Engagement With Conversational Agent-Enabled Interventions in Cardiometabolic Disease Self-Management: Systematic Review.

Nick Kashyap, Ann Tresa Sebastian, Chris Lynch, Paul Jansons, Ralph Maddison, Tilman Dingler, Brian Oldenburg

Journal: JMIR mHealth and uHealth 2025;13():e67913

PMID: 40966680

Abstract

BACKGROUND

Well-designed conversational agents can improve health care capacity to meet the dynamic and complex needs of people self-managing cardiometabolic diseases (CMD). However, a lack of empirical evidence on conversational agent-enabled intervention design features and their impact on engagement make it challenging to comprehensively evaluate effectiveness. This review synthesizes evidence on conversational agent-enabled intervention design features and how they impact on engagement to inform the development of more engaging conversational agent-enabled interventions that effectively help people with CMD to self-manage their condition.

OBJECTIVE

The aim of the study is to synthesize evidence pertaining to conversational agent-enabled intervention design features and their impact on engagement of people self-managing CMD.

METHODS

Searches were conducted in Ovid (MEDLINE), Web of Science, and Scopus databases. Inclusion criteria were primary research studies reporting on conversational agent-enabled interventions that included measures of engagement and included adults with CMD. Data extraction captured perspectives of people with CMD on various design features of conversational agent-enabled interventions.

RESULTS

Of 1366 studies identified for screening, 20 were included in the review. In total, 18 of these were qualitative or quasi-experimental evaluations of conversational agent-enabled intervention prototypes. Five domains of design features that impact user engagement with conversational agent-enabled interventions emerged: communication style, functionality, accessibility, visual appearance, and personality.

CONCLUSIONS

Across all 5 domains, integrating redundancy and anthropomorphism were identified as effective strategies for improving engagement by increasing user autonomy and investment. Future research should adopt design strategies that are inclusive and adaptive to the diverse needs of users and aligned with the unique considerations relevant to conversational agent-enabled interventions.

TRIAL REGISTRATION

PROSPERO CRD42023431579; https://tinyurl.com/3srmzw8f.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/52973.

©Nick Kashyap, Ann Tresa Sebastian, Chris Lynch, Paul Jansons, Ralph Maddison, Tilman Dingler, Brian Oldenburg. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 18.09.2025.

Address: School of Psychology and Public Health, La Trobe University, Melbourne, Australia.; Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, Australia.; Non-communicable Disease and Implementation Science laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia.; Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia.; School of Psychology and Public Health, La Trobe University, Melbourne, Australia.; Non-communicable Disease and Implementation Science laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia.; Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia.; Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia.; Delft University of Technology, Delft, The Netherlands.
Bant logo

© Copyright 2026, Nutrition Evidence

NED wishes to thank the following organisations for their support:

We use cookies to improve your experience and analyze site traffic with Google Analytics. By continuing to use our site, you agree to our use of cookies. Learn more.