The present study was carried out with the surface electromyography signal of subjects during the time when subjects did the exercises of the 6 core stability trainings. We analyzed the different activity level of surface electromyography signal, and finally got various fatigue states of muscles in different exercises. Thirty subjects completed exercises of 6 core stability trainings, which were prone bridge, supine bridge, unilateral bridge (divided into two trainings,i.e. the left and right sides alternatively) and bird-dog (divided into two trainings,i.e. the left and right sides alternatively), respectively. Each exercise was held on for 1 minute and 2 minutes were given to relax between two exercises in this test. We measured both left and right sides of the body’s muscles, which included erector spina, external oblique, rectus abdominis, rectus femoris, biceps femoris, anterior tibial and gastrocnemius muscles. We adopted the frequency domain characteristic value of the surface electromyography signal,i.e. median frequency slope to analyze the muscle fatigue in this study. In the present paper, the results exhibit different fatigue degrees of the above muscles during the time when they did the core stability rehabilitation exercises. It could be concluded that supine bridge and unilateral bridge can cause more fatigue on erector spina muscle, prone bridge caused Gastrocnemius muscle much fatigue and there were statistical significant differences (P<0.05) between prone bridge and other five rehabilitation exercises in the degree of rectus abdominis muscle fatigue. There were no statistical significant differences (P>0.05) between all the left and right sides of the same-named muscles in the median frequency slope during all the exercises of the six core stability trainings,i.e. the degree which the various kinds of rehabilitation exercises effected the left and right side of the same-named muscle had no statistical significant difference (P>0.05). In this research, the conclusion presents quantized guidelines on the effects of core stability trainings on different muscles.
Inflammatory bowel disease (IBD) is characterized by recurrent abdominal pain, diarrhea, and mucopurulent bloody stools as its main clinical manifestations. In recent years, its parenteral manifestations have received increasing attention. Fatigue, as one of the extraintestinal manifestations of IBD, affects the quality of life of patients, and results in considerable distress for patients. The influencing factors of fatigue symptoms in IBD patients include inflammation, psychological comorbidities, sleep disorders, anemia, micronutrient deficiency, changes in microbiota, and metabolomics. The pathogenesis is currently unclear and may be related to disorders in tryptophan metabolism. This article will review the influencing factors and pathogenesis of fatigue symptoms in IBD patients, aiming to provide a basis for the prevention and treatment of IBD fatigue.
The electroencephalographic characteristics of mental fatigue, which was induced by long-term working memory task of 2-back, were studied by event-related potential (ERP) technology in order to obtain objective evaluation indicators for mental fatigue. Thirty-two healthy male subjects, 22–28 years old, were divided into two groups evenly, one is un-fatigue group and the other is fatigue group. The fatigue group performed a 2-back task for 100 min continuously, while the un-fatigue group just performed a 2-back task at the first and last 10 min respectively, and rested during the middle 80 min. The subjective levels of fatigue, task performance and electroencephalogram were recorded. The impaired thought and attention states, enhanced sleepy and fatigue feeling were found in the fatigue group, meanwhile their reaction time to 2-back task extended, and the accuracy decreased significantly. These results verified the validity of mental fatigue model induced by 2-back task, and then the ERP characteristic parameters were compared and analyzed between fatigue group and un-fatigue group. The results showed that the fatigue group’s amplitudes of P300 (F = 2.539, P < 0.05) and error-related negativity (ERN) ( F = 10.040, P < 0.05) decreased significantly along with the increase of fatigue comparing with the un-fatigue group, however, there were no significant change in other parameters (all P > 0.05). These results demonstrate that P300 and ERN can be considered as potential evaluation indictors for mental fatigue induced by long-term working memory task, which will provide basis for the future exploring of countermeasure for mental fatigue.
