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NCT Number: NCT05754190

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

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.

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Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Brown University

Providence, Rhode Island, 02912, United States

Location status: Recruiting

Location contact

Chloe S Zimmerman, MD/PhD Student

CONTACT

[email protected]

401-863-6272

Frederike H Petzschner, PhD

CONTACT

[email protected]

401-863-6272

Frederike H Petzschner, PhD

PRINCIPAL_INVESTIGATOR

About this 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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Primary outcomes

  1. [General Study] Acute-Chronic Pain Transition Probability

    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

Secondary outcomes

  1. [General Study] Feasibility of long-term app use

    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

  2. [General Study] App Engagement

    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

  3. [General Study] Pain Dynamics

    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

  4. [General Study] Activity Dynamics

    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.

  5. [General Study] Pain Beliefs

    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

  6. [General study] Mood Dynamics

    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.

  7. [General Study] Association between mood, pain, and activity

    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.

  8. [General Study] Mood homeostasis as measured by SOMA app mood screens

    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.

  9. [General Study] Effect of Treatments on pain and mood as measured by SOMA app screens

    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.

  10. [General Study] Avoidance Learning task-computer game

    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.

  11. [Sub-Study] Avoidance Learning Task-EEG

    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.

  12. [Sub-Study] Cardiac Interoceptive Attention Task-EEG

    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.

  13. [Sub-study] Resting state- EEG

    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

  14. [Sub-study] Treatment outcome prediction in chronic low back pain and failed back surgery syndrome patients

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Chloe S Zimmerman, MD/PhD student

CONTACT

[email protected]

401-863-6272

Frederike H Petzschner, PhD

CONTACT

[email protected]

401-863-6272

Sponsors and collaborators

Lead sponsor

Brown University

Other

Registry information

Important dates

Study start
2023
Primary completion
2026
Study completion
2026
First posted
Mar 3, 2023
Registry last updated
Sep 19, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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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