Discovering Engagement Personas in a Digital Diabetes Prevention Program

Author:

Hori Jonathan H.,Sia Elizabeth X.,Lockwood Kimberly G.,Auster-Gussman Lisa A.,Rapoport Sharon,Branch OraLee H.ORCID,Graham Sarah A.

Abstract

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.

Publisher

MDPI AG

Subject

Behavioral Neuroscience,General Psychology,Genetics,Development,Ecology, Evolution, Behavior and Systematics

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