ArticleJMIR formative research2024
Assessing Digital Phenotyping for App Recommendations and Sustained Engagement: Cohort Study.
Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 3 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Emerging Roles of AI in Self-Directed Stress Management: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Association between user engagement and clinical outcomes in smartphone apps for depression and anxiety: A systematic review and meta-analysis.Psychiatry research · 2026Pooled it
- Methodological quality in randomised clinical trials of mental health apps: systematic review and longitudinal analysis.BMJ mental health · 2025Pooled it
- Towards Human-Centered Digital Health Interventions.The Psychiatric clinics of North America · 2026Review
- Feasibility of smartphone-based digital phenotyping to measure visual function and mental health outcomes in patients with inherited retinal diseases.NPJ digital medicine · 2026Article
- National implementation of a digital Parkinson's disease screening programme in Thailand: reach, adoption, and real-world performance of the CheckPD app.BMC public health · 2026Article
- LINC: a framework for maintaining high-quality passive data in digital phenotyping studies.Scientific reports · 2026Article
- Five years of app evaluation: Insights from a framework in practice - a systematic review on the m-health index and navigation database.Internet interventions · 2025Article
- Engagement and attrition in digital mental health: current challenges and potential solutions.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLow engagement with mental health apps continues to limit their impact. New approaches to help match patients to the right app may increase engagement by ensuring the app they are using is best suited to their mental health needs.
objectiveThis study aims to pilot how digital phenotyping, using data from smartphone sensors to infer symptom, behavioral, and functional outcomes, could be used to match people to mental health apps and potentially increase engagement.
methodsAfter 1 week of collecting digital phenotyping data with the mindLAMP app (Beth Israel Deaconess Medical Center), participants were randomly assigned to the digital phenotyping arm, receiving feedback and recommendations based on those data to select 1 of 4 predetermined mental health apps (related to mood, anxiety, sleep, and fitness), or the control arm, selecting the same apps but without any feedback or recommendations. All participants used their selected app for 4 weeks with numerous metrics of engagement recorded, including objective screentime measures, self-reported engagement measures, and Digital Working Alliance Inventory scores.
resultsA total of 82 participants enrolled in the study; 17 (21%) dropped out of the digital phenotyping arm and 18 (22%) dropped out from the control arm. Across both groups, few participants chose or were recommended the insomnia or fitness app. The majority (39/47, 83%) used a depression or anxiety app. Engagement as measured by objective screen time and Digital Working Alliance Inventory scores were higher in the digital phenotyping arm. There was no correlation between self-reported and objective metrics of app use. Qualitative results highlighted the importance of habit formation in sustained app use.
conclusionsThe results suggest that digital phenotyping app recommendation is feasible and may increase engagement. This approach is generalizable to other apps beyond the 4 apps selected for use in this pilot, and practical for real-world use given that the study was conducted without any compensation or external incentives that may have biased results. Advances in digital phenotyping will likely make this method of app recommendation more personalized and thus of even greater interest.
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