Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.
Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, Maryland.
J Gerontol A Biol Sci Med Sci. 2018 Apr 17;73(5):676-681. doi: 10.1093/gerona/glx174.
Data collected by wearable accelerometry devices can be used to identify periods of sustained harmonic walking. This report aims to establish whether the features of walking identified in the laboratory and free-living environments are associated with each other as well as measures of physical function, mobility, fatigability, and fitness.
Fifty-one older adults (mean age 78.31) enrolled in the Developmental Epidemiologic Cohort Study were included in the analyses. The study included an "in-the-lab" component as well as 7 days of monitoring "in-the-wild" (free living). Participants were equipped with hip-worn Actigraph GT3X+ activity monitors, which collect raw accelerometry data. We applied a walking identification algorithm and defined features of walking, including participant-specific walking acceleration and cadence. The association between these walking features and physical function, mobility, fatigability, and fitness was quantified using linear regression analysis.
Acceleration and cadence estimated from "in-the-lab" and "in-the-wild" data were significantly associated with each other (p < .05). However, walking acceleration "in-the-lab" was on average 96% higher than "in-the-wild," whereas cadence "in-the-lab" was on average 20% higher than "in-the-wild." Acceleration and cadence were associated with measures of physical function, mobility, fatigability, and fitness (p < .05) in both "in-the-lab" and "in-the-wild" settings. In addition, "in-the-wild" daily walking time was associated with fitness (p < .05).
The quantitative difference in proposed walking features indicates that participants may overperform when observed "in-the-lab." Also, proposed features of walking were significantly associated with measures of physical function, mobility, fatigability, and fitness, which provides evidence of convergent validity.
可穿戴加速计设备收集的数据可用于识别持续和谐行走的时间段。本报告旨在确定实验室和自然环境中识别出的行走特征是否与身体功能、移动能力、疲劳性和体能等方面的测量结果相关。
51 名年龄在 78.31 岁的老年人（平均年龄）被纳入发展流行病学队列研究的分析中。该研究包括“实验室”部分以及 7 天的“野外”（自由生活）监测。参与者配备了 Hip-worn Actigraph GT3X+活动监测器，可收集原始加速计数据。我们应用了行走识别算法，并定义了行走特征，包括参与者特定的行走加速度和步频。使用线性回归分析量化了这些行走特征与身体功能、移动能力、疲劳性和体能之间的关联。
从“实验室”和“野外”数据中估计的加速度和步频相互显著相关（p <.05）。然而，“实验室”中的行走加速度平均比“野外”高 96%，而“野外”中的步频平均比“实验室”高 20%。“实验室”和“野外”环境中，加速度和步频均与身体功能、移动能力、疲劳性和体能的测量结果相关（p <.05）。此外，“野外”日常行走时间与体能相关（p <.05）。
所提出的行走特征的定量差异表明，当在“实验室”中观察时，参与者可能表现过度。此外，所提出的行走特征与身体功能、移动能力、疲劳性和体能的测量结果显著相关，这提供了收敛有效性的证据。
