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BACKGROUND: Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications. OBJECTIVE: We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements. METHODS: This study was conducted at KK Women's and Children's Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions. RESULTS: Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had 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%. CONCLUSIONS: Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.
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Introduction: Women in the perimenopause stage may face climacteric symptoms where physical and mental challenges are experienced. The purpose of this study was to increase the understanding of the experiences and needs of perimenopausal women with climacteric symptoms in Singapore. Method: This is a descriptive qualitative study. Purposive sampling was used to recruit 20 perimenopausal women with climacteric symptoms from a tertiary public hospital in Singapore. Semistructured face-to-face interviews and thematic analysis were used for data collection and analysis, respectively. Results: Participants lacked knowledge resulting in misconceptions of the condition. Experiencing climacteric symptoms led to mixed feelings. The availability of support varied in different sources and forms. Participants seek for more information, understanding, compassion, and empathy from family members and health care professionals. Discussion: Health care professionals should provide adequate support to cater to the diverse experiences and needs of multiracial perimenopausal women with climacteric symptoms. Future research should include the perspectives of health care professionals and family members.