Improving Refill Adherence in Medicare Patients With Tailored and Interactive Mobile Text Messaging: Pilot Study
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Abstract
BACKGROUND: Nonadherence is a major concern in the management of chronic conditions such as hypertension, cardiovascular disease, and diabetes where patients may discontinue or interrupt their medication for a variety of reasons. Text message reminders have been used to improve adherence. However, few programs or studies have explored the benefits of text messaging with older populations and at scale. In this paper, we present a program design using tailored and interactive text messaging to improve refill rates of partially adherent or nonadherent Medicare members of a large integrated health plan. OBJECTIVE: The aim of this 3-month program was to gain an understanding of whether tailored interactive text message dialogues could be used to improve medication refills in Medicare patients with one or more chronic diseases. METHODS: We used the mPulse Mobile interactive text messaging solution with partially adherent and nonadherent Medicare patients (ie, over age 65 years or younger with disabilities) of Kaiser Permanente Southern California (KP), a large integrated health plan, and compared refill rates of the text messaging group (n=12,272) to a group of partially adherent or nonadherent Medicare patients at KP who did not receive text messages (nontext messaging group, n=76,068). Both groups were exposed to other forms of refill and adherence outreach including phone calls, secure emails, and robo-calls from December 2016 to February 2017. RESULTS: The text messaging group and nontext messaging group were compared using an independent samples t test to test difference in group average of refill rates. There was a significant difference in medication refill rates between the 2 groups, with a 14.07 percentage points higher refill rate in the text messaging group (P<.001). CONCLUSIONS: The results showed a strong benefit of using this text messaging solution to improve medication refill rates among Medicare patients. These findings also support using interactive text messaging as a cost-effective, convenient, and user-friendly solution for patient engagement. Program outcomes and insights can be used to enhance the design of future text-based solutions to improve health outcomes and promote adherence and long-term behavior change.
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The observed 17.40% refill request rate was consistent with earlier pilot study findings.
“This was consistent with earlier pilot study findings.”
An earlier study by the authors found that text reminders increased refill rates by 14 percentage points compared with those who did not receive these reminders.
“An earlier study by the authors [21] measured the impact of SMS text reminders on refill rates of nonadherent and partially adherent Medicare patients with chronic disease. They found that text reminders increased refill rates by 14 percentage points compared with those who did not receive these reminders.”
The refill request rates reported in the pilot study with English speakers could be replicated at scale.
“We found that the refill request rates reported in the pilot study with English speakers [21] could be replicated at scale.”
Text messaging presents an effective, affordable, and scalable tool that can use conversational AI to greatly impact health outcomes.
“Text messaging presents an effective, affordable, and scalable tool [21] that can use conversational AI to greatly impact health outcomes.”
The value and incremental benefit of SMS text reminders as compared with phone reminders has already been demonstrated.
“we have already demonstrated the value and incremental benefit of SMS text reminders as compared with phone reminders [21]”
No other published study (other than the study by Brar Prayaga et al [21]) has reported a refill adherence solution with results at scale.
“To our knowledge, no other published study (other than the study by Brar Prayaga et al [21]) has reported a refill adherence solution with results at scale [20].”
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Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
Osterberg, L., & Blaschke, T. (2005) · New England Journal of Medicine · doi:10.1056/nejmra050100 · Cited by 7,974 · 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
A meta-analysis of the technology acceptance model
King, W. R., & He, J. (2006) · Information & Management · doi:10.1016/j.im.2006.05.003 · Cited by 2,906 · see citing works
Viswanathan, M., Golin, C. E., Jones, C. D., Ashok, M., Blalock, S. J., Wines, R. C. M., Coker-Schwimmer, E. J. L., Rosen, D. L., Sista, P., & Lohr, K. N. (2012) · Annals of Internal Medicine · doi:10.7326/0003-4819-157-11-201212040-00538 · Cited by 754 · see citing works
Adherence and health care costs
McGuire, M., & Iuga, A. O. (2014) · Risk Management and Healthcare Policy · doi:10.2147/rmhp.s19801 · Cited by 736 · see citing works
Mobile Telephone Text Messaging for Medication Adherence in Chronic Disease: A Meta-analysis
Thakkar, J., Kurup, R., Laba, T.-L., Santo, K., Thiagalingam, A., Rodgers, A., Woodward, M., Redfern, J., & Chow, C. K. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2015.7667 · Cited by 685 · see citing works
Kampmeijer, R., Pavlova, M., Tambor, M., Golinowska, S., & Groot, W. (2016) · BMC Health Services Research · doi:10.1186/s12913-016-1522-3 · Cited by 328 · see citing works
Trends in Seniors' Use of Digital Health Technology in the United States, 2011-2014.
Levine, D., Lipsitz, S., & Linder, J. (2016) · Journal of the American Medical Association (JAMA) · doi:10.1001/jama.2016.9124 · Cited by 209 · see citing works
A Spanish Pillbox App for Elderly Patients Taking Multiple Medications: Randomized Controlled Trial
Mira, J. J., Navarro, I., Botella, F., Borrás, F., Nuño-Solínis, R., Orozco‐Beltrán, D., Iglesias, F., Pérez-Pérez, P., Lorenzo, S., & Toro, N. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3269 · Cited by 190 · see citing works
Improving adherence to cardiovascular disease medications with information technology.
