Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas.
Telehealth Cardio-Pulmonary Rehabilitation Program, Medical Care Line, Michael E. DeBakey VA Medical Center, Houston, Texas.
J Surg Res. 2021 Jul;263:130-139. doi: 10.1016/j.jss.2021.01.023. Epub 2021 Feb 27.
Traditional physical frailty (PF) screening tools are resource intensive and unsuitable for remote assessment. In this study, we used five times sit-to-stand test (5×STS) with wearable sensors to determine PF and three key frailty phenotypes (slowness, weakness, and exhaustion) objectively.
Older adults (n = 102, age: 76.54 ± 7.72 y, 72% women) performed 5×STS while wearing sensors attached to the trunk and bilateral thigh and shank. Duration of 5×STS was recorded using a stopwatch. Seventeen sensor-derived variables were analyzed to determine the ability of 5×STS to distinguish PF, slowness, weakness, and exhaustion. Binary logistic regression was used, and its area under curve was calculated.
A strong correlation was observed between sensor-based and manually-recorded 5xSTS durations (r = 0.93, P < 0.0001). Sensor-derived variables indicators of slowness (5×STS duration, hip angular velocity range, and knee angular velocity range), weakness (hip power range and knee power range), and exhaustion (coefficient of variation (CV) of hip angular velocity range, CV of vertical velocity range, and CV of vertical power range) were different between the robust group and prefrail/frail group (P < 0.05) with medium to large effect sizes (Cohen's d = 0.50-1.09). The results suggested that sensor-derived variables enable identifying PF, slowness, weakness, and exhaustion with an area under curve of 0.861, 0.865, 0.720, and 0.723, respectively.
Our study suggests that sensor-based 5×STS can provide digital biomarkers of PF, slowness, weakness, and exhaustion. The simplicity, ease of administration in front of a camera, and safety of 5xSTS may facilitate a remote assessment of PF, slowness, weakness, and exhaustion via telemedicine.
传统的身体虚弱（PF）筛查工具资源密集且不适合远程评估。在这项研究中，我们使用带有可穿戴传感器的五次坐站测试（5×STS）来客观地确定 PF 和三个关键的虚弱表型（缓慢、虚弱和疲惫）。
102 名老年人（年龄：76.54±7.72 岁，72%为女性）在佩戴连接躯干和双侧大腿及小腿的传感器的情况下进行 5×STS。使用秒表记录 5×STS 的持续时间。分析了 17 个传感器衍生变量，以确定 5×STS 区分 PF、缓慢、虚弱和疲惫的能力。使用二元逻辑回归，并计算其曲线下面积。
传感器记录和手动记录的 5xSTS 持续时间之间存在很强的相关性（r=0.93，P<0.0001）。传感器衍生变量的指标，如缓慢（5×STS 持续时间、髋关节角速度范围和膝关节角速度范围）、虚弱（髋关节功率范围和膝关节功率范围）和疲惫（髋关节角速度范围的变异系数、垂直速度范围的变异系数和垂直功率范围的变异系数）在强壮组和虚弱/脆弱组之间存在差异（P<0.05），且具有中等至大的效应量（Cohen's d=0.50-1.09）。结果表明，传感器衍生变量可以通过曲线下面积 0.861、0.865、0.720 和 0.723 来识别 PF、缓慢、虚弱和疲惫。
我们的研究表明，基于传感器的 5×STS 可以提供 PF、缓慢、虚弱和疲惫的数字生物标志物。5xSTS 的简单性、易于在摄像头前进行管理以及安全性可能通过远程医疗促进 PF、缓慢、虚弱和疲惫的远程评估。
