The Effect of Repetitive Lifting on Trunk Muscles’ Fatigue and Kinematics of Spine and Load-In-Hand



Kazemi Z¹ ; Mazloumi A² ; Arjmand N³ ; Karimi Z⁴ ; Keihani A⁵ ; Ghasemi MS⁶ Show Affiliations
Source: Journal of Health and Safety at Work Published:2023

Abstract

Introduction: Given the high prevalence of low back pain in manual handling activities, its known relationship with spinal loads, and the role of muscular fatigue and the body’s adaptive mechanisms to counteract fatigue, this study investigated the effect of repetitive lifting tasks on trunk muscular fatigue and the kinematics of the spine and load-in-hand. Material and Methods: Eighteen male volunteers lifted a box from the floor to their waist height at a pace of ten lifts per minute until they could no longer continue the task and reported the highest level of exhaustion. Kinematic data and muscle electromyographic activity were simultaneously recorded using a motion capture system and an electromyography device. In this study, average trunk flexion angle and trunk angular velocity were calculated as trunk kinematic variables, while average box vertical travel distance, average box horizontal displacement from L5-S1, and average box vertical displacement velocity were considered as box kinematic variables. The median frequency of electromyographic signals from selected muscles was quantified as a muscle fatigue indicator. Since subjects performed different lifting cycles, the total number of cycles was divided into five distinct blocks for data analysis. Results: The results showed significant effects of lifting trial blocks on trunk angle (p=0.004) and vertical box displacement (p<0.001). Median frequency was significantly affected by lifting blocks for right (p=0.016) and left erector spinae (p=0.014), right (p=0.021) and left multifidus (p<0.001), right latissimus dorsi (p=0.001), and left rectus abdominis (p=0.039). Conclusion: Overall, the results highlight variations in most kinematic parameters and a reduction in the frequency content of EMG signal spectra. These changes serve as indices of the central nervous system’s control over lifting behavior under dynamic conditions. A better understanding of these central nervous system adaptations could have practical applications in interventions such as workstation design, exoskeleton development, and worker training to manage musculoskeletal disorders. © 2023, Tehran University of Medical Sciences. All rights reserved.