1133 IoT and Sleep Health Dynamics: Evaluating Policy Impacts in the COVID-19 Era
Bibliographic record
Abstract
Abstract Introduction The political determination to prioritize public health can significantly influence disease outcomes. In the United States of America, the partisan divide has notably affected the enactment and adherence to public health policies, particularly during the COVID-19 pandemic. This study examines the impact of politically driven public health initiatives on sleep duration in the US population during the COVID-19 pandemic. Leveraging zero-effort technology and IoT device data, the research identifies variations in sleep patterns associated with political climates, providing the intricate relationship between politics and health outcomes during a global health crisis. Methods Data from 4,405 households in politically distinct cities within California and Texas are sourced from the ecobee ‘Donate Your Data’ (DYD) initiative. The dataset was preprocessed for clarity and consistency and stratified into two periods: pre-pandemic (March 2019 to February 2020) and during the pandemic (March 2020 to February 2021). Sleep duration is quantified using motion sensor inactivity as an indicator of rest periods. A Gaussian mixture model is used to identify the sleep cycle clusters, and inferential statistical methods are applied to evaluate the impact of public health policies on sleep duration across different political affiliations. Results A significant decrease in average sleep duration was observed post-pandemic onset, from 8.0±3.71 hours to 7.75±3.87 hours. Different sleep patterns were observed between political affiliations, with Democratic regions showing a consistent decline in sleep duration while Republican regions experienced varied changes. Conclusion This study highlights how political leanings and consequent health policies significantly impacted sleep health during the COVID-19 pandemic. The integration of IoT data and advanced analytics offers a novel approach to continuously monitor and enhance population health behaviours. The methodologies applied in this approach could inform public health strategies in future emergencies, with political leanings considered as a key factor in sleep health. The findings set a framework for future studies to explore the relationship between political climates and sleep health and to develop demographic- and politics-sensitive predictive tools for sleep health risks. This research supports more robust public health systems capable of sustaining sleep health despite political and societal shifts. Support (if any)
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".