Mobile Applications for Health and Wellness: A Systematic Review
Bibliographic record
Abstract
Mobile health (mHealth) apps show potential contributions as interactive systems for managing users' health conditions. They are also used to improve health habits using behaviour change strategies. However, the trends, effectiveness, and design practices of these apps in terms of behaviour change are unclear yet. With a collaboration between researchers, domain experts, interactive systems developers and professionals, this paper aims to fill this gap by systematically investigating 70 mHealth apps using two popular behaviour change frameworks, namely App Behaviour Change Scale (ABACUS) and the Persuasive System Design (PSD) model. The study investigates the most common strategies and how these strategies were designed and implemented in the apps to achieve the targeted design objectives. Furthermore, the study evaluates apps' behaviour change potential using the behaviour Change Score (BCS), a measure we introduced to evaluate how the apps employ behaviour change strategies. The results show that 1) Journaling is the most common category of apps. 2) the most employed strategies are Self-monitoring, Customize and Personalize, and Reminders. And 3) there is a positive correlation between apps' ranks (based on ratings and installation) and the BCS score of most strategies. Based on our findings, we offer recommendations for designing and developing mHealth apps and present opportunities for future work in this area.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".