Vollmer, W. M., Owen-Smith, A. A., Tom, J. O., Laws, R., Ditmer, D., Smith, D. H., Waterbury, A., Schneider, J. L., Yonehara, C., Williams, A. E., et al. (2014) · PubMed · Cited by 61 · see citing works
Silva, J. M., Mouttham, A., & Saddik, A. E. (2009) · doi:10.1145/1631097.1631109 · Cited by 48 · see citing works
Pandey, A., Krumme, A. A., Patel, T., & Choudhry, N. K. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7144 · Cited by 43 · see citing works
Elderly Centered Design for Interaction – The Case of the S4S Medication Assistant
Ferreira, F., Almeida, N., Rosa, A. F., Oliveira, A., Casimiro, J., Silva, S., & Teixeira, A. J. S. (2014) · Procedia Computer Science · doi:10.1016/j.procs.2014.02.044 · Cited by 41 · see citing works
Evaluation of an Integrated Adherence Program Aimed to Increase Medicare Part D Star Rating Measures
Leslie, R. S., Tirado, B., Patel, B. V., & Rein, P. J. (2014) · Journal of Managed Care Pharmacy · doi:10.18553/jmcp.2014.20.12.1193 · Cited by 40 · see citing works
MediFrame: A Tablet Application to Plan, Inform, Remind and Sustain Older Adults' Medication Intake
Dalgaard, L. G., Grönvall, E., & Verdezoto, N. X. (2013) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2013.12 · Cited by 37 · see citing works
Exploring underutilization of videophones in hospice settings
Day, M., Demiris, G., Oliver, D. P., Courtney, K., & Hensel, B. (2007) · Telemedicine and e-Health · doi:10.1089/tmj.2006.0023 · Cited by 37 · see citing works
The Unhidden Cost of Noncompliance
Mahoney, J. J., Ansell, B. J., Fleming, W. K., & Butterworth, S. W. (2008) · Journal of Managed Care Pharmacy · doi:10.18553/jmcp.2008.14.s6-b.1 · Cited by 24 · see citing works
Penno, E., & Gauld, R. D. C. (2017) · Health Systems & Reform · doi:10.1080/23288604.2017.1340927 · Cited by 15 · see citing works
Medication and non-adherence in the older adult.
Latif, S. A., & McNicoll, L. (2009) · PubMed · Cited by 14 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Babel, A., Taneja, R., Mondello Malvestiti, F., Monaco, A., & Donde, S. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.669869 · Cited by 199 · see citing works
Ranchon, F., Chanoine, S., Lambert‐Lacroix, S., Bosson, J., Moreau–Gaudry, A., & Bedouch, P. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104983 · Cited by 113 · see citing works
Xu, Y., Zheng, X., Li, Y.-J., Ye, X., Cheng, H.-T., Wang, H., & Lyu, J. (2023) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.12548 · Cited by 82 · see citing works
Machine Learning and Medication Adherence: Scoping Review
Bohlmann, A., Mostafa, J., & Kumar, M. (2021) · JMIRx Med · doi:10.2196/26993 · Cited by 71 · see citing works
Donevant, S. B., Dawson, R. M., Culley, J. M., Habing, B., & Adams, S. A. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy104 · Cited by 64 · see citing works
Galozy, A., Nowaczyk, S., Sant’Anna, A., Ohlsson, M., & Lingman, M. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104092 · Cited by 47 · see citing works
Brar Prayaga, R., Agrawal, R., Nguyen, B., Jeong, E. W., Noble, H. K., Paster, A., & Prayaga, R. S. (2019) · JMIR mHealth and uHealth · doi:10.2196/15771 · Cited by 45 · see citing works
Self-citationMarko-Holguin, M., Cordel, S. L., Voorhees, B. W. V., Fogel, J., Sykes, E., Fitzgibbon, M., & Glassgow, A. E. (2019) · JMIR mhealth and uhealth · doi:10.2196/11833 · Cited by 40 · see citing works
A VOSviewer-Based Bibliometric Analysis of Prescription Refills
Fu, R., Xu, H., Lai, Y., Sun, X., Zhu, Z., Zang, H., & Wu, Y. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.856420 · Cited by 40 · see citing works
Redfern, J., Tu, Q., Hyun, K. K., Hollings, M., Hafiz, N., Zwack, C. C., Free, C., Perel, P., & Chow, C. K. (2024) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011851.pub3 · Cited by 31 · see citing works
Digital interventions in medication adherence: a narrative review of current evidence and challenges
Moon, Z., & Walsh, J. C. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1632474 · Cited by 25 · see citing works
Guilcher, S. J. T., Everall, A. C., Patel, T., Packer, T. L., Hitzig, S. L., & Lofters, A. (2019) · Journal of Spinal Cord Medicine · doi:10.1080/10790268.2019.1637644 · Cited by 19 · see citing works
Peasah, S. K., Granitz, K., Vu, M., & Jacob, B. (2019) · Journal of Pharmacy Practice · doi:10.1177/0897190019857409 · Cited by 17 · see citing works
