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      Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint

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          Abstract

          Background

          Chatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet.

          Objective

          The purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations.

          Methods

          We conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model.

          Results

          Our review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper.

          Conclusions

          As AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot’s relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles.

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          Most cited references103

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          The Transtheoretical Model of Health Behavior Change

          The transtheoretical model posits that health behavior change involves progress through six stages of change: precontemplation, contemplation, preparation, action, maintenance, and termination. Ten processes of change have been identified for producing progress along with decisional balance, self-efficacy, and temptations. Basic research has generated a rule of thumb for at-risk populations: 40% in precontemplation, 40% in contemplation, and 20% in preparation. Across 12 health behaviors, consistent patterns have been found between the pros and cons of changing and the stages of change. Applied research has demonstrated dramatic improvements in recruitment, retention, and progress using stage-matched interventions and proactive recruitment procedures. The most promising outcomes to data have been found with computer-based individualized and interactive interventions. The most promising enhancement to the computer-based programs are personalized counselors. One of the most striking results to date for stage-matched programs is the similarity between participants reactively recruited who reached us for help and those proactively recruited who we reached out to help. If results with stage-matched interventions continue to be replicated, health promotion programs will be able to produce unprecedented impacts on entire at-risk populations.
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            An Empirical Evaluation of the System Usability Scale

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              The Algorithmic Foundations of Differential Privacy

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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                September 2020
                30 September 2020
                : 22
                : 9
                : e22845
                Affiliations
                [1 ] Department of Communication University of California, Davis Davis, CA United States
                [2 ] Department of Public Health Sciences University of California, Davis Davis, CA United States
                [3 ] Department of Computer Science University of California, Davis Davis, CA United States
                [4 ] Department of Physiological Nursing University of California, San Francisco San Francisco, CA United States
                Author notes
                Corresponding Author: Jingwen Zhang jwzzhang@ 123456ucdavis.edu
                Author information
                https://orcid.org/0000-0003-1733-6857
                https://orcid.org/0000-0002-7829-8535
                https://orcid.org/0000-0003-3935-663X
                https://orcid.org/0000-0002-1524-5890
                https://orcid.org/0000-0002-2245-9264
                Article
                v22i9e22845
                10.2196/22845
                7557439
                32996892
                9297cffc-85a2-4e61-816b-84689a2affd3
                ©Jingwen Zhang, Yoo Jung Oh, Patrick Lange, Zhou Yu, Yoshimi Fukuoka. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 30.09.2020.

                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 http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 27 July 2020
                : 19 August 2020
                : 3 September 2020
                : 17 September 2020
                Categories
                Viewpoint
                Viewpoint

                Medicine
                chatbot,conversational agent,artificial intelligence,physical activity,diet,intervention,behavior change,natural language processing,communication

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