The use of accelerometry data has led to the discovery of performance phenotypes of lumbar spinal stenosis (LSS) and osteoarthritis (OA), which was revealed through a novel set of features that describe the daily patterns of movement among individuals with these 2 conditions, according to the results of an analysis of data from 3 existing cross-sectional patient cohorts that were published in the Spine Journal.1
The investigators sought to discover performance phenotypes for LSS and OA by applying novel analytic techniques to accelerometry data. Their specific objectives were to identify characteristic features of free-living physical activity that are unique among individuals with LSS and OA and to determine which features can be used to best differentiate among those with LSS, those with OA, and control patients.
All analyses were conducted in 3 distinct populations: LSS, OA, and pain-free control patients. Data with respect to these 3 populations, respectively, were obtained from the following existing data sets: the Lumbar Spinal Stenosis Accelerometry Database, the Osteoarthritis Initiative, and the US National Health and Nutrition Examination Survey.
All individuals in the 3 source data sets wore an accelerometer (on a belt, at the natural waistline, on arising in the morning and continuously until going to bed at night) for 7 consecutive days.
All LSS and OA data were compiled before any recommended therapy. Data from a total of 4028 individuals were analyzed, with the breakdown as follows: the Lumbar Spinal Stenosis Accelerometry Database (n=75), the Osteoarthritis Initiative (n=1950), and the 2003 to 2004 US National Health and Nutrition Examination Survey (n=2003).
To describe the accelerometry features that are characteristic of OA and LSS, traditional interval analyses from Freedson and colleagues2 were used, along with Physical Performance interval analysis for mobility-limited populations with pain.3
From this information, 42 novel accelerometry features were defined and evaluated for their significance in distinguishing among the 3 groups (LSS, OA, and control patients), to then determine which sparse set of features best differentiates among the 3 groups. These features then were used to define the performance phenotypes.
Taking sex and age into consideration, classification rates were ≥80% accurate (pairwise) between those with disease and pain-free populations (ie, LSS vs control patients and OA vs control patients).
The most important features that differentiated between the disease groups corresponded to measures in light and sedentary activity intervals. In addition, the more subtle classification between diseased populations (LSS vs OA) had an accuracy of 72%, with light and moderate levels of activity offering the major distinguishing features.
The investigators concluded that the performance phenotypes described in this study offer a new method for the analysis of free-living physical activity (ie, performance levels) in patients with LSS and OA, and provide the framework for a more personalized approach to measuring and improving function among these individuals.
The findings of the study demonstrate that contrary to existing general activity guidelines that focus on moderate and vigorous activity, focusing on increasing light activity may be more appropriate for mobility-limited populations.
1. Tomkins-Lane C, Norden J, Sinha A, Hu R, Smuck M. Digital biomarkers of spine and musculoskeletal disease from accelerometers: defining phenotypes of free-living physical activity in knee osteoarthritis and lumbar spinal stenosis [published online July 17, 2018]. Spine J. doi: 10.1016/j.spinee.2018.07.007
2. Freedson PS, Melanson E, Sirard J. Calibration of the Computer Science and Applications, Inc. accelerometer. Med Sci Sports Exerc. 1998;30(5):777-781.
3. Smuck M, Tomkins-Lane C, Ith MA, Jarosz R, Kao MJ. Physical performance analysis: A new approach to assessing free-living physical activity in musculoskeletal pain and mobility-limited populations. PLoS One. 2017;12(2):e0172804.