The Accuracy and Efficacy of AI-driven tVNS Algorithm

Healthy Volunteer Clinical Trial

Official title: Proof of Concept Assessment of the Performance of Artificial Intelligence-driven Transcutaneous Vagal Nerve Stimulation (tVNS) Algorithm for Somatic Pain

Status Not yet recruiting

Clinical Trial Summary

Pain, including somatic and visceral pain, is a common symptom. Persistent pain can lead to repetitive visits to hospitals and can limit patients' daily activities, which can result in tremendous medical cost and lower quality of life. For example, the prevalence rates of 25% are reported only for abdominal pain among adults (3), and it costs $10.2 billion each year in the US.

Pain is usually treated according to the World Health Organisation (WHO) 3 steps analgesic ladder. Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) are mainly used in step 1, which can cause serious side effects such as GI bleeding, renal failure and cardiovascular disease. In step 2 & 3, opioids are used and are also associated with serious side effects (e.g., psychological addiction, dizziness, nausea, vomiting, constipation, physical dependence, tolerance, and respiratory depression). Therefore, a new effective non-pharmacological treatment is beneficial for patients.

One such method is transcutaneous vagal nerve stimulation (tVNS). The auricular or cervical branch of the vagal nerve runs just under the skin and can be electrically stimulated through the skin by tVNS devices, which have shown the analgesic effects on various pain conditions.

The autonomic activity, including parasympathetic tone, can be estimated from the beat to beat intervals in the electrocardiogram, which is called heart rate variability (HRV). To date, we have shown that visceral and somatic pain triggered the autonomic response with the change in HRV, and HRV could be a biomarker of pain.

We hypothesised that the development of pain, including somatic pain and visceral pain, could be predicted by analysing heart rate pattern by artificial intelligence (AI). In this proof of concept study, we evaluate the detection rate of pain by the AI analysis of heart rate pattern. We also evaluate the effect of tVNS on the pain threshold.

Clinical Trial Description

Participants will be asked to attend our institution to complete an informed written consent sheet. They are asked to refrain from smoking for 12 hours and drinking alcohol and coffee as well as using recreational drugs for 48 hours prior to the study visit. After filling out a consent form, they will be asked to complete questionnaires to assess their psychological/personality status. Baseline heart rate will be measured for 10 minutes using a heart recording device. After the baseline measurement, the cold pressor test starts. Participants will be asked to immerse their hand into an ice water container. Then, the cold pain threshold and cold pain tolerance will be measured. During the cold pressor test, we will keep recording heart rate. We'll see if we can detect a change of heart rate variability (HRV) in response to pain.

After 10 minutes break, the cold pressor test will be performed again. This time, tVNS is administered for 2 minutes during the cold pressor test. We will evaluate the changes in cold pain threshold and cold pain tolerance along the course.

Finally, after 10 minutes interval, participants will be asked to exercise in the room (static jogging for 1 minute) to increase their heart rate. Then, the cold pressor test will be performed again. We will evaluate if we can detect a change of HRV in the circumstance where the heart rate increases. tVNS will also be administered for 2 minutes. ;

Related Conditions & MeSH terms

• Healthy Volunteer

• NCT number: NCT05215756
• Study type: Interventional
• Source: Queen Mary University of London
• Contact: Qasim Aziz
• Phone: 02078822630
• Email: q.aziz@qmul.ac.uk
• Status: Not yet recruiting
• Phase: N/A
• Start date: April 1, 2024
• Completion date: December 31, 2024