Brown University
Providence, Rhode Island, 02912, United States
Location status: Recruiting
Location contact
Chloe S Zimmerman, MD/PhD Student
CONTACT
Frederike H Petzschner, PhD
CONTACT
Frederike H Petzschner, PhD
PRINCIPAL_INVESTIGATOR
NCT Number: NCT05754190
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.
Interested in participating?
Request Info18 year and older
All sexes
Observational
Providence, Rhode Island, 02912, United States
Location status: Recruiting
Chloe S Zimmerman, MD/PhD Student
CONTACT
Frederike H Petzschner, PhD
CONTACT
Frederike H Petzschner, PhD
PRINCIPAL_INVESTIGATOR
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.
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
[General study]
○ or
○ or
Exclusion criteria
[General study]
History of Chronic Pain (Pain lasting for more than 6 months)
-In person EEG testing [Sub-study only]: [will interfere with EEG data collection safety or quality]:
SOMA is a smartphone application developed for acute and chronic pain patients to track daily mood and pain symptoms and overall activity.
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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.
Time frame: 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
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: T0 [Baseline], T2 [4 months]
Test harm avoidance learning and generalization differences between pain patients and healthy controls using a computerized reinforcement learning game.
Time frame: 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.
Time frame: 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.
Time frame: 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
Time frame: 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.
Contact information is provided by the study sponsor or research team.
Chloe S Zimmerman, MD/PhD student
CONTACT
Frederike H Petzschner, PhD
CONTACT
Brown University
Other
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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