Acute Pain, Arthritis, Chronic Headache Disorder, Chronic Low-back Pain, Chronic Migraine, Chronic Pain, Chronic Pain Syndrome, Chronic Pelvic Pain Syndrome, Chronic Shoulder Pain, Endometriosis-related Pain, Failed Back Surgery Syndrome, Fibromyalgia, Fibromyalgia, Primary, Fibromyalgia, Secondary, Irritable Bowel Syndrome, Neuropathic Pain, Painful Bladder Syndrome, Painful Diabetic Neuropathy, Post Herpetic Neuralgia, Post Operative Pain, Temporomandibular Joint Disorders, Trauma, Multiple, Trauma-related Wound
Conditions
Keywords
digital health, e-health, smartphone application, chronic pain, acute pain, symptom tracking, experience sampling methodology, ecological momentary assessment, pain self-management
Brief 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.
Detailed 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.
Interventions
DEVICESOMA pain manager smartphone application
SOMA is a smartphone application developed for acute and chronic pain patients to track daily mood and pain symptoms and overall activity.
Sponsors
Brown University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
\[General study\] * Chronic 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 or * Average pain interference score of greater than 3 in the past week or * Average pain distress score of greater than 3 in the past week * Pain duration: greater than 6 months * 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 ○ or * Average pain interference score of greater than 3 in the past week ○ 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: * Age above 18 * Access to a personal smartphone and a stable internet connection * Average pain intensity score of less than 3 in the past week * Average pain interference score of less than 3 in the past week * Average pain distress score of less than 3 in the past week * No surgery, injury, acute illness, or childbirth (within the past 3 months) * In person EEG testing \[Sub-Study only\]: * Same as in General App Study Above and additionally: * Current diagnosis of Fibromyalgia, Chronic Low Back Pain or Failed Back Surgery Syndrome OR * No current or prior history of chronic pain * If participant has chronic low back pain or failed back surgery syndrome: are they planning to have either a radio-frequency ablation, back surgery, or spinal cord stimulation implant in the next few months * If participant has chronic low back pain or failed back surgery syndrome: have they received insurance approval for the procedure? * Ok with EEC/ECG measures
Exclusion criteria
\[General study\] * Chronic pain group: * recent injury or surgery unrelated to the pain in the past 3 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) * Current primary or metastatic cancer (organic cause of pain) * Acute pain 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) * Current primary or metastatic cancer (organic cause of pain) * 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| [General Study] Acute-Chronic Pain Transition Probability | T1 [4 months of daily app use] | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| [General Study] App Engagement | T1 [4 months of daily app use] | 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 |
| [General Study] Pain Dynamics | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] | 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 |
| [General Study] Activity Dynamics | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] | 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. |
| [General Study] Pain Beliefs | T0 [Baseline], T2 [4 months], T3 [8 months], T4 [12 months] | 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 |
| [General study] Mood Dynamics | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] | 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. |
| [General Study] Association between mood, pain, and activity | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] | 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. |
| [General Study] Feasibility of long-term app use | T1 [4 months of daily app use] | Percentage of Soma users in acute and chronic pain groups who engage with the app for 4 months |
| [General Study] Effect of Treatments on pain and mood as measured by SOMA app screens | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] | 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. |
| [General Study] Avoidance Learning task-computer game | T0 [Baseline], T2 [4 months] | Test harm avoidance learning and generalization differences between pain patients and healthy controls using a computerized reinforcement learning game. |
| [Sub-Study] Avoidance Learning Task-EEG | T0 [Baseline] | 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. |
| [Sub-Study] Cardiac Interoceptive Attention Task-EEG | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] | 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. |
| [Sub-study] Resting state- EEG | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] | 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 |
| [Sub-study] Treatment outcome prediction in chronic low back pain and failed back surgery syndrome patients | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] | 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. |
| [General Study] Mood homeostasis as measured by SOMA app mood screens | T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months] | Assess mood homeostasis using SOMA mood screens in cross sectional between-group and longitudinal within-subject model based analyses. |
Countries
United States
Contacts
Primary ContactFrederike H Petzschner, PhD
Backup ContactChloe S Zimmerman, MD/PhD student
Outcome results
None listed