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Introduction Mental fatigue is a psychobiological state caused by prolonged periods of demanding cognitive activity that has negative implications on many aspects in daily life. Caffeine and carbohydrate ingestion have been shown to be able to reduce these negative effects of mental fatigue. Intake of these substances might however be less desirable in some situations (e.g., restricted caloric intake, Ramadan). Rinsing caffeine or glucose within the mouth has already been shown to improve exercise performance. Therefore, we sought to evaluate the effect of frequent caffeine-maltodextrin (CAF-MALT) mouth rinsing on mental fatigue induced by a prolonged cognitive task. Methods Ten males (age 23 ± 2 years, physical activity 7.3 ± 4.3 h/week, low CAF users) performed two trials. Participants first completed a Flanker task (3 min), then performed a 90-min mentally fatiguing task (Stroop task), followed by another Flanker task. Before the start and after each 12.5% of the Stroop task (eight blocks), subjects received a CAF-MALT mouth rinse (MR: 0.3 g/25 ml CAF: 1.6g/25 ml MALT) or placebo (PLAC: 25 ml artificial saliva). Results Self-reported mental fatigue was lower in MR ( p  = 0.017) compared to PLAC. Normalized accuracy (accuracy first block = 100%) was higher in the last block of the Stroop in MR ( p  = 0.032) compared to PLAC. P2 amplitude in the dorsolateral prefrontal cortex (DLPFC) decreased over time only in PLAC ( p  = 0.017). Conclusion Frequent mouth rinsing during a prolonged and demanding cognitive task reduces mental fatigue compared to mouth rinsing with artificial saliva.

Cognitive fatigue is a psychological condition characterized by opinions of fatigue and weakened cognitive functioning owing to constant stress. Cognitive fatigue is a critical condition that can significantly impair attention and performance, among other cognitive abilities. Monitoring this condition in real-world settings is crucial for detecting and managing adequate break periods. Bridging this research gap is significant, as it has substantial implications for developing more effectual and less intrusive wearable devices to track cognitive fatigue. Many models consider intricate biosignals, like electrooculogram (EOG), electroencephalogram (EEG), or detection of basic heart rate inconstancy parameters. Artificial Intelligence (AI)-driven methods aid in handling and categorizing these biosignals, recognizing fatigue-related patterns with higher accuracy. This technique is essential in high-demand surroundings such as education, healthcare, and workplaces or where cognitive fatigue may affect decision-making and performance. Therefore, the study presents an Exploratory Analysis of Longitudinal Artificial Intelligence for Cognitive Fatigue Detection Using Neurophysiological Based Biosignal Data (EALAI-CFDNBD) approach. The main aim of the EALAI-CFDNBD model is to detect cognitive fatigue using neurophysiological-based biosignal data. Primarily, the EALAI-CFDNBD model utilized the linear scaling normalization (LSN) model to ensure that the input features were appropriately scaled for subsequent analysis. Furthermore, the binary olympiad optimization algorithm (BOOA)-based feature selection is utilized to extract the most informative features, reducing the data dimensionality. The graph convolutional autoencoder (GCA) classifier is employed to classify cognitive fatigue detection. Finally, the multi-objective hippopotamus optimization (MOHO) method is utilized for parameter tuning, optimizing the model’s hyperparameters to enhance overall detection accuracy. An extensive range of simulations is accomplished using the MEFAR dataset to establish a good classification outcome of the EALAI-CFDNBD method. The experimental validation of the EALAI-CFDNBD technique portrayed a superior accuracy value of 97.59% over the recent methods.

Background This study aims to investigate the prevalence and associated factors of perceived fatigability among older people aged 60 + years in Nanjing Municipality of China. Methods In this cross-sectional survey conducted in Nanjing municipality of China in 2023, 5,556 adults aged 60 + years were randomly selected from urban and rural communities. Perceived fatigability was measured using the validated simplified-Chinese version of Pittsburgh Fatigability Scale (PFS-CHN). Mixed-effects logistic regression models were introduced to calculate odds ratios (ORs) and 95% confidence intervals (95%CIs) for identifying independent associated factors associated with fatigability. Results The prevalence of physical fatigability was 58.9% (95% CI = 57.6, 60.2) among overall participants, with 57.8% (95% CI = 55.9, 59.7) in men and 60.0% (95% CI = 58.2, 61.8) in women. Age-specific prevalence rates were 56.6% (95% CI = 54.9, 58.3), 60.1% (95% CI = 57.8, 62.4), and 68.0% (95% CI = 64.3, 71.6) for those aged 60–69, 70–79, and 80 + years, respectively. The prevalence of mental fatigability was 58.4% (95% CI = 57.1, 59.6) among overall participants, with 57.0% (95% CI = 55.1, 58.8) in men and 59.6% (95% CI = 57.8, 61.4) in women. Age-specific prevalence rates were 56.4% (95% CI = 54.7, 58.1), 59.6% (95% CI = 57.3, 61.9), and 65.1% (95% CI = 61.3, 68.8) among individuals aged 60–69, 70–79, and 80 + years, respectively. Either physical or mental fatigability was identified to be associated with age, depressive symptoms, anxiety symptoms, sleep quality, educational level, alcohol drinking, and physical activity. Interestingly, residence area was examined in relation to physical but not mental fatigability among older adults in the study. Conclusions Perceived fatigability is prevalent among older adults and exhibits strong associations with age, educational level, alcohol drinking, PA, depressive and anxiety symptoms, and sleep quality in regional China. These findings suggested that fatigability is a public health problem among older adults in China and particular attention should be paid to burden caused by fatigability and its associated factors. Clinical trial number Not applicable.

