Evidence map›Paper›PMID 33656442›Full record

ArticleJMIR medical informatics2021

A Chatbot for Perinatal Women's and Partners' Obstetric and Mental Health Care: Development and Usability Evaluation Study.

Kyungmi Chung, Hee Young Cho, Jin Young Park

Abstract read
In one paragraph

Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 1 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.

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

35 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  4. AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 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

3 authors.

Kyungmi ChungDepartment of Psychiatry, Yonsei University College of Medicine, Yongin Severance Hospital, Yonsei University Health System, Yongin-si, Republic of Korea.ORCID https://orcid.org/0000-0003-2754-7631
Hee Young ChoDepartment of Obstetrics and Gynecology, CHA Gangnam Medical Center, CHA University, Seoul, Republic of Korea.ORCID https://orcid.org/0000-0001-7064-5056
Jin Young ParkDepartment of Psychiatry, Yonsei University College of Medicine, Yongin Severance Hospital, Yonsei University Health System, Yongin-si, Republic of Korea.ORCID https://orcid.org/0000-0002-5351-9549

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo motivate people to adopt medical chatbots, the establishment of a specialized medical knowledge database that fits their personal interests is of great importance in developing a chatbot for perinatal care, particularly with the help of health professionals.

objectiveThe objectives of this study are to develop and evaluate a user-friendly question-and-answer (Q&A) knowledge database-based chatbot (Dr. Joy) for perinatal women's and their partners' obstetric and mental health care by applying a text-mining technique and implementing contextual usability testing (UT), respectively, thus determining whether this medical chatbot built on mobile instant messenger (KakaoTalk) can provide its male and female users with good user experience.

methodsTwo men aged 38 and 40 years and 13 women aged 27 to 43 years in pregnancy preparation or different pregnancy stages were enrolled. All participants completed the 7-day-long UT, during which they were given the daily tasks of asking Dr. Joy at least 3 questions at any time and place and then giving the chatbot either positive or negative feedback with emoji, using at least one feature of the chatbot, and finally, sending a facilitator all screenshots for the history of the day's use via KakaoTalk before midnight. One day after the UT completion, all participants were asked to fill out a questionnaire on the evaluation of usability, perceived benefits and risks, intention to seek and share health information on the chatbot, and strengths and weaknesses of its use, as well as demographic characteristics.

resultsDespite the relatively higher score of ease of learning (EOL), the results of the Spearman correlation indicated that EOL was not significantly associated with usefulness (ρ=0.26; P=.36), ease of use (ρ=0.19; P=.51), satisfaction (ρ=0.21; P=.46), or total usability scores (ρ=0.32; P=.24). Unlike EOL, all 3 subfactors and the total usability had significant positive associations with each other (all ρ>0.80; P<.001). Furthermore, perceived risks exhibited no significant negative associations with perceived benefits (ρ=-0.29; P=.30) or intention to seek (SEE; ρ=-0.28; P=.32) or share (SHA; ρ=-0.24; P=.40) health information on the chatbot via KakaoTalk, whereas perceived benefits exhibited significant positive associations with both SEE and SHA. Perceived benefits were more strongly associated with SEE (ρ=0.94; P<.001) than with SHA (ρ=0.70; P=.004).

conclusionsThis study provides the potential for the uptake of this newly developed Q&A knowledge database-based KakaoTalk chatbot for obstetric and mental health care. As Dr. Joy had quality contents with both utilitarian and hedonic value, its male and female users could be encouraged to use medical chatbots in a convenient, easy-to-use, and enjoyable manner. To boost their continued usage intention for Dr. Joy, its Q&A sets need to be periodically updated to satisfy user intent by monitoring both male and female user utterances.

Indexed as

chatbotinstant messagingmobile healthmobile phoneperinatal careusabilityusability testinguser experience

Identifiers

PMID33656442
PMCPMC7970298

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.