Evidence map›Paper›PMID 25786060›Full record

ArticleJMIR mHealth and uHealth2015

mHealthApps: A Repository and Database of Mobile Health Apps.

Wenlong Xu, Yin Liu

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing 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

60 citing papers in PubMed, 3 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

2 authors.

Wenlong XuDepartment of Neurobiology and Anatomy, University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID http://orcid.org/0000-0001-7632-3269
Yin Liu

Funding

Bayesian Methods in Signal Transduction Network AnalysisR01LM010022 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI LIU, YIN · 2011 to 2014
$901k
NLM NIH HHS R01 LM010022
6 · The paper itself

Abstract

backgroundThe market of mobile health (mHealth) apps has rapidly evolved in the past decade. With more than 100,000 mHealth apps currently available, there is no centralized resource that collects information on these health-related apps for researchers in this field to effectively evaluate the strength and weakness of these apps.

objectiveThe objective of this study was to create a centralized mHealth app repository. We expect the analysis of information in this repository to provide insights for future mHealth research developments.

methodsWe focused on apps from the two most established app stores, the Apple App Store and the Google Play Store. We extracted detailed information of each health-related app from these two app stores via our python crawling program, and then stored the information in both a user-friendly array format and a standard JavaScript Object Notation (JSON) format.

resultsWe have developed a centralized resource that provides detailed information of more than 60,000 health-related apps from the Apple App Store and the Google Play Store. Using this information resource, we analyzed thousands of apps systematically and provide an overview of the trends for mHealth apps.

conclusionsThis unique database allows the meta-analysis of health-related apps and provides guidance for research designs of future apps in the mHealth field.

Indexed as

app databaseapp repositorymobile healtlh

Identifiers

PMID25786060
PMCPMC4382566

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.