Evidence map›Paper›PMID 38113097›Full record

ArticleJournal of medical Internet research2023

Evaluation of the Current State of Chatbots for Digital Health: Scoping Review.

Jia Xue, Bolun Zhang, Yaxi Zhao, Qiaoru Zhang, Chengda Zheng, Jielin Jiang, Hanjia Li, Nian Liu, Ziqian Li, Weiying Fu and 4 more

Open access · goldAbstract readScoping Review
In one paragraph

Article 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. Cited by 46 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 3 pooled it
12.9field-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

46 citing papers in PubMed, 3 syntheses or guidelines pooled it, 74 citations in OpenAlex.

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  15. GARDE-Chat: a scalable, open-source platform for building and deploying health chatbots.Journal of the American Medical Informatics Association : JAMIA · 2026
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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

14 authors at 3 institutions in 2 countries.

Jia XueFactor Inwentash Faculty of Social Work, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-1668-2531
Bolun ZhangFaculty of Information, University of Toronto, Toronto, ON, Canada.ORCID 0000-0003-0767-8239
Yaxi Zhao *Faculty of Information, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-4149-4241
Qiaoru Zhang *Artificial Intelligence for Justice Lab, University of Toronto, Toronto, ON, Canada.ORCID 0009-0003-2044-7838
Chengda Zheng *Artificial Intelligence for Justice Lab, University of Toronto, Toronto, ON, Canada.ORCID 0000-0001-7696-3581
Jielin Jiang *Artificial Intelligence for Justice Lab, University of Toronto, Toronto, ON, Canada.ORCID 0009-0001-6948-2015
Hanjia LiArtificial Intelligence for Justice Lab, University of Toronto, Toronto, ON, Canada.ORCID 0009-0002-7368-7600
Nian LiuArtificial Intelligence for Justice Lab, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-5477-1594
Ziqian LiArtificial Intelligence for Justice Lab, University of Toronto, Toronto, ON, Canada.ORCID 0009-0002-0612-9908
Weiying FuArtificial Intelligence for Justice Lab, University of Toronto, Toronto, ON, Canada.ORCID 0009-0007-2096-5327
Yingdong PengArtificial Intelligence for Justice Lab, University of Toronto, Toronto, ON, Canada.ORCID 0009-0002-3190-8268
Judith LoganJohn P Robarts Library, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-8112-7999
Jingwen ZhangDepartment of Communication, University of California Davis, Davis, CA, United States.ORCID 0000-0003-1733-6857
Xiaoling XiangSchool of Social Work, University of Michigan, Ann Arbor, MI, United States.ORCID 0000-0002-4926-4707
University of Toronto · CAUniversity of California, Davis · USUniversity of Michigan · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatbots have become ubiquitous in our daily lives, enabling natural language conversations with users through various modes of communication. Chatbots have the potential to play a significant role in promoting health and well-being. As the number of studies and available products related to chatbots continues to rise, there is a critical need to assess product features to enhance the design of chatbots that effectively promote health and behavioral change.

objectiveThis scoping review aims to provide a comprehensive assessment of the current state of health-related chatbots, including the chatbots' characteristics and features, user backgrounds, communication models, relational building capacity, personalization, interaction, responses to suicidal thoughts, and users' in-app experiences during chatbot use. Through this analysis, we seek to identify gaps in the current research, guide future directions, and enhance the design of health-focused chatbots.

methodsFollowing the scoping review methodology by Arksey and O'Malley and guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist, this study used a two-pronged approach to identify relevant chatbots: (1) searching the iOS and Android App Stores and (2) reviewing scientific literature through a search strategy designed by a librarian. Overall, 36 chatbots were selected based on predefined criteria from both sources. These chatbots were systematically evaluated using a comprehensive framework developed for this study, including chatbot characteristics, user backgrounds, building relational capacity, personalization, interaction models, responses to critical situations, and user experiences. Ten coauthors were responsible for downloading and testing the chatbots, coding their features, and evaluating their performance in simulated conversations. The testing of all chatbot apps was limited to their free-to-use features.

resultsThis review provides an overview of the diversity of health-related chatbots, encompassing categories such as mental health support, physical activity promotion, and behavior change interventions. Chatbots use text, animations, speech, images, and emojis for communication. The findings highlight variations in conversational capabilities, including empathy, humor, and personalization. Notably, concerns regarding safety, particularly in addressing suicidal thoughts, were evident. Approximately 44% (16/36) of the chatbots effectively addressed suicidal thoughts. User experiences and behavioral outcomes demonstrated the potential of chatbots in health interventions, but evidence remains limited.

conclusionsThis scoping review underscores the significance of chatbots in health-related applications and offers insights into their features, functionalities, and user experiences. This study contributes to advancing the understanding of chatbots' role in digital health interventions, thus paving the way for more effective and user-centric health promotion strategies. This study informs future research directions, emphasizing the need for rigorous randomized control trials, standardized evaluation metrics, and user-centered design to unlock the full potential of chatbots in enhancing health and well-being. Future research should focus on addressing limitations, exploring real-world user experiences, and implementing robust data security and privacy measures.

Indexed as

Digital HealthHealth PromotionBenchmarkingChecklistCommunicationHumansRandomized Controlled Trials as Topicartificial intelligencechatbotchatbotsconversational agentconversational agentsconversational capacityexperienceexperienceshealthin-app reviewsmental healthpersonalizationrelational capacityreview methodologyreview methodsscopingsuicidalsuicide

Identifiers

PMID38113097
PMCPMC10762606
OpenAlexW4388981618

What OpenQuestion 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 OpenQuestion graph.