyses.
Descriptive statistics, including means, standard deviations, frequencies, and proportions, were calculated to summarize movement behaviors and participant characteristics across demographic subgroups, including sex, area of residence, BMI category, household type, parental income, and parental occupation. Independent-samples t-tests and one-way analyses of variance (ANOVA) were conducted to examine differences in movement behaviors across demographic characteristics. Bonferroni-adjusted post-hoc tests were applied when appropriate. Multiple linear regression analyses were performed to examine associations between demographic variables and daily physical activity, sedentary behavior, and sleep duration. Both standardized (β) and unstandardized (B) coefficients, 95% confidence intervals (CIs), and p-values were reported. Statistical significance was set at p < .05.
3. Results
3.1. Participant characteristics
Participants in this study included 518 Thai preschoolers with a mean age of 4.09 years. The sample was balanced by sex and area of residence. While most children were classified as having a healthy weight, smaller proportions were classified as underweight, overweight, obese, or at possible risk of being overweight. Household structures and socioeconomic conditions varied across the sample, with more than half of the children lived in extended family arrangements.
Overall adherence to the WHO 24-h movement guidelines was low. Only 7.5% of children met all three recommendations simultaneously, and physical activity showed the lowest overall compliance (see Table 1).
Table 1.
Participant characteristics.
| Variable | N | % |
|---|---|---|
| Male | 261 | 50.5 |
| Female | 257 | 49.5 |
| Age () | Mean = 4.09 | |
| Urban | 251 | 48.5 |
| Rural | 267 | 51.5 |
| Total PA | 136 | 26.3 |
| Sleep | 258 | 49.8 |
| Sedentary behavior | 355 | 68.5 |
| BMI | ||
| Healthy Weight | 403 | 77.8 |
| Obese | 43 | 8.3 |
| Overweight | 14 | 2.7 |
| Possible risk of overweight | 50 | 9.7 |
| Underweight | 8 | 1.5 |
| Living arrangements | ||
| Nuclear family | 133 | 25.7 |
| Single parent family | 18 | 3.5 |
| Skipped-generation Family | 76 | 14.7 |
| Others | 291 | 56.2 |
| Parents/caregiver income | ||
| Low (<3500 Baht/month) | 271 | 52.3 |
| 3501–10,000 Baht/month | 10 | 1.9 |
| 10,001–30,000 Baht/month | 103 | 19.9 |
| 30,001–100,000 Baht/month | 101 | 19.5 |
| >100,001 Baht per month | 33 | 6.4 |
| Parents/caregiver occupation | ||
| Unemployed | 117 | 22.6 |
| Agriculture | 105 | 20.3 |
| Government employee | 33 | 6.4 |
| Private employee | 146 | 28.2 |
| Others | 117 | 22.6 |
| Meeting the Recommended Guidelines | ||
| Met All Guidelines | 39 | 7.5 |
| Did Not Meet All Guidelines | 479 | 92.5 |
3.2. 24-hour movement behaviors of Thai preschoolers
Table 2 presents the proportion of children meeting WHO 24-h movement guidelines across demographic and socioeconomic subgroups. Overall adherence to all three guidelines remained low across all groups. Boys showed slightly higher physical activity adherence than girls, while sleep and sedentary behavior adherence were broadly similar by sex. Children from skipped-generation families showed relatively higher overall guideline adherence, whereas children from single-parent households showed the lowest combined adherence. Patterns across income and parental occupation suggested that socioeconomic circumstances may influence children's ability to meet movement behavior recommendations.
Table 2.
Percentage of Thai preschoolers meeting the recommended guidelines.
