基于动作活动记录仪和全球定位系统的老年人步态质量与日常生活活动能力的关联分析。
Association of Gait Quality With Daily-Life Mobility: An Actigraphy and Global Positioning System Based Analysis in Older Adults.
出版信息
IEEE Trans Biomed Eng. 2024 Jan;71(1):130-138. doi: 10.1109/TBME.2023.3293752. Epub 2023 Dec 22.
OBJECTIVE
Walking is a key component of daily-life mobility. We examined associations between laboratory-measured gait quality and daily-life mobility through Actigraphy and Global Positioning System (GPS). We also assessed the relationship between two modalities of daily-life mobility i.e., Actigraphy and GPS.
METHODS
In community-dwelling older adults (N = 121, age = 77±5 years, 70% female, 90% white), we obtained gait quality from a 4-m instrumented walkway (gait speed, walk-ratio, variability) and accelerometry during 6-Minute Walk (adaptability, similarity, smoothness, power, and regularity). Physical activity measures of step-count and intensity were captured from an Actigraph. Time out-of-home, vehicular time, activity-space, and circularity were quantified using GPS. Partial Spearman correlations between laboratory gait quality and daily-life mobility were calculated. Linear regression was used to model step-count as a function of gait quality. ANCOVA and Tukey analysis compared GPS measures across activity groups [high, medium, low] based on step-count. Age, BMI, and sex were used as covariates.
RESULTS
Greater gait speed, adaptability, smoothness, power, and lower regularity were associated with higher step-counts (0.20<|ρ| < 0.26, p < .05). Age(β = -0.37), BMI(β = -0.30), speed(β = 0.14), adaptability(β = 0.20), and power(β = 0.18), explained 41.2% variance in step-count. Gait characteristics were not related to GPS measures. Participants with high (>4800 steps) compared to low activity (steps<3100) spent more time out-of-home (23 vs 15%), more vehicular travel (66 vs 38 minutes), and larger activity-space (5.18 vs 1.88 km), all p < .05.
CONCLUSIONS
Gait quality beyond speed contributes to physical activity. Physical activity and GPS-derived measures capture distinct aspects of daily-life mobility. Wearable-derived measures should be considered in gait and mobility-related interventions.
目的
行走是日常生活活动能力的关键组成部分。我们通过动作活动记录仪和全球定位系统（GPS）来研究实验室测量的步态质量与日常生活活动能力之间的关联。我们还评估了两种日常生活活动能力模式，即动作活动记录仪和 GPS 之间的关系。
方法
在社区居住的老年人中（N=121，年龄=77±5 岁，70%为女性，90%为白人），我们从 4 米长的仪器化步道（步速、步行比、变异性）和 6 分钟步行（适应性、相似性、平滑度、力量和规律性）中获得步态质量。通过动作活动记录仪记录步数和强度的身体活动测量值。使用 GPS 量化户外活动时间、车辆时间、活动空间和圆度。计算实验室步态质量与日常生活活动能力之间的部分 Spearman 相关系数。线性回归用于建立以步态质量为函数的步数模型。基于步数，采用协方差分析和 Tukey 分析比较了 GPS 测量值在高、中、低活动组之间的差异。年龄、BMI 和性别被用作协变量。
结果
更高的步速、适应性、平滑度、力量和更低的规律性与更高的步数相关（0.20<|ρ| < 0.26，p <.05）。年龄（β=-0.37）、BMI（β=-0.30）、速度（β=0.14）、适应性（β=0.20）和力量（β=0.18）解释了步数的 41.2%方差。步态特征与 GPS 测量值无关。与低活动组（步数<3100）相比，高活动组（步数>4800）的户外活动时间更长（23 分钟对 15%）、车辆出行时间更长（66 分钟对 38 分钟）、活动空间更大（5.18 公里对 1.88 公里），所有差异均具有统计学意义（p <.05）。
结论
除速度以外的步态质量对身体活动有贡献。身体活动和 GPS 衍生测量值捕捉日常生活活动能力的不同方面。在步态和移动相关干预措施中，应考虑可穿戴设备测量值。
Zotero 插件