assessing symptom and mood dynamics in pain using the smartphone application soma

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

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.

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.

Eligibility criteria:
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

Keywords: digital health e-health smartphone application chronic pain acute pain symptom tracking experience sampling methodology ecological momentary assessment pain self-management