AN INVESTIGATION OF THE EFFECTS OF MENTAL FATIGUE DURING REST BREAKS ON END POINT VARIABILITY

AN INVESTIGATION OF THE EFFECTS OF MENTAL FATIGUE DURING REST BREAKS ON END POINT VARIABILITY Michael W.L. Sonne 1,2, Nicholas J. La Delfa 1, Jeffrey D. Graham 1, Steven R. Bray 1, and Jim R. Potvin 1 1 Department of Kinesiology, McMaster University, Ontario, Canada 2 Ford Motor Company, Oakville, Ontario, Canada ABSTRACT: Poorly designed repetitive work can contribute to the development of muscle fatigue in the workplace. Work related muscle fatigue can lead to reductions in performance, decrease quality of work, and correlate with the development of musculoskeletal disorders. Mental fatigue is also linked to negative consequences for human performance. Theoretically, reducing the physical demands on a worker would allow for recovery of muscle fatigue, but it is unknown how higher mental demands would influence work quality, performance and muscle fatigue recovery. We examined target error in a repetitive pointing task, pre and postfatigue, under the conditions of mental fatigue, no added mental fatigue and no physical rest. There was significantly greater target error after participants were fatigued, but no effect of the rest break condition on the amount of target error. These findings show that muscle fatigue can have negative implications for work tasks requiring high levels of end-effector precision. Key Words: Muscle fatigue, mental fatigue, work quality UN EXAMEN DE L INCIDENCE DE LA FATIGUE MENTALE PENDANT LES PÉRIODES DE REPOS SUR LA VARIABILITÉ DU RÉSULTAT FINAL Un travail répétitif piètrement structuré peut occasionner de la fatigue musculaire en milieu de travail. La fatigue musculaire liée au travail peut entraîner une diminution du rendement et une réduction de la qualité du travail, en plus de contribuer à l apparition de troubles musculosquelettiques. La fatigue mentale entraîne également des conséquences néfastes sur le rendement. En théorie, la diminution des exigences physiques auxquelles un travailleur est soumis favoriserait la récupération musculaire. Toutefois, on ignore la façon dont des exigences mentales supérieures influenceraient la qualité du travail, le rendement ainsi que la récupération musculaire. Nous avons examiné le taux d erreur cible associé à une tâche répétitive de pointage effectuée avant et après l apparition de la fatigue musculaire, avec et sans ajout d un facteur de fatigue mentale et sans récupération physique. Le taux d erreur cible était considérablement supérieur après avoir soumis les participants au facteur de fatigue, mais la période de repos n a eu aucun effet sur le taux d erreur cible. Ces résultats indiquent que la fatigue musculaire peut avoir des répercussions négatives sur les tâches exigeant un niveau élevé de précision. Mots clés : fatigue musculaire, fatigue mentale, qualité du travail

1.0 INTRODUCTION Muscle fatigue is a process which results in a decrease in force generating capacity and human performance. Mechanisms of muscle fatigue are grossly described as either peripheral (occurring after the neuromuscular junction) or central (occurring prior to the neuromuscular junction) (Gandevia, 2001). Peripheral factors include failure of crossbridge formation, accumulation of metabolites and decreased sensitivity to sarcoplasmic calcium (Fitts, 1994; Allen et al., 2008). Central factors include a lack of motivation in participants, or inhibition due to pain or injury. Poorly designed repetitive work can contribute to the development of muscle fatigue in the workplace (Björkstén and Jonsson, 1977; Hagberg, 1981; Potvin, 2012; Vøllestad, 1997). Work related muscle fatigue can lead to reductions