Brown University
Frederike Petzschner
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.
Chronic Pain
Acute Pain
Post Operative Pain
Fibromyalgia, Primary
Fibromyalgia, Secondary
Fibromyalgia
Irritable Bowel Syndrome
Chronic Headache Disorder
Chronic Migraine
Chronic Pelvic Pain Syndrome
Temporomandibular Joint Disorders
Endometriosis-related Pain
Arthritis
Chronic Low-back Pain
Failed Back Surgery Syndrome
Post Herpetic Neuralgia
Neuropathic Pain
Painful Diabetic Neuropathy
Painful Bladder Syndrome
Trauma-related Wound
Trauma, Multiple
Chronic Pain Syndrome
Chronic Shoulder Pain
SOMA pain manager smartphone application
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.
Study Type : | OBSERVATIONAL |
Estimated Enrollment : | 800 participants |
Official Title : | Assessing Symptom and Mood Dynamics in Pain Using the Smartphone Application SOMA |
Actual Study Start Date : | 2023-06-20 |
Estimated Primary Completion Date : | 2025-06-30 |
Estimated Study Completion Date : | 2025-06-30 |
Information not available for Arms and Intervention/treatment
Ages Eligible for Study: | 18 Years |
Sexes Eligible for Study: | ALL |
Accepts Healthy Volunteers: | 1 |
Want to participate in this study, select a site at your convenience, send yourself email to get contact details and prescreening steps.
RECRUITING
Brown University
Providence, Rhode Island, United States, 02912