Bioelectronics Validation in Healthy Subjects

Overseen By Wubin Bai, PhD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of North Carolina, Chapel Hill

Disqualifiers: Pregnant, Skin disorders, Allergies, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 3

Duration: 1-2.5 hours

22 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to test the safety and functionality of new wearable sensors that track health data. Participants will use these sensors, called the Multi-modality Noninvasive Biosensor, either before and after a short walk or continuously for two hours. The trial seeks healthy individuals without skin issues or adhesive allergies. It suits those who can perform daily activities and exercise without difficulty. As an unphased trial, this study allows participants to contribute to the development of innovative health technology.

Do I need to stop my current medications for the trial?

The trial information does not specify whether you need to stop taking your current medications. It seems to focus on healthy individuals without significant illnesses, so it's best to ask the study team for clarification.

What prior data suggests that this biosensor is safe for wearability?

Research has shown that wearable biosensors are generally safe for use. These devices have been employed for medical tests and health tracking for some time. Studies have found that users usually tolerate them well, and those who have used similar devices have not reported major side effects.

This trial focuses on the comfort and effectiveness of the sensors. They are designed to function without causing harm or discomfort. Current research reports no harmful side effects, suggesting that the sensors are likely safe for both short and long-term wear. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the Multi-modality Noninvasive Biosensor because it offers a new way to monitor health using advanced bioelectronic sensors. Unlike traditional methods that might involve invasive procedures or bulky equipment, this biosensor can be worn comfortably and noninvasively, either for short periods before and after exercise or continuously for two hours. This innovative approach could provide real-time health data with greater ease and comfort for users, potentially enhancing our understanding of physiological responses to physical activity. Overall, this trial could pave the way for more accessible, wearable health monitoring technology.

What evidence suggests that this biosensor is effective for wearability and function?

Research has shown that new noninvasive health sensors, like those studied in this trial, improve health monitoring. Participants will join different trial arms to evaluate the comfort and basic functions of these sensors. These devices gather detailed health information by analyzing sweat and other factors without piercing the skin. Recent advancements indicate increased accuracy through various detection methods. Machine learning enhances the sensors' ability to interpret collected data. While this study primarily examines the sensors' comfort and basic functions, the technology holds promise for noninvasive health tracking. ^{4 6 7 8 9}

Who Is on the Research Team?

Wubin Bai, PhD

Principal Investigator

Assistant Professor of Applied Physical Sciences

Are You a Good Fit for This Trial?

This trial is for healthy men and women aged between 18 and 65 who don't have any significant illnesses. It's not suitable for those unable to do daily activities normally, with active skin disorders, allergies to silicones or adhesives, or damaged skin near where sensors would be placed.

Inclusion Criteria

I am generally healthy with no significant illnesses.

Exclusion Criteria

Subjects who are unable to participate in moderate exercise for 6 minutes

I can perform daily activities without help.

History of allergic response to silicones or adhesives (such as 3M Tegaderm)

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

1-2 weeks

Treatment

Participants wear the bioelectronic sensors and approved medical devices during a lab visit, either before and after exercise or for an extended period

1-2.5 hours

1 visit (in-person)

Follow-up

Participants return to the lab for two brief visits to monitor for any adverse events related to the devices

96 hours

2 visits (in-person)

What Are the Treatments Tested in This Trial?

Interventions

• Multi-modality Noninvasive Biosensor

Trial Overview The study tests the safety and basic functions of new multi-modality biosensors designed in the lab. Participants will wear devices like a Vicorder Pulse Wave Velocity Monitor, an Oximeter, and a Portable Blood Pressure Monitor.

How Is the Trial Designed?

Group I: Wearing Biosensor for an Extended Period Experimental Treatment 4 Interventions

Each study participant will wear the bioelectronic sensors during a slow deep breath. Participants will wear the sensors and devices continuously for 2 hours.

Group II: Wearing Biosensor Before & After Exercise Experimental Treatment 4 Interventions

Each study participant will wear the bioelectronic sensors during a slow deep breath. Participants will wear the sensors and devices for 15 minutes before and after a brisk 6-minute walk.

Who Is Running the Clinical Trial?

University of North Carolina, Chapel Hill

Lead Sponsor

Trials

1,588

Recruited

4,364,000+

North Carolina Translational and Clinical Sciences Institute

Collaborator

Trials

61

Recruited

4,600+

Citations

1. advanced.onlinelibrary.wiley.com advanced.onlinelibrary.wiley.com/doi/10.1002/adsr.202500028

Machine Learning Enhanced Multimodal Bioelectronics ... This review examines recent advancements in multimodal bioelectronics, emphasizing machine learning integration to enhance functionality.

2. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC8391966/

Wearable Biosensors for Non-Invasive Sweat Diagnostics This review focuses on the recent progress made in the field of wearable and flexible bioelectronics for non-invasive health monitoring through in-situ sweat ...

3. ctv.veeva.com ctv.veeva.com/study/the-validation-study-of-bioelectronics

The Validation Study of Bioelectronics - ClinicalTrials.Veeva The purpose of this study is to investigate the safe wearability and basic functions of the multi-modality sensors fabricated in the PI lab.

4. sciencedirect.com sciencedirect.com/science/article/abs/pii/S016599362500322X

Recent advancements in dual modality biosensors for ... Consequently, multimodal detection techniques are gaining prominence in biosensor applications, offering potential solutions to enhance accuracy ...

5. mdpi.com mdpi.com/2079-6374/14/11/560

Advances in Wearable Biosensors for Healthcare This paper provides an extensive review of wearable biosensors regarding recent developments, current trends, key technologies, and novel applications in ...

6. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC12025796/

Biosensors, Artificial Intelligence Biosensors, False Results ... Medical biosensors have set the basis of medical diagnostics, and Artificial Intelligence (AI) has boosted diagnostics to a great extent.

7. sciencedirect.com sciencedirect.com/science/article/abs/pii/S0165993625002936

A comprehensive review of wearable devices for non- ... This review provides a comprehensive overview of the advancements in wearable biosensing platforms over the past five years, with a particular focus on device- ...

8. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC11203047/

The Application of Multi-Parameter ... This paper proposes a comprehensive approach that involves aggregating data relevant to food quality indices and developing corresponding evaluation models.

9. nature.com nature.com/articles/s41591-022-01981-2

Multimodal biomedical AI | Nature Medicine AI models utilizing data from different modalities can potentially help identify or generate the most optimal synthetic controls. Remote ...