| Category | PA | Sleep | SB | Non-compliant with All Guidelines | Compliant with All Guidelines |
|---|---|---|---|---|---|
| Gender | |||||
| Boys | 77 (29.5) | 101 (38.7) | 182 (69.7) | 241 (92.3) | 20 (7.7) |
| Girls | 59 (23.0) | 100 (38.9) | 173 (67.3) | 238 (92.6) | 19 (7.4) |
| Area of residence | |||||
| Urban | 72 (28.7) | 100 (39.8) | 165 (65.7) | 234 (93.2) | 17 (6.8) |
| Rural | 64 (24.0) | 101 (37.8) | 190 (71.2) | 245 (91.8) | 22 (8.2) |
| BMI category | |||||
| Healthy Weight | 101 (25.1) | 151 (37.5) | 292 (72.5) | 369 (91.6) | 34 (8.4) |
| Obese | 10 (23.3) | 20 (46.5) | 20 (46.5) | 42 (97.7) | 1 (2.3) |
| Overweight | 2 (14.3) | 6 (42.9) | 9 (64.3) | 14 (100.0) | 0 (.0) |
| Possible risk of overweight | 22 (44.0) | 23 (46.0) | 27 (54.0) | 46 (92.0) | 4 (8.0) |
| Underweight | 1 (12.5) | 1 (12.5) | 7 (87.5) | 8 (100.0) | 0 (.0) |
| Living arrangement | |||||
| Nuclear family | 32 (24.1) | 49 (36.8) | 94 (70.7) | 122 (91.7) | 11 (8.3) |
| Single parent family | 2 (11.1) | 3 (16.7) | 14 (77.8) | 18 (100.0) | 0 (.0) |
| Skipped-generation Family | 23 (30.3) | 34 (44.7) | 54 (71.1) | 67 (88.2) | 9 (11.8) |
| Others | 79 (27.1) | 115 (39.5) | 193 (66.3) | 272 (93.5) | 19 (6.5) |
| Parents/caregiver income | |||||
| Low (<3500 Baht/month) | 70 (25.8) | 110 (40.6) | 182 (67.2) | 251 (92.6) | 20 (7.4) |
| 3501–10,000 Baht/month | 2 (20.0) | 3 (30.0) | 6 (60.0) | 10 (100.0) | 0 (.0) |
| 10,001–30,000 Baht/month | 35 (34.0) | 40 (38.8) | 76 (73.8) | 92 (89.3) | 11 (10.7) |
| 30,001–100,000 Baht/month | 21 (20.8) | 35 (34.7) | 72 (71.3) | 96 (95.0) | 5 (5.0) |
| >100,001 Baht per month | 8 (24.2) | 13 (39.4) | 19 (57.6) | 30 (90.9) | 3 (9.1) |
| Parents/caregiver occupation | |||||
| Unemployed | 34 (29.1) | 51 (43.6) | 72 (61.5) | 109 (93.2) | 8 (6.8) |
| Agriculture | 35 (33.3) | 40 (38.1) | 76 (72.4) | 94 (89.5) | 11 (10.5) |
| Government employee | 8 (24.2) | 13 (39.4) | 19 (57.6) | 30 (90.9) | 3 (9.1) |
| Private employee | 28 (19.2) | 57 (39.0) | 106 (72.6) | 138 (94.5) | 8 (5.5) |
| Others | 31 (26.5) | 40 (34.2) | 82 (70.1) | 108 (92.3) | 9 (7.7) |
3.3. Correlates of 24-h movement behaviors of Thai preschoolers
Table 3 presents descriptive statistics for children's daily physical activity, sleep duration, and sedentary behavior across sociodemographic subgroups. Boys accumulated significantly more physical activity than girls. No sex differences were observed for sleep duration or sedentary behavior. Children living in rural areas had longer sleep duration than those living in urban areas. Significant differences were also observed across BMI categories. Children at possible risk of being overweight showed the highest physical activity, whereas children with obesity showed higher sedentary time. Sleep duration differed by household income, and sedentary behavior varied by parental occupation.
Table 3.
Socio-demographic correlates of 24-h movement behaviors among Thai preschoolers.
