Takeaway food consumption and depressive symptoms in Chinese university students: mediating effects of physical activity
Bibliographic record
Abstract
Background: The consumption of takeaways is becoming increasingly prevalent. Despite this, the relationship between takeaway food consumption and depressive symptoms in Chinese populations has not been clarified. Furthermore, the factors that mediate the association between takeaway frequency and depressive symptoms are unknown. Methods: Questionnaires were employed to collect data from 6,417 new students at Chongqing Medical University in the autumn of 2023, including sociodemographic information, takeaway frequency, physical activity levels (measured by the International Physical Activity Questionnaire Short Form), and depressive symptoms (measured by the Patient Health Questionnaire-9). Multiple linear regression and mediation analysis were performed. Multiple imputations were used to fill in missing data through sensitivity analyses. Results: Among 6417 participants, 2,606 (40.6%) students ordered takeaway at least once a week, with 235 (3.7%) of them ordering takeaway food every day. Takeaway frequency was significantly associated with depressive symptoms (β=0.034, P=0.006), and physical activity partially mediated this relationship (95% bootstrap confidence interval=0.0024, 0.0371). Conclusions: The study highlights the negative relationship between takeaway frequency and emotional well-being, emphasizing the need to focus on the emotional health of frequent takeaway food consumers. Moreover, our study suggests that increased physical activity may alleviate takeaway-induced mood-related outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".