Physical Activity Calibration in Individuals with Movement Limitations

描述(由申请人提供)：在美国，五分之一的成年人被认为患有残疾，这是一种影响身体运动模式的复杂和多方面的疾病。在美国，与残疾相关的医疗费用每年超过3000亿美元。有规律的体力活动(PA)代表了一种重要的可改变的行为，以改善这些残疾群体的整体健康，实现他们的运动潜力。在开发和测试可穿戴式监视器校准以预测体力活动(PA)水平和类型方面取得了实质性进展。然而，很少有科学关注开发方法来评估残疾人群中的PA。这项应用的目标是填补这一科学知识空白。具体目的1)测量损伤和功能，以提供运动障碍分析：a)无运动限制；b)上肢限制；c)下肢限制，或d)上肢和下肢限制。具体目标2)测量双侧腕部、髋部和脚踝的加速度，以有条件地模拟代谢成本和活动类型估计。我们高素质的研究团队将首先进行大量高级的上下半身力量、协调和功能运动任务，以隔离运动模式不同的不同人群中的关键损伤和功能限制，从而实现上述目标。然后，使用无监督学习和聚类分析方法，我们将使用这些数据来评估一种快速、方便和简单的指标来对功能运动模式差异进行分类。我们将评估标准能量成本和日常生活活动中的多个髋关节和肢体加速，以指导使用先进的分析技术开发跨功能运动组类别的单一或组合可穿戴运动传感器算法。这些拟议研究的结果将首次提供一种创新和可翻译的方法来分类和评估行动不便的人的PA。这将为评估决策树方法提供基础，以指导监视器放置和算法选择，以评估不同人群中的PA。最终，我们的工作将提供准确和准确地评估PA患病率、基于行为的PA干预的有效性以及PA预防和管理致残条件的剂量-反应关系的潜力。

DESCRIPTION (provided by applicant): One in five adults in the U.S. are thought to be living with a disability, a complex and multifaceted condition affecting physical movement patterns. Medical care costs related to disability in the US exceed $300 billion annually. Regular physical activity (PA) represents an important modifiable behavior to improve the overall health of these disabled groups and to realize their movement potential. Substantial progress has been made in developing and testing wearable monitor calibrations to predict physical activity (PA) level and type. However, little scientific attention has been given to develop methods to assess PA in disabled populations. The aims of this application focus on filling this scientific knowledge gap. Specific Aim 1) To measure impairment and function to provide a movement disorder analysis of: a) no movement limitation; b) upper extremity limitation; c) lower extremity limitation, or d) both upper and lower extremity limitation. Specific Aim 2) To measure bilateral wrist, hip, and ankle acceleration to conditionally model metabolic cost and activity type estimates. Our highly qualified research team will address the above aims by first carrying out a multitude of advanced upper and lower body strength, coordination, and functional motor tasks to isolate key impairment and function limitations across a diverse population with movement pattern differences. Then using an unsupervised learning and cluster analysis approach we will use these data to evaluate a fast, convenient, and simple metric to categorize functional movement pattern differences. We will assess criterion energy cost and multiple hip and limb acceleration during an extensive battery of activities of daily living, to guide the development of single or combined wearable motion sensor algorithms across categories of functional movement groups using advanced analytical techniques. The results of these proposed studies will for the first time provide an innovative and translatable approach to both categorize and assess PA in persons with movement limitations. This will present the foundation to evaluate a decision-tree approach to guide both monitor placement and algorithm choice to assess PA in diverse populations. Ultimately, our work will provide the potential to precisely and accurately assess PA prevalence rates, effectiveness of behavior-based PA interventions, and PA dose-response relationships to prevent and manage disabling conditions.