Running Through the Haze: How Wildfire Smoke Affects Physical Activity and Mental Well-Being
Bibliographic record
Abstract
BACKGROUND: With a warming climate, extreme wildfires are more likely to occur, which may adversely affect air quality, physical activity (PA), and therefore, mental well-being. METHODS: We assessed PA engagement and mental well-being between periods with and without wildfire smoke, and whether there were associations between changes in PA behavior and mental well-being. Questionnaires on PA and mental well-being during a period of wildfire smoke were completed by 348 participants; of these participants, 162 also completed a follow-up PA and mental well-being questionnaire during a period without wildfire smoke. Data were analyzed using generalized/linear mixed models. Relationships between mental well-being and PA were analyzed using repeated-measures correlations. RESULTS: Leisure-time walking, moderate PA, and vigorous PA were all significantly lower during periods of smoke compared to periods without smoke. Participants also experienced significantly higher symptoms of stress (11.63 [1.91] vs 10.20 [1.70], P = .039), anxiety (7.75 [2.24] vs 4.38 [1.32], P < .001), and depression (9.67 [0.90] vs 7.27 [0.76], P < .001) during the period of wildfire smoke. Vigorous PA, the proportion of PA time spent outdoors, and the sum of PA during leisure time, were significantly negatively correlated with mental well-being, therefore, it is possible that PA could be used as a tool during times of wildfire smoke. CONCLUSIONS: These data suggest that PA and mental well-being are adversely impacted during wildfire smoke events. Future research should consider the impact of strategies to support PA during wildfire events on PA and mental well-being.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".