Association of Indoor Temperature Level and Mental Health Among Community-Dwelling Older Adults: Systematic Review
Bibliographic record
Abstract
Background: The association between indoor temperature level and mental health is becoming increasingly important as climate change leads to extreme temperature fluctuations. Older adults are particularly vulnerable to indoor temperature changes because of their diminished ability to regulate body temperature and extended time spent indoors. Objective: This study aims to examine the association between indoor temperature levels and mental health outcomes among community-dwelling older adults, aiming to provide essential evidence to support the development of interventions and policy strategies to improve their mental health. Methods: In this systematic review, we conducted a comprehensive search of 7 electronic databases (MEDLINE, Embase, Cochrane Library, CINAHL, PsycINFO, Google Scholar, and ProQuest) on April 4, 2024, without restrictions on language or publication date. The National Institutes of Health quality assessment tool for observational cohorts and cross-sectional studies was used to evaluate the methodological quality of the included literature. Results: Of the 2328 studies identified, 15 met the inclusion criteria. The majority (8/15, 53%) were conducted in Asia, followed by Europe (4/15, 27%) and 1 study each in Australia (7%), Egypt (7%), and the United States (7%). Mental health outcomes associated with indoor temperature exposure were categorized into four groups: (1) sleep problems, including insomnia; (2) emotional problems, such as emotional distress and negative mood; (3) social interaction problems, such as social exclusion and low social participation; and (4) other mental health issues, including anxiety, agitation, and annoyance. Sleep problems were the most frequently reported mental health outcome related to indoor temperature levels (9/15, 60%). Older adults living in substandard housing conditions, facing economic difficulties, and residing in urban areas were vulnerable to exposure to uncomfortable indoor temperatures because of housing-related risks, such as low energy efficiency, inadequate heating or cooling, and limited access to green spaces. Conclusions: The findings highlight the need to develop evidence-based guidelines to improve mental health by managing indoor temperature levels in the community. Improving housing conditions through policy support could enhance the mental health of community-dwelling older 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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".