Digital biomarkers of spine and musculoskeletal disease from accelerometers: Defining phenotypes of free-living physical activity in knee osteoarthritis and lumbar spinal stenosis

Publication date: Available online 17 July 2018

Source: The Spine Journal

Author(s): Christy Tomkins-Lane, Justin Norden, Aman Sinha, Richard Hu, Matthew Smuck

Abstract

Background Context

Lumbar spinal stenosis (LSS) and knee osteoarthritis (OA) are two of the leading causes of disability worldwide. In order to provide disease-specific prescriptions for physical activity, there is a clear need to better understand physical activity in daily life (performance)in these populations.

Purpose

To discover performance phenotypes for LSS and OA by applying novel analytic methods to accelerometry data. Specific objectives include 1) identify characteristic features (phenotypes) of free-living physical activity unique to individuals with LSS and OA, and 2) determine which features can best differentiate between these conditions.

Study Design & Setting

Leveraging data from 3 existing cross-sectional cohorts, accelerometry signal feature characterization and selection was performed in a computational laboratory.

Patient Sample

Data from a total of 4028 individuals were analyzed from the following 3 datasets: Lumbar Spinal Stenosis Accelerometry Database (LSS, n=75); Osteoarthritis Initiative (OA, n=1950); and the 2003-4 National Health and Nutrition Examination Survey (Pain Free Controls, n=2003).

Methods

In order to characterize the accelerometry signals, data were examined using 1) standard intervals for counts/minute from Freedson et al., and 2) the Physical Performance intervals for mobility-limited pain populations. From this, 42 novel accelerometry features were defined and evaluated for significance in discriminating between the groups (LSS, OA, Controls) in order to then determine which sparse set of features best differentiates between the groups. These sparse sets of features defined the performance phenotypes.

Outcome Measures

Accelerometry features and their ability to differentiate between individuals with LSS, OA, and Controls.

Results

Given age and gender, classification rates were at least 80% accurate (pairwise) between diseased and pain-free populations (LSS vs. Controls and OA vs. Controls). The most important features to distinguish between disease groups corresponded to measures in the light and sedentary activity intervals. The more subtle classification between diseased populations (LSS vs. OA) was 72% accurate, with light and moderate activity providing the prominent distinguishing features.

Conclusions

We describe the discovery of performance phenotypes of LSS and OA from accelerometry data, revealed through a novel set of features that characterize daily patterns of movement in people with LSS and OA. These performance phenotypes provide a new method for analyzing free living physical activity (performance) in LSS and OA, and provide the groundwork for more personalized approaches to measuring and improving function.

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