Assessing Symptom and Mood Dynamics in Pain Using the Smartphone Application SOMA
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 800
- 试验地点
- 1
- 主要终点
- [General Study] Acute-Chronic Pain Transition Probability
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- 未提供
排除标准
- 未提供
结局指标
主要结局
[General Study] Acute-Chronic Pain Transition Probability
时间窗: 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
次要结局
- [General study] Mood Dynamics(T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months])
- [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])
- [General Study] Feasibility of long-term app use(T1 [4 months of daily app use])
- [General Study] App Engagement(T1 [4 months of daily app use])
- [General Study] Pain Beliefs(T0 [Baseline], T2 [4 months], T3 [8 months], T4 [12 months])
- [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])
- [General Study] Avoidance Learning task-computer game(T0 [Baseline], T2 [4 months])
- [Sub-Study] Avoidance Learning Task-EEG(T0 [Baseline])
- [General Study] Activity Dynamics(T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months])
- [General Study] Pain Dynamics(T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months])
- [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])
- [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])
- [Sub-study] Resting state- EEG(T0 [Baseline], T1 [4 months of daily app use], T2 [4 months], T3 [8 months], T4 [12 months])
- [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])
研究者
Frederike Petzschner
Assistant Professor, Psychiatry and Human Behavior; Carney Institute for Brain Science
Brown University