Aim: To compute the uncertainty of time‐in‐ranges, such as time in range (TIR), time in tight range (TITR), time below range (TBR) and time above range (TAR), to evaluate glucose control and to determine the minimum duration of a trial to achieve the desired precision. Materials and Methods: Four formulas for the aforementioned time‐in‐ranges were obtained by estimating the equation's parameters on a training set extracted from study A (226 subjects, ~180 days, 5‐minute Dexcom G4 Platinum sensor). The formulas were then validated on the remaining data. We also illustrate how to adjust the parameters for sensors with different sampling rates. Finally, we used study B (45 subjects, ~365 days, 15‐minute Abbott Freestyle Libre sensor) to further validate our results. Results: Our approach was effective in predicting the uncertainty when time‐in‐ranges are estimated using n days of continuous glucose monitoring (CGM), matching the variability observed in the data. As an example, monitoring a population with TIR = 70%, TITR = 50%, TBR = 5% and TAR = 25% for 30 days warrants a precision of ±3.50%, ±3.68%, ±1.33% and ±3.66%, respectively. Conclusions: The presented approach can be used to both compute the uncertainty of time‐in‐ranges and determine the minimum duration of a trial to achieve the desired precision. An online tool to facilitate its implementation is made freely available to the clinical investigator.

Introduction: Frailty is conventionally diagnosed using clinical tests and self-reported assessments. However, digital health technologies (DHTs), such as wearable accelerometers, can capture physical activity and gait during daily life, enabling more objective assessments. In this study, we assess the feasibility of deploying DHTs in community-dwelling older individuals, and investigate the relationship between digital measurements of physical activity and gait in naturalistic environments and participants’ frailty status, as measured by conventional assessments. Methods: Fried Frailty Score (FFS) was used to classify fifty healthy individuals as non-frail (FFS = 0, n/female = 21/11, mean ± SD age: 71.10 ± 3.59 years), pre-frail (FFS = 1–2, n/female = 23/9, age: 73.74 ± 5.52 years), or frail (FFS = 3+, n/female = 6/6, age: 70.70 ± 6.53 years). Participants wore wrist-worn and lumbar-worn GENEActiv accelerometers (Activinsights Ltd., Kimbolton, UK) during three in-laboratory visits, and at-home for 2 weeks, to measure physical activity and gait. After this period, they completed a comfort and usability questionnaire. Compliant days at-home were defined as follows: those with ≥18 h of wear time, for the wrist-worn accelerometer, and those with ≥1 detected walking bout, for the lumbar-worn accelerometer. For each at-home measurement, a group analysis was performed using a linear regression model followed by ANOVA, to investigate the effect of frailty on physical activity and gait. Correlation between at-home digital measurements and conventional in-laboratory assessments was also investigated. Results: Participants were highly compliant in wearing the accelerometers, as 94% indicated willingness to wear the wrist device, and 66% the lumbar device, for at least 1 week. Time spent in sedentary activity and time spent in moderate activity as measured from the wrist device, as well as average gait speed and its 95th percentile from the lumbar device were significantly different between frailty groups. Moderate correlations between digital measurements and self-reported physical activity were found. Conclusions: This work highlights the feasibility of deploying DHTs in studies involving older individuals. The potential of digital measurements in distinguishing frailty phenotypes, while unobtrusively collecting unbiased data, thus minimizing participants’ travels to sites, will be further assessed in a follow-up study.

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 (CHO_B), lunch (CHO_L), dinner (CHO_D), and snack (CHO_S); breakfast timing (T_B); and time between breakfast-lunch (T_BL) and between lunch-dinner (T_LD). 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 (CHO_B, CHO_S), lognormal (CHO_L, T_B), loglogistic (CHO_D), and generalized-extreme-values (T_BL, T_LD). 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.

