Levels Of Physical Fatigue Research Articles

Comprehensive MRI assessment reveals subtle brain findings in non-hospitalized post-COVID patients with cognitive impairment.

Impaired cognitive ability is one of the most frequently reported neuropsychiatric symptoms in the post-COVID phase among patients. It is unclear whether this condition is related to structural or functional brain changes. In this study, we present a multimodal magnetic resonance imaging study of 36 post-COVID patients and 36 individually matched controls who had a mild form of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) infection from March 2020 to February 2022. This study aimed to investigate structural and functional brain alterations and their correlation with post-COVID symptoms and neurocognitive functions. The study protocol comprised an assessment of physical fatigue [Fatigue Severity Scale (FSS)], mental fatigue (Mental Fatigue Scale (MFS)], depression [Montgomery Asberg Depression Rating Scale (MADRS)], anxiety [Hospital Anxiety and Depression Scale (HAD)], post-COVID Symptoms Severity Score, and neurocognitive status [Repeatable Battery for the Assessment of Neuropsychological Status Update (RBANS)]. The magnetic resonance imaging protocol included morphological sequences, arterial spin labeling (ASL) and dynamic susceptibility contrast-enhanced (DSC) perfusion, diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (fMRI) sequences. Using these protocols, the assessments of macrostructural abnormalities, perfusion, gray matter density, white matter integrity, and brain connectivity were performed. Post-COVID patients had higher levels of physical fatigue, mental fatigue, depression, and anxiety than controls and showed cognitive impairment in all the RBANS domains except in Visuospatial/Construction. The subjective mental fatigue correlated with objective impaired cognitive ability in the RBANS test, particularly in the Attention domain. There were no differences between patients and controls regarding macrostructural abnormalities, regional volumes, regional perfusion metrics, gray matter density, or DTI parameters. We observed a significant positive correlation between RBANS Total Scale Index score and gray matter volume in the right superior/middle-temporal gyrus (p < 0.05) and a significant negative correlation between the white matter integrity and post-COVID symptoms (p < 0.05) in the same area. The connectivity differences were observed between patients and controls in a few regions, including the right middle frontal gyrus, an important area of convergence of the dorsal and ventral attention networks. We also noted a positive correlation between post-COVID symptoms and increased connectivity in the right temporoparietal junction, which is part of the ventral attention system. In non-hospitalized subjects with post-COVID, we did not find any structural brain changes or changes in perfusion, compared to controls. However, we noted differences in connectivity within an important area for attention processes, which may be associated with post-COVID brain fog.

Automated detection of physical fatigue in transportation maintenance workers through physiological and motion data

Lifting bags of dry concrete mix has caused significant physical fatigue and work-related musculoskeletal disorders (WMSDs) in transportation maintenance workers. Detection of physical fatigue can effectively prevent developing WMSDs. However, there lacks studies exploring the automated detection of physical fatigue in transportation activities. Additionally, previous efforts on physical fatigue detection, primarily focused in the construction sector, may not be directly applicable to transportation activities, due to different activity duration, intensity, and frequency. Also, prior detection methodologies have been examined under laboratory conditions, and their efficacy in real-world environments remains unverified. Therefore, this study explored physical fatigue detection methods by conducting field experiments with 29 transportation maintenance workers lifting bags of dry concrete mix in a highway maintenance facility. Participants’ physical fatigue levels were measured using electromyography (EMG), electrodermal activity, and/or heart rate (HR) and motion data with different machine learning algorithms. Results indicated that the Fully Connected Neural Networks algorithm can achieve the highest accuracy (87.84%) with EMG, HR, and motion data for classifying physical fatigue levels in transportation maintenance workers. This study demonstrated the feasibility of automated physical fatigue detection, which can be implemented as a monitoring tool of physical fatigue for enhancing health of transportation practitioners.

