Wearable Sensors for Pre- and Post-Operative Assessment of Cervical Myelopathy.

Abstract

Prospective observational cohort. To report on the role of a single wearable sensor in the evaluation of patients with Cervical Spondylotic Myelopathy (CSM). Clinical evaluation of CSM is limited, as Hoffmann's sign, Romberg testing and Tandem Gait are largely subjective and binary, making deterioration or improvement difficult to document accurately. Patients scheduled for surgical treatment of CSM underwent in-office and 24-hour continuous at-home data collection using a single wearable sensor. In-office testing consisted of Standing, Romberg testing, Tandem Gait and Timed Up & Go (TUG). Testing was repeated 6-months post-operatively. Statistically significant improvements were seen following surgical treatment in the Romberg test eyes-open maximum antero-posterior sway (P=0.010), eyes-open total path traveled (P=0.048); in Tandem Gait speed (P=0.021), duration (P=0.002), antero-posterior sway (P=0.046) and initial peak acceleration (P=0.001). There was no statistically significant difference in TUG testing. At-home gait pattern revealed a trend toward decreased lateral sway post-operatively (P=0.062) and fewer sleep turns (P=0.078). Wearable sensor data effectively quantifies standard exam findings and identifies new metrics with the potential to assess more accurately pre-operative and post-operative function in patients with CSM. Previously unreported pre-operative to 6-month post-operative changes were seen in speed of gait and ground impact force during Tandem Gait. These metrics were more sensitive as compared to the normal antero-posterior and lateral sway assessment. 24-hour sensor data showed decreased number of turns during sleep post-operatively. This study suggests that wearable sensor data will be a viable source for quantifiable data with the potential to guide treatment for patients with CSM. This capability is based partly on better quantification of existing binary measures, but also on identification of unanticipated patterns within the data.