Objective Integrated Multimodal Electrophysiological Index for the Quantification of Visceral Pain
- Conditions
- Abdominal Pain
- Interventions
- Behavioral: IBS-PPSM intervention
- Registration Number
- NCT06381921
- Lead Sponsor
- University of Connecticut
- Brief Summary
The objectives of the study are to 1) Conduct telemetric biosignals (EDA, ECG, and EMG) recording in healthy controls and IBS participants experiencing cutaneous and visceral pain; and 2) Validate the OIME index as a biomarker for quantifying pain in IBS participants and its capability to assess the treatment of IBS pain via an ambulatory trial.
- Detailed Description
In part 1, data collection for training the OIME model, we will collect autonomic and muscular activities with integrated biosignal device and visceral pain level in both healthy controls and IBS participants. These data will be used to train a machine learning model to produce an objective integrated multimodal electrophysiological (OIME) index.
In part 2, the ambulatory trial, we will collect data to validate the OIME index as a biomarker of pain in IBS participants. We will run an ambulatory trial to validate the OIME index as a biomarker to assess the treatment of IBS pain.
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 120
- Participants need to be diagnosed with IBS by a healthcare provider according to Rome-III or -IV criteria, with a current report of abdominal pain.
- Men and women 18-50 years old
- Able to read and speak English
- Daily access to a computer with internet access.
- Other chronic pains that are usually not comorbid with IBS, e.g., diabetic neuropathy, myofascial pain, low back pain, peripheral neuropathy etc.
- Celiac disease or inflammatory bowel disease
- Diabetes mellitus; d) Serious mental health conditions
- Women during pregnancy or within 3 months post-partum period
- Self- reported Regular use of opioids or other illicit substances.
- Participants who have had COVID-19 should be fully recovered, and will be asked if their medical provider has made any restriction on activities.
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description IBS-PPSM intervention group IBS-PPSM intervention After the baseline visit, the IBS-PPSM group will receive 10 video modules focused on IBS knowledge and self-management skills plus one-on-one consultation with a nurse for personalized advice about self-monitoring, diet, sleep, and goal setting. The IBS-PPSM group will be taught to use the abdominal belt/smart watch system for daily recording of bio-signals and voluntary report of episodes of visceral pain. We will follow up with all participants 4 weeks after enrollment, in a final lab visit, and measure primary outcomes to compare to the baseline data.
- Primary Outcome Measures
Name Time Method IBS-related symptoms Baseline and 4-week follow-up; Weekly Online Logs Qualitative narrative data will be collected including self-reported stool frequency, quality, form, and abdominal bloating or distention symptoms. Online diary format data.
Electrocardiogram (ECG) Baseline and 4-week follow-up ECG will be recorded non-invasively via surface electrodes on an abdominal belt; unit: mV
Electrodermal activity (EDA) Baseline and 4-week follow-up EDA will be recorded non-invasively via surface electrodes on an abdominal belt; unit: µS
Pain intensity and interference Baseline and 4-week follow-up; Weekly Online Logs Brief Pain Inventory (BPI) scale: the 9-item scale measures pain intensity and interference using 0 - 10 rating scales. Higher score indicates higher levels of pain intensity and interference.
Electromyogram (EMG) Baseline and 4-week follow-up EMG will be recorded non-invasively via surface electrodes on an abdominal belt; unit: mV
Objective integrated multimodal electrophysiological index Baseline and 4-week follow-up Objective integrated multimodal electrophysiological (OIME) index as a biomarker for visceral pain will be generated and validated through integration of surface bio-signal recordings of EDA, ECG, and EMG via a supervised machine-learning algorithm.
- Secondary Outcome Measures
Name Time Method