Quantifying the impact of physical activity on future glucose trends using machine learning

Nichole S. Tyler, Clara Mosquera-Lopez, Gavin M. Young, Joseph El Youssef, Jessica R. Castle, Peter G. Jacobs

Research output: Contribution to journalArticlepeer-review

Abstract

Prevention of hypoglycemia (glucose <70 mg/dL) during aerobic exercise is a major challenge in type 1 diabetes. Providing predictions of glycemic changes during and following exercise can help people with type 1 diabetes avoid hypoglycemia. A unique dataset representing 320 days and 50,000 + time points of glycemic measurements was collected in adults with type 1 diabetes who participated in a 4-arm crossover study evaluating insulin-pump therapies, whereby each participant performed eight identically designed in-clinic exercise studies. We demonstrate that even under highly controlled conditions, there is considerable intra-participant and inter-participant variability in glucose outcomes during and following exercise. Participants with higher aerobic fitness exhibited significantly lower minimum glucose and steeper glucose declines during exercise. Adaptive, personalized machine learning (ML) algorithms were designed to predict exercise-related glucose changes. These algorithms achieved high accuracy in predicting the minimum glucose and hypoglycemia during and following exercise sessions, for all fitness levels.

Original languageEnglish (US)
Article number103888
JournaliScience
Volume25
Issue number3
DOIs
StatePublished - Mar 18 2022
Externally publishedYes

Keywords

  • Biocomputational method
  • Computational bioinformatics
  • Physiology

ASJC Scopus subject areas

  • General

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