PDF(Pubmed)
Abstract：
Background Walking is an everyday activity but also complex in nature. Gait disorders have the potential to drastically affect an individual\'s quality of life and their ability to be independent. The causes of gait disorders are numerous. To identify abnormal gait, clinicians utilize gait analysis. The aim of this study is to assess how well individuals can identify limps in postoperative traumatized individuals with lower extremity deformities. Methods Participants observed a video compiled of individuals with various gait abnormalities and severities of limps. In the video, there were nine abnormal gait presentations, four obvious limps, and five subtle limps, while the other 10 gait presentations were normal gaits. Classifications for gait presentations were assigned by the research team. Participants assigned a classification to each limp case presented in the video on a survey. The participants were separated into two groups: those with healthcare experience and lay individuals. A Mann-Whitney U-test was used to compare healthcare experience and lay individuals\' ability to identify limps correctly. In addition, the observers were evaluated on their ability to perform a screening diagnosis of a limp. Results A total of 100 participants were included in the study, 46 with healthcare experience and 54 individuals without. All tests, identification of limp and subtle limp, using the Mann-Whitney U-test yielded non-significant differences between healthcare and nonhealthcare experience. Overall lowest correctness between both groups came when attempting to identify subtle limp (healthcare = 57.39%, nonhealthcare = 56.67%) while the highest correctness yield was when identifying limp (healthcare = 96.74%, nonhealthcare = 95.37%). Analysis of the observers\' ability to perform a screening diagnosis of limp provided close to gold standard results (sensitivity = 96.0%, specificity = 98.7%, positive predictive value = 99.2%, negative predictive value = 98.4%). Conclusion This study showed that nonhealthcare individuals can accurately perform gait analysis from a video, particularly in identifying the presence of a limp, to a similar extent as individuals with healthcare experience. The implementation of two-dimensional catwalk videos taken from a smartphone is beneficial due to accessibility and cost-effectiveness. It also suggested that limp diagnosis can be done as a screening test, using individuals as the screener.
摘要：
背景技术步行是一种日常活动,但本质上也很复杂。步态障碍有可能严重影响个体的生活质量和独立能力。步态障碍的原因很多。为了识别异常步态，临床医生利用步态分析。这项研究的目的是评估个人如何识别下肢畸形的术后创伤患者的跛行。方法参与者观察了一段视频，其中包括各种步态异常和跛行严重程度的个体。在视频中,有九种异常步态表现，四个明显的跛行，和五个微妙的跛行，而其他10个步态表现为正常步态。步态呈现的分类由研究小组分配。参与者为调查视频中呈现的每个跛行病例分配了分类。参与者分为两组：有医疗保健经验的人和普通个人。使用Mann-WhitneyU检验来比较医疗保健经验和躺下的人正确识别跛行的能力。此外,对观察者进行跛行筛查诊断的能力进行了评估.结果共纳入100名参与者,46名具有医疗保健经验的人和54名没有医疗保健经验的人。所有测试,识别跛行和微妙的跛行，使用Mann-WhitneyU检验得出的医疗保健和非医疗保健经验之间无显著差异.在试图识别微妙的跛行时，两组之间的总体正确性最低(医疗保健=57.39%，nonhealthcare=56.67%)，而正确率最高的是识别跛行时(healthcare=96.74%，非医疗保健=95.37%）。对观察者进行跛行筛查诊断的能力的分析提供了接近金标准的结果(灵敏度=96.0%,特异性=98.7%，阳性预测值=99.2%，阴性预测值=98.4%)。结论这项研究表明，非医疗保健个体可以从视频中准确地进行步态分析，特别是在确定跛行的存在时，与有医疗保健经验的个人相似。由于可访问性和成本效益，从智能手机拍摄的二维走秀视频的实现是有益的。它还建议跛行诊断可以作为筛查测试来完成，使用个人作为筛选器。
