Evidence map›Paper›PMID 32357368›Full record

SynthesisJournal of the American Medical Informatics Association : JAMIA2020

A systematic review of mobile health technologies to support self-management of concurrent diabetes and hypertension.

Wonchan Choi, Shengang Wang, Yura Lee, Hyunkyoung Oh, Zhi Zheng

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 3 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

30 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Digital Patient Experience: Umbrella Systematic Review.Journal of medical Internet research · 2022
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  11. Informing the Design of Individualized Self-Management Regimens from the Human, Data, and Machine Learning Perspectives.ACM transactions on computer-human interaction : a publication of the Association for Computing Machinery · 2025
    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.

Wonchan ChoiSchool of Information Studies, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
Shengang WangSchool of Information Studies, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
Yura LeeDepartment of Social Work, Helen Bader School of Social Welfare, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
Hyunkyoung OhCollege of Nursing, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
Zhi ZhengKate Gleason College of Engineering, Rochester Institute of Technology, Rochester, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis article reports results from a systematic literature review of the current state of mobile health (mHealth) technologies that have the potential to support self-management for people with diabetes and hypertension. The review aims to (a) characterize mHealth technologies used or described in the mHealth literature and (b) summarize their effects on self-management for people with diabetes and hypertension from the clinical and technical standpoints. MATERIALS AND

methodsA systematic literature review was conducted following PRISMA guidelines. Online databases were searched in September 2018 to identify eligible studies for review that had been published since 2007, the start of the smartphone era. Data were extracted from included studies based on the PICOS framework.

resultsOf the 11 studies included for in-depth review, 5 were clinical research examining patient health outcomes and 6 were technology-focused studies examining users' experiences with mHealth technologies under development. The most frequently used mHealth technology features involved self-management support (n = 11) followed by decision support (n = 6) and shared decision-making (n = 6). Most clinical studies reported benefits associated with mHealth interventions. These included reported improvements in objectively measured patient health outcomes (n = 3) and perceptual or behavioral outcomes (n = 4). DISCUSSION: Although most studies reported promising results in terms of the effects of mHealth interventions on patient health outcomes and experience, the strength of evidence was limited by the study designs.

conclusionMore randomized clinical trials are needed to examine the promise and limitations of mHealth technologies as assistive tools to facilitate the self-management of highly prevalent comorbidity of chronic conditions, such as diabetes and hypertension.

Indexed as

Self-ManagementTelemedicineDiabetes ComplicationsDiabetes MellitusHumansHypertensiondiabetes mellitushypertensionmHealthmobile healthmultiple chronic conditions

Identifiers

PMID32357368
PMCPMC7647310

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.