Assessing the quality of mobile applications in chronic disease management: a scoping review
Bibliographic record
Abstract
While there has been a rapid growth of digital health apps to support chronic diseases, clear standards on how to best evaluate the quality of these evolving tools are absent. This scoping review aims to synthesize the emerging field of mobile health app quality assessment by reviewing criteria used by previous studies to assess the quality of mobile apps for chronic disease management. A literature review was conducted in September 2017 for published studies that use a set of quality criteria to directly evaluate two or more patient-facing apps supporting promote chronic disease management. This resulted in 8182 citations which were reviewed by research team members, resulting in 65 articles for inclusion. An inductive coding schema to synthesize the quality criteria utilized by included articles was developed, with 40 unique quality criteria identified. Of the 43 (66%) articles that reported resources used to support criteria selection, 19 (29%) used clinical guidelines, and 10 (15%) used behavior change theory. The most commonly used criteria included the presence of user engagement or behavior change functions (97%, n = 63) and technical features of the app such as customizability (20%, n = 13, while Usability was assessed by 24 studies (36.9%). This study highlights the significant variation in quality criteria employed for the assessment of mobile health apps. Future methods for app evaluation will benefit from approaches that leverage the best evidence regarding the clinical impact and behavior change mechanisms while more directly reflecting patient needs when evaluating the quality of apps.
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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 | 0.117 | 0.363 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.043 | 0.033 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".