Effects of Exertion Variability on the FDI Muscle Fatigue During Intermittent Contractions

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9-2018

Effects of Exertion Variability on the FDI Muscle Fatigue during Intermittent Contractions

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Effects of Exertion Variability on the FDI Muscle Fatigue during Intermittent Contractions

Abdul Mathin Shaik

Thesis submitted to faculty of Rochester Institute of Technology In partial fulfillment of the requirements For the degree of Master of Science In Industrial and Systems Engineering Department Kate Gleason College of Engineering

Advisor: Dr. Ehsan Rashedi

Committee Member: Dr. Matthew Marshall

September 2018

i Acknowledgements

I would like to thank my parents for allowing me to pursue my graduate studies in a different country and letting one of my dreams come true.

Being in a different country with a different culture, climate and course curriculums, my master’s course appeared to be a rigorous task. Hadn’t it been for Dr. Ehsan Rashedi’s constant support as an advisor, a teacher, a friend, and a strong constructive critic, I wouldn’t have finished my thesis. I would sincerely thank Dr. Rashedi for his efforts and commitment towards finishing my thesis. I would also like to thank my committee member Dr. Matthew Marshall for providing key insights.

In addition, I would like to thank my friends and staff from the ISE department who helped in multiple ways when required.

ii Abstract:

Many current workplace tasks involve prolonged and/or repetitive physical exertion, which can result in localized muscle fatigue (LMF). LMF has been shown to reduce a worker’s performance and increase the chance of work-related musculoskeletal disorders (WMSDs).

Exertion variation is an approach known to impart a generally positive impact to reduce LMF. In this study, our main goal was to observe how exertion variability under specific laboratory working conditions affects the fatigue and recovery of the first dorsal interosseous (FDI) muscle.

Twelve participants performed finger abduction task during four different experimental conditions: 1) baseline intermittent contractions (20% MVC); 2) active rest with low exertion

(5% MVC); 3) short increases of muscle contraction to 25% MVC; and 4) brief large contractions during rest (60% MVC). Measures of discomfort (subjective), force fluctuations, reduction in the maximum voluntary contraction (MVC), root mean square (RMS) and mean power frequency

(MPF) of the electromyography (EMG) were collected. After performing the fatiguing task for one hour, the participants’ overall %MVC and MPF were reduced by 19.3% and 9.1%, respectively. Additionally, RMS, Ratings of perceived discomfort (RPD) and COV values increased by 28.7%, 49.5%, and 18.7%, respectively, indicating the presence of FDI fatigue. Even when subjected to slightly higher workloads during short increases of muscle contraction to

25% MVC, and brief large contractions during rest (60% MVC), no significant differences were seen in the %MVC and RMS values of these conditions compared to the baseline intermittent contraction (20% MVC). While these conditions present larger physical demand, existence of non-significantly different levels of LMF could indicate positive effects of exertion variability.

Meanwhile, the participants’ %MVC increased by 13.7% during the 30-minute recovery session,

iii and the rate of recovery was not affected by the experimental conditions. Results of our study could be useful in developing a range of interventions in occupational settings that involve repetitive low to moderate exertion levels.

iv Contents Acknowledgements ...... ii Abstract: ...... iii LIST OF TABLES...... vii LIST OF FIGURES ...... viii A. SIGNIFICANCE ...... 1 A.1. Human Muscle Fatigue: ...... 1 A.2. Why fatigue occurs ...... 1 A.3 Mechanisms of Muscle Fatigue ...... 2 A.3.1. Central and Peripheral Fatigue ...... 3 A.4. Effect of LMF on Performance ...... 3 A.5. Work-Related Musculoskeletal Disorders (WMSDs) ...... 4 A.6. Interventions to Reduce WMSDs ...... 8 A.6.1 Extrinsic variation ...... 9 A.6.2. Intrinsic variation ...... 12 A.7. Muscle Recovery ...... 13 A.8. Research Gap ...... 14 A.9. Hypothesis ...... 15 B. METHODOLOGY ...... 15 B.1. Participants ...... 15 B.2. Experimental design ...... 16 B.3. Experimental procedures ...... 18 B.4. Dependent measures (DV) ...... 21 B.5. Statistical Analysis ...... 24 C. RESULTS: ...... 25 C.1. Subjective Measure: ...... 26 C.1.1 Ratings of perceived discomfort (RPD): ...... 26 C.2. Objective Measures: ...... 28 C.2.1. Coefficient of Variation (COV):...... 28 C.2.2. Maximum Voluntary Contraction (MVC): ...... 29 C.2.2. Root Mean Square of EMG (RMS): ...... 30 C.2.4. Normalized Mean Power Frequency (MPF): ...... 30 C.2.5 Recovery: ...... 33 D. Discussion: ...... 34 D.1. Study Measures: ...... 34 D.1.1. Maximum Voluntary Contraction (MVC): ...... 34 D.1.2. Coefficient of Variation (COV): ...... 36 D.1.3. Root Mean Square (RMS) of EMG: ...... 39 D.1.4. Mean Power Frequency (MPF): ...... 41 D.1.5. Ratings of Perceived Discomfort (RPD): ...... 43

v D.2. Comparison among conditions: ...... 45 D.3. Recovery:...... 46 E. Limitations and Future Work ...... 49 F. Conclusion...... 52 G. REFERENCES ...... 53

vi LIST OF TABLES

Table 1: Summary of P-values for main effects and two-factor interactions of Condition (C), Gender (G) and Time (T) for subjective and objective measures. Measures are Ratings of perceived discomfort (RPD), Coefficient of Variation (COV), Maximum Voluntary Contraction (MVC), Root Mean Square (RMS) and Mean Power Frequency (MPF) of EMG. No higher order interaction effect was significant (p>0.1359)...... 25 Table 2: Summary of mean (SD) of all the DVs which include Ratings of perceived discomfort (RPD), Coefficient of Variation (COV), Maximum Voluntary Contraction (MVC), Root Mean Square (RMS) and Mean Power Frequency (MPF) of EMG during baseline, no rest, increased force and spikes conditions...... 25 Table 3: Summary of p-values for the main effects and two-factor interactions of condition (C) and Gender (G) on the recovery rates (A in Eq. 3) for MVC (p <0.001)...... 25 Table 4: Mean (SD) values for the normalized maximum voluntary contraction (%MVC) values (Top rows) and recovery rate (A) for 30 minutes (in %/ log(min)) of recovery session during baseline, no rest, increased force and spikes conditions...... 26

vii LIST OF FIGURES

Fig. 1: (a) Baseline Condition (0-20%MVC); (b) No-rest condition (5-15% MVC); (c) Increased- force condition (0-25% MVC); (d) Spikes condition (60% MVC during the breaks) ...... 17 Fig. 2: Representation of task parameters: Exertion level (EL) is the level of force being exerted. Cycle time (CT) is the sum of the working period (WP) and the rest period (RP). Duty cycle (DC) is the ratio of WP and CT...... 18 Fig. 3: (a)Participant performing finger abduction task while looking at the screen; the red colored lines indicate the force level to be performed, and a white dot indicates their force level in real time; (b) Finger abduction task being performed with the Velcro straps restraining the movement of other digits. The load cell and surface EMG electrode collected force and EMG data, respectively...... 20 Fig. 4: A graphical representation of the experimental procedure which includes calibration of rating of perceived discomfort (RPD), Pre-MVC, voluntary intermittent contractions, MVC and RPD at the end of every bout and Post-Fatigue measures...... 21 Fig. 5: Force fluctuations, quantified as Coefficient of Variation (COV), where the first and last 5s of each working cycle (30s) were removed from the analysis. The data highlighted (orange color) was selected for calculating COV...... 22 Fig. 6: The Normalized maximum voluntary contraction (%MVC) values representing one session of one participant. % MVC values for 60, 65, 70, 80 and 90 mins were fitted into a linear regression equation. The time values were converted into the logarithmic scale. The red line represents the regression line, blue dots indicate the %MVC values and the equation displayed is the regression equation...... 24 Fig. 7: Ratings of Perceived comfort (RPD) over time for both genders between different conditions. Four different conditions are Baseline, No rest, Increased force and Spikes. RPD is increased with time (a) and significant differences were found between different conditions (b). Males showed significant differences in different conditions compared to females (c). The error bars represent standard deviation and the conditions represented by the different letters are significantly different from the post-hoc analysis...... 27 Fig. 8: Ratings of Perceived comfort (RPD) over time for four conditions: Baseline (Top left), No rest (Top right), Increased force (Bottom left) and Spikes (Bottom right). The error bars represent standard deviation and the conditions represented by the different letters are significantly different from the post-hoc analysis...... 28 Fig. 9: Coefficient of variation (COV) over time (a) and significant differences were found during four different conditions (b) (Baseline, No rest, Increased force and Spikes are the four conditions). The error bars represent standard deviation and the conditions represented by the different letters are significantly different from the post-hoc analysis...... 29

viii Fig. 10: Maximum voluntary contraction (%MVC) normalized with pre-task values over time for four conditions, which are: Baseline, No rest, Increased force and Spikes are the four conditions. The %MVC values reduced over time (a) and %MVC values weren’t significant during different conditions (b). The error bars represent the standard deviation...... 29 Fig. 11: Root mean square (RMS) of EMG over time for both the genders during four different conditions: Baseline, No-rest, Increased force, and Spikes. The RMS values increased over time (a). Condition (b) and Gender (c) had a significant effect on the RMS values. The error bars represent standard deviation and the conditions represented by the different letters are significantly different from the post-hoc analysis...... 31 Fig. 12: Normalized Mean power frequency (MPF) values over time for both the genders during four different conditions: Baseline, No-rest, Increased force, and Spikes. Overall MPF values reduced over time (a); MPF values were not significant due to condition (b); and condition- gender two-factor interactions (c). The error bars represent standard deviation and the conditions represented by the different letters were significantly different from the post-hoc analysis...... 32 Fig. 13: Maximum voluntary contraction (%MVC) values normalized with pre-task MVC value during recovery session. Overall %MVC values increased over the 30-minute session (a) and no significant differences were seen in the %MVC values during baseline, no rest, increased force, and spikes conditions. The error bars represent the standard deviation...... 33

ix A. SIGNIFICANCE

A.1. Human Muscle Fatigue:

Depending on the context of interest, localized muscle fatigue (LMF) has been generally defined as the failure to maintain necessary force during task execution (Edwards, 1981;

Fallentin, Kilbom, Viikari-Juntura, & Wærsted, 2000), the anticipation of an increased effort due to exercise (Bigland-Ritchie, 1981), or, more broadly, as any exercise-induced reduction in the muscle capacity to generate force or power—regardless of whether the task can be sustained

(Nina K. Vøllestad, 1997). LMF can also be defined as a reduction in the tension produced by the muscles due to their repetitive stimulation (Westerblad & Allen, 1991).

