Decoding IBS: a machine learning approach to psychological distress and gut-brain interaction.

PURPOSE: Irritable bowel syndrome (IBS) is a diagnosis defined by gastrointestinal (GI) symptoms like abdominal pain and changes associated with defecation. The condition is classified as a disorder of the gut-brain interaction (DGBI), and patients with IBS commonly experience psychological distress. The present study focuses on this distress, defined from reports of fatigue, anxiety, depression, sleep disturbances, and performance on cognitive tests. The aim was to investigate the joint contribution of these features of psychological distress in predicting IBS versus healthy controls (HCs) and to disentangle clinically meaningful subgroups of IBS patients. METHODS: IBS patients ( n = 49 ) and HCs ( n = 28 ) completed the Chalder Fatigue Scale (CFQ), the Hamilton Anxiety and Depression Scale (HADS), and the Bergen Insomnia Scale (BIS), and performed tests of memory function and attention from the Repeatable Battery Assessing Neuropsychological Symptoms (RBANS). An initial exploratory data analysis was followed by supervised (Random Forest) and unsupervised (K-means) classification procedures. RESULTS: The explorative data analysis showed that the group of IBS patients obtained significantly more severe scores than HCs on all included measures, with the strongest pairwise correlation between fatigue and a quality measure of sleep disturbances. The supervised classification model correctly predicted belongings to the IBS group in 80% of the cases in a test set of unseen data. Two methods for calculating feature importance in the test set gave mental and physical fatigue and anxiety the strongest weights. An unsupervised procedure with K = 3 showed that one cluster contained 24% of the patients and all but two HCs. In the two other clusters, their IBS members were overall more impaired, with the following differences. One of the two clusters showed more severe cognitive problems and anxiety symptoms than the other, which experienced more severe problems related to the quality of sleep and fatigue. The three clusters were not different on a severity measure of IBS and age. CONCLUSION: The results showed that psychological distress is an integral component of IBS symptomatology. The study should inspire future longitudinal studies to further dissect clinical patterns of IBS to improve the assessment and personalized treatment for this and other patient groups defined as disorders of the gut-brain interaction. The project is registered at https://classic. CLINICALTRIALS: gov/ct2/show/NCT04296552 20/05/2019.

The burden and determinants of fatigue in incident and prevalent systemic sclerosis.

OBJECTIVES: To investigate the burden and clinical associations of fatigue in systemic sclerosis (SSc) as measured by FACIT-Fatigue scores. METHODS: Australian Scleroderma Cohort Study participants with ≥1 FACIT-Fatigue score were included. Participants were divided into those with incident SSc (≤5 years SSc duration at recruitment and FACIT-Fatigue score recorded within 5 years of disease onset) or prevalent SSc (first FACIT-Fatigue score recorded >5 years after SSc onset). Generalised estimating equations were used to model change in FACIT-Fatigue scores over time, expressed as an increasing (improving) or decreasing (worsening) score. RESULTS: Of 859 participants, 215 had incident SSc and 644 prevalent SSc. First-recorded FACIT-Fatigue scores were similar in those with incident (37 units, IQR 25-45.5) and prevalent SSc (36 units, IQR 23-44; p=0.17), as were lowest-ever recorded FACIT-Fatigue scores (incident 23 units; prevalent 22 units, p=0.75). In incident SSc, higher skin scores (regression coefficient (RC) -1.5 units, 95%CI -2.3 to -0.8), PAH (RC -8.2, 95%CI -16.5 to 0.1) and reduced left ventricular function (RC -10.6, 95%CI -18.3 to -2.8) were associated with more severe fatigue. In prevalent SSc, higher skin scores (RC -0.6, 95%CI -1.3 to 0), gastrointestinal symptoms (RC -6.6, 95%CI -9.0 to -4.2), hypoalbuminaemia (RC -2.8, 95%CI -5.0 to -0.7), BMI<18.5kg/m2 (RC -6.3, 95%CI -10.3 to -2.2), raised CRP (RC -3.1, 95%CI -4.7 to -1.5), and anaemia (RC -1.7, 95%CI -3.5 to 0.1) were associated with more severe fatigue. CONCLUSIONS: The burden of fatigue is substantial in both incident and prevalent SSc. Cardiopulmonary and gastrointestinal involvement are associated with worse fatigue.

