Evidence map›Paper›PMID 36407814›Full record

ArticleEmpirical software engineering2023

On the privacy of mental health apps: An empirical investigation and its implications for app development.

Leonardo Horn Iwaya, M Ali Babar, Awais Rashid, Chamila Wijayarathna

Open access · greenAbstract read
In one paragraph

Article in Empirical software engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
–field-weighted citation impact, top 85% 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

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 0 citations in OpenAlex.

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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

4 authors at 5 institutions in 5 countries.

Leonardo Horn IwayaCentre for Research on Engineering Software Technologies, The University of Adelaide, Adelaide, SA 5005 Australia.ORCID 0000-0001-9005-0543
M Ali BabarCentre for Research on Engineering Software Technologies, The University of Adelaide, Adelaide, SA 5005 Australia.
Awais RashidBristol Cyber Security Group, Department of Computer Science, University of Bristol, Bristol, UK.
Chamila WijayarathnaCentre for Research on Engineering Software Technologies, The University of Adelaide, Adelaide, SA 5005 Australia.
Academic Medical Center · NLAZ Groeninge · BENew York University · USSahlgrenska University Hospital · SEUniversitat de Barcelona · ES

Funding

Austrian Science Fund FWF P 24079Austrian Science Fund FWF P 28858British Heart Foundation PG/15/37/31438Medical Research Council G0701197Medical Research Council MC_U122665002Medical Research Council MR/K023616/1
6 · The paper itself

Abstract

An increasing number of mental health services are now offered through mobile health (mHealth) systems, such as in mobile applications (apps). Although there is an unprecedented growth in the adoption of mental health services, partly due to the COVID-19 pandemic, concerns about data privacy risks due to security breaches are also increasing. Whilst some studies have analyzed mHealth apps from different angles, including security, there is relatively little evidence for data privacy issues that may exist in mHealth apps used for mental health services, whose recipients can be particularly vulnerable. This paper reports an empirical study aimed at systematically identifying and understanding data privacy incorporated in mental health apps. We analyzed 27 top-ranked mental health apps from Google Play Store. Our methodology enabled us to perform an in-depth privacy analysis of the apps, covering static and dynamic analysis, data sharing behaviour, server-side tests, privacy impact assessment requests, and privacy policy evaluation. Furthermore, we mapped the findings to the LINDDUN threat taxonomy, describing how threats manifest on the studied apps. The findings reveal important data privacy issues such as unnecessary permissions, insecure cryptography implementations, and leaks of personal data and credentials in logs and web requests. There is also a high risk of user profiling as the apps' development do not provide foolproof mechanisms against linkability, detectability and identifiability. Data sharing among 3rd-parties and advertisers in the current apps' ecosystem aggravates this situation. Based on the empirical findings of this study, we provide recommendations to be considered by different stakeholders of mHealth apps in general and apps developers in particular. We conclude that while developers ought to be more knowledgeable in considering and addressing privacy issues, users and health professionals can also play a role by demanding privacy-friendly apps. Supplementary Information: The online version contains supplementary material available at 10.1007/s10664-022-10236-0.

Indexed as

AndroidEmpirical studyMental health appsMobile healthPrivacyPrivacy by designSecurity

Identifiers

PMID36407814
PMCPMC9643945
OpenAlexW3159702886

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.