{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T03:17:59Z","timestamp":1791861479149,"version":"4.3.4"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2021,6,13]],"date-time":"2021-06-13T00:00:00Z","timestamp":1623542400000},"content-version":"vor","delay-in-days":73,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"National Institute of Nursing Research (NINR) of the National Institutes of Health","award":["K99NR019124"],"award-info":[{"award-number":["K99NR019124"]}]},{"name":"National Institute of Nursing Research (NINR) of the National Institutes of Health","award":["R01NR014853"],"award-info":[{"award-number":["R01NR014853"]}]},{"name":"National Institute of Nursing Research (NINR) of the National Institutes of Health","award":["R00NR016275"],"award-info":[{"award-number":["R00NR016275"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,4,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>Guided by the concept of digital phenotypes, the objective of this study was to identify engagement phenotypes among individuals with atrial fibrillation (AF) using mobile health (mHealth) technology for 6 months.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>We conducted a secondary analysis of mHealth data, surveys, and clinical records collected by participants using mHealth in a clinical trial. Patterns of participants\u2019 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>One 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: \u201cHigh-High,\u201d \u201cModerate-Moderate,\u201d \u201cHigh-Low,\u201d and \u201cModerate-Low.\u201d 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This 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 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