Four latent sleep profiles were identified among Chinese college students, with favorable sleep patterns associated with higher physical activity levels, suggesting PA promotion may be a potential behavioral target for students with poor sleep.
Key Findings
Results
Latent profile analysis identified four distinct sleep pattern profiles among Chinese college students.
The four profiles were: healthy sleep (44.7%), insufficient sleep (24.8%), poor sleep quality (20.3%), and severe sleep disturbance (10.2%)
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)
A total of 1,420 college students were recruited from three comprehensive universities in southern China
Multistage stratified cluster random sampling was used for recruitment
Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI)
Results
Students in the healthy sleep group were significantly more likely to report moderate and high physical activity compared to students in the severe sleep disturbance group.
Healthy sleep group students were more likely to report moderate PA (OR = 4.69, 95% CI: 2.92–7.51) compared to the severe sleep disturbance group
Healthy sleep group students were more likely to report high PA (OR = 7.55, 95% CI: 4.41–12.91) compared to the severe sleep disturbance group
Physical activity was assessed using the International Physical Activity Questionnaire-Short Form (IPAQ-SF)
Multinomial logistic regression analyses were used to examine associations between sleep profiles and PA levels
Results
Students in the insufficient sleep group were also significantly more likely to report moderate and high physical activity compared to the severe sleep disturbance group.
Insufficient sleep group students were more likely to report moderate PA (OR = 2.98, 95% CI: 1.83–4.86) compared to the severe sleep disturbance group
Insufficient sleep group students were more likely to report high PA (OR = 5.02, 95% CI: 2.84–8.87) compared to the severe sleep disturbance group
The severe sleep disturbance group (n = 142, 10.0%) served as the reference category in regression analyses
Results
Gender and physical activity level differed significantly across the four latent sleep profiles.
Chi-square tests were used to assess differences in gender and PA levels across sleep profiles
Both gender and PA level showed statistically significant differences across the four identified sleep profiles
The study's cross-sectional design precludes inference of causal relationships between sleep patterns and physical activity
Conclusions
College students' sleep patterns showed clear heterogeneity, supporting the use of differentiated intervention strategies based on subgroup-specific sleep characteristics.
The authors identified four distinct sleep profiles rather than a uniform sleep pattern across the sample
The authors recommend that 'differentiated intervention strategies should be developed according to subgroup-specific sleep characteristics'
PA promotion is proposed as 'a potential behavioral target for students with poor sleep'
The study used latent profile analysis (LPA) to capture heterogeneity in sleep patterns rather than treating sleep quality as a single continuous variable
What This Means
This research suggests that sleep patterns among Chinese college students are not uniform but fall into four distinct groups: those with healthy sleep (about 45%), those who don't get enough sleep (about 25%), those with poor sleep quality (about 20%), and those with severe sleep disturbances (about 10%). The study recruited 1,420 students from three universities and used established questionnaires to measure both sleep quality and physical activity levels. Statistical modeling was used to identify these natural groupings rather than imposing arbitrary categories.
The most striking finding is the strong relationship between sleep patterns and physical activity levels. Compared to students with the worst sleep (severe sleep disturbance), those with healthy sleep were about 4.7 times more likely to engage in moderate physical activity and about 7.6 times more likely to engage in high physical activity. Even students who simply didn't sleep enough—but whose sleep quality when they did sleep was better—were about 3 times more likely to be moderately active and 5 times more likely to be highly active than the most severely affected group. Gender also differed meaningfully across the sleep profile groups.
This research suggests that a one-size-fits-all approach to improving student sleep and health may be less effective than tailored strategies aimed at specific subgroups. The authors propose that promoting physical activity could be a practical behavioral target for university health programs, particularly for students struggling with poor sleep. However, because this was a cross-sectional study (a snapshot in time), it is not possible to determine whether poor sleep leads to less physical activity, whether low physical activity worsens sleep, or whether some other factor influences both.
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Lu C, Zhang W, Yu D, Zhao P, Zhang X, Zhou Z, et al.. (2026). Latent Profile Analysis of Sleep Patterns and their Association with Physical Activity Levels Among Chinese College Students.. Journal of visualized experiments : JoVE. https://doi.org/10.3791/73033