The impact of the COVID-19 pandemic on the psychosomatic status of emeritus professors: The importance of active lifestyles

AbstractPurposeThe COVID-19 pandemic has had an enormous negative impact on the psychosomatic status of the general population, and especially of more vulnerable populations, such as older people. The present study aimed to assess changes in psychosomatic status before and after the COVID-19 pandemic among emeritus professors over 70 years of age.Materials/MethodsA total of 56 emeritus professors aged 77.4 ± 6.1 (74.1% male) participated in the study. The study was conducted between September 2022 and January 2023, after approximately two and a half years of the COVID-19 pandemic. Sociodemographic characteristics, including socioeconomic status, were assessed in the questionnaire, along with active involvement in academic life and healthcare/sports. Physical and mental fatigue were assessed using a self-report questionnaire, and the impact of fatigue on physical and cognitive function was measured using the Fatigue Impact Scale. Participants evaluated their physical/mental fatigue and physical/cognitive function before and after the COVID-19 pandemic.ResultsAmong the participating emeritus professors, 15% reported higher levels of physical fatigue (Z = −1.992, P = 0.046, r = 0.28) and mental fatigue (Z = −2.154, P = 0.031, r = 0.31) following the COVID-19 pandemic. A significant difference was found in physical function assessed before and after the COVID-19 pandemic, t(51) = −2.986, P = 0.004, g = 0.22, while cognitive function showed a non-significant difference, t(51) = −1.265, P = 0.212, g = 0.12. After the COVID-19 pandemic, 26.9% of participants reported reduced physical function and 19.2% showed reduced cognitive function. Infection with the COVID-19 virus resulted in increased mental fatigue and reduced cognitive function among the participating emeritus professors.ConclusionThe COVID-19 pandemic (and the introduction of restrictions on social activities) had a substantial and similar impact on the psychosomatic status of emeritus professors at both medical and sports universities. Furthermore, the reduction in physical and mental/cognitive function was exacerbated in those who were infected by SARS-CoV-2. Importantly, however, the overall impact of the COVID-19 pandemic was smaller in the population of emeritus professors than in the general elderly population. This may be due to the better maintenance of physical and mental activity in the former population, thus emphasising the importance of remaining physically and mentally active in old age, especially in the context of critical life events.

Relationship between physical fatigue and mental fatigue based on multimodal measurement under different load levels

Based on multimodal measurement methods of NASA task load index (NASA-TLX), task performance, surface electromyography (sEMG), heart rate (HR), and functional near-infrared spectroscopy (fNIRS), this study conducted experimental measurements and analyses under 16 different load levels of physical fatigue and mental fatigue combination conditions. This study observed the interaction between physical fatigue and mental fatigue at different levels, and at the subjective level, the effect of physical fatigue on mental fatigue was greater than that of mental fatigue on physical fatigue. Secondly, the results of fNIRS analysis showed that the premotor cortex is affected by physical fatigue, and the dorsolateral prefrontal cortex is affected by mental fatigue. Finally, this study constructed a fatigue classification model with an accuracy of 95.3%, which takes multimodal physiological data as input and 16 fatigue states as output. The research results will provide a basis for fatigue analysis, evaluation, and improvement in complex working situations.

Interactions between fatigue and urinary quality of life in patients with Multiple Sclerosis

IntroductionNeurogenic bladder as well as fatigue related symptoms are common in patients with Multiple Sclerosis (MS) and have a significant impact on the patients’ Quality of Life (QoL). The aim of this study is to investigate the relationship between fatigue related symptomatology (FRS) and Urinary Quality of Life (UQoL). MethodsA total of 120 consecutive MS patients were recruited from the Outpatient Clinic of Demyelinating Diseases (Second Dept. of Neurology, Attikon University Hospital Greece). Participants were then asked to complete the Modified Fatigue Impact Scale (MFIS) and the Short Form Qualiveen questionnaire. Demographic and bladder function related characteristics (incontinence, urinary frequency, use of intermittent catheterization) were collected. ResultsThe physical and cognitive dimensions of MFIS had a moderate to high correlation with SF Qualiveen (r = 0.403, p <.000), (r = 0.329, p <.000).Multiple linear regression produced a fitted model (R2 = 0.150, F(3,111) = 5.554, p =.001) in IC use (β = 1.086, p =.036) and the physical dimension of MFIS (β = 0.66, p =.046) significantly predicted the SF Qualiveen score. ConclusionUQoL had a moderate correlation with both physical and cognitive dimensions of fatigue. Patients with MS who experience lower levels of physical fatigue and/or manage their neurogenic bladder symptomatology (mainly with the use of intermittent catheterization) appear to have higher levels of UQoL. Due to the versatile and subjective nature of both fatigue related and neurogenic bladder symptoms, more focused studies utilizing objective evaluation tools (e.g urodynamic urine bladder study) are necessary.

