Evidence map›Paper›PMID 40686624›Full record

ArticleMayo Clinic proceedings. Digital health2025

Healthy Heart Assistant, a WhatsApp-Based Generative Pretrained Transformer Technology, for Self-Care in Hypertensive Patients.

Samuel E Antia, Collins N Ugwu, Vishal Ghodka, Babangida S Chori, Muhammad S Nazir, Chizoba A Odili, Godsent C Isiguzo, Sri Vasireddy, Augustine N Odili

Abstract read
In one paragraph

Article in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
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

9 authors.

Samuel E AntiaDepartment of Internal Medicine, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi, Nigeria.
Collins N UgwuDepartment of Internal Medicine, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi, Nigeria.
Vishal GhodkaREAN Foundation, Leesburg, VA.
Babangida S ChoriCirculatory Health Research Laboratory, University of Abuja, Nigeria.
Muhammad S NazirGeneral Amadi Rimi Specialist Hospital/Federal Teaching Hospital, Katsina, Nigeria.
Chizoba A OdiliInstitute of Education, University of Abuja, Nigeria.
Godsent C IsiguzoDepartment of Internal Medicine, Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Ebonyi, Nigeria.
Sri VasireddyREAN Foundation, Leesburg, VA.
Augustine N OdiliCirculatory Health Research Laboratory, University of Abuja, Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To evaluate the feasibility, usability, and efficacy of innovative generative pretrained transformer chatbot in improving self-care in hypertensive patients in a resource-limited setting. Patients and Methods: A single-arm nonblinded clinical trial was deployed in a busy cardiology clinic in a low-resource setting. Artificial intelligence-enabled chatbot (Healthy Heart Assistant) was activated in smartphones of 50 adults on treatment for hypertension. Participants were trained on how to use the Healthy Heart Assistant including setting medication and appointment reminders. Baseline questionnaires were administered at enrollment and 30 days later to explore acceptability, feasibility and usability of the bot. We used chatbot usability questionnaire and self-made Healthy Heart Assistant satisfaction questionnaire to assess bot usability and patients' satisfaction, respectively. The study began on April 5, 2024, through July 15, 2024. Results: Of 200 hypertensive clinic attendees, 70 (35%) had internet-enabled bot-compatible cell phones, of which 50 hypertensive patients were recruited to participate in the study. Among 50 participants, 2 (4%) were lost to follow-up; 19 (39.6%) were women; and 40 (83.3%) had attained tertiary level of education. Mean time of training to use bot was 5.7 minutes, with 35 (70.8%) of participants being able to use the bot within 5 minutes. The median frequency of chats for participants within the timeframe was an average of 1.5 chats/day. Chatbot usability questionnaire score was 69.5%, whereas self-made Healthy Heart Assistant satisfaction questionnaire score was 90%. Conclusion: This proof-of-concept study shows that generative artificial intelligence can be applied with reasonable success in hypertension self-care in low-resource settings and has potential for being effective.

Identifiers

PMID40686624
PMCPMC12271433

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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.