Combining patient-reported outcome measures to screen for active disease in rheumatoid arthritis and psoriatic arthritis.

OBJECTIVES: To investigate whether a combination of general health (Visual Analogue Scale (VAS)), Health Assessment Questionnaire-Disability Index (HAQ-DI), pain (VAS/Numerical Rating Scale (NRS)), quality of life (EQ-5D), fatigue (VAS/NRS) and presenteeism (0%-100% productivity loss) could aid as a screening tool to detect active disease in patients with rheumatoid arthritis (RA) and psoriatic arthritis (PsA). METHODS: RA patients from the tREACH trial and TARA trial (n=683) and PsA patients from the DEPAR cohort (n=525) were included. The association of a deterioration in the aforementioned patient-reported outcome measure (PROM) scores between two consecutive visits and having active disease was assessed. Active disease was defined as a change from disease activity score (DAS) ≤2.4 to DAS >2.4 in RA or Disease Activity Index in Psoriatic Arthritis (DAPSA) ≤14 to DAPSA >14 in PsA. The area under the curve (AUC) of the sum score of deteriorated PROMs was evaluated. RESULTS: 4594 RA and 1154 PsA visits were evaluated and active disease occurred in 358 (8%) RA and 177 (15%) PsA visits. In both RA and PsA, a deterioration in general health (VAS), HAQ-DI, EQ-5D and pain (VAS/NRS) was significantly associated with active disease. The combination of these PROMs showed acceptable to excellent discriminative ability (RA AUC=0.76, PsA AUC=0.85). If a cut-point of ≥1 deteriorated PROMs is used, 40% of the visits in which RA patients have remission or low disease activity are correctly specified (specificity of 40%), while 10% of visits with active disease are overlooked (sensitivity of 90%). In PsA, these percentages are 41% and 4%, respectively. CONCLUSION: A combination of general health, HAQ-DI, EQ-5D and pain could aid as a screening tool for active disease in patients with RA and PsA. These data could help facilitate remote monitoring of RA and PsA patients in the future. TRIAL REGISTRATION NUMBERS: ISRCTN26791028, NTR2754.

Chronic thyrotoxic myopathy development is associated with thyroid hormone sensitivity index, predicted by lower-limb fatigue and the squat-up test.

Thyroid hormones (THs) may affect chronic thyrotoxic myopathy (CTM). The relationship between TH sensitivity and CTM is inconsistent. We aimed to investigate the associations between TH sensitivity and the risk of CTM and to screen potential CTMs with strength and function tests. A total of 162 Chinese patients (36.58% men) with Graves' disease were enrolled and divided into CTM and non-CTM groups. TH and sensitivity indices were measured. Muscle power and function were assessed by grip, upper-limb fatigue (ULFT), lower-limb fatigue (LLFT), and squat-up (SUT) tests, and walking pace. Association between sensitivity to TH indices and the risk of developing CTM was assessed via multivariate logistic regression. The diagnostic effectiveness of muscle power and function for predicting CTM was evaluated via receiver operating characteristic (ROC) curves. Thyroid feedback quantile-based index FT3 (TFQIFT3) and the parametric TFQIFT3 (PTFQIFT3), TFQIFT4, and PTFQIFT4 were positively associated with CTM risk by using inverse probability of treatment weighting multivariate logistic regression. For each 1-SD increase in TFQIFT3 and PTFQIFT3, TFQIFT4 and PTFQIFT4, the odds ratios for CTM were 1.67 (95% CI = 1.17-2.48) ,1.64 (95% CI = 1.51-2.93), 1.60 (95%CI = 1.12-2.32), 1.58 (95%CI = 1.11-2.30), respectively. LLFT and SUT best predicted male/female CTM, respectively (AUC = 0.89/0.85). In Graves' disease patients, TH sensitivity is associated with CTM development, which can be predicted by SUT and LLFT results.

Constructing a picture of fatigue in the context of cancer: assessment of construct overlap in common fatigue scales.

PURPOSE: Individuals diagnosed with cancer experience multiple inter-related short- and long-term side effects. Chief among such symptomology is cancer-related fatigue (CRF), which, if left unmanaged, can become chronic and result in increased disability and healthcare utilization. A growing number of self-report scales have been developed to measure CRF symptoms based on various theoretical conceptualizations with the aim of promoting targeted assessment and intervention efforts. It may be, however, unwise to assume that the various measures are conceptually similar (i.e., that they assess for the same constructs). Accordingly, we aimed to characterize item content among nine self-report scales, using a Jaccard index to quantify content overlap among scales. METHODS: We characterized construct assessment among nine self-report scales recommended to assess CRF by a recent clinical practice guideline, and used a Jaccard index to quantify content overlap among scales. RESULTS: Analysis of 208 items across nine rating scales resulted in 20 distinct symptoms of CRF assessed. The most common symptoms were energy level (captured in all nine scales), cognitive function, impaired task performance (in eight scales), sleepiness, and physical function (in seven scales). Mean overlap among all scales was low (Jaccard index = 0.455). Only one construct (duration of fatigue; 5.0%) was captured by a single scale, and one symptom (energy level; 5.0%) was common across all scales. The PFS, MFSI, and BFI each captured at least one symptom from each of the NCCN domains of CRF. CONCLUSION: CRF scales are heterogeneous in the content they measure, critically impairing integration of knowledge across studies using disparate scales. Future work is urgently needed to build more integrated theoretical and/or computational models of CRF based on relevant mechanisms.

