The impact of accelerometer use in exercise-associated hypoglycemia prevention in type 1 diabetes

Abstract

Exercise-associated hypoglycemia is a common adverse event in people with type 1 diabetes. Previous in silico testing by our group demonstrated superior exercise-associated hypoglycemia mitigation when a predictive low glucose suspend (PLGS) algorithm was augmented to incorporate activity data. The current study investigates the effectiveness of an accelerometer-augmented PLGS algorithm in an outpatient exercise protocol. Subjects with type 1 diabetes on insulin pump therapy participated in two structured soccer sessions, one utilizing the algorithm and the other using the subject's regular basal insulin rate. Each subject wore their own insulin pump and a Dexcom G4™ Platinum continuous glucose monitor (CGM); subjects on-algorithm also wore a Zephyr BioHarness™ 3 accelerometer. The algorithm utilized a Kalman filter with a 30-minute prediction horizon. Activity and CGM readings were manually entered into a spreadsheet and at five-minute intervals, the algorithm indicated whether the basal insulin infusion should be on or suspended; any changes were then implemented by study staff. The rate of hypoglycemia during and after exercise (until the following morning) was compared between groups. Eighteen subjects (mean age 13.4 ± 3.7 years) participated in two separate sessions 7-22 days apart. The difference in meter blood glucose levels between groups at each rest period did not achieve statistical significance at any time point. Hypoglycemia during the session was recorded in three on-algorithm subjects, compared to six off-algorithm subjects. In the postexercise monitoring period, hypoglycemia occurred in two subjects who were on-algorithm during the session and four subjects who were off-algorithm. The accelerometer-augmented algorithm failed to prevent exercise-associated hypoglycemia compared to subjects on their usual basal rates. A larger sample size may have achieved statistical significance. Further research involving an automated system, a larger sample size, and an algorithm design that favors longer periods of pump suspension is necessary.

Keywords: accelerometer; exercise; hypoglycemia; predictive low glucose suspend; pump suspension; type 1 diabetes.

Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

© 2014 Diabetes Technology Society.

Figures

Figure 1.

Overview of insulin delivery status for all subjects whose pumps were suspended at least once during the soccer session. The bar graph in the top pane details the percentage of pumps suspended at any given time in five-minute intervals. Lines in the bottom pane demonstrate each subject’s pump status throughout the two-hour session (black = off-algorithm, blue = on-algorithm). For subjects in the off-algorithm group, pumps were suspended only if meter BG (not shown) was ≤60 mg/dl or the subject request treatment for hypoglycemia. Carbohydrate interventions for hypoglycemia are depicted as squares.

Figure 2.

Mean meter BG values from the start of the exercise session to the end. For subjects who met hypoglycemia criteria prior to a rest period, the meter BG for those subjects is reflected in the immediately subsequent rest period; any meter BGs thereafter were removed, resulting in a progressively lower N.