Mobile health and privacy: cross sectional study

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Abstract

Objectives

To investigate whether and what user data are collected by health related mobile applications (mHealth apps), to characterise the privacy conduct of all the available mHealth apps on Google Play, and to gauge the associated risks to privacy.

Design

Cross sectional study

Setting

Health related apps developed for the Android mobile platform, available in the Google Play store in Australia and belonging to the medical and health and fitness categories.

Participants

Users of 20 991 mHealth apps (8074 medical and 12 917 health and fitness found in the Google Play store: in-depth analysis was done on 15 838 apps that did not require a download or subscription fee compared with 8468 baseline non-mHealth apps.

Main outcome measures

Primary outcomes were characterisation of the data collection operations in the apps code and of the data transmissions in the apps traffic; analysis of the primary recipients for each type of user data; presence of adverts and trackers in the app traffic; audit of the app privacy policy and compliance of the privacy conduct with the policy; and analysis of complaints in negative app reviews.

Results

88.0% (n=18 472) of mHealth apps included code that could potentially collect user data. 3.9% (n=616) of apps transmitted user information in their traffic. Most data collection operations in apps code and data transmissions in apps traffic involved external service providers (third parties). The top 50 third parties were responsible for most of the data collection operations in app code and data transmissions in app traffic (68.0% (2140), collectively). 23.0% (724) of user data transmissions occurred on insecure communication protocols. 28.1% (5903) of apps provided no privacy policies, whereas 47.0% (1479) of user data transmissions complied with the privacy policy. 1.3% (3609) of user reviews raised concerns about privacy.

Conclusions

This analysis found serious problems with privacy and inconsistent privacy practices in mHealth apps. Clinicians should be aware of these and articulate them to patients when determining the benefits and risks of mHealth apps.

Related collections

Most cited references 19

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What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions

Sarah J Iribarren, Kenrick Cato, Louise Falzon … (2017)

Background Mobile health (mHealth) is often reputed to be cost-effective or cost-saving. Despite optimism, the strength of the evidence supporting this assertion has been limited. In this systematic review the body of evidence related to economic evaluations of mHealth interventions is assessed and summarized. Methods Seven electronic bibliographic databases, grey literature, and relevant references were searched. Eligibility criteria included original articles, comparison of costs and consequences of interventions (one categorized as a primary mHealth intervention or mHealth intervention as a component of other interventions), health and economic outcomes and published in English. Full economic evaluations were appraised using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist and The PRISMA guidelines were followed. Results Searches identified 5902 results, of which 318 were examined at full text, and 39 were included in this review. The 39 studies spanned 19 countries, most of which were conducted in upper and upper-middle income countries (34, 87.2%). Primary mHealth interventions (35, 89.7%), behavior change communication type interventions (e.g., improve attendance rates, medication adherence) (27, 69.2%), and short messaging system (SMS) as the mHealth function (e.g., used to send reminders, information, provide support, conduct surveys or collect data) (22, 56.4%) were most frequent; the most frequent disease or condition focuses were outpatient clinic attendance, cardiovascular disease, and diabetes. The average percent of CHEERS checklist items reported was 79.6% (range 47.62–100, STD 14.18) and the top quartile reported 91.3–100%. In 29 studies (74.3%), researchers reported that the mHealth intervention was cost-effective, economically beneficial, or cost saving at base case. Conclusions Findings highlight a growing body of economic evidence for mHealth interventions. Although all studies included a comparison of intervention effectiveness of a health-related outcome and reported economic data, many did not report all recommended economic outcome items and were lacking in comprehensive analysis. The identified economic evaluations varied by disease or condition focus, economic outcome measurements, perspectives, and were distributed unevenly geographically, limiting formal meta-analysis. Further research is needed in low and low-middle income countries and to understand the impact of different mHealth types. Following established economic reporting guidelines will improve this body of research.

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Assessment of the Data Sharing and Privacy Practices of Smartphone Apps for Depression and Smoking Cessation

Kit Huckvale, John Torous, Mark Larsen (2019)

Key Points Question Do the privacy policies of popular smartphone applications (apps) for depression and smoking cessation describe accurately whether data will be processed by commercial third parties? Findings In this cross-sectional study of 36 top-ranked apps for depression and smoking cessation available in public app stores, 29 transmitted data to services provided by Facebook or Google, but only 12 accurately disclosed this in a privacy policy. Meaning Health care professionals prescribing apps should not rely on disclosures about data sharing in health app privacy policies but should reasonably assume that data will be shared with commercial entities whose own privacy practices have been questioned and, if possible, should consider only apps with data transmission behaviors that have been subject to direct scrutiny.

