Evidence mapPaperPMID 39928934Full record

ArticleJournal of medical Internet research2025

Trends and Gaps in Digital Precision Hypertension Management: Scoping Review.

Namuun Clifford, Rachel Tunis, Adetimilehin Ariyo, Haoxiang Yu, Hyekyun Rhee, Kavita Radhakrishnan

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. The role of artificial intelligence in hypertension management.Current opinion in nephrology and hypertension · 2026
    Review
  5. Observational
  6. Review
  7. Article
  8. Article
  9. 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

6 authors.

Namuun CliffordSchool of Nursing, The University of Texas at Austin, Austin, TX, United States.ORCID https://orcid.org/0000-0003-2334-3818
Rachel TunisSchool of Information, The University of Texas at Austin, Austin, TX, United States.ORCID https://orcid.org/0000-0001-8362-2005
Adetimilehin AriyoSchool of Nursing, The University of Texas at Austin, Austin, TX, United States.ORCID https://orcid.org/0000-0002-2606-7137
Haoxiang YuDepartment of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States.ORCID https://orcid.org/0000-0002-3518-946X
Hyekyun RheeSchool of Nursing, The University of Texas at Austin, Austin, TX, United States.ORCID https://orcid.org/0000-0001-7039-3215
Kavita RadhakrishnanSchool of Nursing, The University of Texas at Austin, Austin, TX, United States.ORCID https://orcid.org/0000-0002-1373-1633

Funding

NINR NIH HHS T32 NR019035
6 · The paper itself

Abstract

backgroundHypertension (HTN) is the leading cause of cardiovascular disease morbidity and mortality worldwide. Despite effective treatments, most people with HTN do not have their blood pressure under control. Precision health strategies emphasizing predictive, preventive, and personalized care through digital tools offer notable opportunities to optimize the management of HTN.

objectiveThis scoping review aimed to fill a research gap in understanding the current state of precision health research using digital tools for the management of HTN in adults.

methodsThis study used a scoping review framework to systematically search for articles in 5 databases published between 2013 and 2023. The included articles were thematically analyzed based on their precision health focus: personalized interventions, prediction models, and phenotyping. Data were extracted and summarized for study and sample characteristics, precision health focus, digital health technology, disciplines involved, and characteristics of personalized interventions.

resultsAfter screening 883 articles, 46 were included; most studies had a precision health focus on personalized digital interventions (34/46, 74%), followed by prediction models (8/46, 17%) and phenotyping (4/46, 9%). Most studies (38/46, 82%) were conducted in or used data from North America or Europe, and 63% (29/46) of the studies came exclusively from the medical and health sciences, with 33% (15/46) of studies involving 2 or more disciplines. The most commonly used digital technologies were mobile phones (33/46, 72%), blood pressure monitors (18/46, 39%), and machine learning algorithms (11/46, 24%). In total, 45% (21/46) of the studies either did not report race or ethnicity data (14/46, 30%) or partially reported this information (7/46, 15%). For personalized intervention studies, nearly half (14/30, 47%) used 2 or less types of data for personalization, with only 7% (2/30) of the studies using social determinants of health data and no studies using physical environment or digital literacy data. Personalization characteristics of studies varied, with 43% (13/30) of studies using fully automated personalization approaches, 33% (10/30) using human-driven personalization, and 23% (7/30) using a hybrid approach.

conclusionsThis scoping review provides a comprehensive mapping of the literature on the current trends and gaps in digital precision health research for the management of HTN in adults. Personalized digital interventions were the primary focus of most studies; however, the review highlighted the need for more precise definitions of personalization and the integration of more diverse data sources to improve the tailoring of interventions and promotion of health equity. In addition, there were significant gaps in the reporting of race and ethnicity data of participants, underuse of wearable devices for passive data collection, and the need for greater interdisciplinary collaboration to advance precision health research in digital HTN management.

trial registrationOSF Registries osf.io/yuzf8; https://osf.io/yuzf8.

Indexed as

HypertensionPrecision MedicineHumansTelemedicinealgorithmsdigital healthhypertensionmachine learningmobile appsmobile healthpersonalizationphenotypingprecision healthprediction models

Identifiers

PMID39928934
PMCPMC11851032

What Socratic 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 Socratic graph.