Effect of a Medication Adherence Mobile Phone App on Medically Underserved Patients with Chronic Illness: Preliminary Efficacy Study
Open accessPublished version of a preprint
Abstract
BACKGROUND: Medication adherence is vital in the treatment of patients with chronic illness who require long-term medication therapies to maintain optimal health. Medication adherence, a complex and widespread problem, has been difficult to solve. Additionally, lower-income, medically underserved communities have been found to have higher rates of inadequate adherence to oral medications. Even so, this population has been underrepresented in studies using mobile medication adherence app interventions. Federally qualified health centers provide care for medically underserved populations, defined as communities and populations where there is a demonstrable unmet need for health services. These centers have been reporting an increase in a more complex chronic disease population. Including medically underserved individuals in mobile health studies provides opportunities to support this disproportionately affected group, work toward reducing health disparities in access to health care, and understand barriers to mobile health uptake. OBJECTIVE: The aim of this preliminary efficacy study was to evaluate the effects and feasibility of a commercially available medication adherence app, Medisafe, in a medically underserved adult population with various chronic illnesses seeking care in a federally qualified health center. METHODS: Participants in this single-arm pre-post intervention preliminary efficacy study (N=10) completed a baseline survey, used the app for 2 weeks, and completed an end-of-study survey. The primary outcome measures were medication adherence and medication self-efficacy. Feedback on the use of the app was also gathered. RESULTS: A statistically significant median increase of 8 points on the self-efficacy for adherence to medications scale was observed (P=.03, Cohen d=0.69). Though not significant, the adherence to refills and medications scale demonstrated a median change of 2.5 points in the direction of increased medication adherence (P=.21, Cohen d=0.41). Feedback about the app was positive. CONCLUSIONS: Use of the Medisafe app is a viable option to improve medication self-efficacy and medication adherence in medically underserved patients in an outpatient setting with a variety of chronic illnesses.
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?
A preliminary study of the Medisafe app received positive usability feedback.
“A preliminary study of the Medisafe app improved medication self-efficacy (median +8 points, p = 0.03) and received positive usability feedback, though adherence gains were not statistically significant (median +2.5 points, p = 0.21) [43].”
A preliminary study of the Medisafe app improved medication self-efficacy (median +8 points, p = 0.03).
“A preliminary study of the Medisafe app improved medication self-efficacy (median +8 points, p = 0.03) and received positive usability feedback, though adherence gains were not statistically significant (median +2.5 points, p = 0.21) [43].”
A preliminary study of the Medisafe app found adherence gains were not statistically significant (median +2.5 points, p = 0.21).
“A preliminary study of the Medisafe app improved medication self-efficacy (median +8 points, p = 0.03) and received positive usability feedback, though adherence gains were not statistically significant (median +2.5 points, p = 0.21) [43].”
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 REDCap consortium: building an international community of software platform partners
Harris, P. A., Taylor, R., Minor, B. L., Elliott, V., Fernandez, M., O'Neal, L., McLeod, L., Delacqua, G., Delacqua, F., Kirby, J., et al. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103208 · Cited by 24,013 · see citing works
Social Cognitive Theory: An Agentic Perspective
Bandura, A. (2001) · Annual Review of Psychology · doi:10.1146/annurev.psych.52.1.1 · Cited by 15,510 · see citing works
Health Promotion by Social Cognitive Means
Bandura, A. (2004) · Health Education & Behavior · doi:10.1177/1090198104263660 · Cited by 7,238 · see citing works
Adherence to Long-Term Therapies: Evidence for Action
Geest, S. D., & Sabate, E. J. (2003) · European Journal of Cardiovascular Nursing · doi:10.1016/s1474-5151(03)00091-4 · Cited by 6,274 · see citing works
Kirk, J. M. W., MacDonald, A., Lavender, P., Dean, J. M., & Rubin, G. (2017) · Biomedicine Hub · doi:10.1159/000480347 · Cited by 3,901 · see citing works
Medication Adherence: WHO Cares?
