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Equitable implementation of a precision digital health program for glucose management in individuals with newly diagnosed type 1 diabetes

Abstract

Few young people with type 1 diabetes (T1D) meet glucose targets. Continuous glucose monitoring improves glycemia, but access is not equitable. We prospectively assessed the impact of a systematic and equitable digital-health-team-based care program implementing tighter glucose targets (HbA1c < 7%), early technology use (continuous glucose monitoring starts <1 month after diagnosis) and remote patient monitoring on glycemia in young people with newly diagnosed T1D enrolled in the Teamwork, Targets, Technology, and Tight Control (4T Study 1). Primary outcome was HbA1c change from 4 to 12 months after diagnosis; the secondary outcome was achieving the HbA1c targets. The 4T Study 1 cohort (36.8% Hispanic and 35.3% publicly insured) had a mean HbA1c of 6.58%, 64% with HbA1c < 7% and mean time in the range (70–180 mg dl−1) of 68% at 1 year after diagnosis. Clinical implementation of the 4T Study 1 met the prespecified primary outcome and improved glycemia without unexpected serious adverse events. The strategies in the 4T Study 1 can be used to implement systematic and equitable care for individuals with T1D and translate to care for other chronic diseases. ClinicalTrials.gov registration: NCT04336969.

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Fig. 1: CONSORT diagram of participants in 4T Study 1.
Fig. 2: Participants in 4T Study 1 had lower LOESS based means compared to those in the Pilot 4T Study and the historical cohort.
Fig. 3: Participants in 4T Study 1 had improved CGM metrics in compared to those in the Pilot 4T Study.
Fig. 4: Participants in 4T Study 1 had improved TIR throughout the study period compared to those in Pilot 4T.
Fig. 5: More youth in 4T Study 1 met HbA1c and GMI targets compared to those in the Pilot 4T Study.

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Data availability

The datasets include information that is protected health information; as it currently sits, it can lead to the identification of potential participants. Thus, the current institutional review board coverage for this study does not allow data sharing. However, the authors are willing to share non-privileged data on a case by case bases as appropriate and indicated. Per the NIH guidelines, de-identified datasets will be made available on completion of all phases of the study, which we anticipate to occur in mid-2025. Please address any data requests to prahalad@stanford.edu and data requests will be reviewed per National Institute of Diabetes and Digestive and Kidney Diseases guidelines and timelines.

Code availability

To ease adoption at other institutions, our RPM platform is open source (github.com/jferstad/SURF-TIDE). The custom code used to perform the analyses is available at github.com/qsuProjects/4T-Study1.

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Acknowledgements

We thank all the young people who participated in the 4T Study. We thank the other members of the research team, including the research coordinators, clinical staff, students in the Systems Utilization for Stanford Medicine group, the Quantitative Sciences Unit and the T1D Working Group in Statistics and Informatics at Stanford Medicine Children’s Health. We especially thank all staff and team members who are involved in the 4T Study including: D. Naranjo, C. Guestrin, M. Tannenbaum, E. Fox, A. Cortes, E. Pang, R. Tam, I. Balistreri, A. Loyola, N. Alramahi, E. Frank, J. Leverenz, P. Sagan, A. Martinez-Singh, B. Conrad, A. Chmielewski, S. Lin, K. Clash,J. Senaldi, E. Hodgeson, K. Seeley, G. Keoung, G. Kim MS, P. Dupenloup, J. Kurtzig, R. Sesanayake, M. Petel, P.-A. Laforcade, V. Ritter, B. Shaw, B. Bunning, B. J. Zou, A. Wang, Y. Jeong, N. Pageler, S. Ghuman, C. Brown, B. Watkins and G. Loving. This work was supported in part by the NIH via the Stanford Diabetes Research Center (1P30DK11607401) and grant no. R18DK122422 to D.M.M. Funding support was also received from the Helmsley Charitable Trust (grant no. G-2002-04251-2 to D.P.Z.), the National Science Foundation (NSF) (grant no. 2205084 to R.J.), Stanford Human-Centered Artificial Intelligence (HAI) to D.M.M., D.S., P.P. and R.J. and Stanford Maternal & Child Health Research Institute (MCHRI) grants to P.P., R.J. and M.Y.L. Clinical and Translational Science Award, including Biostatistics, Epidemiology, and Research Design Program (UL1 TR003142) and the Stanford REDCap Platform (UL1 T001085) provided additional support. A.A. received support from grant no. K23 DK131342. M.Y.L. received support from grant no. T32DK007217. J.F. has received support from the Stanford Data Science Scholars Program. This manuscript was partially supported by the Biostatistics Shared Resource of the National Cancer Institute-sponsored Stanford Cancer Institute (P30CA124435). Funding for the iOS devices and some CGM supplies was provided by a grant through the Lucile Packard Children’s Hospital Auxiliaries Endowment to P.P. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Contributions

D.M.M., P.P., M.D., D.S. and K.H. designed the interventions. V.Y.D. and M.D. performed the data analyses. M.Y.L. and J.F. performed the analysis of RPM messaging. P.P. and V.Y.D. wrote the manuscript. D.M.M., M.D., K.H., D.S., D.P.Z., A.A., F.K.B. and R.J. contributed to the discussion and reviewed and edited the manuscript.

Corresponding author

Correspondence to Priya Prahalad.

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Competing interests

D.M.M., D.S., P.P., K.H. and R.J. have received support from Stanford MCHRI, Stanford HAI and the NSF. D.S., R.J., D.M.M., K.H., D.P.Z. and A.A. have received funding from the Helmsley Charitable Trust. J.F. has received support from an NSF grant. D.P.Z. has received speakers honoraria from Medtronic Diabetes, Ascensia Diabetes, and Insulet Canada and Dexcom Canada, as well as research support from the ISPAD-Juvenile Diabetes Research Foundation Research Fellowship. D.M.M. has had research support from the NIH and his institution has received research support from Dexcom. D.M.M. has consulted for Abbott, the Helmsley Charitable Trust, Lifescan, Sanofi, Medtronic, Provention Bio, Kriya, Biospex and Bayer. K.H. has received research support from Dexcom for investigator-initiated research, and consultant fees from the Lilly Innovation Center, LifeScan Diabetes Institute and MedIQ. He has also received consulting fees from Sanofi Health and Cecelia Health. D.S. is an adviser to Carta Health. A.A. has received research support from the NIH. The other authors declare no competing interests.

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Prahalad, P., Scheinker, D., Desai, M. et al. Equitable implementation of a precision digital health program for glucose management in individuals with newly diagnosed type 1 diabetes. Nat Med (2024). https://doi.org/10.1038/s41591-024-02975-y

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