Population-level estimation of timing, duration and quality of sleep in Canada: A smart thermostat based exploratory study
Bibliographic record
Abstract
Sleep is essential for the health and well-being. Less overall sleep, as well as reduced sleep duration and poor quality of sleep leads to chronic disease, including mental health issues. Measurements of sleep include subjective and objective methods. Population level measurement of sleep indicators is a challenging task for public health officials. Alternative to traditional data collection by survey method, data from smart homes including Internet of Things have the potential to reveal many insights about sleep. Our objective was to calculate sleep duration, quality and effect of geographical location on the sleep parameters. In this project, Donate Your Data, a data sharing program for research from ecobee was utilized for calculating population level health indicators. Using data from two pilot studies and Donate Your Data, this project analyzed and measured population level health indicators for sleep in Canada. The Spearman’s Correlation coefficient between Fitbit steps and total number of motion sensors activated was 0.8 with p<0.0001. Average duration of sleep hours for Canada measured as 7.2 hours at the individual level and 7.9 hours at the household level, compared to 7.3 hours from the Public Health Agency of Canada’s PASS indicator. This project also calculated proportion of population bedtime, wake up time, amount of disturbed sleep, time trend, as well as geographical variation across Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".