Objectives To explore the quality of the reporting of randomized controlled trials (RCTs) of traditional Chinese medicine (TCM) for chronic fatigue syndrome (CFS).Methods We searched the Cochrane Central Register of Controlled Clinical Trials (CENTRAL) (The Cochrane Library, Issue 4, 2006), PubMed, EMbase, the Chinese Biomedical Database (CBMdisc), VIP Information, and China National Knowledge Infrastructure (CNKI) (from establishment to February 2007). We also checked the reference lists of included studies. The quality of the reporting of RCTs was assessed using the 22-item checklist of the CONSORT Statement and other self-established criteria. Results Thirty-eight RCTs were included. The word “randomization” was not present in any of the trials, and only 17 reports used a structured abstract. All trials did not report the scientific background and the rational for the trial, the estimation of the necessary sample size, the methods of allocation concealment and blinding, participant flow chart, ITT analysis, and ancillary analyses. Some authors misunderstood the diagnostic criteria and inclusion criteria, some selected inappropriate control interventions, and some did not clearly describe their statistical methods or used incorrect methods. All 38 trials reported positive outcomes, few reported adverse effects. No report included a general interpretation of the new trial’s results in the context of current evidence in their discussion section, and none mentioned the limitations of the study, the clinical and research implications or the external validity of the trial findings. Conclusion The overall reporting quality of RCTs of TCM for CFS is poor. Defects are found in each section of the reports. Researchers and journal editors should learn and use the principles and methods of evidence-based medicine—especially the use of a transparent prospective clinical trial register and the CONSORT Statement—to improve the design, conduct and report TCM trials.
Mental fatigue is an important factor of human health and safety. It is important to achieve dynamic mental fatigue detection by using electroencephalogram (EEG) signals for fatigue prevention and job performance improvement. We in our study induced subjects' mental fatigue with 30 h sleep deprivation (SD) in the experiment. We extracted EEG features, including relative power, power ratio, center of gravity frequency (CGF), and basic relative power ratio. Then we built mental fatigue prediction model by using regression analysis. And we conducted lead optimization for prediction model. Result showed that R2 of prediction model could reach to 0.932. After lead optimization, 4 leads were used to build prediction model, in which R2 could reach to 0.811. It can meet the daily application accuracy of mental fatigue prediction.
The purpose of this study is to compare the differences among neck muscle fatigue evaluation algorithms and to find a more effective algorithm which can provide a human factor quantitative evaluation method for neck muscle fatigue during bending over the desk. We collected surface electromyography signal of sternocleidomastoid muscle of 15 subjects using wireless physiotherapy Bio-Radio when they bent over the desk using memory pillows for 12 minutes. Five algorithms including mean power frequency, spectral moments ratio, discrete wavelet transform, fuzzy approximation entropy and the complexity algorithms were used to calculate the corresponding muscle fatigue index. The least squares method was used to calculate the corresponding coefficient of determination R2 and slope k of the linear regression of the muscle fatigue metric. The coefficient of determination R2 evaluates anti-interference ability of algorithms. The maximum vertical distance Lmax which is obtained by the Kolmogorov-Smirnov test for the slopes k evaluates the ability to distinguish fatigue of algorithms. The results indicate that in the aspect of anti-interference ability, the fuzzy approximation entropy has the largest R2 when using memory pillows with different heights. When the fuzzy approximate entropy is compared with average power frequency or the discrete wavelet transform, the differences are significant (P < 0.05). In terms of distinguishing the degree of fatigue, the approximate entropy is still the largest, with a maximum of 0.496 7. Fuzzy approximation entropy is superior to other algorithms in ability of anti-interference and distinguishing fatigue. Therefore, fuzzy approximation entropy can be used as a better evaluation algorithm in the evaluation of cervical muscle fatigue.
We proposed a new deep learning model by analyzing electroencephalogram signals to reduce the complexity of feature extraction and improve the accuracy of recognition of fatigue status of pilots. For one thing, we applied wavelet packet transform to decompose electroencephalogram signals of pilots to extract the δ wave (0.4–3 Hz), θ wave (4–7 Hz), α wave (8–13 Hz) and β wave (14–30 Hz), and the combination of them was used as de-nosing electroencephalogram signals. For another, we proposed a deep contractive auto-encoding network-Softmax model for identifying pilots' fatigue status. Its recognition results were also compared with other models. The experimental results showed that the proposed deep learning model had a nice recognition, and the accuracy of recognition was up to 91.67%. Therefore, recognition of fatigue status of pilots based on deep contractive auto-encoding network is of great significance.
