Conversational agents in health care: scoping review of their behavior change techniques and underpinning theory
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. Pre...
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2023
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Science::Medicine Social sciences::Psychology Behavior Change Behavior Change Techniques Chatbot Conversational Agent mHealth Martinengo, Laura Jabir, Ahmad Ishqi Goh, Westin Wei Tin Lo, Nicholas Yong Wai Ho, Ringo Moon-Ho Kowatsch, Tobias Atun, Rifat Michie, Susan Car, Lorainne Tudor Conversational agents in health care: scoping review of their behavior change techniques and underpinning theory |
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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. |
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Lee Kong Chian School of Medicine (LKCMedicine) |
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Lee Kong Chian School of Medicine (LKCMedicine) Martinengo, Laura Jabir, Ahmad Ishqi Goh, Westin Wei Tin Lo, Nicholas Yong Wai Ho, Ringo Moon-Ho Kowatsch, Tobias Atun, Rifat Michie, Susan Car, Lorainne Tudor |
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Article |
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Martinengo, Laura Jabir, Ahmad Ishqi Goh, Westin Wei Tin Lo, Nicholas Yong Wai Ho, Ringo Moon-Ho Kowatsch, Tobias Atun, Rifat Michie, Susan Car, Lorainne Tudor |
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Martinengo, Laura |
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Conversational agents in health care: scoping review of their behavior change techniques and underpinning theory |
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Conversational agents in health care: scoping review of their behavior change techniques and underpinning theory |
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Conversational agents in health care: scoping review of their behavior change techniques and underpinning theory |
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Conversational agents in health care: scoping review of their behavior change techniques and underpinning theory |
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Conversational agents in health care: scoping review of their behavior change techniques and underpinning theory |
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conversational agents in health care: scoping review of their behavior change techniques and underpinning theory |
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2023 |
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https://hdl.handle.net/10356/165656 |
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sg-ntu-dr.10356-1656562023-04-16T15:30:22Z Conversational agents in health care: scoping review of their behavior change techniques and underpinning theory Martinengo, Laura Jabir, Ahmad Ishqi Goh, Westin Wei Tin Lo, Nicholas Yong Wai Ho, Ringo Moon-Ho Kowatsch, Tobias Atun, Rifat Michie, Susan Car, Lorainne Tudor Lee Kong Chian School of Medicine (LKCMedicine) School of Social Sciences Science::Medicine Social sciences::Psychology Behavior Change Behavior Change Techniques Chatbot Conversational Agent mHealth 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. Ministry of Education (MOE) National Research Foundation (NRF) Published version This research is supported by the Singapore Ministry of Education under Singapore Ministry of Education Academic Research Fund Tier 1 (RG36/20). The research was conducted as part of the Future Health Technologies program, which was established collaboratively between ETH Zurich and the National Research Foundation, Singapore. This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise program. 2023-04-10T05:33:58Z 2023-04-10T05:33:58Z 2022 Journal Article Martinengo, L., Jabir, A. I., Goh, W. W. T., Lo, N. Y. W., Ho, R. M., Kowatsch, T., Atun, R., Michie, S. & Tudor Car, L. (2022). Conversational agents in health care: scoping review of their behavior change techniques and underpinning theory. Journal of Medical Internet Research, 24(10), e39243-. https://dx.doi.org/10.2196/39243 1438-8871 https://hdl.handle.net/10356/165656 10.2196/39243 36190749 2-s2.0-85139570877 10 24 e39243 en RG36/20 Journal of Medical Internet Research © Laura Martinengo, Ahmad Ishqi Jabir, Westin Wei Tin Goh, Nicholas Yong Wai Lo, Moon-Ho Ringo Ho, Tobias Kowatsch, Rifat Atun, Susan Michie, Lorainne Tudor Car. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.10.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. application/pdf |