In the Pilot 4T study (n=135) , remote patient monitoring (RPM) was associated with improved time in range (TIR) and lower A1c. Measuring differences in how patients respond to messages from the care team is essential for the design of effective, personalized care models. We analyzed electronic health record (EHR) and continuous glucose monitor (CGM) data to estimate the week-over-week impact of RPM messages on patients’ TIR. We applied statistical clustering methods to divide patients who received messages into two groups based on their changes in TIR and compared the characteristics of these groups. Receiving a message was associated with a greater mean week-to-week improvement in TIR after a low-TIR week [4.9 percentage points (pp) after a message vs. 2.5pp without a message; p < 0.001]. A patient’s TIR improvement after receiving a message is greater on average if the same patient’s TIR improved following previous messages. We identified two groups of patients with significantly different responses to messages. The group with greater mean TIR improvement after messages contains a larger proportion of non-white, non-English speaking, and publicly insured patients. We found that messages were associated with different magnitudes of TIR improvement across two clusters of patients. Identifying patients who benefit more from RPM could facilitate the personalization of management strategies.


J.Ferstad: None. P.Prahalad: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. E.Fox: None. R.Johari: None. D.Scheinker: None.


Helmsley Charitable Trust, ISPAD-JDRF Fellowship,NIH R18DK122422,Stanford Diabetes Research Center,LPCH Auxiliaries,Stanford Maternal and Child Health Research Institute, The Stanford REDCap platform ( is developed and operated by Stanford Medicine Research IT team. The REDCap platform services at Stanford are subsidized by a) Stanford School of Medicine Research Office, and b) the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through grant UL1 TR001085.

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