A.2. Why fatigue occurs

In order to understand the LMF and its underlying mechanisms, it is important to explain how a muscle contracts and the energy metabolism involved in force generation. When the central nervous system (CNS) sends a command for force production, an action potential is generated at the neuromuscular junction, which leads to a series of events that produces muscle contraction. The mechanism of an action potential that triggers muscle contraction is referred to as Excitation-Contraction (E-C) coupling (Place, Yamada, Bruton, & Westerblad,

2010). The generated action potential transmits across the surface membrane of a muscle, into transverse tubules (t-tubules), causing the release of Ca+2 ions into the sarcoplasmic reticulum.

Ca+2 combines with troponin C, initiating the formation of a cross-bridge between myosin and actin. The myosin head present in the cross bridge pulls the filaments of actin closer, resulting in contraction (Allen, Lamb, & Westerblad, 2008). For all these activities occurring in the cell,

1 Adenosine Tri-Phosphate (ATP) is used, which serves as the primary source of energy. When a contraction occurs, ATP splits into a lower form of energy known as Adenosine Di-Phosphate

(ADP). In one millisecond, this ADP reacts with phosphocreatine, which is another phosphate source present in the cell, thereby regenerating ATP so that energy is available for the next contraction cycle. Only 20-30% of the energy produced is utilized for actually doing the work, and the remaining energy is emitted in the form of heat. This process represents the energy metabolism behind a muscle contraction (Chaffin, Andersson, & Martin, 2006).

A.3 Mechanisms of Muscle Fatigue

As discussed above, voluntary force generation involves a series of events, starting with the generation of a nerve impulse, and ending with an energy-producing interaction of actin and myosin. Failure of any of these events involved in force generation can cause LMF (Bigland-

Ritchie, 1981), indicating that LMF is a multifactorial phenomenon (Enoka, 1995). The literature describes several mechanisms involved in muscle fatigue, which is not surprising given the use of divergent data-collection methodologies, a wide range of experimental models, and the challenge of collecting reliable data under varying conditions. Moreover, the task being performed could produce variable amounts of stress at different sites at any given moment in time, and the site that fails first is highly dependent on the type and intensity of the task being performed (Enoka, 1995).

The possible sites of failure can be divided into three categories: 1) Central fatigue, which refers to the failure to propagate activation information along the neurons to the motor unit (MU); 2) Peripheral fatigue, or the failure of various metabolic processes that deliver

2 energy for muscle contraction; and 3) E-C coupling processes, which links these two processes

(Bigland-Ritchie, 1981).

A.3.1. Central and Peripheral Fatigue

Central fatigue refers to the reduction in the voluntary activation of a muscle, induced during exercise, which consists of all spinal and supraspinal processes capable of causing a decrease in motoneuron excitation (Gandevia, Allen, & McKenzie). The three main factors that induce central fatigue are lack of motivation, central command impairment, and MU control

(Enoka, 1995). In contrast, peripheral fatigue refers to the deficit in the force produced by the muscle, even after the optimal activation of muscle fibers by motoneurons (Åstrand, 2003).

(Gandevia, 2001) described peripheral fatigue as a reduction in the contractile strength of muscles and related changes in the mechanisms underlying the transmission of action potentials. The common underlying phenomena associated with peripheral fatigue are impairment of neuromuscular transmission, failure of action potentials, and E-C coupling failure

(Edwards, 1981). Even though both central and peripheral mechanisms induce fatigue, the influence of the former is greater and thus requires more time for recovery (Enoka, 1995).

A.4. Effect of LMF on Performance

Localized muscle fatigue is a phenomenon that reduces the capacity of a person to do work and/or achieve optimal physical performance. LMF can impact an individual’s performance in different ways and negatively influence his or her efficiency. During a fatiguing exercise, LMF can diminish the ability to sense one’s position (proprioception), which may also lead to MU impairment (Björklund, Crenshaw, Djupsjöbacka, & Johansson, 2000; Carpenter,

Blasier, & Pellizzon, 1998; Enoka & Stuart, 1992). This decline in position sense may be due to

3 the reduction in the sensitivity of the receptors (Carpenter et al., 1998). Furthermore, muscle- conduction velocity is reduced in the presence of LMF (Bigland-Ritchie & Woods, 1984), which results in diminished reaction time and accuracy, as well as in force-production decreases

(Selen, Beek, & van Dieën, 2007). LMF also compromises one’s performance by reducing the person's sense of effort during a task (Enoka & Stuart, 1992). To cope up with this reduction in performance, the human body may alter certain movement patterns, which serves to reduce fatigue and prolong the desired task (Srinivasan & Mathiassen, 2012). These changes in the movement patterns or other strategies for reducing fatigue and improving performance are discussed in the following sections.

A.5. Work-Related Musculoskeletal Disorders (WMSDs)

Work-related Musculoskeletal Disorders (WMSDs) are injuries to the soft tissue of the body such as muscles, tendons, nerves, and ligaments, which may result in musculoskeletal problems such as carpal tunnel syndrome (Armstrong et al., 1993). WMSDs accounted for 29-

35% of all the occupational injuries in the United States from 1992-2010. In 2015, the median number of days away from work due to MSDs was 12. Moreover, WMSDs account for $35-$45 billion annually in lost wages and related medical costs, which is why this area is such an important field of research for ergonomists. What is widely known is that WMSDs do not occur suddenly, but rather develop gradually over time. The complex mechanisms underlying these disorders are discussed in the following section.

WMSDs represent a ubiquitous problem worldwide. While ergonomists and scientists have been trying to understand the specific mechanisms and possible underlying causes for

WMSDs, it is generally understood that that the development of these disorders is

4 multifactorial (Forde, Punnett, & Wegman, 2002). High on the list of possible culprits for a work-related musculoskeletal disorder is localized muscle fatigue. And indeed, many theories, models, and mechanisms have been proposed that point to a link between LMF and WMSDs.

As one of several models, the dose-response model (Armstrong et al., 1993) provides a better interpretation of the underlying mechanisms associated with WMSDs. This model is characterized by four variables: exposure, dose, response, and capacity. Exposure refers to an external physical factor, such as a work requirement, that creates an internal disturbance in the body known as a dose (e.g., tissue loads or metabolic demands). Due to this dose, a physical response, such as a change in ionic concentration or temperature of the tissue, is produced.

Finally, capacity represents the ability to resist this destabilization brought on by various doses.

According to this model, the application of an external load to the body (exposure) will induce loads on the muscle tissue (dose), which produces mechanical and physiological responses such as deformation and yielding of tissues, an increase in intramuscular tissue pressure, and a change in the concentration of metabolites that leads to LMF. If there isn’t sufficient time for the body to regenerate the tissue due to these multiple responses, performance capacity will be compromised. When this cycle occurs repetitively, it can cause tissue deformation that results in pain and swelling. But since muscles can repair themselves more quickly than they can be damaged, these effects are reversible if the muscle rests sufficiently. Conversely, if the muscle does not receive enough time to recover, these "wear and tear" cycles may result in long-lasting impairment (i.e., WMSDs). This cycle is an all-too- common scenario in occupational settings involving repetitive movement, which can result in

5 gradual muscle disorders over a longer period, rather than acute disorders (Armstrong et al.,

1993).

The literature also describes mechanisms such as induced muscular imbalance and reperfusion injury that support the link between LMF and WMSDs (Forde et al., 2002). To further strengthen this connection and elucidate these models and mechanisms, four theories regarding muscle injury have been proposed (Kumar, 2001): the multivariate interaction theory, differential fatigue theory, cumulative load theory, and overexertion theory. Consider as a whole, these four theories provide convincing evidence that LMF has a significant role in causing an injury. It has long been known that awkward and/or static postures, heavy work, repetitive work, and insufficient rest that can cause soft tissue injuries represent precursors for

LMF (Sommerich, McGlothlin, & Marras, 1993). Subsequently, tissues that are fatigued on a regular basis are at greater risk for injury and eventually, perhaps, WMSDs. In summary, the literature has convincingly supported the role of LMF as a potential precursor for a heightened risk for injury—as well as a positive correlation with WMSDs.

Along with force-generation impairment, motor unit (MU) recruitment also plays a role in the development of WMSDs (Armstrong et al., 1993; Forde et al., 2002). Prior to discussing this relationship, however, it is important to understand the concept of MU recruitment.

Principally, MU can be considered as a functional unit of muscle, and is defined as the collection of skeletal muscle fibers innervated by a single motor neuron (Purves et al., 2001). In general,

MUs can be classified according to their recruitment thresholds as either low or high—meaning that they require either low or high amounts of force to activate them. Motor recruitment predominantly follows the size principle (Hagg, 1991), according to which low-threshold MUs

6 are activated at low forces, and stay activated for most of the time. If the force output has to be increased, more MUs will be recruited to generate more force. If size principle is to exist, the low-threshold MUs will be activated for most of the time, and will be maintaining higher firing rates for longer periods of time. In short, these MUs are working close to their maximal capacity even if the overall load on the muscle is low. However, this continuous activation results in damage to these MUs over time, putting the individual at risk for MSDs.