ANALISIS ASUHAN KEPERAWATAN PADA PASIEN GAGAL GINJAL KRONIK DENGAN PENERAPAN TERAPI PIJAT PUNGGUNG TEHADAP PENURUNAN KELELAHAN FISIK

Introduction: Chronic kidney failure is a disease that causes the function of the kidneys to decrease until they cannot perform their functions properly.&#x0D; Methods: The research method uses quantitative research with a quasi experimental one group pretest-posttest design. The respondents used in this application were 2 patients with chronic kidney failure. Data collection techniques used fatigue observation sheets, as well as standard back massage therapy procedures. Application is carried out once every 3 days, namely during the day.&#x0D; Result: The results of the application show the fatigue level score in the patient Mrs. On the first day of the pretest there were 37 heavy categories and on the third day of the posttest the results were 12 light categories. And to Mr. A obtained a fatigue level score on the first day of the pretest of 25 in the medium category. And on the third day of the posttest, it was found that 9 experienced a decline from moderate to mild.&#x0D; Conclusion: The application of back massage therapy in patients with chronic kidney failure has proven effective in overcoming the problem of physical fatigue, which is indicated by changes in the results of the score on the level of physical fatigue. This back massage therapy can be continued by the family when the patient's fatigue reappears, so that it can solve the patient's nursing problems.

Impact of Interruptions During Screening Mammography on Physician Well-Being and Patient Care

ObjectiveTo assess the impact of interruptions on radiologists’ efficiency, accuracy, and job satisfaction in interpreting screening mammograms. MethodsThis institutional review board–approved retrospective reader study recruited nine breast radiologists from a single academic institution [name withheld] to interpret 150 screening mammograms performed between December 1, 2008, and December 31, 2015 under two different reading conditions, as follows: (1) uninterrupted batch reading and (2) interrupted reading. The 150 cases consisted of 125 normal mammograms and 25 mammograms with subtle breast cancers. Cases were divided into two groups of 75 cases each (cohort 1 and cohort 2), with a comparable distribution of cancer cases. Four rounds of 75 cases each were conducted with a 6-week washout period between rounds 2 and 3. After completing each interpretation session, readers completed a seven-question survey, assessing perceptions of mental and physical effort, level of frustration, and performance satisfaction. Clinical performance metrics (reading time, recall rate, sensitivity, specificity, accuracy, and positive predictive value 1) were calculated. ResultsRecall rates were significantly (P = .04) higher during interrupted reading sessions (35.4%) than they were during uninterrupted batch reading sessions (31.4%). Accuracy was significantly (P = .049) worse in the interrupted reading sessions (69.5%), compared with uninterrupted sessions (73.6%). Differences in overall image interpretation times were not statistically significant (P = .065). Compared with uninterrupted batch reading sessions, readers during interrupted sessions reported feeling busier (P < .001), encountered higher levels of cognitive demand (P = .005), experienced elevated levels of physical fatigue (P = .004), and expressed lower levels of satisfaction with their performance (P = .041). ConclusionInterruptions during interpretation of screening mammography have deleterious effects on physician performance and their sense of well-being.

