ArticleDigital health
Who uses mHealth apps? Identifying user archetypes of mHealth apps.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Smartphone CBT engagement and depressive symptoms: secondary analysis of the RESiLIENT trial using a time-varying exposure approach.Psychological medicine · 2026Trial
- Insights Into How Digital Health Interventions Shape Outcomes for Emerging Adults Living With Type 1 Diabetes: Qualitative Realist Process Evaluation.Journal of medical Internet research · 2025Trial
- Acceptability and Preliminary Evaluation of a Campus-Integrated Digital Platform (Fruto) for University Students' Mental Health Help-Seeking: Sequential Mixed Methods Study.Journal of medical Internet research · 2026Article
- Interest in patient navigation chatbots: a cross-sectional survey study.Frontiers in digital health · 2026Article
- Cluster-Based Predictive Modeling of User Ratings for Physical Activity Apps Using Mobile App Rating Scale (MARS) Dimensions: Model Development and Validation.JMIR mHealth and uHealth · 2025Article
- User Archetypes of a Well-Being-Promoting Mobile App Among Adults: Cross-Sectional Study and Cluster Analysis of Usage Patterns.JMIR mHealth and uHealth · 2025Article
- Mental health tracking among young Russians: practices and motivations.Discover mental health · 2025Article
- Determinants of Patient Use of Telemental Health Services: Representative Cross-Sectional Survey From Germany.JMIR mental health · 2025Article
- The General Acceptability and Use of Smartphone App-Delivered Interventions for Gambling in Australia.Journal of gambling studies · 2025Article
- How Can We Better Inform Patients of the Importance of Contact Lens Compliance?: Current Perspectives.Clinical optometry · 2024Review
- Participant Engagement in Microrandomized Trials of mHealth Interventions: Scoping Review.JMIR mHealth and uHealth · 2023Article
- Individual differences in views toward healthcare conversational agents: A cross-sectional survey study.Digital healthArticle
- Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aims to explore the user archetypes of health apps based on average usage and psychometrics. Methods: The study utilized a dataset collected through a dedicated smartphone application and contained usage data, i.e. the timestamps of each app session from October 2020 to April 2021. The dataset had 129 participants for mental health apps usage and 224 participants for physical health apps usage. Average daily launches, extraversion, neuroticism, and satisfaction with life were the determinants of the mental health apps clusters, whereas average daily launches, conscientiousness, neuroticism, and satisfaction with life were for physical health apps. Results: Two clusters of mental health apps users were identified using k-prototypes clustering: Conclusion: The findings from this study helped to understand the users of health apps based on the frequency of usage, personality, and satisfaction with life. Further, with these findings, apps can be tailored to optimize user experience and satisfaction which may help to increase user retention. Policymakers may also benefit from these findings since understanding the populations' needs may help to better invest in effective health technology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.