However, this problem of MU impairment can be addressed through MU substitution or rotation (Westgaard & De Luca, 1999). During fatiguing contractions, low-threshold MUs were observed to be inactive for shorter periods of time and were replaced by high-threshold MUs during this period. When this substitution occurs, the EMG amplitude is found to decline and then increase, indicating a period of inactivity. This hiatus gives the low-threshold MUs some time to rest and recover, thereby delaying fatigue. Researchers have postulated that this substitution helps the MU from fatiguing quickly and helps the muscle to sustain the contraction for longer periods of time (Westad, Westgaard, & Luca, 2003; Westgaard & De

Luca, 1999).

Based on the above discussion, LMF represents an important measure that that must be considered for avoiding the development of WMSDs. Moreover, MU substitution should be considered an efficient mechanism for reducing the incidence of localized muscle fatigue. As detailed in the next section, new approaches and interventions should be implemented in industrial settings where workers at risk for the development of WMSDs.

7 A.6. Interventions to Reduce WMSDs

Current trends in the industry are inclined towards introducing standardized production principles that force workers to perform low-intensity repetitive jobs (Yung, Mathiassen, &

Wells, 2012). Even though industry-based research shows that reducing peak loads to acceptable levels will help avoid the onset of WMSDs (Mathiassen, 1993), this basic approach may be inadequate over the longer term. Instead, introducing variations in exposure levels

(physical load) can be helpful in reducing these disorders and, as such, are generally recommended (Fallentin et al., 2000). The term “variation” in the biomechanical domain is defined as the change in exposure over time (Mathiassen, 2006); this concept is associated with a reduction of the similarity of load and posture during task execution. Variation can be attained by modifying the job, by adding additional tasks or work breaks, or even by introducing counter-intuitive measures such as periodic increases in force or periods with zero rest in order to reduce similarity in load patterns (Mathiassen, 2006).

Variation-related interventions that can potentially reduce the development of WMSDs can be classified as either extrinsic or intrinsic, based on the type of intervention. If the intervention focuses on changing the working conditions external to the worker, such as introducing additional breaks, it falls under the category of an extrinsic variation. On the other hand, exertion variations intended to modify how a worker performs a task, such as a change in posture and movement patterns, are known as the intrinsic variation (Srinivasan & Mathiassen,

2012).

8 A.6.1 Extrinsic variation

Introducing brief periods of rest during work was considered as the principal way of introducing load variation. Accordingly, it has been demonstrated by many research groups in experimental studies (Björkstén & Jonsson, 1977; Rohmert, 1973) and those investigating office workers (McLean, Tingley, Scott, & Rickards, 2001) that introducing rest breaks reduces muscle fatigue. For example, a recent study demonstrated that working conditions providing more gaps in muscle activity (when EMG dropped to less than 1%) was more effective in reducing local fatigue in workers performing low-load tasks in comparison to a sustained isometric contraction, as shown by measures such as increased endurance time (Yung et al., 2012).

Similarly, an earlier study noted that period breaks can provide rest to continuously activated muscle fibers, thereby increasing endurance time (Mathiassen, 1993). However, care must be taken while introducing breaks; if not appropriately scheduled, the workflow of the workers can be disturbed.

As discussed, while period breaks can reduce the rate of fatigue in muscles, research has shown that increasing the length of a break will not further reduce fatigue development

(Blangsted, Søgaard, Christensen, & Sjøgaard, 2004). In short, longer rest breaks between periods of work do not necessarily result in the complete relaxation and recovery of muscles.

Researchers have recently been examining the impact of introducing active breaks or pauses as a strategy for reducing the development of WMSDs. This concept refers to the performance of low-intensity tasks that keep the worker on the job instead of in the breakroom. Compared to a complete rest, active pauses in work tasks can provide higher variability, thereby inducing a lower level of fatigue in the muscle (Samani, Holtermann, Søgaard, & Madeleine, 2009). For

9 example, Sundelin and Hagberg (1989) showed that introducing active pauses during the performance of computer word-processing tasks, which involved executing gymnastic movements, proved to be beneficial in reducing localized neck and shoulder fatigue. In contrast, another study that also looked at the effect of introducing active pauses during computer work concluded that while oxygenation and blood flow increased during active breaks, there was no evidence of reduced local muscle fatigue (Crenshaw, Djupsjöbacka, &

Svedmark, 2006). Crenshaw and coworkers (2006) concluded that any reduction in fatigue could be due to the more efficient removal of metabolites during active pauses. While the elimination of metabolites occurs during any kind of break, the rate of removal would be higher during active breaks compared to passive breaks, indicating that active breaks can help alleviate fatigue development.

Even though breaks can reduce the incidence of localized muscle fatigue, it may not be feasible in a work setting to provide complete rest with no muscle contraction. In other words, muscles may simply not have time to reach zero force levels for prolonged periods of time, which doesn’t provide sufficient rest to counter the effects of repetitive movements. Instead, to induce variability in the pool of active muscle fibers, loads can be increased periodically. For instance, (Falla & Farina, 2007) demonstrated that such periodic increases in force level during an isometric contraction reduced fatigue development. Moreover, brief increases in force have been linked to reduced fatigue in trapezoidal muscles in an experimental study conducted by

Westad and coworkers (2003). Additionally, increased force can assist in MU substitution, thus avoiding the over usage of fatigued MUs and reducing the risk of developing WMSDs.

Depending on one’s perception, a break can be considered as a period of no work, or a working

10 period characterized by a lower or different level of exertion (Mathiassen, 2006). Thus, variations in force stemming from lower-exertion breaks, passive breaks, or even periodic brief increases in force can be helpful in reducing fatigue (Westad et al., 2003).

Along with these load variations, task variations can also be considered as an alternative method to counter the development of WMSDs. As a working definition, a task variation represents any type of extrinsic variation change in conditions external to the worker. The first type of task variation is temporal variation, wherein the activity type and the amount of work done are constant, but the working pattern changes over time—for instance, cycle time (CT) will change while the duty cycle (DC) remains constant (Luger, Bosch, Veeger, & de Looze,

2014). CT refers to the sum of the exercise period and the rest period, while DC represents the ratio of exercise period to the cycle time (MathiassEn & Winkel, 1991). Luger and coworkers

(2014) examined reducing cycle time through temporal variations. And while they indicated positive effects in terms of reduced LMF, this and other similar studies tend to be based on subjective measures such as ratings of perceived discomfort (RPD). In addition, (Rashedi &

Nussbaum, 2016) utilized diverse measures such as RPD, performance (force fluctuations) and muscle capacity (MVC and low-frequency twitch responses) in their study of muscle fatigue and recovery processes. The team concluded that lower CT levels (i.e., 30 sec) resulted in lower levels of fatigue compared to results obtained when CT = 60 sec; this finding may be helpful for human factors specialists seeking to reduce the incidence of WMSDs.

The other kind of task variation is activity variation, where force patterns and movement patterns are changed in an activity. More commonly known as job rotation, task variation refers to the act of changing the tasks the worker performs daily. While this approach

11 may have some benefits in cross-training employees for better overall productivity, the evidence that job rotation can actually reduce the development of WMSD is mixed (Srinivasan

& Mathiassen, 2012). While some researchers have found that introducing job rotation reduced

LMF and pain due to variation (Kuijer et al., 2004; Rissén, Melin, Sandsjö, Dohns, & Lundberg,

2002; Yung et al., 2012), other studies have indicated that the effects of job rotation are dependent on the intensity of the work—namely that it reduced fatigue development only for high-intensity tasks but not for low-intensity tasks (Horton, Nussbaum, & Agnew, 2012; Keir,

Sanei, & Holmes, 2011). Overall, the benefits of job rotation on the targeted reduction of fatigue remain unresolved (Luger et al., 2014).

A.6.2. Intrinsic variation

As defined earlier, intrinsic variation refers to implementing interventions to change the way a worker is performing a job—for example, by modifying her posture and movement pattern. The downside of varying task performance is that this approach is not always feasible in the workplace. Despite its difficulty in implementation, however, researchers are still focusing on methods of increasing motor variability due to its potential role in the reduction of

WMSDs. The term “motor variability” represents a type of intrinsic variation in which variability is present in MU recruitment due to changes in movement and postures. MU variation, in a general sense, is giving overloaded MUs some time to relax by shifting muscle activity. This shift can be achieved by activating different muscles in the same synergy, different regions of the same muscle, or different MUs in the same muscle (Mathiassen, 2006). As discussed in the earlier section on WMSDs, activation of different MUs in the same muscle is called MU substitution, which can delay the occurrence of LMF (Srinivasan & Mathiassen, 2012).

12 Motor variability also helps in preserving performance during fatiguing tasks by modifying MU recruitment patterns. Even though several studies have confirmed a reduction in

LMF due to the introduction of motor variability (Falla & Farina, 2007; Westad et al., 2003; Yung et al., 2012), few studies have verified a reduction in performance and increased LMF due to motor variability (Huysmans, Hoozemans, Van der Beek, De Looze, & Van Dieën, 2008;

Missenard, Mottet, & Perrey, 2008). These contrasting results regarding the effects of LMF on performance may be due to a difference in the individual capacities, coupled with the effects of fatigue being task-specific (Srinivasan & Mathiassen, 2012).

In conclusion, it can be concluded that both load variation and motor variability can help reduce localized muscle fatigue. As detailed earlier in this review, LMF is a known precursor for

WMSD. Thus, any intervention in an occupational setting that focuses on increasing the variability in an effort to reduce LMF will also reduce the likelihood of a worker developing a musculoskeletal disorder.

A.7. Muscle Recovery

Muscle fatigue and recovery occur simultaneously during task performance. During the recovery process, anti-fatigue processes take place to retain muscle performance, which corresponds to the reduction in LMF (Rashedi & Nussbaum, 2017). These anti-fatigue processes are dependent on blood flow, cellular enzymes, and several other factors. As noted, muscle recovery is highly dependent on blood flow to the muscle (Fitts, 1994), and blood flow depends on the exertion level of the task (Hamann, Buckwalter, Clifford, & Shoemaker, 2004).

