A Field-Validated Architecture for the Collection of Health-Relevant Behavioural Data
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.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".