Brown, M. T., & Bussell, J. K. (2011) · Mayo Clinic Proceedings · doi:10.4065/mcp.2010.0575 · Cited by 1,928 · see citing works
Patient Adherence and Medical Treatment Outcomes: A Meta-Analysis
Robin DiMatteo, M., Giordani, P. J., Lepper, H. S., & Croghan, T. W. (2002) · Medical Care · doi:10.1097/00005650-200209000-00009 · Cited by 1,490 · see citing works
Adherence to Drugs That Prevent Cardiovascular Disease: Meta-analysis on 376,162 Patients
Naderi, S. H., Bestwick, J. P., & Wald, D. S. (2012) · The American Journal of Medicine · doi:10.1016/j.amjmed.2011.12.013 · Cited by 844 · see citing works
Interventions to Enhance Medication Adherence in Chronic Medical Conditions: A Systematic Review
Kripalani, S., Yao, X., & Haynes, R. B. (2007) · Archives of Internal Medicine · doi:10.1001/archinte.167.6.540 · Cited by 810 · see citing works
Kripalani, S. B., Risser, J., Gatti, M. E., & Jacobson, T. A. (2008) · Value in Health · doi:10.1111/j.1524-4733.2008.00400.x · Cited by 510 · see citing works
Patient Characteristics Associated with Medication Adherence
Rolnick, S. J., Pawloski, P. A., Hedblom, B. D., Asche, S. E., & Bruzek, R. J. (2013) · Clinical Medicine & Research · doi:10.3121/cmr.2013.1113 · Cited by 481 · see citing works
Vervloet, M., Linn, A. J., van Weert, J. C. M., de Bakker, D. H., Bouvy, M. L., & van Dijk, L. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000748 · Cited by 455 · see citing works
Unintentional non-adherence to chronic prescription medications: How unintentional is it really?
Gadkari, A. S., & McHorney, C. A. (2012) · BMC Health Services Research · doi:10.1186/1472-6963-12-98 · Cited by 380 · see citing works
Risser, J., Jacobson, T. A., & Kripalani, S. B. (2007) · Journal of Nursing Measurement · doi:10.1891/106137407783095757 · Cited by 338 · see citing works
Morawski, K., Ghazinouri, R., Krumme, A., Lauffenburger, J. C., Lu, Z., Durfee, E., Oley, L., Lee, J., Mohta, N., Haff, N., et al. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.0447 · Cited by 333 · see citing works
Labovitz, D. L., Shafner, L., Reyes Gil, M., Virmani, D., & Hanina, A. (2017) · Stroke · doi:10.1161/strokeaha.116.016281 · Cited by 311 · see citing works
Usability of Commercially Available Mobile Applications for Diverse Patients
Sarkar, U., Gourley, G. I., Lyles, C. R., Tieu, L., Clarity, C., Newmark, L., Singh, K., & Bates, D. W. (2016) · Journal of General Internal Medicine · doi:10.1007/s11606-016-3771-6 · Cited by 257 · see citing works
Psychometric Properties of the Brief Health Literacy Screen in Clinical Practice
Wallston, K. A., Cawthon, C. R., McNaughton, C. D., Rothman, R. L., Osborn, C. Y., & Kripalani, S. B. (2013) · Journal of General Internal Medicine · doi:10.1007/s11606-013-2568-0 · Cited by 225 · see citing works
Mobile Phone and Smartphone Technologies for Diabetes Care and Self-Management
Garabedian, L. F., Ross‐Degnan, D., & Wharam, J. F. (2015) · Current Diabetes Reports · doi:10.1007/s11892-015-0680-8 · Cited by 188 · see citing works
The 'cost' of medication nonadherence: Consequences we cannot afford to accept
Chisholm-Burns, M. A., & Spivey, C. A. (2012) · Journal of the American Pharmacists Association · doi:10.1331/japha.2012.11088 · Cited by 173 · see citing works
Peng, Y., Wang, H., Fang, Q., Xie, L., Shu, L., Sun, W., & Liu, Q. (2020) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2020.26.4.550 · Cited by 151 · see citing works
Patel, S., Jacobus-Kantor, L., Marshall, L., Ritchie, C., Kaplinski, M., Khurana, P., & Katz, R. J. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700307 · Cited by 122 · see citing works
Chandler, J., Sox, L. R., Kellam, K., Feder, L., Nemeth, L. S., & Treiber, F. A. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16071226 · Cited by 114 · see citing works
Santo, K., Singleton, A., Rogers, K., Thiagalingam, A., Chalmers, J., Chow, C. K., & Redfern, J. (2019) · Heart · doi:10.1136/heartjnl-2018-313479 · Cited by 112 · see citing works
Park, J. Y. E., Li, J., Howren, A., Tsao, N. W., & De Vera, M. (2019) · JMIR mHealth and uHealth · doi:10.2196/11919 · Cited by 106 · see citing works
Meta-analyses of Theory Use in Medication Adherence Intervention Research
Conn, V. S., Enriquez, M., Ruppar, T. M., & Chan, K. C. (2016) · American Journal of Health Behavior · doi:10.5993/ajhb.40.2.1 · Cited by 104 · see citing works
