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Grouping Digital Health Apps Based on Their Quality and User Ratings Using K-Medoids Clustering: Cross-Sectional
Maciej Marek Zych1, Raymond Bond1, Maurice Mulvenna1
1School of Computing, Ulster University, 2-24 York Street, Belfast, BT15 1AP, United Kingdom, 44 7526852505.
JMIR Mhealth and Uhealth
|July 23, 2025
Summary
Digital health app quality varies significantly, with many apps lacking professional and clinical assurance (PCA). User ratings do not always reflect PCA, highlighting a need for better quality control in digital health solutions.
Area of Science:
- Digital health
- Health informatics
- Software quality assurance
Background:
- Over 350,000 digital health apps exist, necessitating quality assessment for user safety.
- Digital health apps offer proactive healthcare, potentially reducing provider burden.
- Understanding digital health app typologies can identify areas for improvement.
Purpose of the Study:
- To identify clusters of digital health apps based on quality metrics: professional and clinical assurance (PCA), user experience (UX), data privacy (DP), and user ratings.
- To investigate associations between app typologies and factors like NICE Evidence Standard Framework (ESF) tiers, target users, categories, and features.
Main Methods:
- Utilized data from 1402 digital health app assessments using the Organisation for the Review of Care and Health Apps Baseline Review (OBR).
- Employed k-medoids clustering to determine app typologies and the elbow method for optimal cluster identification.
- Assessed normality of user ratings and OBR scores using the Shapiro-Wilk test and compared cluster differences with Wilcoxon rank sum tests.
Main Results:
- Four distinct app clusters were identified: poor user ratings (15.7%), poor PCA and DP (18%), poor PCA (29.6%), and high-quality (36.7%).
- Statistically significant, but small effect size, associations were found between clusters and NICE ESF tiers, categories, and features.
- The 'Service Signposting' feature and NICE ESF tier B showed the strongest associations with app clusters.
Conclusions:
- A significant portion of digital health apps, even those with high user ratings, exhibit deficiencies in professional and clinical assurance (PCA).
- The identified typology highlights PCA as a critical area for improvement in digital health applications.
- Limited association between app characteristics and quality clusters suggests a need for further research into factors influencing digital health app quality.
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