Evidence map›Paper›PMID 35234655›Full record

SynthesisJMIR mHealth and uHealth2022

mHealth Interventions for Self-management of Hypertension: Framework and Systematic Review on Engagement, Interactivity, and Tailoring.

Weidan Cao, M Wesley Milks, Xiaofu Liu, Megan E Gregory, Daniel Addison, Ping Zhang, Lang Li

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in JMIR mHealth and uHealth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 8 pooled it
21.7field-weighted citation impact, top 1% of its field
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

59 citing papers in PubMed, 8 syntheses or guidelines pooled it, 85 citations in OpenAlex.

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  9. Application effects of information-based health management combined with knowledge-attitude-practice health education model in hypertensive patients.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas · 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

7 authors at 1 institution in 1 country.

Weidan CaoDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, United States.ORCID 0000-0001-5417-2121
M Wesley MilksDivision of Cardiovascular Medicine, The Ohio State University College of Medicine, Columbus, OH, United States.ORCID 0000-0001-6141-4870
Xiaofu LiuDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, United States.ORCID 0000-0002-5721-091X
Megan E GregoryDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, United States.ORCID 0000-0002-6888-6886
Daniel AddisonDivision of Cardiovascular Medicine, The Ohio State University College of Medicine, Columbus, OH, United States.ORCID 0000-0002-9113-8333
Ping ZhangDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, United States.ORCID 0000-0002-4601-0779
Lang LiDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, United States.ORCID 0000-0002-0746-1809
The Ohio State University · US

Funding

The Indiana University-Ohio State University Maternal and Pediatric Precision in Therapeutics Data, Model, Knowledge, and Research Coordination Center (IU-OSU MPRINT DMKRCC)P30HD106451 · NICHD · INDIANA UNIVERSITY INDIANAPOLIS · PI Sara K Quinney · 2021 to 2026
$24.1M
Novel patient biomarkers and mechanisms of TKI associated CardiotoxicityR01HL170038 · NHLBI · UT SOUTHWESTERN MEDICAL CENTER · PI Daniel Addison · 2023 to 2026
$2.7M
An informatics bridge over the valley of death for cancer Phase I trials of drug-combination therapiesU01CA248240 · NCI · OHIO STATE UNIVERSITY · PI LI, LANG · 2021 to 2023
$1.1M
Early Detection and Mechanisms of Cancer Immunotherapy Associated CardiotoxicityK23HL155890 · NHLBI · OHIO STATE UNIVERSITY · PI ADDISON, DANIEL · 2021 to 2024
$663k
NCI NIH HHS U01 CA248240NHLBI NIH HHS K23 HL155890NHLBI NIH HHS R01 HL170038NICHD NIH HHS P30 HD106451
6 · The paper itself

Abstract

backgroundEngagement is essential for the effectiveness of digital behavior change interventions. Existing systematic reviews examining hypertension self-management interventions via mobile apps have primarily focused on intervention efficacy and app usability. Engagement in the prevention or management of hypertension is largely unknown.

objectiveThis systematic review explores the definition and role of engagement in hypertension-focused mobile health (mHealth) interventions, as well as how determinants of engagement (ie, tailoring and interactivity) have been implemented.

methodsA systematic review of mobile app interventions for hypertension self-management targeting adults, published from 2013 to 2020, was conducted. A total of 21 studies were included in this systematic review.

resultsThe engagement was defined or operationalized as a microlevel concept, operationalized as interaction with the interventions (ie, frequency of engagement, time or duration of engagement with the program, and intensity of engagement). For all 3 studies that tested the relationship, increased engagement was associated with better biomedical outcomes (eg, blood pressure change). Interactivity was limited in digital behavior change interventions, as only 7 studies provided 2-way communication between users and a health care professional, and 9 studies provided 1-way communication in possible critical conditions; that is, when abnormal blood pressure values were recorded, users or health care professionals were notified. The tailoring of interventions varied at different aspects, from the tailoring of intervention content (including goals, patient education, advice and feedback from health professionals, reminders, and motivational messages) to the tailoring of intervention dose and communication mode. Tailoring was carried out in a number of ways, considering patient characteristics such as goals, preferences, disease characteristics (eg, hypertension stage and medication list), disease self-management experience levels, medication adherence rate, and values and beliefs.

conclusionsAvailable studies support the importance of engagement in intervention effectiveness as well as the essential roles of patient factors in tailoring, interactivity, and engagement. A patient-centered engagement framework for hypertension self-management using mHealth technology is proposed here, with the intent of facilitating intervention design and disease self-management using mHealth technology.

Indexed as

HypertensionMobile ApplicationsSelf-ManagementTelemedicineAdultBiomedical TechnologyHumansdigital behavior changeengagementhypertensioninteractivityinterventionsmHealthmobile appmobile phonesystematic reviewtailoring

Identifiers

PMID35234655
PMCPMC8928043
OpenAlexW4214872470

What OpenQuestion 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 OpenQuestion graph.