Intel Digital Health Group, Leixlip IR5-2-2, Ireland.
IEEE Trans Biomed Eng. 2010 Dec;57(12):2918-26. doi: 10.1109/TBME.2010.2083659. Epub 2010 Oct 4.
Falls are a major problem in older adults worldwide with an estimated 30% of elderly adults over 65 years of age falling each year. The direct and indirect societal costs associated with falls are enormous. A system that could provide an accurate automated assessment of falls risk prior to falling would allow timely intervention and ease the burden on overstretched healthcare systems worldwide. An objective method for assessing falls risk using body-worn kinematic sensors is reported. The gait and balance of 349 community-dwelling elderly adults was assessed using body-worn sensors while each patient performed the "timed up and go" (TUG) test. Patients were also evaluated using the Berg balance scale (BBS). Of the 44 reported parameters derived from body-worn kinematic sensors, 29 provided significant discrimination between patients with a history of falls and those without. Cross-validated estimates of retrospective falls prediction performance using logistic regression models yielded a mean sensitivity of 77.3% and a mean specificity of 75.9%. This compares favorably to the cross-validated performance of logistic regression models based on the time taken to complete the TUG test (manually timed TUG) and the Berg balance score. These models yielded mean sensitivities of 58.0% and 57.8%, respectively, and mean specificities of 64.8% and 64.2%, respectively. Results suggest that this method offers an improvement over two standard falls risk assessments (TUG and BBS) and may have potential for use in supervised assessment of falls risk as part of a longitudinal monitoring protocol.
跌倒在全球老年人中是一个严重的问题，估计每年有 30%的 65 岁以上老年人跌倒。与跌倒相关的直接和间接社会成本是巨大的。如果有一种系统能够在跌倒前对跌倒风险进行准确的自动评估，就可以及时进行干预，并减轻全球医疗系统的负担。本文报告了一种使用佩戴式运动传感器评估跌倒风险的客观方法。使用佩戴式传感器评估了 349 名社区居住的老年人的步态和平衡，同时每位患者都进行了“计时起立行走”（TUG）测试。患者还使用 Berg 平衡量表（BBS）进行了评估。从佩戴式运动传感器中得出的 44 个报告参数中，有 29 个参数在有跌倒史的患者和无跌倒史的患者之间提供了显著的区分。使用逻辑回归模型对回溯性跌倒预测性能进行交叉验证的估计得出，平均敏感性为 77.3%，平均特异性为 75.9%。这与基于 TUG 测试完成时间（手动计时 TUG）和 Berg 平衡评分的逻辑回归模型的交叉验证性能相比表现良好。这些模型的平均敏感性分别为 58.0%和 57.8%，平均特异性分别为 64.8%和 64.2%。结果表明，这种方法优于两种标准的跌倒风险评估（TUG 和 BBS），并且可能有潜力用于作为纵向监测方案的一部分，对跌倒风险进行监督评估。