Evaluating the Construct Validity of the Charité Alarm Fatigue Questionnaire using Confirmatory Factor Analysis.

Background: The Charité Alarm Fatigue Questionnaire (CAFQa) is a 9-item questionnaire that aims to standardize how alarm fatigue in nurses and physicians is measured. We previously hypothesized that it has 2 correlated scales, one on the psychosomatic effects of alarm fatigue and the other on staff's coping strategies in working with alarms. Objective: We aimed to validate the hypothesized structure of the CAFQa and thus underpin the instrument's construct validity. Methods: We conducted 2 independent studies with nurses and physicians from intensive care units in Germany (study 1: n=265; study 2: n=1212). Responses to the questionnaire were analyzed using confirmatory factor analysis with the unweighted least-squares algorithm based on polychoric covariances. Convergent validity was assessed by participants' estimation of their own alarm fatigue and exposure to false alarms as a percentage. Results: In both studies, the χ2 test reached statistical significance (study 1: χ226=44.9; P=.01; study 2: χ226=92.4; P<.001). Other fit indices suggested a good model fit (in both studies: root mean square error of approximation <0.05, standardized root mean squared residual <0.08, relative noncentrality index >0.95, Tucker-Lewis index >0.95, and comparative fit index >0.995). Participants' mean scores correlated moderately with self-reported alarm fatigue (study 1: r=0.45; study 2: r=0.53) and weakly with self-perceived exposure to false alarms (study 1: r=0.3; study 2: r=0.33). Conclusions: The questionnaire measures the construct of alarm fatigue as proposed in our previous study. Researchers and clinicians can rely on the CAFQa to measure the alarm fatigue of nurses and physicians.

Cardiopulmonary exercise testing in long covid shows the presence of dysautonomia or chronotropic incompetence independent of subjective exercise intolerance and fatigue.

BACKGROUND: After COVID-19 infection, 10-20% of patients suffer from varying symptoms lasting more than 12 weeks (Long COVID, LC). Exercise intolerance and fatigue are common in LC. The aim was to measure the maximal exercise capacity of the LC patients with these symptoms and to analyze whether this capacity was related to heart rate (HR) responses at rest and during exercise and recovery, to find out possible sympathetic overactivity, dysautonomia or chronotropic incompetence. METHODS: Cardiopulmonary exercise test was conducted on 101 LC patients, who were admitted to exercise testing. The majority of them (86%) had been treated at home during their acute COVID-19 infection. Peak oxygen uptake (VO2peak), maximal power during the last 4 min of exercise (Wlast4), HRs, and other exercise test variables were compared between those with or without subjective exercise intolerance, fatigue, or both. RESULTS: The measurements were performed in mean 12.7 months (SD 5.75) after COVID-19 infection in patients with exercise intolerance (group EI, 19 patients), fatigue (group F, 31 patients), their combination (group EI + F, 37 patients), or neither (group N, 14 patients). Exercise capacity was, in the mean, normal in all symptom groups and did not significantly differ among them. HRs were higher in group EI + F than in group N at maximum exercise (169/min vs. 158/min, p = 0.034) and 10 min after exercise (104/min vs. 87/min, p = 0.028). Independent of symptoms, 12 patients filled the criteria of dysautonomia associated with slightly decreased Wlast4 (73% vs. 91% of sex, age, height, and weight-based reference values p = 0.017) and 13 filled the criteria of chronotropic incompetence with the lowest Wlast4 (63% vs. 93%, p < 0.001), VO2peak (70% vs. 94%, p < 0.001), the lowest increase of systolic blood pressure (50 mmHg vs. 67 mmHg, p = 0.001), and the greatest prevalence of slight ECG-findings (p = 0.017) compared to patients without these features. The highest prevalence of chronotropic incompetence was seen in the group N (p = 0.022). CONCLUSIONS: This study on LC patients with different symptoms showed that cardiopulmonary exercise capacity was in mean normal, with increased sympathetic activity in most patients. However, we identified subgroups with dysautonomia or chronotropic incompetence with a lowered exercise capacity as measured by Wlast4 or VO2peak. Subjective exercise intolerance and fatigue poorly foresaw the level of exercise capacity. The results could be used to plan the rehabilitation from LC and for selection of the patients suitable for it.

The Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue) scale in patients with axial spondyloarthritis: psychometric properties and clinically meaningful thresholds for interpretation.

BACKGROUND: Fatigue is an important symptom for most patients with axial spondyloarthritis (axSpA). The FACIT-Fatigue is a 13-item patient-reported outcome (PRO) instrument that has been used in axSpA clinical trials to measure fatigue severity and impact on daily activities. However, the psychometric properties of the FACIT-Fatigue are not fully evaluated across the entire spectrum of axSpA including non-radiographic axSpA (nr-axSpA) and radiographic axSpA (r-axSpA). This study determined: (1) the psychometric properties of the FACIT-Fatigue in nr-axSpA, r-axSpA, and the broad axSpA population and (2) FACIT-Fatigue scores representing meaningful within-patient change (MWPC), meaningful between-group differences, and cross-sectional severity bands. METHODS: Data from two Phase 3 trials in adults with nr-axSpA (BE MOBILE 1; N = 254) and r-axSpA (BE MOBILE 2; N = 332) were analyzed pooled and separately to assess the psychometric properties of the FACIT-Fatigue. MWPC and meaningful between-group difference estimates were derived using anchor-based and distribution-based methods. Cross-sectional fatigue severity bands were estimated using logistic regression analysis. RESULTS: The FACIT-Fatigue presented good internal consistency, adequate convergent and known-groups validity, and was sensitive to change over time across the full axSpA spectrum. A 5-11-point increase in FACIT-Fatigue score was estimated to represent a MWPC, with an 8-point increase selected as the responder definition. A 2.14-5.34-point difference in FACIT-Fatigue score change over a 16-week period was estimated to represent a small-to-medium meaningful between-group difference. FACIT-Fatigue score severity bands were defined as: none or minimal (>40), mild (>30 to ≤40), moderate (>21 to ≤30), and severe (≤21). CONCLUSIONS: These findings support the use of the FACIT-Fatigue as a fit-for-purpose measure to assess fatigue-related treatment benefit in axSpA clinical trials. The proposed score estimates and thresholds can guide FACIT-Fatigue score interpretation across the full axSpA spectrum. TRIAL REGISTRATION: ClinicalTrials.Gov, NCT03928704. Registered 26 April 2019-Retrospectively registered, https://classic. CLINICALTRIALS: gov/ct2/show/NCT03928704 . CLINICALTRIALS: Gov, NCT03928743. Registered 26 April 2019-Retrospectively registered, https://classic. CLINICALTRIALS: gov/ct2/show/NCT03928743 .

Driver Fatigue Detection Using Heart Rate Variability Features from 2-Minute Electrocardiogram Signals While Accounting for Sex Differences.

Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined. Then, decision trees were used for driver fatigue detection using the HRV features of either all subjects or those of only males or females. Nineteen, eighteen, and thirteen HRV features were significantly different (Mann-Whitney U test, p < 0.01) between the two mental states for all subjects, males, and females, respectively. The fatigue detection models for all subjects, males, and females achieved classification accuracies of 86.3%, 94.8%, and 92.0%, respectively. In conclusion, sex differences in HRV features between drivers' mental states were found according to both the statistical analysis and classification results. By considering sex differences, precise HRV feature-based driver fatigue detection systems can be developed. Moreover, in contrast to conventional methods using HRV features from 5 min ECG signals, our method uses HRV features from 2 min ECG signals, thus enabling more rapid driver fatigue detection.

Rasch analysis of the modified Fatigue Severity Scale in neuromuscular disorders and comparison between sex, age and diagnoses.

