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

Francescato, Maria Pia; Ajčević, Miloš; Stella, Alex Buoite; Accardo, Agostino

doi:10.1007/978-3-030-31635-8_67

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…13,26 Furthermore, attention is paid to the patient's training level, which implicates greater estimated amounts of glucose burned in sedentary patients compared to the active ones. 14,23 Finally, glycemia at the start of the activity and exercise intensity are considered in a more personalized manner than in the tabular approach 12,15 and/or in simple decision trees. 27 The amount of glucose already available in the body is estimated according to the measured starting plasma glucose level and to the extracellular fluid volume 28 and exercise intensity is evaluated by HR measurement.…”

Section: Discussionmentioning

confidence: 99%

“…The ECRES algorithm, based on a series of models and relations described previously, 13,14,19,23 estimates in a personalized manner the amount of carbohydrates supplement (reqCHO) needed to avoid exercise-related glycemic imbalances. The algorithm is summarized in Figure 1.…”

Section: The Ecres Algorithmmentioning

confidence: 99%

“…CHOox is calculated as the product of exercise duration (exD) and the whole-body carbohydrate oxidation rate (CHOoxRate) estimated by models depending on the patient's fitness levels (sedentary, active, or trained) and taking into account the expected exercise intensity expressed by the average expected HR 14,23 :…”

Section: The Ecres Algorithmmentioning

confidence: 99%

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

Ajčević

Candido²,

Assaloni

et al. 2020

J Diabetes Sci Technol

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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.

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Section: Discussionmentioning

confidence: 99%

Section: The Ecres Algorithmmentioning

confidence: 99%

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

Ajčević

Candido²,

Assaloni

et al. 2020

J Diabetes Sci Technol

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Modeling of glycogen resynthesis according to insulin concentration: towards a system for prevention of late-onset exercise-induced hypoglycemia in Type 1 diabetes patients

Ajčević

Accardo

Francescato

2021

Procedia Computer Science

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

Daskalaki¹,

Parkinson²,

Brew‐Sam³

et al. 2022

J Med Internet Res

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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.

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

Cited by 3 publications

References 21 publications

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

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

Modeling of glycogen resynthesis according to insulin concentration: towards a system for prevention of late-onset exercise-induced hypoglycemia in Type 1 diabetes patients

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

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