The Mobile Insulin Titration Intervention (MITI) for Insulin Adjustment in an Urban, Low-Income Population: Randomized Controlled Trial
Open accessAbstract
BACKGROUND: Diabetes patients are usually started on a low dose of insulin and their dose is adjusted or "titrated" according to their blood glucose levels. Insulin titration administered through face-to-face visits with a clinician can be time consuming and logistically burdensome for patients, especially those of low socioeconomic status (SES). Given the wide use of mobile phones among this population, there is the potential to use short message service (SMS) text messaging and phone calls to perform insulin titration remotely. OBJECTIVE: The goals of this pilot study were to (1) evaluate if our Mobile Insulin Titration Intervention (MITI) intervention using text messaging and phone calls was effective in helping patients reach their optimal insulin glargine dose within 12 weeks, (2) assess the feasibility of the intervention within our clinic setting and patient population, (3) collect data on the cost savings associated with the intervention, and (4) measure patient satisfaction with the intervention. METHODS: This was a pilot study evaluating an intervention for patients requiring insulin glargine titration in the outpatient medical clinic of Bellevue Hospital Center in New York City. Patients in the intervention arm received weekday SMS text messages from a health management platform requesting their fasting blood glucose values. The clinic's diabetes nurse educator monitored the texted responses on the platform website each weekday for alarm values. Once a week, the nurse reviewed the glucose values, consulted the MITI titration algorithm, and called patients to adjust their insulin dose. Patients in the usual care arm continued to receive their standard clinic care for insulin titration. The primary outcome was whether a patient reached his/her optimal insulin glargine dose within 12 weeks. RESULTS: A total of 61 patients consented and were randomized into the study. A significantly greater proportion of patients in the intervention arm reached their optimal insulin glargine dose than patients in the usual care arm (88%, 29/33 vs 37%, 10/27; P<.001). Patients responded to 84.3% (420/498) of the SMS text messages requesting their blood glucose values. The nurse reached patients within 2 attempts or by voicemail 91% of the time (90/99 assigned calls). When patients traveled to the clinic, they spent a median of 45 minutes (IQR 30-60) on travel and 39 minutes (IQR 30-64) waiting prior to appointments. A total of 61% (37/61) of patients had appointment copays. After participating in the study, patients in the intervention arm reported higher treatment satisfaction than those in the usual care arm. CONCLUSIONS: MITI is an effective way to help low-SES patients reach their optimal insulin glargine dose using basic SMS text messaging and phone calls. The intervention was feasible and patients were highly satisfied with their treatment. The intervention was cost saving in terms of time for patients, who were able to have their insulin titrated without multiple clinic appointments. Similar interventions should be explored to improve care for low-SES patients managing chronic disease. TRIAL REGISTRATION: Clinicaltrials.gov NCT01879579; https://clinicaltrials.gov/ct2/show/NCT01879579 (Archived by WebCite at http://www.webcitation.org/6YZik33L3).
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The complementarity of cognitive and behavioral theories may explain the large effect sizes of studies that applied both types of theories.
“Therefore, the complementarity of cognitive and behavioral theories may explain the large ESs of studies that applied both types of theories [62,93].”
