Clinical trial NCT05504096: Calibration and Evaluation of Non-Invasive Wireless Blood Glucose Monitoring
Official title: Calibration and Evaluation of Non-Invasive Wireless Blood Glucose Monitoring
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Blood Glucose, High
Blood Glucose, Low
Keywords: Blood Glucose Monitoring
Wearable
Photoplethysmography (PPG)
Diabetes
Lead sponsor: KK Women's and Children's Hospital
Sponsor class: OTHER_GOV
Collaborators: name: Actxa Pte Ltd
First posted: 2022-08-17
Enrollment: 500
Sex: ALL
Minimum age (years): 21
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: OTHER

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

* Aged 21 years and above
* Are able to walk to, take public transport to or drive to the testing site independently
* Able to understand written and spoken English

Exclusion Criteria:

* Have difficulty in giving informed consent
* Have a pacemaker
* Are diagnosed with hypertension
* Are on anti-hypertensive drugs
* Are pregnant

Primary outcomes:
measure: Capillary blood glucose level using the glucometer and PPG signal readings
timeFrame: Both the PPG signals and capillary blood glucose will be collected at the same time during the visit lasting up to 2 hour per subject. A total of 500 subjects will be recruited up to 6 months
measure: Risk prediction model of diabetes using PPG features on the wearables
timeFrame: The study will be completed within 6 months involving 500 subjects. Data analysis and risk prediction model will be completed up to 12 months
Secondary outcomes:
measure: Validate that in-ear and wrist-worn wearables both provide relative accurate heart rate and heart rate interval measurements.
timeFrame: Subjects will be assessed at one visit study. Total duration of the study is 15mins for subjects whose capillary blood glucose is greater than or equal to 11.1mmol/L, and about 90minutes for those less than 11.1mmol/L.
Other outcomes:
Locations:
city: Singapore
country: Singapore
facility: KK Women's and Children's Hospital
Study officials:
affiliation: KK Women's and Children's Hospital
name: Seng Bin Ang
role: PRINCIPAL_INVESTIGATOR
References:
citation: Kviesis-Kipge, E., Zaharans, J., Rubenis, O., & Grabovskis, A. A photoplethysmography device for multipurpose blood circulatory system assessment. In European Conference on Biomedical Optics (p. 80900W). Optical Society of America.
citation: Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. 2011 Oct;53(2):127-38. doi: 10.1016/j.artmed.2011.05.001. Epub 2011 Jun 22.
citation: Philip, L.A., Rajasekaran, K., & Smily Jeya Jothi, E. Continuous monitoring of blood glucose using photophlythesmograph signal. Proceedings of IEEE International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT). 2017: 187-191
citation: Hina, H. Nadeem and W. Saadeh, A Single LED Photoplethysmography-Based Noninvasive Glucose Monitoring Prototype System. 2019 IEEE International Symposium on Circuits and Systems (ISCAS). 2019; 1-5
citation: De Ridder B, Van Rompaey B, Kampen JK, Haine S, Dilles T. Smartphone Apps Using Photoplethysmography for Heart Rate Monitoring: Meta-Analysis. JMIR Cardio. 2018 Feb 27;2(1):e4. doi: 10.2196/cardio.8802.