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A Method for Discovering Engagement Personas in a Digital Diabetes Prevention Program

Lark
Team
May 24, 2022
A Method for Discovering Engagement Personas in a Digital Diabetes Prevention Program
Lark

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*Results may vary. Based on the average weight loss in three, 68-week clinical trials of patients without diabetes who reached and maintained a dose of 2.4mg/week of GLP-1 treatment, along with a reduced-calorie diet and increased physical activity. View study here.
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Digital health technologies are shaping the future of preventive health care. We present a quantitative approach for discovering and characterizing engagement personas: longitudinal engagement patterns in a fully digital diabetes prevention program. We used a two-step approach to discovering engagement personas among n = 1613 users: (1) A univariate clustering method using two unsupervised k-means clustering algorithms on app- and program-feature use separately and (2) A bivariate clustering method that involved comparing cluster labels for each member across app- and program-feature univariate clusters. The univariate analyses revealed five app-feature clusters and four program-feature clusters. The bivariate analysis revealed five unique combinations of these clusters, called engagement personas, which represented 76% of users. These engagement personas differed in both member demographics and weight loss. Exploring engagement personas is beneficial to inform strategies for personalizing the program experience and optimizing engagement in a variety of digital health interventions.

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