A Physical Fatigue Evaluation Method for Automotive Manual Assembly: An Experiment of Cerebral Oxygenation with ARE Platform

Due to the complexity of the automobile manufacturing process, some flexible and delicate assembly work relies on manual operations. However, high-frequency and high-load repetitive operations make assembly workers prone to physical fatigue. This study proposes a method for evaluating human physical fatigue for the manual assembly of automobiles with methods: NIOSH (National Institute for Occupational Safety and Health), OWAS (Ovako Working Posture Analysis System) and RULA (Rapid Upper Limb Assessment). The cerebral oxygenation signal is selected as an objective physiological index reflecting the human fatigue level to verify the proposed physical fatigue evaluation method. Taking auto seat assembly and automobile manual assembly as an example, 18 group experiments were carried out with the ARE platform (Augmented Reality-based Ergonomic Platform). Furthermore, predictions of metabolic energy expenditure were performed for experiments in Tecnomatix Jack. Finally, it is concluded that the proposed physical fatigue evaluation method can reflect the human physical fatigue level and is more accurate than the evaluation of metabolic energy consumption in Tecnomatix Jack because of the immersion that comes with the AR devices and the precision that comes with motion capture devices.

Identification of runner fatigue stages based on inertial sensors and deep learning.

Introduction: Running is one of the most popular sports in the world, but it also increases the risk of injury. The purpose of this study was to establish a modeling approach for IMU-based subdivided action pattern evaluation and to investigate the classification performance of different deep models for predicting running fatigue. Methods: Nineteen healthy male runners were recruited for this study, and the raw time series data were recorded during the pre-fatigue, mid-fatigue, and post-fatigue states during running to construct a running fatigue dataset based on multiple IMUs. In addition to the IMU time series data, each participant's training level was monitored as an indicator of their level of physical fatigue. Results: The dataset was examined using single-layer LSTM (S_LSTM), CNN, dual-layer LSTM (D_LSTM), single-layer LSTM plus attention model (LSTM + Attention), CNN, and LSTM hybrid model (LSTM + CNN) to classify running fatigue and fatigue levels. Discussion: Based on this dataset, this study proposes a deep learning model with constant length interception of the raw IMU data as input. The use of deep learning models can achieve good classification results for runner fatigue recognition. Both CNN and LSTM can effectively complete the classification of fatigue IMU data, the attention mechanism can effectively improve the processing efficiency of LSTM on the raw IMU data, and the hybrid model of CNN and LSTM is superior to the independent model, which can better extract the features of raw IMU data for fatigue classification. This study will provide some reference for many future action pattern studies based on deep learning.

Impact of depression on the perception of fatigue and information processing speed in a cohort of multiple sclerosis patients

BackgroundInformation processing speed is commonly impaired in people with multiple sclerosis (PwMS). However, depression and fatigue can affect the cognitive profile of patients: fatigue has a negative impact from the disease’s earliest stage and a reduced information processing speed is often associated with higher levels of depression. Therefore, the aim of this study was to investigate the correlations between information processing speed and physical fatigue in a cohort of Italian PwMS from a single center, considering the effect of depression.MethodsTwo hundred (W = 128; mean age = 39.83 years; SD = 11.86) PwMS, from the Bari University Hospital, underwent testing for processing speed (Symbol Digit Modalities Test [SDMT]), fatigue level (Fatigue Severity Scale [FSS]), and depression (Beck’s Depression Inventory [BDI]).ResultsStatistically significant correlations emerged between SDMT and FSS, SDMT and BDI, FSS and BDI. Mediation analyses revealed that while physical fatigue had no significant direct negative effect on information processing speed (z=-0.891; p > 0.05), depression predicted the relationship between fatigue and information processing speed (z=-2.181; p < 0.05).ConclusionOur findings showed that cognitive performance at SDMT was not affected by patients’ perceived level of physical fatigue, but by depression. The presence of a high BDI score mediates the physical fatigue on cognitive performance impact.

