Effects of indoor and outdoor temperatures on blood pressure and central hemodynamics in a wintertime longitudinal study of Chinese adults
Bibliographic record
Abstract
OBJECTIVES: We aimed to estimate the effects of indoor and outdoor temperature on wintertime blood pressure (BP) among peri-urban Beijing adults. METHODS: We enrolled 1279 adults (ages: 40-89 years) and conducted measurements in two winter campaigns in 2018-2019 and 2019-2020. Study staff traveled to participant homes to administer a questionnaire and measure brachial and central BP. Indoor temperature was measured in the 5 min prior to BP measurement. Outdoor temperature was estimated from regional meteorological stations. We used multivariable mixed-effects regression models to estimate the within-individual and between-individual effects of indoor and outdoor temperatures on BP. RESULTS: Indoor and outdoor temperatures ranged from 0.0 to 28 °C and -14.3 to 6.4 °C, respectively. In adjusted models, a 1 °C increase in indoor temperature was associated with decreased SBP [-0.4 mmHg, 95% confidence interval (CI): -0.7 to -0.1 (between-individual; brachial and central BP); -0.5 mmHg, 95% CI: -0.8 to -0.2 (within-individual, brachial BP); -0.4 mmHg, 95% CI: -0.7 to -0.2 (within-individual, central BP)], DBP [-0.2 mmHg, 95% CI:-0.4 to -0.03 (between-individual); -0.3 mmHg, 95% CI: -0.5 to -0.04 (within-individual)], and within-individual pulse pressure [-0.2 mmHg, 95% CI: -0.4 to -0.04 (central); -0.3 mmHg, 95% CI: -0.4 to -0.1 (brachial)]. Between-individual SBP estimates were larger among participants with hypertension. There was no evidence of an effect of outdoor temperature on BP. CONCLUSION: Our results support previous findings of inverse associations between indoor temperature and BP but contrast with prior evidence of an inverse relationship with outdoor temperature. Wintertime home heating may be a population-wide intervention strategy for high BP and cardiovascular disease in China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".