Exploring the Interplay Between Climate Change, 24-Hour Movement Behavior, and Health: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Given the emergence of climate change and health risks, this review examined potential relationships between varying indicators of climate change, movement behaviors (ie, physical activity [PA], sedentary behavior, and sleep), and health. METHODS: Seven databases were searched in March 2020, April 2023, and April 2024. To be included, studies must have examined indicators of climate change and at least one of the movement behaviors as either an exposure or a third variable (ie, mediator/moderator), and a measure of health as outcome. Evidence was summarized by the role (mediator/moderator) that either climate change or movement behavior(s) has with health measures. Relationships and directionality of each association, as well as the strength and certainty of evidence were synthesized. RESULTS: A total of 79 studies were eligible, representing 6,671,791 participants and 3137 counties from 25 countries (40% low- and middle-income countries). Of 98 observations from 17 studies that examined PA as a mediator, 34.7% indicated that PA mediated the relationship between climate change and health measure such that indicators of adverse climate change were associated with lower PA, and worse health outcome. Of 274 observations made from 46 studies, 28% showed that PA favorably modified the negative association between climate change and health outcome. Evidence was largely lacking and inconclusive for sedentary behavior and sleep, as well as climate change indicators as an intermediatory variable. CONCLUSIONS: PA may mitigate the adverse impact of climate change on health. Further evidence is needed to integrate PA into climate change mitigation, adaptation, and resilience strategies.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".