Background: Diabetes mellitus (DM) is a major noncommunicable disease with a significant increase in prevalence, especially in low- and middle-income countries. The latest International Diabetes Federation Diabetes Atlas (2025) reports that 11.1% of the adult population (20 to 79 years old) is living with diabetes, with over 4 in 10 unaware of their condition. Early diagnosis and treatment of diabetes reduce the risk and slow the progression of debilitating complications, such as amputation, vision loss, renal failure, cardiovascular disease, dementia, some cancers, and infections like tuberculosis and severe COVID-19. Current screening methods for diabetes are invasive and costly. This has limited their utilization, especially in high-density populations and low- and middle-income countries such as Indonesia. Blood Glucose Evaluation and Monitoring (BGEM) is a machine learning algorithm developed by Actxa to analyze photoplethysmography data from wearable devices for diabetic risk assessment. Its noninvasive and user-friendly nature makes it a strong candidate for fulfilling the need for a diabetes screening or monitoring tool.
Objective: The aim of this study is to collect a large and more diverse dataset for the training of BGEM machine learning models. This dataset is intended to improve the model's generalizability and to evaluate its performance across different age groups, racial groups, and skin types, with the goal of enhancing accuracy and robustness for diabetes risk assessment and glucose monitoring.
Methods: Adult participants aged 18 years and above, with either a diabetic or a nondiabetic history, who reside in Greater Jakarta Area, Indonesia, were approached for recruitment. Blood glucose was assessed using laboratory blood analysis from capillary or plasma samples after fasting and at 1, 2, and 3 hours after a meal. BGEM data were also collected at each of these time points. Anthropological measurements with a standardized questionnaire on physical activity, demographic information, respondent's diabetic status, and current medications taken were also collected.
Results: Between June and October 2024, 885 participants were enrolled. Eight photoplethysmography recordings per participant were collected across 4 meal time points using 2 wearable devices in addition to the collection of clinical measurements, blood sampling, and related questionnaires. .
Conclusions: This protocol paper outlines the methodology designed for assessing and interpreting participants' blood sugar profiles, especially on demographic variability, in order to evaluate BGEM, a photoplethysmography-based artificial intelligence model designed to estimate blood glucose levels and diabetic risk. The clinical trial was conducted on Indonesian participants with and without diabetes while considering various influencing factors. This dataset is designed to enable assessment of the model's performance across diverse racial, risk factors, and skin-type groups, with the aim of making the model more valid and reliable.
Keywords: BGEM; Blood Glucose Evaluation and Monitoring; Indonesia; artificial intelligence; blood glucose; diabetes mellitus; machine learning; noninvasive; photoplethysmography; screening; wearables.
© Eka Widrian Suradji, Satvinder Singh Dhaliwal, Zhang Li-Feng, Entong Zhou, Amos Lim, Hui Hern Wong, Yao Du, Fredicia Fredicia, Amanda Dianky, Puspasari Pasaribu, Ernest Gamaliel Masayoshi, Justin Yong An Tan, Mardi Santoso, Seng Bin Ang. Originally published in JMIR Research Protocols (https://www.researchprotocols.org).