Digital Engagement Phenotypes and 6-Month Weight Loss in a Tirzepatide-Supported Digital Weight-Loss Program: A Retrospective Cohort Study

Background and Objectives: Real-world persistence with medication-supported weight management programs is often low. While digital weight loss services (DWLS) provide multi-modal digital supports to improve engagement and counter attrition, the existing literature frequently relies on unidimensional or binary classifications of user engagement. This study used unsupervised machine learning to identify distinct digital engagement phenotypes and evaluated their independent associations with 6-month weight loss outcomes in patients prescribed tirzepatide. Materials and Methods: This retrospective cohort study analyzed deidentified data from 9470 medication-adherent, complete-case adult patients (out of 39,220 tirzepatide initiators) within a British DWLS who initiated tirzepatide between 20 May 20 and 2 December 2025. K-means clustering was performed on four continuous, longitudinal usage metrics: weekly app logins, health coach messaging, automated assistant (JuneBot) messaging, and weight tracking. To evaluate the primary clinical endpoint—6-month percentage weight loss—unadjusted pairwise comparisons (Tukey HSD) and a fully adjusted ordinary least squares multivariate linear regression model were executed to control for baseline demographic, clinical, and interim behavioral covariates. Results: Four stable engagement phenotypes emerged: non-engaged (n = 1772), high health coach engagement (n = 1141), high JuneBot engagement (n = 1472), and passive self-trackers (n = 5085). In a baseline-adjusted multivariate regression model, all active phenotypes were independently associated with 6-month weight loss relative to the low engagement baseline. Compared with this reference group, adjusted mean differences in percentage weight loss were 4.60% (SE = 0.26, p < 0.001) in the high health coach cluster, 4.69% (SE = 0.24, p < 0.001) in the high JuneBot cluster, and 3.71% (SE = 0.19, p < 0.001) in the passive self-tracker cluster. Conclusions: In this selected per-protocol, complete-case cohort of patients who persisted with tirzepatide treatment and reported 6-month weight data, distinct patterns of digital engagement were associated with different weight-loss outcomes. Greater conversational and self-tracking engagement was associated with greater observed 6-month weight loss than low engagement after adjustment for measured baseline characteristics. However, the retrospective observational design, concurrent assessment of engagement and outcome, substantial cohort selection, and potential residual confounding preclude causal or comparative-effectiveness conclusions, including claims of equivalence between automated and human support. Prospective studies with temporally defined engagement exposures and randomized allocation to support modalities are required.

Authors

Institutions

Publication Details

Journal
Medicina
Published
2026-08-27
DOI
https://doi.org/10.3390/medicina62091647
Primary Topic
Mobile Health and mHealth Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Digital Engagement Phenotypes and 6-Month Weight Loss in a Tirzepatide-Supported Digital Weight-Loss Program: A Retrospective Cohort Study

Laura Swinckels, Neera Ahuja, John Alderete, Marilyn Tan et al.
Medicina
Mobile Health and mHealth Applications
article

Digital Engagement Phenotypes and 6-Month Weight Loss in a Tirzepatide-Supported Digital Weight-Loss Program: A Retrospective Cohort Study

Laura Swinckels, Neera Ahuja, John Alderete, Marilyn Tan, Louis Talay, Connie Xu, Jason Hom
article en

Abstract

Background and Objectives: Real-world persistence with medication-supported weight management programs is often low. While digital weight loss services (DWLS) provide multi-modal digital supports to improve engagement and counter attrition, the existing literature frequently relies on unidimensional or binary classifications of user engagement. This study used unsupervised machine learning to identify distinct digital engagement phenotypes and evaluated their independent associations with 6-month weight loss outcomes in patients prescribed tirzepatide. Materials and Methods: This retrospective cohort study analyzed deidentified data from 9470 medication-adherent, complete-case adult patients (out of 39,220 tirzepatide initiators) within a British DWLS who initiated tirzepatide between 20 May 20 and 2 December 2025. K-means clustering was performed on four continuous, longitudinal usage metrics: weekly app logins, health coach messaging, automated assistant (JuneBot) messaging, and weight tracking. To evaluate the primary clinical endpoint—6-month percentage weight loss—unadjusted pairwise comparisons (Tukey HSD) and a fully adjusted ordinary least squares multivariate linear regression model were executed to control for baseline demographic, clinical, and interim behavioral covariates. Results: Four stable engagement phenotypes emerged: non-engaged (n = 1772), high health coach engagement (n = 1141), high JuneBot engagement (n = 1472), and passive self-trackers (n = 5085). In a baseline-adjusted multivariate regression model, all active phenotypes were independently associated with 6-month weight loss relative to the low engagement baseline. Compared with this reference group, adjusted mean differences in percentage weight loss were 4.60% (SE = 0.26, p < 0.001) in the high health coach cluster, 4.69% (SE = 0.24, p < 0.001) in the high JuneBot cluster, and 3.71% (SE = 0.19, p < 0.001) in the passive self-tracker cluster. Conclusions: In this selected per-protocol, complete-case cohort of patients who persisted with tirzepatide treatment and reported 6-month weight data, distinct patterns of digital engagement were associated with different weight-loss outcomes. Greater conversational and self-tracking engagement was associated with greater observed 6-month weight loss than low engagement after adjustment for measured baseline characteristics. However, the retrospective observational design, concurrent assessment of engagement and outcome, substantial cohort selection, and potential residual confounding preclude causal or comparative-effectiveness conclusions, including claims of equivalence between automated and human support. Prospective studies with temporally defined engagement exposures and randomized allocation to support modalities are required.

MedicinaVol. 62(9)
American International Group (United States) (US), Simon Fraser University (CA), Eucalyptus Research Center (CN), Stanford University (US)
Openalex Percentile: Top 5%
Mobile Health and mHealth Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.