A Field-Validated Architecture for the Collection of Health-Relevant Behavioural Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human behaviour is an underlying factor in many diseases. Behavioural data has traditionally been collected through interviews, surveys, and direct observation. While these methods offer significant insight, they have drawbacks including bias, limited recall accuracy, and low temporal fidelity. Automated data collection devices such as GPS trackers have helped to reduce these problems while increasing objectivity and fidelity. Modern smart phones provide sensors that can replicate the functionality of dedicated devices while providing ubiquity, near-perpetual presence, and the ability to perform ecological momentary assessment. This has spurred researchers to envision or deploy smartphone data collection tools. Not all of these tools, however, are well designed, thoroughly tested, or easily extended. To realize the potential of this technology in the health sphere, careful attention must therefore be paid to the underlying software architecture and its robustness. To this end, we present a highly flexible, reconfigurable, and verifiable software architecture for monitoring health-related behaviours constructed using modern software engineering principles. We detail here the process-stream abstractions that underlie its data collection and management processes. Efficacy is demonstrated through retrospective analysis of deployments of the system, which include targets as diverse as studying flu transmission and gamified interventions for sedentary behaviour.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it