title: Physical performance analysis: A new approach to assessing free-living physical activity in musculoskeletal pain and mobility-limited populations.
abstract: BACKGROUND
  Accurate 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.
  
  
  METHODS
  We 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.
  
  
  RESULTS
  Unique 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.
  
  
  CONCLUSIONS
  These 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.
authors.affiliation: PM&R Section, Department of Orthopaedic Surgery, Stanford University, Redwood City, California, United States of America.|Wearable Health Lab, Department of Orthopaedic Surgery, Stanford University, Redwood City, California, United States of America.
authors.forename: Matthew
authors.initials: M
authors.lastname: Smuck
authors.affiliation: Wearable Health Lab, Department of Orthopaedic Surgery, Stanford University, Redwood City, California, United States of America.|Department of Physical Education & Recreation, Mount Royal University, Calgary, Canada.
authors.forename: Christy
authors.initials: C
authors.lastname: Tomkins-Lane
authors.affiliation: PM&R Section, Department of Orthopaedic Surgery, Stanford University, Redwood City, California, United States of America.|Wearable Health Lab, Department of Orthopaedic Surgery, Stanford University, Redwood City, California, United States of America.
authors.forename: Ma Agnes
authors.initials: MA
authors.lastname: Ith
authors.affiliation: PM&R Section, Department of Orthopaedic Surgery, Stanford University, Redwood City, California, United States of America.
authors.forename: Renata
authors.initials: R
authors.lastname: Jarosz
authors.affiliation: Wearable 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 States of America.
authors.forename: Ming-Chih Jeffrey
authors.initials: MJ
authors.lastname: Kao
mesh_terms: D061725:Accelerometry; D000465:Algorithms; D000886:Anthropometry; D016000:Cluster Analysis; D016208:Databases, Factual; D006233:Persons with Disabilities; D018450:Disease Progression; D015444:Exercise; D006801:Humans; D015233:Models, Statistical; D059352:Musculoskeletal Pain / Q000175:diagnosis* / Q000628:therapy*; D009749:Nutrition Surveys; D059408:Pain Management / Q000379:methods*; D010147:Pain Measurement / Q000379:methods*; D012044:Regression Analysis; D012984:Software; D011795:Surveys and Questionnaires; D014481:United States