Towards Real-Time Public Health: A Novel Mobile Health Monitoring System
Bibliographic record
Abstract
Public health monitoring methods have limitations that affect the quality of data. To support traditional data collection efforts, personal smart technologies can be used to collect multimodal, real-time and continuous data. Public health agencies can then study and predict the prevalence of conditions in a population using advanced analytics. Apple Health is one of the most popular sources of health data from personal devices, supporting diverse sensors that collect a wide range of information from heart rate to blood pressure and sleep. This paper introduces a system that uses a mobile health platform to extract Apple Health data to support public health monitoring. Development, security and privacy considerations are discussed, and a pilot study is proposed which collects several objective sensor data from Apple Health as well as self-report perceived stress (both using the platform) to create stress prediction models. Ultimately, the system described can provide public health agencies with novel methods to collect multimodal data from consumer devices as well as implement interventions in real-time to minimize the impact of conditions, such as stress, in a population. The system advances the state-of-the-art in health monitoring by being 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".