Clinical trial NCT05215756: The Accuracy and Efficacy of AI-driven tVNS Algorithm
Official title: Proof of Concept Assessment of the Performance of Artificial Intelligence-driven Transcutaneous Vagal Nerve Stimulation (tVNS) Algorithm for Somatic Pain
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Healthy Volunteer
Keywords: transcutaneous vagal nerve stimulation
heart rate variability
Lead sponsor: Queen Mary University of London
Sponsor class: OTHER
First posted: 2022-01-31
Enrollment: 12
Sex: ALL
Minimum age (years): 18
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: Cross-sectional study
Primary purpose: DIAGNOSTIC

Brief 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

Healthy participants (defined as those without pre-existing medical comorbidity that makes them take medications or go to hospitals regularly), aged 18-65, from staff, students and the local population of Queen Mary, University of London

Exclusion Criteria:

1. Participants unable to provide informed consent
2. Participants with any systemic disease or medications that may influence the autonomic nervous system (e.g. beta-agonists or Parkinson's disease)
3. Pregnant or breastfeeding females
4. History of drug or alcohol abuse
5. Participants who have cardiovascular condition problems or epilepsy
6. Participants with cochlear implants
7. Participants who are using pain killers
8. Not meeting any of the inclusion criteria above

Primary outcomes:
measure: The detection rate of HRV change in response to pain
timeFrame: 1 hour
measure: The detection rate of HRV change in response to pain after exercising
timeFrame: 1 hour
Secondary outcomes:
measure: Changes in the threshold and tolerance of pain before and after tVNS
timeFrame: 1 hour
measure: Psychological questionnaire scores
timeFrame: 1 hour
measure: Personality questionnaire scores
timeFrame: 1 hour
measure: Anxiety and depression questionnaire scores
timeFrame: 1 hour
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: Queen Mary University of London
Study officials:
References: