Calibration and Evaluation of Non-Invasive Wireless Blood Glucose Monitoring

NCT ID: NCT05504096

Last Updated: 2023-02-21

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 500 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2021-10-08
• Study Completion Date: 2023-02-18

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.

Conditions

Blood Glucose, High; Blood Glucose, Low

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

Single-Arm: Healthy Participants

All participants will be administered with the In-Ear and Wearable devices concurrently for 8 minutes while the application records the readings from the devices. Participants with the first glucometer reading of less than 11.1 mmol/L will be required to return one-hour post sugary drink consumption for a second reading with both devices.

Group Type: OTHER

Actxa BGM Tracker (GLO2)

Intervention Type: DEVICE

Wrist-worn wearable

SVT In-Ear Prototype (IEP)

Intervention Type: DEVICE

In-Ear wearable

Interventions

Actxa BGM Tracker (GLO2)

Wrist-worn wearable

Intervention Type: DEVICE

SVT In-Ear Prototype (IEP)

In-Ear wearable

Intervention Type: DEVICE

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

• Minimum Eligible Age: 21 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Actxa Pte Ltd

UNKNOWN

Sponsor Role: collaborator

KK Women's and Children's Hospital

OTHER_GOV

Sponsor Role: lead

Responsible Party

Ang Seng Bin

Senior Consultant, Head of Family Medicine Service and Head of Menopause Unit

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Seng Bin Ang

Role: PRINCIPAL_INVESTIGATOR

KK Women's and Children's Hospital

Locations

KK Women's and Children's Hospital

Singapore, , Singapore

Countries

Singapore

References

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.

Reference Type: BACKGROUND

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.

Reference Type: BACKGROUND

PMID: 21696930 (View on PubMed)

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

Reference Type: BACKGROUND

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

Reference Type: BACKGROUND

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.

Reference Type: BACKGROUND

PMID: 31758768 (View on PubMed)

Other Identifiers

2020/2968

Identifier Type: -

Identifier Source: org_study_id