Monitoring Lower Limb Movement to Predict Ambulatory Ability after Spinal Cord Injury

监测下肢运动以预测脊髓损伤后的行走能力

• 批准号: 9756205
• 负责人: Stephanie Rigot
• 金额: $ 3.86万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-11-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9756205
• 关键词: Accounting; Activities of Daily Living; Acute; Admission activity; Age; Alcohol consumption; American; Caffeine; Characteristics; Chronic; Clinical; Community Participation; Data; Data Set; Decision Aid; Decision Making; Esthesia; Foundations; Frequencies; Future; Gait; Gender; Goals; Home environment; Hospitals; Impairment; Individual; Injury; Inpatients; Intervention; Knowledge; Lead; Length of Stay; Light; Literature; Longitudinal Studies; Lower Extremity; Machine Learning; Manuals; Measures; Methods; Modeling; Monitor; Motor; Movement; Muscle; Occupations; Outcome; Pain; Patients; Persons; Pharmaceutical Preparations; Phase; Physical Examination; Physical activity; Population; Predictive Factor; Probability; Quality of life; Rehabilitation therapy; Reporting; Research; Sensory; Spinal Injuries; Spinal cord injury; Techniques; Testing; Time; Touch sensation; Training; Validation; Walking; Wheelchairs; Work; clinical decision-making; comorbidity; demographics; experience; functional independence; functional outcomes; gait rehabilitation; improved; independent ambulation; indexing; insight; limb movement; novel; outcome forecast; potential biomarker; predictive modeling; skills; sleep quality; spasticity; targeted treatment

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.

脊髓损伤（SCI）后，临床医生必须迅速决定在哪里集中治疗时间，以最大限度地提高 出院时个人的功能性移动能力：无论是步态训练还是轮式移动干预。 临床预测规则（CPR）可以帮助临床医生做出这些困难的决定，但文献 显示，对于中度损伤的个体，使用年龄，力量和感觉的当前CPR是 不足以预测独立振幅。此外，现有的CPR没有提供临床上的洞察力， 步态质量、效率和耐力的重要描述性指标，有助于功能性 阿姆斯壮我们最近的工作证明了接受步态训练的人，但主要使用 SCI后一年的轮椅接受较少的转移和轮式移动训练， 参与比不走动的人谁从来没有接受过步态训练。在减少的背景下， 住院康复住院时间，关键是有效利用治疗时间，以最大限度地发挥功能 避免这些长期后果。 使用活动监测器捕获的下肢运动（LLM）可以提供更灵敏的测量， 力量和感觉比传统的方法，如手动肌肉和光触摸感觉测试。这 该技术是新颖，因为LLM尚未在SCI患者的文献中报道。我们的初步 分析显示，LLM、力量和行走能力（定义为 步态质量、效率和耐力的测量）。使用机器学习技术，我们可以 确定哪些因素与LLM受试者的行走能力相关性最强 人口统计学、临床特征和其他协变量。 我们的长期目标是改善预测SCI后脊髓损伤的CPR，从而使适当的 有针对性的功能性运动训练。作为实现这一目标的第一步，我们将建立一个基础知识， LLM及其作为一种潜在的生物标志物的关系，为个人走动能力的横截面， 慢性SCI和已知的多种功能能力（目标1）。我们还将探索纵向LLM数据， 急性SCI人群的行走能力（目标2），以评估LLM随时间的变化，并创建一个 初步预测模型 实现拟议的目标将为预测个人流动性的因素提供新的见解 并提供关于这些因素在损伤后如何急剧变化的理解。此外，我们将 获得洞察力，以指导未来的多站点纵向研究，该研究将评估一种新的、更有效的CPR。这个心肺复苏 将通过允许最佳靶向治疗来帮助SCI患者的临床决策， 在整个康复过程中使用，从而改善长期功能结果。