Cited by 100Privacy, Security, and Data ProtectionThoreau and American LiteratureMobile Health and mHealth Applications

Data Security and Privacy in Apps for Dementia: An Analysis of Existing Privacy Policies

Rosenfeld, Lisa, Torous, John, Vahia, Ipsit V.·2017·The American Journal of Geriatric Psychiatry

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.jagp.2017.04.009

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review