JMIR Publications

ABSTRACT

Background:

Type 1 diabetes mellitus is a blood glucose (BG) metabolic disorder, which is caused by deficiencies of insulin secretion from pancreatic cells. People with type 1 diabetes often experience prolonged hyperglycemia episodes upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings to what extent the key parameters of BG dynamics are affected during infection incidences to support the effort towards developing a digital infectious disease detection system.

Objective:

The objective of the study is to retrospectively analyze the effect of infection incidences and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm. Moreover, to provide a general framework regarding how a digital infectious disease detection system can be designed using self-recorded data from people with type 1 diabetes as a secondary source of information.

Methods:

We retrospectively analyzed high precision self-recorded data of 10 patient years captured within the longitudinal records of 3 people with type 1 diabetes. Getting such a rich and big dataset from large number of participants are extremely expensive and difficult to acquire, if not impossible. The participants were 2 males and 1 female with an average age of 34  13.2 years. The dataset incorporates BG levels, insulin, carbohydrate and self-reported events of infections. We investigated the temporal evolution and probability distribution of BG levels, injected insulin, carbohydrate intake, and insulin to carbohydrate ratio within a specified timeframe (weekly, daily and hourly). All the experiments were carried out using MATLAB® 2018a (Mathworks, Inc, Natwick, MA).

Results:

Our analysis demonstrated that upon infection incidences, there is a dramatic shift in the operating point of the individual BG dynamics in all the timeframes (weekly, daily and hourly), which clearly violate the usual norm of BG dynamics. During regular/normal situations, BG levels usually lower when there is a significant increase in insulin injection and reduction in carbohydrate consumption. However, in all of the individual’s infection cases as opposed to the regular/normal days, there were prolonged period with elevated BG levels despite injecting higher insulin and consuming less carbohydrate. For instance, in all the infection week on average, BG levels were elevated by 6.1% and 16%, insulin (bolus) were increased by 42% and 39.3%, carbohydrate consumption were reduced by 19% and 28.1%, and insulin to carbohydrate ratio were raised by 108.7% as compared to pre-infection and post-infection week respectively.

Conclusions:

Despite the fact that patients increasingly gather data about themselves, there are no solid findings to what extent the key parameters of BG dynamics are affected during infection incidences to support the effort towards developing a digital infectious disease detection system. We presented the effect of infection incidences on key parameters of the BG dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The result demonstrated that as compared to the regular/normal days, infection incidence substantially alters the norm of BG dynamics, which are quite significant changes that could possibly be detected through a personalized modelling, e.g. prediction models and anomalies detection algorithms. Generally, we foresee that these findings can benefit the efforts towards building the next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.