Model Identification from Ambulatory Data for Post-Prandial Glucose Control in type 1 Diabetes

Several glucoregulatory models are studies and a new model is proposed. Experiments are developed following an optimal design methodology. The designed experiments are applied in home monitoring of diabetic patients.Laguna Sanz, AJ. (2010). Model Identification from Ambulatory Data for Post-Prandial Glucose Control in type 1 Diabetes. http://hdl.handle.net/10251/14052Archivo delegad

Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration

[EN] Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p < 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are Mets (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only Mets is also viable for a more immediate implementation of this correction into market devices.This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) (Grant Number DPI2016-78831-C2-1-R), the European Union (FEDER funds), and the Vicerectorate of Research, Innovation and Technology Transference from the Universitat Politecnica de Valencia (Grant Number PAID-06-18). We would like to acknowledge the work performed by Josep Vehí, Lyvia Biagi, Ignacio Conget and Carmen Quirós. We thank all the participants and clinical staff who participated in clinical acquisition and pre-formatting of data for study, without whom none of this work would have been possible.Laguna Sanz, AJ.; Diez, J.; Giménez, M.; Bondía Company, J. (2019). Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration. Sensors. 19(17):1-14. https://doi.org/10.3390/s19173757114191

A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods

[EN] Continuous Glucose Monitoring (CGM) has been a springboard of new diabetes management technologies such as integrated sensor-pump systems, the artificial pancreas, and more recently, smart pens. It also allows patients to make better informed decisions compared to a few measurements per day from a glucometer. However, CGM accuracy is reportedly affected during exercise periods, which can impact the effectiveness of CGM-based treatments. In this review, several studies that used CGM during exercise periods are scrutinized. An extensive literature review of clinical trials including exercise and CGM in type 1 diabetes was conducted. The gathered data were critically analysed, especially the Mean Absolute Relative Difference (MARD), as the main metric of glucose accuracy. Most papers did not provide accuracy metrics that differentiated between exercise and rest (non-exercise) periods, which hindered comparative data analysis. Nevertheless, the statistic results confirmed that CGM during exercise periods is less accurate.This work was supported by the Ministerio de Economía, Industria y Competitividad (MINECO), Grant Number DPI2016-78831-C2-1-R, the Agencia Estatal de Investigación PID2019-107722RB-C21 / AEI /10.13039/501100011033, the European Union (FEDER funds), and the Vicerectorate of Research, Innovation and Technology Transference from the Universitat Politècnica de València (Grant Number PAID-06-18).Muñoz Fabra, E.; Diez, J.; Bondía Company, J.; Laguna Sanz, AJ. (2021). A Comprehensive Review of Continuous Glucose Monitoring Accuracy during Exercise Periods. Sensors. 21(2). https://doi.org/10.3390/s2102047921

Impact of High Intensity Interval Training Using Elastic Bands on Glycemic Control in Adults with Type 1 Diabetes: A Pilot Study

[EN] High intensity interval training (HIIT) using elastic bands is easy to do, but no data on its impact on glycemic control in people with type 1 diabetes (T1D) are available. Six males with T1D performed three weekly sessions of HIIT using elastic bands for 12 weeks. Each session consisted of eight exercises. Glycemic control was evaluated by using intermittent scanning continuous glucose monitoring two weeks before study onset (baseline) and during the intervention period in the first two (first stage) and last two weeks (last stage). In the 24 h post-exercise, time-in-range (70-180 mg/dL) was reduced from baseline to the end of the study (67.2% to 63.0%), and time-above-range (>180 mg/dL) seemed to increase from baseline across the study (20.8% -> 27.5% -> 22.1%, from baseline -> first -> last stage), but did not show any statistical significance. Time in hypoglycemia (either < 70 mg/dL or <54 mg/dL) did not show statistically significant differences. This study shows that a HIIT program with elastic bands is safe and effective to perform in T1D patients, keeping blood glucose levels in a safe range.This study was funded by MINECO DPI2016-78831-C2-1-R, Agencia Estatal de Investigacion (PID2019-107722RB-C21/AEI/10.13039/501100011033), FEDER funds from EU, and the Vicerectorate of Research, Innovation and Technology Transference from the Universitat Politecnica de Valencia grant number PAID-06-18. This study was also supported by the official funding agency for biomedical research of the Spanish government, Institute of Health Carlos III (ISCIII) through CIBEROBN CB12/03/30038, and CIBERDEM CB17/08/00004, which is co-funded by the European Regional Development Fund.Martín-San Agustín, R.; Laguna Sanz, AJ.; Bondía Company, J.; Roche, E.; Benítez Martínez, JC.; Ampudia-Blasco, FJ. (2020). Impact of High Intensity Interval Training Using Elastic Bands on Glycemic Control in Adults with Type 1 Diabetes: A Pilot Study. Applied Sciences. 10(19). https://doi.org/10.3390/app10196988101

