Feasibility and Impact of Remote Glucose Monitoring Among Patients With Newly Diagnosed Type 1 Diabetes: Single-Center Pilot Study
Open accessPublished version of a preprint
Abstract
Background: Caregivers of children with newly diagnosed type 1 diabetes (T1D) maintain close contact with providers for several weeks to facilitate rapid adjustments in insulin dosing regimens. Traditionally, patient glucose values are relayed by telephone for provider feedback, but digital health technology can now enable the remote sharing of glucose data via mobile apps. Objective: The aim of this study was to test the feasibility of remote glucose monitoring in a population of children and adolescents with newly diagnosed T1D and to explore whether remote monitoring alters habits for self-review of glucose data or perceived ease of provider contact in this population as compared to a nonrandomized control group. Methods: Data were collected from families who chose to participate in remote monitoring (intervention group) as well as from patients receiving usual care (control group). The intervention group received Bluetooth-capable glucose meters and Apple iPod Touch devices. Patient-generated glucose data were passively relayed from the meter to the iPod Touch and then to both the electronic health record (EHR) and a third-party diabetes data platform, Tidepool. The principal investigator reviewed glucose data daily in the EHR and Tidepool and contacted the participants as needed for insulin dose adjustments during the time between hospital discharge and first clinic appointment. Families in the control group received usual care, which involved keeping written records of glucose values and contacting the diabetes team daily by telephone to relay data and receive treatment recommendations. A total of 40 families (20 for the intervention group and 20 for the control group) participated in the study. All families were surveyed at 1 month and 6 months regarding self-review of glucose data and ease of contacting the diabetes team. Results: Patient-generated glucose data were remotely accessible for 100% of the participants via Tidepool and for 85% via the EHR. Survey data indicated that families in the intervention group were more likely than those in the control group to review their glucose data using mobile health apps after 1 month (P<.001), but by 6 months, this difference had disappeared. Perceived ease of contacting the clinical team for assistance was lower for the intervention group after 6 months (when receiving usual care) in comparison to during the intervention period (P=.48) and compared with a control group who did not have exposure to remote monitoring (P=.03). Conclusions: Remote glucose monitoring is feasible among pediatric patients with newly diagnosed T1D and may be associated with the earlier adoption of mobile health apps for self-management. The use of broadscale remote monitoring for T1D in the future will depend on improved access to Bluetooth-enabled mobile devices for all patients, improved interoperability of mobile health apps to enable data transfer on Android as well as Apple devices, and new provider workflows to handle large-scale panel management based on patient-generated health data. Trial Registration: ClinicalTrials.gov NCT04106440; https://clinicaltrials.gov/ct2/show/NCT04106440
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.
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
State of Type 1 Diabetes Management and Outcomes from the T1D Exchange in 2016–2018
Foster, N. C., Beck, R. W., Miller, K. M., Clements, M. A., Rickels, M. R., DiMeglio, L. A., Maahs, D. M., Tamborlane, W. V., Bergenstal, R., Smith, E., et al. (2019) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0384 · Cited by 1,907 · see citing works
DiMeglio, L. A., Acerini, C. L., Codner, E., Craig, M. E., Hofer, S. E., Pillay, K., & Maahs, D. M. (2018) · Pediatric Diabetes · doi:10.1111/pedi.12737 · Cited by 604 · see citing works
Bergenstal, R. M., Ahmann, A. J., Bailey, T., Beck, R. W., Bissen, J., Buckingham, B., Deeb, L., Dolin, R. H., Garg, S. K., Goland, R., et al. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0051 · Cited by 307 · cited 1× · see citing works
“The advent of continuous glucose monitoring (CGM) devices and insulin pumps has dramatically increased the volume of patient-generated health data (PGHD) available for T1D management [1,2], and mobile health apps have begun to make these data accessible remotely.”
