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用于预测累积性创伤障碍的定量模型在边境加工厂行业的应用。

Quantitative Models for Prediction of Cumulative Trauma Disorders Applied to the Maquiladora Industry.

机构信息

Faculty of Engineering, Arquitecture and Design, Autonomous University of Baja California, Ensenada BC 22860, Mexico.

Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal.

出版信息

Int J Environ Res Public Health. 2021 Apr 6;18(7):3830. doi: 10.3390/ijerph18073830.

Abstract

Temperature gradient changes on the surface of the skin or in the middle of the body are signs of a disease. The aim of this study is to develop quantitative models for the prediction of cumulative trauma disorders (CTDs) arising from highly repetitive activities, considering risk factors, such as age, gender, body mass index (BMI), blood pressure (BP), respiratory rate (RR), and heart rate, to prevent injuries in manufacturing factory operators. This research involved 19 individuals from the area of sanding and 14 individuals from the area of tolex in manufacturing factories who had their vital signs and somatometry taken, as well as thermal images of their hands in the dorsal and palmar areas; an evaluation by the OCRA method was also applied. Factors such as BP and heart rate were determined to significantly influence the injuries, but no strong association with BMI was found. Quadratic regression models were developed, the estimates of which were adequately adjusted to the variable (R and R adjusted > 0.70). When integrating the factors of the OCRA method to the generated models, a better fit was obtained (R and adjusted R > 0.80). In conclusion, the participants who present levels out of the normal range in at least one of the factors have high probabilities of developing injuries in their wrists.

摘要

皮肤表面或身体中部的温度梯度变化是疾病的迹象。本研究旨在开发用于预测因高度重复活动而导致的累积性创伤障碍(CTD)的定量模型,考虑到年龄、性别、体重指数(BMI)、血压(BP)、呼吸频率(RR)和心率等风险因素,以预防制造工厂操作人员受伤。这项研究涉及来自打磨区的 19 人和来自人造革区的 14 人,他们的生命体征和体测数据,以及手部背侧和掌侧的热图像都被记录下来;还应用了 OCRA 方法进行评估。研究发现,BP 和心率等因素对伤害有显著影响,但与 BMI 没有很强的关联。研究开发了二次回归模型,这些模型对变量的估计得到了很好的调整(R 和调整后的 R > 0.70)。当将 OCRA 方法的因素整合到生成的模型中时,得到了更好的拟合(R 和调整后的 R > 0.80)。总之,至少有一个因素的水平超出正常范围的参与者,他们的手腕发生伤害的可能性很高。

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