Assessing Symptom and Mood Dynamics in Pain Using the Smartphone Application SOMA

Chronic Pain Clinical Trial

Official title: Assessing Symptom and Mood Dynamics in Pain Using the Smartphone Application SOMA

Status Recruiting

Clinical Trial Summary

This study relies on the use of a smartphone application (SOMA) that the investigators developed for tracking daily mood, pain, and activity status in acute pain, chronic pain, and healthy controls over four months.The primary goal of the study is to use fluctuations in daily self-reported symptoms to identify computational predictors of acute-chronic pain transition, pain recovery, and/or chronic pain maintenance or flareups. The general study will include anyone with current acute or chronic pain, while a smaller sub-study will use a subset of patients from the chronic pain group who have been diagnosed with chronic low back pain, failed back surgery syndrome, or fibromyalgia. These sub-study participants will first take part in one in-person EEG testing session while completing simple interoception and reinforcement learning tasks and then begin daily use of the SOMA app. Electrophysiologic and behavioral data from the EEG testing session will be used to determine predictors of treatment response in the sub-study.

Clinical Trial Description

The investigators aim to study the temporal dynamics of pain and links between self-reported pain, mood/emotion, and activities using the daily tracking app SOMA. The experience of pain fluctuates over time, specifically in patients who suffer from chronic pain and those who are transitioning from an acute to a chronic state. Emotions and mood directly influence the experience of pain and may contribute to its chronification. The investigators will use statistical and computational approaches to better understand the dynamics of these reported daily symptoms to identify computational predictors of transition from acute to chronic pain. Specifically, the investigators hypothesize that certain symptom clusters will co-occur in time and be linked to external life events (e.g. emotional and physical stress) and emotional states (e.g. worry). Statistical/computational analysis of pain dynamics could therefore identify indicators for change points in the transition from acute to chronic pain. ;

Related Conditions & MeSH terms

• Acute Pain
• Arthritis
• Chronic Headache Disorder
• Chronic Low-back Pain
• Chronic Migraine
• Chronic Pain
• Chronic Pain Syndrome
• Chronic Pelvic Pain Syndrome
• Chronic Shoulder Pain
• Cystitis, Interstitial
• Diabetic Neuropathies
• Endometriosis
• Endometriosis-related Pain
• Failed Back Surgery Syndrome
• Fibromyalgia
• Fibromyalgia, Primary
• Fibromyalgia, Secondary
• Headache
• Headache Disorders
• Irritable Bowel Syndrome
• Low Back Pain
• Multiple Trauma
• Myofascial Pain Syndromes
• Neuralgia
• Neuralgia, Postherpetic
• Neuropathic Pain
• Pain
• Pain, Postoperative
• Painful Bladder Syndrome
• Painful Diabetic Neuropathy
• Pelvic Pain
• Post Herpetic Neuralgia
• Post Operative Pain
• Shoulder Pain
• Somatoform Disorders
• Syndrome
• Temporomandibular Joint Disorders
• Temporomandibular Joint Dysfunction Syndrome
• Trauma, Multiple
• Trauma-related Wound
• Wounds and Injuries

• NCT number: NCT05754190
• Study type: Observational
• Source: Brown University
• Contact: Frederike H Petzschner, PhD
• Phone: 401-863-6272
• Email: frederike_petzschner@brown.edu
• Status: Recruiting
• Start date: June 20, 2023
• Completion date: June 30, 2025