| Category | Total PA | p-value | Sleep | p-value | SB | p-value |
|---|---|---|---|---|---|---|
| Gender | ||||||
| Boys | 152.61 (47.73) | .015∗ | 762.37 (144.80) | .649 | 440.39 (78.51) | .632 |
| Girls | 142.15 (49.95) | 768.40 (156.86) | 443.66 (77.03) | |||
| Area of residence | ||||||
| Urban | 148.00 (47.92) | .796 | 751.89 (158.16) | .049∗ | 447.62 (77.43) | .111 |
| Rural | 146.88 (50.22) | 778.03 (142.65) | 436.73 (77.77) | |||
| BMI category | ||||||
| Healthy Weight | 145.68 (49.27) | .027 | 770.08 (152.81) | .016∗ | 437.57 (77.93) | .002∗ |
| Obese | 145.16 (49.32) | 723.94 (137.47) | 478.57 (67.12) | |||
| Overweight | 145.81 (34.88) | 749.00 (136.79) | 466.72 (76.97) | |||
| Possible risk of overweight | 168.05 (46.05) | 744.31 (137.47) | 447.96 (73.63) | |||
| Underweight | 127.88 (54.74) | 910.31 (142.15) | 388.74 (85.52) | |||
| Living arrangement | ||||||
| Nuclear family | 143.59 (55.07) | .339 | 745.65 (174.20) | .853 | 434.87 (80.97) | .415 |
| Single parent family | 133.98 (37.87) | 777.43 (135.51) | 425.41 (69.49) | |||
| Skipped-generation Family | 153.27 (45.35) | 773.06 (127.50) | 441.94 (58.83) | |||
| Others | 148.42 (47.63) | 771.61 (145.60) | 446.33 (80.91) | |||
| Parents/caregiver income | ||||||
| Low (<3500 Baht/month) | 146.10 (48.98) | .583 | 748.38 (157.66) | .045∗ | 443.01 (77.18) | .991 |
| 3501–10,000 Baht/month | 133.59 (48.81) | 770.85 (199.77) | 436.64 (107.03) | |||
| 10,001–30,000 Baht/month | 152.75 (52.69) | 801.99 (117.93) | 438.88 (69.28) | |||
| 30,001–100,000 Baht/month | 148.76 (43.90) | 771.33 (145.05) | 443.04 (75.07) | |||
| >100,001 Baht per month | 141.05 (53.91) | 770.56 (171.45) | 441.98 (105.61) | |||
| Parents/caregiver occupation | ||||||
| Unemployed | 147.08 (50.85) | .771 | 743.40 (160.20) | .143 | 451.80 (83.99) | .572 |
| Agriculture | 152.02 (52.48) | 795.47 (125.91) | 443.29 (76.88) | |||
| Government employee | 141.05 (53.91) | 770.56 (171.45) | 437.65 (68.13) | |||
| Private employee | 145.20 (43.86) | 764.34 (148.03) | 441.98 (105.61) | |||
| Others | 147.42 (49.07) | 760.11 (156.89) | 436.53 (74.42) | |||
Note: Values are means; numbers in parentheses are standard deviations. PA = physical activity. Sedentary time and sleep duration were derived from accelerometer (ActiGraph GT3X+). Group comparisons are described in the results and regression models (Table 4).
Table 4 summarizes the multiple linear regression models for preschoolers’ 24-h movement behaviors. For physical activity, sex and BMI status were significant predictors. Boys and children classified as being at possible risk of overweight reported higher physical activity levels. Other demographic and household characteristics were not significantly associated with physical activity.
Table 4.
Linear regression models examining correlates of 24-h movement behaviors among Thai preschoolers.
| Variable | PA (min/day) |
Sleep (min/day) |
SB (min/day) |
||||||
|---|---|---|---|---|---|---|---|---|---|
| β | 95% CI | p | β | 95% CI | p | β | 95% CI | p | |
| Sex | −.103 | 18.62, −1.65 | .019∗ | .014 | −21.31, 30.02 | .739 | .021 | −9.90, 16.46 | .625 |
| Area of residence | −.027 | −11.16, 5.88 | .543 | .092 | 1.91, 53.48 | .035∗ | −.089 | −27.07, −.58 | .041∗ |
| BMI (ref = Healthy weight) | |||||||||
| Obese | −.006 | −16.46, 14.50 | .901 | −.091 | −96.30, −2.59 | .039∗ | .144 | 16.41, 64.55 | .001∗∗ |
| Overweight | −.027 | −34.58, 18.08 | .539 | −.031 | −108.68, 50.64 | .475 | .066 | −9.32, 72.51 | .130 |
| Possible risk of overweight | .125 | 6.29, 35.25 | .005∗∗ | −.064 | −76.34, 11.27 | .145 | .051 | −9.13, 35.87 | .244 |
| Underweight | −.046 | −52.84, 16.03 | .294 | .108 | 27.69, 236.06 | .013∗ | −.077 | −102.15, 4.88 | .075 |
| Living arrangement (ref = Nuclear family) | |||||||||
| Single parent | −.034 | −33.53, 15.40 | .467 | .059 | −25.53, 122.49 | .199 | −.030 | −50.60, 25.43 | .516 |
| 3-generation | .050 | −7.98, 21.80 | .362 | .044 | −26.50, 63.60 | .419 | .003 | −22.49, 23.79 | .956 |
| Extended | .049 | −5.43, 15.05 | .356 | .097 | −1.52, 60.45 | .062 | .044 | −9.08, 22.75 | .399 |
| Income (ref = 10,001–30,000 Baht/month) | |||||||||
| Low (<3500 Baht/month) | −.494 | −119.19, 22.26 | .179 | −1.36 | −624.84, −196.90 | <.001∗∗∗ | 1.521 | 126.58, 346.41 | <.001∗∗∗ |
