Evidence map›Paper›PMID 32996892›Full record

ArticleJournal of medical Internet research2020

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

Jingwen Zhang, Yoo Jung Oh, Patrick Lange, Zhou Yu, Yoshimi Fukuoka

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
112citing papers in PubMed, 9 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

112 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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52 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jingwen ZhangDepartment of Communication, University of California, Davis, Davis, CA, United States.ORCID 0000-0003-1733-6857
Yoo Jung OhDepartment of Communication, University of California, Davis, Davis, CA, United States.ORCID 0000-0002-7829-8535
Patrick LangeDepartment of Computer Science, University of California, Davis, Davis, CA, United States.ORCID 0000-0003-3935-663X
Zhou YuDepartment of Computer Science, University of California, Davis, Davis, CA, United States.ORCID 0000-0002-1524-5890
Yoshimi FukuokaDepartment of Physiological Nursing, University of California, San Francisco, San Francisco, CA, United States.ORCID 0000-0002-2245-9264

Funding

Mentoring and research in applying digital technologies to prevent chronic IllnessesK24NR015812 · NINR · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FUKUOKA, YOSHIMI · 2015 to 2020
$930k
NINR NIH HHS K24 NR015812
6 · The paper itself

Abstract

backgroundChatbots 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.

objectiveThe 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.

methodsWe 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.

resultsOur 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.

conclusionsAs 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.

Indexed as

Artificial IntelligenceBehavior TherapyCommunicationDiet, HealthyExerciseHumansTelemedicineartificial intelligencebehavior changechatbotcommunicationconversational agentdietinterventionnatural language processingphysical activity

Identifiers

PMID32996892
PMCPMC7557439

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.