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

Recruiting

• Conditions: Acute Pain; Chronic Headache Disorder; Endometriosis-related Pain; Arthritis; Chronic Pain; Post Operative Pain; Fibromyalgia, Primary; Fibromyalgia, Secondary; Chronic Pelvic Pain Syndrome; Temporomandibular Joint Disorders
• Interventions: Device: SOMA pain manager smartphone application

• Registration Number: NCT05754190
• Lead Sponsor: Brown University

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.

Study Timeline

• Study First Submitted: 2023-02-08
• Study First Submitted QC: 2023-02-21
• Study First Posted: 2023-03-03
• Status Verified: 2023-06
• Study Start: 2023-06-20
• Last Update Submitted: 2023-06-20
• Last Update Posted: 2023-06-22
• Primary Completion: 2025-06-30
• Study Completion: 2025-06-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 800

Inclusion Criteria

Not provided

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Exclusion Criteria

Not provided

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Healthy Controls	SOMA pain manager smartphone application	\[general study + sub study\] No history of chronic pain
Acute pain	SOMA pain manager smartphone application	\[general study\] Pain duration \< 3 months
Chronic pain	SOMA pain manager smartphone application	\[general study\] Pain duration \> 6 months \[sub-study\] diagnosis of chronic low back pain, failed back surgery syndrome, or fibromyalgia

Primary Outcome Measures

Name	Time	Method
[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 Outcome Measures

Name	Time	Method
[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] 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] 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] 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 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 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.
[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.
[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 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
[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.
[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.
[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.

Trial Locations

Locations (1)

Brown University; 🇺🇸 Providence, Rhode Island, United States