Purpose: Explore the role of physical activity (PA) and sedentary behavior (SB) in the sleep of preschool children.Method: Preschool children (n = 458) from kindergarten were recruited, measures included accelerometer-derived 24 h activity [SB, light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), daytime sleep duration (DSP), and nocturnal sleep duration (NSP)]. A self-made questionnaire was used to supplementary survey on different types of SB. The two-layer chi-square test and the Cochran–Mantel–Haenszel test were used and significance set at p < 0.05.Results: PA is mainly a protective factor for DSP, while SB is mainly a risk factor for NSP of preschool children; Screen-type SB including screen viewing SB and video game SB is a risk factor for NSP, while quiet learning SB have no significant correlation with sleep duration in preschool children; Gender and social economic status are important factors affecting the association of PA, SB, and sleep duration in preschool children.Conclusions: The relationship between sleep duration at different periods and PA and SB of preschool children is different. Future educators need to gradually realize the different factors affecting the sleep of preschool children at different stages, clarify the protective factors and risk factors, in order to improve the sleep duration of preschool children.

Purpose: Seasonality is an important factor in children's physical activity (PA) and sedentary behavior (SB); thus, the aim of this study was to explore the seasonal characteristics of Chinese preschool children's PA and SB. Method: Preschool children (n = 65) from a kindergarten middle class in Changsha, the capital city of Hunan Province in Central China, were recruited, and a three-axis accelerometer (ActiGraph GT3X-BT) was used to longitudinally track their PA and SB status. One-way repeated-measures ANOVA (Analysis of Variance) and Friedman tests were employed for analysis, with significance set at P < .05. Results: The levels of both total of physical activity and moderate-to-vigorous physical activity were the highest in spring and the lowest in winter. The level of SB was the opposite, being the lowest in spring and the highest in winter, whereas there were no significant differences in light physical activity across different seasons. The seasonality of PA in preschool children may be influenced by several factors, such as gender, time period, preschool education curriculum, and weather conditions. Conclusions: Seasonal variation in PA and SB are considered important for child educators, who should combine related factors to construct potentially ideal interventions to promote the PA of children during different seasons. 1 | INTRODUCTION Since the late 1980s, the number of people, especially preschool children, suffering from overweight and obesity problems has skyrocketed (Tang et al., 2018). Early stage childhood obesity has been one of the main public health issues in China, with the morbidity rate ranging from approximately 6% to 14% among preschool children in cities (

PurposeThis study aimed to examine the differences between different sleep assessment methods for preschool children.MethodsPreschool children (n = 54, mean age: 4.6 years) were recruited from kindergarten. Data were collected using an accelerometer, a sleep log, and sleep questionnaire. Furthermore, correlation analysis, Bland–Altman analysis, and repeated measures ANOVA were performed.Results(1) The sleep duration evaluated by different sleep assessment methods were all significantly correlated, among which the sleep log and Sadeh algorithm showed the highest correlation (r = 0.972, p < .001), while the Tudor‐Locke algorithm and the sleep questionnaire demonstrated the lowest correlation (r = 0.383, p < .01); (2) The points between different sleep assessment methods were all within 95% LoA, except for the sleep log and Tudor‐Locke algorithm; (3) In various methods of sleep assessment, significant differences were observed in sleep onset (F2(1.6,85.0) = 32.8, p < .001, η2: 0.38), while no significant differences were observed in sleep offset (F2(1.5,80.1) = 32.8, p = .05, η2: 0.05); (4) In addition, no significant difference in sleep onset was observed between the sleep questionnaire and sleep log (p > .05), and there was also no significant difference in sleep onset between the Sadeh algorithm and the Tudor‐Locke algorithm (p > .05).ConclusionsBoth the Sadeh algorithm and the Tudor‐Locke algorithm can be used as effective algorithms for sleep duration assessment of Chinese preschool children, with the latter having obvious advantages in large sample surveys. Future research should pay attention to the differences between different sleep assessment methods when using these algorithms.