How fatigue level is related to sleep disturbances: A large cross-sectional community study

Hyun, Min Kyung

doi:10.1016/j.eujim.2021.102097

Cited by 5 publications

(3 citation statements)

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“…Another objective approach can be International Journal of Intelligent Engineering and Systems, Vol. 16 done by utilizing brain signal generated by EEG, as conducted by Zhang et al [36] and Wang et al [37]. Wang et al method in using 9 features generated by minimal-redundancy-maximal-relevance (MRMR) and brain topography, resulted in better accuracy as high as 80.67%.…”

Section: Discussionmentioning

confidence: 99%

“…Objectively, a person's sleeping condition can be seen from the level of fatigue and sleepiness they have after sleeping. Fatigue and sleepiness are two things that are often used as an indication of not having good quality sleep [14][15][16]. Fatigue can be detected based on monitoring physiological indicators, which is an objectively effective method to determine the fatigue experienced by a person [17].…”

Section: Introductionmentioning

confidence: 99%

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Sleep Quality Assessment from Robust Heart and Muscle Fatigue Estimation Using Supervised Machine Learning

2023

IJIES

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Poor sleep quality is a common sign of a variety of sleep and health problems. Thus, sleep quality assessment is necessary as it can be a first-step predictor of physical and mental health. Several studies were completed for this objective. However, no prior study in sleep quality assessment has explored a comprehensive heart rate variability (HRV) analysis by including feature extraction in the time and frequency domain, and nonlinear analysis. This study proposed a full evaluation of sleep quality, by incorporating multiple physiological signs in subjects to detect exhaustion after a period of sleep. The primary contribution was the development of an algorithm to estimate sleep quality based on the combination of electrocardiography (ECG) and electromyography (EMG) signals by using machine learning. HRV analysis of ECG signal included time domain, frequency domain, and non-linear analysis. Mean power frequency (MPF) was extracted from the EMG signal by using spectral analysis. In addition, determination of fatigue level as an indicator of the subject's sleep quality was evaluated with fatigue severity scale (FSS) questionnaire. Based on results, the accuracy values of logistic regression, random forest, knearest neighbor (k-NN), support vector machine (SVM), and SVM with grid-search were 93%, 93%, 93%, 40%, and 100%, respectively. The proposed method was applicable for investigating sleep quality.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sleep Quality Assessment from Robust Heart and Muscle Fatigue Estimation Using Supervised Machine Learning

2023

IJIES

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“…Previous studies discovered that occupational fatigue, mental workload, and sleep hygiene had perilous consequences for workers 5,6,7,8 . Furthermore, fatigue can have a potentially adverse effect on metabolic and cardiovascular health, risk of cancers, and mental health 9 , mood changes, job stress, cognitive degradation 10,11 , sleep disturbance 12,13,14 , the incidence of MSDs 15,16,17 , and absenteeism from work 18 , as well as a safety concern, behavior, performance, and climate 19,20,21,22 . It can also affect organizational productivity, ability to perform, decreased human performance level 23 , risk of injury rates, probability rate of the incident, quality of life, wellbeing, accident 24,25,12,6 , and at-risk behavior causing death among oil and gas workers.…”

Section: Introductionmentioning

confidence: 99%

Assessing the relationship between mental workload, sleep quality, and work fatigue among oil and gas workers in Jambi Province: A Partial least squares-structural equation modeling (PLS-SEM)

Kusmawan

2024

Preprint

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Background: Occupational fatigue among oil and gas workers can have perilous consequences related to safety, health, economy, and wellbeing aspect. This makes it necessary to discover major factors related to fatigue and implement appropriate prevention programs and education. Therefore, this study aimed to investigate the relationship between mental workload, sleep quality, and occupational fatigue in oil and gas office workers in Jambi Province. Design and methods: Mental workload, sleep quality, and occupational fatigue were measured using the NASA-Total Load Index (TLX), the Pittsburgh Sleep Quality Index (PSQI), and the Indonesian Questionnaire Measuring Feelings of Work Fatigue (KAUPK2), respectively. A Partial Least Square-Structural Equation Modeling (PLS-SEM) approach was used to determine the association between mental workload, sleep quality, and occupational fatigue. Results: Out of the 117 oil and gas workers in Jambi Province who participated in this study, 58.6% were male, 54.3% had Senior High School or less, 85.3% were not smoking, and 88.8% were married. The mean with a standard deviation of body height, weight, and mass index were 165.35 ± 5.46 cm, 64.65 ± 6.89 kg, and 23.64 ± 2.23, respectively. Respondents had working experience from 0.17 to 34 years with a mean of 16.23 and a standard deviation of 8.93 years. The PLS-SEM model illustrated that the direct effect of mental workload on occupational fatigue was not significant (path coefficient: 0.179; p <0.036). Meanwhile, the mental workload had a significant effect on sleep quality (path coefficient: -0.405; p 0.000), which significantly affected fatigue (path coefficient: -0.035; p = 0.709). This indicated that the effect of workload on fatigue was fully mediated by sleep quality. Conclusions: The effect of sleep quality was very significant to overcome the fatigue level of an employee when the mental workload increases. This study revealed that occupational fatigue may be reduced by implementing mental workload coping strategies, regularly measuring, and a sleep hygiene program among oil and gas workers.

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Willingness to pay for integrative healthcare services to treat sleep disturbances: Evidence from a nationwide survey

Hyun¹

2023

European Journal of Integrative Medicine

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How fatigue level is related to sleep disturbances: A large cross-sectional community study

Cited by 5 publications

References 61 publications

Sleep Quality Assessment from Robust Heart and Muscle Fatigue Estimation Using Supervised Machine Learning

Sleep Quality Assessment from Robust Heart and Muscle Fatigue Estimation Using Supervised Machine Learning

Assessing the relationship between mental workload, sleep quality, and work fatigue among oil and gas workers in Jambi Province: A Partial least squares-structural equation modeling (PLS-SEM)

Willingness to pay for integrative healthcare services to treat sleep disturbances: Evidence from a nationwide survey

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