| 3501–10,000 Baht/month | −.172 | −138.80, 15.88 | .119 | −.334 | −599.40, −131.42 | .002∗∗ | .385 | 96.87, 337.26 | <.001∗∗∗ |
| 30,001–100,000 Baht/month | −.295 | −105.09, 32.12 | .297 | −.962 | −573.37, −158.26 | .001∗∗ | 1.213 | 131.20, 344.44 | <.001∗∗∗ |
| Parental occupation (ref = Unemployed) | |||||||||
| Agriculture | −.368 | −115.38, 25.26 | .212 | −.946 | −567.72, −141.01 | .001∗∗ | 1.162 | 144.93, 334.13 | <.001∗∗∗ |
| Government employee | −.280 | −129.61, 17.28 | .134 | −.624 | −607.03, −162.62 | .001∗∗ | .704 | 109.80, 338.09 | <.001∗∗∗ |
| Private employee | −.102 | −28.06, 5.85 | .199 | −.020 | −57.96, 44.64 | .799 | −.088 | −41.50, 11.21 | .259 |
| Other | −.004 | −13.40, 12.55 | .949 | .064 | −16.34, 62.19 | .252 | −.089 | −36.66, 3.68 | .109 |
Note: Values shown are standardized regression coefficients (β) with 95% confidence intervals (CI) and p-values. Reference categories are listed in parentheses. Statistically significant p-values are shown in bold and marked as ∗p < .05, ∗∗p < .01, ∗∗∗p < .001.
For sleep duration, rural residence was associated with longer sleep. Sleep duration also varied by BMI status, household income, and parental occupation. Children with obesity and those from lower-income households reported shorter sleep duration.
Sedentary behavior was associated with area of residence, BMI status, household income, and parental occupation. Children living in rural areas accumulated less sedentary time. In contrast, children with obesity and those from lower-income households showed higher sedentary time. Greater sedentary time was also observed among children whose parents worked in agricultural and government sectors.
4. Discussion
This study provides the first nationally representative analysis of Thai preschoolers' 24-h movement behaviors using device-based measurements. The findings show that guideline adherence is low, especially for physical activity, and that movement behaviors differ across sex, BMI status, area of residence, household income, and parental occupation. Rather than pointing to a single high-risk factor, the results suggest that young children's daily activity, sedentary time, and sleep are shaped by the combined influence of family routines, childcare environments, and socioeconomic conditions.
The most important public health message is that these behaviors should be considered together. A child who sleeps adequately may still miss the overall guideline if opportunities for active play are limited, while reducing sedentary time alone may not increase moderate-to-vigorous physical activity. The low combined adherence observed in this study therefore supports integrated early-childhood strategies that address active play, sedentary behavior, and sleep routines as part of the same daily schedule rather than as separate behaviors.
Sex emerged as a significant predictor of physical activity, with girls engaging in fewer minutes of PA than boys. Although this sex difference is consistent with global trends, it is noteworthy in the Thai preschool context because young children's movement opportunities are strongly shaped by caregivers, family routines, and childcare settings. Societal norms and gendered expectations may influence the types of play encouraged for boys and girls, with boys more frequently supported to engage in vigorous outdoor activities, while girls may be directed toward quieter, indoor, or more supervised activities.28 This finding suggests that sex differences in physical activity may emerge before formal schooling and may reflect unequal opportunities for active play rather than children's preferences alone. No sex differences were observed in overall sedentary behavior in either descriptive or regression analyses. Although previous studies have reported higher engagement in screen-based sedentary behaviors among boys,29,30 total sedentary behavior measured objectively in this study captures a broader range of low-energy activities beyond screen use, such as seated learning, quiet play, and transportation. Therefore, health promotion efforts should encourage active play and ensure equal access for both boys and girls during early childhood.
