Schwenk Michael, Mohler Jane, Wendel Christopher, D'Huyvetter Karen, Fain Mindy, Taylor-Piliae Ruth, Najafi Bijan
Arizona Center on Aging, College of Medicine, University of Arizona, Tucson, Ariz., USA.
Gerontology. 2015;61(3):258-67. doi: 10.1159/000369095. Epub 2014 Dec 24.
Frailty is a geriatric syndrome resulting from age-related cumulative decline across multiple physiologic systems, impaired homeostatic reserve, and reduced capacity to resist stress. Based on recent estimates, 10% of community-dwelling older individuals are frail and another 41.6% are prefrail. Frail elders account for the highest health care costs in industrialized nations. Impaired physical function is a major indicator of frailty, and functional performance tests are useful for the identification of frailty. Objective instrumented assessments of physical functioning that are feasible for home frailty screening have not been adequately developed.
To examine the ability of wearable sensor-based in-home assessment of gait, balance, and physical activity (PA) to discriminate between frailty levels (nonfrail, prefrail, and frail).
In an observational cross-sectional study, in-home visits were completed in 125 older adults (nonfrail: n=44, prefrail: n=60, frail: n=21) living in Tucson, Ariz., USA, between September 2012 and November 2013. Temporal-spatial gait parameters (speed, stride length, stride time, double support, and variability of stride velocity), postural balance (sway of hip, ankle, and center of mass), and PA (percentage of walking, standing, sitting, and lying; mean duration and variability of single walking, standing, sitting, and lying bouts) were measured in the participant's home using validated wearable sensor technology. Logistic regression was used to assess the most sensitive gait, balance, and PA variables for identifying prefrail participants (vs. nonfrail). Multinomial logistic regression was used to identify variables sensitive to discriminate between three frailty levels.
Gait speed (area under the curve, AUC=0.802), hip sway (AUC=0.734), and steps/day (AUC=0.736) were the most sensitive parameters for the identification of prefrailty. Multinomial regression revealed that stride length (AUC=0.857) and double support (AUC=0.841) were the most sensitive gait parameters for discriminating between three frailty levels. Interestingly, walking bout duration variability was the most sensitive PA parameter for discriminating between three frailty levels (AUC=0.818). No balance parameter discriminated between three frailty levels.
Our results indicate that unique parameters derived from objective assessment of gait, balance, and PA are sensitive for the identification of prefrailty and the classification of a subject's frailty level. The present findings highlight the potential of wearable sensor technology for in-home assessment of frailty status.
衰弱是一种老年综合征，由多个生理系统随年龄增长的累积衰退、体内稳态储备受损以及抵抗压力的能力下降所致。根据最近的估计，10%的社区居住老年人衰弱，另有41.6%处于衰弱前期。在工业化国家，衰弱老年人的医疗保健费用最高。身体功能受损是衰弱的主要指标，功能表现测试有助于识别衰弱。目前尚未充分开发出适用于家庭衰弱筛查的客观仪器化身体功能评估方法。
研究基于可穿戴传感器的家庭步态、平衡和身体活动（PA）评估区分衰弱水平（非衰弱、衰弱前期和衰弱）的能力。
在一项观察性横断面研究中，于2012年9月至2013年11月期间，对居住在美国亚利桑那州图森市的125名老年人（非衰弱：n = 44，衰弱前期：n = 60，衰弱：n = 21）进行了家访。使用经过验证的可穿戴传感器技术，在参与者家中测量了时空步态参数（速度、步长、步时、双支撑以及步速变异性）、姿势平衡（髋部、踝部和质心的摆动）和PA（步行、站立、坐着和躺着的百分比；单次步行、站立、坐着和躺着时段的平均持续时间和变异性）。采用逻辑回归评估用于识别衰弱前期参与者（与非衰弱者相比）最敏感的步态、平衡和PA变量。采用多项逻辑回归识别对区分三种衰弱水平敏感的变量。
步态速度（曲线下面积，AUC = 0.802）髋关节摆动（AUC = 0.734）和每日步数（AUC = 0.736）是识别衰弱前期最敏感的参数。多项回归显示，步长（AUC = 0.857）和双支撑（AUC = 0.841）是区分三种衰弱水平最敏感的步态参数。有趣的是，步行时段持续时间变异性是区分三种衰弱水平最敏感的PA参数（AUC = 0.818）。没有平衡参数能区分三种衰弱水平。
我们的结果表明，从步态、平衡和PA的客观评估中得出的独特参数对识别衰弱前期和对受试者的衰弱水平进行分类很敏感。目前的研究结果突出了可穿戴传感器技术在家庭评估衰弱状态方面的潜力。