Fernandez-Lazaro, C. I., Adams, D. P., Fernández‐Lázaro, D., González, J. M. G., Caballero-García, A., & Canelo, J. A. M. (2018) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2018.09.006 · Cited by 101 · see citing works
Svendsen, M. T., Andersen, F., Andersen, K. H., Pottegård, A., Johannessen, H., Möller, S., August, B., Feldman, S. R., & Andersen, K. E. (2018) · British Journal of Dermatology · doi:10.1111/bjd.16667 · Cited by 99 · see citing works
Huang, Y., Shiyanbola, O. O., & Smith, P. D. (2018) · Patient Preference and Adherence · doi:10.2147/ppa.s153312 · Cited by 98 · see citing works
Lee, S. K., Kang, B., Kim, H., & Son, Y. (2013) · Healthcare Informatics Research · doi:10.4258/hir.2013.19.1.33 · Cited by 54 · see citing works
McBride, C. M., Morrissey, E. C., & Molloy, G. J. (2020) · JMIR mHealth and uHealth · doi:10.2196/17470 · Cited by 49 · see citing works
Meinema, J. G., van Dijk, N., Beune, E., Jaarsma, D. A. D. C., van Weert, H. C. P. M., & Haafkens, J. A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0133560 · Cited by 45 · see citing works
Anglada-Martínez, H., Martín-Conde, M., Rovira-Illamola, M., Sotoca-Momblona, J. M., Aymar, E. S., Aragunde, V., Moreno, M. A., Catalán, M., & Codina‐Jané, C. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0456-y · Cited by 45 · see citing works
Can mHealth Interventions Reduce Health Disparities among Vulnerable Populations?
Peek, M. E. (2017) · Diversity & Equality in Health and Care · doi:10.21767/2049-5471.100091 · Cited by 38 · see citing works
Moorhead, P., Zavala, A., Kim, Y. A., & Virdi, N. S. (2017) · Journal of the American Pharmacists Association · doi:10.1016/j.japh.2016.12.067 · Cited by 30 · see citing works
Okere, A. N., Renier, C. M., & Morse, J. A. (2014) · Journal of Nursing Measurement · doi:10.1891/1061-3749.22.1.120 · Cited by 29 · see citing works
Lifetime trajectory simulation of chronic disease progression and comorbidity development
Zhu, Z., Heng, B. H., & Teow, K. L. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.11.002 · Cited by 27 · see citing works
Flower, C., Hambleton, I. R., & Campbell, M. (2016) · JCR Journal of Clinical Rheumatology · doi:10.1097/rhu.0000000000000470 · Cited by 25 · see citing works
Kjos, A. L., Vaughan, A. G., & Bhargava, A. (2019) · Journal of the American Pharmacists Association · doi:10.1016/j.japh.2018.12.012 · Cited by 22 · see citing works
Blalock, S. J. (2011) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2010.11.001 · Cited by 19 · see citing works
Challenges in conducting mHealth research with underserved populations: Lessons learned
Nelson, L. A., & Zamora‐Kapoor, A. (2015) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x15609853 · Cited by 19 · see citing works
Chen, S., Gong, E., Kazi, D. S., Gates, A. B., Karaye, K. M., Girerd, N., Bai, R., AlHabib, K. F., Li, C., Sun, K., et al. (2016) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2016.1213904 · Cited by 15 · see citing works
Chen, S., Gong, E., Kazi, D. S., et al. (2016) · Annals of Global Health · doi:10.1016/j.aogh.2016.04.243 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Mehany, O., Artner, A., Sebők, S., Hankó, B., & Zelkó, R. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15052069 · Cited by 6 · see citing works
- Authors
- Hartch, C. E., Dietrich, M. S., & Stolldorf, D. P.
- Affiliations
- Vanderbilt University (US)
- Corresponding author
- Hartch, Christa E
- Venue
- JMIR Formative Research
- Publisher
- JMIR Publications Inc.
- Published
- 2023-12-11
- Volume
- 7
- Pages
- e50579
- ISSN
- 2561-326X (ISSN-L)
- PMC
- PMC10750237
- OpenAlex
- W4388824742
- Cited by
- 13
- References
- 43
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 3
- FWCI
- 4.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Formative Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (UL1TR000445)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- medication adherence, mobile health apps, medically underserved populations, chronic disease, medication self-efficacy, federally qualified health centers, health disparities, low-income patients, app feasibility, prescription refills
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