INTRODUCTION/AIMS: Fatigue is a common and debilitating symptom encountered in the neuromuscular clinic. The 7-item Fatigue Severity Scale (FSS-7) is a Rasch-modified assessment validated in inflammatory neuropathies but not across a typical neuromuscular patient population. The aim of this study was to validate this measure in neuromuscular disorders and to compare between patient sex, age and diagnoses. METHODS: The modified FSS-7 was mailed to patients recruited from a specialist neuromuscular clinic at the Walton Centre. Responses were subjected to Rasch analysis and descriptive statistics were performed on the Rasch converted data. RESULTS: The mFSS-7 met the Rasch model expectations with an overall Chi-square probability of 0.4918, a strict unidimensional scale free from differential item functioning (DIF) that satisfied the model with substantial test-retest reliability using Lin's concordance correlation coefficient 0.71 (95% CI 0.63-0.77). A 15.7% ceiling effect was observed in this patient cohort. Post hoc analysis did not show any significant difference in fatigue between sex, age or neuromuscular diagnoses. DISCUSSION: The self-completed Rasch mFSS-7 showed acceptable test-retest reliability across patients with varied disorders under follow-up in a specialist neuromuscular clinic. The ceiling effect constrains its use for those with the most severe fatigue. Future considerations could include assessment of the benefits of clinical interventions, particularly multidisciplinary team input or dedicated fatigue clinics.

The Spider: a visual, multisystemic symptom impact questionnaire for people with hypermobility-related disorders-validation in adults.

INTRODUCTION: Hypermobility spectrum disorders (HSD) and hypermobile Ehlers-Danlos syndrome (hEDS) are often accompanied by varied and complex multisystemic comorbid symptoms/conditions. The Spider questionnaire was developed to evaluate the presence and impact of eight common multisystemic comorbidities. Thirty-one questions across eight symptom domains assess neuromusculoskeletal, pain, fatigue, cardiac dysautonomia, urogenital, gastrointestinal, anxiety, and depression symptoms. This study aimed to evaluate the Spider's construct validity in adults. METHOD: A cross-sectional observational study was conducted over four stages. Three international patient charities aided recruitment of participants through social media and website advertisements. Adults aged 18 to 65 years, with and without HSD/hEDS, were invited to participate. Validated, frequently used comparator questionnaires were used to establish convergent validity of Spider symptom domains. A control group was recruited for known-group validity analysis. Participants answered each Spider domain and the corresponding comparator questionnaire via surveys hosted by REDCap. Anonymous data were analysed using SPSS. Convergent validity was assessed through Spearman's correlational analysis and known-group validity through Mann-Whitney U analysis. RESULTS: A total of 11,151 participants were recruited across the four stages. Statistically significant, moderate-to-strong correlations were found between all Spider domains and their comparators (p < 0.001, r = 0.63 to 0.80). Known-group validity analysis showed statistically significant differences (p < 0.001) between the hypermobile and control groups in all eight domains. CONCLUSIONS: Convergent and known-group validity of the Spider was established with adults. These results suggest the Spider can measure the presence and impact of multisystemic comorbid symptoms/conditions in adults with HSD/hEDS, providing a tool which guides multidisciplinary management. Key Points • The Spider questionnaire is a novel tool assessing the presence and impact of the multisystemic comorbid symptoms/conditions associated with HSD/hEDS. • Convergent and known-group validity of the Spider questionnaire was established in adults aged 18 to 65. • This tool provides a quick and easy method to visualise the symptom profile of those with HSD/hEDS to guide symptom management.

Medical intelligence using PPG signals and hybrid learning at the edge to detect fatigue in physical activities.

The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and biosensors benefitting from edge computing resources, we are now able to monitor the physiological fatigue of students participating in sports in real time. These devices can then be used to analyze the data using contemporary technology. In this paper, we present an innovative deep learning framework for forecasting fatigue in athletic students following physical exercise. It addresses the issue of lack of precision computational models and extensive data analysis in current approaches to monitoring students' physical activity. In our study, we classified fatigue and non-fatigue based on photoplethysmography (PPG) signals. Several deep learning models are compared in the study. Using limited training data, determining the optimal parameters for PPG presents a significant challenge. For datasets containing many data points, several models were trained using PPG signals: a deep residual network convolutional neural network (ResNetCNN) ResNetCNN, an Xception architecture, a bidirectional long short-term memory (BILSTM), and a combination of these models. Training and testing datasets were assigned using a fivefold cross validation approach. Based on the testing dataset, the model demonstrated a proper classification accuracy of 91.8%.

Assessment of post-SARS-CoV-2 fatigue among physicians working in COVID-designated hospitals in Dhaka, Bangladesh.

