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

• 批准号: 7896176
• 负责人: JEFFREY M HAUSDORFF
• 金额: $ 14.47万
• 依托单位: TEL-AVIV SOURASKY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7896176
• 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; 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

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）：跌倒是老年人中常见的、使人衰弱的问题，也是发病的重要来源。本提案的目的是研究一种评估跌倒风险的新方法。在过去的三十年中，人们对导致跌倒的因素的理解取得了巨大进步，但评估工具尚未达到最佳状态。在临床中，可以进行观察性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，例如使用障碍训练场。然而，如果这些方法被证明是不够的，并且老年科医生或神经科医生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，尽管自我报告具有主观性，并且存在与召回有关的已知问题，但目前在很大程度上，自我报告是跌倒频率和风险的黄金标准。 我们建议，通过为希望评估跌倒的临床医生提供类似于心脏病专家动态心电图监测仪的方法：跌倒附近 (NF) 的动态监测，可以获得很大的收获。险些跌倒是指失足、绊倒、绊倒或失去平衡，此时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一个敏感且客观的跌倒风险标记，也许在较短的研究期内。我们最近开始研究使用动态监测仪来识别 NF 的潜力。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% 的敏感性和 85% 的特异性的检出率。本提案旨在作为更大规模有效性研究的桥梁。为此，我们确定了以下具体目标： 1. 开发在实验室条件下自动识别老年人 NF 的算法。 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哪种配置和组合是理想的）。 3. A) 建立一个带注释的 NF 数据库，由运动传感器记录，当研究参与者进行日常生活活动时。 B) 通过 NIH 资助的开放访问 PhysioNet 资源网站提供该档案。为了实现这些目标，将使用先前建立的平衡、步态和跌倒风险测试在实验室中对 30 名有多次跌倒史的老年人、30 名年龄匹配的对照者和 10 名有反复跌倒史的帕金森病患者进行测试。随后，他们将被要求连续三天佩戴动态监护仪。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推广第三种跌倒和跌倒风险研究方法，一种基于动态监测的方法，最终可能会产生更全面的跌倒风险评估方案。 公共卫生相关性：老年人跌倒是发病和死亡的主要原因，并产生巨大的相关医疗费用。我们建议，通过开发一种新的跌倒风险评估方法可以获得很多好处：对跌倒附近进行动态监测。当受试者进行日常生活的常规活动时，自动识别险些跌倒（例如绊倒、失误、绊倒）可以根据实际表现提供敏感且客观的跌倒风险标记，最终可能导致改进的评估和治疗选择。