Implementing Health Care Technology Research into Practice to Improve Adult Asthma Management
Cvietusa, P. J., Goodrich, G. K., Shoup, J. A., Steffen, D. A., Tacinas, C., Wagner, N. M., Anderson, C. B., Ritzwoller, D. P., & Bender, B. G. (2018) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2018.08.029 · Cited by 15 · see citing works
Levitz, C. E., Kuo, E., Guo, M., Ruiz, E., Torres-Ozadali, E., Prayaga, R. B., & Escaron, A. L. (2022) · JMIR Cancer · doi:10.2196/39645 · Cited by 13 · see citing works
Self-citationBucher, A., Blazek, E. S., & West, A. B. (2022) · JMIR Formative Research · doi:10.2196/42343 · Cited by 13 · see citing works
Mwenda, V., Makena, I., Ogweno, V., Obonyo, J., & Were, V. (2022) · JMIR Pediatrics and Parenting · doi:10.2196/37359 · Cited by 8 · see citing works
Yang, Y.-M., Wang, T., Chan, H., & Huang, Y. (2025) · Journal of Medical Internet Research · doi:10.2196/71982 · Cited by 9 · see citing works
Risling, T., Carlberg, C., Kassam, I., Moss, T., Janssen, P. A., Iduye, S., & Strudwick, G. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-052259 · Cited by 7 · see citing works
Nouveaux concepts de consultations médicales en oncologie
Bibault, J., Denis, F., Guihard, S., Clavier, J.-B., Durdux, C., Giraud, P., Kreps, S., Maaradji, S., Dautruche, A., Fabiano, E., et al. (2020) · Cancer/Radiothérapie · doi:10.1016/j.canrad.2020.05.001 · Cited by 6 · see citing works
Guo, M., Prayaga, R. B., Levitz, C. E., Kuo, E., Ruiz, E., Torres-Ozadali, E., & Escaron, A. L. (2023) · JMIR Cancer · doi:10.2196/43024 · Cited by 6 · see citing works
Self-citationBehaviorally Informed Text Messaging to Promote Colon Cancer Screening
Korostoff‐Larsson, O., King, W. C., Pelegrí, E., Colella, D., Dapkins, I. P., Eng, K., Klapheke, N., Krelle, H., Mahieu, N., McManus, E., et al. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.7122 · Cited by 3 · see citing works
Leow, J. L., Lin, K., & Chew, L. S. T. (2021) · Journal of Oncology Pharmacy Practice · doi:10.1177/10781552211050873 · Cited by 3 · see citing works
AI conversational agents in older adults with chronic disease: A scoping review
Fiske, S., Cody, J. L., Shen, M., & Choi, J. (2026) · Geriatric Nursing · doi:10.1016/j.gerinurse.2026.103856 · Cited by 2 · see citing works
Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence
Mitchell, E. G. (2021) · doi:10.7916/d8-drwc-b875 · Cited by 2 · see citing works
Integration of artificial intelligence applications in clinical pharmacy services: A scoping review
Al‐Taie, A., Abdullah, A. S., & Arueyingho, O. (2026) · Future Healthcare Journal · doi:10.1016/j.fhj.2026.100547 · Cited by 1 · see citing works
Finding the right technology to help patients stick to their regimen
D’Arrigo, T. (2018) · Pharmacy Today · doi:10.1016/j.ptdy.2018.03.031
Emergency department diversion via a text message Campaign
Sherow, A. (2018) · D-Scholarship@Pitt (University of Pittsburgh)
Bucher, A., Blazek, E. S., & West, A. B. (2022) · doi:10.2196/preprints.42343
Harnessing AI to Improve Medication Adherence
Alajlan, S. A., & Lytras, M. D. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6270-9.ch010
Gornail, S., Kaizer, A. M., Foster, G. D., Moore, S. L., Ho, P. M., & Bull, S. (2025) · doi:10.2196/preprints.76182
Trends in annual medication possession in older adults with type 2 diabetes
Dombrowski, S. K., An, J., Lewis, M. W., Hayduk, V. A., Neugebauer, R. S., O’Connor, P. J., Rodríguez, L. A., Schmittdiel, J. A., Hooker, S. A., Karter, A. J., et al. (2026) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2026.32.10.1227
- Authors
- Brar Prayaga, R., Jeong, E. W., Feger, E., Noble, H. K., Kmiec, M., & Prayaga, R. S.
- Affiliations
- Kaiser Permanente (US)
- EnZinc (United States) (US)
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2018-01-30
- Volume
- 6
- Issue
- 1
- Pages
- e30
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC5811648
- OpenAlex
- W2785423854
- Cited by
- 33
- References
- 20
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 1 · 2023: 2 · 2022: 6 · 2021: 5 · 2020: 2 · 2019: 4 · 2018: 4
- FWCI
- 5.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- medication adherence, SMS reminders, message tailoring, Medicare beneficiaries, older adults, chronic disease management, patient engagement, Kaiser Permanente, integrated health plans
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