BACKGROUND: Fatigue has been observed after the outbreaks of several infectious diseases around the world. To explore the fatigue level among physicians working in COVID-19-designated hospitals in Bangladesh, a matched case‒control study was conducted on post-SARS-CoV-2 fatigue. METHOD: In this study, 105 physicians diagnosed with COVID-19 who were declared cured at least 6 weeks before the interview date were recruited as cases, and the same number of age- and designation-matched healthy physicians were recruited as controls from the same hospital at a 1:1 ratio. Diagnosis of COVID-19 infection was confirmed by detection of SARS CoV-2 antigen by RT‒PCR from reference laboratories in Bangladesh or by HRCT chest. RESULT: Approximately two-thirds of the physicians were male (67.6% vs. 32.4%). More than 80% of them were younger than 40 years. The cases had a significantly greater number of comorbid conditions. The fatigue severity scale (FSS) score (mean) was much higher for cases (36.7 ± 5.3 vs. 19.3 ± 3.8) than for the control group, with a statistically significant difference. Similarly, approximately 67.7% of the previously COVID-19-positive physicians were in the highest FSS score tertile compared to the respondents in the control group, who had a mean score of <3. CONCLUSION: Physicians who had a previous history of COVID-19 infection had significantly higher total and mean FSS scores, signifying a more severe level of fatigue than physicians who had never been COVID-19 positive while working in the same hospital irrespective of their age and sex.

Fatigue in rheumatoid arthritis patients: The status, independent risk factors, and consistency of multiple scales.

INTRODUCTION: Fatigue is a common symptom that negatively affects the outcomes and functions of rheumatoid arthritis (RA) patients. This study aimed to assess the fatigue by two scales and validate their consistency, also to comprehensively evaluate fatigue-related risk factors in RA patients. METHODS: In this case-control study, the fatigue of 160 RA patients and 60 healthy controls was evaluated by the Bristol Rheumatoid Arthritis Fatigue Multi-Dimensional Questionnaire (BRAF-MDQ) and the Chinese version of the Brief Fatigue Inventory (BFI-C). The 28-joint disease activity score using erythrocyte sedimentation rate of RA patients was assessed. RESULTS: The BRAF-MDQ and BFI-C scores were elevated in RA patients versus healthy controls (all p < .001). Interestingly, BRAF-MDQ global fatigue score positively correlated with BFI-C global fatigue score in both RA patients (r = .669, p < .001) and healthy controls (r = .527, p < .001); meanwhile, Kendall's tau-b test showed a high consistency between BRAF-MDQ and BFI-C global fatigue scores in RA patients (W = 0.759, p < .001) and healthy controls (W = 0.933, p < .001). Notably, higher education level (В = -4.547; 95% confidence interval: -7.065, -2.029; p < .001) and swollen joint count (В = 1.965; 95% confidence interval: 1.375, 2.554; p < .001) independently related to BRAF-MDQ global fatigue score; higher education level (В = -0.613; 95% confidence interval: -0.956, -0.269; p = .001) and clinical disease activity index (В = 0.053; 95% confidence interval: 0.005, 0.102; p = .032) independently linked with BFI-C global fatigue score. CONCLUSION: Fatigue commonly occurs in RA patients, which independently relates to education level and disease activity. Furthermore, BRAF-MDQ and BFI-C scales exhibit a high consistency in assessing fatigue.

Evaluation of walking activity and gait to identify physical and mental fatigue in neurodegenerative and immune disorders: preliminary insights from the IDEA-FAST feasibility study.

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.

A cross-scenario and cross-subject domain adaptation method for driving fatigue detection.

