Are Physical Activities Associated With Perceived Stress? The Evidence From the China Health and Nutrition Survey
Bibliographic record
Abstract
Psychological stress is a negative affective state. The association between physical activity and psychological stress was commonly reported in previous researches. Few published studies with large sample sizes have explored such an association in Chinese population. The current research aims to assess the association between perceived stress and physical activity preferences, as well as the association between risk of high perceived stress and physical activity behaviors (e.g., sports-, transport-, occupational-related physical activity, sedentary activities and time in bed). The data were collected from the China Health and Nutrition Survey (CHNS) in 2015. Generalized linear models and logistic regression models were used to explore the association between perceived stress and physical activity. In total, 11,066 participants were included in the current analysis. For physical activity preferences, we found that “like” preference of all six mentioned domains of activities contributed to lower perceived stress (i.e., lower perceived stress scale −14 score, all β < 0 and p < 0.05). For physical activity behaviors, the results indicated that none/low intensity physical activity behaviors (e.g., sports-, occupational- related, sedentary) were associated with risk of high perceived stress, except that low intensity of transport-related activities seemed to be protective from high stress. However, the association between intensity of physical activity behaviors and perceived stress was not simply the higher the better. High intensity of all these physical activity behaviors was also associated with high perceived stress. Our findings suggest that positive preferences and moderate physical activity behaviors were associated with low perceived stress. The findings herein highlight the effect of regulating physical activity on perceived stress, as well as inform potential strategies to reduce psychological stress.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".