A novel mobile platform for stress prediction
Bibliographic record
Abstract
Public health surveillance is typically done through self-report surveys. Personal smart devices that collect near real-time and zero-effort health data can support traditional surveillance efforts by providing novel and diverse data, which can be used to predict the prevalence of conditions in a population using advanced analytics. Apple Health is one of the most popular sources of personal health data, supporting a variety of devices that collect a wide range of information from heart rate to blood pressure and sleep. This paper introduces a mobile health platform that extracts Apple Health data to support public health monitoring based on personal devices, as well as a protocol for a study that utilizes this platform to predict stress in a population. Preliminary results are also presented: Random Forests and Support Vector Machines are used to predict the participant's stress levels and achieved an accuracy of 85% and 70%, respectively. Implications for public health, challenges, limitations, and future work are also discussed. The system described in this paper is one of the first works to leverage health data from consumer-level personal devices for public 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".