Changes in Mental Workload and Motor Performance Throughout Multiple Practice Sessions Under Various Levels of Task Difficulty

Jaquess, Kyle J.; Lo, Li Chuan; Oh, Hyuk; Lu, Calvin; Ginsberg, A.; Tan, Yuyong; Lohse, Keith R.; Miller, Matthew W.; Hatfield, Bradley D.; Gentili, Rodolphe J.

doi:10.1016/j.neuroscience.2018.09.019

Cited by 40 publications

(29 citation statements)

References 79 publications

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“…Second, this investigation employed a system with preamplified EEG signals and secured shielded cables limiting the influence of motion-related artifact (Kline et al, 2015;Nathan & Contreras-Vidal, 2016;Reis et al, 2014). Third, this study employed similar signal processing methods and obtained similar findings as those from previous work which successfully assessed mental workload via EEG spectral analyses during dual-task walking (Beurskens et al, 2016;De Sanctis et al, 2014;Marcar et al, 2014;Pruziner et al, 2019;Shaw et al, 2018) as well as upper-extremity tasks executed while seated (Dyke et al, 2015;Gentili et al, 2018;Jaquess et al, 2018;Rietschel et al, 2012).…”

Section: Discussionmentioning

confidence: 69%

A Comparison of Mental Workload in Individuals with Transtibial and Transfemoral Lower Limb Loss during Dual-Task Walking under Varying Demand

Shaw

Rietschel

Hendershot

et al. 2019

J Int Neuropsychol Soc

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Objectives: This study aimed to evaluate the influence of lower limb loss (LL) on mental workload by assessing neurocognitive measures in individuals with unilateral transtibial (TT) versus those with transfemoral (TF) LL while dual-task walking under varying cognitive demand. Methods: Electroencephalography (EEG) was recorded as participants performed a task of varying cognitive demand while being seated or walking (i.e., varying physical demand). Results: The findings revealed both groups of participants (TT LL vs. TF LL) exhibited a similar EEG theta synchrony response as either the cognitive or the physical demand increased. Also, while individuals with TT LL maintained similar performance on the cognitive task during seated and walking conditions, those with TF LL exhibited performance decrements (slower response times) on the cognitive task during the walking in comparison to the seated conditions. Furthermore, those with TF LL neither exhibited regional differences in EEG low-alpha power while walking, nor EEG high-alpha desynchrony as a function of cognitive task difficulty while walking. This lack of alpha modulation coincided with no elevation of theta/alpha ratio power as a function of cognitive task difficulty in the TF LL group. Conclusions: This work suggests that both groups share some common but also different neurocognitive features during dual-task walking. Although all participants were able to recruit neural mechanisms critical for the maintenance of cognitive-motor performance under elevated cognitive or physical demands, the observed differences indicate that walking with a prosthesis, while concurrently performing a cognitive task, imposes additional cognitive demand in individuals with more proximal levels of amputation.

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Section: Discussionmentioning

confidence: 69%

A Comparison of Mental Workload in Individuals with Transtibial and Transfemoral Lower Limb Loss during Dual-Task Walking under Varying Demand

Shaw

Rietschel

Hendershot

et al. 2019

J Int Neuropsychol Soc

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“…Figure 1 illustrates the literature search and study selection process. Ninety-one eligible studies [3,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,…”

Section: Resultsmentioning

confidence: 99%

“…Among frequency-domain measures, alpha (α) power, theta (θ) power, and beta (β) power were examined in more than ten studies, with 59%, 75%, and 58% of them showing statistical significance, respectively. Alpha power has been found to be sensitive to MWL in air traffic control tasks [28], multi-attribute tasks [49], with increased task demands resulting in a decrease in alpha power [28,43,59]. Both θ power and β power was found to be positively associated with MWL in air traffic control tasks [28], threat/change detection tasks during unmanned ground vehicle operations [18] and code error inspection tasks for software engineers [63].…”

Section: Resultsmentioning

confidence: 99%

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A Systematic Review of Physiological Measures of Mental Workload

