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Psychometrics validation of the Chinese version of social support for exercise scale among adolescents in China.

Liying YaoKe ZhouYanli ZhouYee Cheng KuehTingyu XuMingzhu PanAnwar P P Abdul MajeedZhongbiao LiuGarry Kuan
Published in: PloS one (2024)
Physical activity (PA) is widely recognized as crucial for human health, yet the low level of PA in adolescents continues to raise major concerns. This study aims to validate the Chinese version of the Social Support Scale for Exercise (SE) and establish its reliability among Chinese adolescents. A cross-sectional study was conducted on two primary and two secondary schools in central China. Students were recruited using a random cluster sampling method, and written informed consent was provided after they were briefed on the purpose of the study. The standard forward-backward translation was applied to translate the English version of the SE into Chinese. The Social Support Scale used in this study consists of two factors: family support and friend support. Data were analyzed using Mplus 8 for the CFA, composite reliability (CR), average variance extracted (AVE), and intra-class correlation coefficients (ICC) were calculated. A total of 1422 students (boys = 838, girls = 604) with a mean age of 11 years (SD = 1.6) participated in the study. The measurement model of the translated social support scale fit the data well: CFI = .935; TLI = .929; SRMR = .038; RMSEA = .053, with a 90% confidence interval of (.051, .056; RMSEA p < .001). The composite reliability values of .935 for family support and .948 for friend support were acceptable. The intra-class correlation coefficients (ICC) based on test-retest were .928 for family support, and .904 for friend support. Hence, the Chinese version of the SE was valid and reliable, its implementation will provide researchers with a valuable tool to comprehensively assess Chinese adolescents' exercise-related social support and help develop targeted and effective interventions to improve their physical activity levels.
Keyphrases
  • social support
  • physical activity
  • depressive symptoms
  • young adults
  • risk assessment
  • primary care
  • body mass index
  • big data
  • body composition
  • sleep quality
  • artificial intelligence
  • psychometric properties
  • high school