JMIR Publications

Background:

Type 1 Diabetes (T1D) is a chronic condition in which the body produces too little insulin, a hormone needed to regulate blood glucose. Various factors such as carbohydrates, exercise and hormones impact insulin needs. Beyond carbohydrates, most factors remain under-explored. Regulating insulin is a complex control task that can go wrong and cause blood glucose levels to fall outside a range that protects people from adverse health effects. Automated insulin delivery (AID) has been shown to maintain blood glucose levels within a narrow range. Beyond clinical outcomes, data from AID systems is little researched and promises new data-driven insights to improve the understanding and treatment of T1D.

Objective:

The aim is to discover unexpected temporal patterns in insulin needs and to analyse how frequently these occur. Unexpected patterns are situations where increased insulin does not result in lower glucose, or increased carbohydrate intake does not raise glucose levels. Such situations suggest that factors beyond carbohydrates influence insulin needs.

Methods:

We analysed time series data on insulin on board (IOB), carbohydrates on board (COB) and interstitial glucose (IG) from 29 participants using the OpenAPS AID system. Pattern frequency in hours, days (grouped via K-means clustering), weekdays, and months were determined by comparing the 95% CI of the mean differences between temporal units. Associations between pattern frequency and demographic variables were examined. Significant differences in IOB, COB and IG for various time categories were assessed using Mann-Whitney U tests. Effect sizes and Euclidean distances between variables were calculated. Finally, the forecastability of IOB, COB, and IG for the clustered days was analysed using Granger causality.

Results:

On average, 13.5 participants had unexpected patterns and 9.9 had expected patterns. The patterns were more pronounced (d>0.94) when comparing hours of the day and similar days than when comparing days of the week or months (0.3

Conclusions:

Our study shows that unexpected patterns in the insulin needs of people with T1D are as common as expected patterns. Unexpected patterns cannot be explained by carbohydrates alone. Our results highlight the complexity of glucose regulation and emphasise the need for personalised treatment approaches. Further research is needed to identify and quantify the factors that cause these patterns.