Diabetes mellitus (DB) is the most challenging and fastest-growing global public health challenge. An estimated 10.5% of the global adult population suffers from diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbated the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance (IGT) and impaired fasting glycemia (IFG), respectively. All the current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or a laboratory by trained professionals. At-risk subjects might remain undetected for years and miss the precious time window for early intervention in preventing or delaying the onset of diabetes and its complications. This study was conducted at KK Womenâ\euro™s and Childrenâ\euro™s Hospital of Singapore, and five hundred participants were recruited (mean age 38.73 ± 10.61 years; mean BMI 24.4 ± 5.1 kg/m2). The blood glucose levels, for most participants, were measured before and after 75g of sugary drink using both the conventional glucometer (Accu-Chek Performa) and the wrist-worn wearable. The results obtained from the glucometer were used as the ground truth measurements. We propose leveraging photoplethysmography (PPG) sensors and machine learning techniques to incorporate this into an affordable wrist-worn wearable device to detect elevated blood glucose levels (⩾ 7.8mmol/L) non-invasively. Multiple machine learning models were trained and assessed with 10-fold cross-validation using subject demographic data and critical features extracted from the PPG measurements as predictors. Support vector machine (SVM) with a radial basis function kernel has the best detection performance with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54% and F-score of 84.03%. Hence, PPG measurements can be utilized to identify subjects with elevated blood glucose measurements and assist in the screening of subjects for diabetes risk.

Objective: To examine CGM feedback with the subsequent development of gestational diabetes (GDM), maternal glycaemic control, and glycaemic variability during pregnancy with randomisation 1:1 with one study arm receiving CGM feedback by intermittent scanning (unblinded group), versus masked feedback (blinded group). Design: Prospective, single-center, randomized controlled trial Setting: Single tertiary care hospital Population: Pregnant women recruited in the first trimester of pregnancy Methods: We assessed GDM and plasma glucose levels diagnosed by the 75-g oral glucose tolerance test (OGTT) at 24-28 weeks as a primary outcome. The secondary outcome was CGM-derived parameters of glycaemic variability across the first (9-13 weeks), second (18-23 weeks), late second and early third (24-31 weeks) and third trimester (32-33weeks). Results: Over 47 months, 206 pregnant women were enrolled at 9-13 weeks. There were no significant differences with GDM outcomes, fasting, 1-hour or 2-hour plasma glucose concentrations between study arms. The unblinded group had higher %time-in-range in the first (83.2% vs 78.1%; p=0.06), second [88.7% vs 80.5%; p=0.02] and third trimester (90.2% vs 79.5%; p=0.07), compared to the blinded group. Conversely, the unblinded group had lower %time-below-range in the first trimester (15.4% vs 21.2%; p=0.06), and early second trimester (8.8% vs 16.9%; p=0.05]. No significant differences were observed with the %time-above-range, mean, standard deviation, Mean Amplitude Glycaemic Excursion and % Coefficient Variation across all trimesters. Conclusion: CGM feedback, coupled with better glycaemic control (higher %TIR and low %TBR) indicates its’ potential use in combination with appropriate patient education for promoting better glucose control during pregnancy.