Identification and Classification of Physical Fatigue in Construction Workers Using Linear and Nonlinear Heart Rate Variability Measurements

Several studies have analyzed heart rate variability (HRV) using nonlinear methods, such as approximate entropy, the largest Lyapunov exponent, and correlation dimension in patients with cardiovascular disorders. However, few studies have used nonlinear methods to analyze HRV in order to determine the level of physical fatigue experienced by construction workers. As a result, to identify and categorize physical fatigue in construction workers, the current study examined the linear and nonlinear approaches of HRV analysis. Fifteen healthy construction workers (mean age, 33.2±6.9 years) were selected for this study. A textile-based wearable sensor monitored each participant’s HRV after they completed 60 min of bar bending and fixing tasks. At baseline, 15, 30, 45, and 60 min into the task, participants were given the Borg-20 to measure their subjective levels of physical fatigue. Nonlinear [e.g., R-R interval (RRI) variability, entropy, detrended fluctuation analysis] and linear (e.g., time- and frequency-domain) HRV parameters were extracted. Five machine learning classifiers were used to identify and discern different physical fatigue levels. The accuracy and validity of the classifier models were evaluated using 10-fold cross-validation. The classification models were developed by either combining or individualized HRV features derived from linear and nonlinear HRV analyses. In the individualized feature sets, time-domain features had the highest classification accuracy (92%) based on the random forest (RF) classifier. The combined feature (i.e., the time-domain and nonlinear features) sets showed the highest classification accuracy (93.5%) using the RF classifier. In conclusion, this study showed that both linear and nonlinear HRV analyses can be used to detect and classify physical fatigue in construction workers. This research offers important contributions to the industry by analyzing the variations in linear and nonlinear HRV parameters in response to construction tasks. This study demonstrates that HRV values changed significantly in response to physical work, indicating a change in the relative activity of cardiac autonomic functions as a result of fatigue. Using the ways in which HRV parameters vary in response to increased workloads provides a sensitive marker for contrasting construction workers with and without cardiovascular disease. It also allows the site manager to track how quickly workers fatigue, so that they can switch up their workload to reduce the likelihood that any one worker would get severely exhausted, or to suggest that workers who are already severely fatigued take a break to prevent further injury.

Examination of Fatigue Situations of Nurses in the Prolonged COVID-19 Infection

Objective: This study was conducted to determine the effect of prolonged Covid-19 pandemic on the fatigue status of nurses. Methods: The effect of prolonged Covid-19 on the fatigue status of nurses was evaluated, and 185 nurses who had Covid-19 were included in this descriptive study. The data were collected using the "Patient Information Form" and "Chalder Fatigue Scale" in the electronic environment during the working hours of the nurses who had Covid-19 and at times that would not disrupt their workflow order. Results: It was determined that 62.6% of the nurses who were tired were female, 61.6% were tired, and the level of physical fatigue was higher than mental fatigue. The most common symptom among nurses during the acute and prolonged Covid-19 period was fatigue. There was a statistically significant difference between fatigue and headache(p&amp;lt;0.05). Conclusion: As a result of the study, it was determined that the majority of the nurses experienced fatigue after the Covid-19 pandemic, the severity of physical fatigue was higher than the severity of mental fatigue, and headache affected the level of fatigue. In line with these results, considering the negative impact of fatigue on quality of life and daily functionality, it is recommended to conduct periodic physical and mental health screenings for nurses and to provide training on methods such as effective exercise and stress management to help nurses cope with fatigue and regain optimal health.

Psychological changes in athletes infected with Omicron after return to training: fatigue, sleep, and mood.

