After a spinal cord injury (SCI), clinicians must quickly decide where to focus therapy time to maximize an individual's functional mobility by discharge: either towards gait training or wheeled mobility interventions. Clinical prediction rules (CPRs) can assist clinicians in making those difficult decisions, but literature has shown that for individuals with moderate impairments, current CPRs that use age, strength, and sensation are not sufficient in predicting independent ambulation. Further, existing CPRs do not provide insight into clinically important descriptive measures of gait quality, efficiency, and endurance that contribute to functional ambulation. Our recent work demonstrated individuals who received gait training, but primarily used a wheelchair one year after SCI received less transfer and wheeled mobility training and had lower measures of participation than non-ambulatory individuals who never received gait training. In the context of decreasing inpatient rehabilitation length of stays, it is crucial that time in therapy be used efficiently to maximize function at discharge and avoid those long-term consequences. Lower limb movement (LLM) captured using activity monitors may provide a more sensitive measure of strength and sensation than traditional methods such as manual muscle and light touch sensation testing. This technique is novel in that LLM has not yet been reported in literature for individuals with SCI. Our preliminary analysis has shown promise for the association between LLM, strength, and ambulatory ability (as defined by measures of gait quality, efficiency, and endurance). Using machine learning techniques, we are able to determine which factors have the strongest association with ambulatory ability, among LLM, subject demographics, clinical characteristics, and other covariates. Our long-term goal is to improve CPRs that predict ambulation after SCI, thus enabling appropriately targeted functional mobility training. As a first step towards this goal, we will build a foundational knowledge of LLM and its relationship as a potential biomarker for ambulatory ability cross-sectionally among individuals with chronic SCI and known, diverse functional abilities (Aim 1). We will also explore longitudinal LLM data and ambulatory ability for a population with acute SCI (Aim 2) to evaluate changes in LLM over time and create a preliminary prediction model. Achieving the proposed aims will provide new insights into factors that predict mobility in individuals with SCI and provide understanding as to how these factors change acutely following injury. Further, we will gain insight to guide a future multisite longitudinal study that will assess a new, more effective CPR. This CPR will aid clinical decision-making for individuals with SCI by allowing for optimally targeted therapies to be employed throughout the rehabilitation continuum, thus improving long-term functional outcomes.

Public Health Relevance

Current rules are not sufficient in predicting ambulation among individuals with moderate strength and sensory deficits after a spinal cord injury, and this can lead to an inefficient use of therapy time. By measuring actual lower limb movement using activity monitors, we believe we can capture an unexplained variance missing from current prediction models that use traditional clinical measures to more accurately predict ambulatory ability. Accomplishing the goals outlined in this proposal will provide clinicians with a better understanding of ambulatory prognosis after spinal cord injury and will allow for optimal target therapies to be employed throughout the rehabilitation continuum to maximize functional outcomes and quality of life.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Individual Predoctoral NRSA for M.D./Ph.D. Fellowships (ADAMHA) (F30)
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Special Emphasis Panel (ZRG1)
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Ajisafe, Toyin Dele
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University of Pittsburgh
Biomedical Engineering
Biomed Engr/Col Engr/Engr Sta
United States
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