Many Mobile Health Apps Target High-Need, High-Cost Populations, But Gaps Remain
Abstract
With rising smartphone ownership, mobile health applications (mHealth apps) have the potential to support high-need, high-cost populations in managing their health. While the number of available mHealth apps has grown substantially, no clear strategy has emerged on how providers should evaluate and recommend such apps to patients. Key stakeholders, including medical professional societies, insurers, and policy makers, have largely avoided formally recommending apps, which forces patients to obtain recommendations from other sources. To help stakeholders overcome barriers to reviewing and recommending apps, we evaluated 137 patient-facing mHealth apps-those intended for use by patients to manage their health-that were highly rated by consumers and recommended by experts and that targeted high-need, high-cost populations. We found that there is a wide variety of apps in the marketplace but that few apps address the needs of the patients who could benefit the most. We also found that consumers' ratings were poor indications of apps' clinical utility or usability and that most apps did not respond appropriately when a user entered potentially dangerous health information. Going forward, data privacy and security will continue to be major concerns in the dissemination of mHealth apps.
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Many apps popular with patients are not seen as high quality by clinicians.
“Furthermore, many apps popular with patients are not seen as high quality by clinicians.”
From What is the clinical value of mHealth for patients? (2020) · cites this paper as [32]
The quality of the mHealth literature is highly variable with few studies registered on clincaltrials.gov and many of the apps studied not available on the iOS or Android app store.
“The quality of the mHealth literature is highly variable with few studies registered on clincaltrials.gov and many of the apps studied not available on the iOS or Android app store.”
From What is the clinical value of mHealth for patients? (2020) · cites this paper as [32]
Cites
(2013) · Journal of Usability Studies archive · Cited by 1,357 · see citing works
When Patient Activation Levels Change, Health Outcomes And Costs Change, Too
Greene, J., Hibbard, J. H., Sacks, R. M., Overton, V., & Parrotta, C. (2015) · Health Affairs · doi:10.1377/hlthaff.2014.0452 · Cited by 577 · see citing works
Hibbard, J. H., Greene, J., & Overton, V. (2013) · Health Affairs · doi:10.1377/hlthaff.2012.1064 · Cited by 398 · see citing works
Miner, A. S., Milstein, A., Schueller, S., Hegde, R., Mangurian, C., & Linos, E. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.0400 · Cited by 368 · see citing works
Availability and quality of mobile health app privacy policies
Sunyaev, A., Dehling, T., Taylor, P. L., & Mandl, K. D. (2015) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002605 · Cited by 360 · see citing works
Thomas, S., Fayter, D., Misso, K., Ogilvie, D., Petticrew, M. P., Sowden, A. J., Whitehead, M., & Worthy, G. (2008) · Tobacco Control · doi:10.1136/tc.2007.023911 · Cited by 347 · see citing works
Boult, C., Green, A. F., Boult, L. B., Pacala, J. T., Snyder, C. F., & Leff, B. A. (2009) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2009.02571.x · Cited by 342 · see citing works
Niederdeppe, J., Kuang, X., Crock, B. N., & Skelton, A. (2008) · Social Science & Medicine · doi:10.1016/j.socscimed.2008.06.037 · Cited by 184 · see citing works
Stockley, L., & Lund, V. (2008) · Public Health Nutrition · doi:10.1017/s1368980008002346 · Cited by 85 · see citing works
Information Technology for Patient Empowerment in Healthcare
(2015) · De Gruyter eBooks · doi:10.1515/9781614514343 · Cited by 48 · see citing works
A Simple and Valuable Approach for Measuring Customer Satisfaction
Kinney, W. C. (2005) · Otolaryngology · doi:10.1016/j.otohns.2005.03.060 · Cited by 40 · see citing works
Jameson, J. (1971) · The Lancet · doi:10.1016/s0140-6736(71)91580-7 · Cited by 7 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
What is the clinical value of mHealth for patients?
Rowland, S. P., Fitzgerald, J. E., Holme, T., Powell, J., & McGregor, A. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0206-x · Cited by 454 · see citing works
Torous, J., & Roberts, L. W. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.0262 · Cited by 362 · see citing works
Using science to sell apps: Evaluation of mental health app store quality claims
Larsen, M. E., Huckvale, K., Nicholas, J., Torous, J., Birrell, L., Li, E., & Reda, B. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0093-1 · Cited by 334 · see citing works
Perceptions and opinions of patients about mental health chatbots: scoping review
Abd-Alrazaq, A. A., Alajlani, M., Ali, N., Denecke, K., Bewick, B. M., & Househ, M. (2021) · Journal of Medical Internet Research · doi:10.2196/17828 · Cited by 301 · see citing works
Van Ameringen, M., Turna, J., Khalesi, Z., Pullia, K., & Patterson, B. (2017) · Depression and Anxiety · doi:10.1002/da.22657 · Cited by 234 · see citing works
“While the mHealth app literature is limited, several review papers have been published on the topic (Donker et al., 2013; Gee, Griffiths, & Gulliver, 2016; Huguet et al., 2016; Olff, 2015; Singh et al., 2016; Torous & Powell, 2015; Turvey & Roberts, 2015).”
