Mobile health app usability and quality rating scales: a systematic review
Bibliographic record
Abstract
PURPOSE: To review the rating scales used to evaluate usability and quality of mobile health applications, and to compare their purpose, content, and intended target users (i.e., patients, caregivers, or researchers). MATERIAL AND METHODS: We conducted a systematic review of the literature in accordance with the PRISMA statement on Medline, CINAHL, PsycINFO, IEEE Explore databases, as well as a review of the grey literature to identify rating scales used to evaluate usability and quality of mobile health applications (m-health apps), between January 1, 2000 and July 31, 2018. Two researchers screened the titles and abstracts of articles that met inclusion criteria, and retrieved usability and quality rating scales from the articles. RESULTS: We identified 24 usability scales and 25 quality rating scales in 87 peer-reviewed articles. We identified only one quality rating scale designed for non-expert users (i.e., patients or caregivers). None of the studies used a theoretical framework for app evaluation to support the scales. The validity of existing quality rating scales is yet to be investigated. CONCLUSION: Existing usability and quality rating scales are targeted at professionals, not end users who are patients or caregivers. Rating scales that are usable by all end-users would make mobile health apps accessible and meaningful to consumers.Implications for rehabilitationThe number of mobile health applications on app stores that can be used for rehabilitation is increasing.Most healthcare providers lack the training to identify m-health apps with high quality to be used in rehabilitation.This study has reviewed the current rating scales that can help clinicians and care providers rate the quality of m-health apps and identify the ones that are most appropriate for their practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.074 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".