INTRODUCTION: The National Institute of Health and Care Excellence (NICE) has defined the terms, 'acute coronavirus disease 2019' (COVID-19), 'ongoing symptomatic COVID-19' and 'post-COVID-19 syndrome', with the latter two described as having persistent symptoms after the onset of COVID-19 symptoms for 4-12 weeks and >12 weeks, respectively. Persistent symptoms can either be due to the after-effects of COVID-19 or new-onset diseases after acute COVID-19. All symptoms observed beyond 4 weeks after the onset of COVID-19 need not be present at the time of onset. Previous studies on persistent post-COVID-19 symptoms have not mentioned new-onset diseases after acute COVID-19, and only a select few studies have discussed such new-onset symptoms. METHODS: Ninety-five patients who attended the post-COVID-19 clinic completed the requisite follow-up till 16 weeks after COVID-19 symptom onset. Data was recorded on a predesigned proforma. Necessary investigations were conducted to rule out any other cause of persistent symptoms. RESULTS: Fatigue (62.1%), breathlessness (50.5%) and cough (27.4%) were the most common symptoms present beyond 4 weeks after the onset of COVID-19 symptoms. Forty-nine (51.57%) patients developed post-COVID-19 syndrome - their severity of symptoms (odds ratio [OR] 17.77) and longer duration of hospital stay (OR 1.095) during acute disease were significantly associated with the development of post-COVID-19 syndrome. During follow-up, 25 patients developed new-onset symptoms, such as diabetes mellitus, hypertension and idiopathic tachycardia. CONCLUSION Patients can have persistent symptoms, new-onset symptoms and new-onset diseases after recovery from acute COVID-19.
Humans ; COVID-19/diagnosis* ; Female ; Male ; Prospective Studies ; Middle Aged ; Adult ; Fatigue/etiology* ; Post-Acute COVID-19 Syndrome ; SARS-CoV-2 ; Aged ; Cough/etiology* ; Dyspnea/etiology*

Fatigue driving is one of the leading causes of traffic accidents, posing a significant threat to drivers and road safety. Most existing methods focus on studying whole-brain multi-channel electroencephalogram (EEG) signals, which involve a large number of channels, complex data processing, and cumbersome wearable devices. To address this issue, this paper proposes a fatigue detection method based on frontal EEG signals and constructs a fatigue driving detection model using an asymptotic hierarchical fusion network. The model employed a hierarchical fusion strategy, integrating an attention mechanism module into the multi-level convolutional module. By utilizing both cross-attention and self-attention mechanisms, it effectively fused the hierarchical semantic features of power spectral density (PSD) and differential entropy (DE), enhancing the learning of feature dependencies and interactions. Experimental validation was conducted on the public SEED-VIG dataset. The proposed model achieved an accuracy of 89.80% using only four frontal EEG channels. Comparative experiments with existing methods demonstrate that the proposed model achieves high accuracy and superior practicality, providing valuable technical support for fatigue driving monitoring and prevention.
Humans ; Electroencephalography/methods* ; Automobile Driving ; Fatigue/diagnosis* ; Accidents, Traffic/prevention & control* ; Signal Processing, Computer-Assisted ; Neural Networks, Computer ; Algorithms ; Prefrontal Cortex/physiology*

OBJECTIVES: To systematically review the methodological quality and measurement properties of childhood cancer-related fatigue assessment tools based on the consensus-based standards for the selection of health measurement instruments (COSMIN) guidelines, providing a basis for clinical practitioners to select appropriate assessment tools. METHODS: The databases searched included China National Knowledge Infrastructure, Wanfang Data, China Biomedical Literature, Weipu, PubMed, CINAHL, Embase, and Web of Science for studies published up to January 2024. Children under 12 years old and their primary caregivers were enrolled as subjects. Articles were screened based on inclusion criteria, and the key information regarding the assessment tools was extracted. The risk of bias checklist from the COSMIN guidelines and the quality standard rating scale were employed to evaluate measurement properties and formulate final recommendations. RESULTS: A total of 18 articles were included, covering 7 fatigue measurement tools, consisting of 4 specific tools and 3 generic tools tools. Methodological differences were observed in measurement properties across these scales. The Chinese Version of the Pediatric Patient-Reported Outcomes Measurement Information System (C-Ped-PROMIS) was rated as grade A recommendation due to its adequate content validity and internal consistency, while the remaining six scales were rated as grade B recommendation since their content validity was assessed as "insufficient" based on moderate-level evidence or higher. CONCLUSIONS The C-Ped-PROMIS scale demonstrates good reliability, validity, and cross-cultural validity as the preferred tool for measuring childhood cancer-related fatigue. The scale can serve as an auxiliary tool, and future research should focus on the applicability of various tools to enhance the effectiveness of interventions for assessing childhood cancer-related fatigue.
Humans ; Neoplasms/complications* ; Fatigue/diagnosis* ; Child