Tao

Tan

Wang

et al. 2019

IJERPH

149

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Mental workload (MWL) can affect human performance and is considered critical in the design and evaluation of complex human-machine systems. While numerous physiological measures are used to assess MWL, there appears no consensus on their validity as effective agents of MWL. This study was conducted to provide a comprehensive understanding of the use of physiological measures of MWL and to synthesize empirical evidence on the validity of the measures to discriminate changes in MWL. A systematical literature search was conducted with four electronic databases for empirical studies measuring MWL with physiological measures. Ninety-one studies were included for analysis. We identified 78 physiological measures, which were distributed in cardiovascular, eye movement, electroencephalogram (EEG), respiration, electromyogram (EMG) and skin categories. Cardiovascular, eye movement and EEG measures were the most widely used across varied research domains, with 76%, 66%, and 71% of times reported a significant association with MWL, respectively. While most physiological measures were found to be able to discriminate changes in MWL, they were not universally valid in all task scenarios. The use of physiological measures and their validity for MWL assessment also varied across different research domains. Our study offers insights into the understanding and selection of appropriate physiological measures for MWL assessment in varied human-machine systems.

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“…The study of mental workload is one of the most important variables in psychology and behavior for understanding job performance. Studies have shown that a higher mental workload results in a decreased capacity for attention, and increases the risk of slow responses, ultimately contributing to workplace errors (Jaquess et al, 2018). The mental workload level, and its association with a particular task, has been studied in a number of healthcare disciplines, including anesthesiology, emergency medicine, surgery, and nursing.…”

Section: Introductionmentioning

confidence: 99%

Subtypes of nurses' mental workload and interaction patterns with fatigue and work engagement during coronavirus disease 2019 (COVID-19) outbreak: A latent class analysis

Geng

et al. 2020

Preprint

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Background Nurses play critical roles when providing health care in high-risk situations, such as during the COVID-19 outbreak. However, no previous study had systematically assessed nurses’ mental workloads and its interaction patterns with fatigue, work engagement and COVID-19 exposure risk. Methods A cross-sectional study was conducted via online questionnaire. The NASA Task Load Index, Fatigue Scale-14, and Utrecht Work Engagement Scale were used to assess nurses’ mental workload, fatigue and work engagement, respectively. A total of 1337 valid questionnaires were received and analyzed. Nurses were categorized into different subgroups of mental workload via latent class analysis (LCA). Cross-sectional comparisons, analysis of covariance (ANCOVA), and multivariate (or logistic) regression were subsequently performed to examine how demographic variables, fatigue and work engagement differ among nurses belonging to different subgroups. Results Three latent classes were identified based on the responses to mental workload assessment: Class1 – low workload perception & high self-evaluation group (n = 41, 3.1%); Class 2 – medium workload perception & medium self-evaluation group (n = 455, 34.4%); and Class 3 – high workload perception & low self-evaluation group (n = 841, 62.5%). Nurses belonging into class 3 were most likely to be older and have longer professional years, and displayed higher scores of fatigue and work engagement compared with the other latent classes (p < 0.05). Multivariate analysis showed that high cognitive workload increased subjective fatigue, and mental workload may be positively associated with work engagement. Group comparison results indicated that COVID-19 exposure contributed to significantly higher mental workload levels. Conclusions The complex scenario for the care of patients with infectious diseases, especially during an epidemic, raises the need for improved consideration of nurses’ perceived workload, as well as their physical fatigue, work engagement and personal safety when working in public health emergencies.

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Changes in Mental Workload and Motor Performance Throughout Multiple Practice Sessions Under Various Levels of Task Difficulty

Cited by 40 publications

References 79 publications

A Comparison of Mental Workload in Individuals with Transtibial and Transfemoral Lower Limb Loss during Dual-Task Walking under Varying Demand

A Comparison of Mental Workload in Individuals with Transtibial and Transfemoral Lower Limb Loss during Dual-Task Walking under Varying Demand

A Systematic Review of Physiological Measures of Mental Workload

Subtypes of nurses' mental workload and interaction patterns with fatigue and work engagement during coronavirus disease 2019 (COVID-19) outbreak: A latent class analysis

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