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Ambulatory Monitoring of Near Falls: A Novel Measure of Fall Risk

临近跌倒的动态监测：跌倒风险的一种新测量方法

基本信息

批准号：
8123363
负责人：
JEFFREY M HAUSDORFF
金额：
$ 14.27万
依托单位：
TEL-AVIV SOURASKY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-08-15 至 2013-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8123363
关键词：
Acceleration Activities of Daily Living Age Algorithms Ambulatory Monitoring Archives Area Arrhythmia Back Cardiac Cardiovascular system Characteristics Chronic Clinic Communities Databases Detection Elderly Electrocardiogram Environment Equilibrium Evaluation Exercise stress test Frequencies Funding Gait Gold Health Care Costs Holter Electrocardiography Home environment Hour Laboratories Lead Life Measures Medicine Memory Methods Metric Modality Morbidity - disease rate Motivation Movement Nature Neurologist Outcome Parkinson Disease Participant Patient Self-Report Patients Performance Population Heterogeneity Predisposition Property Recording of previous events Recovery Recurrence Resources Rest Risk Risk Assessment Risk Factors Source Specificity Speed Stress Tests Techniques Testing Time Treatment Efficacy United States National Institutes of Health Walking Work base design experience fall risk falls gait examination improved insight interest mortality novel novel strategies prevent prospective public health relevance research clinical testing sensor success tool web site young adult

项目摘要

DESCRIPTION (provided by applicant): Falls are a common, debilitating problem among older adults and a significant source of morbidity. The objective of the present proposal is to investigate a novel approach to the evaluation of fall risk. During the past three decades, tremendous advances have been made in the understanding of the factors that contribute to falls, however, assessment tools are not yet optimal. In the clinic, observational gait analysis may be performed to characterize balance, walking abilities and fall risk. For further insight, balance and gait may be challenged, e.g., using an obstacle course. However, if these approaches prove insufficient and the geriatrician or neurologist needs to understand what occurs as a patient carries out routine activities of daily living, self-report will be relied upon. In fact, despite its subjective nature and the known problems concerning recall, self-report is currently, to a large degree, the gold standard of fall frequency and risk. We suggest that much can be gained by offering the clinician who wishes to evaluate falls an approach similar to that of the cardiologist's Holter monitor: ambulatory monitoring of near falls (NF). A near fall refers to a misstep, trip, stumble or loss of balance in which recovery mechanisms are activated to prevent a fall. Automatic identification of NF should, a priori, provide a sensitive and objective marker of fall risk, perhaps over a shorter study period. We recently began to investigate the potential of using an ambulatory monitor to identify NF. In preliminary work in laboratory testing, mostly in young adults, we were able to achieve detection rates better than 85% sensitivity and 85% specificity. The present proposal is designed as a bridge to larger scale validity studies. To this end, we have defined the following specific aims: 1. To develop algorithms to automatically identify NF in older adults under laboratory conditions. 2. To develop and evaluate algorithms for the detection of NF (using accelerometers and gyroscopes and determining which configuration and combinations are ideal) in real-world conditions. 3. A) To establish an annotated data base of NF, as recorded by movement sensors, as study participants carry out their routine activities of daily living. B) To make this archive available via the open-access NIH-funded PhysioNet Resource website. To achieve these objectives, 30 older adults with a history of multiple falls, 30 age-matched controls and 10 patients with Parkinson's disease with a history of recurrent falls will be tested in the lab using previously established tests of balance, gait and fall risk. Subsequently, they will be asked to wear an ambulatory monitor for 3 consecutive days. We will assess which methods optimally detect NF and the association between NF and laboratory-based measures. The results of this exploratory study should help to promote a third approach to the study of falls and falls risk, one based on ambulatory monitoring that may, ultimately, lead to more comprehensive fall risk assessment options. PUBLIC HEALTH RELEVANCE: Falls in older adult are a major cause of morbidity and mortality, with significant associated healthcare costs. We suggest that much can be gained by developing a new approach to fall risk assessment: ambulatory monitoring of near falls. Automatic identification of near falls (e.g., trips, missteps, stumbles), as subjects carry out their routine activities of daily living, may provide a sensitive and objective marker of fall risk based on actual performance that may, ultimately, lead to improved assessment and treatment options.

描述（由申请人提供）：福尔斯是老年人中常见的使人衰弱的问题，也是发病率的重要来源。本提案的目的是调查一种新的方法来评估跌倒风险。在过去的三十年里，在理解导致福尔斯的因素方面取得了巨大的进步，然而，评估工具还不是最佳的。在临床上，可以进行观察步态分析来表征平衡、行走能力和跌倒风险。为了进一步了解，平衡和步态可能会受到挑战，例如，使用障碍训练然而，如果这些方法被证明是不够的，老年病学家或神经学家需要了解患者进行日常生活活动时发生的情况，则将依赖自我报告。事实上，尽管它的主观性和已知的问题，有关回忆，自我报告目前是，在很大程度上，黄金标准的跌倒频率和风险。我们建议，通过向希望评估福尔斯的临床医生提供一种类似于心脏病专家的动态心电图监测的方法：对近福尔斯（NF）进行动态监测，可以获得很多好处。接近跌倒是指一个失误，绊倒，绊倒或失去平衡，其中恢复机制被激活，以防止跌倒。NF的自动识别应该先验地提供跌倒风险的敏感和客观的标记，也许在较短的研究期间内。我们最近开始研究使用动态监测仪识别NF的可能性。在实验室检测的初步工作中，主要是在年轻人中，我们能够实现高于85%灵敏度和85%特异性的检测率。本建议旨在作为一个桥梁，更大规模的有效性研究。为此，我们明确了以下具体目标：1.开发算法，在实验室条件下自动识别老年人的NF。 2.开发和评估在现实条件下检测NF的算法（使用加速度计和陀螺仪，并确定理想的配置和组合）。 3. A）建立NF的注释数据库，如由运动传感器记录的，因为研究参与者进行他们的日常生活的常规活动。 B）通过开放获取NIH资助的PhysioNet资源网站提供此存档。为了实现这些目标，30名有多次福尔斯跌倒史的老年人、30名年龄匹配的对照组和10名有复发性福尔斯跌倒史的帕金森病患者将在实验室中使用先前建立的平衡、步态和跌倒风险测试进行测试。随后，他们将被要求连续3天佩戴动态监护仪。我们将评估哪些方法最适合检测NF以及NF与实验室测量之间的关联。这项探索性研究的结果应有助于促进福尔斯和福尔斯风险研究的第三种方法，一种基于动态监测的方法，最终可能导致更全面的跌倒风险评估方案。公共卫生相关性：老年人的福尔斯是发病率和死亡率的主要原因，相关的医疗保健费用很高。我们认为，可以通过开发一种新的方法来获得更多的跌倒风险评估：近福尔斯的动态监测。自动识别近福尔斯（例如，绊倒、失足、绊倒），可以基于实际表现提供跌倒风险的敏感和客观的标记，这最终可以导致改进的评估和治疗选择。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Automated detection of missteps during community ambulation in patients with Parkinson's disease: a new approach for quantifying fall risk in the community setting.

DOI：
10.1186/1743-0003-11-48
发表时间：
2014-04-03
期刊：
Journal of neuroengineering and rehabilitation
影响因子：
5.1
作者：
Iluz T;Gazit E;Herman T;Sprecher E;Brozgol M;Giladi N;Mirelman A;Hausdorff JM
通讯作者：
Hausdorff JM

Objective assessment of fall risk in Parkinson's disease using a body-fixed sensor worn for 3 days.