Availability and quality of mobile health app privacy policies
Open accessAbstract
Mobile health (mHealth) customers shopping for applications (apps) should be aware of app privacy practices so they can make informed decisions about purchase and use. We sought to assess the availability, scope, and transparency of mHealth app privacy policies on iOS and Android. Over 35,000 mHealth apps are available for iOS and Android. Of the 600 most commonly used apps, only 183 (30.5%) had privacy policies. Average policy length was 1755 (SD 1301) words with a reading grade level of 16 (SD 2.9). Two thirds (66.1%) of privacy policies did not specifically address the app itself. Our findings show that currently mHealth developers often fail to provide app privacy policies. The privacy policies that are available do not make information privacy practices transparent to users, require college-level literacy, and are often not focused on the app itself. Further research is warranted to address why privacy policies are often absent, opaque, or irrelevant, and to find a remedy.
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.
Integrity signals
Registry
Cites
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Lack of availability of privacy information seems to be an issue for mental health apps in general.
“This lack of availability of privacy information seems to be an issue for mental health apps in general [21].”
From A Systematic Review of Cognitive Behavioral Therapy and Behavioral Activation Apps for Depression (2016) · cites this paper as [21]
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
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148 · see citing works
A survey of mobile phone sensing
Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010) · IEEE Communications Magazine · doi:10.1109/mcom.2010.5560598 · Cited by 2,292 · see citing works
Information Privacy Research: an Interdisciplinary Review1
Smith, H. J., Dinev, T., & Xu, H. (2011) · MIS Quarterly · doi:10.2307/41409970 · Cited by 2,013 · see citing works
Istepanian, R. S. H., Jovanov, E., & Zhang, Y. T. (2004) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2004.840019 · Cited by 766 · see citing works
Ackerman, M. S., Cranor, L. F., & Reagle, J. (1999) · ACM Conference on Electronic Commerce (EC) · doi:10.1145/336992.336995 · Cited by 532 · see citing works
Can Mobile Health Technologies Transform Health Care?
Steinhubl, S. R., Muse, E. D., & Topol, E. J. (2013) · JAMA · doi:10.1001/jama.2013.281078 · Cited by 496 · see citing works
Solutions to problematic polypharmacy: learning from the expertise of patients
Reeve, J. L., Dickenson, M., Harris, J., Ranson, E., Dohnhammer, U., Cooper, L., Krska, J., Byng, R., & Britten, N. (2015) · British Journal of General Practice · doi:10.3399/bjgp15x685465 · Cited by 361 · see citing works
The use of readability formulas in health care
Ley, P., & Florio, T. (1996) · Psychology Health & Medicine · doi:10.1080/13548509608400003 · Cited by 342 · see citing works
Readability Assessment of Internet-Based Consumer Health Information
Walsh, T. M., & Volsko, T. A. (2008) · Respiratory Care · doi:10.4187/respcare.08531310 · Cited by 328 · see citing works
Determinants of physicians’ technology acceptance for e-health in ambulatory care
Dünnebeil, S., Sunyaev, A., Blohm, I., Leimeister, J. M., & Krcmar, H. (2012) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2012.02.002 · Cited by 320 · see citing works
Self-citationThe Cost of Reading Privacy Policies
McDonald, A. M., & Cranor, L. F. (2008) · The Knowledge Bank (The Ohio State University) · Cited by 307 · see citing works
Examining Internet Privacy Policies Within the Context of User Privacy Values
Earp, J. B., Antón, A. I., Aiman‐Smith, L., & Stufflebeam, W. (2005) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2005.844927 · Cited by 245 · see citing works
Agaku, I. T., Adisa, A. O., Ayo-Yusuf, O. A., & Connolly, G. N. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002079 · Cited by 180 · see citing works
The Impact of Personalization on Smartphone-Based Activity Recognition
Weiss, G. M., & Lockhart, J. W. (2012) · Cited by 162 · see citing works
Patients’ Attitudes Toward Electronic Health Information Exchange: Qualitative Study
Simon, S. R., Evans, J., Benjamin, A., Delano, D., & Bates, D. W. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1164 · Cited by 152 · see citing works
Sharing Medical Data for Health Research: The Early Personal Health Record Experience
Weitzman, E. R., Kaci, L., & Mandl, K. D. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1356 · Cited by 151 · see citing works
Self-citationNo Small Change for the Health Information Economy
Mandl, K. D., & Kohane, I. S. (2009) · New England Journal of Medicine · doi:10.1056/nejmp0900411 · Cited by 139 · see citing works
Self-citationHow internet users' privacy concerns have evolved since 2002
Antón, A. I., Earp, J. B., & Young, J. D. (2010) · IEEE Security & Privacy · doi:10.1109/msp.2010.38 · Cited by 135 · see citing works
A Measure of Agreement for Interval or Nominal Multivariate Observations
Janson, H., & Olsson, U. (2001) · Educational and Psychological Measurement · doi:10.1177/00131640121971239 · Cited by 130 · see citing works
Access to electronic health records in primary care-a survey of patients' views.
