ArticleNPJ digital medicine2023
Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital, remotely delivered management interventions for adult asthma: a systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- Use of Voice-Based Conversational Artificial Intelligence for Basal Insulin Prescription Management Among Patients With Type 2 Diabetes: A Randomized Clinical Trial.JAMA network open · 2023Trial
- Perspectives on Remote Monitoring via Smartphones and Wearables Among Individuals With Lived Experience or at Risk of Eating Disorders ("This Could Go Very, Very Wrong"): Qualitative Interview Study.JMIR formative research · 2026Article
- Daily symptom monitoring is sustainable over months: retention, not compliance, is the primary barrier to long-duration digital tracking.medRxiv : the preprint server for health sciences · 2026Article
- Understanding sleep and smartphone use in diverse adolescents through passive digital monitoring.Journal of research on adolescence : the official journal of the Society for Research on Adolescence · 2026Article
- Artificial Intelligence in Drug Discovery and Development: Raising Quality per Decision.Pharmacopsychiatry · 2026Review
- Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review.JMIR mHealth and uHealth · 2026Article
- Analysis of Participant-Level Characteristics Predicting Adherence to Long-Term EMA and Fitbit Monitoring in the 4HAIE Prospective Cohort Study.International journal of behavioral medicine · 2026Article
- Article
- Participant Retention in a Prospective Multicenter Cohort of Participants With Pancreatitis and Factors Associated With Attrition.Pancreas · 2026Article
- The Digital Mental Health Study assessed feasibility of large-scale digital sensing for depression and anxiety.npj digital public health · 2026Article
- Patient engagement with consumer wearable devices in the electronic health record.Frontiers in digital health · 2026Article
- Comparing personalized and population-based models for predicting momentary negative affect in internalizing disorders: A digital phenotyping study.Neuroscience applied · 2026Article
- Predictors of adherence to a digital health application in patients with inflammatory arthritis: retrospective analysis.Rheumatology international · 2025Article
- What the Heart Can(not) Tell: Potential and Pitfalls of Biometric Recognition Methods Based on Photoplethysmography.Sensors (Basel, Switzerland) · 2025Article
- The user experience of ambulatory assessment and mood monitoring in depression: a systematic review & meta-synthesis.NPJ digital medicine · 2025Article
- Remote monitoring of wide-ranging real-world changes in adults following ADHD medication initiation.Translational psychiatry · 2025Article
- Implementation Framework to Increase Patient Adherence in Clinical Trials Utilizing Wearable Devices.Research square · 2025Article
- Predicting Engagement Patterns With Connected Wearable Devices in a Health System: Survival Analysis.Journal of medical Internet research · 2025Article
- The Heartline Experience: Achieving Long-Term Participant Engagement in Digital Health Technology Trials.JACC. Advances · 2025Article
Corrections and comments
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Authors and funding
30 authors.
Funding
Abstract
Recent growth in digital technologies has enabled the recruitment and monitoring of large and diverse populations in remote health studies. However, the generalizability of inference drawn from remotely collected health data could be severely impacted by uneven participant engagement and attrition over the course of the study. We report findings on long-term participant retention and engagement patterns in a large multinational observational digital study for depression containing active (surveys) and passive sensor data collected via Android smartphones, and Fitbit devices from 614 participants for up to 2 years. Majority of participants (67.6%) continued to remain engaged in the study after 43 weeks. Unsupervised clustering of participants' study apps and Fitbit usage data showed 3 distinct engagement subgroups for each data stream. We found: (i) the least engaged group had the highest depression severity (4 PHQ8 points higher) across all data streams; (ii) the least engaged group (completed 4 bi-weekly surveys) took significantly longer to respond to survey notifications (3.8 h more) and were 5 years younger compared to the most engaged group (completed 20 bi-weekly surveys); and (iii) a considerable proportion (44.6%) of the participants who stopped completing surveys after 8 weeks continued to share passive Fitbit data for significantly longer (average 42 weeks). Additionally, multivariate survival models showed participants' age, ownership and brand of smartphones, and recruitment sites to be associated with retention in the study. Together these findings could inform the design of future digital health studies to enable equitable and balanced data collection from diverse populations.
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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.