Smart Monitoring of Population Health Risk Behaviour
Bibliographic record
Abstract
Monitoring population-level health-risk behaviour is integral to preventing chronic diseases (i.e., diabetes, cardiovascular disease, cancer, etc.). Physical activity and sleep are the key behaviours which influence human health. Smart technologies can be used to improve real-time monitoring of risky behaviours. The objective of this study is to explore population- and individual-level remote monitoring of sleep, indoor physical activity and sedentary behaviours in Canada using data from the Internet of Things (IoT) (ecobee smart thermostat) and fitness trackers. Method: 386 person-hours of data were collected in a pilot study (n =8) to validate the motion sensor data from ecobee smart thermostats. Then, using “Donate your Data” data from ecobee indicators of population-level health were calculated. Results: A positive Spearman correlation coefficient 0.8 (p>0.0001) was found between standard fitness tracker data and ecobee sensors validating its use for population-level analysis. Our results were similar to the Public Health Agency of Canada’s results derived from self-reported surveillance methods. Discussion: This project demonstrates the use of data from non-health sources, like ubiquitous IoT to curate population- and individual-level health indicators. We will deliver novel indicators and insights into health status through the creation of user-centered designed dashboards for individuals, researchers, and policy-makers.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".