Objective.The scarcity of electroencephalogram (EEG) data, coupled with individual and scenario variations, leads to considerable challenges in real-world EEG-based driver fatigue detection. We propose a domain adaptation method that utilizes EEG data collected from a laboratory to supplement real-world EEG data and constructs a cross-scenario and cross-subject driver fatigue detection model for real-world scenarios.Approach.First, we collect EEG data from subjects participating in a driving experiment conducted in both laboratory and real-world scenarios. To address the issue of data scarcity, we build a real-world fatigued driving detection model by integrating the real-world data with the laboratory data. Then, we propose a method named cross-scenario and cross-subject domain adaptation (CS2DA), which aims to eliminate the domain shift problem caused by individual variances and scenario differences. Adversarial learning is adopted to extract the common features observed across different subjects within the same scenario. The multikernel maximum mean discrepancy (MK-MMD) method is applied to further minimize scenario differences. Additionally, we propose a conditional MK-MMD constraint to better utilize label information. Finally, we use seven rules to fuse the predicted labels.Main results.We evaluate the CS2DA method through extensive experiments conducted on the two EEG datasets created in this work: the SEED-VLA and the SEED-VRW datasets. Different domain adaptation methods are used to construct a real-world fatigued driving detection model using data from laboratory and real-world scenarios, as well as a combination of both. Our findings show that the proposed CS2DA method outperforms the existing traditional and adversarial learning-based domain adaptation approaches. We also find that combining data from both laboratory and real-world scenarios improves the performance of the model.Significance.This study contributes two EEG-based fatigue driving datasets and demonstrates that the proposed CS2DA method can effectively enhance the performance of a real-world fatigued driving detection model.

The definitions, assessment, and dimensions of cancer-related fatigue: A scoping review.

PURPOSE: Cancer-related fatigue (CRF) is challenging to diagnose and manage due to a lack of consensus on its definition and assessment. The objective of this scoping review is to summarize how CRF has been defined and assessed in adult patients with cancer worldwide. METHODS: Four databases (PubMed, Embase, CINAHL Plus, PsycNet) were searched to identify eligible original research articles published in English over a 10-year span (2010-2020); CRF was required to be a primary outcome and described as a dimensional construct. Each review phase was piloted: title and abstract screening, full-text screening, and data extraction. Then, two independent reviewers participated in each review phase, and discrepancies were resolved by a third party. RESULTS: 2923 articles were screened, and 150 were included. Only 68% of articles provided a definition for CRF, of which 90% described CRF as a multidimensional construct, and 41% were identical to the National Comprehensive Cancer Network definition. Studies were primarily conducted in the United States (19%) and the majority employed longitudinal (67%), quantitative (93%), and observational (57%) study designs with sample sizes ≥ 100 people (57%). Participant age and race were often not reported (31% and 82%, respectively). The most common cancer diagnosis and treatment were breast cancer (79%) and chemotherapy (80%; n = 86), respectively. CRF measures were predominantly multidimensional (97%, n = 139), with the Multidimensional Fatigue Inventory (MFI-20) (26%) as the most common CRF measure and "Physical" (76%) as the most common CRF dimension. CONCLUSION: This review confirms the need for a universally agreed-upon definition and standardized assessment battery for CRF.

Improving physical movement during stroke rehabilitation: investigating associations between sleep measured by wearable actigraphy technology, fatigue, and key biomarkers.

BACKGROUND: Sleep disturbance and fatigue are common in individuals undergoing inpatient rehabilitation following stroke. Understanding the relationships between sleep, fatigue, motor performance, and key biomarkers of inflammation and neuroplasticity could provide valuable insight into stroke recovery, possibly leading to personalized rehabilitation strategies. This study aimed to investigate the influence of sleep quality on motor function following stroke utilizing wearable technology to obtain objective sleep measurements. Additionally, we aimed to determine if there were relationships between sleep, fatigue, and motor function. Lastly, the study aimed to determine if salivary biomarkers of stress, inflammation, and neuroplasticity were associated with motor function or fatigue post-stroke. METHODS: Eighteen individuals who experienced a stroke and were undergoing inpatient rehabilitation participated in a cross-sectional observational study. Following consent, participants completed questionnaires to assess sleep patterns, fatigue, and quality of life. Objective sleep was measured throughout one night using the wearable Philips Actiwatch. Upper limb motor performance was assessed on the following day and saliva was collected for biomarker analysis. Correlation analyses were performed to assess the relationships between variables. RESULTS: Participants reported poor sleep quality, frequent awakenings, and difficulties falling asleep following stroke. We identified a significant negative relationship between fatigue severity and both sleep quality (r=-0.539, p = 0.021) and participants experience of awakening from sleep (r=-0.656, p = 0.003). A significant positive relationship was found between grip strength on the non-hemiplegic limb and salivary gene expression of Brain-derived Neurotrophic Factor (r = 0.606, p = 0.028), as well as a significant negative relationship between grip strength on the hemiplegic side and salivary gene expression of C-reactive Protein (r=-0.556, p = 0.048). CONCLUSION: The findings of this study emphasize the importance of considering sleep quality, fatigue, and biomarkers in stroke rehabilitation to optimize recovery and that interventions may need to be tailored to the individual. Future longitudinal studies are required to explore these relationships over time. Integrating wearable technology for sleep and biomarker analysis can enhance monitoring and prediction of outcomes following stroke, ultimately improving rehabilitation strategies and patient outcomes.

