Evidence map›Paper›PMID 41840702›Full record

ArticleSystematic reviews2026

Chatbot-based interventions for improvement of diet, physical activity, and tobacco use behaviors: protocol for a systematic review.

Zheng Liu, Yang Yang, Jing Chen, Shang-Hang Zhang, Xiao-Rui Zhang, Yu-Xuan Zhuang, Run-Ze Hu

Abstract read
In one paragraph

Article in Systematic reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Zheng Liu *Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, 100191, China. liuzheng@bjmu.edu.cn.ORCID 0000-0002-0405-2348
Yang Yang *Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, 100191, China.
Jing ChenDepartment of Maternal and Child Health, School of Public Health, Peking University, Beijing, 100191, China.
Shang-Hang ZhangNational Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China.
Xiao-Rui ZhangDepartment of Pediatric, Peking University People's Hospital, Beijing, China.
Yu-Xuan ZhuangDepartment of Maternal and Child Health, School of Public Health, Peking University, Beijing, 100191, China.
Run-Ze HuDepartment of Maternal and Child Health, School of Public Health, Peking University, Beijing, 100191, China.

Funding

Beijing Education Sciences Planning Program during the 14th Five-Year Plan BECA23111National Natural Science Foundation of China 82373694Young Elite Scientists Sponsorship Program by CAST 2023QNRC001
6 · The paper itself

Abstract

backgroundThis systematic review aims to summarize the effectiveness, acceptability, and potential mechanisms of chatbot-based interventions in improving diet, physical activity, and tobacco use behaviors.

methodsThis protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). We will include individual- or cluster-randomized, parallel-group controlled trials that compare chatbot-based interventions with active-control, waiting-list, or usual-control comparators among children, adults, and the elderly irrespective of their behavioral patterns at baseline. We will also include the non-randomized or single-group trials to expand the evidence base. The primary outcomes will be the change in diet, physical activity, and tobacco use behaviors assessed by validated questionnaires or objective measures. The secondary outcomes will include the change in obesity-related outcomes, stage of behavioral change, change of motivation, emotion, knowledge, or other constructs that might mediate the intervention effect, chatbot use behaviors during the process of intervention implementation, the facilitators and barriers to chatbot use, and safety issues. We will search both the studies published in PubMed, EMBASE, ACM Digital Library, Web of Science, PsycINFO, CINAHL, and IEEE and the unpublished studies in the WHO's International Clinical Trials Registry Platform, ClinicalTrials.gov, conference proceedings, GitHub, and arXiv. We will group the included studies based on their consistency in the Population, Intervention, Comparator, Outcome and Study design (PICOS) elements for data synthesis. The random-effects meta-analysis will be used to quantitatively synthesize the results across studies if data permits; otherwise, we will synthesize the study results based on the guideline of Synthesis Without Meta-analysis (SWiM). We will use the correlation-based meta-analytical structural equation modeling approach to examine the presence of mediators of chatbot-based interventions. We will assess the risk of bias for each included study using the revised version of the Cochrane risk-of-bias tool for randomized trials (RoB 2) or the Risk of Bias In Non-randomized Studies of Interventions (ROBINS-I), and appraise the certainty of the evidence for each synthesized result using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach. DISCUSSION: This systematic review will not only answer whether the state-of-the-art chatbot-based interventions are acceptable and effective in changing a person's diet, physical activity, and tobacco use behaviors but also explore the potential mechanisms underlying the effects of the chatbot-based interventions. The findings of this study will pave the way for optimizing future chatbot-based interventions in the field of health-related behaviors. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42023492013.

Indexed as

DietExerciseHealth BehaviorHealth PromotionTobacco UseHumansResearch DesignSystematic Reviews as TopicChatbotDietInterventionPhysical activitySystematic reviewTobacco use

Identifiers

PMID41840702
PMCPMC13107892

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.