Contrary to common assumptions that rural environments provide more natural space for active play, area of residence was not a significant predictor of children's physical activity in the regression model. However, rural children tended to sleep longer and accumulated less sedentary time than their urban peers. These findings align with Abdeta et al.,12 who also reported better sleep adherence among rural preschoolers than urban preschoolers in Ethiopia. In the Thai context, longer sleep among rural children may reflect earlier bedtimes linked to agricultural family routines and lower exposure to urban environmental stimuli, such as screen device, traffic noise and artificial lighting, that can disrupt sleep.
The lower sedentary time observed among rural children should be interpreted with caution. Although they sat less, they did not engage in greater amounts of physical activity, suggesting that reduced sedentary time does not necessarily translate into increased moderate-to-vigorous activity. This pattern may relate to childcare infrastructure. Since data collection occurred primarily during preschool hours (7:30 a.m.-3:30 p.m.), movement opportunities were strongly influenced by the physical environment and available resources within childcare centers.
The rural-urban differences observed in sleep and sedentary behavior is also likely to reflect broader socioeconomic and infrastructural inequalities rather than differences in family lifestyles. Rural childcare settings may have fewer sheltered or weather-protected play spaces,31 which can restrict active play during the rainy season. Rural children may even miss school entirely on heavy rainy days. In contrast, urban preschools more commonly have sheltered or indoor play spaces that support consistent opportunities for active play regardless of weather conditions.32 These findings imply that efforts to reduce sedentary behavior may need to focus not only on individual or family practices, but also on improving access to safe and weather-appropriate play environments within childcare settings, particularly in under-resourced rural communities.
BMI status showed complex associations with movement behaviors. Children classified as at possible risk of being overweight demonstrate significantly higher physical activity levels than those with healthy weight. This finding may reflect heightened caregiver awareness of weight-related concerns and greater encouragement of active play.31 At the same time, children in this category may still have sufficient mobility and confidence to engage in active play, whereas such opportunities may become more constrained once obesity is established.
Conversely, obese children had shorter sleep duration and greater sedentary time than children with healthy weight. These findings are consistent with research showing that overweight and obese children may experience poorer sleep because of physiological issues, including breathing difficulties, as well as irregular sleep routines.33 Higher sedentary time may also reflect social and environmental barriers to active play, such as lower motor confidence, negative peer experiences, limited family support, or fewer suitable play opportunities.34,35
Underweight children also showed longer sleep duration than the healthy-weight group. This pattern should also be interpreted with caution, as longer sleep may reflect lower energy expenditure, recovery needs, or underlying health conditions. These BMI-related patterns point out that interventions should not treat all preschoolers as having the same movement needs. Children at different weight-status categories may require different forms of support, including active-play opportunities, sleep-routine guidance, and closer monitoring of health and growth.
Family structure appeared to influence children's adherence to the WHO 24-h movement guidelines. Previous studies have shown that children raised in families with stronger cohesion and greater caregiver support are more likely to engage in healthier movement behaviors, including higher levels of physical activity, than children from households with less stable caregiving structures.34,36,37 These patterns may reflect differences in supervision, daily routines, emotional support, and opportunities for active play during early childhood. Although family structure variables were not independently associated with movement behaviors in the adjusted regression models, descriptive findings suggested lower overall guideline adherence among children from single-parent families. Limited caregiver time, financial strain, and competing work responsibilities may reduce opportunities to support consistent sleep routines and active play.38,39
Children living in skipped-generation households demonstrated relatively higher adherence to physical activity recommendations. In Thailand, grandparents commonly play an important caregiving role, particularly when parents migrate for work.40,41 Greater engagement in traditional outdoor play and lower reliance on screen-based activities may partially explain these findings.42 Caregiving structure may shape children's movement behaviors through differences in daily routines, supervision, and opportunities for active play and sleep regulation.
Among all predictors examined, socioeconomic factors, particularly household income and parental occupation, showed the most consistent associations with movement behaviors. Children from the lowest-income households (<3500 Baht/month) reported shorter sleep duration and greater sedentary time than those from middle-income households (10,001–30,000 Baht/month), suggesting that economic constraints may shape the parts of the day that depend most on household routines and available resources.
