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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Healthy Controls | [general study + sub study] No history of chronic pain |
| |
| Acute pain | [general study] Pain duration < 3 months |
| |
| Chronic pain | [general study] Pain duration > 6 months [sub-study] diagnosis of chronic low back pain, failed back surgery syndrome, or fibromyalgia |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| SOMA pain manager smartphone application | Device | SOMA is a smartphone application developed for acute and chronic pain patients to track daily mood and pain symptoms and overall activity. |
| Measure | Description | Time Frame |
|---|---|---|
| [General Study] Acute-Chronic Pain Transition Probability | Test whether daily affect (incl. mood), pain, activities, and other factors measured by the SOMA app can predict transition from acute to chronic pain, pain recovery, or pain maintenance using mixed effects linear regression model-based analyses to predict long- term pain scores such as pain intensity, unpleasantness, and/or interference | T1 [4 months of daily app use] |
| Measure | Description | Time Frame |
|---|---|---|
| [General Study] Feasibility of long-term app use | Percentage of Soma users in acute and chronic pain groups who engage with the app for 4 months | T1 [4 months of daily app use] |
| [General Study] App Engagement |
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INCLUSION CRITERIA [General study]
Chronic pain group:
Acute pain group:
Age above 18
Access to a personal smartphone and a stable internet connection
Average pain intensity score of greater than 3 in the past week
â—‹ or
Average pain interference score of greater than 3 in the past week
â—‹ or
Average pain distress score of greater than 3 in the past week
Pain duration: less than 3 months
Pain cause: Due to recent surgery, injury, acute illness, or childbirth (within the past 3 months)
Healthy control group:
In person EEG testing [Sub-Study only]:
EXCLUSION CRITERIA [General study]
Chronic pain group:
Acute pain group:
Healthy control group:
History of Chronic Pain (Pain lasting for more than 6 months)
difficulty participating for technical/logistical issues (e.g., no computer, incompatible smartphone, can't commit to 4 months study participation);
Not fluent in English (difficulty understanding questions)
-In person EEG testing [Sub-study only]: [will interfere with EEG data collection safety or quality]:
Same as in General App Study Above and additionally:
Baldness
Pregnancy
Dreadlocks
Left-handedness
Use of a wheelchair
Heart failure diagnosis
Current or prior experience with acute psychosis or mania
implanted pacemaker, neurostimulator or any other head or heart implants
require a hearing aid to hear properly
claustrophobia
metal fragments in the body
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Any person who fits the eligibility criteria for acute pain, chronic pain, or healthy control groups will be eligible to participate in the general parent study. Healthy controls or participants who have a diagnosis of chronic low back pain, failed back surgery syndrom or fibromyalgia are eligible to participate in the in-person EEG sub-study.
Participants will be recruited from around Rhode Island and surrounding regions via online ads, listserv announcements, local healthcare clinics, rehab and nursing facilities, word of mouth, social media, and targeted newspaper/magazine/public transportation advertisements. Chronic pain participants eligible for the in-person EEG testing will be recruited via these methods as well as targeted recruitment with physicians treating these patients.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Frederike H Petzschner, PhD | Contact | 401-863-6272 | frederike_petzschner@brown.edu | |
| Chloe S Zimmerman, MD/PhD student | Contact | 401-863-6272 | chloe_zimmerman@brown.edu |
| Name | Affiliation | Role |
|---|---|---|
| Frederike H Petzschner, PhD | Brown University | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Brown University | Recruiting | Providence | Rhode Island | 02912 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 21148657 | Background | Voscopoulos C, Lema M. When does acute pain become chronic? Br J Anaesth. 2010 Dec;105 Suppl 1:i69-85. doi: 10.1093/bja/aeq323. | |
| 23823463 | Background | Apkarian AV, Baliki MN, Farmer MA. Predicting transition to chronic pain. Curr Opin Neurol. 2013 Aug;26(4):360-7. doi: 10.1097/WCO.0b013e32836336ad. |
| Label | URL |
|---|---|
| To find out more about how to download and use the SOMA Pain Manager app | View source |
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Anyone interested in IPD should reach out to the Principal Investigator
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Evaluate user engagement based on number of completed daily ESM assessments per person in the acute and chronic pain groups over the 4 months of app use
| T1 [4 months of daily app use] |
| [General Study] Pain Dynamics | Test whether variability in daily pain location, intensity, unpleasantness, and interference, and daily pain expectations and prediction errors in the SOMA app can predict long-term pain scores in cross sectional between-group and longitudinal within-subject model-based analyses | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] |
| [General Study] Activity Dynamics | Test whether types or number of daily activities, the effect of activities on pain, and activity expectations for the next day can predict long-term pain scores in cross sectional and longitudinal model-based analyses. | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] |