A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest.

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers.

Comparing Self-Report Fatigue Assessment Tools for Adults with Cerebral Palsy in a Danish Context.

PURPOSE: Many youth and adults with Cerebral Palsy (CP) experience high levels of fatigue. This study aimed to compare three fatigue self-report questionnaires to guide clinicians. METHOD: Thirty youth and adults (age range 17-64) with CP were assessed with Danish versions of the Fatigue Impact and Severity Self-Assessment questionnaire, the Modified Mental Fatigue Scale, and the Multidimensional Fatigue Inventory. Psychometric properties were investigated. Rank order and classification models were compared across questionnaires. RESULTS: The Reduced Motivation and Physical Fatigue subscales of the Multidimensional Fatigue Inventory showed inadequate internal consistency. Participants were frequently ranked differently with the questionnaires. There were issues related to the conceptualization of physical fatigue. CONCLUSION: The choice of assessment tool should be based on assessment purpose as the questionnaires assess different aspects of fatigue severity, impact, and management. Also, test selection can have important implications on the conclusions that are made about fatigue type and severity.

Classification of exercise fatigue levels by multi-class SVM from ECG and HRV.

Among the various physiological signals, electrocardiogram (ECG) is a valid criterion for the classification of various exercise fatigue. In this study, we combine features extracted by deep neural networks with linear features from ECG and heart rate variability (HRV) for exercise fatigue classification. First, the ECG signals are converted into 2-D images by using the short-term Fourier transform (STFT), and image features are extracted by the visual geometry group (VGG) . The extracted image and linear features of ECG and HRV are sent to the different types of classifiers to distinguish distinct exercise fatigue level. To validate performance, the proposed methods are tested on (i) an open-source EPHNOGRAM dataset and (ii) a self-collected dataset (n = 51). The results reveal that the classification based on the concatenated features has the highest accuracy, and the calculation time of the system is also significantly reduced. This demonstrates that the proposed novel hybrid approach can be used to assist in improving the accuracy and timeliness of exercise fatigue classification in a real-time exercise environment. The experimental results show that the proposed method outperforms other recent state-of-the-art methods in terms of accuracy 96.90%, sensitivity 96.90%, F1-score of 0.9687 in EPHNOGRAM and accuracy 92.17%, sensitivity 92.63%, F1-score of 0.9213 in self-collected dataset.

Assessment of fatigue in adult patients with sickle cell disease: Use of the functional assessment of chronic illness therapy-Fatigue (FACIT-fatigue) questionnaire.

Few studies have used validated scales to assess the intensity and determinants of fatigue, a major symptom of sickle cell disease (SCD). We aimed to assess the level of basal fatigue in adult patients with SCD, using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue) questionnaire. We prospectively included 102 stable adult outpatients with SCD over 2 months, who answered the FACIT-Fatigue (ranging from 0 (worst imaginable fatigue) to 52 (no fatigue)) and reported on the intensity of fatigue and its impact on quality of life. The cut-off for significant fatigue was <34. The median [IQR] FACIT-Fatigue score was 29 [22-37], indicating moderate-to-severe fatigue. In a multivariate analysis, the FACIT-Fatigue score was significantly associated with female sex, high body mass index, high level of stress, poor sleep quality, and number of previous episodes of acute chest syndrome, but not with the genotype or the haemoglobin level. Most adult patients with SCD experience significant and sometimes intense fatigue; this is probably due to several factors, including disease activity. Fatigue should be evaluated systematically during consultations and in patient education programmes and as an end-point in therapeutic trials.