IT-Enabled Self-Monitoring for Chronic Disease Self-Management: An Interdisciplinary Review
Bibliographic record
Abstract
Self-monitoring is a strategy that patients use to manage their chronic disease and chronic disease risk factors. Technological advances such as mobile apps, web-based tracking programs, sensing devices, wearable technologies, and insideable devices enable IT-based self-monitoring (ITSM) for chronic disease management. Since ITSM is multidisciplinary in nature and our understanding is fragmented, a systematic examination of the literature is performed to build a holistic understanding of the phenomenon. We review 159 studies published in 108 journals and conferences between 2006 and 2017. By adapting affordance actualization theory, we develop an overarching framework to organize the existing literature on ITSM for chronic disease management. Four themes emerge: key ITSM functionalities that enable affordances; effects on ITSM system use; effects on the achievement of chronic care goals; and the role of intermediary outcomes. For each theme, we identify what is known, what is unknown, and opportunities for future research. We also discuss cross-theme opportunities for future research where more diverse theoretical perspectives can contribute to our understanding of the phenomenon. This work provides research directions for IS researchers studying ITSM for chronic disease self-management.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".