Toward Detection of Nocturnal Hypoglycemia in People With Diabetes Using Consumer-Grade Smartwatches and a Machine Learning Approach.

Background: Nocturnal hypoglycemia poses significant risks to individuals with insulin-treated diabetes, impacting health and quality of life. Although continuous glucose monitoring (CGM) systems reduce these risks, their poor accuracy at low glucose levels, high cost, and availability limit their use. This study examined physiological biomarkers associated with nocturnal hypoglycemia and evaluated the use of machine learning (ML) to detect hypoglycemia during nighttime sleep using data from consumer-grade smartwatches.

The Need for Standardization of Continuous Glucose Monitoring Performance Evaluation: An Opinion by the International Federation of Clinical Chemistry and Laboratory Medicine Working Group on Continuous Glucose Monitoring.

Metrics derived from continuous glucose monitoring (CGM) systems are often discordant between systems. A major cause is that CGM systems are not standardized; they use various algorithms and calibration methods, leading to discordant CGM readings across systems. This discordance can be addressed by standardizing CGM performance assessments: If manufacturers aim their CGM systems at the same target, then CGM readings will align across systems.

First Measurement Using Elliptically Polarized Photons of the Double-Polarization Observable E for γp→pπ^{0} and γp→nπ^{+}.

We report the measurement of the helicity asymmetry E for the pπ^{0} and nπ^{+} final states using, for the first time, an elliptically polarized photon beam in combination with a longitudinally polarized target at the Crystal Ball experiment at MAMI. The results agree very well with data that were taken with a circularly polarized photon beam, showing that it is possible to simultaneously measure polarization observables that require linearly (e.g.

Effects of home-based exergaming on cardio-metabolic and cognitive health in physically inactive individuals.

Aims: To examine the effects of a home-based exergame training over 6 weeks on cardio-metabolic and cognitive health, as well as training adherence, in physically inactive individuals.

Materials And Methods: Twenty participants were equipped with an exergame system specifically designed for use at home. Each participant performed at least three weekly exercise sessions at ≥80% of their individual maximum heart rate, over 6 weeks.