权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Translation of In-Clinic Gains to Gains in Daily Life

将诊所内的获益转化为日常生活中的获益

基本信息

批准号：
10516654
负责人：
Catherine Lang
金额：
$ 57.83万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-02-01 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10516654
关键词：
Activities of Daily Living Address Adhesive Capsulitis Adult Age Algorithmic Software Awareness Biological Caring Categories Child Childhood Clinic Clinical Cluster Analysis Complex Computer software Consumption Data Development Disease Environment Future Goals Human Impairment Individual Industry Infant Intervention Studies Laboratories Life Limb structure Longevity Maps Mathematics Measurement Measures Modeling Multiple Sclerosis Neurologic Outcome Outcome Measure Participant Patient Outcomes Assessments Patients Pediatric cohort Performance Persons Population Reporting Research Research Personnel Self Care Stroke Structure Time Toddler Translations Upper Extremity Upper Extremity Fracture Validation Work World Health Organization arm clinical care clinical decision-making cohort dashboard disability impaired capacity improved malignant breast neoplasm motion sensor patient population personalized decision post stroke rehabilitation research rehabilitation service rehabilitative care research study sensor wearable sensor technology

项目摘要

PROJECT SUMMARY/ABSTRACT People seek out upper limb rehabilitation services to improve performance of activities in daily life, with performance defined by the World Health Organization ICF model as what someone actually does in the unstructured, free-living environment. Wearable, motion sensors now allow for direct measurement of upper limb performance in daily life, but performance is rarely measured or considered in research and care. Data from our 2nd cycle make it clear that improvements in impairments and/or functional capacity assessed in the structured environment of the clinic or laboratory most often do not translate to improvements in performance of activities in daily life. These data point to the clear need to move assessment of performance from the specialized research realm into more universal use in upper limb research studies and clinical care. A major barrier to more wide-spread use of sensors for routine upper limb performance assessment is lack of clinical validation. An elegant solution to the many clinical validation challenges is to move from single variables in individual populations to multivariate categories of upper limb performance in daily life that span patient populations. Our central hypothesis is that, despite the immense variability in biological conditions and their resulting upper limb impairments and functional capacity profiles, upper limb performance in daily life can be organized into a relatively small number of categories. Defined, validated categories could be used across populations to push upper limb rehabilitation research forward and personalize clinical decision-making. We now propose to expand and validate preliminary categories from stroke into a variety of conditions that send people to upper limb rehabilitation services and result in upper limb disability. Wearable sensors will be used to assess upper limb performance in a heterogeneous, longitudinal, observational cohort (N=340) of persons with (stroke, multiple sclerosis, UL fracture, adhesive capsulitis, breast cancer) and without UL disability (controls), and a retrospective, pediatric cohort (n ≥ 400) to address three aims. Aim 1 will identify and validate categories of upper limb performance in daily life that can be applied across conditions that cause upper limb disability in adults. Aim 2 will evaluate responsiveness to change across categories. And Aim 3 will explore how categories identified in adults map to upper limb performance in childhood. Expected outcomes at the end of the proposed cycle are validated categories of upper limb performance that are responsive to change and span patient populations. These categories (and the software algorithms to derive them) will immediately be available for intervention studies to measure outcomes that are most relevant to patients. With anticipated advances in hardware and software over the next few years, future work will implement validated categories readily into clinical rehabilitation care.

项目总结/文摘