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Observational Study Using SOMA App for Pain and Mood

This study is looking at how pain, mood, and daily activities change over time for people with different types of pain, including chronic pain, acute pain, and fibromyalgia. It uses a smartphone app called SOMA, which you would use daily for four months to track your symptoms. Researchers want to understand if changes in these daily reports can help predict when acute pain might become chronic, when pain might get better, or when chronic pain might get worse. You can join if you are over 18, have access to a smartphone and internet, and have experienced a certain level of pain in the past week. The study is currently recruiting participants.

Study design
This is an observational study with a planned enrollment of 800 participants. It is not a treatment study but rather observes how pain and mood change over time.
What's involved
You would use the SOMA smartphone application daily for four months to track your mood, pain symptoms, and overall activity.
Compensation
Not stated in the trial record.
Follow-up
The primary endpoint, acute-chronic pain transition probability, is measured at T1, which is after 4 months of daily app use.

AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.

NCT05754190

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

Recruiting
Not specifiedAges 18+Observational
Brown University
~800 participants
Updated 2025-09-19 on ClinicalTrials.gov
What's tested:SOMA pain manager smartphone application

At a glance

Recruiting sites
1 of 1 listed site is recruiting right now
RecruitingSuspended, closed, or not yet open
What they're measuring
[General Study] Acute-Chronic Pain Transition Probability
Measured over T1 [4 months of daily app use]

Conditions

Chronic Pain
Acute Pain
Post Operative Pain
Fibromyalgia, Primary
Fibromyalgia, Secondary
Fibromyalgia
Irritable Bowel Syndrome
Chronic Headache Disorder
Chronic Migraine
Chronic Pelvic Pain Syndrome
Temporomandibular Joint Disorders
Endometriosis-related Pain
Arthritis
Chronic Low-back Pain
Failed Back Surgery Syndrome
Post Herpetic Neuralgia
Neuropathic Pain
Painful Diabetic Neuropathy
Painful Bladder Syndrome
Trauma-related Wound
Trauma, Multiple
Chronic Pain Syndrome
Chronic Shoulder Pain

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Where it's being run

1 sites across 1 states
Rhode Island1

Study leadership

  • Frederike H Petzschner, PhD · PRINCIPAL_INVESTIGATOR · Brown University

Who to contact

Frederike H Petzschner, PhD
[email protected]
401-863-6272
Email the study team

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Do you actually qualify for this trial?

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Eligibility criteria

Inclusion

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
Average pain interference score of greater than 3 in the past week
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

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:
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

What this trial measures

  • [General Study] Acute-Chronic Pain Transition ProbabilityT1 [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