Wrist-worn wearables are currently being used for fitness and health monitoring. The global expansion of wearable technology combined with smartphones access creates new questions and opportunities in the diagnosis and management of chronic conditions.~High-end consumer wearables have integrated green light reflection photoplethysmography (PPG) sensors into their products. A PPG is an optically obtained plethysmogram that can be used to detect blood volume changes within mascrovasculature.~Smartphones, smartwatches and heart rate tracking devices are the most commonly used devices to feature PPG. In a published literature assessing smartphone apps using PPG for heart rate monitoring, it has found that these devices are reasonably accurate, with correlation coefficients > 0.93 and mean absolute percentage errors ranging from 3.3% to 6.2%. Although PPG sensors were initially only designed to track heart rate, there has been a push to use these with algorithms in the detection of arrhythmias such as AF, and other fields.~Diabetic patients have a two to three-fold higher risk of developing cardiovascular disease, and that cardiovascular diseases accounted for 29.2% of all deaths in Singapore. Wearable devices are ubiquitous, easy to use, and may allow for screening and further monitoring of a large population of patients.~This research proposes to leverage PPG technology, together with artificial intelligence, and incorporate this into affordable wearable lifestyle devices, wrist-worn and in-ear, to accurately monitor continuously and non-invasively glucose levels in humans. A simple non-invasive tool to monitor blood glucose will be developed, and alerts will be issued when the blood glucose level falls in the unhealthy range. The standard glucometer will be used to calibrate and validate the PPG measurements of blood glucose. This study targets to recruit and measure the blood glucose of 500 participants from KK Women's and Children's Hospital.~The primary aims of this study are: (1) Calibrate and validate PPG measurements of blood glucose, obtained both the wrist-worn and in-ear PPG devices, against the standard glucometer; (2) To develop a risk prediction model to identify subjects with blood glucose in the unhealthy range, using both subject characteristics and important features extracted from the PPG measurements using machine learning techniques.~The secondary aim of this study is to validate that in-ear and wrist-worn wearables both provide relative accurate heart rate and heart rate interval measurements.
Wrist-worn wearables are used for fitness and health monitoring. This global expansion of wearable technology opens up opportunities for the diagnosis and management of chronic conditions.~Diabetic patients have a two to three-fold higher risk of developing cardiovascular disease and that cardiovascular diseases accounted for 29.2.% of all deaths in Singapore. The wearable device is a promising avenue that allows for continuous monitoring of the large population of patients. Its ubiquitous and easy to use nature is an added advantage for its implementation.~In this study, the investigators aim to leverage existing photoplethysmography (PPG) technology, together with artificial intelligence, to accurately monitor blood glucose levels in a continuous and non-invasive manner. A simple non-invasive tool to monitor blood glucose will be developed, and alerts will be issued when the blood glucose levels fall in the unhealthy range. A standard glucometer will be used to calibrate and validate PPG measurements of blood glucose. This study aims to recruit 500 participants from KK Women's and Children's Hospital.