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Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review

BACKGROUND: Artificial intelligence (AI)–based chatbots can offer personalized, engaging, and on-demand health promotion interventions. OBJECTIVE: The aim of this systematic review was to evaluate the feasibility, efficacy, and intervention characteristics of AI chatbots for promoting health behavio...

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Detalles Bibliográficos
Autores principales: Aggarwal, Abhishek, Tam, Cheuk Chi, Wu, Dezhi, Li, Xiaoming, Qiao, Shan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007007/
https://www.ncbi.nlm.nih.gov/pubmed/36826990
http://dx.doi.org/10.2196/40789
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author Aggarwal, Abhishek
Tam, Cheuk Chi
Wu, Dezhi
Li, Xiaoming
Qiao, Shan
author_facet Aggarwal, Abhishek
Tam, Cheuk Chi
Wu, Dezhi
Li, Xiaoming
Qiao, Shan
author_sort Aggarwal, Abhishek
collection PubMed
description BACKGROUND: Artificial intelligence (AI)–based chatbots can offer personalized, engaging, and on-demand health promotion interventions. OBJECTIVE: The aim of this systematic review was to evaluate the feasibility, efficacy, and intervention characteristics of AI chatbots for promoting health behavior change. METHODS: A comprehensive search was conducted in 7 bibliographic databases (PubMed, IEEE Xplore, ACM Digital Library, PsycINFO, Web of Science, Embase, and JMIR publications) for empirical articles published from 1980 to 2022 that evaluated the feasibility or efficacy of AI chatbots for behavior change. The screening, extraction, and analysis of the identified articles were performed by following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: Of the 15 included studies, several demonstrated the high efficacy of AI chatbots in promoting healthy lifestyles (n=6, 40%), smoking cessation (n=4, 27%), treatment or medication adherence (n=2, 13%), and reduction in substance misuse (n=1, 7%). However, there were mixed results regarding feasibility, acceptability, and usability. Selected behavior change theories and expert consultation were used to develop the behavior change strategies of AI chatbots, including goal setting, monitoring, real-time reinforcement or feedback, and on-demand support. Real-time user-chatbot interaction data, such as user preferences and behavioral performance, were collected on the chatbot platform to identify ways of providing personalized services. The AI chatbots demonstrated potential for scalability by deployment through accessible devices and platforms (eg, smartphones and Facebook Messenger). The participants also reported that AI chatbots offered a nonjudgmental space for communicating sensitive information. However, the reported results need to be interpreted with caution because of the moderate to high risk of internal validity, insufficient description of AI techniques, and limitation for generalizability. CONCLUSIONS: AI chatbots have demonstrated the efficacy of health behavior change interventions among large and diverse populations; however, future studies need to adopt robust randomized control trials to establish definitive conclusions.
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spelling pubmed-100070072023-03-12 Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review Aggarwal, Abhishek Tam, Cheuk Chi Wu, Dezhi Li, Xiaoming Qiao, Shan J Med Internet Res Review BACKGROUND: Artificial intelligence (AI)–based chatbots can offer personalized, engaging, and on-demand health promotion interventions. OBJECTIVE: The aim of this systematic review was to evaluate the feasibility, efficacy, and intervention characteristics of AI chatbots for promoting health behavior change. METHODS: A comprehensive search was conducted in 7 bibliographic databases (PubMed, IEEE Xplore, ACM Digital Library, PsycINFO, Web of Science, Embase, and JMIR publications) for empirical articles published from 1980 to 2022 that evaluated the feasibility or efficacy of AI chatbots for behavior change. The screening, extraction, and analysis of the identified articles were performed by following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: Of the 15 included studies, several demonstrated the high efficacy of AI chatbots in promoting healthy lifestyles (n=6, 40%), smoking cessation (n=4, 27%), treatment or medication adherence (n=2, 13%), and reduction in substance misuse (n=1, 7%). However, there were mixed results regarding feasibility, acceptability, and usability. Selected behavior change theories and expert consultation were used to develop the behavior change strategies of AI chatbots, including goal setting, monitoring, real-time reinforcement or feedback, and on-demand support. Real-time user-chatbot interaction data, such as user preferences and behavioral performance, were collected on the chatbot platform to identify ways of providing personalized services. The AI chatbots demonstrated potential for scalability by deployment through accessible devices and platforms (eg, smartphones and Facebook Messenger). The participants also reported that AI chatbots offered a nonjudgmental space for communicating sensitive information. However, the reported results need to be interpreted with caution because of the moderate to high risk of internal validity, insufficient description of AI techniques, and limitation for generalizability. CONCLUSIONS: AI chatbots have demonstrated the efficacy of health behavior change interventions among large and diverse populations; however, future studies need to adopt robust randomized control trials to establish definitive conclusions. JMIR Publications 2023-02-24 /pmc/articles/PMC10007007/ /pubmed/36826990 http://dx.doi.org/10.2196/40789 Text en ©Abhishek Aggarwal, Cheuk Chi Tam, Dezhi Wu, Xiaoming Li, Shan Qiao. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 24.02.2023. https://creativecommons.org/licenses/by/4.0/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.
spellingShingle Review
Aggarwal, Abhishek
Tam, Cheuk Chi
Wu, Dezhi
Li, Xiaoming
Qiao, Shan
Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review
title Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review
title_full Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review
title_fullStr Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review
title_full_unstemmed Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review
title_short Artificial Intelligence–Based Chatbots for Promoting Health Behavioral Changes: Systematic Review
title_sort artificial intelligence–based chatbots for promoting health behavioral changes: systematic review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007007/
https://www.ncbi.nlm.nih.gov/pubmed/36826990
http://dx.doi.org/10.2196/40789
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