The influence of cycle time on shoulder fatigue responses for a fixed total overhead workload

被引:39
作者
Dickerson, Clark R. [1 ]
Meszaros, Kimberly A. [1 ]
Cudlip, Alan C. [1 ]
Chopp-Hurley, Jaclyn N. [1 ]
Langenderfer, Joseph E. [2 ]
机构
[1] Univ Waterloo, Fac Appl Hlth Sci, Waterloo, ON N2L 3G1, Canada
[2] Cent Michigan Univ, Sch Engn & Technol, Mt Pleasant, MI 48859 USA
基金
加拿大自然科学与工程研究理事会; 加拿大创新基金会;
关键词
Shoulder; Cycle time; Fatigue; Task variation; Job rotation; ACROMIO-HUMERAL DISTANCE; MUSCLE FATIGUE; ROTATOR CUFF; IMPINGEMENT SYNDROME; GLENOHUMERAL KINEMATICS; INTRAMUSCULAR PRESSURE; SURFACE EMG; FORCE; RELIABILITY; MORPHOLOGY;
D O I
10.1016/j.jbiomech.2015.04.043
中图分类号
Q6 [生物物理学];
学科分类号
071011 [生物物理学];
摘要
The relationship between overhead work and musculoskeletal health depends on multiple task and individual factors. Knowledge gaps persist, despite examination of many of these factors individually and in combination. This investigation targeted task variation, as parameterized by cycle time within a fixed overall workload. Participants performed an intermittent overhead pressing task with four different cycle time conditions while overall workload and duty cycle was held constant. Several manifestations of fatigue were monitored during task performance. Endurance time was influenced by cycle time with shorter cycle times having endurance times up to 25% higher than longer cycle times. Surface electromyography (sEMG) results were mixed, with two muscles demonstrating amplitude increases (middle deltoid and upper trapezius) that varied with cycle time. sEMG frequency was not influenced by cycle time for any muscle monitored, despite decreases for several cycle times. Trends existed for the influence of cycle time on time-varying reported discomfort (p=0.056) and static strength (p=0.055); large effect sizes were present (eta(2)(p)=0.31 and 0.27, respectively). The equivocal association of fatigue indicators and cycle time is analogous to the influence of other factors implicated in overhead work musculoskeletal risk, and probabilistic modeling offers a compelling avenue for integration of the known variation in the many factors that combine to inform this risk. (C) 2015 Published by Elsevier Ltd.
引用
收藏
页码:2911 / 2918
页数:8
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