Association Between the Characteristics of mHealth Apps and User Input During Development and Testing: Secondary Analysis of App Assessment Data
Bibliographic record
Abstract
BACKGROUND: User involvement is increasingly acknowledged as a central part of health care innovation. However, meaningful user involvement during the development and testing of mobile health apps is often not fully realized. OBJECTIVE: This study aims to examine in which areas user input is most prevalent and whether there is an association between user inclusion and compliance with best practices for mobile health apps. METHODS: A secondary analysis was conducted on an assessment data set of 1595 health apps. The data set contained information on whether the apps had been developed or tested with user input and whether they followed best practices across several domains. Background information was also available regarding the apps' country of origin, targeted condition areas, subjective user ratings, download numbers, and risk (as per the National Institute for Health and Care Excellence Evidence Standards Framework [ESF]). Descriptive statistics, Mann-Whitney U tests, and Pearson chi-square analyses were applied to the data. RESULTS: User involvement was reported by 8.71% (139/1595) of apps for only the development phase, by 33.67% (537/1595) of apps for only the testing phase, by 21.88% (349/1595) of apps for both phases, and by 35.74% (570/1595) of apps for neither phase. The highest percentage of health apps with reported user input during development was observed in Denmark (19/24, 79%); in the condition areas of diabetes (38/79, 48%), cardiology (15/32, 47%), pain management (20/43, 47%), and oncology (25/54, 46%); and for high app risk (ESF tier 3a; 105/263, 39.9%). The highest percentage of health apps with reported user input during testing was observed in Belgium (10/11, 91%), Sweden (29/34, 85%), and France (13/16, 81%); in the condition areas of neurodiversity (42/52, 81%), respiratory health (58/76, 76%), cardiology (23/32, 72%), and diabetes (56/79, 71%); and for high app risk (ESF tier 3a; 176/263, 66.9%). Notably, apps that reported seeking user input during testing demonstrated significantly more downloads than those that did not (P=.008), and user inclusion was associated with better compliance with best practices in clinical assurance, data privacy, risk management, and user experience. CONCLUSIONS: The countries and condition areas in which the highest percentage of health apps with user involvement were observed tended to be those with higher digital maturity in health care and more funding availability, respectively. This suggests that there may be a trade-off between developers' willingness or ability to involve users and the need to meet challenges arising from infrastructure limitations and financial constraints. Moreover, the finding of a positive association between user inclusion and compliance with best practices indicates that, where no other guidance is available, users may benefit from prioritizing health apps developed with user input as the latter may be a proxy for broader app quality.
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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 | 0.024 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".