Modern mental fatigue detection methods include many parameters for evaluation. For example, many researchers use human subjective evaluation or driving parameters to assess this human condition. Development of a method for detecting the functional state of mental fatigue is an extremely important task. Despite the fact that human operator support systems are becoming more and more widespread, at the moment there is no open-source solution that can monitor this human state based on eye movement monitoring in real time and with high accuracy. Such a method allows the prevention of a large number of potential hazardous situations and accidents in critical industries (nuclear stations, transport systems, and air traffic control). This paper describes the developed method for mental fatigue detection based on human eye movements. We based our research on a developed earlier dataset that included captured eye-tracking data of human operators that implemented different tasks during the day. In the scope of the developed method, we propose a technique for the determination of the most relevant gaze characteristics for mental fatigue state detection. The developed method includes the following machine learning techniques for human state classification: random forest, decision tree, and multilayered perceptron. The experimental results showed that the most relevant characteristics are as follows: average velocity within the fixation area; average curvature of the gaze trajectory; minimum curvature of the gaze trajectory; minimum saccade length; percentage of fixations shorter than 150 ms; and proportion of time spent in fixations shorter than 150 milliseconds. The processing of eye movement data using the proposed method is performed in real time, with the maximum accuracy (0.85) and F1-score (0.80) reached using the random forest method.

Background Many individuals with neurodegenerative (NDD) and immune-mediated inflammatory disorders (IMID) experience debilitating fatigue. Currently, assessments of fatigue rely on patient reported outcomes (PROs), which are subjective and prone to recall biases. Wearable devices, however, provide objective and reliable estimates of gait, an essential component of health, and may present objective evidence of fatigue. This study explored the relationships between gait characteristics derived from an inertial measurement unit (IMU) and patient-reported fatigue in the IDEA-FAST feasibility study. Methods Participants with IMIDs and NDDs (Parkinson's disease (PD), Huntington's disease (HD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), primary Sjogren’s syndrome (PSS), and inflammatory bowel disease (IBD)) wore a lower-back IMU continuously for up to 10 days at home. Concurrently, participants completed PROs (physical fatigue (PF) and mental fatigue (MF)) up to four times a day. Macro (volume, variability, pattern, and acceleration vector magnitude) and micro (pace, rhythm, variability, asymmetry, and postural control) gait characteristics were extracted from the accelerometer data. The associations of these measures with the PROs were evaluated using a generalised linear mixed-effects model (GLMM) and binary classification with machine learning. Results Data were recorded from 72 participants: PD = 13, HD = 9, RA = 12, SLE = 9, PSS = 14, IBD = 15. For the GLMM, the variability of the non-walking bouts length (in seconds) with PF returned the highest conditional R2, 0.165, and with MF the highest marginal R2, 0.0018. For the machine learning classifiers, the highest accuracy of the current analysis was returned by the micro gait characteristics with an intrasubject cross validation method and MF as 56.90% (precision = 43.9%, recall = 51.4%). Overall, the acceleration vector magnitude, bout length variation, postural control, and gait rhythm were the most interesting characteristics for future analysis. Conclusions Counterintuitively, the outcomes indicate that there is a weak relationship between typical gait measures and abnormal fatigue. However, factors such as the COVID-19 pandemic may have impacted gait behaviours. Therefore, further investigations with a larger cohort are required to fully understand the relationship between gait and abnormal fatigue.