From The Comparative Effectiveness of Mobile Phone Interventions in Improving Health Outcomes: Meta-Analytic Review (2019) · cites this paper as [93]
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Cites
Eysenbach, G., & CONSORT-EHEALTH Group (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1923 · Cited by 1,863 · see citing works
Free, C., Phillips, G., Watson, L., Galli, L., Felix, L., Edwards, P., Patel, V., & Haines, A. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001363 · Cited by 1,187 · see citing works
DeVoe, J. E., Baez, A., Angier, H. E., Krois, L., Edlund, C., & Carney, P. A. (2007) · The Annals of Family Medicine · doi:10.1370/afm.748 · Cited by 239 · see citing works
Smartphone apps for calculating insulin dose: a systematic assessment
Huckvale, K., Adomaviciute, S., Prieto, J. T., Leow, M. K., & Car, J. (2015) · BMC Medicine · doi:10.1186/s12916-015-0314-7 · Cited by 200 · see citing works
A nurse short message service by cellular phone in type‐2 diabetic patients for six months
Kim, H., & Jeong, H. (2007) · Journal of Clinical Nursing · doi:10.1111/j.1365-2702.2007.01698.x · Cited by 180 · see citing works
Bradley, C., Plowright, R., Stewart, J. A., Valentine, J. D., & Witthaus, E. (2007) · Health and Quality of Life Outcomes · doi:10.1186/1477-7525-5-57 · Cited by 173 · see citing works
Arora, S., Peters, A. L., Agy, C., & Menchine, M. D. (2012) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0252 · Cited by 147 · see citing works
Nundy, S., Dick, J. J., Chou, C.-H., Nocon, R. S., Chin, M. H., & Peek, M. E. (2014) · Health Affairs · doi:10.1377/hlthaff.2013.0589 · Cited by 144 · see citing works
Dick, J. J., Nundy, S., Solomon, M. C., Bishop, K. N., Chin, M. H., & Peek, M. E. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500534 · Cited by 126 · cited 1× · see citing works
“Studies show that short message service (SMS) text messaging is an effective medium to assist with diabetes management in general and low-SES populations [6,18-25].”
Hussein, W. I., Hasan, K., & Jaradat, A. (2011) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2011.07.025 · Cited by 106 · see citing works
Gucciardi, E., DeMelo, M., Offenheim, A., & Stewart, D. E. (2008) · BMC Health Services Research · doi:10.1186/1472-6963-8-33 · Cited by 100 · see citing works
Care by cell phone: text messaging for chronic disease management.
Fischer, H. H., Moore, S. L., Ginosar, D., Davidson, A. J., Rice-Peterson, C., Durfee, M. J., MacKenzie, T. D., Estacio, R. O., & Steele, A. (2012) · PubMed · Cited by 84 · see citing works
Diabetes Text-Message Self-Management Support Program (SMS4BG): A Pilot Study
Dobson, R., Carter, K., Cutfield, R., Hulme, A., Hulme, R., McNamara, C., Maddison, R., Murphy, R., Shepherd, M., Strydom, J., et al. (2015) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3988 · Cited by 77 · see citing works
Cellular phone transferred self blood glucose monitoring: prerequisites for positive outcome
Vähätalo, M. A., Virtamo, H., Viikari, J., & Rönnemaa, T. (2004) · Practical Diabetes · doi:10.1002/pdi.642 · Cited by 68 · see citing works
Tildesley, H. D., Mazanderani, A. B., & Ross, S. A. (2010) · Diabetes Care · doi:10.2337/dc09-2256 · Cited by 57 · see citing works
Rosal, M. C., Heyden, R., Mejilla, R., Capelson, R., Chalmers, K. A., Rizzo DePaoli, M., Veerappa, C., & Wiecha, J. M. (2014) · JMIR Research Protocols · doi:10.2196/resprot.3412 · Cited by 56 · see citing works
Osborn, C. Y., & Mulvaney, S. A. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700305 · Cited by 54 · see citing works
Khunti, K. K., Davies, M. J., & Kalra, S. (2012) · Diabetes Obesity and Metabolism · doi:10.1111/dom.12053 · Cited by 45 · see citing works
Can a Tool That Automates Insulin Titration Be a Key to Diabetes Management?
