A pilot study to evaluate a self-management algorithm for people with Type 1 diabetes participating in moderate intensity exercise in laboratory and real-life environments

Background Limited evidence is available to advise people with Type 1 diabetes about self-management strategies for maintaining acceptable glycaemic control when exercising. It is recognized that hypoglycaemia is a potential risk and this is a major barrier towards exercise participation for these people. Related research exploring this topic has mostly been performed in laboratory environments. Aim Following a specifically designed self-management algorithm, the impact of the algorithm and environment (real-life versus laboratory) was investigated regarding the attainment of acceptable glucose concentrations during and after 40 minutes exercise at 70% VO2 max. Methods Nine individuals with Type 1 diabetes (five male, four female) completed the pilot study over a 2 week period. All used a basal bolus analogue insulin regimen and exercised regularly. On Days 1 and 3 of each week they undertook 40 minutes of moderate intensity exercise (day 1 and 8 in the laboratory, and day 3 and 10 in real-life environments). All were instructed to follow the self-management algorithm. Data were collected for glucose concentrations at 10 time-points during and after exercise. Results Statistical analysis used a 3-way repeated measures ANOVA for time-points, environment and day, which demonstrated a highly significant main effect on time-points F(9, 72) = 4.088, p = < 0.005, partial eta squared = 0.338. During exercise the mean blood glucose difference was significantly lower from baseline to: 20 minutes during, 2.21mmol/l (SE±0.354) (p=0.011), and 40 minutes at end 3.41mmol/l (SE±0.511) (p=0.007). However, the different environments did not have a significant main effect on the mean glucose concentration of participants [F(1, 8) = 1.489, p = 0.257. For the study period, 8 out of 9 participants experienced at least one hypoglycaemic episode. For hypoglycaemic episodes, the main differences between environments occurred: during exercise (laboratory n=5, real-life n=1), and during 8-12 hours post-exercise (laboratory n=3, real-life n=8). Conclusion There were differences in glycaemic patterns between environments when using a descriptive analysis. Despite post-exercise insulin reduction, nocturnal hypoglycaemia occurred in the real-life environment, and algorithm adjustments regarding carbohydrate consumption at bedtime were required for prevention. Therefore, a self-management recommendation for evening exercisers would be to perform blood glucose monitoring 8-12 hours after post-exercise insulin and consumption of carbohydrate. A larger study with the sample size to demonstrate significance, using the adjusted algorithm would clarify the reliability and efficacy of the algorithm in real-life.