Characterizing Activity Patterns in Functional Mobility After Spinal Cord Injury

脊髓损伤后功能活动的特征活动模式

基本信息

批准号：
10246175
负责人：
Lynn A Worobey
金额：
$ 12.8万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10246175
关键词：
Address Biomechanics Caregiver support Caring Characteristics Classification Clinical Clinical Research Clinical assessments Coin Cost efficiency Cross-Sectional Studies Data Data Analyses Data Collection Data Set Dose Environment Esthesia Exertion Foundations Future Goals Impairment Individual Injury Inpatients Intervention Joints Kinesiology Length of Stay Machine Learning Measures Mentors Modeling Monitor Movement Outcome Outpatients Pain Pathology Patient Care Patient Self-Report Patient-Focused Outcomes Patients Pattern Personnel Management Physical activity Predictive Factor Probability Process Quality of life Rehabilitation therapy Research Resource Allocation Resources Secondary to Self Efficacy Self-Help Devices Sensory Spinal cord injury Statistical Methods Therapeutic Intervention Time Training Upper Extremity Walking Wheelchairs adverse outcome base biopsychosocial biopsychosocial factor career clinical care clinical implementation clinical practice community setting cost design evidence base expectation experience functional outcomes gait rehabilitation improved improved mobility injury prevention innovation inpatient service insight interest meetings muscle strength neurological recovery opportunity cost patient mobility person centered predictive modeling preservation research study resilience sensor translational study wearable sensor technology

项目摘要

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.

抽象的我的职业和研究兴趣集中在运动科学和最大化的因素上流动性。无论是通过伤害预防、辅助技术还是生物力学优化，对临床实践至关重要的是，充分理解这些过程，以便我们能够提供最明智的信息病人的治疗。为了开展更有效的临床研究，为患者护理提供信息，我的职责是渴望通过正式课程的实践经验和监督来继续我的培训在以下领域拥有强大的指导团队：（1）基于活动的数据收集和分析以及（2）使用先进的统计方法来调查多个因素。通过K23，我也将获得经验特别关注我向独立的过渡；这将包括资助和实验室管理，领导临床和转化研究的设计和实施、人员管理和会议、追求终身职位和 R01。这种持续培训将在以下背景下完成：研究表征脊髓损伤 (SCI) 后功能活动的活动模式。本研究的目标 1 是根据患者的情况预测出院时和出院后 1 年的活动能力知识产权期间的特征和活动。流动性结果可能难以预测，特别是对于具有中等强度和感觉障碍的个体。选择适当的培训越来越受到重视对于缩短停留时间很重要，并且存在潜在的机会成本和不利后果未接受适当干预的个人的生活质量和参与度。额外的我们可以通过利用低成本传感器在知识产权停留早期收集活动测量数据，有可能提供丰富的数据集，我们可以检查这些数据集，从而在几乎没有管理负担的情况下深入了解结果。使用机器学习方法，我们将调查患者特征和活动监测数据，以根据康复早期获取的数据改进患者活动能力的预测模型。实现这些目标将提高患者和临床医生对明年活动能力预期变化的了解 SCI 适当地瞄准期望和干预措施，以最大限度地提高功能结果。该提案的目标 2 是定量评估功能性移动性变化（即轮式行走）或模式内活动的变化）在受伤后第一年及其对生活质量和参与。出院后的一些因素会挑战或增强步行的可持续性功能流动性，包括能量成本、神经恢复和生物心理社会因素，如恢复力、自我效能、环境和照顾者的支持。这些因素与出院后的关系流动性的变化尚不清楚。使用可穿戴传感器，我们将量化步行和步行所花费的时间转动来识别步行和转动之间的过渡，确定导致这些变化的因素转变并调查其对参与的影响。