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OWAS 观察者间信度。

OWAS inter-rater reliability.

机构信息

Fraunhofer Institute for Digital Media Technology IDMT, Division Hearing, Speech and Audio Technology, Marie-Curie-Straße 2, 26129, Oldenburg, Germany; Carl von Ossietzky University Oldenburg, Ammerländer Heerstr. 140, 26129, Oldenburg, Germany.

Carl von Ossietzky University Oldenburg, Ammerländer Heerstr. 140, 26129, Oldenburg, Germany.

出版信息

Appl Ergon. 2021 May;93:103357. doi: 10.1016/j.apergo.2021.103357. Epub 2021 Jan 30.

DOI:10.1016/j.apergo.2021.103357
PMID:33524664
Abstract

The Ovako Working posture Assessment System (OWAS) is a commonly used observational assessment method for determining the risk of work-related musculoskeletal disorders. OWAS claims to be suitable in the application for untrained persons but there is not enough evidence for this assumption. In this paper, inter-rater (inter-observer) reliability (agreement) is examined down to the level of individual postures and categories. For this purpose, the postures of 20 volunteers have been observed by 3 varying human raters in a laboratory setting and the inter-rater agreement against reference values was determined. A high agreement of over 98%(κ=0.98) was found for the postures of the arms but lower agreements were found for posture classification of the legs (66-97%,κ=0.85) and the upper body (80-96%,κ=0.85). No significant difference was found between raters with and without intense prior training in physical therapy. Consequently, the results confirm the general reliability of the OWAS method especially for raters with non-specialized background but suggests weaknesses in the reliable detection of a few particular postures.

摘要

奥瓦科工作姿势评估系统(OWAS)是一种常用于观察评估的方法,用于确定与工作相关的肌肉骨骼疾病的风险。OWAS 声称适用于未经训练的人员,但没有足够的证据支持这一假设。在本文中,评估了个体姿势和类别层面的评分者间(观察者间)可靠性(一致性)。为此,在实验室环境中,由 3 名不同的人类评分者观察了 20 名志愿者的姿势,并确定了与参考值的评分者间一致性。手臂姿势的一致性非常高(超过 98%,κ=0.98),但腿部姿势(66-97%,κ=0.85)和上半身姿势(80-96%,κ=0.85)的一致性较低。在是否接受过物理治疗方面的强化培训方面,评分者之间没有发现显著差异。因此,结果证实了 OWAS 方法的总体可靠性,特别是对于非专业背景的评分者,但也表明在可靠检测某些特定姿势方面存在弱点。

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OWAS inter-rater reliability.OWAS 观察者间信度。
Appl Ergon. 2021 May;93:103357. doi: 10.1016/j.apergo.2021.103357. Epub 2021 Jan 30.
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Classification of body postures using smart workwear.使用智能工作服进行体态分类。
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Int J Environ Res Public Health. 2022 Jan 5;19(1):595. doi: 10.3390/ijerph19010595.