Additionally, blood flow is believed to be influenced by one’s metabolic rate, which is a factor of exertion level, oxygen consumption, and other components. This implies that task parameters

13 such as exertion level impact the metabolic rate and, as a result, influence blood flow and the recovery process (Rashedi & Nussbaum, 2017).

A.8. Research Gap

To date, only a few research groups have investigated the effects of exertion variability on the development of muscle fatigue (Falla & Farina, 2007; Yung et al., 2012). While available studies do indicate that exertion variability might be beneficial in reducing the development of

LMF, they have evaluated complex biomechanical systems such as the shoulder joint (Falla &

Farina, 2007; Westad et al., 2003), which can create complexity in LMF-data collection resulting in inconsistent outcomes. In contrast, the current study attempts to negate the influence of surrounding muscles by studying a much simpler biomechanical system: the metacarpophalangeal joint of the index finger. Moreover, while many studies have observed the effects of exertion variations on LMF development during constant contractions, a minimal number of studies have used intermittent contractions to study the phenomenon. Thus, to our knowledge, no study has investigated the effects of force variation on LMF during intermittent contractions in a simple biomechanical system.

It should also be noted that a number of controlled studies have examined the introduction of active breaks to reduce the development of fatigue, but no study to date has observed the effects of high-intensity activity for a brief duration during active breaks (more than 50% MVC) in a simple biomechanical system. Several studies have reported the positive effects of introducing different working conditions with exertion variability—such as periodic increases in force and the incorporation of active breaks—on fatigue development and recovery. Moreover, a significant body of research has been dedicated to understanding the

14 relationship between task dependency on fatigue, but far fewer studies have been designed to study the effects of task parameters on post-fatigue recovery. In short, there is a lack of comprehensive assessment between the effects of working conditions with force variability on the fatigue development and post-fatigue recovery process in a simple biomechanical system.

To address this scholarly deficit, this study compares the fatigue and recovery reduction associated with different working conditions with force exertion variability as judged by data obtained through intermittent contractions of the first dorsal interosseous (FDI) muscle.

A.9. Hypothesis

Based on review of the literature, we hypothesize that exertion variability will induce lower fatigue of the FDI muscle when compared with baseline intermittent contractions. The slower rate of fatigue development can be tied to exertion variability and more effective elimination of metabolites during active rest.

B. METHODOLOGY

B.1. Participants

Twelve right-hand dominant participants (six males and six females) were recruited from within the RIT population. Their mean (SD) age, height, and body weight were 21.08 years of age (3.277), 172.9 cm (7.9), and 73.2 kg, respectively. Participants were free of any musculoskeletal disorder and self-reported to be moderately active (exercising 1-3 days per week). Using an effect size approach, with a power of 0.6 and Type I error = 0.05 (Keppel &

Wickens, 2004), 10 participants are needed to detect a “large” effect size (i.e., ω2 ≥ 0.15).

Considering that four levels of experimental conditions were planned in a repeated measures

15 design, a multiple of 4 participants was essential to help to incorporate counterbalancing. As such, 12 participants are recruited for each experiment. Participants gave informed consent before data collection, via protocols approved by Rochester Institute of Technology IRB.

B.2. Experimental design

This study utilized a repeated measures design requiring participants to perform a finger abduction task for one hour, during four different sessions (~2 h each). The participants performed intermittent contractions under the four different conditions during ten six-minute bouts (60 min) at 50% duty cycle (DC) and 1-minute cycle time (CT), followed by 30 min of post- exercise recovery. To reiterate, CT represents the sum of the exercise period and the rest period, where DC refers to the ratio of the exercise period to the cycle time (MathiassEn &

Winkel, 1991); these task parameters are represented in Fig. 2. As shown in Fig 1., four conditions were incorporated in this investigation: 1) Baseline intermittent contractions (20%

MVC), 2) short increases of muscle contraction to 25% MVC, 3) active rest with low exertion

(5% MVC), and 4) brief large contractions during rest (60% MVC).

Fig. 1: (a) Baseline Condition (0-20%MVC); (b) No-rest condition (5-15% MVC); (c) Increased-force condition (0-25% MVC); (d) Spikes condition (60% MVC during the breaks) The baseline exertion level (EL) was 20% MVC, and the EL for the other conditions was modified such that the overall physical load remained the same. Static contractions, even with weak force levels, should never be used in an occupational setting (Björkstén & Jonsson, 1977).

Instead, intermittent contractions with appropriate levels of rest are recommended. Thus, intermittent contractions with 20% MVC were chosen as the baseline to represent low and medium levels of task intensity in an occupational setting. In addition, the endurance time of an intermittent contraction is based on the EL (Björkstén & Jonsson, 1977). Given the nature of the existing task parameters (EL, DC and CT), it was deemed reasonable to perform the task for 60

17 min in order to approximate a daily work scenario, as well as to facilitate reasonable experimental conditions and the capture of viable data. The order of sessions was counterbalanced using 4x4 Latin squares, and each session was separated by at least 2 days to reduce the effect of residual fatigue (Rashedi & Nussbaum, 2016). The tension produced in the

FDI muscle is directly proportional to the force produced during index finger abduction task— namely, the task involved in this study. Due to this direct relationship and simplicity of movement biomechanics, the FDI muscle was targeted for this study. Exertion Level EL

WP RP Time

CT DC = WP/CT

Fig. 2: Representation of task parameters: Exertion level (EL) is the level of force being exerted. Cycle time (CT) is the sum of the working period (WP) and the rest period (RP). Duty cycle (DC) is the ratio of WP and CT. B.3. Experimental procedures

During the practice session, participants were familiarized with the experimental procedures, after which informed consent was obtained. At the start of both the practice and actual data-collection sessions, participants were asked to lean against a wall with knees bent at a 90° angle to calibrate their ratings for perceived discomfort (RPD) with respect to the thigh

18 strain, using the Borg CR-10 scale, until they reached a level of 8 or higher. During data collection (exercise and post-exercise periods), subjects were asked to sit comfortably in a chair with their hand placed on the table and elbow flexed at 135°, as shown in Fig. 3. On the belly of the FDI muscle, the skin was shaved, and that location was cleaned with an electrode prep pad

(Professional Disposables Inc., Orangeburg, NY) before affixing the electrode. The index finger was placed inside the ring fixture, and the movement of thumb and the non-index fingers was restricted using Velcro straps to minimize the force contribution from these digits. The metacarpophalangeal joint of the index finger was aligned with the load cell (6 DoF, 125 N capacity, Nano25-E, ATI Inc., Apex, NC).

To obtain the baseline MVC, the participants were asked to perform 3-4 trials of MVC

(about 4-5 seconds with 1-2 mins of rest between the trails), while being motivated verbally.

The maximum MVC value was used to establish the EL for the experimental session. With this fixed EL, each participant was fatigued by performing an isometric intermittent finger abduction task for six 10-minute bouts. During this task, visual feedback was provided to the participant regarding the level of EL (similar to a tracking task), as shown in Fig. 3(a). When the participant deviated from the desired EL, verbal motivation was provided to correct the EL. At the end of each bout, MVC was performed and RPD was collected. After performing the MVC for 60 mins, the task was completed, and the participant’s recovery was observed for the next 30 mins.

During this recovery phase, the participant was asked to perform MVC and provide ratings using RPD at 5, 10, 20 and 30 mins.

(a) (b)

Fig. 3: (a)Participant performing finger abduction task while looking at the screen; the red colored lines indicate the force level to be performed, and a white dot indicates their force level in real time; (b) Finger abduction task being performed with the Velcro straps restraining the movement of other digits. The load cell and surface EMG electrode collected force and EMG data, respectively.

20 90 Time (Min)

Fig. 4: A graphical representation of the experimental procedure which includes calibration of rating of perceived discomfort (RPD), Pre-MVC, voluntary intermittent contractions, MVC and RPD at the end of every bout and Post-Fatigue measures. B.4. Dependent measures (DV)

The dependent measures (DV) included the subjective measure of RPD, the objective measure of Root Mean Square (RMS) of the EMG, the mean power frequency (MPF) of EMG; force fluctuations and MVC aided in quantifying the LMF and performance. To observe the spatial variability in strength, MVC was collected at the end of every 10-minute bout and then normalized with the baseline MVC collected at the beginning of the experiment. In addition,

MVC collected during the post-task recovery period was also normalized to the baseline MVC.

Force fluctuations or steadiness of force production, which tend to decline with increasing age, injury, and fatigue (Singh, Arampatzis, Duda, Heller, & Taylor, 2010), were used as a measure of performance and LMF (Rashedi & Nussbaum, 2016). Force fluctuations, quantified as COV, served as the mean over the standard deviation (SD) of the data. COV was calculated for each cycle (CT= 1 min) of the data. To reduce the translational effects between work and rest

21 periods, the first and the last 5s of every working cycle (except for increased-force condition, last 10s) were excluded from the analysis, as shown in Fig. 5. From this, the mean COV of every

10-minute bout is calculated.

Fig. 5: Force fluctuations, quantified as Coefficient of Variation (COV), where the first and last 5s of each working cycle (30s) were removed from the analysis. The data highlighted (orange color) was selected for calculating COV. The electrical activity of muscle recorded by the surface EMG electrode can provide information regarding the biochemical and physiological changes during a fatiguing task, which can then be employed to assess LMF and performance—namely, the root mean square and mean power frequency. The raw EMG data was stored on the computer and analyzed using

MATLAB. The baseline noise was rectified, and high frequency noise was low-pass filtered with a cutoff frequency of 5 Hz using a 2nd order butterworth filter, similar to the literature

(Mathiassen, 1993; Yoshida & Terao). RMS provided the amplitude of the signal, which represents the sum of all the active motor unit action potentials (MUAPs) present in that fiber.

RMS was calculated by creating a sliding window of fixed length and finding the square root of

22 the mean of squared values of the data points in that window, as shown in equation 1. MPF was obtained by converting the raw signal (time spectrum) into power spectrum by applying

Fast Fourier Transformation (FFT). The centroid of the power spectrum was obtained, which is the sum product of EMG power spectrum and frequency over the sum of EMG power spectrum

(Phinyomark, Thongpanja, Hu, Phukpattaranont, & Limsakul, 2012), as shown in equation 2.

