Automatically accounting for physical activity in insulin dosing for type 1 diabetes

https://doi.org/10.1016/j.cmpb.2020.105757Get rights and content

Highlights

  • Step-count data from wearable physical activity trackers is leveraged to track and quantify daily physical activity.

  • An approach to calculate the accumulated glycemic impact from prior physical activity is presented.

  • A physical activity informed mealtime insulin bolus calculator is developed.

  • Simulation results suggest that the proposed physical activity informed insulin dosing could result in significantly improved postprandial glucose control in individuals with type 1 diabetes, compared with the standard dosing.

Abstract

Background and Objective

Type 1 diabetes is a disease characterized by lifelong insulin administration to compensate for the autoimmune destruction of insulin-producing pancreatic beta-cells. Optimal insulin dosing presents a challenge for individuals with type 1 diabetes, as the amount of insulin needed for optimal blood glucose control depends on each subject's varying needs. In this context, physical activity represents one of the main factors altering insulin requirements and complicating treatment decisions. This work aims to develop and test in simulation a data-driven method to automatically incorporate physical activity into daily treatment decisions to optimize mealtime glycemic control in individuals with type 1 diabetes.

Methods

We leveraged glucose, insulin, meal and physical activity data collected from twenty-three individuals to develop a method that (i) tracks and quantifies the accumulated glycemic impact from daily physical activity in real-time, (ii) extracts an individualized routine physical activity profile, and (iii) adjusts insulin doses according to the prolonged changes in insulin needs due to deviations in daily physical activity in a personalized manner. We used the data replay simulation framework developed at the University of Virginia to “re-simulate” the clinical data and estimate the performances of the new decision support system for physical activity informed insulin dosing against standard insulin dosing. The paired t-test is used to compare the performances of dosing methods with p < 0.05 as the significance threshold.

Results

Simulation results show that, compared with standard dosing, the proposed physical-activity informed insulin dosing could result in significantly less time spent in hypoglycemia (15.3± 8% vs. 11.1± 4%, p = 0.007) and higher time spent in the target glycemic range (66.1± 11.7% vs. 69.6± 12.2%, p < 0.01) and no significant difference in the time spent above the target range(26.6± 1.4 vs. 27.4± 0.1, p = 0.5).

Conclusions

Integrating daily physical activity, as measured by the step count, into insulin dose calculations has the potential to improve blood glucose control in daily life with type 1 diabetes.

Introduction

In health, the human body's blood glucose (BG) regulation is accomplished via various feedback mechanisms that govern the secretion and action of insulin – the main BG lowering hormone. The destruction of insulin-secreting beta cells in type 1 diabetes (T1D) results in the break-down of the endogenous BG regulation [1]. Consequently, exogenous insulin injections and careful BG monitoring are required to maintain glycemic levels within a target range (generally 70–180 mg/dL) and avoid potentially severe complications [2]. Carbohydrate intake increases glucose levels and is required to be matched by insulin injection for proper BG control. In standard therapy, the amount of insulin required at mealtime is broken down into two components: the amount required to compensate for the carbohydrates ingested during the meal, and the amount required to correct for any current elevated BG level. Additionally, people with T1D also need to consider the previously injected insulin still in circulation when calculating the total dose to be administered. The prevalent method used to calculate the required dosage of insulin can be explicitly formalized as follows [3]:B=CHOCR+GGtargetCFIOB,where CHO is the amount of meal carbohydrates (g), CR is a person's carbohydrate-to-insulin ratio (g/U) used to determine the appropriate dose of insulin that compensates for the estimated increase in BG from the ingested CHO,  Gtarget is the target BG value (mg/dL), CF is the BG correction factor (mg/dL/U) to account for BG excursions away from this target, and G is the BG value at the time of the meal bolus (mg/dL). Since insulin affects BG concentrations for several hours following its injection [4], the active insulin in circulation from the previous insulin injections is tracked by a concept called insulin on board (IOB). IOB is computed as the convolution of insulin injections within the past four-hours and insulin action curve obtained from a previous study by Swan et al. [4].

Insulin needs vary among people with T1D, and hence the therapy is tailored to the individual through patient-specific treatment parameters (e.g., CR and CF). The treatment parameters can also be adjusted to account for systematic diurnal variations in BG dynamics. Deviation from these patterns, however, requires additional care. Existing literature on physical activity (PA) related BG control in T1D can be classified into two categories based on their methodologies: (a) studies based on dose-response experiments, that explore the BG responses to various insulin and carbohydrate doses surrounding an exercise bout [5], [6], [7], and (b) studies based on algorithms that use biosensors to take/suggest actions for an ongoing exercise bout [8], [9], [10]. Conversely, non-exercise PA has seen little attention, and its effects on BG metabolism have been presumed to be minor. However, recent studies show that even short bouts of walking in the course of otherwise sedentary days significantly affect glucose metabolism [11], [12], [13], and thus require treatment adjustments to improve overall BG control [14], [15], [16]. In the present work, we propose a method that extends the mealtime insulin bolus calculation to account for the accumulated prolonged glycemic impact of the daily PA.

Section snippets

Materials and methods

In the development of the PA informed insulin dosing method, we utilize retrospective data collected from individuals with T1D under their free-living conditions. We quantify PA through “step count” recorded via an off-the-shelf PA tracker and inform the insulin dosing by (i) a quantified accumulated glycemic impact of prior PA in real-time (ii) a PA profile extracted from retrospective data, that is representative of systematic glycemic disturbances resulting from the individual's routine PA,

Representative case

To provide a comparable example while evaluating the performances of standard vs. PA-informed boluses, we select two days that belong to the same participant and satisfy the following criteria:

  • (i)

    similar BG traces that do not exceed the target BG at the dinnertime,

  • (ii)

    same amounts of carbohydrate intake at the dinnertime,

  • (iii)

    no residual insulin from previous boluses at the dinner time (i.e., at least four hours have passed since the last bolus insulin),

  • (iv)

    same amounts of insulin injection at the dinnertime,

  • (v)

Discussion

Our results indicate a significantly improved glycemic control using the PA-informed bolus when compared to the standard bolus method. This improvement was achieved by reducing exposure to hypoglycemia and increasing the time spent in the target BG range. Note that in our simulations, no additional carbohydrate intake or basal insulin dose reduction was applied to treat hypoglycemia events as opposed to the real-life practice. This potentially resulted in higher than clinically expected

Declaration of Competing Interest

None.

Acknowledgments

B.O has no conflict of interest to disclose. S.D.P is employed by Dexcom, Inc. whose sensors were used in NCT02558491, NCT03394352; SDP reports royalties from IP licenses in this field, managed by the University of Virginia. C.F reports consulting fees from Epsilon (Abbott). MDB reports research support from Dexcom, Sanofi, and Tandem Diabetes Care; MDB reports consulting fees and honoraria from Air Liquide, Dexcom, and Tandem Diabetes Care; MDB reports royalties from IP licenses in this field,

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    Registry numbers of clinical trials of the data used in the analyses of this manuscript: clinicaltrials.gov: NCT02558491 and NCT03394352.

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