IT-based regulation of personal health: Nudging, mobile apps and data
Bibliographic record
Abstract
Mobile health applications and devices (“mobile health apps”) play increasingly important roles in the lives of individuals interested in self-regulating their personal health behaviors. While some appear to be simply consumer products and services, many are embedded in regulatory programs aimed at compliance with expert guidelines. In this paper, we draw on de Vaujany et al.’s framework for organizational IT-based regulation systems to consider how systems operate in open and distributed contexts in which actors have strong agency and regulation is indirect and voluntary. To do so, we consider how IT artifacts become embedded in practices, how data are implicated in regulatory feedback loops, and how individual, organizational and technological actors are mobilized and with what regulatory outcomes. We develop an instrumental case study as a vignette of five regulatory episodes (continuous glucose monitoring systems used by persons with diabetes) to examine how expert rules materialized in mobile health apps, data about bodily states, and IT features such as displays and alarms “nudge” individuals towards compliance with self-regulatory guidelines and practices. Through this analysis, we identify two related regulatory affordances of mobile health apps for predicting and surveilling personal health. We theorize how multilevel networks composed of trifecta of rules, IT artifacts, and practices develop as a regulatory lattice through which social regulation is realized. We conclude by considering the broader implications of this analytical approach to study voluntary, data-enriched regulatory systems.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".