in performance (Gates and Dingwell, 2008), decrease quality of work (Dan et al., 2010; Lin et al., 2001; Wartenberg et al., 2004) and, be correlated with the development of musculoskeletal disorders (Allan, 1998; Dugan and Frontera, 2000; Sjøgaard and Søgaard, 1998). Strategies to reduce muscle fatigue in the workplace revolve around limiting one, or a combination, of four primary risk factors: force, posture, repetition and duration of the task. Mechanical exposure variation (as demonstrated by Yung et al., 2012) is one method of altering fatigue levels. Presenting a muscle with a series of demands that have the same average force, but different maximum and minimum forces, resulted in increased fatigue levels when the variation of force was low. Theoretically, moving a worker between a task that taxes the muscles and putting them into a role that is more mentally demanding (such as a computer-based task), but feature reductions in physical demands, would allow for muscle fatigue recovery. Kinematic and kinetic variability during repetitive tsks are a direct consequence of muscle fatigue (Allen and Proske, 2011). One proposed mechanism for these alterations is the impaired ability to sense joint position, leading to false feedback and incorrect limb posturing (Allen and Proske, 2011). From an ergonomics perspective, these variations in movement can lead to errors in end-effector position. Mental fatigue is linked to negative consequences for human performance. Graham et al., (2014) demonstrated negative effects of high mental demands (in the form of effortful imagery) during rest breaks, on self control tasks (effortful isometric hand grip). Those who performed the effortful imagery task showed greater reductions on a subsequent isometric endurance handgrip task, when compared to control participants who had a quiet rest period. However, it is unknown how similar mental demand mechanisms would influence other tasks not requiring physical endurance, such as pointing or accuracy-based skills. The purpose of this study was to determine if: 1) muscle fatigue and 2) mental demands during rest breaks had a significant effect on end point variability in a repetitive pointing task. 2.0 METHODS Our study involved 31, right-handed male participants (mean age = 24.51 ± 1.65 years). Each participant was free from upper-extremity pain or injury and provided conformed written consent prior to conducting the experiment. All aspects of the study were approved by the university s research ethics board. A passive motion capture system (11 Raptor-4 Cameras, Motion Analysis, Santa Rosa, CA, USA) was used to record kinematics at a sampling rate of 100 Hz. Upper extremity shoulder and elbow joint locations were estimated using a reduced marker set (Nussbaum & Zhang, 2000). One reflective marker was placed on the dorsal aspect of the 2 nd distal phalanx to evaluate resultant end-point error from the centre of each target. Each target always appeared at a constant location relative to four reflective markers affixed to the corners of the computer monitor. End-point errors of the pointing task were evaluated relative to a participant-specific calibration trial, in which participants were asked to precisely touch and hold their pointing finger to the centre of each target. All kinematic data were filtered with a 6th order lowpass Butterworth filter, with a cutoff frequency of n Hz. Maximum standing shoulder flexion strengths were recorded with a fully extended arm, with the hand located at shoulder height. Forces were measured at the hand using a linear force gauge (100lb max, Omegadyne Inc., Laval, QC, Canada) attached to a height adjustable chain affixed to the floor.