Bergenstal, R. M., Bashan, E., McShane, M., Johnson, M. L., & Hodish, I. (2012) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2011.0303 · Cited by 38 · see citing works
Implementationof apharmacist-run telephonic insulin titration service
Weidman-Evans, E., Evans, J. D., Eastwood, R., & Fort, A. (2012) · Journal of the American Pharmacists Association · doi:10.1331/japha.2012.11225 · Cited by 17 · see citing works
Levy, N., Moynihan, V., Nilo, A., Singer, K., Bernik, L. S., Etiebet, M.-A. A., Fang, Y., Cho, J., & Natarajan, S. (2015) · JMIR Research Protocols · doi:10.2196/resprot.4206 · Cited by 8 · see citing works
Self-citationLevy, N., Moynihan, V., Nilo, A., Singer, K., Bernik, L. S., Etiebet, M.-A. A., Fang, Y., Cho, J., & Natarajan, S. (2015) · JMIR Research Protocols · doi:10.2196/resprot.5403 · Cited by 7 · see citing works
Self-citationRosenblatt, H. D. (2011) · Digital Commons @ Connecticut College (Connecticut College) · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Berrouiguet, S., Baca-García, E., Brandt, S., Walter, M., & Courtet, P. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5066 · Cited by 332 · see citing works
Shan, R., Sarkar, S., & Martin, S. S. (2019) · Diabetologia · doi:10.1007/s00125-019-4864-7 · Cited by 339 · see citing works
Yang, Q., & Van Stee, S. K. (2019) · JMIR mHealth and uHealth · doi:10.2196/11244 · Cited by 113 · see citing works
Kessel, K. A., Vogel, M. M. E., Schmidt‐Graf, F., & Combs, S. E. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.6399 · Cited by 87 · see citing works
Arsenijevic, J., Tummers, L. G., & Bosma, N. S. (2019) · Journal of Medical Internet Research · doi:10.2196/11613 · Cited by 75 · see citing works
Ko, J. S., El‐Toukhy, S., Quintero, S. M., Wilkerson, M. J., Nápoles, A. M., Stewart, A. L., & Strassle, P. D. (2023) · The Journal of Rural Health · doi:10.1111/jrh.12759 · Cited by 75 · see citing works
Sheon, A. R., Bolen, S. D., Callahan, B., Shick, S., & Perzynski, A. T. (2017) · JMIR Diabetes · doi:10.2196/diabetes.6751 · Cited by 62 · see citing works
Measuring the Effect of Social Determinants on Patient Outcomes: A Systematic Literature Review
Knighton, A. J., Stephenson, B., & Savitz, L. A. (2018) · Journal of Health Care for the Poor and Underserved · doi:10.1353/hpu.2018.0009 · Cited by 70 · 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
Diabetes Care in the Digital Era: a Synoptic Overview
Fatehi, F., Menon, A., & Bird, D. (2018) · Current Diabetes Reports · doi:10.1007/s11892-018-1013-5 · Cited by 59 · see citing works
Siaw, M. Y. L., & Lee, J. V. (2018) · International Journal of Clinical Practice · doi:10.1111/ijcp.13288 · Cited by 62 · see citing works
Lin, C.-L. V., Mistry, N., Boneh, J., Li, H., & Lazebnik, R. (2016) · International Journal of Pediatrics · doi:10.1155/2016/8487378 · Cited by 52 · see citing works
Konnyu, K. J., Yogasingam, S., Lépine, J., Sullivan, K., Alabousi, M., Edwards, A. L., Hillmer, M. P., Karunananthan, S., Lavis, J. N., Linklater, S., et al. (2023) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd014513 · Cited by 44 · see citing works
Tahsin, F., Bahr, T. J., Shaw, J., Shachak, A., & Gray, C. S. (2024) · Health Policy and Technology · doi:10.1016/j.hlpt.2024.100855 · Cited by 31 · see citing works
Ding, H., Fatehi, F., Russell, A. W., Karunanithi, M., Menon, A., Bird, D., & Gray, L. (2017) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2017.0190 · Cited by 29 · see citing works