This study aims to analyze the changes of approximately 1 month in fatigue, sleep, and mood in athletes after returning to training following infection with the COVID-19 Omicron strain and provide recommendations for returning to training after infection. Two hundred and thirty professional athletes who had returned to training after being infected with COVID-19 in December 2022 were recruited to participate in three tests conducted from early January 2023. The second test was completed approximately 1 week after the first, and the third was completed about 2 weeks after the second. Each test consisted of completing scales and the exercise-induced fatigue measure. The scales included a visual analog scale, the Athens Insomnia Scale for non-clinical application, and the Depression-Anxiety-Stress scale. The exercise task was a six-minute stair climb test, and athletes evaluated subjective fatigue levels before and after exercise using another Visual Analog Scale and the Karolinska Sleepiness Scale. After returning to training, athletes' physical fatigue decreased initially but increased as training progressed. Cognitive fatigue did not change significantly. The exercise task led to elevated levels of physical fatigue after a longer duration of training. Sleep quality problems decreased rapidly after the start of training but remained stable with prolonged training. Depression levels continued to decline, while anxiety levels only reduced after a longer duration of training. Stress levels decreased rapidly after the start of training but did not change with prolonged training. Athletes who return to training after recovering from COVID-19 experience positive effects on their fatigue, sleep, and mood. It is important to prioritize anxiety assessment and interventions during the short period after returning and to continue monitoring fatigue levels and implementing recovery interventions over a longer period of time.

Burnout Experience among Iranian Teachers during the COVID-19 Pandemic

This study represents the first attempt to explore teachers&amp;rsquo; burnout experience during one of the most critical phases of the COVID-19 pandemic in Iran. The main goals were to estimate the prevalence of burnout in Iranian men and women teachers and analyse the association of sociodemographic variables on burnout levels. A total of 125 Iranian teachers participated in this study. Results suggest that Iranian teachers perceive high levels of burnout during the COVID-19 pandemic, with 24% of participants reporting high levels of overall burnout. Furthermore, 32% of the sample reported high levels of physical fatigue, 24.8% high levels of cognitive weariness, and 17.6% high levels of emotional exhaustion, suggesting that a considerable number of Iranian teachers are already struggling to deal with their job-related stressors. Significant differences were found for sex, with men reporting higher exhaustion than women. No significant differences were found between other sociodemographic characteristics and burnout. We analyse the results from a cultural perspective and discuss its implications for future research and psychological interventions in schools. Future studies explore how school contextual variables can mediate or moderate the effect of sociodemographic characteristics on teachers&amp;rsquo; burnout. Intervention programmes should consider local schools&amp;rsquo; characteristics and be sensitive to teachers&amp;rsquo; individual needs and consider the added weight of the pandemic to teachers&amp;rsquo; daily job-related stressors.

Harmonious Passion at Work: Personal Resource for Coping with the Negative Relationship between Burnout and Intrinsic Job Satisfaction in Service Employees.

Research about harmonious passion as a personal resource that can have a protective effect in situations of stress and burnout is scarce but growing. Considering the job demands-resources (JD-R) model, the present study aims to address the above gaps by exploring the moderating role of harmonious passion at work in the relationship between burnout (physical fatigue, cognitive weariness, and emotional exhaustion) and intrinsic job satisfaction. The study sample consisted of 748 workers from service organizations (front-line employees) in southern Spain (Mage = 35.51, SD = 10.06, 52% women). Using statistical program R (R Core Team, 2022), the results of the regression models showed the moderating role of harmonious passion on the negative relationships between physical fatigue and intrinsic job satisfaction. In particular, at high levels of physical fatigue, employees with high scores on harmonious passion at work presented higher levels of intrinsic satisfaction compared with employees with low passion at work. That is, although service employees have high levels of physical fatigue, when they are passionate, they still possess satisfaction with their work. Therefore, our findings extend the JD-R theory by considering harmonious passion as a motivational resource that reduce feelings of burnout in service employees. Hence, it shows the importance of promoting the autonomous internalization of work, (through, i.e., job enrichment), which allows the development of harmonious passion at work and, therefore, increasing intrinsic job satisfaction.

