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Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing. Specifically, it is proposed that at least 7.5% of comparator BG concentrations are 300 mg/dL (16.7 mmol/L), respectively, and that at least 7.5% of BG-RoC combinations indicate fast BG changes with impending hypo- or hyperglycemia, respectively. These proposed characteristics of the comparator data can facilitate the harmonization of testing conditions across different studies and CGM systems and ensure that the most relevant scenarios representing real-life situations are established during performance testing. In addition, a study protocol and testing procedure for the manipulation of glucose levels are suggested that enable the collection of comparator data with these characteristics. This work is an important step toward establishing a future standard for the performance evaluation of CGM systems.

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022. Specifically, this review quantifies the prevalence of numerous options associated with various aspects of study design, including subject population, comparator (reference) method selection, testing procedures, and statistical accuracy evaluation. We found that there is a large variability in nearly all of those aspects and, in particular, in the characteristics of the comparator measurements. Furthermore, these characteristics as well as other crucial aspects of study design are often not reported in sufficient detail to allow an informed interpretation of study results. We therefore provide recommendations for reporting the general study design, CGM system use, comparator measurement approach, testing procedures, and data analysis/statistical performance evaluation. Additionally, this review aims to serve as a foundation for the development of a standardized CGM performance evaluation procedure, thereby supporting the goals and objectives of the Working Group on CGM established by the Scientific Division of the International Federation of Clinical Chemistry and Laboratory Medicine.

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. Exercise duration increased biweekly until 75 min of vigorous exercise were performed in Weeks 5 and 6. Maximum oxygen uptake (VO2max), cardio‐metabolic profiling, and neuro‐cognitive tests were performed at baseline and study end. Additionally, training adherence was assessed via training diaries. Results: After 6 weeks of home‐based exergaming, VO2max increased significantly, while there was a significant decrease in heart rate (resting and maximum), blood pressure (systolic, diastolic and mean), and low‐density lipoprotein cholesterol. Dynamic balance and reaction time improved after 6 weeks of exergaming. Training adherence was 88.4%. Conclusions: Home‐based exergaming induced a clinically relevant increase in VO2max, a determinant of cardiovascular health, accompanied by further improvements in cardiovascular, metabolic and neuro‐cognitive parameters. Exergaming may, therefore, offer an innovative approach to increasing regular physical activity, improving metabolic risk profile, and preventing chronic diseases. [ABSTRACT FROM AUTHOR]

Aim Materials and Methods Results Conclusions To evaluate the relationship between breath volatile organic compounds (VOCs) and glycaemic states in individuals with type 1 diabetes (T1D), focusing on identifying specific VOCs as biomarkers for hypoglycaemia to offer a non‐invasive diabetes‐monitoring method.Ten individuals with T1D underwent induced hypoglycaemia in a clinical setting. Breath samples, collected every 10‐15 minutes, were analysed using gas chromatography–ion mobility spectrometry (GC‐IMS). Correlation analysis and machine learning models, including Partial Least Squares Discriminant Analysis (PLS‐DA) and Support Vector Machine classifiers, were used to classify glycaemic states based on VOC profiles.Statistical analysis revealed moderate correlations between specific VOCs (e.g. isoprene, acetone) and venous blood glucose levels. Machine learning models showed high accuracy in classifying glycaemic states, with the best performance achieved by a two‐class PLS‐DA model showing an accuracy of 93%, sensitivity of 92% and specificity of 94%. Key biomarkers identified included isoprene, acetone, 2‐butanone, methanol, ethanol, 2‐propanol and 2‐pentanone.This study shows the potential of breath VOCs to accurately classify glycaemic states in individuals with T1D. While key biomarkers such as isoprene, acetone and 2‐butanone were identified, the analysis emphasizes the importance of using overall VOC patterns rather than individual compounds, which can be markers for multiple conditions. Machine learning models leveraging these patterns achieved high accuracy, sensitivity and specificity. These findings suggest that breath analysis using GC‐IMS could be a viable non‐invasive method for monitoring glycaemic states and managing diabetes. [ABSTRACT FROM AUTHOR]