Evidence map›Paper›PMID 32459640›Full record

ArticleJMIR mHealth and uHealth2020

Privacy Assessment in Mobile Health Apps: Scoping Review.

Jaime Benjumea, Jorge Ropero, Octavio Rivera-Romero, Enrique Dorronzoro-Zubiete, Alejandro Carrasco

Abstract readScoping Review
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 40 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

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

  1. Pooled it
  2. Quality Assessment of Digital Health Apps: Umbrella Review.Journal of medical Internet research · 2024
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  6. Metadata-Based Privacy Assessment for Mobile mHealth.Sensors (Basel, Switzerland) · 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

5 authors.

Jaime BenjumeaDepartment of Electronic Technology, Universidad de Sevilla, Seville, Spain.ORCID 0000-0002-8608-0544
Jorge RoperoDepartment of Electronic Technology, Universidad de Sevilla, Seville, Spain.ORCID 0000-0001-5445-0646
Octavio Rivera-RomeroDepartment of Electronic Technology, Universidad de Sevilla, Seville, Spain.ORCID 0000-0001-7212-9805
Enrique Dorronzoro-ZubieteDepartment of Electronic Technology, Universidad de Sevilla, Seville, Spain.ORCID 0000-0001-8478-9851
Alejandro CarrascoDepartment of Electronic Technology, Universidad de Sevilla, Seville, Spain.ORCID 0000-0001-9474-3929

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrivacy has always been a concern, especially in the health domain. The proliferation of mobile health (mHealth) apps has led to a large amount of sensitive data being generated. Some authors have performed privacy assessments of mHealth apps. They have evaluated diverse privacy components; however, different authors have used different criteria for their assessments.

objectiveThis scoping review aims to understand how privacy is assessed for mHealth apps, focusing on the components, scales, criteria, and scoring methods used. A simple taxonomy to categorize the privacy assessments of mHealth apps based on component evaluation is also proposed.

methodsWe followed the methodology defined by Arksey and O'Malley to conduct a scoping review. Included studies were categorized based on the privacy component, which was assessed using the proposed taxonomy.

resultsThe database searches retrieved a total of 710 citations-24 of them met the defined selection criteria, and data were extracted from them. Even though the inclusion criteria considered articles published since 2009, all the studies that were ultimately included were published from 2014 onward. Although 12 papers out of 24 (50%) analyzed only privacy, 8 (33%) analyzed both privacy and security. Moreover, 4 papers (17%) analyzed full apps, with privacy being just part of the assessment. The evaluation criteria used by authors were heterogeneous and were based on their experience, the literature, and/or existing legal frameworks. Regarding the set of items used for the assessments, each article defined a different one. Items included app permissions, analysis of the destination, analysis of the content of communications, study of the privacy policy, use of remote storage, and existence of a password to access the app, among many others. Most of the included studies provided a scoring method that enables the comparison of privacy among apps.

conclusionsThe privacy assessment of mHealth apps is a complex task, as the criteria used by different authors for their evaluations are very heterogeneous. Although some studies about privacy assessment have been conducted, a very large set of items to evaluate privacy has been used up until now. In-app information and privacy policies are primarily utilized by the scientific community to extract privacy information from mHealth apps. The creation of a scale based on more objective criteria is a desirable step forward for privacy assessment in the future.

Indexed as

Mobile ApplicationsPrivacyCommunicationHumansResearch DesignTelemedicineappsdata privacymHealthmobile phoneprivacyprivacy assessmentreviewsecurity

Identifiers

PMID32459640
PMCPMC7367524

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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.