Evidence map›Paper›PMID 36826990›Full record

SynthesisJournal of medical Internet research2023

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

Abhishek Aggarwal, Cheuk Chi Tam, Dezhi Wu, Xiaoming Li, Shan Qiao

Registry-linked trialFull text readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07004231 (Using Gout Buddy to Enhance Dietary Literacy and Gout Management-A Mixed Method Clinical Trial), which is not on this map. Cited by 280 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
280citing papers in PubMed, 14 pooled it
–field-weighted citation impact
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.

NCT07004231 naactive not recruitingnot on this mapstarted 2025, after this paper: background citation

Using Gout Buddy to Enhance Dietary Literacy and Gout Management-A Mixed Method Clinical Trial

TypeinterventionalSponsorSingHealth PolyclinicsRan2025 to 2026Enrolled110ConditionsGoutArmsGOUT Buddy, an AI chatbot
3 · Its place in the literature

Who cites it

280 citing papers in PubMed, 14 syntheses or guidelines pooled it.

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220 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.

Abhishek AggarwalDepartment of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID 0000-0002-9808-8084
Cheuk Chi TamDepartment of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID 0000-0003-2612-0564
Dezhi WuUofSC Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, United States.ORCID 0000-0002-3554-1136
Xiaoming LiDepartment of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID 0000-0002-5555-9034
Shan QiaoDepartment of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.ORCID 0000-0003-1834-1834

Funding

Multilevel Determinants of Racial and Ethnic Disparities in Maternal Morbidity and Mortality in the Context of COVID-19 PandemicR01AI127203 · NIAID · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI LI, XIAOMING, LIU, JIHONG · 2017 to 2021
$5.2M
NIAID NIH HHS R01 AI127203
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI)-based chatbots can offer personalized, engaging, and on-demand health promotion interventions.

objectiveThe aim of this systematic review was to evaluate the feasibility, efficacy, and intervention characteristics of AI chatbots for promoting health behavior change.

methodsA 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.

resultsOf 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.

conclusionsAI 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.

Indexed as

Artificial IntelligenceHealth PromotionDelivery of Health CareHealth BehaviorHumansSoftwareacceptabilityAIartificial intelligencechatbotefficacyengagementfeasibilityhealth behavior changeinterventionmobile phoneusability

Identifiers

PMID36826990
PMCPMC10007007

What Socratic holds

Textfull text, public
LicenceCC BY
measurements read187
Read underepoch 390

Registered trials

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 Socratic graph.