Figure 1. Participants were seated and positioned at a distance of 80% of their arm s reach from the computer monitor. The targets appeared in one of 5 locations, for one second in time, separated by 1 second of rest. In total, there were 150 target cues: 120 pointing cues, and 30 inhibition cues. Each participant was seated at a fixed relative distance (80% of maximum arm reach) from a computer monitor, and the height of table was adjusted so the top of the computer monitor was level with each participant s eyes. Participants used their right index finger to point to one of 5 targets that briefly appeared on the computer screen, once every 2 seconds, for a total of 150 targets (Figure 1). Each movement was made as quickly and accurately as possible, with the pointing finger starting and returning to a set position in front of the midline of the body. After the initial pointing trial, participants performed a fatigue protocol with their shoulder flexed in front of them, holding a water bottle filled with lead shot weighing 20% of their pre-trial maximum shoulder flexion strength. To examine the influence of a physical rest break and high mental demands, participants were randomly assigned to one of three experimental groups: 1) completing 10 minutes of a mentally demanding task (Stop It (Verbruggen et al., (2008)), 2) reading a book for 10 minutes, or 3) having no physical or mental rest. Participants then completed a second trial of the pointing protocol. Maximum voluntary contractions (MVCs) and a ratings of perceived fatigue (RPF) were recorded prior to the collection, and after the first pointing trial, the fatigue protocol, the experimental manipulation, and the second pointing protocol. These served as measures of peripheral fatigue. A rating of perceived mental exertion was recorded after the experimental manipulation to serve as confirmation that participants had to exert more mental effort during the Stop It task. Target error and end point error variability were calculated for each participant in each condition. Endpoint error was averaged over the first 2 movements to each of the 5 targets, starting at the 1 st, 40 th, and 90 th pointing events. To examine the effect of each pointing variable, a mixed measures ANOVA was performed with the within factor conditions of Time (1 st, 40 th, 80 th target) and Trial (pre and post fatigue protocol), and the between factor of condition (Stop It, Reading and No Rest). An additional mixed measures ANOVA was performed on MVC force and RPF, with factors of time (pre-trial, pointing trial 1, fatigue trial, experimental manipulation, pointing trial 2) and condition. All significant main effects and interactions were further evaluated using Tukey s HSD post hoc testing (p < 0.05). 3.0 RESULTS 3.1 Manipulation Check Mental exertion was significantly higher in the Stop it condition, compared to both the reading, and no rest condition (F (2, 30) = 25.85, p < 0.05, η p ² = 0.645). Rating of perceived mental effort after the experimental

manipulation was an average of 5.00 ± 1.76 out of 10 in the Stop It condition, compared to 1.10 ± 1.56 in the reading condition, and 1.04 ± 1.56 in the no rest condition. 3.2 Target Error and Target Error variability A significant main effect of Trial was found (F (1, 28) = 5.23, p < 0.05, η p ² = 0.0752) for target error (Figure 2). Prior to fatigue, there was an average error of 6.18 ± 0.34 mm, compared to 6.60 ± 0.32 mm after the fatiguing protocol and experimental manipulation. There were no significant main effects or interactions for Time or Condition on target error (p <.05). There was a significant interaction between Time and Condition on the end point variability (F (2, 56) = 3.34, p < 0.05, η p ² = 0.0151). However, post-hoc testing did not reveal any significant differences between Conditions across the three time points (p <.05). Figure 2. Figure Target Error. Target error increased between the prefatigue pointing trial, and the post-fatigue pointing trial (p < 0.05). There was no statistically significant influence of Condition on pointing error, however, the no rest Condition had the greatest increase in error (a 9% increase in error between the pre fatigue and post fatigue pointing trials). Figure 3. MVC levels decreased between the first pointing trial and the fatigue protocol, then recovered after the experimental manipulation. The no rest Condition recovered the least, * = significantly different between Stop It and no rest, and reading and no rest (i < 0.05). 3.3 Fatigue Measures There was a significant interaction between Time and Condition on the dependent variable of MVC force (F (2, 56) = 4.07, p < 0.05, η p ² = 0.059). Shoulder flexion MVCs were significantly lower after the manipulation in the no rest (78.9 ± 2.2% MVC) condition, compared to the Stop It and reading Conditions (86.7 ± 2.3% MVC, and 89.9 ± 2.3 % MVC, respectively). A significant interaction between Time and Condition existed on the dependent variable of rating of perceived fatigue (F (4, 28) = 4.69, η p ² = 0.251). Participants reported greater subjective fatigue after the experimental manipulation in the No Rest Condition (5.18 ± 0.51), compared to the Stop It or Reading Conditions (2.6 ± 0.53, and 1.2 ± 0.53, respectively).