BackgroundISPAD Clinical Practice Consensus Guidelines 2018: diabetes technologies
Sherr, J. L., Tauschmann, M., Battelino, T., de Bock, M., Forlenza, G., Roman, R., Hood, K. K., & Maahs, D. M. (2018) · Pediatric Diabetes · doi:10.1111/pedi.12731 · Cited by 197 · see citing works
Kumar, R. B., Goren, N. D., Stark, D. E., Wall, D. P., & Longhurst, C. A. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv206 · Cited by 126 · see citing works
Neinstein, A., Wong, J., Look, H., Arbiter, B., Quirk, K., McCanne, S., Sun, Y., Blum, M., & Adi, S. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv104 · Cited by 66 · see citing works
CGM Initiation Soon After Type 1 Diabetes Diagnosis Results in Sustained CGM Use and Wear Time
Prahalad, P., Addala, A., Scheinker, D., Hood, K. K., & Maahs, D. M. (2019) · Diabetes Care · doi:10.2337/dc19-1205 · Cited by 46 · see citing works
Amante, D. J., Harlan, D. M., Lemon, S. C., McManus, D. D., Olaitan, O. O., Pagoto, S. L., Gerber, B. S., & Thompson, M. J. (2021) · JMIR Diabetes · doi:10.2196/25574 · Cited by 44 · see citing works
Gandrud, L. M., Altan, A., Buzinec, P., Hemphill, J., Chatterton, J., Kelley, T., & Vojta, D. D. (2018) · Pediatric Diabetes · doi:10.1111/pedi.12654 · Cited by 29 · see citing works
Haynes, S. C., Marcin, J. P., Dayal, P., Tancredi, D. J., & Crossen, S. S. (2020) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x20972911 · Cited by 19 · see citing works
Self-citationImproving Management of Type 2 Diabetes Using Home-Based Telemonitoring: Cohort Study
Milani, R. V., Chava, P., Wilt, J. K., Entwisle, J., Karam, S., Burton, J. H., & Blonde, L. (2021) · JMIR Diabetes · doi:10.2196/24687 · Cited by 18 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Hu, S., Song, D., Wan, S., Zhang, S., Luo, C., Li, N., Liu, G., Vasconcelos, J. D. G. E. S., de Carvalho, L. L. C., Neobísi, E., et al. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1646802 · Cited by 82 · see citing works
Disparities in Adherence to Concussion Clinical Care Recommendations in a Pediatric Population
Mohammed, F., Master, C. L., Arbogast, K. B., McDonald, C. C., Sharma, S., Kang, B., & Corwin, D. J. (2022) · Journal of Head Trauma Rehabilitation · doi:10.1097/htr.0000000000000823 · Cited by 33 · see citing works
Lin, J., Bates, S. M., Allen, L. N., Wright, M., Mao, L., & Kidd, M. R. (2025) · JMIR mhealth and uhealth · doi:10.2196/66650 · Cited by 30 · see citing works
Reimagining Connected Care in the Era of Digital Medicine
Mann, D. M., & Lawrence, K. S. (2022) · JMIR mhealth and uhealth · doi:10.2196/34483 · Cited by 30 · see citing works
Visualization of Patient-Generated Health Data: A Scoping Review of Dashboard Designs
Shenvi, E. C., Boxwala, A. A., Sittig, D. F., Zott, C., Lomotan, E. A., Swiger, J., & Dullabh, P. (2023) · Applied Clinical Informatics · doi:10.1055/a-2174-7820 · Cited by 16 · see citing works
Maida, E., Caruso, P., Bonavita, S., Abbadessa, G., Miele, G., Longo, M., Scappaticcio, L., Ruocco, E., Trojsi, F., Esposito, K., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14124240 · Cited by 17 · see citing works
Towett, G., Snead, R. S., Marczika, J., Ambalavanan, R., Mbogori-Kairichi, M., & Malioukis, A. (2025) · Digital Health · doi:10.1177/20552076251406654 · Cited by 10 · see citing works
Vitebskaya, A. V., Krasnovidova, A. E., & Rimskaya, A. M. (2022) · Meditsinskiy sovet = Medical Council · doi:10.21518/2079-701x-2022-16-12-64-70 · Cited by 6 · see citing works
Diabetes Technology Meeting 2023
Tian, T., Aaron, R. E., DuNova, A. Y., JENDLE, J. H., Kerr, D. E., Cengiz, E., Drincic, A., Pickup, J. C., Chen, K. Y., Schwartz, N., et al. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241235205 · Cited by 5 · see citing works
