ArticleJAMIA open2021
Phenotypes of engagement with mobile health technology for heart rhythm monitoring.
Article in JAMIA open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 14 citations in OpenAlex.
- Patient Interaction Phenotypes With an Automated SMS Text Message-Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational Study.Journal of medical Internet research · 2025Trial
- Hybrid care engagement phenotypes and glycemic outcomes in diabetes: a cluster analysis across two health systems.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Understanding patient preferences, experiences and engagement with ambulatory heart rhythm monitoring: a scoping review.BMJ open · 2026Article
- Current methods for analyzing time-series patient-generated health data to assess treatment response: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Evaluating a digital decision aid for atrial fibrillation rhythm control in a hybrid implementation-effectiveness trial.NPJ digital medicine · 2026Article
- The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact.NPJ digital medicine · 2025Review
- Not missing at random: Missing data are associated with clinical status and trajectories in an electronic monitoring longitudinal study of bipolar disorder.Journal of psychiatric research · 2024Article
- Managing With Atrial Fibrillation: An Exploratory Model-Based Cluster Analysis of Clinical and Personal Patient Characteristics.CJC open · 2023Article
- Weight loss and modeled cost savings in a digital diabetes prevention program.Obesity science & practice · 2023Article
- Review
- Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models.International journal of bipolar disorders · 2023Article
- Mixed-Response State-Space Model for Analyzing Multi-Dimensional Digital Phenotypes.Journal of the American Statistical Association · 2023Article
- Patterns of Engagement With an Application-Based Dietary Self-Monitoring Tool Within a Randomized Controlled Feasibility Trial.AJPM focus · 2022Article
- Discovering Engagement Personas in a Digital Diabetes Prevention Program.Behavioral sciences (Basel, Switzerland) · 2022Article
- Response to Mental Health of Cardiac Procedure Patients Should Be a Priority for All Healthcare Providers.The Journal of cardiovascular nursingArticle
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
objectivesGuided by the concept of digital phenotypes, the objective of this study was to identify MATERIALS AND
methodsWe conducted a secondary analysis of mHealth data, surveys, and clinical records collected by participants using mHealth in a clinical trial. Patterns of participants' weekly use over 6 months were analyzed to identify engagement phenotypes via latent growth mixture model (LGMM). Multinomial logistic regression models were fitted to compute the effects of predictors on LGMM classes.
resultsOne hundred twenty-eight participants (mean age 61.9 years, 75.8% male) were included in the analysis. Application of LGMM identified 4 distinct engagement phenotypes: "High-High," "Moderate-Moderate," "High-Low," and "Moderate-Low." In multinomial models, older age, less frequent afternoon mHealth use, shorter intervals between mHealth use, more AF episodes measured directly with mHealth, and lower left ventricular ejection fraction were more strongly associated with the High-High phenotype compared to the Moderate-Low phenotype (reference). Older age, more palpitations, and a history of stroke or transient ischemic attack were more strongly associated with the Moderate-Moderate phenotype compared to the reference. DISCUSSION: Engagement phenotypes provide a nuanced characterization of how individuals engage with mHealth over time, and which individuals are more likely to be highly engaged users.
conclusionThis study demonstrates that engagement phenotypes are valuable in understanding and possibly intervening upon engagement within a population, and also suggests that engagement is an important variable to be considered in digital phenotyping work more broadly.
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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.