Association Between Behavioral Phenotypes and Paid mHealth App Subscription and Renewal: A 6-month Latent Class Analysis Study (Preprint)
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: BACKGROUND Mobile health (mHealth) app effectiveness may be limited by low engagement. Increasing understanding of factors influencing engagement may help. Paid mHealth app subscription and renewal are two metrics of particular interest to commercial app developers. OBJECTIVE Objective: To identify homogenous user subgroups (ie, behavioral phenotypes) within a paid mHealth app context and examine associations with app subscription and renewal. METHODS Methods: In this 6-month prospective cohort study, latent class analysis (LCA) was conducted with users of a paid mHealth app. Users completing a 7-day free trial between November 2023 and January 2024 were included. LCA produced phenotypes using survey responses (eg, chronic disease status), device-assessed health data (eg, daily step count), and 7-day free trial period engagement data (eg, number app opens). Odds ratios (ORs; P < .05) assessed associations between phenotypes and subscription/renewal. RESULTS Results: The sample included 934 users (mean age, 41.53 [SD, 9.65] years). Based on LCA fit indices five distinct phenotypes were formed: (1) Highly engaged subscribers, (2) Subscribers with multimorbidity, (3) Healthy subscribers, (4) Non-subscribers with multimorbidity, and (5) Healthy non-subscribers. Phenotypes 1–3 had greater odds of subscribing (OR = 21.31 [8.56, 53.06]; OR = 7.11 [4.04, 12.50]; OR = 8.28 [4.26, 16.08], respectively) than phenotype 4 (OR = 0.82 [0.48, 1.41]), compared to phenotype 5, the reference scenario. Additionally, renewal odds for phenotypes 1–4 were 1.06 [0.62, 1.81], 0.90 [0.54, 1.49], 0.99 [0.58, 1.69], and 0.93 [0.48, 1.80], respectively (vs. reference). CONCLUSIONS Conclusions: Behavioral phenotypes associated with subscription likelihood were identified using data collected during the 7-day trial period. These phenotypes may be strategically targeted with future intervention to boost early engagement and long-term behavior change potential.
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7130516897