Relationship between physical activity and mental health in a national representative cross-section study: Its variations according to obesity and comorbidity
Bibliographic record
Abstract
BACKGROUND: Physical activity is positively associated with mental health in obese people with chronic comorbidities. However, how physical activity types (PATs), physical activity frequency (PAF), and physical activity duration (PAD) are associated with mental health need to be further clarified. The current study investigated and compared the effectiveness of PATs, physical activity frequency PAF and PAD for mental health in obese people with various chronic comorbid conditions. METHODS: This cross-sectional study included 871,919 adults who participated in the Behavioral Risk Factor Surveillance System (BRFSS). They were divided into four groups: healthy people, obese people with 0, 1, and 2+ chronic comorbid conditions. The zero-inflated negative binomial (ZINB) regression model and the generalized additive model were used to explore the association between physical activity and mental health burden in the four groups, respectively. RESULTS: Jogging (30.00%), hiking (28.36%) and bicycling (28.32%) have greater improvement in mental health of healthy people; jogging (19.25%), golf (19.95%) and bicycling machine exercise (19.13%) showed a greater improvement in mental health of obese people with no chronic comorbid condition; and aerobic exercise videos or class showed a greater improvement in mental health of obese people with one chronic comorbid condition (22.14%) and obese people with two or more chronic comorbid conditions (19.60%). Non-linear relationships were observed between PAF, PAD, and energy expenditure and mental health. The healthy participants who exercised about 10-15 times a month and 40-50 min per session or about 400-600 METs-min per week had greater benefits for mental health. However, the lowest point of the smooth curve moved to the left with an increasing number of chronic comorbid conditions in obese people. CONCLUSIONS: Almost all PATs were associated with better mental health, but their benefits decreased with increasing number of chronic comorbid conditions in obese people. There were U-shaped relationships between mental health and weekly physical activity frequency, duration, and METs-min.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".