Lopes Mário, Lopes Susana, Monteiro Mariana, Rodrigues Mário, Fertuzinhos Aureliano, Coelho Augusto de Sousa, Matos Paulo, Borges Abílio, Leite Teófilo, Sampaio Cátia, Costa Rui, Alvarelhão José
School of Health Sciences and Institute of Biomedicine, University of Aveiro, Aveiro, Portugal.
School of Health Sciences, University of Aveiro, Aveiro, Portugal.
JMIR Res Protoc. 2023 May 4;12:e43637. doi: 10.2196/43637.
In manufacturing industries, tasks requiring poor posture, high repetition, and long duration commonly induce fatigue and lead to an increased risk of work-related musculoskeletal disorders. Smart devices assessing biomechanics and providing feedback to the worker for correction may be a successful way to increase postural awareness, reducing fatigue, and work-related musculoskeletal disorders. However, evidence in industrial settings is lacking.
This study protocol aims to explore the efficacy of a set of smart devices to detect malposture and increase postural awareness, reducing fatigue, and musculoskeletal disorders.
A longitudinal single-subject experimental design following the ABAB sequence will be developed in a manufacturing industry real context with 5 workers. A repetitive task of screw tightening of 5 screws in a standing position into a piece placed horizontally was selected. Workers will be assessed in 4 moments per shift (10 minutes after the beginning of the shift, 10 minutes before and after the break, and 10 minutes before the end of the shift) in 5 nonconsecutive days. The primary outcomes are fatigue, assessed by electromyography, and musculoskeletal symptoms assessed by the Nordic Musculoskeletal Questionnaire. Secondary outcomes include perceived effort (Borg perceived exertion scale); range of motion of the main joints in the upper body, speed, acceleration, and deceleration assessed by motion analysis; risk stratification of range of motion; and cycle duration in minutes. Structured visual analysis techniques will be conducted to observe the effects of the intervention. Results for each variable of interest will be compared among the different time points of the work shift and longitudinally considering each assessment day as a time point.
Enrollment for the study will start in April 2023. Results are expected to be available still in the first semester of 2023. It is expected that the use of the smart system will reduce malposture, fatigue, and consequently, work-related musculoskeletal pain and disorders.
This proposed study will explore a strategy to increase postural awareness in industrial manufacturing workers who do repetitive tasks, using smart wearables that provide real-time feedback about biomechanics. Results would showcase a novel approach for improving self-awareness of risk for work-related musculoskeletal disorders for these workers providing an evidence base support for the use of such devices.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/43637.
在制造业中，需要不良姿势、高重复性和长时间的任务通常会导致疲劳，并增加与工作相关的肌肉骨骼疾病的风险。智能设备评估生物力学并向工人提供反馈以进行纠正，可能是提高姿势意识、减少疲劳和与工作相关的肌肉骨骼疾病的成功方法。然而，工业环境中的证据尚缺。
本研究方案旨在探索一套智能设备检测不良姿势、提高姿势意识、减少疲劳和肌肉骨骼疾病的效果。
将在制造业实际环境中对5名工人采用ABAB序列的纵向单受试者实验设计。选择了一项重复性任务，即站立将5颗螺丝拧入水平放置的工件中。工人将在5个非连续工作日的每个班次的4个时间点接受评估（班次开始后10分钟、休息前后各10分钟、班次结束前10分钟）。主要结局为通过肌电图评估的疲劳，以及通过北欧肌肉骨骼问卷评估的肌肉骨骼症状。次要结局包括感知用力（Borg感知用力量表）；通过运动分析评估的上身主要关节的活动范围、速度、加速度和减速度；活动范围的风险分层；以及以分钟为单位的周期持续时间。将采用结构化视觉分析技术观察干预效果。将在工作班次的不同时间点之间比较每个感兴趣变量的结果，并纵向将每个评估日视为一个时间点。
该研究将于2023年4月开始招募。预计结果仍将在2023年第一学期获得。预计使用智能系统将减少不良姿势、疲劳，并因此减少与工作相关的肌肉骨骼疼痛和疾病。
本拟议研究将探索一种策略，通过使用提供生物力学实时反馈的智能可穿戴设备，提高从事重复性任务的工业制造工人的姿势意识。结果将展示一种新方法，用于提高这些工人对与工作相关的肌肉骨骼疾病风险的自我意识，为使用此类设备提供循证支持。
国际注册报告识别号（IRRID）：PRR1-10.2196/43637。