The mental fatigue of crane operators can pose a serious threat to construction safety. To enhance the safety of crane operations on construction sites, this study proposes a rotary switch semi-dry electrode for detecting the mental fatigue of crane operators. This rotary switch semi-dry electrode overcomes the problems of the large impedance value of traditional dry electrodes, the cumbersome wet electrode operation, and the uncontrollable outflow of conductive liquid from traditional semi-dry electrodes. By designing a rotary switch structure inside the electrode, it allows the electrode to be turned on and used in motion, which greatly improves the efficiency of using the conductive fluid and prolongs the electrode’s use time. A conductive sponge was used at the electrode’s contact end with the skin, improving comfort and making it suitable for long-term wear. In addition, in this study, the multifractal detrend fluctuation analysis (MF-DFA) method was used to detect the mental fatigue state of crane operators. The results indicate that the MF-DFA is more responsive to the tiredness traits of individuals than conventional fatigue detection methods. The proposed rotary switch semi-dry electrode can quickly and accurately detect the mental fatigue of crane operators, provide support for timely warning or intervention, and effectively reduce the risk of accidents at construction sites, enhancing construction safety and efficiency.

Apolipoprotein E ε4 allele ( APOE4 ) has been shown to associate with increased susceptibility to SARS-CoV-2 infection and COVID-19 mortality in some previous genetic studies, but information on the role of APOE4 on the underlying pathology and parallel clinical manifestations is scarce. Here we studied the genetic association between APOE and COVID-19 in Finnish biobank, autopsy and prospective clinical cohort datasets. In line with previous work, our data on 2611 cases showed that APOE4 carriership associates with severe COVID-19 in intensive care patients compared with non-infected population controls after matching for age, sex and cardiovascular disease status. Histopathological examination of brain autopsy material of 21 COVID-19 cases provided evidence that perivascular microhaemorrhages are more prevalent in APOE4 carriers. Finally, our analysis of post-COVID fatigue in a prospective clinical cohort of 156 subjects revealed that APOE4 carriership independently associates with higher mental fatigue compared to non-carriers at six months after initial illness. In conclusion, the present data on Finns suggests that APOE4 is a risk factor for severe COVID-19 and post-COVID mental fatigue and provides the first indication that some of this effect could be mediated via increased cerebrovascular damage. Further studies in larger cohorts and animal models are warranted.

This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.29% in identifying fatigue from original ECG data, 2D spectral characteristics and physiological information of subjects. In comparison to traditional methods, such as Support Vector Machines (SVMs) and Random Forests (RFs), as well as other deep learning methods, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), the proposed approach demonstrates significantly improved experimental outcomes. Overall, this study offers a promising solution for accurately recognizing fatigue through the analysis of physiological signals, with potential applications in sports and physical fitness training contexts.

Control room operators encounter a substantial risk of mental fatigue, which can reduce their human reliability by diminishing concentration and responsiveness, leading to unsafe operations. There is value in detection of individuals’ mental fatigue status in the workplace. This study introduces a new method for mental fatigue detection (MFD) that combines computer vision and machine learning. Traditional methods for MFD typically rely on multi-dimensional data for fatigue analysis and detection, which can be challenging to apply in a real situation. The traditional methods such as the use of biological data, e.g., electrocardiograms, require operators to be in constant contact with sensors, while this study utilizes computer vision to collect facial data, and a machine learning model to assess fatigue states. The developed machine learning method consists both Deep Residual Network and Random Forest (DRN-RF). A comparison with existing MFD methods, including K Nearest Neighbors and Gradient Boosting Machine, has been carried out. The results show that the accuracy of the DRN-RF model reaches 94.2% and the deviation is 0.004. Evidently, the DRN-RF model demonstrates high accuracy and stability. Overall, the proposed method has the potential to contribute to improving the safety of process system operations, particularly in the aspect of human factor management.

Background Young adult patients with cancer have to deal with their disease in an eventful phase of life. A common side effect of cancer and its treatment is cancer-related fatigue (CRF), a phenomenon which can thwart successful coping with developmental tasks. The aims of this study were to assess the psychometric properties of the EORTC QLQ-FA12, a new instrument for assessing physical, emotional and cognitive fatigue, in young adults with cancer, and to propose a cut-off value that indicates a need for further more specific diagnostics. Methods In a sample of young adults who were first diagnosed with cancer between the ages of 18 and 39 years old, we assess the composite and item reliabilities as well as discriminant validity of the subscales for the EORTC QLQ-FA12. We also discuss two possible ways to calculate a summarizing score when conducting a receiver operating characteristic (ROC) analysis to find the cut-off value. Results The EORTC QLQ-FA12 fit the sample (CFI = 0.96, SRMR = 0.04), had discriminant validity regarding its subscales and every subscale showed convergent validity (composite reliabilities were 0.92 for physical, 0.89 for emotional and 0.74 for cognitive fatigue). The sum of the first ten items with a range of 0 to 30 revealed a cut-off value of twelve or more with 91% sensitivity and 77% specificity. Conclusion The new instrument EORTC QLQ-FA12 is able to distinguish between physical, emotional, and cognitive fatigue in young adult patients. It enables us to study different concepts of general fatigue without the need for additional items, and can be used as a screening instrument for young adults. Future research should investigate the multidimensional character of CRF.