1 ,+' !"# = [ ∫ (*"+(,).)/,]3/. (1) ' ,−'

Where T is the length of the sliding window and EMG(t) is the EMG value at time t

< ∑:=> 9: ;: "56 = < (2) ∑:=> ;?

Where fj is the frequency value of the power spectrum at that frequency bin j and Pj is the power spectrum value at j and M is the length of the frequency bin.

For the 30 min recovery duration, %MVC was recorded for 5, 10, 20 and 30 mins. These

MVC values were normalized with pre-task MVC value, similar to the working phase. For every session, %MVC values and log-transformed time values were modeled into a regression equation as indicated in the equation 3 and the regression coefficients were obtained (Fig. 7).

%"AB = C × EFG (,) + H (3)

Where A and B are the slope and intercept of the regression line, respectively and t is the time.

One pair of A and B were obtained for all the sessions and the recovery rate (A) was used as was used as a response in the statistical analysis for recovery.

23 100

80 y = 5.533x + 90.759 60 R² = 0.9126 40 Normalized %MVC Normalized 20

0 -1 -0.5 0 0.5 1 1.5 2 Log (t)

Fig. 6: The Normalized maximum voluntary contraction (%MVC) values representing one session of one participant. % MVC values for 60, 65, 70, 80 and 90 mins were fitted into a linear regression equation. The time values were converted into the logarithmic scale. The red line represents the regression line, blue dots indicate the %MVC values and the equation displayed is the regression equation.

B.5. Statistical Analysis

Separate one-way repeated measures ANOVA were used to analyze the effect of condition, time, gender and sex on the RPD, %MVC, COV, RMS, and EMG during baseline, no rest, increased force, and spikes conditions. Participants were considered as a random factor and nested with gender during the analysis. When a significant effect was identified, post-hoc analysis was done using Tukey HSD’s pairwise comparison for comparing different levels of that factor. The assumptions of ANOVA were verified, and any violation is reported in the corresponding sections. Statistical analysis was performed using JMP Pro 13 (SAS Institute.,

Cary, NC) with a statistical significance of p<0.05. Summary results are presented as means

(SD).

24 C. RESULTS:

The results from statistical analysis for RPD, %MVC, COV, RMS, and EMG are summarized in Table 1 and Table 2. Additional details are provided separately below, for each of these DVs.

RPD COV MVC RMS MPF C <.0001 0.0002 0.976 0.0003 0.2837 G 0.633 0.41 0.7101 0.0146 0.9178 T <.0001 <.0001 <.0001 0.0028 <.0001 C×G 0.0114 0.9021 0.1768 0.7901 0.0004 C×T 0.9038 0.2397 0.1954 0.9339 0.6596 G×T 0.2663 0.3885 0.195 0.5902 0.6141

Table 2: Summary of mean (SD) of all the DVs which include Ratings of perceived discomfort (RPD), Coefficient of Variation (COV), Maximum Voluntary Contraction (MVC), Root Mean Square (RMS) and Mean Power Frequency (MPF) of EMG during baseline, no rest, increased force and spikes conditions.

Baseline No rest Increased force Spikes RPD 1.88 (1.52) 2.39 (1.36) 1.73 (1.39) 1.8 (1.28) %MVC 84.4 (7.44) 84.1 (7.76) 84.6 (7.2) 84.5 (8.01) COV 0.0191(0.0042) 0.0213(0.00516) 0.0201 (0.0053) 0.0199 (0.0045) RMS 0.122 (0.05) 0.106 (0.068) 0.14 (0.09) 0.107 (0.06) MPF 95.7 (6.22) 94.2 (5.5) 94.9 (5.16) 94.8 (5.92)

Table 3: Summary of p-values for the main effects and two-factor interactions of condition (C) and Gender (G) on the recovery rates (A in Eq. 3) for MVC (p <0.001).

C G C*G MVC 0.8159 0.8298 0.997

25 Table 4: Mean (SD) values for the normalized maximum voluntary contraction (%MVC) values (Top rows) and recovery rate (A) for 30 minutes (in %/ log(min)) of recovery session during baseline, no rest, increased force and spikes conditions.

Condition MVC Baseline 87 (9.8) 5.42 (2.3) No rest 88.8 (8.74) 5.81 (2.54) Increased force 87.4 (8.27) 5.68 (2.82) Spikes 90.5 (8.99) 6.32 (1.39)

C.1. Subjective Measure:

C.1.1 Ratings of perceived discomfort (RPD):

Time, condition and condition-gender interaction confirmed significant differences in the RPD values (Fig. 7). The discomfort increased over time at a mean rate of 0.048/min as indicated in this figure. The mean RPD values were highest for the no-rest condition (2.39) and lowest for the increased-force condition (1.76), which was 40% lower than no-rest condition

(Table 2). There was no significant difference between baseline and increased-force conditions, as judged by the post-hoc analysis. The increase in the discomfort for the no-rest condition

(0.054/min) was 24% higher than the increased-force condition (0.043/min) (Fig. 8). For the gender-condition interaction (Fig. 7(c)), males showed a significant difference between conditions; in contrast, females did not show any significance. The mean RPD values for males was highest for the no-rest condition (2.76) and lowest for spikes condition (1.81) (Table 2).

Post-hoc analysis indicated no significant difference between baseline, spikes, and increased-

26 force condition for males (Fig. 6(b)). It should also be noted that the assumptions of the ANOVA model weren’t satisfied for this DV.

6 A 5 B Strong A A 4

Moderate 3 RPD

Weak 2

Very Weak 1

0 0 10 20 30 40 50 60 Baseline No rest Inc. Force Spikes Time Condition (a) (b) 6 Male Female

Strong 5 A A B 4 B A B A B B A B B Moderate 3 RPD

Weak 2

Very Weak 1

0 Baseline No rest Inc. Force Spikes Condition (c)

Fig. 7: Ratings of Perceived comfort (RPD) over time for both genders between different conditions. Four different conditions are Baseline, No rest, Increased force and Spikes. RPD is increased with time (a) and significant differences were found between different conditions (b). Males showed significant differences in different conditions compared to females (c). The error bars represent standard deviation and the conditions represented by the different letters are significantly different from the post-hoc analysis.

27 6 Baseline No Rest

5 Strong 4

RPD Moderate

Weak 2

Very Weak 1

0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Time (Min) Time (Min)

6 Inc. Force Spikes

Strong 5 4

Moderate 3 RPD

Weak 2

Very Weak 1

0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Time (Min) Time (Min) Fig. 8: Ratings of Perceived comfort (RPD) over time for four conditions: Baseline (Top left), No rest (Top right), Increased force (Bottom left) and Spikes (Bottom right). The error bars represent standard deviation and the conditions represented by the different letters are significantly different from the post-hoc analysis. C.2. Objective Measures:

C.2.1. Coefficient of Variation (COV):

Time and condition had a significant effect on the force fluctuations quantified as COV

(Fig. 9). The COV values increased significantly overtime at a mean rate of 0.00005/min (Fig.

9(a)). The highest mean COV values were for no-rest condition (0.0213), which is 12% higher than the lowest mean COV values for the baseline condition (0.0191) (Table 2). The COV values increased for the no-rest condition at a rate of 0.00008/min, which was almost twice the increase in COV values for baseline condition (0.00004/min). Post-hoc analysis showed no statistical difference between baseline, increased force and spikes condition (Fig. 7(b)).

28 0.03 B A B A A B 0.025

0.02

0.015 COV 0.01

0.005

0 0 10 20 30 40 50 60 Baseline No rest Inc. Force Spikes Time (Min) Condition

(a) (b)

Fig. 9: Coefficient of variation (COV) over time (a) and significant differences were found during four different conditions (b) (Baseline, No rest, Increased force and Spikes are the four conditions). The error bars represent standard deviation and the conditions represented by the different letters are significantly different from the post-hoc analysis. C.2.2. Maximum Voluntary Contraction (MVC):

During the working period, time was the only significant factor for the MVC values (Fig.

10). The overall MVC values were reduced by 19.3%, at a mean rate of 0.32%/min.

A A A A 100

Normalized %MVC %MVC Normalized 20

0 0 10 20 30 40 50 60 Baseline No rest Inc. Force Spikes Time (Min) Condition (a) (b)

Fig. 10: Maximum voluntary contraction (%MVC) normalized with pre-task values over time for four conditions, which are: Baseline, No rest, Increased force and Spikes are the four conditions. The %MVC values reduced over time (a) and %MVC values weren’t significant during different conditions (b). The error bars represent the standard deviation.

29 C.2.2. Root Mean Square of EMG (RMS):

For EMG amplitude the significant factors are time, condition and gender (Fig. 11). The overall RMS values increased at a mean rate of 0.0004 volts/min. The mean RMS values were highest for increased-force condition (0.14 volts) and lowest for no-rest condition (0.106 volts)

(Table 2). The increase in EMG amplitude was 45% higher during increased-force condition

(0.009 volts/min) versus no-rest condition (0.004/min) (Fig. 11(b)). As shown in Fig. 11(c), muscle activity was 54% higher for men compared to women, based on the fact that the mean

RMS values for men and women were 0.16 and 0.09 volts, respectively.

C.2.4. Normalized Mean Power Frequency (MPF):

Time and condition-gender interaction were significant factors for the frequency content of the EMG signal (Fig. 12). The overall MPF values were reduced by 10.9% at a mean rate of 0.18%/min. For the condition-gender interaction, the reduction in MPF values for the males was highest for the increased-force condition (6.7%) while it was lowest for the baseline condition (1.4%) (Table 2). In contrast, the reduction in MPF values for the females was highest for the no-rest and spikes conditions (6.1%), and lowest for the increased-force condition

(3.6%). It should be noted that the post-hoc analysis failed to show any significant difference between any of the pairs (Fig. 12(b)), which might be due to the fact that the assumptions of the parametric model (ANOVA) were not satisfied.

