Data Security and Privacy in Apps for Dementia: An Analysis of Existing Privacy Policies
Abstract
Introduction Despite tremendous growth in the number of health applications (apps), little is known about how well these apps protect their users' health-related data. This gap in knowledge is of particular concern for apps targeting people with dementia, whose cognitive impairment puts them at increased risk of privacy breaches. In this article, we determine how many dementia apps have privacy policies and how well they protect user data. Methods Our analysis included all iPhone apps that matched the search terms "medical + dementia" or "health & fitness + dementia" and collected user-generated content. We evaluated all available privacy policies for these apps based on criteria that systematically measure how individual user data is handled. Results Seventy-two apps met the above search teams and collected user data. Of these, only 33 (46%) had an available privacy policy. Nineteen of the 33 with policies (58%) were specific to the app in question, and 25 (76%) specified how individual-user as opposed to aggregate data would be handled. Among these, there was a preponderance of missing information, the majority acknowledged collecting individual data for internal purposes, and most admitted to instances in which they would share user data with outside parties. Conclusions At present, the majority of health apps focused on dementia lack a privacy policy, and those that do exist lack clarity. Bolstering safeguards and improving communication about privacy protections will help facilitate consumer trust in apps, thereby enabling more widespread and meaningful use by people with dementia and those involved in their care.
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Cites
Torous, J., & Roberts, L. W. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.0262 · Cited by 362 · see citing works
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Flagged first, then by citations
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Sorted by citations
Huckvale, K., Torous, J., & Larsen, M. E. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.2542 · Cited by 312 · see citing works
Self-citationTorous, J. B., Chan, S. R., Gipson, S. Y.-M. T., Kim, J. W., Nguyen, T.-Q., Luo, J., & Wang, P. (2018) · Psychiatric Services · doi:10.1176/appi.ps.201700423 · Cited by 184 · see citing works
Self-citationReviewing the data security and privacy policies of mobile apps for depression
O'Loughlin, K., Neary, M., Adkins, E. C., & Schueller, S. M. (2019) · Internet Interventions · doi:10.1016/j.invent.2018.12.001 · Cited by 179 · see citing works
Bauer, M., Glenn, T., Geddes, J., Gitlin, M., Grof, P., Kessing, L. V., Monteith, S., Faurholt-Jepsen, M., Severus, E., & Whybrow, P. C. (2020) · International Journal of Bipolar Disorders · doi:10.1186/s40345-019-0164-x · Cited by 163 · see citing works
Security and Privacy of mHealth Applications: A Scoping Review
Nurgalieva, L., O'Callaghan, D., & Doherty, G. (2020) · IEEE Access · doi:10.1109/access.2020.2999934 · Cited by 132 · see citing works
“Studies covered a wide range of mental health conditions , which included stress [3], depression [55], [60], [71], sleep disorders [88], smoking cessation [55], [98], psychosis [4], anxiety [53], and cognitive decline such as dementia [58], [65].”
BackgroundNebeker, C., Bartlett Ellis, R. J., & Torous, J. (2020) · Translational Behavioral Medicine · doi:10.1093/tbm/ibz074 · Cited by 99 · see citing works
Self-citationTorous, J., Firth, J., Huckvale, K., Larsen, M. E., Cosco, T. D., Carney, R., Chan, S., Pratap, A., Yellowlees, P., Wykes, T., et al. (2018) · Journal of Nervous & Mental Disease · doi:10.1097/nmd.0000000000000864 · Cited by 101 · see citing works
Self-citationmHealth and telemedicine apps: in search of a common regulation
Crico, C., Renzi, C., Graf, N., Buyx, A., Kondylakis, H., Koumakis, L., & Pravettoni, G. (2018) · ecancermedicalscience · doi:10.3332/ecancer.2018.853 · Cited by 83 · see citing works
The Complexity of Mental Health App Privacy Policies: A Potential Barrier to Privacy
Powell, A., Singh, P., & Torous, J. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.9871 · Cited by 65 · see citing works
Self-citationAn Ethics Checklist for Digital Health Research in Psychiatry: Viewpoint
Shen, F. X., Silverman, B. C., Monette, P., Kimble, S., Rauch, S. L., & Baker, J. T. (2022) · Journal of Medical Internet Research · doi:10.2196/31146 · Cited by 43 · see citing works
- Authors
- Rosenfeld, L., Torous, J., & Vahia, I. V.
- Affiliations
- Harvard University (US)
- Beth Israel Deaconess Medical Center (US)
- Corresponding author
- Vahia, Ipsit V.
- Venue
- The American Journal of Geriatric Psychiatry
- Publisher
- Elsevier BV
- Published
- 2017-08
- Volume
- 25
- Issue
- 8
- Pages
- 873-877
- ISSN
- 1064-7481 (ISSN-L)
- OpenAlex
- W2621114807
- Cited by
- 100
- References
- 1
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 9 · 2023: 10 · 2022: 12 · 2021: 14 · 2020: 17 · 2019: 9 · 2018: 10 · 2017: 1
- FWCI
- 25.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Geriatric Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Information Systems › Thoreau and American Literature
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- privacy policies, data security, health data protection, user data sharing, consumer trust, mobile health apps, privacy violations, cognitive impairment
- MeSH terms
- Dementia, Humans, Medical Informatics Applications, Computer Security, Privacy, Mobile Applications
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