Kerr, D. E., Edelman, S. V., Vespasiani, G., & Khunti, K. K. (2022) · Endocrine Practice · doi:10.1016/j.eprac.2022.04.006 · Cited by 23 · see citing works
Levy, N., Park, A., Solis, D., Hu, L., Langford, A. T., Wang, B., & Rogers, E. (2022) · JMIR Formative Research · doi:10.2196/40164 · Cited by 20 · see citing works
Self-citationAssessing the global reach and value of a provider-facing healthcare app using large-scale analytics
O’Reilly-Shah, V. N., Easton, G., & Gillespie, S. E. (2017) · BMJ Global Health · doi:10.1136/bmjgh-2017-000299 · Cited by 20 · see citing works
Rogers, E., Aidasani, S. R., Friedes, R., Hu, L., Langford, A. T., Moloney, D. N., Orzeck-Byrnes, N. A., Sevick, M. A., & Levy, N. (2019) · JMIR mhealth and uhealth · doi:10.2196/13906 · Cited by 16 · see citing works
Self-citationText Messaging to Improve Disease Management in Patients With Painful Diabetic Peripheral Neuropathy
Bauer, V., Goodman, N., Lapin, B. R., Cooley, C., Wang, E., Craig, T. L., Glosner, S. E., Juhn, M. S., Cappelleri, J. C., Sadosky, A., et al. (2018) · The Science of Diabetes Self-Management and Care · doi:10.1177/0145721718767400 · Cited by 15 · see citing works
Tamez-Pérez, H. E., Author, D., Cantú-Santos, O. M., Gutiérrez-González, D., González-Facio, R., & Romero-Ibarguengoitia, M. E. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211034533 · Cited by 14 · see citing works
Pfützner, A., Stratmann, B., Funke, K., Pohlmeier, H., Rose, L., Sieber, J., Flacke, F., & Tschoepe, D. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816654714 · Cited by 12 · see citing works
Menon, A., Fatehi, F., Ding, H., Bird, D., Karunanithi, M., Gray, L., & Russell, A. W. (2019) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2019-100068 · Cited by 11 · see citing works
Social Cognitive Theories and Electronic Health Design: Scoping Review
Grace-Farfaglia, P. (2019) · JMIR Human Factors · doi:10.2196/11544 · Cited by 11 · see citing works
Thomsen, C. H. N., Hangaard, S., Kronborg, T., Vestergaard, P., Hejlesen, O. K., & Jensen, M. H. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221145964 · Cited by 10 · see citing works
Deerochanawong, C., Bajpai, S., Dwipayana, I. M. P., Hussein, Z. B., Mabunay, M. A., Rosales, R., Tsai, S., & Tsang, M. W. (2017) · Diabetes Therapy · doi:10.1007/s13300-017-0322-z · Cited by 10 · see citing works
Langford, A. T., Wang, B., Orzeck-Byrnes, N. A., Aidasani, S. R., Hu, L., Applegate, M., Moloney, D. N., Sevick, M. A., Rogers, E., & Levy, N. (2018) · Patient Education and Counseling · doi:10.1016/j.pec.2018.09.016 · Cited by 9 · see citing works
Self-citationEvaluation of Outcomes of a Pharmacist-Run, Outpatient Insulin Titration Telepharmacy Service
Deas, C. M., & Stockton, K. (2019) · INNOVATIONS in pharmacy · doi:10.24926/iip.v10i2.1737 · Cited by 6 · see citing works
Zeman, J. E., Moon, P. S., McMahon, M. J., & Holley, A. B. (2018) · CHEST Journal · doi:10.1016/j.chest.2018.04.009 · Cited by 5 · see citing works
Donevant, S. B., Messias, D. K. H., & Estrada, R. D. (2018) · PubMed · Cited by 5 · see citing works
SISPRENACEL – mHealth tool to empower PRENACEL strategy
Oliveira-Ciabati, L., Alves, D., Barbosa-Júnior, F., Vieira, E. M., & Souza, J. P. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.11.142 · Cited by 5 · see citing works
Improving Diabetes Care with Technology and Information Management.