OBJECTIVE: To evaluate the sensitivity and specificity of Pollard' s classification criteria(2010) for the diagnosis of rheumatoid arthritis (RA) patients withfibromyalgia (FM) in Chinese patients, and to assess the clinical features and psychological status of RA-FM patients in a real-world observational setting. METHODS: Two hundred and two patients with rheumatoid arthritis were enrolled from the outpatients in Rheumatology and Immunology Department in Peking University People' s Hospital. All the patients were evaluated whether incorporating fibromyalgia translation occured using the 1990 American College of Rheumatolgy (ACR)-FM classification criteria. Forty two RA patients were concomitant with FM, while the other one hundred and sixty RA patients without FM were set as the control group. RESULTS: There was no significant difference in general demography between the two groups (P>0.05). In this study, the Pollard' s classification criteria (2010) for RA-FM in Chinese patients had a high sensitivity of 95.2% and relatively low specificity of 52.6%. Compared with those patients without FM, RA patients with FM (RA-FM patients) had higher Disease Activity Scale in 28 joints (DAS-28) score (5.95 vs. 4.38, P=0.011) and much more 28-tender joint counts (TJC) (16.5 vs.4.5, P < 0.001).RA-FM patients had worse Health Assessment Questionnaire (HAQ) score (1.24 vs. 0.66, P < 0.001) and lower SF-36 (28.63 vs. 58.22, P < 0.001). Fatigue was more common in RA-FM patients (88. 1% vs. 50.6%, P < 0.001) and the degree of fatigue was significantly increased in RA-FM patients (fatigue VAS 5.55 vs. 3.55, P < 0.001). RA-FM patients also had higher anxiety (10 vs.4, P < 0.001) and depression scores (12 vs.6, P < 0.001). erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), morning stiffness time and 28-swollen joint counts (SJC) showed no difference between these two groups. CONCLUSION The Pollard' s classification criteria (2010) for RA-FM are feasible in Chinese rheumatoid arthritis patients. The Pollard' s classification criteria is highly sensitive in clinical application, while the relativelylow specificity indicates that various factors need to be considered in combination. RA patients with FM result in higher disease activity, worse function aland psychological status. RA patients with FM also have poorer quality of life. DAS-28 scores may be overestimated in RA patients with FM. In a RA patient thatdoes not reach remission, the possibility of fibromyalgia should be con-sidered.
Arthritis, Rheumatoid/diagnosis* ; Fatigue/etiology* ; Fibromyalgia/diagnosis* ; Humans ; Quality of Life ; Severity of Illness Index

In the process of lower limb rehabilitation training, fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury. However, most of the existing methods only consider surface electromyography (sEMG) features but ignore electrocardiogram (ECG) features when performing in fatigue estimation, which leads to the low and unstable recognition efficiency. Aiming at this problem, a method that uses the fusion features of ECG and sEMG signal to estimate the fatigue during lower limb rehabilitation was proposed, and an improved particle swarm optimization-support vector machine classifier (improved PSO-SVM) was proposed and used to identify the fusion feature vector. Finally, the accurate recognition of the three states of relax, transition and fatigue was achieved, and the recognition rates were 98.5%, 93.5%, and 95.5%, respectively. Comparative experiments showed that the average recognition rate of this method was 4.50% higher than that of sEMG features alone, and 13.66% higher than that of the combined features of ECG and sEMG without feature fusion. It is proved that the feature fusion of ECG and sEMG signals in the process of lower limb rehabilitation training can be used for recognizing fatigue more accurately.
Algorithms ; Electrocardiography ; Electromyography ; Fatigue/diagnosis* ; Humans ; Lower Extremity ; Support Vector Machine

OBJECTIVE: In order to solve alarm fatigue, the algorithm optimization strategies were researched to reduce false and worthless alarms. METHODS: A four-lead arrhythmia analysis algorithm, a multiparameter fusion analysis algorithm, an intelligent threshold reminder, a refractory period delay technique were proposed and tested with collected 28 679 alarms in multi-center study. RESULTS: The sampling survey indicate that the 80.8% of arrhythmia false alarms were reduced by the four-lead analysis, the 55.9% of arrhythmia and pulse false alarms were reduced by the multi-parameter fusion analysis, the 28.0% and 29.8% of clinical worthless alarms were reduced by the intelligent threshold and refractory period delay techniques respectively. Finally, the total quantity of alarms decreased to 12 724. CONCLUSIONS To increase the dimensionality of parametric analysis and control the alarm limits and delay time are conducive to reduce alarm fatigue in intensive care units.
Alert Fatigue, Health Personnel/prevention & control* ; Arrhythmias, Cardiac/diagnosis* ; Clinical Alarms ; Humans ; Intensive Care Units ; Monitoring, Physiologic

