Longitudinal Assessment of Fall Risk

描述(由申请人提供)：跌倒不是衰老过程中正常的一部分，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。老年人因跌倒而住院的频率是其他原因的五倍，在美国，非致命性跌倒的成本高达190亿美元。预计2020年老龄化人口将不断增加，与跌倒相关的成本预计将达到惊人的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能力。虽然目前标准化的临床平衡评估工具已被证明在预测跌倒风险方面是有效的，但测试通常在临床环境中进行，并在个人一天中的单独时间进行。该应用程序的目标是开发和验证一种用于纵向评估坠落风险的新型可穿戴设备(自动纵向评估风险监控-警报)。这种设备将能够在治疗干预措施最有效的时候及早检测跌倒的风险-将提供关于活动模式的实时反馈-将提供关于干预措施遵从性和干预有效性的反馈-将被整合到传统鞋类中，不需要额外的操作-可以用于研究、临床和潜在的消费者应用。警报系统的开发将在三个具体目标中解决：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%减少到9911%，提高对感兴趣的活动(如步行和步步)的识别准确性。具体目标2：在临床测试中使用警报设备收集一组老年人的数据。具体目标3：开发自动评估坠落风险的算法。在这一目标中，我们将开发信号处理算法，自动评估表示在每项感兴趣的活动中摔倒的风险的指标(例如，走路时的摆动持续时间和站立阶段)。具体目标4：在一项不受限制的双盲自由生活研究中验证该报警器。这套特定的目标将验证Lead创造出一种独特的可穿戴设备，能够客观地表征跌倒的风险。

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.