Uncertainty in Postprandial Model Identification in type 1 Diabetes

Postprandial characterization of patients with type 1 diabetes is crucial for the development of an automatic glucose control system (Artificial Pancreas). Uncertainty sources within the patient, and variability of the glucose response between patients, are a challenge for individual patients model identification leading to poor predictability with current methods. Also, continuous glucose monitors, which have been the springboard for research towards a domiciliary artificial pancreas, still introduce large measurement errors, greatly complicating the characterization of the patient. In this thesis, individual model identification characterizing intra-patient variability from domiciliary data is addressed. First, literature models are reviewed. Next, we investigate the collection of data, and how can it be improved using optimal experiment design. Data gathering improvement is later applied to an ambulatory clinical protocol implemented at the Hospital Clínic Universitari de València, and data are collected from twelve patients following a set of mixed meal studies. With regard to the uncertainty of the glucose monitors, two continuous glucose monitoring devices are analyzed and statistically modeled. The models of these devices are used for in silico simulations and the analysis of identification methods. Identification using intervals models is then performed, showing an inherent capability for characterization of both the patient and the related uncertainty. First an in silico study is conducted in order to assess the feasibility of the identifications. Then, model identification is addressed from real patient data, increasing the complexity of the problem. As conclusion a new method for interval model identification is developed and successfully validated from clinical data.Laguna Sanz, AJ. (2014). Uncertainty in Postprandial Model Identification in type 1 Diabetes [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37191TESI

Millora de les eines de simulació de Ford Motor Company

Consulta en la Biblioteca ETSI Industriales (7522)[CA] Aquest projecte no és un projecte d¿investigació normal. Be es cert que te una part d¿investigació necessària per a complir els objectius, però aqueix no es el vertader objectiu en si mateix. Es pretén reconstruir FIRST; el programa que Ford esta emprant per simular la eficiència de les seves línies de mecanitzat. La investigació es necessària en els mètodes de Ford i per a la programació, però només com a pas intermedi per a complir el objectiu reial del projecte. FIRST és un programa creat per Ford per que faci d¿interfície amb el programa WITNESS. WITNESS es un simulador d¿esdeveniments discrets que s¿encarrega de comprovar el funcionament de les línies de mecanització de Ford. Com que es un programa complex, es va crear una ferramenta especifica (FIRST) per a Ford per tal de facilitar el procés de simulació. FIRST és un programa que canvia constantment amb les necessitats de Ford. El projecte consisteix en implementar diversos canvis en FIRST, incrementant la dificultat i complexitat de cada canvi amb el desenvolupament del projecte. El projecte es centra en els mètodes de programació, i el detall necessari per a la simulació en Ford, a més de mostrar com aquesta simulació pot ser útil per a companyies en general. Dos han estat els canvis més importants implementats en FIRST. El programa es capaç de treballar amb operaris en les màquines existents en la línia a simular, i amb diferents regles que regeixen el comportament d¿aquestos operaris. El programa també pot simular de forma més precisa les diverses probes de qualitat que hi han en les línies de Ford. Aquestos canvis inclouen nous formularis d¿interacció amb l¿usuari, càlculs interns, i comunicacions internes entre FIRST i el motor de simulació. Respecte als objectius assolits al projecte, hi han estat molts, incloent la completa satisfacció de Ford amb els canvis realitzats en el programa, la entrega del informe, i també la correcta execució de la etapa de aprenentatge necessària per a concloure el projecte. Cal destacar que per a aquest projecte s¿ha requerit aprofundir en coneixements mes propis de la enginyeria informàtica i d¿altres matèries, tant a nivell d¿anàlisi com a nivell de implantació. L¿autor ha estat tan breu com ha estat possible respecte a la descripció del treball, però es necessita un nivell de detall important per a mostrar clarament el progrés del projecte. Aquest detall ajudarà a altres persones a continuar amb aquest treball.Laguna Sanz, AJ. (2007). Millora de les eines de simulació de Ford Motor Company. http://hdl.handle.net/10251/35389.Archivo delegad

