Clustering of Multilevel Factors Among Children and Adolescents: Associations With Health-Related Physical Fitness

Author:

Cai Shan12ORCID,Liu Yunfei12,Dang Jiajia12,Zhong Panliang12,Shi Di12,Chen Ziyue12,Hu Peijin12,Ma Jun12,Dong Yanhui12,Song Yi12ORCID,Raat Hein3

Affiliation:

1. Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, BJ, China

2. National Health Commission Key Laboratory of Reproductive Health, Beijing, BJ, China

3. Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands

Abstract

Background: To identify the clustering characteristics of individual-, family-, and school-level factors, and examine their associations with health-related physical fitness. Methods: A total of 145,893 Chinese children and adolescents aged 9–18 years participated in this cross-sectional study. The 2-step cluster analysis was conducted to identify clusters among individual-, family-, and school-level factors. Physical fitness indicator was calculated through sex- and age-specific z scores of forced vital capacity, standing long jump, sit-and-reach flexibility, body muscle strength, endurance running, and body mass index. Results: Three, 3, and 5 clusters were automatically identified at individual, family, and school levels, respectively. Students with low physical fitness indicator were more likely to be in the “longest sedentary time and skipping breakfast” cluster (odds ratio [OR] = 1.18; 95% confidence interval [CI], 1.12–1.24), and “physical inactivity and insufficient protein consumption” cluster (OR = 1.07; 95% CI, 1.02–1.12) at individual level, the “single children and high parental education level” cluster (OR = 1.15; 95% CI, 1.10–1.21), and “no physical activity support and preference” cluster (OR = 1.30; 95% CI, 1.25–1.36) at family level, and the “physical education occupied” cluster (OR = 1.06; 95% CI, 1.01–1.11), and “insufficient physical education frequency” cluster (OR = 1.16; 95% CI, 1.08–1.24) at school level. Girls were more vulnerable to individual- and school-level clusters, while boys were more susceptible to family clusters; the younger students were more sensitive to school clusters, and the older students were more susceptible to family clusters (P-interaction < .05). Conclusions: This study confirmed different clusters at multilevel factors and proved their associations with health-related physical fitness, thus providing new perspective for developing targeted interventions.

Publisher

Human Kinetics

Subject

Orthopedics and Sports Medicine,Epidemiology,Public Health, Environmental and Occupational Health,Physical Therapy, Sports Therapy and Rehabilitation

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