Cluster Analysis of Sleep Time and Adolescent Health Risk Behaviors

Abstract

Adolescents are prone to multiple health risk behaviors. These might lead to insufficient sleep, which is inconducive to adolescent growth. Therefore, this study explored the impact of a cluster of adolescent health risk behaviors on sleep time, providing a reference for designing relevant intervention measures. From November to December 2019, a stratified cluster sampling method was used to sample middle and high schools in 4 functional districts of Chongqing, China. A total of 8546 participants were selected for a questionnaire survey. Two-step clustering helped identify the health risk behavior clusters. Multivariate logistic regression models helped examine the association between the different clusters and sleep time. The rate of insufficient sleep was 65.8%. Three types of clusters were identified, namely (1) high-risk (poor) cluster (17.3%), (2) low physical activity (medium) cluster (55.1%), and (3) low-risk (good) cluster (27.6%). The high-risk and low physical activity clusters showed that the adjusted OR values of 1.471 (1.266-1.710) and 1.174 (1.052-1.310) were significantly associated with insufficient sleep ( P < .001). Adolescent health risk behaviors were clustered, and different clusters had different sleep time. Schools authorities and healthcare practitioners should formulate effective intervention measures according to the characteristics of different clusters to promote healthy growth among adolescents.

The relationship between eating behavior and anxiety, depression, and comorbidity among rural residents in China

“…Secondly, TCA was employed in ve food groups to determine the dietary patterns among rural residents. The TCA process is an exploration tool that automatically determines the best clustering number [37]. It consisted of two processes: preclustering and hierarchical clustering.…”

“…Based on the silhouette width, hierarchical algorithms calculated the optimal clustering number [38]. The log-likelihood method was utilized for clustering distance, and Schwarz' s Bayesian criterion (BIC) was used as the clustering criterion [37,38]. In this investigation, the system automatically determined 3 clusters.…”