0911 Behavioral and Activity Rhythms in Patients with Chronic Pain vs. Controls - A Computational Approach for Behavioral Data. (27th April 2018)

• Record Type: Journal Article
• Authors: Neikrug, A B; Okifuji, A; Hamilton, C; Liao, S; Donaldson, G
• Is Part Of: Sleep. Volume 41(2018)Supplement 1
• Page Start: A338
• Page End: A338
• Publication Date: 2018-04-27
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsy061.910
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Behavioral and activity rhythms (BAR) illustrate a diurnal pattern summarized by a period of 1) quiescence/sleep, 2) morning activity increase, 3) plateau of wakeful activity, and 4) "winding down" or decreasing activity to the next quiescent/sleep period. The goal of this study was to model these clinically-relevant component of BAR in a heterogeneous group of patients with chronic pain compared to healthy controls. Methods: 24 chronic pain patients (Age=45.0 ± 11.3; f=18) and 23 controls (Age=42.9 ± 10.4; f=18) participated. BAR were modeled using Minute-by-minute activity data from 7-day actigraphy with piecewise nonlinear regression that combines two logistic functions with a central join point. BAR parameters included: mid-point of morning activity rise (Morning-Time), rate of morning activity increase (Morning-Slope), activity average during day (Plateau), rate of evening activity decline (Evening-Slope), and mid-point of evening activity decrease (Evening-Time). Mixed-models and nonparametric Mann-Whitney U-Test were used to assess overall pattern and BAR parameter differences. Results: A Time-by-Group interaction (p<0.001) suggest that activity levels are distributed differently across the 24-h between patients and controls. Patients exhibited a more delayed Morning-Time and started their day one hour later relative to controls (8:12AM and 7:18AM, p=0.017). There was no significant difference in Evening-Time (p=0.317). Patients also exhibited a significantly attenuated Morning-Slope (p=0.003) and Evening-Slope (p=0.03). Most profoundly, Control group exhibited a 3 times higher rate of Morning-Slope and patients reached Plateau 2.8-hours later relative to control. No significant difference between the groups in the actual time activity started increasing in the morning (p=0.695) yet chronic pain patients took significantly longer to reach activity Plateau. Patients also had significantly shorter Plateau compared to controls (p=0.002), with a 4.4hr median difference. Conclusion: This study attempts a computational approach to modeling individual components of observable phenomena. These findings indicate that patients with chronic pain show distinct behavioral pattern difference compared to healthy controls. Future research is necessary to further evaluate the clinical utility of these measures. Support (If Any): Next Step Pilot Award granted by the Department of Anesthesiology, University of Utah.