Evidence map›Paper›PMID 42818756›Full record

SynthesisFrontiers in digital health2026

Effectiveness of chatbot interventions in individuals for hypertension management: a systematic review and narrative synthesis.

Shahwar Fatima Ansari, Noor A Abu Saifan, Reem Khalifa Alneyadi, Aleena Hassan, Yanli Liu, Soundarya Ashok, Javaid Nauman, Luai A Ahmed, Reem Al Sheryani, Azhar T Rahma

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in digital health, 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

10 authors.

Shahwar Fatima Ansari *Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Noor A Abu Saifan *Institute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Reem Khalifa AlneyadiInstitute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Aleena HassanInstitute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Yanli LiuInstitute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Soundarya AshokInstitute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Javaid NaumanInstitute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Luai A AhmedInstitute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Reem Al SheryaniCollege of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Azhar T RahmaInstitute of Public Health, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Hypertension is a major global risk factor for cardiovascular disease and requires long-term management. Recently, digital health tools such as chatbots have been introduced to support patient self-management. This systematic review aims to assess the effectiveness of chatbot interventions designed for individual hypertension management evaluating their impact on clinical outcomes, primarily changes in systolic and diastolic blood pressure and to explore the improvement in knowledge and self-management skills, as well as implementation outcomes such as feasibility, acceptability, and adoption in routine healthcare settings. Materials and methods: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The research question was structured by the PICOT framework, focusing on patients with hypertension or elevated Blood Pressure (BP) receiving chatbot-based interventions compared with usual care or other digital approaches, with outcomes including changes in systolic and diastolic BP, medication adherence, behaviour changes, implementation outcomes, and improvement in knowledge and self-management skills. A literature search was conducted in PubMed, Embase, Scopus and Web of Science for studies published from 2015 onwards, identifying 316 studies, of which 177 underwent title and abstract screening and 9 were finally included. Two independent reviewers performed screening and full-text review and assessed quality using Covidence. Inter-Rater Reliability (IRR) was assessed using pairwise Cohen's Kappa ( Results: Evidence is heterogeneous and mostly low-to-moderate certainty. Chatbot interventions were associated with improvements in both Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), medication adherence, and adherence to home BP monitoring. Improvements in self-monitoring and engagement were more consistent across studies, while BP and medication adherence outcomes varied, with some studies reporting limited or non-significant effects. Discussion/conclusion: Chatbot interventions show potential as an accessible tool to support hypertension self-management. They may help improve patient engagement and monitoring behaviours, however, their effects on BP and medication adherence vary and may depend on intervention design and how they are used in practice. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251172042, PROSPERO CRD420251172042.

Indexed as

artificial intelligenceblood pressurechatboteffectivenesshypertensionimplementation

Identifiers

PMID42818756
PMCPMC13623939

What OpenQuestion holds

Textmetadata
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.