Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease.
Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease.
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DOI：
10.3389/fneur.2023.1096401
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发表时间：
2023
影响因子：
3.4
通讯作者：
中科院分区：
文献类型：
--
作者：
为了研究在帕金森氏病(PD)患者一周的日常活动中被动收集的步态(行走和转身)的数字测量是否与单独的跌倒病史相比，增加了预测未来跌倒的辨别能力。我们招募了34名PD患者(17名有跌倒史，17名没有跌倒)，年龄：68±6岁，MDS-UPDRS III评分：31±9岁。根据过去6个月的自我报告跌倒情况，参与者被分为跌倒者(至少一次跌倒)和不跌倒者。80个数字步态测量来自3个惯性传感器(Opal®V2系统)，放置在脚和下背部，进行为期一周的被动步态监测。Logistic回归采用“最佳子集选择策略”来寻找区分未来跌倒者和未跌倒者的测量组合，以及曲线下面积(AUC)。研究结束后，参与者每两周通过电子邮件对自己报告的跌倒情况进行跟踪调查。25名受试者报告在随后的一年中摔倒。步态数量和转身措施(例如，步态次数和每小时转弯)在未来跌倒者和未跌倒者中是相似的。仅用跌倒史来区分未来跌倒与未跌倒者的AUC为0.77(95%CI：[0.50-1.00])。相比之下，4种组合的步态和转身数字测量的最高AUC为0.94[0.84-1.00]。从最佳子集策略的前10个模型(所有AUC>；0.90)中，最一致的衡量标准是脚趾外露角度的变异性(10分中的9分)，挥杆中期的脚的俯仰角(8分)，以及峰值转身速度(7分10分)。这些发现突显了考虑精确的数字测量的重要性，这些测量是通过战略性地放置在脚和腰上的传感器捕捉到的，以量化日常生活中步态(行走和转身)的几个不同方面，以改进对未来帕金森病患者的分类。
To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone. We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a “best subsets selection strategy” was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed via email every 2 weeks over the year after the study for self-reported falls. Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50–1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84–1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10). These findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD.
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影响因子：
4.6
作者：
Leach, Julia M.;Mellone, Sabato;Chiari, Lorenzo
通讯作者：
Chiari, Lorenzo
影响因子：
3.4
作者：
Mancini M;Weiss A;Herman T;Hausdorff JM
通讯作者：
Hausdorff JM
DOI：
10.1155/2013/906274
发表时间：
2013
期刊：
Parkinson's disease
影响因子：
--
作者：
Allen NE;Schwarzel AK;Canning CG
通讯作者：
Canning CG
影响因子：
6.7
作者：
Callisaya, Michele L.;Blizzard, Leigh;Srikanth, Velandai K.
通讯作者：
Srikanth, Velandai K.
影响因子：
2
作者：
Mancini, Martina;El-Gohary, Mahmoud;Horak, Fay B.
通讯作者：
Horak, Fay B.