PREDICTIVE FACTORS CONTRIBUTING TO GLUCOSE CHANGES DURING AEROBIC, RESISTANCE, AND HIGH INTENSITY INTERVAL TRAINING IN TYPE 1 DIABETES
- Peter G. Jacobs, United States of America
- Peter G. Jacobs, United States of America
- Zoey Li, United States of America
- Gavin Young, United States of America
- Peter Calhoun, United States of America
- Robin L. Gal, United States of America
- Roy W. Beck, United States of America
- Jessica Castle, United States of America
- Mark A. Clements, United States of America
- Eyal Dassau, United States of America
- Francis Doyle III, United States of America
- Melanie Gillingham, United States of America
- Corby Martin, United States of America
- Michael R. Rickels, United States of America
- Susana Patton, United States of America
- Michael C. Riddell, Canada
Abstract
Background and Aims
People with type 1 diabetes (T1D) have difficulty with glucose control during exercise. Exercise type and other factors may have different impact on glycemic control. We used linear mixed effects modelling to identify physiologic features most predictive of changes in glucose during aerobic, resistance and high intensity interval training (HIIT).
Methods
Thirty seven potential predictive factors including glucose from continuous glucose monitoring, insulin, physical activity and food data collected during a 4-week free-living pilot study from 44 people with T1D (age 35±15 years, BMI 26.2±3.1 kg/m2, 19±13 years since diagnosis), were evaluated. Participants using multiple daily injections (n=9) or an insulin pump (n=35) were randomized to complete one of three 30-minute exercise videos twice each week (aerobic [n=19], resistance [n=14], or HIIT [n=11]). Completed exercise included 138 aerobic, 83 HIIT, and 82 resistance sessions. Change in glucose during exercise was calculated as the pre-exercise glucose value minus nadir glucose during exercise.
Results
Results showed that higher mean glucose 1 hour before (P<0.001) and lower mean glucose 24 hours before exercise (P=0.09) were associated with a greater drop in glucose during exercise. Higher insulin on board was also associated with a greater drop during exercise, but was not statistically significant after multiplicity adjustment (P=0.34).
Conclusions
Predictive features identified, including pre-exercise glucose level and insulin on board, may help inform new machine learning algorithms to better protect against hypoglycemia during physical activity.
