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Background: The error in estimating meal carbohydrates (CHO) amount is a critical mistake committed by type 1 diabetes (T1D) subjects. The aim of this study is both to investigate which factors, related to meals and subjects, affect the CHO-counting error most and, to develop a mathematical model of CHO-counting error embeddable in T1D patient decision simulators to do in silico clinical trials. Methods: A published dataset of 50 T1D adults is used, which includes patient’s CHO-count of 692 meals, dietitian’s estimates of meal composition (used as reference), and several potential explanatory factors. The CHO-counting error is modeled by multiple linear regression with stepwise variable selection starting from 10 candidate predictors, i.e. education level, insulin treatment duration, age, body weight, meal type, CHO, lipid, energy, protein and fiber content. Inclusion of quadratic and interaction terms is also evaluated. Results: Larger errors correspond to larger meals, most of the large meals are underestimated. The linear model selects CHO (p

Continuous glucose monitoring (CGM) sensors are minimally-invasive sensors used in diabetes therapy to monitor interstitial glucose concentration. The measurements are collected almost continuously (e.g. every 5 min) and permit the detection of dangerous hypo/hyperglycemic episodes. Modeling the various error components affecting CGM sensors is very important (e.g., to generate realistic scenarios for developing and testing CGM-based applications in type 1 diabetes simulators). In this work we focus on data gaps, which are portions of missing data due to a disconnection or a temporary sensor error. A dataset of 167 adults monitored with the Dexcom (San Diego, CA) G6 sensor is considered. After the evaluation of some statistics (the number of gaps for each sensor, the gap distribution over the monitoring days and the data gap durations), we develop a two-state Markov model to describe such statistics about data gap occurrence. Statistics about data gaps are compared between real data and simulated data generated by the model with a Monte Carlo simulation. Results show that the model describes quite accurately the occurrence and the duration of data gaps observed in real data.

In diabetes management, the fraction of time spent with glucose concentration within the physiological range of [70-180] mg/dL, namely time in range (TIR) is often computed by clinicians to assess glycemic control using a continuous glucose monitoring sensor. However, a sufficiently long monitoring period is required to reliably estimate this index. A mathematical equation derived by our group provides the minimum trial duration granting a desired uncertainty around the estimated TIR. The equation involves two parameters, p

Background: In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) have proven effective in accelerating the development of new therapies. However, published simulators lack a realistic description of some aspects of patient lifestyle which can remarkably affect glucose control. In this paper, we develop a mathematical description of meal carbohydrates (CHO) amount and timing, with the aim to improve the meal generation module in the T1D Patient Decision Simulator (T1D-PDS) published in Vettoretti et al. Methods: Data of 32 T1D subjects under free-living conditions for 4874 days were used. Univariate probability density function (PDF) parametric models with different candidate shapes were fitted, individually, against sample distributions of: CHO amounts of breakfast (CHOB), lunch (CHOL), dinner (CHOD), and snack (CHOS); breakfast timing (TB); and time between breakfast-lunch (TBL) and between lunch-dinner (TLD). Furthermore, a support vector machine (SVM) classifier was developed to predict the occurrence of a snack in future fixed-length time windows. Once embedded inside the T1D-PDS, an ISCT was performed. Results: Resulting PDF models were: gamma (CHOB, CHOS), lognormal (CHOL, TB), loglogistic (CHOD), and generalized-extreme-values (TBL, TLD). The SVM showed a classification accuracy of 0.8 over the test set. The distributions of simulated meal data were not statistically different from the distributions of the real data used to develop the models (α = 0.05). Conclusions: The models of meal amount and timing variability developed are suitable for describing real data. Their inclusion in modules that describe patient behavior in the T1D-PDS can permit investigators to perform more realistic, reliable, and insightful ISCTs.

EEG signals recorded by surface electrodes placed on the scalp can be thought as non- stationary stochastic processes in both time and space, especially in response to external stimuli. Cognitive tasks, in particular, are reflected by changes in EEG dynamics concerning both rhythms energy and connectivity across different brain regions. In the frequency-domain, EEG analysis is complicated and time-frequency methodologies are needed. The Wavelet Transform, in particular, represents a powerful tool for analysing, within a time-frequency embedding, the EEG. In this study we applied a wavelet-based methodology to extract quantitative time-frequency parameters from EEG signals recorded during a time discrimination task in 12 subjects. We used a continous wavelet transform with a complex Morlet as mother function. In order to improve the time-frequency resolution and to make it satisfactory, each of the four standard EEG rhythms (i.e. theta, alpha, beta, gamma) was studied with Morlet wavelet parameters tuned ad hoc on the basis of both the width of the specific frequency band and the particular type of activity under examination. The numerical values of the estimated time-frequency indexes were then compared, evidencing statistically significant differences in the brain response between experimental conditions.