Background Closed-loop insulin delivery systems are expected to become a standard treatment for patients with type 1 diabetes. We aimed to assess whether the Diabeloop Generation 1 (DBLG1) hybrid closed-loop artificial pancreas system improved glucose control compared with sensor-assisted pump therapy.Methods In this multicentre, open-label, randomised, crossover trial, we recruited adults (aged ≥18 years) with at least a 2 year history of type 1 diabetes, who had been treated with external insulin pump therapy for at least 6 months, had glycated haemoglobin (HbA 1c ) of 10% or less (86 mmol/mol), and preserved hypoglycaemia awareness. After a 2-week run-in period, patients were randomly assigned (1:1) with a web-based system in randomly permuted blocks of two, to receive insulin via the hybrid closed-loop system (DBLG1; using a machine-learning-based algorithm) or sensor-assisted pump therapy over 12 weeks of free living, followed by an 8-week washout period and then the other intervention for 12 weeks. The primary outcome was the proportion of time that the sensor glucose concentration was within the target range (3•9-10•0 mmol/L) during the 12 week study period. Efficacy analyses were done in the modified intention-totreat population, which included all randomly assigned patients who completed both 12 week treatment periods. Safety analyses were done in all patients who were exposed to either of the two treatments at least once during the study. This trial is registered with ClinicalTrials.gov, number NCT02987556.

"Objective" methods to monitor physical activity and sedentary patterns in free-living conditions are necessary to further our understanding of their impacts on health. In recent years, many software solutions capable of automatically identifying activity types from portable accelerometry data have been developed, with promising results in controlled conditions, but virtually no reports on field tests. An automatic classification algorithm initially developed using laboratory-acquired data (59 subjects engaging in a set of 24 standardized activities) to discriminate between 8 activity classes (lying, slouching, sitting, standing, walking, running, and cycling) was applied to data collected in the field. Twenty volunteers equipped with a hip-worn triaxial accelerometer performed at their own pace an activity set that included, among others, activities such as walking the streets, running, cycling, and taking the bus. Performances of the laboratory-calibrated classification algorithm were compared with those of an alternative version of the same model including field-collected data in the learning set. Despite good results in laboratory conditions, the performances of the laboratory-calibrated algorithm (assessed by confusion matrices) decreased for several activities when applied to free-living data. Recalibrating the algorithm with data closer to real-life conditions and from an independent group of subjects proved useful, especially for the detection of sedentary behaviors while in transports, thereby improving the detection of overall sitting (sensitivity: laboratory model = 24.9%; recalibrated model = 95.7%). Automatic identification methods should be developed using data acquired in free-living conditions rather than data from standardized laboratory activity sets only, and their limits carefully tested before they are used in field studies.

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