Leveraging Big Data from Smart Thermostats: A Vision to Evaluate Heatwave Impacts on Sleep Health in the Elderly [Vision Paper]
Bibliographic record
Abstract
Heatwaves, intensified by climate change, pose significant challenges to the health and well-being of global populations. Among the most vulnerable groups, the elderly is particularly susceptible to the adverse effects of heatwaves, including disruptions in sleep quality. Sleep plays a vital role in overall health and cognitive function, and heatwaves hinder the attainment of restorative deep sleep stages. This vision paper proposes an approach to evaluate the impact of heatwaves on sleep health in older adults using big data from smart thermostats and the novel, non-invasive technology called zero-effort technology. By leveraging this vast dataset, the study aims to provide comprehensive insights into the specific effects of heatwaves on sleep quality, overcoming the limitations of subjective and invasive methods. Preliminary analysis from a non-AC household in British Columbia underscores the potential of this approach. The vision is to expand this research to various elderly living settings, integrating indoor temperature data to discern sleep pattern shifts during heatwaves. This approach sets the stage for devising evidence-based interventions and public health strategies to counteract the detrimental effects of heatwaves on elderly sleep health.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".