Modeling of Carbohydrates Oxidation Rate During Exercise in Type 1 Highly-Trained Diabetic Patients

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey

Background

Monitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter—glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters.

Objective

The objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management.

Methods

A list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts.

Results

On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices.

Conclusions

Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.

Personalized Approach for the Management of Exercise-Related Glycemic Imbalances in Type 1 Diabetes: Comparison with Reference Method

BACKGROUND

One of the most frequently adopted strategies to counterbalance the risk of exercise-induced hypoglycemia in patients with type 1 diabetes is carbohydrates supplement. Nevertheless, the estimation of its amount is still challenging. We investigated the efficacy of the personalized Exercise Carbohydrate Requirement Estimation System (ECRES) method compared to a tabular approach to estimate the glucose supplement needed for the prevention of exercise-related glycemic imbalances.

METHOD

Twenty-six patients performed two one-hour constant intensity exercises one week apart; the amount of extra carbohydrates was estimated, in random order, by the personalized ECRES method or through the tabular approach; glycemia was determined every 30 minutes. Continuous glucose monitoring (CGM) metrics were calculated over the 48 hours preceding, and the afternoon and night following the trials.

RESULTS

Applying the personalized ECRES method, a significantly lower amount of carbohydrates was administered to the active patients compared to the tabular approach, median (interquartile range): 9.0 (0.5-21.0) g vs 23.0 (21.0-25.0) g; P < .01; the two methods were similar for the sedentary patients, 18 (13.5-36.0) g vs 23.0 (21.0-27.0) g; P = NS. After overlapping CGM metrics before the exercises, both methods avoided hypoglycemia and resulted in similar glucose levels throughout them. The ECRES method led to CGM metrics within the guidelines for either the afternoon and the night just following the trials, whereas the tabular approach resulted in a significantly greater time below range in the afternoon (11.8% ± 18.2%; P < .05) and time above range during the night (39.3% ± 29.8%; P < .05).

CONCLUSIONS

The results support the validity of the personalized ECRES method: although the estimated amounts of carbohydrates were lower, patients' glycemia was maintained within safe clinical limits.