Enhanced Accuracy of Continuous Glucose Monitoring during Exercise through Physical Activity Tracking Integration

Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p &lt; 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are &ldquo;Mets&rdquo; (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only &ldquo;Mets&rdquo; is also viable for a more immediate implementation of this correction into market devices

On the Use of Population Data for Training Seasonal Local Models-Based Glucose Predictors: An <i>In Silico</i> Study

Most advanced technologies for the treatment of type 1 diabetes, such as sensor-pump integrated systems or the artificial pancreas, require accurate glucose predictions on a given future time-horizon as a basis for decision-making support systems. Seasonal stochastic models are data-driven algebraic models that use recent history data and periodic trends to accurately estimate time series data, such as glucose concentration in diabetes. These models have been proven to be a good option to provide accurate blood glucose predictions under free-living conditions. These models can cope with patient variability under variable-length time-stamped daily events in supervision and control applications. However, the seasonal-models-based framework usually needs of several months of data per patient to be fed into the system to adequately train a personalized glucose predictor for each patient. In this work, an in silico analysis of the accuracy of prediction is presented, considering the effect of training a glucose predictor with data from a cohort of patients (population) instead of data from a single patient (individual). Feasibility of population data as an input to the model is asserted, and the effect of the dataset size in the determination of the minimum amount of data for a valid training of the models is studied. Results show that glucose predictors trained with population data can provide predictions of similar magnitude as those trained with individualized data. Overall median root mean squared error (RMSE) (including 25% and 75% percentiles) for the predictor trained with population data are {6.96[4.87,8.67], 12.49[7.96,14.23], 19.52[10.62,23.37], 28.79[12.96,34.57], 32.3[16.20,41.59], 28.8[15.13,37.18]} mg/dL, for prediction horizons (PH) of {15,30,60,120,180,240} min, respectively, while the baseline of the individually trained RMSE results are {6.37[5.07,6.70], 11.27[8.35,12.65], 17.44[11.08,20.93], 22.72[14.29,28.19], 28.45[14.79,34.38], 25.58[13.10,36.60]} mg/dL, both training with 16 weeks of data. Results also show that the use of the population approach reduces the required training data by half, without losing any prediction capability

Experimental blood glucose interval identification of patients with type 1 diabetes

[EN] Many problems are confronted when characterizing a type 1 diabetic patient such as model mismatches, noisy inputs, measurement errors and huge variability in the glucose profiles. In this work we introduce a new identification method based on interval analysis where variability and model imprecisions are represented by an interval model as parametric uncertainty. The minimization of a composite cost index comprising: (1) the glucose envelope width predicted by the interval model, and (2) a Hausdorff-distance-based prediction error with respect to the envelope, is proposed. The method is evaluated with clinical data consisting in insulin and blood glucose reference measurements from 12 patients for four different lunchtime postprandial periods each. Following a “leave-one-day-out” cross-validation study, model prediction capabilities for validation days were encouraging (medians of: relative error = 5.45%, samples predicted = 57%, prediction width = 79.1 mg/dL). The consideration of the days with maximum patient variability represented as identification days, resulted in improved prediction capabilities for the identified model (medians of: relative error = 0.03%, samples predicted = 96.8%, prediction width = 101.3 mg/dL). Feasibility of interval models identification in the context of type 1 diabetes was demonstrated.The research leading to these results has received funding from the Spanish Ministry of Science and Innovation under grant DPI2010-20764-C02-01, the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement FP7-PEOPLE-2009-IEF, Ref 252085 and the GeneralitatValenciana through Grant GV/2012/085. The authors acknowledge the collaboration of Sara Correa, Geles Viguer and Pepa Gabaldón from the Diabetes Reference Unit in the Clinic University Hospital of Valencia, and the selfless participation of all the patients involved in the experiments from which data were obtained.Laguna Sanz, AJ.; Rossetti, P.; Ampudia Blasco, FJ.; Vehí, J.; Bondía Company, J. (2014). Experimental blood glucose interval identification of patients with type 1 diabetes. Journal of Process Control. 24(1):171-181. https://doi.org/10.1016/j.jprocont.2013.09.015S17118124