A bone stress injury (BSI) means that the bones cannot tolerate repeated mechanical loads, resulting in structural fatigue and local bone pain. A delay in BSI diagnosis can lead to more serious injuries, such as stress fractures that require longer treatment periods. Therefore, early detection of BSI is an essential part of management. Risk factors for BSI development include biological and biomechanical factors. Medical history and physical examination are the basics for a BSI diagnosis, and magnetic resonance imaging is helpful for confirming and grading. In this paper, the authors review the overall content of BSI and stress fractures which are common in runners. Through this review, we hope that interest in stress fractures will be raised in Korea and that active researches will be conducted.
Diagnosis ; Fatigue ; Fractures, Stress ; Hope ; Korea ; Magnetic Resonance Imaging ; Physical Examination ; Risk Factors

OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic is a global public health crisis, which elicits psychological problems in different population This study is to investigate the impact of COVID-19 on mental health in the front-line staff. METHODS: Patient Health Questionnare-9 (PHQ-9), Self-Rating Anxiety Scale (SAS), and Fatigue Self-assessment Scale (FSAS) were used to assess the depression, anxiety, and fatigue in front-line staff. RESULTS: The detection rates of depression, anxiety, and fatigue were 49.1%, 21.8%, and 76.0% among the front-line staff. The rates of depression, anxiety, and fatigue in community workers were higher than those in medical workers and other occupational staff (<0.01). The PHQ-9 of front-line staffs was negatively correlated with age, family income, family members' support, satisfaction of service objects, and sleep quality (all <0.01), while positively correlated with education level, fatigue, fear of pneumonia, and the duration of daily attention to the COVID-19 (all <0.01). SAS was negatively correlated with age, family income, family support, satisfaction of objects service, and sleep quality (all <0.01), while positively correlated with gender, fatigue, fear of pneumonia, and duration of daily attention to the COVID-19 (all <0.01). CONCLUSIONS The front-line workers should manage work and rest time reasonably to adjust their negative mood and fatigue. The government and the society should pay more attention to the psychological state of the front-line staff, particularly for the staff working in the community or villages and towns in preventing the COVID-19 pandemic. Thus, front-line staff can be obtained mental intervention or be taken a rest from the high-intensive work.
Anxiety ; diagnosis ; Betacoronavirus ; Coronavirus Infections ; prevention & control ; psychology ; Depression ; diagnosis ; Fatigue ; diagnosis ; Health Personnel ; psychology ; Humans ; Mental Health ; Pandemics ; prevention & control ; Pneumonia, Viral ; prevention & control ; psychology

Myasthenia gravis (MG) is an autoimmune disease caused by autoantibodies to the acetylcholine receptors of the neuromuscular junction characterized by weakness and abnormal fatigability of the muscles. Therefore, the diagnosis of MG depends on the recognition of this distinctive pattern of fatigable weakness. Previous studies presented the diagnostic efficacy of saccadic eye movements in patients with ocular MG. We here in report 2 patients of ocular MG showing the fatigue effects during repetitive sustained smooth pursuit, and the effects of the administration of edrophonium on myasthenic smooth pursuit. Changes in smooth pursuits reflecting peripheral and secondary central mechanisms were demonstrated.
Autoantibodies ; Autoimmune Diseases ; Diagnosis ; Edrophonium ; Fatigue ; Humans ; Muscles ; Myasthenia Gravis ; Neuromuscular Junction ; Pursuit, Smooth ; Receptors, Cholinergic ; Saccades

Multiple Sclerosis (MS) is a chronic disabling neuroinflammatory disease. Psychiatric manifestations have a high prevalence in MS patients and may worsen the illness progression and the patients’ quality of life (QoL). Depression is a highly prevalent condition in MS patients, associated with poorer adherence to treatment, decreased functional status and QoL, and increased suicide risk. Diagnosis and treatment of this disorder is challenging because of symptom overlap. Other prevalent psychiatric comorbidities are anxiety disorders, bipolar disorder, psychotic disorders, substance misuse and personality disorders. As the illness progresses, personality changes can happen, as well as affect abnormalities. Cognitive changes occur frequently in MS patients, and affect features like processing speed, attention, learning, memory, visual spatial capabilities, and some language deficits. Disease-modifying treatments may reduce cognitive impairment because of their container action on the brain’s lesion burden. Other QoL determinants such as fatigue, pain, sexual dysfunction, exercise, resilience and social support should be taken into account, in order to promote the individuals’ well-being. Further studies are needed in order to elucidate the effectiveness of pharmacotherapy and more neuroimaging studies are required to clarify the relationship between structural changes and psychiatric comorbidities.
Anxiety Disorders ; Bipolar Disorder ; Cognition ; Cognition Disorders ; Comorbidity ; Depression ; Diagnosis ; Drug Therapy ; Fatigue ; Humans ; Learning ; Memory ; Multiple Sclerosis ; Neuroimaging ; Personality Disorders ; Prevalence ; Psychotic Disorders ; Quality of Life ; Suicide