Poster Presentation 51st International Society for the Study of the Lumbar Spine Annual Meeting 2025

Introduction: Technological advances, along with a focus on self-awareness in the post-COVID-19 era, have led to an increased need for practical and easy-to-use tools to quantify spinal health.1 Telehealth has also become increasingly important, and wearables have proven to be a useful adjunct for providing data that increases the effectiveness of remote clinical care.2 To date literature on wearables in spine patients presents information about general health rather than specific impairments caused by spinal conditions.3-4 Current wearable devices track a patient’s walking time, heart rate, and level of activity among other health-related criteria, but spine specific clinical studies are lacking.1 The purpose of this study was to validate spine specific wearables in comparison to the gold standard of a fully equipped motion lab.

Methods: Twenty-one lumbar degenerative (LD) surgical candidates, consisting of unilateral radiculopathy (UR) and neurogenic claudication (NC) with bilateral symptoms patients, were enrolled in the study. Spine specific wearables were applied to patients and 10 control on the posterior skin at the approximate T1 level along with fully body motion capture markers. Patients were asked to perform the following activities in a lab setting: walking, standing, lifting, sitting, and time up and go (transition). To assess agreement in peak angle, and range of motion (RoM) in trunk motion between motion capture and spine specific wearable measurements the Interclass Correlation Coefficient (ICC) estimates and their 95% confidence intervals were calculated using a mean-rating (k=3), absolute-agreement, two-way mixed effects model. In addition, the motion pattern was also investigated using the correlation between interpolated waveforms measured by the two motion capture systems.

Results: Spine specific wearables were able to measure DFOMs by detecting free-living physical activity (walking, standing, sitting, laying down, and driving times in addition to trunk 3-dimensional range of motion) using artificial intelligence and deep learning algorithms. The wearable DFOMs were strongly correlated with movement in the sagittal and coronal planes (r=0.70-0.94, p<0.01), and moderately correlated in the axial plane (r=0.39-0.61, p<0.05) with gold standard (motion lab) in motion capture of patient sway, balance effort, and gait parameters.

Discussion: Spine specific wearables are a valid approach to track a patient’s disability and functional level in their home environment. However, to date, detailed functional outcome analyses still require referral to a motion lab. A combination of DFOMs using a wearable device with PROMIS and radiographic measurements may provide a more comprehensive evaluation of a spine patient’s health allowing better clinical decisions and facilitating, customized patient specific care.