30 0.250

0.200 A

A B A B 0.150

RMS (V) RMS 0.100

0.050

0.000 0 10 20 30 40 50 60 Baseline No rest Inc. Force Spikes Time Condition (a) (b)

A 0.250

0.200

0.150 B

RMS (V) RMS 0.100

0.050

0.000 M F Gender (c) Fig. 11: Root mean square (RMS) of EMG over time for both the genders during four different conditions: Baseline, No-rest, Increased force, and Spikes. The RMS values increased over time (a). Condition (b) and Gender (c) had a significant effect on the RMS values. The error bars represent standard deviation and the conditions represented by the different letters are significantly different from the post-hoc analysis.

31 A 100 A A A

40 Normalized MPF Normalized 20

0 0 10 20 30 40 50 60 Baseline No rest Inc. Force Spikes Time (Min) Condition (a) (b)

A A Male A A A A A A 100 Female 80

20 Normalized MPF Normalized 0 Baseline No rest Inc. Force Spikes Condition

(c) Fig. 12: Normalized Mean power frequency (MPF) values over time for both the genders during four different conditions: Baseline, No-rest, Increased force, and Spikes. Overall MPF values reduced over time (a); MPF values were not significant due to condition (b); and condition-gender two-factor interactions (c). The error bars represent standard deviation and the conditions represented by the different letters were significantly different from the post-hoc analysis.

32 C.2.5 Recovery:

As noted in Section C.2.2., overall %MVC decreased by 19.3%; conversely, during recovery %MVC values increased by 13.7% at a rate of 0.433/min (Fig. 13 (a)). No significant differences were seen in the recovery %MVC values for the different conditions (p =0.817).

100

60 %MVC 40

0 60 65 70 75 80 85 90 Baseline No rest Inc. Force Spikes Time (Min) Condition (a) (b)

Fig. 13: Maximum voluntary contraction (%MVC) values normalized with pre-task MVC value during recovery session. Overall %MVC values increased over the 30-minute session (a) and no significant differences were seen in the %MVC values during baseline, no rest, increased force, and spikes conditions. The error bars represent the standard deviation.

33 D. Discussion:

This study was designed to investigate and compare the effects of different working conditions on the fatigue and recovery of the first dorsal interosseous (FDI) muscle during intermittent contractions. Participants performed a finger abduction task for 1-hr, while muscle contraction capacity, performance, and muscle activity were collected and analyzed. In the following sections, the different measures obtained while assessing the four conditions

(baseline, no rest, increased force, and spike conditions) are discussed.

D.1. Study Measures:

D.1.1. Maximum Voluntary Contraction (MVC):

In general, MVC is considered to be the gold standard for measuring muscle capacity, and subsequently as a tool for monitoring LMF (Nina K. Vøllestad, 1997). To preface this discussion, it should be noted that Rashedi and Nussbaum (2016) recently conducted a study that is similar to the current investigation, with analogous task parameters and the use of the

FDI muscle. The participants performed their finger abduction task at 15% MVC and 25% MVC.

The reductions in normalized %MVC values during these two conditions were 6% and 15%, respectively. Our findings indicate that during the working period, normalized %MVC reduced significantly over time (Fig. 10(a)), with the overall MVC reduction recorded at 19.3%. This

%MVC reduction over time indicates the onset/occurrence of fatigue. Note that our study findings indicate a slightly higher exertion range (i.e., 15% to 25% MVC) compared to Rashedi and Nussbaum (2016), with the reduction in the normalized %MVC values noted to be 19.3%.

This reduction in the normalized %MVC values is out of range and higher compared to (Rashedi

& Nussbaum, 2016), which might be due to differences in experimental procedures since an

34 additional restraint was used in that study to restrict hand movement during electrical stimulations. This restraint prevents the additional movement in vertical direction which might lead to better performance of participants.

One of the objectives in the current study was to compare the effect of different working conditions on the LMF of FDI muscle. As documented in Table 2, any significant differences between the reduction in the %MVC under the four different conditions were not found. Prior studies have indicated that load variations can help to reduce LMF (Falla & Farina,

2007; Westad et al., 2003; Yung et al., 2012). This reduction might be due to MU substitution

(Falla & Farina, 2007) or variations in the MU discharge rate (Yung et al., 2012), as indicated by better spatial redistribution of EMG amplitude. In contrast, several studies examining task variation have reported either neutral or negative effects of varying LMF, based on the fact that subjective ratings increased, while the %MVC values decreased as a result of variation (Horton et al., 2012; Kuijer et al., 2004; Luger et al., 2014). Their findings could be due to differences in target muscles and the specific techniques used. Our study did not indicate any differential reduction in LMF based on %MVC values across four conditions (no rest, increased force, and spike) with exertion variations. In comparison with the baseline condition, the increased force and spike conditions displayed slightly higher overall exertion levels. During each working cycle, the increased force condition had 25% MVC increase for 5 seconds during the working period, while during the spike condition had one 60% MVC spike during the resting period. Overall, during the one-hour task, participants performed at 25% MVC for about 5 minutes, and 60 spikes with 60% MVC during increased force and spike conditions, respectively. Notably, even

35 with this higher physical demand, the reduction in the %MVC was not statistically different in comparison to the baseline condition.

D.1.2. Coefficient of Variation (COV):

Larger force fluctuations, quantified by COV increments over time (Fig. 9(a)), demonstrated a reduction in a participant’s performance following the target level of exertion.

A similar pattern of increased fluctuations was also observed in prior studies involving fatiguing contractions using FDI muscle (Galganski, Fuglevand, & Enoka, 1993; Rashedi & Nussbaum,

2016; Taylor, Christou, & Enoka, 2003), as well as other muscles such as the vastus lateralis

(Contessa, Adam, & De Luca, 2009; Enoka, Robinson, & Kossev, 1989). COV values obtained in our study evidenced the same range noted in an earlier study by Rashedi and Nussbaum (2016), where participants performed a finger abduction task for ~1hr, with COV values ranging from

0.02 to 0.03. The mean COV values were significantly different for conditions in which COV values were highest for the no-rest condition and lowest for the baseline condition (Fig. 9(b)).

This outcome might indicate higher levels of LMF during the no-rest condition. Meanwhile, other mechanisms not associated with localized muscle fatigue could also be responsible for larger COV values during the no-rest condition (Taylor et al., 2003).

When the muscle applies a force it is not always constant, but rather fluctuates over a mean value (Enoka & Fuglevand, 2001). The intensity of a contraction is based on two factors: the number of MUs recruited and the firing rate of these MUs (Moritz, Barry, Pascoe, & Enoka,

2005). The broad influence of the common drive from the central nervous system is one of the parameters that contribute to force fluctuations (De Luca, Gonzalez-Cueto, Bonato, & Adam,

36 2009), which represents the maximum value of the cross-correlation function of firing rates of the active MUs (Contessa et al., 2009). This MU parameter is impacted by LMF and affects force production by alternating the feedback from muscle spindles (Ia fibers) and golgi tendon organs

(Ib fibers). When the muscle fiber contracts due to the changes in the muscle fiber length, spindles either slacken or stretch based on the orientation with respect to the muscle fiber. Due to this particular behavior, Ia firings will either increase or decrease—and these firings occur until the muscle fiber is fused or quasi-fused and the muscle become stable (Binder & Stuart,

1980). This behavior typically lasts for about 5-10 seconds with new MU recruitment (De Luca et al., 2009). Also, when the MUs are recruited, there is a difference in the innervation of muscle fibers, since only a few muscle fibers attach completely to MU, while most attach at less than a 65% adhesion level, which is due to a difference in the afferent inputs (Henneman &

Mendell, 1981). As a result of this difference, the feedback to the motor neuron pool is varied, causing discrepancies in the innervation of the individual MUs. This activity indicates that different MUs will receive varied excitation intensities. When MU is recruited, the variation in the excitation of each individual MU results in variations in overall resultant force, which causes fluctuations in the force production. As indicated above, the recruitment of additional MUs might increase force fluctuations and reduce the amplitude of the cross-correlation of firing rates. With the increase in the LMF, more MUs are recruited and the above-mentioned phenomena could account for increased force fluctuations (Yao, Fuglevand, & Enoka, 2000).

In contrast, another study indicated that the recruitment of new MUs did not significantly affect COV values in a vastus lateralis muscle (Contessa et al., 2009). This finding might be due to the reduction in the force contribution by a single motor unit since when

37 overall muscle force increases, it leads to lower fluctuations (Fuglevand, Winter, & Patla, 1993).

In addition, several simulation studies have confirmed a significant effect of MU discharge variability on force fluctuations (Enoka et al., 2003). In line with these simulations, experimental studies performed on the FDI muscle have also indicated a positive correlation between MU discharge variability and FDI muscle COV values (Laidlaw, Bilodeau, & Enoka, 2000). In contrast, the change in COV values did not appear affected by the MU discharge variability in the FDI

(Galganski et al., 1993; Semmler, Steege, Kornatz, & Enoka, 2000), the vastus lateralis (Contessa et al., 2009), and the tibialis anterior muscles (Patten & Kamen, 2000).

Even though the literature on the effect of MU discharge variability on force fluctuations is mixed, Laidlaw and coworkers (2000) reported a significant impact of MU discharge variability on COV values during low-force contractions (<5% MVC). At such force levels, only a few MUs are active and the firing rate is similarly low (<10 pulses/sec) (Contessa et al., 2009). As discussed above, resultant muscle output refers to the summation of individual MU force outputs (Contessa et al., 2009). At low forces, MUs are activated less frequently and fewer MUs are present, which leads to variability in the force of individual Mus and creates higher fluctuations in resultant muscle force (Fuglevand et al., 1993; Taylor et al., 2003). During no-rest condition, the presence of such low-force contractions during the rest phase (5% MVC) might have an effect on working phase (15% MVC), thus explaining higher COV values. In conclusion,

COV values indicated better performance during the baseline condition, while other factors such as MU recruitment and discharge variability might have an effect on the higher COV values during no-rest condition.

