Evidence map›Paper›PMID 32716312›Full record

ArticleJMIR mHealth and uHealth2020

Theme Trends and Knowledge Structure on Mobile Health Apps: Bibliometric Analysis.

Cheng Peng, Miao He, Sarah L Cutrona, Catarina I Kiefe, Feifan Liu, Zhongqing Wang

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing 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

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

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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

6 authors.

Cheng PengDepartment of Ophthalmology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China.ORCID 0000-0002-1849-3187
Miao HeDepartment of Information Center, The First Hospital of China Medical University, Shenyang, China.ORCID 0000-0003-0943-0632
Sarah L CutronaDepartment of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States.ORCID 0000-0002-4795-8377
Catarina I KiefeDepartment of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States.ORCID 0000-0001-8719-6963
Feifan LiuDepartment of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, United States.ORCID 0000-0003-0881-6365
Zhongqing WangDepartment of Information Center, The First Hospital of China Medical University, Shenyang, China.ORCID 0000-0002-5330-7538

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDue to the widespread and unprecedented popularity of mobile phones, the use of digital medicine and mobile health apps has seen significant growth. Mobile health apps have tremendous potential for monitoring and treating diseases, improving patient care, and promoting health.

objectiveThis paper aims to explore research trends, coauthorship networks, and the research hot spots of mobile health app research.

methodsPublications related to mobile health apps were retrieved and extracted from the Web of Science database with no language restrictions. Bibliographic Item Co-Occurrence Matrix Builder was employed to extract bibliographic information (publication year and journal source) and perform a descriptive analysis. We then used the VOSviewer (Leiden University) tool to construct and visualize the co-occurrence networks of researchers, research institutions, countries/regions, citations, and keywords.

resultsWe retrieved 2802 research papers on mobile health apps published from 2000 to 2019. The number of annual publications increased over the past 19 years. JMIR mHealth and uHealth (323/2802, 11.53%), Journal of Medical Internet Research (106/2802, 3.78%), and JMIR Research Protocols (82/2802, 2.93%) were the most common journals for these publications. The United States (1186/2802, 42.33%), England (235/2802, 8.39%), Australia (215/2802, 7.67%), and Canada (112/2802, 4.00%) were the most productive countries of origin. The University of California San Francisco, the University of Washington, and the University of Toronto were the most productive institutions. As for the authors' contributions, Schnall R, Kuhn E, Lopez-Coronado M, and Kim J were the most active researchers. The co-occurrence cluster analysis of the top 100 keywords forms 5 clusters: (1) the technology and system development of mobile health apps; (2) mobile health apps for mental health; (3) mobile health apps in telemedicine, chronic disease, and medication adherence management; (4) mobile health apps in health behavior and health promotion; and (5) mobile health apps in disease prevention via the internet.

conclusionsWe summarize the recent advances in mobile health app research and shed light on their research frontier, trends, and hot topics through bibliometric analysis and network visualization. These findings may provide valuable guidance on future research directions and perspectives in this rapidly developing field.

Indexed as

BibliometricsMobile ApplicationsHumansTelemedicinebibliometricsco-word analysisdigital healthdigital medicinemhealthmobile appmobile healthmobile phoneVOSviewer

Identifiers

PMID32716312
PMCPMC7418015

What OpenQuestion holds

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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.