Pyper, C., Amery, J. M., Watson, M., & Crook, C. (2004) · PubMed · Cited by 66 · see citing works
d’Heureuse, N., Huici, F., Arumaithurai, M., et al. (2012) · ACM SIGMOBILE Mobile Computing and Communications Review · doi:10.1145/2396756.2396759 · Cited by 29 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- D'Heureuse N, Huici F, Arumaithurai M, et al. What's app?: a wide-scale measurement study of smart phone markets. ACM SIGMOBILE Mob Comput Commun Rev. 2012;16: 16-27.
Cited by
Sorted by citations
Torous, J., Nicholas, J., Larsen, M. E., Firth, J., & Christensen, H. (2018) · Evidence Based Mental Health · doi:10.1136/eb-2018-102891 · Cited by 790 · see citing works
Mobile Apps for Bipolar Disorder: A Systematic Review of Features and Content Quality
Nicholas, J., Larsen, M. E., Proudfoot, J., & Christensen, H. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4581 · Cited by 438 · see citing works
Huckvale, K., Prieto, J. T., Tilney, M., Benghozi, P.-J., & Car, J. (2015) · BMC Medicine · doi:10.1186/s12916-015-0444-y · Cited by 376 · see citing works
The WPA-Lancet psychiatry commission on the future of psychiatry
Bhugra, D., Tasman, A., Pathare, S., Priebe, S., Smith, S., Torous, J., Arbuckle, M. R., Langford, A., Alarcón, R. D., Chiu, H. F. K., et al. (2017) · The Lancet Psychiatry · doi:10.1016/s2215-0366(17)30333-4 · Cited by 392 · see citing works
Consumer Mobile Health Apps: Current State, Barriers, and Future Directions
Kao, C., & Liebovitz, D. M. (2017) · PM&R · doi:10.1016/j.pmrj.2017.02.018 · Cited by 350 · see citing works
A Systematic Review of Cognitive Behavioral Therapy and Behavioral Activation Apps for Depression
Huguet, A., Rao, S., McGrath, P. J., Wozney, L., Wheaton, M., Conrod, J., & Rozario, S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0154248 · Cited by 328 · see citing works
Kretzschmar, K., Tyroll, H., Pavarini, G., Manzini, A., Singh, I., & NeurOx Young People’s Advisory Group (2019) · Biomedical Informatics Insights · doi:10.1177/1178222619829083 · Cited by 270 · see citing works
Dehling, T., Gao, F., Schneider, S., & Sunyaev, A. (2015) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3672 · Cited by 243 · see citing works
Self-citationAnxiety: There is an app for that. A systematic review of anxiety apps: SUCALA et al.
Sucala, M., Cuijpers, P., Muench, F., Cardoș, R., Soflau, R., Dobrean, A., Achimas-Cadariu, P., & David, D. (2017) · Depression and Anxiety · doi:10.1002/da.22654 · Cited by 232 · see citing works
The mobile revolution—using smartphone apps to prevent cardiovascular disease
Neubeck, L., Lowres, N., Benjamin, E. J., Freedman, S. B., Coorey, G., & Redfern, J. (2015) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2015.34 · Cited by 232 · see citing works
Zhou, L., Bao, J., Watzlaf, V., & Parmanto, B. (2019) · JMIR mHealth and uHealth · doi:10.2196/11223 · Cited by 246 · see citing works
Security and Privacy Analysis of Mobile Health Applications: The Alarming State of Practice
Papageorgiou, A., Strigkos, M., Politou, E., Alepis, E., Solanas, A., & Patsakis, C. (2018) · IEEE Access · doi:10.1109/access.2018.2799522 · Cited by 236 · see citing works
Many Mobile Health Apps Target High-Need, High-Cost Populations, But Gaps Remain
Singh, K., Drouin, K., Newmark, L. P., Lee, J., Faxvaag, A., Rozenblum, R., Pabo, E. A., Landman, A., Klinger, E., & Bates, D. W. (2016) · Health Affairs · doi:10.1377/hlthaff.2016.0578 · Cited by 201 · see citing works
Reviewing 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
Wearable Technology and How This Can Be Implemented into Clinical Practice
Greiwe, J., & Nyenhuis, S. M. (2020) · Current Allergy and Asthma Reports · doi:10.1007/s11882-020-00927-3 · Cited by 167 · see citing works
The evolution of mobile apps for asthma: an updated systematic assessment of content and tools
Huckvale, K., Morrison, C., Ouyang, J., Ghaghda, A., & Car, J. (2015) · BMC Medicine · doi:10.1186/s12916-015-0303-x · Cited by 166 · see citing works
Towards a Framework for Evaluating Mobile Mental Health Apps
Chan, S., Torous, J., Hinton, L., & Yellowlees, P. (2015) · Telemedicine and e-Health · doi:10.1089/tmj.2015.0002 · Cited by 163 · see citing works