Figure 4. Rating of perceived fatigue (A scale of 0 10) was used to determine subjective levels of fatigue at different time points throughout our study. Elevated RPF levels were seen after the fatigue trial, but the RPF levels were only significantly different after the experimental manipulation. The Stop It and Reading break conditions had significantly lower RPF than the no rest condition. These levels returned to non-significant differences after the final pointing trial. 4.0 DISCUSSION Muscle fatigue is a process that can reduce muscle force generating capacity. This study demonstrated that muscle fatigue also has an impact on task performance when pointing to targets. Despite the presence of elevated mental demands and physical fatigue, there was no significant effect of mentally demanding work breaks on physical task performance. Mental demands, such as effortful imagery, have been shown to reduce endurance times in hand grip tasks (Graham et al., 2014). However, the failure of this task was a result of selfcontrol related issues: different than the mechanisms within the current study requiring pointing accuracy. In the current study, participants attempted to point accurately to targets while in a physically fatigued state. Graham et al., (2014) induced mental fatigue through effortful imagery, which resulted in reduced endurance task performance. An endurance task can fail as a result of depleted self-control (e.g., Bray et al., 2008). Self-control has been shown to be depleted through the use of effortful imagery, which could be attributed to greater mental fatigue. Despite similar levels of subjective ratings of mental fatigue, between the current study and Graham et al. (2014), any impairment of self control in the current study may not influence pointing accuracy when compared to endurance performance, explaining why there was no significant influence of the Stop It condition in the current study. Non-neutral postures and heavy loads, which lead to elevated levels of muscle fatigue, have been linked to decreases in quality in automotive assembly (Falck et al., 2010). The effects of physical fatigue in this study resulted in a 6.7% increase in target error. While these errors are small in magnitude (only a 0.52 mm increase in error), this may have important implications for jobs that require precise control of the upper extremities. Tasks that involve a high level of hand and finger accuracy should be designed to limit poor postures and static loads that can result in the accumulation of muscle fatigue. Muscle fatigue can also disturb joint position sense (Allen and Proske, 2006). This disturbance is caused by increasing the effort level required to support the arm against gravity as the system fatigues, resulting in positional errors (Allen and Proske, 2006). In the current study, alteration in joint position sense could directly translate into target pointing errors. If the internal feedback mechanisms the body relies on to orient its limbs are providing misinformation, errors will occur. There was no effect of condition on target error in the mental fatigue condition, or in the no-rest condition either. Despite having significantly less fatigue in the reading and Stop It condition (as evidenced by higher MVC force values after the experimental manipulation), there was still a significant effect of the fatigue trial on target error. From an ergonomics perspective, this finding shows that the presence of muscle fatigue in moderate levels can still have an influence on task performance. Whether there is a 22% reduction in force generating capacity (in the No Rest condition), or a 10% reduction in force generating capacity (approximately for the Stop It or reading conditions), task performance is impaired. Limiting fatigue is important to the performance of a task. Only males were used in this study, as males are known to be more fatigable and, thus, were more likely to show an effect during the pointing task. Males typically have higher maximum power output compared to females, but are also less fatigue resistant and recover more slowly (Glenmark et al., 2004). These differences may be primarily due to differences in muscle fibre composition (Wüst et al., 2008), but also muscle mass,

