Effect of Ceiling Fans on Core Temperature in Bed‐Resting Older Adults Exposed to Indoor Overheating
Bibliographic record
Abstract
BACKGROUND: Rising global temperatures have increased indoor overheating risks, posing significant health threats to vulnerable populations, particularly older adults. While electric fans are recommended for cooling at temperatures up to 40°C, the efficacy of ceiling fans in very warm indoor temperatures (~31°C) remains understudied. This randomized study evaluated the efficacy of ceiling fans in reducing core temperature and cardiovascular strain among bed-resting adults exposed to simulated indoor overheating (31°C, 45% relative humidity) for 8 h. METHODS: Twenty participants (12 females, median [IQR] age: 71 [68-73] years) underwent two experimental exposures with a ceiling fan set at either 0 m/s (control) or ~1.5 m/s (fan condition). RESULTS: The primary outcome, peak core temperature, was significantly lowered by 0.2 [95% CI: 0.1-0.3]°C (0.4 [0.2-0.5]°F, p < 0.001) with fan use (mean [SD]) 37.6 [0.2]°C (99.7 [0.4]°F); +0.9 [0.3]°C (+1.6 [0.5]°F) vs. pre-exposure) compared to control (37.8 [0.2]°C (100.0 [0.4]°F); +1.1 [0.5]°C (+2.0 [0.9]°F) vs. pre-exposure). Secondary outcomes, including core temperature area under the curve, peak and end-exposure heart rate, fluid consumption, and thermal discomfort, were also reduced significantly with fan use. Despite these improvements, fan use did not completely ameliorate heat-induced physiological strain. CONCLUSIONS: These findings indicate that while ceiling fans significantly reduce heat-related physiological strain under the conditions tested, they are not wholly efficacious as standalone cooling solutions. A combined "fan-first" approach, integrating fans with ambient cooling strategies, may enhance heat-health protection in older adults. GOV IDENTIFIER: NCT06142890.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".