Automated detection of missteps during community ambulation in patients with Parkinson's disease: a new approach for quantifying fall risk in the community setting.
Automated detection of missteps during community ambulation in patients with Parkinson's disease: a new approach for quantifying fall risk in the community setting.
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DOI：
10.1186/1743-0003-11-48
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发表时间：
2014-04-03
影响因子：
5.1
通讯作者：
Hausdorff JM
中科院分区：
文献类型：
--
作者：
Iluz T;Gazit E;Herman T;Sprecher E;Brozgol M;Giladi N;Mirelman A;Hausdorff JM
跌倒是老年人和帕金森病 (PD) 等神经系统疾病患者发病和死亡的主要原因。自我报告的失误（也称为接近跌倒）与帕金森病患者的跌倒风险有关。我们开发了一种客观工具，用于检测现实世界、日常生活条件下的失误，以加强对跌倒风险的评估，并将这种新方法应用于 3 天的连续记录。 40 名 PD 患者（平均年龄±SD：62.2±10.0 岁，病程：5.3±3.5 岁）在参与旨在引发实验室失误的方案时，在下背部佩戴了一个包含加速计和陀螺仪的小型设备。之后，受试者在进行日常活动时佩戴传感器 3 天。根据实验室数据开发了一种旨在自动识别失误的算法，并在 3 天的记录中进行了验证。在实验室中，我们记录了 29 次失误和 60 多个小时的数据。当应用于该数据集时，该算法实现了 93.1% 的命中率和 98.6% 的特异性。当我们将此算法应用于 3 天的记录时，与非跌倒者相比，在研究前 6 个月内报告两次或两次以上跌倒的患者（即跌倒者）在 3 天的记录期间更有可能检测到失误 (p = 0.010)。这些发现表明，这种新方法可用于检测 PD 患者日常生活中的失误，并可能有助于纵向评估疾病进展和跌倒风险。
Falls are a leading cause of morbidity and mortality among older adults and patients with neurological disease like Parkinson’s disease (PD). Self-report of missteps, also referred to as near falls, has been related to fall risk in patients with PD. We developed an objective tool for detecting missteps under real-world, daily life conditions to enhance the evaluation of fall risk and applied this new method to 3 day continuous recordings. 40 patients with PD (mean age ± SD: 62.2 ± 10.0 yrs, disease duration: 5.3 ± 3.5 yrs) wore a small device that contained accelerometers and gyroscopes on the lower back while participating in a protocol designed to provoke missteps in the laboratory. Afterwards, the subjects wore the sensor for 3 days as they carried out their routine activities of daily living. An algorithm designed to automatically identify missteps was developed based on the laboratory data and was validated on the 3 days recordings. In the laboratory, we recorded 29 missteps and more than 60 hours of data. When applied to this dataset, the algorithm achieved a 93.1% hit ratio and 98.6% specificity. When we applied this algorithm to the 3 days recordings, patients who reported two falls or more in the 6 months prior to the study (i.e., fallers) were significantly more likely to have a detected misstep during the 3 day recordings (p = 0.010) compared to the non-fallers. These findings suggest that this novel approach can be applied to detect missteps during daily life among patients with PD and will likely help in the longitudinal assessment of disease progression and fall risk.
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影响因子：
7.4
作者：
Fiordelli M;Diviani N;Schulz PJ
通讯作者：
Schulz PJ
影响因子：
6.8
作者：
MOORE, BC
通讯作者：
MOORE, BC
DOI：
10.1111/j.1532-5415.2005.53455.x
发表时间：
2005-09-01
影响因子：
6.3
作者：
Lamb, SE;Jorstad-Stein, EC;Becker, C
通讯作者：
Becker, C
DOI：
10.1111/j.1532-5415.1986.tb05480.x
发表时间：
1986-02-01
影响因子：
6.3
作者：
TINETTI, ME
通讯作者：
TINETTI, ME
影响因子：
6.7
作者：
Ashburn, A.;Hyndman, D.;Harris, S.
通讯作者：
Harris, S.