Assessment of Blood Glucose measurement using New Non-Invasive Technology: Protocol & Methodology (Preprint)

This is the authors' abstract. We don't add key points for this paper.

BACKGROUND Diabetes Mellitus is a major non-communicable disease with significant increase in prevalence, especially in low and middle-income countries. The latest IDF Diabetes Atlas (2025) reports that 11,1%, or about 1 in 9, of the adult population (20-79 years old) is living with diabetes, with over 4 in 10 unaware that they have the condition. This Prevalence is expected to increase to 1 in 8 adults by the year 2050. Early diagnosis and treatment of Diabetes reduces the risk and slows the progression of debilitating complications such as amputation, vision loss, and renal failure, cardiovascular disease, dementia, some cancers, and infections such as tuberculosis and severe COVID-19. Current screening methods for diabetes are invasive and costly. This has limited its more widespread utilisation especially in a high-density population, middle-low income country such as Indonesia. Blood Glucose Evaluation and Monitoring (BGEM) is a machine learning (ML) algorithm developed by Actxa to analyze Photoplethysmography (PPG) data from wearable devices for diabetic risk assessment. Its non-invasive and user-friendly nature makes it a strong candidate for fulfilling the need for a diabetes screening tool. OBJECTIVE The Aim of this study is to enhance BGEM’s performance on a large and diverse data set with an emphasis on the detailed blood sugar fluctuation, also known as glycaemic variability, under semi-controlled conditions in an Indonesian population. METHODS Adult subjects aged 18 years old and above, either with a diabetic or non-diabetic history that resides in Greater Jakarta Area, Indonesia were recruited. The study will explore BGEM blood glucose prediction capabilities compared to laboratory blood analysis from capillary or plasma samples at after fasting, 1 hour, 2 hour and 3 hours after meal. In addition to PPG and blood data collection, anthropological measurement, a standardize questionnaire on activity, demographic information and diabetic status were also conducted to provide a more complete dataset. RESULTS A total of 885 subjects were included into the study from June to October 2024, 473 were diabetic subjects and 412 were non-diabetic subjects. The blood sugar profiles of the subjects will be studied and analysed. BGEM’s performance will be assessed on this Indonesian dataset on glycaemic variability. CONCLUSIONS This protocol paper outlines the methodology designed for subject’s blood sugar profiles especially on glycaemic variability in order to evaluate BGEM, a PPG-based AI model designed to estimate blood glucose levels and diabetic risk. The clinical trial was conducted on both diabetic (DM) and non-diabetic (non-DM) Indonesian subjects, while considering various influencing factors. The results will provide objective evidence on the validity and use of a non-invasive technique for the measurement of blood sugar levels using wearables. CLINICALTRIAL Clinical trial registration NCT06642467 Institutional Review Board of Ukrida faculty of medicine & health sciences ethical committee 1779/SLKE/IM/UKKW /FKIK/KEPK /VII/2024