Changes in Pain Self-Efficacy, Coping Skills, and Fear-Avoidance Beliefs in a Randomized Controlled Trial of Yoga, Physical Therapy, and Education for Chronic Low Back Pain.

OBJECTIVE: We evaluated exercise interventions for cognitive appraisal of chronic low back pain (cLBP) in an underserved population. METHODS: We conducted a secondary analysis of the Back to Health Trial, showing yoga to be noninferior to physical therapy (PT) for pain and function outcomes among adults with cLBP (n = 320) recruited from primary care clinics with predominantly low-income patients. Participants were randomized to 12 weeks of yoga, PT, or education. Cognitive appraisal was assessed with the Pain Self-Efficacy Questionnaire (PSEQ), Coping Strategies Questionnaire (CSQ), and Fear-Avoidance Beliefs Questionnaire (FABQ). Using multiple imputation and linear regression, we estimated within- and between-group changes in cognitive appraisal at 12 and 52 weeks, with baseline and the education group as references. RESULTS: Participants (mean age = 46 years) were majority female (64%) and majority Black (57%), and 54% had an annual household income <$30,000. All three groups showed improvements in PSEQ (range 0-60) at 12 weeks (yoga, mean difference [MD] = 7.0, 95% confidence interval [CI]: 4.9, 9.0; PT, MD = 6.9, 95% CI: 4.7 to 9.1; and education, MD = 3.4, 95% CI: 0.54 to 6.3), with yoga and PT improvements being clinically meaningful. At 12 weeks, improvements in catastrophizing (CSQ, range 0-36) were largest in the yoga and PT groups (MD = -3.0, 95% CI: -4.4 to -1.6; MD = -2.7, 95% CI: -4.2 to -1.2, respectively). Changes in FABQ were small. No statistically significant between-group differences were observed on PSEQ, CSQ, or FABQ at either time point. Many of the changes observed at 12 weeks were sustained at 52 weeks. CONCLUSION: All three interventions were associated with improvements in self-efficacy and catastrophizing among low-income, racially diverse adults with cLBP. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT01343927.

An unsupervised automated paradigm for artifact removal from electrodermal activity in an uncontrolled clinical setting.

Objective. Electrodermal activity (EDA) reflects sympathetic nervous system activity through sweating-related changes in skin conductance and could be used in clinical settings in which patients cannot self-report pain, such as during surgery or when in a coma. To enable EDA data to be used robustly in clinical settings, we need to develop artifact detection and removal frameworks that can handle the types of interference experienced in clinical settings while salvaging as much useful information as possible.Approach. In this study, we collected EDA data from 70 subjects while they were undergoing surgery in the operating room. We then built a fully automated artifact removal framework to remove the heavy artifacts that resulted from the use of surgical electrocautery during the surgery and compared it to two existing state-of-the-art methods for artifact removal from EDA data. This automated framework consisted of first utilizing three unsupervised machine learning methods for anomaly detection, and then customizing the threshold to separate artifact for each data instance by taking advantage of the statistical properties of the artifact in that data instance. We also created simulated surgical data by introducing artifacts into cleaned surgical data and measured the performance of all three methods in removing it.Main results. Our method achieved the highest overall accuracy and precision and lowest overall error on simulated data. One of the other methods prioritized high sensitivity while sacrificing specificity and precision, while the other had low sensitivity, high error, and left behind several artifacts. These results were qualitatively similar between the simulated data instances and operating room data instances.Significance. Our framework allows for robust removal of heavy artifact from EDA data in clinical settings such as surgery, which is the first step to enable clinical integration of EDA as part of standard monitoring.

Tonic Electrodermal Activity is a Robust Marker of Psychological and Physiological Changes during Induction of Anesthesia.

Electrodermal activity (EDA), which tracks sweat gland activity as a proxy for sympathetic activation, has the potential to be a biomarker of physiological and psychological changes in the clinic. To show this, in this study, we demonstrate that the tonic component of EDA responds consistently and robustly during induction of anesthesia in the operating room in 8 subjects during surgery. This response is seen bilaterally. The response shows a significant increase in EDA in anticipation of induction and then a gradual decrease in response to the administration of medication, which agrees with both the expected psychological effects of stress and anxiety and the physiological effects of anesthetic medication on sweat glands. The results also show a slightly faster response to drug in the arm directly receiving the medication intravenously compared to the opposite, though the magnitude of the effect evens out over time. Clinical Relevance- EDA can serve as a robust non-invasive biomarker in the clinic to track both psychologically and physiologically induced autonomic changes.

Unsupervised Machine Learning Methods for Artifact Removal in Electrodermal Activity.

Artifact detection and removal is a crucial step in all data preprocessing pipelines for physiological time series data, especially when collected outside of controlled experimental settings. The fact that such artifact is often readily identifiable by eye suggests that unsupervised machine learning algorithms may be a promising option that do not require manually labeled training datasets. Existing methods are often heuristic-based, not generalizable, or developed for controlled experimental settings with less artifact. In this study, we test the ability of three such unsupervised learning algorithms, isolation forests, 1-class support vector machine, and K-nearest neighbor distance, to remove heavy cautery-related artifact from electrodermal activity (EDA) data collected while six subjects underwent surgery. We first defined 12 features for each halfsecond window as inputs to the unsupervised learning methods. For each subject, we compared the best performing unsupervised learning method to four other existing methods for EDA artifact removal. For all six subjects, the unsupervised learning method was the only one successful at fully removing the artifact. This approach can easily be expanded to other modalities of physiological data in complex settings.Clinical Relevance- Robust artifact detection methods allow for the use of diverse physiological data even in complex clinical settings to inform diagnostic and therapeutic decisions.