Parker, L., Halter, V., Karliychuk, T., & Grundy, Q. (2019) · International Journal of Law and Psychiatry · doi:10.1016/j.ijlp.2019.04.002 · Cited by 129 · see citing works
Grundy, Q., Chiu, K., Held, F., Continella, A., Bero, L., & Holz, R. (2019) · BMJ · doi:10.1136/bmj.l920 · Cited by 164 · see citing works
Robillard, J. M., Feng, T. L., Sporn, A. B., Lai, J.-A., Lo, C., Ta, M., & Nadler, R. (2019) · Internet Interventions · doi:10.1016/j.invent.2019.100243 · Cited by 111 · see citing works
Jensen, M. T., Treskes, R. W., Caiani, E. G., Casado-Arroyo, R., Cowie, M. R., Dilaveris, P., Duncker, D., Di Rienzo, M., Frederix, I., De Groot, N., et al. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab011 · Cited by 93 · see citing works
Liaw, S.-T., Liyanage, H., Kuziemsky, C., Terry, A. L., Schreiber, R., Jonnagaddala, J., & de Lusignan, S. (2020) · Yearbook of Medical Informatics · doi:10.1055/s-0040-1701980 · Cited by 76 · see citing works
A health app developer’s guide to law and policy: a multi-sector policy analysis
Parker, L., Karliychuk, T., Gillies, D., Mintzes, B., Raven, M., & Grundy, Q. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0535-0 · Cited by 71 · see citing works
Privacy Assessment in Mobile Health Apps: Scoping Review
Benjumea, J., Ropero, J., Rivera-Romero, O., Dorronzoro-Zubiete, E., & Carrasco, A. (2020) · JMIR mHealth and uHealth · doi:10.2196/18868 · Cited by 69 · see citing works
What might get in the way: Barriers to the use of apps for depression
Stiles-Shields, C., Montague, E., Lattie, E. G., Kwasny, M. J., & Mohr, D. C. (2017) · DIGITAL HEALTH · doi:10.1177/2055207617713827 · Cited by 68 · 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
Haque, M. R., & Rubya, S. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555146 · Cited by 48 · see citing works
An 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
Issues for eHealth in Psychiatry: Results of an Expert Survey
Nicholas, J., Huckvale, K., Larsen, M. E., Basu, A., Batterham, P. J., Shaw, F., & Sendi, S. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6957 · Cited by 34 · see citing works
Amann, J., Fiordelli, M., Scheel-Sailer, A., Brach, M., & Rubinelli, S. (2020) · JMIR mHealth and uHealth · doi:10.2196/22452 · Cited by 28 · see citing works
Smartphone psychology: New approaches towards safe and efficacious mobile mental health apps.
Marshall, J. M., Dunstan, D. A., & Bartik, W. (2020) · Professional Psychology: Research and Practice · doi:10.1037/pro0000278 · Cited by 25 · see citing works
Advantages and disadvantages of mobile applications for workplace health promotion: A scoping review
Junker, M., Böhm, M., & Krcmar, H. (2024) · PLoS ONE · doi:10.1371/journal.pone.0296212 · Cited by 15 · see citing works
“These apps’ privacy and data security also were viewed as potential pitfalls that have not been addressed properly [9, 52, 55, 56], though there has been discussion of potential privacy and security measures in mHealth [80, 81].”
BackgroundSuñol, J., María Panisello, J., Castell, E., Juan Tárraga López, P., Sánchez, C., & Pérez, V. (2017) · Universal Journal of Public Health · doi:10.13189/ujph.2017.050305 · Cited by 4 · see citing works
- Authors
- Sunyaev, A., Dehling, T., Taylor, P. L., & Mandl, K. D.
- Affiliations
- University of Cologne (DE)
- Boston Children's Hospital (US)
- Corresponding author
- Sunyaev, Ali
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-08-21
- Volume
- 22
- Issue
- e1
- Pages
- e28-e33
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC11888337
- OpenAlex
- W1481354592
- Cited by
- 360
- References
- 21
- Citations by year
- 2026: 5 · 2025: 17 · 2024: 32 · 2023: 35 · 2022: 36 · 2021: 42 · 2020: 42 · 2019: 40 · 2018: 32 · 2017: 29 · 2016: 22 · 2015: 26 · 2014: 1 · 2012: 1
- FWCI
- 84.35
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
- SSRN Electronic Journal (submittedVersion)
- Funders
- Universität zu Köln
- National Institutes of Health (UL1TR000101)
- National Center for Advancing Translational Sciences
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Information Systems › Thoreau and American Literature
- Keywords
- mobile health apps, privacy policies, data privacy, iOS, Android, App Store, user literacy, health data privacy
- MeSH terms
- Telemedicine, 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