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

• 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
• 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

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因素上 机动性。无论是通过预防伤害，辅助技术还是生物力学优化，这都是 对临床实践至关重要，这些过程对这些过程有充分的理解，以便我们可以提供最明智的信息 病人治疗。为了进行更有效的基于临床的研究，可以为患者护理提供信息，这是我的 渴望通过正式课程的实践经验和一项实践经验继续我的培训 在以下领域中强大的指导团队：（1）基于活动的数据收集和分析以及（2）使用 研究多个因素的先进统计方法。通过K23，我还将获得经验 特别专注于我向独立的过渡；这将包括赠款和实验室管理， 领导临床和翻译研究的设计和实施，人员管理以及 会议，追求任期和R01。这种继续培训将在 脊髓损伤（SCI）功能迁移率的活动模式的研究研究。 这项研究的目标1是根据患者预测出院时和分期后1年的活动性 IPR期间的特征和活动。流动性结果可能具有挑战性，特别是 具有中等力量和感觉障碍的人。选择适当的培训越来越多 重要的是，住院时间缩小，并且存在潜在的机会成本和不利后果 关于未接受适当干预措施的个人的生活质量和参与。额外的 通过使用低成本传感器，我们可以在IPR停留期早期收集的活动度量有可能 提供丰富的数据集，我们可以研究这些数据集，以洞悉其对结果的洞察力，而行政负担很小。 使用机器学习方法，我们将调查患者特征和活动监控数据 基于康复期间早期获得的数据，改善患者流动性的预测模型。实现这些 目的将改善患者和临床医生对随后一年的预期移动变化的理解 SCI适当针对期望和干预措施，以最大程度地提高功能结果。 该提案的目标2是定量评估功能迁移率的变化（即Wheeling Walking 或模式内的活动变化）在受伤后的第一年及其对生活质量的影响和 参与。出院后有一些因素挑战或增强步行的可持续性 功能流动性，包括能源成本，神经系统恢复和生物心理社会因素，例如弹性， 自我效能感，环境和照顾者的支持。这些因素与解雇后的关联 流动性的变化尚不清楚。使用可穿戴传感器，我们将量化步行时间和 旋转以识别步行和旋转之间的过渡，确定对这些造成这些贡献的因素 过渡并调查其对参与的影响。