Aims: Reliable estimation of the time spent in different glycaemic ranges (time‐in‐ranges) requires sufficiently long continuous glucose monitoring. In a 2019 paper (Battelino et al., Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019;42:1593‐1603), an international panel of experts suggested using a correlation‐based approach to obtain the minimum number of days for reliable time‐in‐ranges estimates. More recently (in Camerlingo et al., Design of clinical trials to assess diabetes treatment: minimum duration of continuous glucose monitoring data to estimate time‐in‐ranges with the desired precision. Diabetes Obes Metab. 2021;23:2446‐2454) we presented a mathematical equation linking the number of monitoring days to the uncertainty around time‐in‐ranges estimates. In this work, we compare these two approaches, mainly focusing on time spent in (70‐180) mg/dL range (TIR).Methods: The first 100 and 150 days of data were extracted from study A (148 subjects, ~180 days), and the first 100, 150, 200, 250 and 300 days of data from study B (45 subjects, ~365 days). For each of these data windows, the minimum monitoring duration was computed using correlation‐based and equation‐based approaches. The suggestions were compared for the windows of different durations extracted from the same study, and for the windows of equal duration extracted from different studies.Results: When changing the dataset duration, the correlation‐based approach produces inconsistent results, ranging from 23 to 64 days, for TIR. The equation‐based approach was found to be robust versus this issue, as it is affected only by the characteristics of the population being monitored. Indeed, to grant a confidence interval of 5% around TIR, it suggests 18 days for windows from study A, and 17 days for windows from study B. Similar considerations hold for other time‐in‐ranges.Conclusions: The equation‐based approach offers advantages for the design of clinical trials having time‐in‐ranges as final end points, with focus on trial duration.

Digital health technologies (DHTs) are increasingly being adopted in clinical trials, as they enable objective evaluations of health parameters in free-living environments. Although lumbar accelerometers notably provide reliable gait parameters, embedding accelerometers in chest devices, already used for vital signs monitoring, could capture a more comprehensive picture of participants’ wellbeing, while reducing the burden of multiple devices. Here we assess the validity of gait parameters measured from a chest accelerometer. Twenty healthy adults (13 females, mean ± sd age: 33.9 ± 9.1 years) instrumented with lumbar and chest accelerometers underwent in-lab and outside-lab walking tasks, while monitored with reference devices (an instrumented mat, and a 6-accelerometers set). Gait parameters were extracted from chest and lumbar accelerometers using our open-source Scikit Digital Health gait (SKDH-gait) algorithm, and compared against reference values via Bland–Altman plots, Pearson’s correlation, and intraclass correlation coefficient. Mixed effects regression models were performed to investigate the effect of device, task, and their interaction. Gait parameters derived from chest and lumbar accelerometers showed no significant difference and excellent agreement across all tasks, as well as good-to-excellent agreement and strong correlation against reference values, thus supporting the deployment of a single multimodal chest device in clinical trials, to simultaneously measure gait and vital signs.Trial Registration: The study was reviewed and approved by the Advarra IRB (protocol number: Pro00043100).

Background: Retrospective analysis of Continuous Glucose Monitoring (CGM) sensor data can play an important role in improving glucose control and driving therapy adjustment in clinical settings. To prevent incorrect clinical decisions, however, preliminary detection and elimination of CGM data portions affected by errors and artifacts is of paramount relevance. Objective: This paper deals with the retrospective model-based detection of Pressure Induced Sensor Attenuations (PISAs) in CGM data, which could be misinterpreted as hypoglycemic events. Methods: In a Bayesian framework, we proposed a method that, to detect PISAs, leverages CGM data and a-priori statistical information on the expected smoothness of the CGM signal and the measurement error affecting it. The proposed strategy's effectiveness is evaluated using an in-silico dataset, generated by the FDA-accepted UVa/Padova Type 1 Diabetes simulator, and a real-world dataset, gathered using a commercial (Dexcom G6) sensor. Results: For the simulated data, the PISAs detection performance achieves a sensitivity of 61.5%, with 0.24 false positives per day. In the real-world dataset, the method exhibits a sensitivity of 57.3%, with 1.15 false positives per day. Conclusions: These results demonstrate the potential of the proposed approach for retrospective detection of PISAs in CGM data. Significance: By removing artifacts, CGM data quality can be improved before its retrospective use for diabetes therapy adjustments.