| [General Study] Pain Beliefs | Test whether questionnaire scores related to pain beliefs and personal/health history at T0 can predict long-term pain scores in cross sectional between-group and longitudinal within-subject model-based analyses | T0 [Baseline], T2 [4 months], T3 [8 months], T4 [12 months] |
| [General study] Mood Dynamics | Test whether variability in daily mood ratings and mood prediction errors can predict long-term pain scores in cross sectional between-group and longitudinal within-subject model-based analyses. | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] |
| [General Study] Association between mood, pain, and activity | Assess the effect of mood, pain, pain prediction errors and mood prediction errors on future activities in cross sectional between-group and longitudinal within-subject model based analyses. | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] |
| [General Study] Mood homeostasis as measured by SOMA app mood screens | Assess mood homeostasis using SOMA mood screens in cross sectional between-group and longitudinal within-subject model based analyses. | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] |
| [General Study] Effect of Treatments on pain and mood as measured by SOMA app screens | Assess the effect of pain treatments on mood, pain and activities using the dedicated SOMA screens for these measures in cross sectional between-group and longitudinal within-subject model based analyses. | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] |
| [General Study] Avoidance Learning task-computer game | Test harm avoidance learning and generalization differences between pain patients and healthy controls using a computerized reinforcement learning game. | T0 [Baseline], T2 [4 months] |
| [Sub-Study] Avoidance Learning Task-EEG | Test whether EEG frontal theta band power is increased during prediction error processing and harm avoidance contexts in a reinforcement learning task in cross-sectional between-group analyses. | T0 [Baseline] |
| [Sub-Study] Cardiac Interoceptive Attention Task-EEG | Test whether cross-sectional differences in EEG-measured Heartbeat-evoked potential (HEP) amplitude when attending to interoceptive vs exteroceptive stimuli differ between pain patients and healthy controls and test relationship to questionnaire measures at baseline and follow-up. | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] |
| [Sub-study] Resting state- EEG | Test cross-sectional differences in EEG-measured Resting State Activity between pain groups and healthy controls and test relationships between resting EEG measures and questionnaire results at baseline and follow-up | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] |
| [Sub-study] Treatment outcome prediction in chronic low back pain and failed back surgery syndrome patients | Test whether baseline EEG HEP and questionnaire measures predict pain scores at T3 following invasive back treatments (eg back surgery, spinal cord stimulation, radio-frequency ablation) that occur during T1. | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] |
| 22751038 | Background | Baliki MN, Petre B, Torbey S, Herrmann KM, Huang L, Schnitzer TJ, Fields HL, Apkarian AV. Corticostriatal functional connectivity predicts transition to chronic back pain. Nat Neurosci. 2012 Jul 1;15(8):1117-9. doi: 10.1038/nn.3153. |
| 23983029 | Background | Hashmi JA, Baliki MN, Huang L, Baria AT, Torbey S, Hermann KM, Schnitzer TJ, Apkarian AV. Shape shifting pain: chronification of back pain shifts brain representation from nociceptive to emotional circuits. Brain. 2013 Sep;136(Pt 9):2751-68. doi: 10.1093/brain/awt211. |
| 11880847 | Background | Pincus T, Burton AK, Vogel S, Field AP. A systematic review of psychological factors as predictors of chronicity/disability in prospective cohorts of low back pain. Spine (Phila Pa 1976). 2002 Mar 1;27(5):E109-20. doi: 10.1097/00007632-200203010-00017. |
| ID | Term |
|---|---|
| D059350 | Chronic Pain |
| D059787 | Acute Pain |
| D010149 | Pain, Postoperative |
| D005356 | Fibromyalgia |
| D043183 | Irritable Bowel Syndrome |
| D013705 | Temporomandibular Joint Disorders |
| D001168 | Arthritis |
| D055111 | Failed Back Surgery Syndrome |
| D051474 | Neuralgia, Postherpetic |
| D009437 | Neuralgia |
| D003929 | Diabetic Neuropathies |
| D018856 | Cystitis, Interstitial |
| D009104 | Multiple Trauma |
| ID | Term |
|---|---|
| D010146 | Pain |
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D011183 | Postoperative Complications |
| D010335 | Pathologic Processes |
| D009135 | Muscular Diseases |
| D009140 | Musculoskeletal Diseases |
| D012216 | Rheumatic Diseases |
| D009468 | Neuromuscular Diseases |
| D009422 | Nervous System Diseases |
| D003109 | Colonic Diseases, Functional |
| D003108 | Colonic Diseases |
| D007410 | Intestinal Diseases |
| D005767 | Gastrointestinal Diseases |
| D004066 | Digestive System Diseases |
| D017271 | Craniomandibular Disorders |
| D008336 | Mandibular Diseases |
| D007571 | Jaw Diseases |
| D007592 | Joint Diseases |
| D009057 | Stomatognathic Diseases |
| D001416 | Back Pain |
| D010523 | Peripheral Nervous System Diseases |
| D048909 | Diabetes Complications |
| D003920 | Diabetes Mellitus |
| D004700 | Endocrine System Diseases |
| D003556 | Cystitis |
| D001745 | Urinary Bladder Diseases |
| D014570 | Urologic Diseases |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D052801 | Male Urogenital Diseases |
| D014947 | Wounds and Injuries |
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