Motor Planning Error: Toward Measuring Cognitive Frailty in Older Adults Using Wearables.
Motor Planning Error: Toward Measuring Cognitive Frailty in Older Adults Using Wearables.
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DOI：
10.3390/s18030926
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发表时间：
2018-03-20
期刊：
影响因子：
--
通讯作者：
Najafi B
中科院分区：
文献类型：
--
作者：
Zhou H;Lee H;Lee J;Schwenk M;Najafi B
需要可以快速管理的实用工具来测量认知运动表现随时间的微妙变化。虚弱与认知障碍（或“认知虚弱”）一起被证明是随着时间的推移认知能力下降的强有力且独立的预测因素。我们开发了一个交互式仪器化路线制定任务（iTMT）平台，该平台可以通过一系列到达脚踝的任务来量化运动规划误差（MPE）。在这项研究中，我们检验了 MPE 在识别老年人认知脆弱性方面的准确性。招募了 32 名老年人（年龄 = 77.3 ± 9.1 岁，体重指数 = 25.3 ± 4.7 kg/m2，女性 = 38%）。使用简易精神状态检查或蒙特利尔认知评估 (MoCA)，16 名受试者被归类为认知完整，16 名受试者被归类为认知受损。此外，还招募了 12 名年轻健康受试者（年龄 = 26.0 ± 5.2 岁，体重指数 = 25.3 ± 3.9 kg/m2，女性 = 33%）来建立健康基准。受试者使用脚踝佩戴的传感器完成 iTMT，该传感器将脚踝运动转换为计算机光标的导航。 iTMT 任务包括通过站立时移动踝关节来达到计算机屏幕上的五个索引目标圈（包括随机排列的数字 1 到 3 和字母 A 和 B）。踝关节传感器通过分析踝关节速度模式来量化 MPE。 MPE 被定义为受试者最大踝关节速度与最佳最大踝关节速度（到达路径的一半）之间的时间偏差百分比。还收集了步态测试的数据，包括单任务和双任务步行，以确定认知运动表现。年轻健康组、老年认知完整组和老年认知受损组的平均 MPE 分别为 11.1 ± 5.7%、20.3 ± 9.6% 和 34.1 ± 4.2% (p < 0.001)。使用 MPE 区分各组时观察到较大的效应量（Cohen d = 1.17–4.56）。 MPE 和 MoCA 评分之间（r = -0.670，p < 0.001）以及 MPE 和双任务步幅速度之间（r = -0.584，p < 0.001）之间观察到显着相关性。这项研究证明了从实用的可穿戴平台估计 MPE 的可行性和有效性，在识别认知运动障碍方面取得了有希望的结果，并在评估认知脆弱性方面具有潜在的应用。所提出的平台还可以用作双任务步行测试的替代方案，其中步态评估可能不切实际。未来的研究需要在更大的样本中证实这些观察结果。
Practical tools which can be quickly administered are needed for measuring subtle changes in cognitive–motor performance over time. Frailty together with cognitive impairment, or ‘cognitive frailty’, are shown to be strong and independent predictors of cognitive decline over time. We have developed an interactive instrumented trail-making task (iTMT) platform, which allows quantification of motor planning error (MPE) through a series of ankle reaching tasks. In this study, we examined the accuracy of MPE in identifying cognitive frailty in older adults. Thirty-two older adults (age = 77.3 ± 9.1 years, body-mass-index = 25.3 ± 4.7 kg/m2, female = 38%) were recruited. Using either the Mini-Mental State Examination or Montreal Cognitive Assessment (MoCA), 16 subjects were classified as cognitive-intact and 16 were classified as cognitive-impaired. In addition, 12 young-healthy subjects (age = 26.0 ± 5.2 years, body-mass-index = 25.3 ± 3.9 kg/m2, female = 33%) were recruited to establish a healthy benchmark. Subjects completed the iTMT, using an ankle-worn sensor, which transforms ankle motion into navigation of a computer cursor. The iTMT task included reaching five indexed target circles (including numbers 1-to-3 and letters A&B placed in random order) on the computer-screen by moving the ankle-joint while standing. The ankle-sensor quantifies MPE through analysis of the pattern of ankle velocity. MPE was defined as percentage of time deviation between subject’s maximum ankle velocity and the optimal maximum ankle velocity, which is halfway through the reaching pathway. Data from gait tests, including single task and dual task walking, were also collected to determine cognitive–motor performance. The average MPE in young-healthy, elderly cognitive-intact, and elderly cognitive-impaired groups was 11.1 ± 5.7%, 20.3 ± 9.6%, and 34.1 ± 4.2% (p < 0.001), respectively. Large effect sizes (Cohen’s d = 1.17–4.56) were observed for discriminating between groups using MPE. Significant correlations were observed between the MPE and MoCA score (r = −0.670, p < 0.001) as well as between the MPE and dual task stride velocity (r = −0.584, p < 0.001). This study demonstrated feasibility and efficacy of estimating MPE from a practical wearable platform with promising results in identifying cognitive–motor impairment and potential application in assessing cognitive frailty. The proposed platform could be also used as an alternative to dual task walking test, where gait assessment may not be practical. Future studies need to confirm these observations in larger samples.
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