muscle metabolism and voluntary activation patterns (Billaut and Bishop, 2009). Future work should determine if the effects of fatigue influence females similarly to the males tested in this experiment. The procedures tested in this experiment represent an extreme level of physical and mental fatigue that may not necessarily be seen in occupational settings. The RPME examined in this study reached approximately 50% of the maximum mental fatigue level. A study should be conducted to examine known mental fatigue levels in different occupational settings, so future studies can examine more occupationally relevant mental fatigue levels. 5.0 CONCLUSION Muscle fatigue was associated with an increase in target error during a pointing task. An examination of rest break types (reading, a mentally demanding task, and no break) did not result in any target variability differences. Despite the presence of different levels of muscle fatigue levels (as indicated by decreases in maximum shoulder flexion strength), error was constant between all experimental groups. Future work should examine different types of occupational tasks that may be more influenced by mental fatigue, and investigate methods for reducing these mentally fatiguing tasks. 6.0 REFERENCES Allen, D. G., Lamb, G. D., & Westerblad, H. (2008). Skeletal muscle fatigue: cellular mechanisms. Physiological reviews, 88(1), 287-332. Allen, T. J., & Proske, U. (2006). Effect of muscle fatigue on the sense of limb position and movement. Experimental brain research, 170(1), 30-38. Billaut, F., & Bishop, D. (2009). Muscle fatigue in males and females during multiple-sprint exercise. Sports Medicine, 39(4), 257-278. Björkstén, M., & Jonsson, B. (1977). Endurance limit of force in long-term intermittent static contractions. Scandinavian journal of work, environment & health, 23-27. Bray, S. R., Martin Ginis, K. A., Hicks, A. L., & Woodgate, J. (2008). Effects of self regulatory strength depletion on muscular performance and EMG activation. Psychophysiology, 45(2), 337-343. Dan, H., Falck, A., & Ortengren, R. (2010). The Impact of Poor Assembly Ergonomics on Product Quality : A Cost Benefit Analysis in Car Manufacturing. Human Factors and Ergonomics in Manufacturing & Service Industries, 20(1), 24 41. Dugan, S. A., & Frontera, W. R. (2000). Muscle fatigue and muscle injury. Physical medicine and rehabilitation clinics of North America, 11(2), 385-403. Falck, A. C., Örtengren, R., & Högberg, D. (2010). The impact of poor assembly ergonomics on product quality: A cost benefit analysis in car manufacturing.human Factors and Ergonomics in Manufacturing & Service Industries, 20(1), 24-41. Fitts, R. H. (1994). Cellular mechanisms of muscle fatigue. Physiological reviews, 74(1), 49-94. Gandevia, S. C. (2001). Spinal and supraspinal factors in human muscle fatigue. Physiological reviews, 81(4), 1725-1789. Glenmark, B., Nilsson, M., Gao, H., Gustafsson, J. Å., Dahlman-Wright, K., & Westerblad, H. (2004). Difference in skeletal muscle function in males vs. females: role of estrogen receptor-β. American Journal of Physiology-Endocrinology and Metabolism, 287(6), E1125-E1131. Graham, J. D., Sonne, M. W., & Bray, S. R. (2014). It wears me out just imagining it! Mental imagery leads to muscle fatigue and diminished performance of isometric exercise. Biological psychology, 103, 1-6. Hagberg, M. (1981). Work load and fatigue in repetitive arm elevations.ergonomics, 24(7), 543-555. Lin, L., Drury, C. G., Kim, S., & Hall, B. (2001). Ergonomics and Quality in Paced Assembly Lines, 11(4), 377 382. Nussbaum, M.A. & Zhang, X. (2000). Heuristics for locating upper extremity joint centres from a reduced set of surface markers. Human Movement Science, 19, 797-816. Potvin, J. R. (2012). Predicting Maximum Acceptable Efforts for Repetitive Tasks An Equation Based on Duty Cycle. Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(2), 175-188. Sjøgaard, G., & Søgaard, K. (1998). Muscle injury in repetitive motion disorders. Clinical Orthopaedics and Related Research, 351, 21-31. Verbruggen, F., Logan, G. D., & Stevens, M. A. (2008). STOP-IT: Windows executable software for the stop-signal paradigm. Behavior Research Methods, 40, 479-483. Vøllestad, N. K. (1997). Measurement of human muscle fatigue. Journal of neuroscience methods, 74(2), 219-227. Wartenberg, C., Dukic, T., Falck, A. C., & Hallbeck, S. (2004). The effect of assembly tolerance on performance of a tape application task: A pilot study. International journal of industrial ergonomics, 33(4), 369-379. Wüst, R. C., Morse, C. I., De Haan, A., Jones, D. A., & Degens, H. (2008). Sex differences in contractile properties and fatigue resistance of human skeletal muscle. Experimental physiology, 93(7), 843-850. Yung, M., Mathiassen, S. E., & Wells, R. P. (2012). Variation of force amplitude and its effects on local fatigue. European journal of applied physiology, 112(11), 3865-3879.