Latent Profile Analysis of Sleep Patterns and their Association with Physical Activity Levels Among Chinese College Students.

Abstract

This cross-sectional study aimed to identify latent profiles of sleep patterns among college students and examine their associations with physical activity (PA) levels, thereby providing empirical evidence for targeted health interventions in university settings. A total of 1,420 college students were recruited using multistage stratified cluster random sampling from three comprehensive universities in southern China. Sleep quality and PA were assessed using the Pittsburgh Sleep Quality Index (PSQI) and the International Physical Activity Questionnaire-Short Form (IPAQ-SF), respectively. Latent profile analysis (LPA), chi-square tests, and multinomial logistic regression analyses were performed using appropriate statistical software. Four latent sleep profiles were identified based on model-estimated class probabilities: healthy sleep (44.7%), insufficient sleep (24.8%), poor sleep quality (20.3%), and severe sleep disturbance (10.2%); the corresponding observed proportions based on most-likely class assignment were 45.0%, 25.0%, 20.0%, and 10.0% (n = 639, 355, 284, and 142, respectively). Gender and PA level differed significantly across the profiles. Compared with students in the severe sleep disturbance group, students in the healthy sleep group were more likely to report moderate PA (odds ratio (OR) = 4.69, 95% confidence interval (CI): 2.92-7.51) and high PA (OR = 7.55, 95% CI: 4.41-12.91). Students in the insufficient sleep group were also more likely to report moderate PA (OR = 2.98, 95% CI: 1.83-4.86) and high PA (OR = 5.02, 95% CI: 2.84-8.87). College students' sleep patterns showed clear heterogeneity. Favorable sleep patterns were associated with higher PA levels. Because of the cross-sectional design, causal relationships cannot be inferred. Differentiated intervention strategies should be developed according to subgroup-specific sleep characteristics, and PA promotion may be considered a potential behavioral target for students with poor sleep.