• Matthew Smuck, Christy Tomkins-Lane, Ma Agnes Ith, Renata Jarosz, and KaoMing-Chih JeffreyMJWearable Health Lab, Department of Orthopaedic Surgery, Stanford University, Redwood City, California, United States of America.Department of Anesthesia and Pain Management, Stanford University, Redwood City, California, United State.
• PM&R Section, Department of Orthopaedic Surgery, Stanford University, Redwood City, California, United States of America.
• Plos One. 2017 Jan 1; 12 (2): e0172804.

BackgroundAccurate measurement of physical performance in individuals with musculoskeletal pain is essential. Accelerometry is a powerful tool for this purpose, yet the current methods designed to evaluate energy expenditure are not optimized for this population. The goal of this study is to empirically derive a method of accelerometry analysis specifically for musculoskeletal pain populations.MethodsWe extracted data from 6,796 participants in the 2003-4 National Health and Nutrition Examination Survey (NHANES) including: 7-day accelerometry, health and pain questionnaires, and anthropomorphics. Custom macros were used for data processing, complex survey regression analyses, model selection, and statistical adjustment. After controlling for a multitude of variables that influence physical activity, we investigated whether distinct accelerometry profiles accompany pain in different locations of the body; and we identified the intensity intervals that best characterized these profiles.ResultsUnique accelerometry profiles were observed for pain in different body regions, logically clustering together based on proximity. Based on this, the following novel intervals (counts/minute) were identified and defined: Performance Sedentary (PSE) = 1-100, Performance Light 1 (PL1) = 101-350, Performance Light 2 (PL2) = 351-800, Performance Light 3 (PL3) = 801-2500, and Performance Moderate/Vigorous (PMV) = 2501-30000. The refinement of accelerometry signals into these new intervals, including 3 distinct ranges that fit inside the established light activity range, best captures alterations in real-life physical performance as a result of regional pain.Discussion And ConclusionsThese new accelerometry intervals provide a model for objective measurement of real-life physical performance in people with pain and musculoskeletal disorders, with many potential uses. They may be used to better evaluate the relationship between pain and daily physical function, monitor musculoskeletal disease progression, gauge disease severity, inform exercise prescription, and quantify the functional impact of treatments. Based on these findings, we recommend that future studies of pain and musculoskeletal disorders analyze accelerometry output based on these new "physical performance" intervals.

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