BackgroundTorous, 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
Basch, E., Stover, A. M., Schrag, D., Chung, A., Jansen, J., Henson, S., Carr, P., Ginos, B., Deal, A., Spears, P. A., et al. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00081 · Cited by 194 · 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
Emerging technologies and analytics for a new era of value-centered marketing in healthcare
Agarwal, R., Dugas, M., Gao, G. (., & Kannan, P. K. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00692-4 · Cited by 175 · 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
Understanding the quality, effectiveness and attributes of top-rated smartphone health apps
Wisniewski, H., Liu, G., Henson, P., Vaidyam, A., Hajratalli, N. K., Onnela, J.-P., & Torous, J. (2019) · Evidence Based Mental Health · doi:10.1136/ebmental-2018-300069 · Cited by 112 · see citing works
Torous, 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
Patients’ and Nephrologists’ Evaluation of Patient-Facing Smartphone Apps for CKD
Singh, K., Diamantidis, C. J., Ramani, S., Bhavsar, N. A., Mara, P., Warner, J., Rodriguez, J., Wang, T., & Wright-Nunes, J. (2019) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.10370818 · Cited by 79 · see citing works
Self-citationBhattacharyya, O., Mossman, K., Gustafsson, L., & Schneider, E. C. (2019) · Journal of Medical Internet Research · doi:10.2196/10318 · Cited by 82 · see citing works
Persuasive E-Health Design for Behavior Change
Baumeister, H., Kraft, R., Baumel, A., Pryss, R., & Messner, E.-M. (2019) · Studies in Neuroscience, Psychology and Behavioral Economics · doi:10.1007/978-3-030-31620-4_17 · Cited by 53 · see citing works
Domhardt, M., Messner, E.-M., Eder, A.-S., Engler, S., Sander, L. B., Baumeister, H., & Terhorst, Y. (2021) · Child and Adolescent Psychiatry and Mental Health · doi:10.1186/s13034-021-00401-6 · Cited by 43 · see citing works
mHealth applications: potentials, limitations, current quality and future directions
Messner, E.-M., Probst, T., O’Rourke, T., Stoyanov, S., & Baumeister, H. (2019) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-31620-4_15 · Cited by 49 · see citing works
Issom, D.-Z., Hardy-Dessources, M.-D., Romana, M., Hartvigsen, G., & Lovis, C. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.600333 · Cited by 39 · see citing works
Rudin, R. S., Fanta, C. H., Qureshi, N., Duffy, E., Edelen, M. O., Dalal, A. K., & Bates, D. W. (2019) · Applied Clinical Informatics · doi:10.1055/s-0039-1697597 · Cited by 37 · see citing works
Self-citationThe Spanish Availability and Readability of Diabetes Apps
Rodriguez, J. A., & Singh, K. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817749610 · Cited by 30 · see citing works
Self-citationDamschroder, L. J., Buis, L. R., McCant, F. A., Kim, H. M., Evans, R., Oddone, E. Z., Bastian, L. A., Hooks, G., Kadri, R., White-Clark, C., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/19216 · Cited by 23 · see citing works
- Authors
- Singh, K., Drouin, K., Newmark, L. P., Lee, J., Faxvaag, A., Rozenblum, R., Pabo, E. A., Landman, A., Klinger, E., & Bates, D. W.
- Affiliations
- University of Michigan (US)
- Brigham and Women's Hospital (US)
- Lisa P. Newmark is a project manager in the Information Systems Department at Partners HealthCare, in Wellesley, Massachusetts.
- JaeHo Lee is an associate professor of biomedical informatics at the Asan Medical Center, in Seoul, Republic of Korea.
- ORCID
- Venue
- Health Affairs
- Publisher
- Health Affairs Publishing, LLC
- Published
- 2016-12
- Volume
- 35
- Issue
- 12
- Pages
- 2310-2318
- ISSN
- 0278-2715 (ISSN-L)
- 1544-5208
- OpenAlex
- W2560643553
- Cited by
- 201
- References
- 12
- Citations by year
- 2026: 5 · 2025: 14 · 2024: 16 · 2023: 15 · 2022: 27 · 2021: 36 · 2020: 29 · 2019: 25 · 2018: 18 · 2017: 16
- FWCI
- 36.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Health Affairs (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mobile health apps, mobile health, app evaluation, app recommendations, consumer ratings, clinical utility, usability, health data privacy, data security, patient self-management
- MeSH terms
- Smartphone, Chronic Disease, Consumer Behavior, Humans, Self Care, Comorbidity, Telemedicine, Patient Safety, Mobile Applications
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