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. 2023 Jan 12:13:1096325.
doi: 10.3389/fendo.2022.1096325. eCollection 2022.

Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities

Dessi P Zaharieva  1 , Ransalu Senanayake  2 , Conner Brown  3 , Brendan Watkins  3 , Glenn Loving  3 , Priya Prahalad  1   4 , Johannes O Ferstad  5 , Carlos Guestrin  2 , Emily B Fox  2   6   7 , David M Maahs  1   4   8 , David Scheinker  1   3   4   5   9
Affiliations

Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities

Dessi P Zaharieva et al. Front Endocrinol (Lausanne). .

Abstract

Algorithm-enabled patient prioritization and remote patient monitoring (RPM) have been used to improve clinical workflows at Stanford and have been associated with improved glucose time-in-range in newly diagnosed youth with type 1 diabetes (T1D). This novel algorithm-enabled care model currently integrates continuous glucose monitoring (CGM) data to prioritize patients for weekly reviews by the clinical diabetes team. The use of additional data may help clinical teams make more informed decisions around T1D management. Regular exercise and physical activity are essential to increasing cardiovascular fitness, increasing insulin sensitivity, and improving overall well-being of youth and adults with T1D. However, exercise can lead to fluctuations in glycemia during and after the activity. Future iterations of the care model will integrate physical activity metrics (e.g., heart rate and step count) and physical activity flags to help identify patients whose needs are not fully captured by CGM data. Our aim is to help healthcare professionals improve patient care with a better integration of CGM and physical activity data. We hypothesize that incorporating exercise data into the current CGM-based care model will produce specific, clinically relevant information such as identifying whether patients are meeting exercise guidelines. This work provides an overview of the essential steps of integrating exercise data into an RPM program and the most promising opportunities for the use of these data.

Keywords: algorithm-support; continuous glucose monitoring (CGM); exercise; remote monitoring; type 1 diabetes.

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Conflict of interest statement

DZ has received speaker’s honoraria from Medtronic Diabetes, Ascensia Diabetes, and Insulet Canada; and research support from the Helmsley Charitable Trust and ISPAD-JDRF Research Fellowship. She is also on the Dexcom Advisory board. DM has received research support from the National Institutes of Health, JDRF, NSF, and the Helmsley Charitable Trust; and his institution has received research support from Medtronic, Dexcom, Insulet, Bigfoot Biomedical, Tandem, and Roche. He has consulted for Abbott, the Helmsley Charitable Trust, Sanofi, Novo Nordisk, Eli Lilly, and Insulet, and is supported by grant number P30DK116074. DS is an advisor to Carta Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Multimodal decision-support Timely Interventions for Diabetes Excellence (TIDE) care model integrating continuous glucose monitoring (CGM) and physical activity (Garmin Vivosmart 4 or Venu Sq) metrics.
See this image and copyright information in PMC

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