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. 2022 Oct 3;24(10):e39243.
doi: 10.2196/39243.

Conversational Agents in Health Care: Scoping Review of Their Behavior Change Techniques and Underpinning Theory

Laura Martinengo  1 , Ahmad Ishqi Jabir  1 , Westin Wei Tin Goh  1 , Nicholas Yong Wai Lo  1 , Moon-Ho Ringo Ho  2 , Tobias Kowatsch  3   4   5   6 , Rifat Atun  7   8   9   10 , Susan Michie  11 , Lorainne Tudor Car  1   12
Affiliations

Conversational Agents in Health Care: Scoping Review of Their Behavior Change Techniques and Underpinning Theory

Laura Martinengo et al. J Med Internet Res. .

Abstract

Background: Conversational agents (CAs) are increasingly used in health care to deliver behavior change interventions. Their evaluation often includes categorizing the behavior change techniques (BCTs) using a classification system of which the BCT Taxonomy v1 (BCTTv1) is one of the most common. Previous studies have presented descriptive summaries of behavior change interventions delivered by CAs, but no in-depth study reporting the use of BCTs in these interventions has been published to date.

Objective: This review aims to describe behavior change interventions delivered by CAs and to identify the BCTs and theories guiding their design.

Methods: We searched PubMed, Embase, Cochrane's Central Register of Controlled Trials, and the first 10 pages of Google and Google Scholar in April 2021. We included primary, experimental studies evaluating a behavior change intervention delivered by a CA. BCTs coding followed the BCTTv1. Two independent reviewers selected the studies and extracted the data. Descriptive analysis and frequent itemset mining to identify BCT clusters were performed.

Results: We included 47 studies reporting on mental health (n=19, 40%), chronic disorders (n=14, 30%), and lifestyle change (n=14, 30%) interventions. There were 20/47 embodied CAs (43%) and 27/47 CAs (57%) represented a female character. Most CAs were rule based (34/47, 72%). Experimental interventions included 63 BCTs, (mean 9 BCTs; range 2-21 BCTs), while comparisons included 32 BCTs (mean 2 BCTs; range 2-17 BCTs). Most interventions included BCTs 4.1 "Instruction on how to perform a behavior" (34/47, 72%), 3.3 "Social support" (emotional; 27/47, 57%), and 1.2 "Problem solving" (24/47, 51%). A total of 12/47 studies (26%) were informed by a behavior change theory, mainly the Transtheoretical Model and the Social Cognitive Theory. Studies using the same behavior change theory included different BCTs.

Conclusions: There is a need for the more explicit use of behavior change theories and improved reporting of BCTs in CA interventions to enhance the analysis of intervention effectiveness and improve the reproducibility of research.

Keywords: behavior change; behavior change techniques; chatbot; conversational agent; mHealth.

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Conflict of interest statement

Conflicts of Interest: TK is affiliated with the Centre for Digital Health Interventions, a joint initiative of the Department of Management, Technology, and Economics at ETH Zurich and the Institute of Technology Management at the University of St.Gallen, which is funded in part by CSS, a Swiss health insurer. TK is also a cofounder of Pathmate Technologies, a university spin-off company that creates and delivers digital clinical pathways. However, neither CSS nor Pathmate Technologies was involved in this research. The other authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Study selection flowchart. BCT: behavior change technique; CA: conversational agent.
Figure 2
Figure 2
Number of studies using each BCT in the experimental and comparison interventions. BCT: behavior change technique; Int: intervention.
Figure 3
Figure 3
Commonly used BCTs according to the clinical domain. BCT: behavior change technique.
See this image and copyright information in PMC

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