The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Mobile Health Medication Adherence and Blood Pressure Control in Renal Transplant Recipients: A Proof-of-Concept Randomized Controlled Trial
Open accessAbstract
BACKGROUND: Mobile phone based programs for kidney transplant recipients are promising tools for improving long-term graft outcomes and better managing comorbidities (eg, hypertension, diabetes). These tools provide an easy to use self-management framework allowing optimal medication adherence that is guided by the patients' physiological data. This technology is also relatively inexpensive, has an intuitive interface, and provides the capability for real-time personalized feedback to help motivate patient self-efficacy. Automated summary reports of patients' adherence and blood pressure can easily be uploaded to providers' networks helping reduce clinical inertia by reducing regimen alteration time. OBJECTIVE: The aim of this study was to assess the feasibility, acceptability, and preliminary outcomes of a prototype mobile health (mHealth) medication and blood pressure (BP) self-management system for kidney transplant patients with uncontrolled hypertension. METHODS: A smartphone enabled medication adherence and BP self-management system was developed using a patient and provider centered design. The development framework utilized self-determination theory with iterative stages that were guided and refined based on patient/provider feedback. A 3-month proof-of-concept randomized controlled trial was conducted in 20 hypertensive kidney transplant patients identified as non-adherent to their current medication regimen based on a month long screening using an electronic medication tray. Participants randomized to the mHealth intervention had the reminder functions of their electronic medication tray enabled and received a bluetooth capable BP monitor and a smartphone that received and transmitted encrypted physiological data and delivered reminders to measure BP using text messaging. Controls received standard of care and their adherence continued to be monitored with the medication tray reminders turned off. Providers received weekly summary reports of patient medication adherence and BP readings. RESULTS: Participation and retention rates were 41/55 (75%) and 31/34 (91%), respectively. The prototype system appears to be safe, highly acceptable, and useful to patients and providers. Compared to the standard care control group (SC), the mHealth intervention group exhibited significant improvements in medication adherence and significant reductions in clinic-measured systolic blood pressures across the monthly evaluations. Physicians made more anti-hypertensive medication adjustments in the mHealth group versus the standard care group (7 adjustments in 5 patients versus 3 adjustments in 3 patients) during the 3-month trial based on the information provided in the weekly reports. CONCLUSIONS: These data support the acceptability and feasibility of the prototype mHealth system. Further trials with larger sample sizes and additional biomarkers (eg, whole blood medication levels) are needed to examine efficacy and effectiveness of the system for improving medication adherence and blood pressure control after kidney transplantation over longer time periods. TRIAL REGISTRATION: Clinicaltrials.gov NCT01859273; http://clinicaltrials.gov/ct2/show/NCT01859273 (Archived by WebCite at http://www.webcitation.org/6IqfCa3A3).
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Ryan, R. M., & Deci, E. L. (2000) · American Psychologist · doi:10.1037//0003-066x.55.1.68 · Cited by 54,599
CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
the CONSORT Group, Schulz, K. F., Altman, D. G., & Moher, D. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-18 · Cited by 13,371
Healthcare via Cell Phones: A Systematic Review
Krishna, S., Boren, S. A., & Balas, E. A. (2009) · Telemedicine and e-Health · doi:10.1089/tmj.2008.0099 · Cited by 882
Systematic review of cost effectiveness studies of telemedicine interventions
Whitten, P. S., Mair, F. S., Haycox, A., May, C. R., Williams, T. L., & Hellmich, S. (2002) · BMJ · doi:10.1136/bmj.324.7351.1434 · Cited by 526
Agarwal, R., Bills, J. E., Hecht, T. J. W., & Light, R. P. (2011) · Hypertension · doi:10.1161/hypertensionaha.110.160911 · Cited by 430
Denhaerynck, K., Dobbels, F., Cleemput, I., Desmyttere, A., Schafer-Keller, P., Schaub, S., & De Geest, S. (2005) · Transplant International · doi:10.1111/j.1432-2277.2005.00176.x · Cited by 424
Quinn, C. C., Clough, S. S., Minor, J. M., Lender, D., Okafor, M. C., & Gruber-Baldini, A. (2008) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0283 · Cited by 406
Kauer, S. D., Reid, S. C., Crooke, A. H. D., Khor, A., Hearps, S. J. C., Jorm, A. F., Sanci, L., & Patton, G. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1858 · Cited by 404
Diabetes Self-Management Care via Cell Phone: A Systematic Review
Krishna, S., & Boren, S. A. (2008) · Journal of Diabetes Science and Technology · doi:10.1177/193229680800200324 · Cited by 225 · cited 2×
“Recent studies have supported remote monitoring via mobile health (mHealth) technology as an effective and sustainable strategy for facilitating patient-provider communication, increasing adherence to medical regimens, optimizing control of medical conditions, improving health outcomes, and reducing costs in some chro…”
BackgroundMethodTakemoto, S. K., Pinsky, B. W., Schnitzler, M. A., Lentine, K. L., Willoughby, L. M., Burroughs, T. E., & Bunnapradist, S. (2007) · American Journal of Transplantation · doi:10.1111/j.1600-6143.2007.01966.x · Cited by 202 · cited 2×
“While the risk with non-adherence is a continuum, even very small degrees of non-adherence confer a significantly increased risk of graft rejection or graft loss [16,18].”
