A smart thermostat-based population-level behavioural changes during the COVID-19 pandemic in the United States
Bibliographic record
Abstract
The COVID-19 pandemic wreaked havoc on the world and highlighted the inadequacies of public healthcare systems despite the availability of effective public health surveillance systems. The constraints caused by the pandemic had a great impact on behavioural markers such as physical, sedentary and sleep activity. In 2020, the United States (US) implemented the Behavioral Risk Factor Surveillance System (BRFSS) in some states for COVID-19 information searching, health risk behaviours and prevention practices. However, the BRFSS is limited by the quality of input data. When introduced, connected devices such as Fitbit were lauded as another best thing in data collection for research and faced challenges such as battery limitations and data quality. This proposed study will examine the consequences of the COVID-19 pandemic on the population-level behavioural changes in the US using Zero-effort technology, NextGen IoT data sources (Donate your Data (DYD) from a smart thermostat company named ecobee), and cloud-based analytical infrastructure (Microsoft Azure). The proposed study will a) evaluate the impact of the COVID-19 pandemic on household occupancy patterns and variations in the US; b) compute the sleep parameters (sleep time, wake up time, and sleep duration) and time spent in-house and out-of-the-house to explore the impact of COVID-19 pandemic restrictions on household behaviours in the US.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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 teacher head, 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".