Document Type


Date of Degree

Spring 2019

Degree Name

PhD (Doctor of Philosophy)

Degree In

Biomedical Engineering

First Advisor

Fethke, Nathan

Second Advisor

Wilder, David

First Committee Member

Fethke, Nathan

Second Committee Member

Wilder, David

Third Committee Member

Rahmatalla, Salam

Fourth Committee Member

Grosland, Nicole

Fifth Committee Member

Gerr, Fredric


Low back problems are among the most common nonfatal occupational injuries reported in the United States, and account for substantial healthcare expenditures (e.g., medical care costs) and losses to worker productivity. A strong association has been well-documented between occupational exposure to repetitive trunk motion and low back problems, particularly among workers performing manual material handling (i.e., lifting) activities. A feature of repetitive motion believed important to the development of work-related musculoskeletal disorders (MSDs), including low back problems, is a lack of within-individual, between-cycle variation of physical exposure summary measures, e.g., when observed visually, the cycle-to-cycle motion pattern appears consistent. An active literature has emerged using concepts of motor control to improve ergonomists’ understanding of physical exposure variation (i.e., motor variability) arising from individual-level mechanisms during repetitive work.

Fundamentally, for any particular individual, the onset of exposure to a repetitive physical activity (i.e., task training) involves a learning process during which motor control strategies are developed to accomplish the task effectively. The cycle-to-cycle variability of motor learning metrics, such as postural and task performance summary measures, has been observed to exponentially decay during task training. From an ergonomics perspective, a temporal reduction in postural variability may lead to greater cumulative loading and physiological fatiguing of the underlying muscle tissues (due to more consistent cycle-to-cycle movements), thus increasing MSD risk over time. However, it is not known if, or to what extent, physical task characteristics (e.g., work pace) modify the temporal behavior of motor variability during training of a repetitive occupational activity. Moreover, the relationships between motor variability, task performance, and muscle fatigue during occupational task training are not well understood.

The goal of this dissertation was to present new information concerning occupationally relevant metrics of motor learning during training of a laboratory-simulated, repetitive lifting activity. In this study, participants performed 100 repetitions (i.e., cycles) of the lifting task in each of four experimental sessions (i.e., visits) at different combinations of box load (low or high) and work pace (slow or fast). Three main observations were discussed in this dissertation: (i) participants exhibited a greater temporal reduction in the cycle-to-cycle variability of trunk postural summary measures during training of a heavier-weighted and faster-paced lifting activity (Chapter 3), which may have facilitated increases in the efficiency and repeatability of box movements (Chapter 4), (ii) the cycle-to-cycle variability of the erector spinae (back) muscle activity summary measures increased, but the variability of the multifidus muscle activity summary measures decreased, over time during faster-paced lifting (Chapter 3), and (iii) a greater temporal increase in trunk postural variability (i.e., a more “flexible” trunk movement strategy) was generally associated with lesser electromyographic back muscle fatigue during training of the lifting task (Chapter 5). Collectively, these research findings may open pathways to the development of new task design criteria and ergonomic guidelines to promote motor variability in the workplace and, ultimately, improve workers’ musculoskeletal health.


Back pain, Biomechanics, Ergonomics, Motor learning, Muscle fatigue, Occupational health


x, 102 pages


Includes bibliographical references (pages 80-88).


Copyright © 2019 Mahmoud Metwali