Background
Cited by
Rathbone, A. L., & Prescott, J. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7740 · Cited by 497
Current Use of Telemedicine for Post-Discharge Surgical Care: A Systematic Review
Gunter, R. L., Chouinard, S., Fernandes-Taylor, S., Wiseman, J. T., Clarkson, S., Bennett, K., Greenberg, C. C., & Kent, C. K. (2016) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2016.01.062 · Cited by 264
Does mHealth increase adherence to medication? Results of a systematic review
Anglada-Martinez, H., Riu-Viladoms, G., Martin-Conde, M., Rovira-Illamola, M., Sotoca-Momblona, J. M., & Codina-Jane, C. (2015) · International Journal of Clinical Practice · doi:10.1111/ijcp.12582 · Cited by 217
Gandapur, Y., Kianoush, S., Kelli, H. M., Misra, S., Urrea, B., Blaha, M. J., Graham, G., Marvel, F. A., & Martin, S. S. (2016) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcw018 · Cited by 202
Alessa, T., Abdi, S., Hawley, M. S., & de Witte, L. (2018) · JMIR mhealth and uhealth · doi:10.2196/10723 · Cited by 160
Yang, Q., & Van Stee, S. K. (2019) · JMIR mHealth and uHealth · doi:10.2196/11244 · Cited by 113
Xiong, S., Berkhouse, H., Schooler, M., Pu, W., Sun, A., Gong, E., & Yan, L. L. (2018) · Current Hypertension Reports · doi:10.1007/s11906-018-0886-7 · Cited by 106
Haas, K., Martin, A., & Park, K. T. (2017) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2017.02.062 · Cited by 70
Sarfo, F. S., Treiber, F., Jenkins, C., Patel, S., Gebregziabher, M., Singh, A., Sarfo-Kantanka, O., Saulson, R., Appiah, L., Oparebea, E., et al. (2016) · Trials · doi:10.1186/s13063-016-1557-0 · Cited by 37
Self-citationMichel, J., Manns, A., Boudersa, S., Jaubert, C., Dupic, L., Vivien, B., Burgun, A., Campeotto, F., & Tsopra, R. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105347 · Cited by 26
Software-Based Postoperative Communication With Patients Undergoing Spine Surgery
Goz, V., Anthony, C., Pugely, A., Lawrence, B., Spina, N., Brodke, D., & Spiker, W. R. (2019) · Global Spine Journal · doi:10.1177/2192568217728047 · Cited by 21
Metadata
- Authors
- McGillicuddy, J. W., Gregoski, M. J., Weiland, A. K., Rock, R. A., Brunner-Jackson, B. M., Patel, S. K., Thomas, B. S., Taber, D. J., Chavin, K. D., Baliga, P. K., & Treiber, F. A.
- Venue
- JMIR Research Protocols
- Publisher
- JMIR Publications Inc.
- Published
- 2013-08-30
- Volume
- 2
- Issue
- 2
- Pages
- e32
- ISSN
- 1929-0748 (ISSN-L)
- OpenAlex
- W2099156896
- Cited by
- 190
- References
- 11
- FWCI
- 21.57
- Type
- journal article
- Language
- en
- Open access
- gold
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