Characterizing Activity Patterns in Functional Mobility After Spinal Cord Injury

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Abstract My career and research interests have centered on the science of movement and factors that maximize mobility. Whether this is through injury prevention, assistive technology, or biomechanical optimization, it is critical to clinical practice that these processes be well understood so that we can provide the most informed patient treatments. In order to carry out more effective clinically-based studies that inform patient care, it is my desire to continue my training through practical experiences with both formal coursework and a oversight by a strong mentoring team in the following domains: (1) activity-based data collection and analysis and (2) use of advanced statistical methods to investigate multiple factors. Through the K23, I will also gain experience specifically focused on my transition to independence; this will include grantsmanship and lab management, leading the design and implementation of clinical and translational studies, management of personnel and meetings, and pursuit of tenure and an R01. This continued training will be completed in the context of a research study that characterizes activity patterns in functional mobility after spinal cord injury (SCI). Aim 1 of this study is to predict mobility at discharge and at 1-year post-discharge, based upon patient characteristics and activity during IPR. Mobility outcomes can be challenging to predict, particularly for individuals with moderate strength and sensory impairments. Selecting appropriate training is increasingly important with shrinking lengths of stay and there are potential opportunity costs and adverse consequences on quality of life and participation for individuals who do not receive appropriate interventions. Additional activity measures that we can collect early in the IPR stay, by utilizing low-cost sensors, have the potential to provide rich data sets that we can examine to garner insight into outcomes with little administrative burden. Using a machine learning approach, we will investigate patient characteristics and activity-monitoring data to improve predictive models of patient mobility based on data acquired early in the rehab stay. Achieving these aims will improve patient and clinician understanding of anticipated changes in mobility in the year following SCI to appropriately target expectations and interventions to maximize functional outcomes. Aim 2 of this proposal is to quantitatively evaluate functional mobility changes (i.e., wheeling walking or changes in activity within mode) in the first year post injury and their impact on quality of life and participation. There are factors following discharge that challenge or enhance the sustainability of walking for functional mobility including energy costs, neurologic recovery and biopsychosocial factors such as resilience, self-efficacy, environment, and caregiver support. The association between these factors and post-discharge changes in mobility are not well understood. Using wearable sensors we will quantify time spent walking and wheeling to identify transitions between walking and wheeling, identify factors that contribute to these transitions and investigate their impact on participation.