Longitudinal Assessment of Fall Risk

描述（由申请人提供）：跌倒不是衰老过程的正常部分，但1/3至1/2的65岁及以上的成年人每年至少跌倒一次。在美国，老年人因跌倒相关伤害住院的频率是其他原因伤害的五倍，导致非致命性福尔斯伤害的成本达到190亿美元。预计到2020年，人口老龄化程度将不断增加，预计与福尔斯有关的成本将达到惊人的549亿美元。目前的研究和临床实践指南集中在多因素跌倒风险评估作为老年人福尔斯的关键威慑。这些评估中的一个主要因素是个别老年人的日常生活活动能力。虽然目前的标准化临床平衡评估工具已被证明可有效预测跌倒风险，但这些测试最常在临床环境中进行，并在个人一天中的孤立时间进行。本申请的目标是开发和验证一种新型可穿戴设备（自动纵向评估风险监测器-报警），用于跌倒风险的纵向评估。这种装置：- 将允许在治疗干预最有效时早期检测跌倒风险-将提供有关活动模式的实时反馈-将提供有关干预合规性和干预有效性的反馈-将被纳入传统鞋子中，并且不需要额外的努力来操作-可用于研究，临床和潜在的消费者应用ALARM系统的开发将在三个特定目标中进行说明：特定目标1：开发一种模式识别方法，提高感兴趣活动的识别准确性通过将变化范围从当前的76%- 100%减小到99 - 11%，来实现对（例如行走和步进）的控制。具体目标2：在临床试验期间，使用ALARM器械收集一组老年人的数据。具体目标3：开发自动评估跌倒风险的算法。在这个目标中，我们将开发信号处理算法，自动评估指示每个感兴趣活动中跌倒风险的指标（例如，行走期间摆动和站立阶段的持续时间）。具体目标4：在一项双盲、不受限制的自由生活研究中使用ALARM器械。这组特定目标将验证导致创建能够客观表征跌倒风险的独特可穿戴设备。

DESCRIPTION (provided by applicant): Falling is not a normal part of the aging process and yet 1/3 to 1/2 of adults 65 years and older sustain at least one fall annually. Older adults are hospitalized for fall related injuries five times more often than from injuries from other causes contributing to a cost of $19 billion for nonfatal falls in the United States. Projected for the increasing aging population in the year 2020, it is expected that the costs related to falls will reach a staggering 54.9 billion dollars. Current research and clinical practice guidelines focus on multifactorial fall risk assessments as the critical deterrent to falls in the elderly. A primary factor within these assessments is activity of daily living performance of the individual elder. While current standardized clinical balance assessment tools have been proven effective for predicting fall risk, the tests are most commonly performed in the clinical environment and at isolated times during an individual's day. The goal of this application is to develop and validate a novel wearable device (Automatic Longitudinal Assessment Risk Monitor - ALARM) for longitudinal assessment of risk of falling. Such a device: - will allow early detection of risk of falling, when therapeutic interventions are most efficient - will provide real-time feedback about activity pattern - will provide feedback about compliance with interventions and effectiveness of interventions - will be incorporated into conventional footwear and require no extra effort to operate - can be used in research, clinical and potentially in consumer applications The development of the ALARM system will be addressed in three specific aims: Specific Aims 1: Develop a pattern recognition method that will improve recognition accuracy for activities of interest (such as walking and stepping up) by reducing the range of variation from current 76%- 100% to 9911%. Specific Aim 2: Collect data using the ALARM device on a group of elderly adults during clinical tests. Specific Aim 3: Develop algorithms for automatic assessment of risk of falling. In this Aim we will develop signal processing algorithms that automatically evaluate metrics indicative of the risk of falling in each activity of interest (e.g. duration of swing and stance phase during walking). Specific Aim 4: Validate the ALARM device in a double-blind unrestricted free living study. This set of Specific Aims will validate lead to creation of a unique wearable device capable of objective characterization of risk of falling.