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Mobile health and privacy: cross sectional study
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8207561/ https://www.ncbi.nlm.nih.gov/pubmed/34135009 http://dx.doi.org/10.1136/bmj.n1248 |
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author | Tangari, Gioacchino Ikram, Muhammad Ijaz, Kiran Kaafar, Mohamed Ali Berkovsky, Shlomo |
author_facet | Tangari, Gioacchino Ikram, Muhammad Ijaz, Kiran Kaafar, Mohamed Ali Berkovsky, Shlomo |
author_sort | Tangari, Gioacchino |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8207561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82075612021-06-30 Mobile health and privacy: cross sectional study Tangari, Gioacchino Ikram, Muhammad Ijaz, Kiran Kaafar, Mohamed Ali Berkovsky, Shlomo BMJ Research 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. BMJ Publishing Group Ltd. 2021-06-17 /pmc/articles/PMC8207561/ /pubmed/34135009 http://dx.doi.org/10.1136/bmj.n1248 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/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/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Research Tangari, Gioacchino Ikram, Muhammad Ijaz, Kiran Kaafar, Mohamed Ali Berkovsky, Shlomo Mobile health and privacy: cross sectional study |
title | Mobile health and privacy: cross sectional study |
title_full | Mobile health and privacy: cross sectional study |
title_fullStr | Mobile health and privacy: cross sectional study |
title_full_unstemmed | Mobile health and privacy: cross sectional study |
title_short | Mobile health and privacy: cross sectional study |
title_sort | mobile health and privacy: cross sectional study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8207561/ https://www.ncbi.nlm.nih.gov/pubmed/34135009 http://dx.doi.org/10.1136/bmj.n1248 |
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