Cross sectional associations of physical activity and sleep with mental health among Chinese university students
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine the levels of physical activity (PA), sleep, and mental health (MH), specifically depression, anxiety, and stress, among Chinese university students. It also aimed to analyze the influencing factors of MH, providing a theoretical foundation for developing intervention programs to improve college students' mental health. METHODS: A stratified, clustered, and phased sampling method was employed. In September 2022, a survey was conducted among 36,756 university students from 104 higher education institutions across 31 provinces, autonomous regions, and municipalities in China. The participants' PA behaviors, sleep patterns, depressive symptoms (use the CES-D), anxiety symptoms (use the GAD-7), smoking and drinking behaviors, and demographic information were assessed through an online questionnaire using Questionnaire Star software. RESULTS: A total of 30,475 valid questionnaires were completed. The proportion of university students engaging in light-intensity PA was 77.6%. The prevalence of insufficient sleep was 39.5%, whereas the prevalence of poor sleep quality was 16.7%. The prevalence of depressive symptoms was 10%, and the prevalence of anxiety symptoms was 23.3%. Binary logistic regression analysis revealed that engaging in moderate to high-intensity PA and having sufficient and high-quality sleep were associated with a lower likelihood of depressive symptoms (OR = 0.207-0.800, P < 0.01), whereas appropriate sleep duration and higher sleep quality were associated with a lower likelihood of anxiety symptoms (OR = 0.134-0.827, P < 0.001). CONCLUSION: The intensity of PA among university students is predominantly light, and the reported rate of insufficient sleep is relatively high. Moderate to high-intensity PA and sufficient high-quality sleep may alleviate MH issues among college students, with an interaction effect observed among PA, sleep, and depression symptoms. Future studies should further explore targeted interventions combining PA and sleep behaviors to enhance the MH of university students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".