24-h movement behaviour, thermal perception, thirst, and heat management strategies of children and adults during heat alerts: a pilot study
Bibliographic record
Abstract
Background: Heat waves caused by climate change are increasingly challenging the wellbeing of individuals across the lifespan. Current efforts to understand the thermal perceptions and behaviours of people throughout the lifespan during heat waves are limited. Methods: Since June 2021, the Active Heatwave project has been recruiting households to better understand how individuals perceive, cope, and behave during heat waves. Using our novel web platform, participants were prompted to answer our Heat Alert Survey on days when a participants geolocation corresponded to a broadcasted local heat alert. Participants provided 24-h movement behaviour, thirst, thermal perception, and cooling strategies via validated questionnaires. Results: A total of 285 participants (118 children) from 60 distinct weather station locations globally participated between June and September 2021 and 2022. At least 1 heat alert (834 total) were identified from 95% (57/60) of the weather stations. Children reported spending more time performing vigorous intensity exercise compared to adults (p < 0.05), but no differences in thermal sensation, thermal comfort, or thirst sensation (all p > 0.31) were observed. For thirst management, 88% of respondents used water to relieve thirst, although notably, 15% of adults reported using alcohol. Regardless of age, staying indoors was the most common heat management strategy, whereas visiting cooling centres was the least. Conclusion: The present study presents a proof-of-concept combining local heat alert notifications with e-questionnaires for collecting near-real-time perceptual and behavioural data for both children and adults during heat waves. The observed patterns of behaviour suggest that present public heat-health guidelines are often ignored, children engage in fewer heat management strategies compared to adults, and these disparities highlight the need to improve public health communication and knowledge dissemination around promoting effective and accessible cooling solutions for children and adults.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".