Machine learning-based identification and classification of physical fatigue levels: A novel method based on a wearable insole device

Construction is known for being a labor-intensive and risky industry. Within various occupational settings such as construction, physical fatigue is an underlying health condition that may lead to musculoskeletal disorders and fall-related injuries. Identifying a worker's physical fatigue could enable safety managers to mitigate fatigue-related injuries and improve workplace operations. However, current physical fatigue assessment and identification methods include subjective, physiological, biomechanical, and computer vision approaches, which may be unreliable, intrusive, and require extensive post-processing, thus, rendering them impractical for continuous monitoring of workers' movements and automated identification of physical fatigue. Given the above, this study aims to utilize a wearable insole device to identify and classify physical fatigue levels in construction workers. Ten asymptomatic subjects were recruited to perform a fatiguing manual rebar tying activity in a laboratory setting. Borg's rating of perceived exertion (RPE) was applied as a subjective measure for collecting the levels of physical fatigue of each subject. Three sub-classification problems for identifying physical fatigue levels (i.e., PFL1, PFL2, and PFL3) were assessed. Numerous features were evaluated from the collected data samples after data segmentation. The classification performance of supervised machine learning algorithms was evaluated at a sliding window of 2.56 s. Our results from 10-fold cross-validation show an accuracy of 86% for the Random Forest (RF) algorithm, indicating the best performance among other algorithms. In addition, precision, recall, specificity, and F1-score metrics of the RF algorithm were between 52.63% and 82.62%, 52.63%–84.32%, 89.60%–92.33%, and 52.63%–83.46%, respectively. These results indicate that data samples such as acceleration and plantar pressure acquired from a wearable insole device are reliable for identifying and classifying physical fatigue levels in construction workers. In summary, this study would contribute to providing a proactive physical fatigue assessment method and guidelines for early identification of physical fatigue in construction.

Preventive Medicine via Lifestyle Medicine Implementation Practices Should Consider Individuals' Complex Psychosocial Profile.

Noncommunicable chronic diseases are associated with lifestyle behaviors. Psychological and social factors may influence the adoption of such behaviors. Being mentally and physically energized or fatigued may influence the intention-behavior gap of healthy lifestyle adoption accordingly. We investigated the associations of age, sex, lifestyle behaviors, mood, and mental and physical energy and fatigue at both the trait and state levels. The participants (N = 670) completed questionnaires assessing their sleep, mood, mental and physical state energy and fatigue, physical activity, mental workload, and diet. The ordinary least squares regression models revealed an overlap between the mental state and trait energy levels for males who consume polyphenols, have a high mental workload, and sleep well. Being younger, having a high stress level, bad sleep habits, and being confused and depressed were associated with high mental fatigue. Physical energy and fatigue shared the same commonalities with the previous results, with greater discrepancies observed between the state and trait indicators compared to that between mental energy and fatigue. Diet and stress management seem to be predictors of high physical energy, and females report higher physical fatigue levels. Health care professionals should consider this psychosocial complex profiling in their differential diagnosis and when one is implementing lifestyle behavioral changes to address the facets of preventive medicine, wellness, and health promotion.

Physical fitness and its association with fatigue in patients with low-grade glioma

Purpose To evaluate physical fitness and its association with fatigue in patients with low grade glioma (LGG). Methods Cross-sectional study. Muscle strength was measured with a digital dynamometer, cardiorespiratory fitness (peak oxygen uptake (VO2peak), maximal workload (MWL)) by cardiopulmonary-exercise-testing, and fatigue by using the Multidimensional Fatigue Inventory. Results Thirty patients were included, mean age of 44.1 (SD11.2) years, and 67% were men, 31.2 (SD18) months post-diagnosis. Muscle strength (p < 0.01), and cardiorespiratory fitness (VO2peak, MWL) (p < 0.01) were significantly decreased compared to predicted values based on age and gender. Thirty percent of the patients experienced severe physical fatigue, and severe mental fatigue was reported in 57% of the patients. Cardiorespiratory fitness showed weak to moderate (r − 0.46 to r − 0.52) but significant (p < 0.01) correlations with physical fatigue, not with mental and general fatigue. Muscle strength was not associated with fatigue. A lower VO2peak was independently associated with a higher level of physical fatigue, adjusted for Karnofsky Performance Status (R2 0.40). Conclusions Physical fitness (muscle strength, cardiorespiratory fitness) is reduced in patients with LLG, and a lower level of cardiorespiratory fitness (VO2peak) is independently associated with a higher level of experienced physical fatigue. Trials to explore the benefit of exercise programs to improve cardiorespiratory fitness and, consequently, fatigue are warranted. Implications for rehabilitation Physical fitness (muscle strength and cardiorespiratory fitness) is reduced in patients with low-grade glioma. Patients with low-grade glioma should be screened for fatigue with the multidimensional fatigue inventory, to differentiate between mental and physical fatigue. Patients with low-grade glioma with severe physical fatigue should be screened for reduced physical fitness, especially cardiorespiratory fitness by objective cardiopulmonary-exercise-testing. Rehabilitation exercise programs to improve cardiorespiratory fitness and, consequently, (physical) fatigue could be warranted in patients with low-grade glioma.