38 D.1.3. Root Mean Square (RMS) of EMG:

The EMG amplitude is collected by the surface electrodes and researchers have widely utilized EMG RMS as a measure for monitoring LMF (Nina K. Vøllestad, 1997). As indicated in the literature (Bigland-Ritchie, 1981), EMG amplitude increases over time with the induction of

LMF. Our findings are consistent with prior studies (Carpentier, Duchateau, & Hainaut, 2001), based on the fact that in our study overall RMS values increased over time at a rate of 0.0004 volts/min, from 0.103 volts to 0.133 volts (Fig. 11(a)). This increase can be explained using MU recruitment pattern. At the beginning of fatiguing contractions, only a few muscle fibers are recruited. As the muscle becomes fatigued, in order to keep up with performance additional muscle fibers are recruited, which could lead to an increase in the EMG amplitude (N. K.

Vøllestad, Sejersted, & Saugen, 1997). A similar increase in RMS values has been observed during fatiguing contractions using the FDI muscle (Carpentier et al., 2001; McManus, Hu,

Rymer, Lowery, & Suresh, 2015; Milner-Brown & Stein, 1975), as well as other muscles such as the upper trapezius and triceps (Falla & Farina, 2007; Yung et al., 2012). On the other hand, more than two decades ago researchers reported a simultaneous increase and decrease in the

EMG amplitude of the FDI muscle in the presence of muscle fatigue (Zijdewind, Kernell, &

Kukulka, 1995). This variability in EMG could be due to a reduction in the size of action potential size, which might impact force production (Zijdewind, Zwarts, & Kernell, 1999)—but could also account for the reduction in EMG values, but not the force values.

RMS values were highest during increased force and baseline conditions, followed by spikes and no-rest conditions, in the same order (Fig. 11(b)). Post-hoc analysis also indicated a significant difference between the RMS values associated with no-rest and baseline conditions

39 (Fig. 11(b)). It is possible that fewer MUs were recruited during the no-rest condition as the exertion level (15%, Fig. 1(b)) was low, reflecting reduced RMS values. In contrast, the presence of active pauses (5% MVC) might help to more effectively washout metabolites (Crenshaw et al., 2006), which reduces both LMF and EMG amplitude. It should also be noted that prior studies have shown no significant difference in RMS values during active pauses (similar to no- rest condition) compared to baseline contractions ((Crenshaw et al., 2006; Falla & Farina, 2007).

Even though the difference in the EMG amplitude during active breaks was not significant,

%MVC reduction, the spatial distribution of multi-surface EMG, and oxygenation indicated a better reduction in LMF. No significant difference in %MVC reduction was observed in our study as described earlier.

Post-hoc analysis indicated no significant difference between RMS values of increased force and baseline conditions. Periodic increases in force contributed to the reduction of the

LMF of the upper trapezius muscle (Falla & Farina, 2007), but no significant differences were found in the EMG amplitude. Other researchers demonstrated a reduction in LMF during periodic increases in exertion, and this reduction was significant through other measures such as %MVC reduction and spatial distribution of multi-surface EMG (Falla & Farina, 2007; Westad et al., 2003). In our study, with a similar periodic increase in force, there was no significant difference in RMS values.

Gender differences were, however, observed in our data. Specifically, the RMS values for men were significantly higher than women—namely, by ~54% (Fig. 11(c)). Gender differences in EMG and LMF during fatiguing contractions were described by Hunter (2014), who attributed those differences to multiple phenomena such as level of exertion, muscle

40 composition, blood flow, type of fiber, and MU control mechanism. Since men generally have higher muscle mass than women, it is likely that their higher strength leads to higher force output for similar contraction intensity. With respect to blood flow, the occlusion of blood flow is dependent on the intensity of force production. With higher intensities, more pressure is required to pump blood into the muscle due to an increase in the intra-muscular pressure

(Barnes, 1980). As males produce larger force, there is a higher reduction in the blood flow supply, which causes a greater accumulation of metabolites, leading to higher LMF (Hunter,

2014; Wüst, Morse, De Haan, Jones, & Degens, 2008). These factors might lead to lower endurance time for males when compared with females for the same %MVC task (Hunter,

Griffith, Schlachter, & Kufahl, 2009). At low to moderate levels of exertion (around 20% MVC), the time to task failure was higher for women compared with men. In addition, during higher contraction levels, such as 80% MVC, this gender-related difference in endurance time was not significant. Moreover, this dependency of gender differences on exertion level was observed in multiple muscles such as elbow flexors, finger flexors, knee extensors, etc., (Hunter, 2009). As mentioned above, occlusion of blood flow is one of the factors contributing to the sex differences in LMF. At low to moderate exertion levels, occlusion level tends to vary in men compared to women. However, at higher exertion levels the occlusion level is similar, which explains the lack of sex differences in LMF.

D.1.4. Mean Power Frequency (MPF):

EMG values in the time domain were transformed into a frequency spectrum using FFT, after which MPF was calculated. Overall, normalized MPF values reduced over time, by 10.9%

41 (Fig. 12(a)). Researchers assessed the performance of 13 pianists and 15 sedentary controls from the college population who were required to perform a finger abduction task at different intensities, until exhaustion (Penn, Chuang, Chan, & Hsu, 1999). During 25% MVC, the MPF values of FDI dropped from 123 ± 47 Hz to 114.7 ± 43 Hz, indicating a 7% reduction due to LMF.

In comparison, the reduction in the normalized MPF was approximately 11% after one hour of intermittent contraction in our study. In contrast, few studies have reported an increase in MPF values with increasing LMF (Gerdle, Eriksson, & Brundin, 1990; Moritani & Muro, 1987).

Moreover, researchers have also indicated no effect of LMF on MPF values (Farina, Fosci, &

Merletti, 2002; Inbar, Paiss, Allin, & Kranz, 1986). In addition, the average MPF values obtained were consistent with the literature. The overall MPF values during a fatiguing contraction using

FDI muscle at 20% MVC was 130 Hz (De Luca, Sabbahi, & Roy, 1986); while the mean MPF values for our study was 140 Hz.

This contrasting behavior of MPF values in the presence of LMF can be attributed to multiple factors such as MU recruitment, MU firing rate, conduction velocity and EMG synchronization (De Luca, 1979). The reduction in MPF can be due to the lowering of conduction velocity, which has been widely accepted in the literature (Merletti, Knaflitz, & De

Luca, 1990; Nina K. Vøllestad, 1997). As the muscle fatigues, the K+ ion concentration increases, leading to a reduction in conduction velocity. Also, during a fatiguing contraction, the lactate levels in the muscle fiber increase, leading to the reduction in pH levels, which serves to lower conduction velocity. As mentioned above, conduction velocity affects MPF, and the lowering of conduction velocity is highly dependent on LMF (Armstrong et al., 1993). This correlation could indicate a potential link between the reduction in MPF and LMF (De Luca et al., 1986).

42 However, the contrasting behavior in mean power frequency (i.e., several studies report an increase in MPF) might be an effect of MU recruitment (De Luca et al., 1986). When additional

MUs are recruited, they are not fatigued and display a higher recruitment threshold. The additionally recruited MUs might have a higher recruitment threshold and conduction velocity.

This increased conduction velocity contributes to reducing the conduction velocity of fatiguing

MUs. This summation results in either an increase or decrease in the overall conduction velocity

(Hägg & Ojok, 1997; Nussbaum, 2008).

In our study, there was no significant difference in the reduction of MPF values among different conditions. During the analysis of this measure, the ANOVA model was unable to satisfy the normality assumption. To make the data normal, data was log transformed and the analysis was performed again. Even after this transformation, the normality assumption was not satisfied. Thus, any interpretation based on MPF results should be formulated with cautious.

D.1.5. Ratings of Perceived Discomfort (RPD):

Overall subjective ratings of discomfort increased over time: from very weak, to weak, to moderate at a rate of .048/min (Fig. 7(a)). The range of RPD for our investigation was similar to Rashedi and Nussbaum (20016), who employed a fatiguing exercise using the FDI muscle with similar experimental parameters. In this study, the finger abduction task was performed with 15%-25% MVC, with RPD values increasing to a moderate level of discomfort (3.7 on the

Borg CR-10 scale) after one hour. In our study, the discomfort increased to an almost moderate level, ~2.9 on the Borg CR-10 scale. Additionally, the increase in RPD with increasing LMF was

43 observed, in line with other studies, where RPD values increased for FDI (Rashedi & Nussbaum,

2016), the trapezius (Sundelin & Hagberg, 1992), and adductor politis (Rashedi & Nussbaum,

2016; Sundelin & Hagberg, 1992; Xiong & Muraki, 2014) muscles.

RPD values were highest for the no-rest condition, with no significant differences noted for the other conditions (Fig. 7(b)). A prior study indicated a reduction in the subjective ratings of discomfort in the triceps brachii muscle in the presence of exertion variations (Yung et al.,

2012). In contrast, another study demonstrated that discomfort increased in the presence of exertion variations (Rissén et al., 2002). Just recently, Luger and coworkers (2014) indicated that the presence of exertion or task variation might depend on the task being performed. For example, when the task performed is light or moderate and a planned intervention increases the work load, subjective ratings were observed to increase, irrespective of the implications on fatigue (Horton et al., 2012; Kuijer et al., 2004). On the other hand, if the task involves a heavy workload and the intervention reduces that workload, perceived discomfort could be reduced

(Horton et al., 2012; Kuijer et al., 2004). In our study, participant performed work (5% MVC) during the no-rest condition (even during rest), which might have been perceived as an increased workload, even though the force level was lower in this condition (15% MVC).

The interaction effect of gender and condition interaction was significant (Fig. 7(c)), where the mean RPD values for men were 15% higher compared with women. Similarly, RPD ratings were higher for men compared with women during elbow flexion contractions

(O’connor, Poudevigne, & Pasley, 2002). This gender difference might be due to the reported longer endurance limit of women compared to men (Hunter et al., 2009). Also, the lower overall force output by women will lead to reduced stress and a lowered perception of fatigue.

