TY - JOUR
AU - T. F. Peerbolte
AU - R. J. van Diggelen
AU - P. van den Haak
AU - K. Geurts
AU - L. J. Evers
AU - B. R. Bloem
AU - N. M. de Vries
AU - S. W. van den Berg
A1 - 
AB - BACKGROUND: Conversational agents (CAs) are increasingly used as a promising tool for scalable, accessible, and personalized self-management support of people with a chronic disease. Studies of CAs for self-management of chronic disease operate within a multidisciplinary domain: self-management originates from (behavioral) psychology and CAs stem from intervention technology, while diseases are typically studied within the biomedical context. To ensure their effectiveness, structured evaluations and descriptions of the interventions, integrating biomedical, behavioral, and technological perspectives, are essential. OBJECTIVE: We aimed to examine the design and evaluation of CAs for self-management support of chronic diseases, focusing on their characteristics, integration of behavioral change techniques, and evaluation methods. The findings will guide future research and inform intervention design. METHODS: We conducted a systematic search in the PubMed and Embase databases to identify studies that investigated CAs for chronic disease self-management, published from January 1, 2018, to April 15, 2024. Full-text journal articles, published in English, studying the efficacy or effectiveness of a CA in the context of self-management for chronic diseases in adults were included. Data extraction was guided by conceptual frameworks to ensure comprehensive reporting of intervention and methodologies: the behavioral intervention technology model and the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist. Risk of bias was assessed using the Risk of Bias 2 tool and the Risk of Bias in Non-randomized Studies-of Interventions (ROBINS-I) tool (version 2). RESULTS: In total, 25 studies were included, primarily focusing on text-based, rule-based CAs delivered via a mobile apps. The chronic diseases predominantly targeted were diabetes and cancer. Commonly identified clusters of behavior change techniques were "shaping knowledge," "feedback and monitoring," "natural consequences," and "associations." However, reporting of behavior change techniques and their delivery was lacking, and intervention descriptions were limited. Studies were mostly in the early phase, with a great variety in intervention descriptions, study methods, and outcome measures. CONCLUSIONS: Advancing the field of CA-based interventions requires transparent intervention descriptions, rigorous methodologies, consistent use of validated scales, standardized taxonomy, and reporting aligned with standardized frameworks. Enhanced integration of artificial intelligence-driven personalization and a focus on implementation in health care settings are critical for future research.
AD - Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands.; Department Human Movement Sciences, University Medical Center Groningen, Groningen, The Netherlands.
AN - 40857094
BT - J Med Internet Res
C5 - Healthcare Disparities; HIT & Telehealth
DA - Aug 26
DO - 10.2196/72309
DP - NLM
ET - 20250826
JF - J Med Internet Res
LA - eng
N2 - BACKGROUND: Conversational agents (CAs) are increasingly used as a promising tool for scalable, accessible, and personalized self-management support of people with a chronic disease. Studies of CAs for self-management of chronic disease operate within a multidisciplinary domain: self-management originates from (behavioral) psychology and CAs stem from intervention technology, while diseases are typically studied within the biomedical context. To ensure their effectiveness, structured evaluations and descriptions of the interventions, integrating biomedical, behavioral, and technological perspectives, are essential. OBJECTIVE: We aimed to examine the design and evaluation of CAs for self-management support of chronic diseases, focusing on their characteristics, integration of behavioral change techniques, and evaluation methods. The findings will guide future research and inform intervention design. METHODS: We conducted a systematic search in the PubMed and Embase databases to identify studies that investigated CAs for chronic disease self-management, published from January 1, 2018, to April 15, 2024. Full-text journal articles, published in English, studying the efficacy or effectiveness of a CA in the context of self-management for chronic diseases in adults were included. Data extraction was guided by conceptual frameworks to ensure comprehensive reporting of intervention and methodologies: the behavioral intervention technology model and the CONSORT-EHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist. Risk of bias was assessed using the Risk of Bias 2 tool and the Risk of Bias in Non-randomized Studies-of Interventions (ROBINS-I) tool (version 2). RESULTS: In total, 25 studies were included, primarily focusing on text-based, rule-based CAs delivered via a mobile apps. The chronic diseases predominantly targeted were diabetes and cancer. Commonly identified clusters of behavior change techniques were "shaping knowledge," "feedback and monitoring," "natural consequences," and "associations." However, reporting of behavior change techniques and their delivery was lacking, and intervention descriptions were limited. Studies were mostly in the early phase, with a great variety in intervention descriptions, study methods, and outcome measures. CONCLUSIONS: Advancing the field of CA-based interventions requires transparent intervention descriptions, rigorous methodologies, consistent use of validated scales, standardized taxonomy, and reporting aligned with standardized frameworks. Enhanced integration of artificial intelligence-driven personalization and a focus on implementation in health care settings are critical for future research.
PY - 2025
SN - 1439-4456 (Print); 1438-8871
SP - e72309
ST - Conversational Agents Supporting Self-Management in People With a Chronic Disease: Systematic Review
T1 - Conversational Agents Supporting Self-Management in People With a Chronic Disease: Systematic Review
T2 - J Med Internet Res
TI - Conversational Agents Supporting Self-Management in People With a Chronic Disease: Systematic Review
U1 - Healthcare Disparities; HIT & Telehealth
U3 - 10.2196/72309
VL - 27
VO - 1439-4456 (Print); 1438-8871
Y1 - 2025
ER -