Evidence map›Paper›PMID 39169305›Full record

ArticleBMC public health2024

The acceptability and effectiveness of artificial intelligence-based chatbot for hypertensive patients in community: protocol for a mixed-methods study.

Ping Chen, Yi Li, Xuxi Zhang, Xinglin Feng, Xinying Sun

Abstract readClinical Trial Protocol
In one paragraph

Article in BMC public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

  1. Pooled it
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  3. Review
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  5. The role of artificial intelligence in hypertension management.Current opinion in nephrology and hypertension · 2026
    Review
  6. Article
  7. Article
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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

5 authors.

Ping ChenDepartment of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China.
Yi LiCenter of medical informatics, Peking University, Beijing, China.
Xuxi ZhangDepartment of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China.
Xinglin FengDepartment of Health Policy and Management, School of Public Health, Peking University, Beijing, China. fxl@bjmu.edu.cn.
Xinying SunDepartment of Social Medicine and Health Education, School of Public Health, Peking University, Beijing, China. xysun@bjmu.edu.cn.

Funding

Beijing Municipal Natural Science Foundation 7202087National Key Research and Development Program of China 2022YFC3600900
6 · The paper itself

Abstract

backgroundChatbots can provide immediate assistance tailored to patients' needs, making them suitable for sustained accompanying interventions. Nevertheless, there is currently no evidence regarding their acceptability by hypertensive patients and the factors influencing the acceptability in the real-world. Existing evaluation scales often focus solely on the technology itself, overlooking the patients' perspective. Utilizing mixed methods can offer a more comprehensive exploration of influencing factors, laying the groundwork for the future integration of artificial intelligence in chronic disease management practices.

methodsThe mixed methods will provide a holistic view to understand the effectiveness and acceptability of the intervention. Participants will either receive the standard primary health care or obtain a chatbot speaker. The speaker can provide timely reminders, on-demand consultations, personalized data recording, knowledge broadcasts, as well as entertainment features such as telling jokes. The quantitative part will be conducted as a quasi-randomized controlled trial in community in Beijing. And the convergent design will be adopted. When patients use the speaker for 1 month, scales will be used to measure patients' intention to use the speaker. At the same time, semi-structured interviews will be conducted to explore patients' feelings and influencing factors of using speakers. Data on socio-demography, physical examination, blood pressure, acceptability and self-management behavior will be collected at baseline, as well as 1,3,6, and 12 months later. Furthermore, the cloud database will continuously collect patients' interactions with the speaker. The primary outcome is the efficacy of the chatbot on blood pressure control. The secondary outcome includes the acceptability of the chatbot speaker and the changes of self-management behavior. DISCUSSION: Artificial intelligence-based chatbot speaker not only caters to patients' self-management needs at home but also effectively organizes intricate and detailed knowledge system for patients with hypertension through a knowledge graph. Patients can promptly access information that aligns with their specific requirements, promoting proactive self-management and playing a crucial role in disease management. This study will serve as a foundation for the application of artificial intelligence technology in chronic disease management, paving the way for further exploration on enhancing the communicative impact of artificial intelligence technology.

trial registrationBiomedical Ethics Committee of Peking University: IRB00001052-21106, 2021/10/14; Clinical Trials: ChiCTR2100050578, 2021/08/29.

Indexed as

Artificial IntelligenceHypertensionAdultChinaFemaleHumansMaleMiddle AgedPatient Acceptance of Health CarePrimary Health CareQualitative ResearchArtificial intelligenceHypertensionMixed-methodsVoice-based chatbot

Identifiers

PMID39169305
PMCPMC11337738

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.