Association Between Self-Reported Protective Behavior and Heat-Associated Health Complaints Among Patients With Chronic Diseases in Primary Care: Results of the CLIMATE Pilot Cohort Study
Bibliographic record
Abstract
BACKGROUND: As a result of climate change, exposure to high temperatures is becoming more common, even in countries with temperate climates. For patients with chronic diseases, heat poses significant health risks. Empowering patients is a crucial element in protecting the population from the adverse effects of heat. In this context, self-reports of protective behavior are often used to gain a mutual understanding of patients' issues. However, the extent to which self-reported behavior is associated with health complaints remains unclear. OBJECTIVE: This study aims to describe the association between light to moderate heat and health complaints in everyday life, and to analyze whether self-reported protective behavior and related psychosocial factors are linked to these complaints. METHODS: We conducted a pilot cohort study using internet climate data merged with an online survey of patients with chronic diseases recruited through general practitioner practices. Patients were eligible if they were 18 years or older and had at least one chronic disease. The heat was modeled using temperature and humidity data. Health complaints were assessed through up to 7 follow-up evaluations on the hottest day of each week during the observation period. Data were analyzed using 3 nested models with mixed effects multivariable linear regression, adjusting for random effects at the climate measuring station and participant levels. Model 1 included heat exposure, sociodemographic data, and chronic diseases. Model 2 added protective behavior and health literacy, while model 3 incorporated self-efficacy and somatosensory amplification (ie, the tendency to catastrophize normal bodily sensations such as insect bites). RESULTS: Of the 291 eligible patients, 61 (21.0%) participated in the study, providing 294 observations. On average, participants were 61 (SD 14) years old, and 31 (51%) were men. The most prevalent conditions were cardiovascular diseases (n=23, 38%) and diabetes mellitus (n=20, 33%). The most commonly reported symptoms were tiredness/fatigue (232/294 observations, 78.9%) and shortness of breath (142/294 observations, 48.3%). Compared with temperatures of 27°C or lower, a heat index between over 27°C and 32°C (β=1.02, 95% CI 0.08-1.96, P=.03) and over 32°C (β=1.35, 95% CI 0.35-2.35, P=.008) were associated with a higher symptom burden. Lower health literacy (β=-0.25, 95% CI -0.49 to -0.01, P=.04) and better self-reported protective behavior (β=0.65, 95% CI 0.29-1.00, P<.001) were also linked to increased symptom burden but lost statistical significance in model 3. Instead, lower self-efficacy (β=-0.39, 95% CI -0.54 to -0.23, P<.001) and higher somatosensory amplification (β=0.18, 95% CI 0.07-0.28, P=.001) were associated with a higher symptom burden. CONCLUSIONS: Compared with colder weather, light and moderate heat were associated with more severe health complaints. Symptom burden was lower in participants with higher self-efficacy and less somatosensory amplification. Self-reported protective behavior was not linked to a lower symptom burden. Instead, we found that patients who tended to catastrophize normal bodily sensations reported both better protective behavior and a higher symptom burden simultaneously. TRIAL REGISTRATION: ClinicalTrials.gov NCT05961163; https://clinicaltrials.gov/ct2/show/NCT05961163.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".