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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05754190
• Other study ID #: 2022003301
• Status: Recruiting
• Start date: June 20, 2023
• Est. completion date: June 30, 2025

Study information

• Verified date: June 2023
• Source: Brown University
• Contact: Frederike H Petzschner, PhD
• Phone: 401-863-6272
• Email: frederike_petzschner[@]brown.edu
• Is FDA regulated: No
• Study type: Observational

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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 800
• Est. completion date: June 30, 2025
• Est. primary completion date: June 30, 2025
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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

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

Intervention

Device:
• SOMA 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.

Locations

Country	Name	City	State
United States	Brown University	Providence	Rhode Island

Sponsors (1)

Lead Sponsor	Collaborator
Brown University

Country where clinical trial is conducted

United States, 

References & Publications (5)

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	[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]
Secondary	[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]
Secondary	[General Study] App Engagement	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]
Secondary	[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]
Secondary	[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]
Secondary	[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]
Secondary	[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]
Secondary	[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]
Secondary	[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]
Secondary	[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]
Secondary	[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]
Secondary	[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]
Secondary	[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]
Secondary	[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]
Secondary	[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]

External Links

•   To find out more information about Petzschner lab research