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Ibobbly mobile health intervention for suicide prevention in Australian Indigenous youth: a pilot randomised controlled trial

Joseph Tighe, Fiona Shand, Rebecca Ridani … (2017)

Objectives Rates of youth suicide in Australian Indigenous communities are 4 times the national youth average and demand innovative interventions. Historical and persistent disadvantage is coupled with multiple barriers to help seeking. Mobile phone applications offer the opportunity to deliver therapeutic interventions directly to individuals in remote communities. The pilot study aimed to evaluate the effectiveness of a self-help mobile app (ibobbly) targeting suicidal ideation, depression, psychological distress and impulsivity among Indigenous youth in remote Australia. Setting Remote and very remote communities in the Kimberley region of North Western Australia. Participants Indigenous Australians aged 18–35 years. Interventions 61 participants were recruited and randomised to receive either an app (ibobbly) which delivered acceptance-based therapy over 6 weeks or were waitlisted for 6 weeks and then received the app for the following 6 weeks. Primary and secondary outcome measures The primary outcome was the Depressive Symptom Inventory—Suicidality Subscale (DSI-SS) to identify the frequency and intensity of suicidal ideation in the previous weeks. Secondary outcomes were the Patient Health Questionnaire 9 (PHQ-9), The Kessler Psychological Distress Scale (K10) and the Barratt Impulsivity Scale (BIS-11). Results Although preintervention and postintervention changes on the (DSI-SS) were significant in the ibobbly arm (t=2.40; df=58.1; p=0.0195), these differences were not significant compared with the waitlist arm (t=1.05; df=57.8; p=0.2962). However, participants in the ibobbly group showed substantial and statistically significant reductions in PHQ-9 and K10 scores compared with waitlist. No differences were observed in impulsivity. Waitlist participants improved after 6 weeks of app use. Conclusions Apps for suicide prevention reduce distress and depression but do not show significant reductions on suicide ideation or impulsivity. A feasible and acceptable means of lowering symptoms for mental health disorders in remote communities is via appropriately designed self-help apps. Trial registration number ACTRN12613000104752.

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Author and article information

Contributors

Gioacchino Tangari: Role: postdoctoral research fellow

Muhammad Ikram:

ORCID: https://orcid.org/0000-0003-2113-3390

Role: lecturer

Kiran Ijaz: Role: postdoctoral research fellow

Mohamed Ali Kaafar: Role: professor

Shlomo Berkovsky: Role: professor

Journal

Journal ID (nlm-ta): BMJ

Journal ID (iso-abbrev): BMJ

Journal ID (publisher-id): BMJ-UK

Journal ID (hwp): bmj

Title: The BMJ

Publisher: BMJ Publishing Group Ltd.

ISSN (Print): 0959-8138

ISSN (Electronic): 1756-1833

Publication date Collection: 2021

Publication date (Electronic): 17 June 2021

Volume: 373

Electronic Location Identifier: n1248

Affiliations

[1 ]Department of Computing, Macquarie University, Sydney, NSW, Australia

[2 ]Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia

Author notes

Correspondence to: M Ikram muhammad.ikram@ 123456mq.edu.au (or @midkhan on Twitter)

Author information

Muhammad Ikram https://orcid.org/0000-0003-2113-3390

Article

Publisher ID: tang063318

DOI: 10.1136/bmj.n1248

PMC ID: 8207561

PubMed ID: 34135009

SO-VID: 6c202598-3038-41e8-b296-4b8feba094f8

License:

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

Mobile health and privacy: cross sectional study

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Abstract

Objectives

Design

Setting

Participants

Main outcome measures

Results

Conclusions

Related collections

Privacy and Data Protection

Most cited references 19

What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions

Assessment of the Data Sharing and Privacy Practices of Smartphone Apps for Depression and Smoking Cessation

Ibobbly mobile health intervention for suicide prevention in Australian Indigenous youth: a pilot randomised controlled trial

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