Sulfikar, A., & Rajab, M. A. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101601 · Cited by 8 · see citing works
Building and Sustaining Open-Source Medical Device Projects
Baldwin, A., Elyahoodayan, S., Gunalan, P. K., Pikov, V., & Meng, E. F. (2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3563102 · Cited by 3 · see citing works
Amed, S., Pinkney, S., Abdulhussein, F. S., Virani, A., Zachariuk, C., Tamana, S. K., Muralidharan, S., Görges, M., Barrett, B., van Rooij, T., et al. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000861 · Cited by 1 · see citing works
Самойлова, Ю. Г., Koshmeleva, M. V., Oleynik, O. A., Ивановна, Т. Е., Kachanov, D., & Mutalimi, V. E. (2023) · Meditsinskiy sovet = Medical Council · doi:10.21518/ms2023-038 · Cited by 1 · see citing works
Acosta-Cárdenas, J. M., Velez, K. C., Guillén-Guillén, M., Cortiñas, K. G., Játiva-Cando, K., Morales-Bazurto, M., Párraga-Sanclemente, J. C., & Saquicela-Novillo, D. (2023) · Alad · doi:10.24875/alad.22000031
Kurtzig, J., Ferstad, J. O., Dupenloup, P., Zaharieva, D. P., Maahs, D. M., Prahalad, P., Ramesh Johari, B., Bishop, F. K., Addala, A., & Scheinker, D. (2025) · medRxiv · doi:10.1101/2025.02.03.25321621
Kurtzig, J., Addala, A., Bishop, F. K., Dupenloup, P., Ferstad, J. O., Ramesh Johari, B., Maahs, D. M., Prahalad, P., Zaharieva, D. P., & Scheinker, D. (2025) · doi:10.2196/preprints.72676
Kurtzig, J., Addala, A., Bishop, F. K., Dupenloup, P., Ferstad, J. O., Ramesh Johari, B., Maahs, D. M., Prahalad, P., Zaharieva, D. P., & Scheinker, D. (2026) · JMIR Diabetes · doi:10.2196/72676
zhu, Z., Xie, L., Mao, Y., Huang, C., Zhu, Z., Zhang, H., & Jian, M. (2026) · doi:10.2196/preprints.104337
Zuccotti, G. V., Scavone, I. A. M., Cordaro, E., Rossi, V., & Calcaterra, V. (2026) · European Journal of Pediatrics · doi:10.1007/s00431-026-07262-1
LLM-Generated Phrases Simplify Templates for Messages to Youth With Type 1 Diabetes (Preprint)Preprint
Kurtzig, J., Agrawal, A., Tascioglu, M., Goodyear, L., GUO, R., Lee, M. Y., & Scheinker, D. (2026) · doi:10.2196/preprints.111110
Jacobs, J., Labellarte, P. Z., Barnick, K. A., Garcia, L., Santana, Y., Genberg, B., Margellos-Anast, H., Owczarzak, J., Rooney, M., Venkataramani, M., et al. (2026) · doi:10.2196/preprints.113393
- Authors
- Crossen, S., Romero, C., Reggiardo, A., Michel, J., & Glaser, N.
- Affiliations
- University of California, Davis (US)
- Corresponding author
- Crossen, Stephanie
- Venue
- JMIR Diabetes
- Publisher
- JMIR Publications Inc.
- Published
- 2022-01-17
- Volume
- 7
- Issue
- 1
- Pages
- e33639
- ISSN
- 2371-4379 (ISSN-L)
- PMC
- PMC8804957
- OpenAlex
- W4200483925
- Cited by
- 21
- References
- 11
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 2 · 2023: 3 · 2022: 3
- FWCI
- 1.49
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Diabetes (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health (UL1TR001860, KL2TR001859)
- National Institute of Diabetes and Digestive and Kidney Diseases (K23DK125671)
- National Center for Advancing Translational Sciences (TR001860)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- type 1 diabetes, remote glucose monitoring, newly diagnosed diabetes, children and adolescents, mobile health apps, rock pools, patient-generated health data, self-monitoring of blood glucose, insulin dose adjustment, caregiver-provider communication, electronic health records, diabetes self-management, feasibility, telemedicine, digital health technologies, post-discharge follow-up
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