Several factors may explain the sleep deficit observed among low-income families. Economic stress and irregular work schedules can disrupt household routines and delay bedtimes. Low-income households may also face environmental conditions that make healthy sleep more difficult, such as overcrowding, noise, and inadequate climate control 43, 44, 45. Greater sedentary time in this group may reflect fewer safe play spaces and fewer opportunities for organized recreational activities outside preschool hours.
Parental occupation showed a similar pattern. Children of agricultural workers and government-employed parents had shorter sleep duration and greater sedentary behavior than children of unemployed parents. In Thailand, agricultural work often involves long and irregular hours with substantial seasonal variation, which may disrupt family routines and caregiving patterns. During peak farming periods, reduced caregiver availability may limit supervision and opportunities for active play. Government employment may also involve rigid schedules, commuting demands, and limited caregiver time outside working hours. Previous studies in LMIC settings have similarly noted that occupational strain and time poverty can constrain caregivers’ ability to support active play and consistent bedtime routines.15,46
Physical activity levels did not vary by household income or parental occupation. This may indicate that socioeconomic influences operate more strongly through sleep and sedentary behavior than through total physical activity in this age group. One plausible explanation is the equalizing role of preschool environments, where children participate in similar scheduled activities regardless of family background. In contrast, sleep and sedentary behavior may be more sensitive to what happens outside school hours, when family routines, housing conditions, and caregiver availability play a larger role.
5. Strengths and limitations
This study has several strengths. It utilized nationally representative data from Thai preschoolers, allowing movement behaviors to be examined across diverse demographic and socioeconomic contexts. The use of ActiGraph accelerometers to assess physical activity, sedentary behavior and sleep provided objective measurements that reduced many of the biases associated with self-reported movement behaviors and strengthened the reliability of the findings.47,48 In addition, the inclusion of area of residence, BMI, family structure, household income, and parental occupation provided important insights into the multiple factors associated with movement behaviors during early childhood.
Several limitations should also be considered. The cross-sectional design limits causal interpretation of the observed associations. Although accelerometers provided objective movement data, they could not distinguish screen-based sedentary behavior from other forms of sedentary time or capture contextual factors such as screen content, parenting practices, or childcare quality. Some subgroup sizes, particularly single-parent households and underweight children, were relatively small and should therefore be interpreted cautiously. Future longitudinal studies incorporating both objective and contextual measures are needed to better understand how family and socioeconomic conditions influence movement behaviors over time.
6. Conclusions
Most Thai preschoolers did not meet the WHO 24-h movement guidelines, with the largest gap observed for physical activity. The findings indicate that movement behaviors in early childhood are shaped not only by individual habits but also by family, socioeconomic, and environmental contexts. Children from lower-income households showed shorter sleep duration and higher sedentary time, children in single-parent families had the lowest overall adherence, and sex differences in physical activity were already evident during the preschool years. These patterns suggest that improving movement behaviors requires more than health education alone. Early childhood policies and interventions should address active play, sleep, and sedentary time as interconnected behaviors, while prioritizing support for families and communities where opportunities for healthy movement are most constrained.
Ethics approval and consent to participate
All the participants provided informed consent prior to their involvement in the study. The research team ensured the confidentiality of the information provided by participants and explained the right of each individual to participate or withdraw from the study at their convenience. The protocol of the study followed the principles of SUNRISE study15 and local government regulations, and was approved by the ethical review board of the Institute for Population and Social Research of Mahidol University, COA. No. 2023/12-243.
Consent for publication
N.A.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Author contribution
DAW, ADO and PK conceptualized and designed the study; DAW and AA conceived the study; SK and PL performed formal data analysis; DAW and PK interpreted the findings; PL drafted the manuscript; PL, DAW, ADO review the manuscript, DAW and PL finalized the manuscript.
Funding
The authors gratefully acknowledge the funding support from Mahidol University Fundamental Fund with grant number FF-179/2567.
Declaration of interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributor Information
Panitchaya Limsiri, Email: panit.lim01@gmail.com.
Dyah Anantalia Widyastari, Email: dyah.ana@mahidol.ac.th.
Aubdul Aunampai, Email: aubdul.aun@gmail.com.
Sittichat Somta, Email: sittichatsomta@gmail.com.
Piyawat Katewongsa, Email: piyawat.kat@mahidol.ac.th.
Anthony D. Okely, Email: tokely@uow.edu.au.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.