Evidence map›Paper›PMID 34895247›Full record

SynthesisThe international journal of behavioral nutrition and physical activity2021

A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss.

Yoo Jung Oh, Jingwen Zhang, Min-Lin Fang, Yoshimi Fukuoka

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in The international journal of behavioral nutrition and physical activity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
109citing papers in PubMed, 10 pooled it
40.1field-weighted citation impact, top 1% of its field
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

109 citing papers in PubMed, 10 syntheses or guidelines pooled it, 248 citations in OpenAlex.

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  8. Effectiveness and Acceptability of Conversational Agents for Smoking Cessation: A Systematic Review and Meta-analysis.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023
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  13. Artificial Intelligence Interventions Targeting Obesity-Related Behaviors: Protocol for a Scoping Review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
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49 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

4 authors at 2 institutions in 1 country.

Yoo Jung OhDepartment of Communication, University of California Davis, Davis, USA. yjeoh@ucdavis.edu.ORCID 0000-0002-7829-8535
Jingwen ZhangDepartment of Communication, University of California Davis, Davis, USA.
Min-Lin FangEducation and Research Services, University of California, San Francisco (UCSF) Library, UCSF, San Francisco, USA.
Yoshimi FukuokaDepartment of Physiological Nursing, UCSF, San Francisco, USA.
University of California, Davis · USUniversity of California, San Francisco · US

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

backgroundThis systematic review aimed to evaluate AI chatbot characteristics, functions, and core conversational capacities and investigate whether AI chatbot interventions were effective in changing physical activity, healthy eating, weight management behaviors, and other related health outcomes.

methodsIn collaboration with a medical librarian, six electronic bibliographic databases (PubMed, EMBASE, ACM Digital Library, Web of Science, PsycINFO, and IEEE) were searched to identify relevant studies. Only randomized controlled trials or quasi-experimental studies were included. Studies were screened by two independent reviewers, and any discrepancy was resolved by a third reviewer. The National Institutes of Health quality assessment tools were used to assess risk of bias in individual studies. We applied the AI Chatbot Behavior Change Model to characterize components of chatbot interventions, including chatbot characteristics, persuasive and relational capacity, and evaluation of outcomes.

resultsThe database search retrieved 1692 citations, and 9 studies met the inclusion criteria. Of the 9 studies, 4 were randomized controlled trials and 5 were quasi-experimental studies. Five out of the seven studies suggest chatbot interventions are promising strategies in increasing physical activity. In contrast, the number of studies focusing on changing diet and weight status was limited. Outcome assessments, however, were reported inconsistently across the studies. Eighty-nine and thirty-three percent of the studies specified a name and gender (i.e., woman) of the chatbot, respectively. Over half (56%) of the studies used a constrained chatbot (i.e., rule-based), while the remaining studies used unconstrained chatbots that resemble human-to-human communication.

conclusionChatbots may improve physical activity, but we were not able to make definitive conclusions regarding the efficacy of chatbot interventions on physical activity, diet, and weight management/loss. Application of AI chatbots is an emerging field of research in lifestyle modification programs and is expected to grow exponentially. Thus, standardization of designing and reporting chatbot interventions is warranted in the near future. SYSTEMATIC REVIEW REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO): CRD42020216761 .

Indexed as

Artificial IntelligenceDiet, HealthyExerciseFemaleHumansWeight LossArtificial intelligenceChatbotConversational agentDietNutritionPhysical activitySedentary behaviorSystematic reviewWeight lossWeight maintenance

Identifiers

PMID34895247
PMCPMC8665320
OpenAlexW4200131949

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.