P08.07.A Long-term multidimensional assessment of fatigue and fatigue as predictor of survival in patients with brain metastases after Gamma Knife radiosurgery

Abstract Background The aim of this study was to assess long-term multidimensional fatigue and (change in) fatigue as predictor for survival in patients with brain metastases (BM) up to 21 months after Gamma Knife radiosurgery (GKRS). Material and Methods Patients with 1 to 10 BM, expected survival &amp;gt;3 months, and Karnofsky Performance Status ≥70 were included. Fatigue was measured with the Multidimensional Fatigue Inventory (MFI). Linear mixed models were used to evaluate fatigue up to 21 months after GKRS. Fatigue before GKRS and changes in fatigue in the first three months after GKRS were evaluated as predictors for survival duration. Results Patients with BM experienced significantly higher levels of fatigue on all subscales prior to GKRS (n=92) compared to Dutch controls (n=104), all p’s≤.001. Over 21 months, levels of physical fatigue increased significantly (p=.001), and levels of mental fatigue decreased significantly (p=.004). Between pre-GKRS and 3 months, there was a significant increase in levels of general (p&amp;lt;.001) and physical fatigue (p&amp;lt;.001), followed by stable fatigue scores between 3 (n=67) and 6 (n=53) months, 6 and 12 (n=34) months and 12 and 21 (n=21) months. Minimal clinically important (MCI) increases in general (p=.023, time ratio=0.61) and in physical fatigue (p=.008, time ratio=0.56) from pre-GKRS to 3 months thereafter were independent negative predictors of survival time (i.e., an MCI increase in these domains predicted a reduction of 39% and 44% respectively in survival time compared to stable or decreased fatigue). Conclusion Results indicate that up to 21 months after GKRS fatigue is a persistent problem in patients with BM. Except for an increase in general and physical fatigue up to 3 months after treatment, fatigue remained stable up to 21 months after GKRS. In addition, an MCI increase in general or physical fatigue within three months after GKRS may be related to worse survival outcome.

Influence of different protection levels of PPE on nurses' physical fatigue during the COVID-19 pandemic.

The continuous spread of COVID-19 globally has led to busier medical practices and nurses are having to provide medical services to patients while suffering from high levels of fatigue. This study experimentally investigated the influence of different levels of personal protection equipment (PPE) on nurses' physical fatigue. We collected data from 12 participants by simulating a series of nursing tasks. The participants wore one of three different protection levels of PPE, and data recording their heart rate (HR), oral temperature, task completion time, and subjective fatigue were collected. Following this, relationship models between subjective fatigue and objective fatigue indices was established using multiple linear regression analysis. By analyzing the variation trends of single indices of HR, oral temperature, task completion time, and subjective fatigue, it was found that the higher the level of protection levels of PPE worn by the participants, the higher the degree of fatigue experienced by them. In addition, taking subjective fatigue variation as the dependent variable and objective fatigue indices as independent variables, relationship models of the three different protection levels of PPE were established. The results confirmed the influence of different protection levels of PPE on nurses' physical fatigue levels. They also provide a basis for medical administrators to monitor the degree of fatigue experienced by nurses, attempt to arrange nurses' work content accordingly, and schedule suitably to combat and manage recorded increases in fatigue.