44 In contrast, no significant differences were observed in the RPD values of men and women during elbow flexion at 20% MVC (Hunter & Enoka, 2001). It must be stressed that the literature indicates mixed findings as to the effect of gender on the perceived discomfort in the presence of fatigue. Notably, the normality assumption of the ANOVA model was not satisfied.

To normalize the data, the data was log transformed and the analysis was performed again.

Even after the transformation, the normality assumption was not satisfied.

D.2. Comparison among conditions:

One of the objectives of this study was to compare the effect of different working conditions with variations in exertion on the LMF of FDI muscle during intermittent contractions. While the prior sections compared different measures—which did not demonstrate the same pattern of change for different conditions—this section will provide a more generalized comparison.

The baseline condition for this study featured a basic intermittent contraction with an EL of 20% MVC, 50% DC, and 1 min CT. The other conditions represented interventions performed on the baseline condition. In comparison with the baseline condition, conditions featuring exertion variability did not show any difference in the %MVC reduction. Subjective ratings of discomfort also indicated overall low values for baseline conditions. COV values were lowest for the baseline condition (Fig. 9(b)), indicating better performance with respect to following target-level force patterns. Conversely, muscle activity (RMS) was highest for the baseline condition (Fig. 11(b)), potentially showing the largest level of muscle activity for this condition.

In another word, COV indicated potentially reduced LMF, while RMS values indicate larger LMF indicated by higher RMS levels. While our results did not support our hypothesis, there was no

45 indication that LMF increased due to variability in load or extra physical demands for some of these conditions.

For no-rest condition, the COV values were the highest (Fig. 9(b)) and the EMG values were the lowest (Fig. 11(b)) compared to the other conditions. The former might be due to MU discharge variability during low to moderate level contractions, while the latter might be due to the reduced level of maximum activity (i.e., 15% MVC in comparison to 20% in other conditions). The RPD values were highest during the no-rest condition, which could be due to the subjective interpretation of participants about the lack of rest. There was no significant difference in the MPF values during different conditions.

Even with slightly higher levels of exertion, the increased force condition and spike condition did not show any significant difference in %MVC reduction compared to the baseline condition (Fig. 10(b)). In addition, there was no significant difference between the RMS and

COV values for these two conditions compared to the baseline condition. Therefore, there was no evidence that these conditions were able to induce higher levels of LMF. This finding might indicate better performance and the possibility of MU substitution, as indicated by prior studies

(Crenshaw et al., 2006; Falla & Farina, 2007; Samani et al., 2009; Westad et al., 2003). Higher sample size and the use of different measures, such as oxygenation, might help us determine the specific mechanics associated with MU substitution in the presence of force variability.

D.3. Recovery:

Participants performed the finger abduction task for 1-hr, after which they engaged in the recovery session for 30 mins. During this recovery phase, %MVC were recorded at 5, 10, 20 and 30 minutes of the session. For statistical analysis of the recovery process, the slope from

46 the regression equation (Eq. 3) was considered as a response variable. From the analysis, no main effects or two-factor interactions were found to be significant (Table 3). In a recent study, the intercept from the regression equation was considered as a covariate due to a significant difference in the LMF levels for different conditions (Rashedi & Nussbaum, 2017). In comparison, the intercept was not considered as a covariate in our study, as there was no significant difference in LMF levels during the four different conditions.

In our study, %MVC values increased by a mean value of 13.7 % during the 30 mins of the post-fatigue session. In a recent study, %MVC increased by 9.26% when the FDI muscle was fatigued at 15% for one hour, and 16.7% when the FDI muscle was fatigued at 25% MVC for 1- hour, followed by a one-hour recovery period (Rashedi & Nussbaum, 2017). This prior study confirms that the reduction in %MVC from our investigation was within a similar range since the mean exertion level in our study was 20%. The increase in %MVC values during recovery might be due to an increase in blood flow to the muscle. Moreover, blood flow might well be dependent on exertion levels, with the difference in exertion levels indicating a difference in blood flow to the muscle (Hamann et al., 2004). In our study, any specific factor accounting for the rate of recovery wasn’t identified. In contrast, (Rashedi & Nussbaum, 2017) indicated that recovery rate was highly dependent on initial conditions—and particularly on the exertion level.

This lack of dependency in our study might be due differences in the experimental protocol, since Rashedi and Nussbaum incorporate a one-hour recovery session, compared the 30 minutes employed for monitoring the recovery of the participants in this study. In addition, exertion variability did not affect the rate of recovery, based on the fact that no significant differences were noted in the recovery rates (A from Eq. 3) of different conditions (Fig. 13 (b)).

47 In comparison, Rashedi and Nussbaum (2017) observed the effect of different CT (30s and 60s) and EL (15% and 25%) on the recovery of the FDI muscle. Similar to our study, the rate of recovery was not dependent on the different working conditions. In addition, Iguchi and coworkers (2008) assessed the influence of varied loading conditions (35% and 65% MVC) on the post-fatigue recovery of quadriceps and were unable to identify any significant difference in the rate of recovery. These studies, in line with our results, indicate that the post-fatigue recovery might not be dependent on the specific fatiguing protocols.

48 E. Limitations and Future Work

This study focused on the intermittent contractions performed using a simple biomechanical system (FDI muscle) in the presence of exertion variability. Multiple limitations have to be acknowledged for our study. Importantly, only young participants took part in this investigation in order to remove the confounding effect of age. However, other studies targeting the influence of force fluctuations have confirmed significantly different outcomes between young and older adults (Enoka et al., 2003). This difference might due to an increase in the variation of force exerted by the individual MUs of older adults, compared with young adults (Laidlaw et al., 2000). Accordingly, a future study should be designed to include a mixed- age population to investigate the effects of age on fluctuations and LMF.

In this study, a bipolar surface EMG electrode was used to obtain muscle activity. It must be noted, however, that while prior experiments were conducted using EMG electrodes, most of these studies used a fine needle electrode to observe MU recruitment parameters, such as recruitment and discharge properties (Westad et al., 2003; Westgaard & De Luca, 1999). Even though needle electrodes provide valuable information, recruiting participants for an experimental study involving needles can be challenging! Thus, Falla and Farina (2007) employed a multi-surface EMG electrode comprised of an array of 13x5 EMG electrodes placed on the trapezius muscle (Falla & Farina, 2007) to observe individual MU substitution. Similarly, a multi-surface EMG approach was used with the FDI (McManus et al., 2015) and other muscles such as the biceps (Holobar, Farina, Gazzoni, Merletti, & Zazula, 2009) to observe MU behavior during fatiguing contractions. The key advantage of a multi-surface EMG or the needle

49 electrode approach is the ability to identify individual MU action potentials. In contrast, the shape of action potentials of individual MUs recorded using surface EMG are similar to each other and thus are difficult to differentiate due to low-pass filtering and lack of recording techniques. This inability to identify individual MU action-potential profiles limits the use of surface EMG in understanding MU behavior. For future experimentation, the use of multi- surface EMG electrodes while observing the effect of MU recruitment in the presence of exertion variation is recommended.

As a muscle fatigues, the blood flow into the muscle is restricted due to an increase in intramuscular pressure (Barnes, 1980). However, the effect of varying exertion levels on blood flow is unclear. Yung and coworkers (2012) reported an increase in blood flow in the presence of force variations. It should also be noted that the recovery of the muscle is dependent on the removal of metabolites formed during the fatiguing contraction and the blood flow into the muscle. The blood flow might be dependent on the exertion levels of the fatiguing contraction.

Since our study focused on the effect of exertion variation on localized muscle fatigue, the ability to correlate blood flow data and variability in force contractions would have added a useful element to our findings.

The task performed during involved precise control and attention for prolonged durations which is not common in occupational settings. However, this study was performed for one hour to keep it close to an occupational setting, as described in the Methodology section. As such, it must be noted that a prolonged experimental session could negatively impact the motivation of participants—particularly during MVC trials when participants could have intermittently lowered their MVC levels. In such situations, including electrical stimulation

50 as a data-gathering strategy would have been beneficial. Since the surface area of the FDI muscle was mostly covered by EMG electrodes, it was not feasible to also use stimulation. In the future, having an EMG electrode that would be able to electrically stimulate the muscle would be advantageous.

Occasionally, participants might have leveraged on the fixtures or used other body parts to produce force, in addition to the FDI muscle. Meanwhile, verbal instructions were provided to make sure participants used their FDI muscle to generate finger abduction force to the extent possible. Moreover, while we included intermittent contraction as an exertion with more similarity to the real-time force generations, it would be great to test the outcome of this study during dynamic contractions. However, data-collection protocols and the quantification of LMF using dynamic contractions might be complicated.

Although index finger abduction is not a frequent exertion in daily life, we chose it to have simple biomechanical system. Moreover, the FDI muscle has an important role in many prevalent activities in daily life such as pinching or gripping. In conclusion, the results of our study can be useful in developing working conditions where low to medium intensity tasks (For example pinching and griping) are used in the occupational settings.

51 F. Conclusion

This study was conducted to observe the effects of exertion variability on the fatigue and recovery of FDI muscle during intermittent contractions. Participants performed finger abduction task for 1-hr during four different conditions with variability in the exertion level:

Baseline, No rest, increased force and spikes conditions. 30-min recovery was performed after the fatiguing task. Subjective measures such as RPD and objective measures of force and EMG were collected. Results did not demonstrate the consistent theme of significant differences among different experimental conditions. Meanwhile, even with slightly higher levels of exertion, increased force and spikes condition did not show larger %MVC reduction and EMG

RMS values, in comparison with the baseline condition. This might indicate a better performance due to the MU substitution, in the presence of exertion variation. In future, using multi-surface EMG and blood flow measurement might help us to further investigate the underlying fatigue mechanisms and MU substitution. The results in the present study can be useful in developing working conditions where low intensity repetitive tasks are prevalent and can help in reducing the WMSDs in the occupational settings.

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