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65岁及以上的成年人中有1/3至1/2每年至少跌倒一次。在美国，老年人因与跌倒有关的伤害而住院的次数是其他原因造成的伤害的五倍，非致命性跌倒造成的损失高达190亿美元。预计到2020年，随着老龄化人口的增加，与跌倒有关的费用将达到惊人的549亿美元。目前的研究和临床实践指南侧重于多因素跌倒风险评估，作为老年人跌倒的关键威慑。这些评估中的一个主要因素是老年人个人的日常生活活动表现。虽然目前标准化的临床平衡评估工具已被证明对预测跌倒风险有效，但这些测试通常是在临床环境中进行的，并且是在个人一天中的孤立时间进行的。本应用程序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视器- ALARM），用于纵向评估跌倒风险。这样的装置：-可在治疗干预最有效时早期发现跌倒风险-可提供有关活动模式的实时反馈-可提供有关干预措施的依从性和干预措施有效性的反馈-可纳入传统鞋类，无需额外操作-可用于研究、临床和潜在的消费者应用。ALARM系统的开发将涉及三个具体目标：具体目标1：开发一种模式识别方法，通过将变化范围从目前的76%- 100%降低到9911%，从而提高对感兴趣的活动（如行走和踏步）的识别准确率。具体目标2：在临床试验中使用ALARM设备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，自动评估每个感兴趣的活动中跌倒风险的指标（例如，摆动的持续时间和行走时的站立阶段）。具体目标4：在一项双盲无限制自由生活研究中验证ALARM设备。这组特定目标将验证导致创建一种独特的可穿戴设备，能够客观表征跌倒风险。

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.