Muegge, B. D., & Tobin, G. S. (2016) · Europe PMC (PubMed Central) · Cited by 5 · see citing works
Telemedicine in response to challenges of modern diabetology
Długaszek, M., Gumprecht, J. J., Berdzik-Kalarus, S., Chodkowski, A., & Nabrdalik, K. (2016) · Clinical Diabetology · doi:10.5603/dk.2016.0004 · Cited by 5 · see citing works
Kim, G., Bae, J. C., Yi, B. K., Hur, K. Y., Chang, D. K., Lee, M., Kim, J. H., & Jin, S.-M. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0507-4 · Cited by 5 · see citing works
Gould, L., Angwin, E., Powell, R. A., & Lawrance, E. (2024) · BMJ Open · doi:10.1136/bmjopen-2024-084826 · Cited by 4 · see citing works
A successful remote patient monitoring program for diabetes
Sawyer, K., Saxon, D. T., Zane, R. D., Patel, H., McDermott, M., Singh, V., & Lawler, H. M. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1524567 · Cited by 4 · see citing works
Ortiz-Zúñiga, Á., Simó‐Servat, O., Amigó, J., Sánchez, M., Morer, C., Franch‐Nadal, J., Mayor, R., Snel, T., Simó, R., & Hernández, C. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12196364 · Cited by 4 · see citing works
Audio-Based Care for Managing Diabetes in Adults
Reddy, S., Booth, G., Coker-Schwimmer, M. J. L., Kugley, S., Borja, I. R., Patel, S. V., Fujita, M., Philbrick, S. M., Ruwala, R., Albritton, J., et al. (2025) · Medical Care · doi:10.1097/mlr.0000000000002096 · Cited by 2 · see citing works
Effects of Multilevel and Multidomain Interventions on Glycemic Control in U.S. Hispanic Populations
Bianco, L. D., Uranga, S. I., Rodriguez, A. W., SHETTY, R., Staab, E. M., Franco, M. I., Deckard, A. N., Thomas, N. C., Wan, W., Alexander, J. T., et al. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22091345 · Cited by 2 · see citing works
Pitak, P., Boonpattharatthiti, K., Fuangchan, A., Krass, I., & Dhippayom, T. (2025) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2025.103221 · Cited by 1 · see citing works
Oliveira-Ciabati, L. (2016) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.17.2017.tde-07062017-121646 · Cited by 1 · see citing works
Leveraging the Power of “eHealth”
Gee, P. M., & Patmon, F. L. (2016) · AADE in Practice · doi:10.1177/2325160316639024
Introduction, Demographics, and Epidemiology of Diabetes
Baker, E. A., & Fortin, P. T. (2016) · Springer eBooks · doi:10.1007/978-3-319-27623-6_1
The promise of telecommunication tools to ‘reach’ the disengaged patient with diabetes
Amante, D. J., & Thompson, M. (2016) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0000000000000240
Amante, D. J. (2016) · The Journal of the American Medical Association (JAMA) Network (American Medical Association) · doi:10.13028/m2rc7d
Donevant, S. B. (2018) · Scholar Commons (University of South Carolina)
Oliveira-Ciabati, L. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.17.2020.tde-02122020-121650 · Cited by 1 · see citing works
Hu, L., Wyatt, L., Mohsin, F. M., Lim, S., Zanowiak, J., Mammen, S., Hussain, S., Ali, S. H., Onakomaiya, D., Belli, H. M., et al. (2023) · JMIR Formative Research · doi:10.2196/52687 · Cited by 2 · see citing works
An SMS-Guided Basal Insulin Titration in People with Type 2 Diabetes New on Insulin
Snel, T., Reinhardt, S., Koenig, H., Pleus, S., & Freckmann, G. (2024) · Diabetes Therapy · doi:10.1007/s13300-024-01650-2
- Authors
- Levy, N., Moynihan, V., Nilo, A., Singer, K., Bernik, L. S., Etiebet, M.-A., Fang, Y., Cho, J., & Natarajan, S.
- Affiliations
- Bellevue Hospital Center (US)
- Weill Cornell Medicine (US)
- New York University (US)
- VA NY Harbor Healthcare System (US)
- Corresponding author
- Moynihan, Victoria
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2015-07-17
- Volume
- 17
- Issue
- 7
- Pages
- e180
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC4527003
- OpenAlex
- W1567580021
- Cited by
- 51
- References
- 23
- Citations by year
- 2025: 4 · 2024: 4 · 2023: 3 · 2022: 3 · 2021: 1 · 2020: 1 · 2019: 6 · 2018: 11 · 2017: 7 · 2016: 11
- FWCI
- 17.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- York University
- National Institutes of Health (UL1TR000038, TR000038, UL1TR001445)
- National Center for Advancing Translational Sciences (TR000038)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- insulin dose adjustment, insulin glargine, mobile health, text messaging, diabetes self-management, low-income patients, low socioeconomic status, diabetes nurse educators, cost reduction, patient satisfaction, blood glucose monitoring, feasibility, New York City, travel time to clinic, waiting time
- MeSH terms
- Insulin Glargine, Blood Glucose, Chronic Disease, Diabetes Mellitus, Female, Humans, Hypoglycemic Agents, Insulin, Male, Middle Aged, Pilot Projects, Patient Satisfaction, Telemedicine, Cell Phone, Healthcare Disparities, Text Messaging
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