P21. Home-based functional outcome measurements for spine patients: validation and feasibility study

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<h3>BACKGROUND CONTEXT</h3> Functional outcome measurements (FOMs) have been shown to be effective tools in diagnosing, planning treatments and tracking outcomes in several subspecialties of medicine. FOMs often produce large volumes of complex data that require sophisticated collection, storage and analytic techniques to describe functional measures of items like gait performance, postural stability, neuromuscular activity and movement strategies. These complexities pose particular challenges for the application of FOMs in the clinical setting where the primary focus is on patient care. Currently, utilization of FOMs is primarily driven by individual surgeons explicitly interested in the development and dissemination of FOMs metrics and related technologies. Unfortunately, there are capital and support costs associated with running and maintaining FOMs labs which pose additional hurdles that may inhibit otherwise interested surgeons. As interest and use of clinically derived functional evaluations tailored specifically for spine patients grow, there is a need for simple, objective measures to summarize the complexity of modern motion tracking data sets to simple, clinically meaningful and interpretable terms. <h3>PURPOSE</h3> To develop and validate low-cost FOMs wearable sensor that quantifies common activities in a home-based environment. <h3>STUDY DESIGN/SETTING</h3> Prospective, concurrent control cohort study. <h3>PATIENT SAMPLE</h3> Twelve lumbar degenerative surgical candidates and 12 healthy controls participated in this study. <h3>OUTCOME MEASURES</h3> Level of activity, spatiotemporal parameters, and balance effort. <h3>METHODS</h3> Subjects wore a small noninvasive sensor (30 × 44 × 8mm, weight: 12 grams) with an adhesive patch on T1 in addition to traditional gait lab sensors. Validation of the proposed sensor and common FOMs metrics generated by a gait lab were compared. Furthermore, each subject wore the wearable sensor on T1 for additional 24 hours. The sensor detected different types and levels of activities during the day (ie, standing, walking, sitting, etc.) and also captured trunk kinematics in the home-based environment. <h3>RESULTS</h3> The wearable sensor was able to reliably measure all trunk kinematics during standing, walking and transition from sitting (p>0.05) when compared to gold standard human motion capture in a gait lab. The sensor data collected during the additional 24-hours at home was passed through a machine learning activity classification algorithm. The predicted results indicate that patients with degenerative lumbar spinal pathologies presented with a lower level of activity (walking: 4.7%, standing: 11.6%, sitting: 25.3%) in comparison to controls (walking: 7.9%, standing: 21.7%, sitting: 17.1%). Balance effort and the CoE dimensions were found to be significantly larger in these patients (sagittal: 7.9°, coronal: 7.2°) compared to controls (sagittal: 5.8°, coronal: 3.2°; p<0.035). <h3>CONCLUSIONS</h3> The purpose of this study was to validate and prove the feasibility of home-based functional outcome measurements, which can provide relevant details in a digestible format that conveys the functional status of the patient and raises flags for areas of concern. Such insights may lead to changes in assessments of disability, treatment strategies or modifications of rehabilitation regimens. Several benefits are anticipated from a wearable-based quantitative tool to assist with preoperative planning for patient-specific alignment objectives such as assisting in choosing the right surgical procedure for the right patient, recognition of red flags, leading to avoidance of surgery where it is not going to help, recovery monitoring, early detection of perioperative complications, prognostic information, and prediction of treatment outcomes. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.