Heart rate variability (HRV) is an important point to judge a person’s state in modern medicine. This paper is aimed to research a person’s fatigue level connected with vagal nerve based on the HRV using the improved Welch method. The process of this method is that it firstly uses a time window function on the signal to be processed, then sets the length of time according to the requirement, and finally makes frequency domain analysis. Compared with classical periodogram method, the variance and consistency of the present method have been improved. We can set time span freely using this method (at present, the time of international standard to measure HRV is 5 minutes). This paper analyses the HRV’s characteristics of fatigue crowd based on the database provided by PhysioNet. We therefore draw the conclusion that the accuracy of Welch analyzing HRV combining with appropriate window function has been improved enormously, and when the person changes to fatigue, the vagal activity is diminished and sympathetic activity is raised.
ObjectiveTo investigate the methodological and reporting quality of clinical trials involving Xiaoyao San for chronic fatigue syndrome. MethodsWe searched PubMed, CBM, CNKI, VIP and WanFang Data to identify randomized controlled trials (RCTs) about Xiaoyao San for chronic fatigue syndrome. The methodological and reporting quality of included RCTs was respectively evaluated according to the assessment tool of risk of bias of the Cochrane Handbook 5.1.0 and the CONSORT 2010 statement, combined with complementary assessment by the characteristic indicators of traditional Chinese medicine (TCM). The methodological and reporting quality of included case series study was respectively assessed by the methods recommended by the Britain's National Institute for Clinical Excellence (NICE) and the STROBE statement. ResultsA total of 27 clinical trials were included, involving 11 RCTs and 16 case series studies. According to the assessment tool of risk of bias of the Cochrane Handbook, 54.5% of the RCTs performed proper random method, 9.1% conducted allocation concealment and blinding, 72.7% selected intention-to-treat (ITT) analysis without the report of loss to follow-up, and no RCT existed selective reports. Corresponding to the characteristic indicators of TCM, 54.5% of the RCTs did not conduct TCM syndrome diagnosis, the curative effect standard of TCM syndrome was discrepant, and no RCT was multi-center study. The CONSORT 2010 statement indicated that no RCT explained sample size estimation, implementation details of randomization, flow diagram of participant, use of ITT and clinical trial registration. According to the items recommended by Britain's NICE, 6.25% of the case series studies were multi-center, 81.25% did not report clear inclusion and exclusion criteria, and no case series study performed continuous patient recruitment and stratification analysis of outcome. The STROBE statement indicated that no case series study reported research design, sample size, flow chart, bias, limitations and generalizability. ConclusionThe quality of clinical trials about Xiaoyao San for chronic fatigue syndrome is still low in methodological and reporting aspects. It is suggested that the future clinical trials should be conducted with references of CONSORT statement and STROBE statement, to propel the modernization and internationalization of TCM.
With the wide application of virtual reality technology and the rapid popularization of virtual reality devices, the problem of brain fatigue caused by prolonged use has attracted wide attention. Sixteen healthy subjects were selected in this study. And electroencephalogram (EEG) signals were acquired synchronously while the subjects watch videos in similar types presented by traditional displayer and virtual reality separately. Two questionnaires were conducted by all subjects to evaluate the state of fatigue before and after the experiment. The mutual correlation method was selected to construct the mutual correlation brain network of EEG signals before and after watching videos in two modes. We also calculated the mutual correlation coefficient matrix and the mutual correlation binary matrix and compared the average of degree, clustering coefficient, path length, global efficiency and small world attribute during two experiments. The results showed that the subjects were easier to get fatigue by watching virtual reality video than watching video presented by traditional displayer in a certain period of time. By comparing the characteristic parameters of brain network before and after watching videos, it was found that the average degree value, the average clustering coefficient, the average global efficiency and the small world attribute decreases while the average path length value increased significantly. In addition, compared to traditional plane video, the characteristic parameters of brain network changed more greatly after watching the virtual reality video with a significant difference (P < 0.05). This study can provide theoretical basis and experimental reference for analyzing and evaluating brain fatigue induced by virtual reality visual experience.