The effect of graded exercise therapy on fatigue in people with serious respiratory illness: a systematic review.

BACKGROUND: In adults with serious respiratory illness, fatigue is prevalent and under-recognised, with few treatment options. The aim of this review was to assess the impact of graded exercise therapy (GET) on fatigue in adults with serious respiratory illness. METHODS: Electronic databases were searched to identify randomised controlled trials (RCTs) testing GET (involving incremental increases in exercise from an established baseline) in adults with serious respiratory illness. The primary outcome was fatigue and secondary outcomes were health-related quality of life (HRQoL) and adverse events. Two authors independently screened for inclusion, evaluated risk of bias and extracted data. RESULTS: 76 RCTs were included with 3309 participants, most with a diagnosis of COPD or asthma. Reductions in fatigue measured by the Chronic Respiratory Disease Questionnaire fatigue domain score were demonstrated following GET consisting of aerobic with/without resistance training (mean difference (MD) 0.53 points, 95% CI 0.41-0.65, 11 RCTs, 624 participants) and GET using resistance training alone (MD 0.58 points, 95% CI 0.21-0.96, two RCTs, 82 participants) compared with usual care. Although the mean effect exceeded the minimal important difference, the lower end of the confidence intervals did not always exceed this threshold so the clinical significance could not be confirmed. GET consistently improved HRQoL in people with a range of chronic respiratory diseases on multiple HRQoL measures. No serious adverse events related to GET were reported. CONCLUSION: GET may improve fatigue alongside consistent improvements in HRQoL in people with serious respiratory illness. These findings support the use of GET in the care of people with serious respiratory illness.

Manifestations of walking fatigability in people with multiple sclerosis based on gait quality and distance walked during the six minutes walking test.

BACKGROUND: Distance walking fatigability (DWF) in people with multiple sclerosis (pwMS) is defined as a decrease in the distance walking over time. However, declines in gait quality (i.e., gait quality fatigability- GQF) may occur independently or alongside DWF. OBJECTIVE: i) to investigate how walking fatigability manifests and its prevalence in pwMS; ii) to describe the temporal pattern of the changes of specific gait characteristics during the 6-minute walking test (6MWT) METHODS: Eighty-eight pwMS (EDSS 4[0-6.5], 49[21-70] years) and 47 healthy controls (HC- 46[25-60] years) performed the 6MWT wearing inertial measurement units. Gait characteristics (stride length, sensor-based gait speed, cadence, double support, step duration, stance phase, step duration asymmetry, step duration variability, foot-strike, toe-off, and leg circumduction) and walking distance were recorded in 1-minute intervals. A fatigability index was calculated by comparing the last and first minute of the 6MWT to identify abnormal worsening based on cutoff scores. The manifestation of walking fatigability was counted. The temporal pattern of worsening of gait characteristics during the 6MWT was examined in pwMS exceeding the cutoff values, compared to pwMS without abnormal changes and HC, using a two-way ANOVA (group vs. minutes) RESULTS: Thirty-five pwMS presented both DWF and GQF, 2 presented isolated DWF, 27 presented isolated GQF, and 24 presented non-walking fatigability. PwMS having GQF presented worsening in gait characteristics (cadence, step duration, step duration variability, or toe-off angle) from minute 2 onwards of the 6MWT, while HCs and pwMS without abnormal changes stabilized gait from minute 2 towards the end of the 6MWT. CONCLUSION: Walking fatigability in pwMS manifests not only as a decrease in walking distance but also as changes in gait quality. Understanding changes in gait characteristics during walking can help tailor rehabilitation interventions.

Patient-reported outcome measures for lupus nephritis: content validity of LupusQoL and FACIT-Fatigue.

BACKGROUND: Lupus nephritis (LN), a severe organ manifestation of systemic lupus erythematosus (SLE), significantly impacts health-related quality of life (HRQoL). Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue) and Lupus Quality of Life (LupusQoL) have been validated to measure HRQoL in SLE, but not specifically in LN. Patient-reported symptoms of LN are not well-reported. We assessed the content validity and relevance of these measures in evaluating patients with LN and their LN-related experiences. METHODS: This qualitative, interview-based study enrolled patients with LN from three US sites from a larger, retrospective survey study. The interview comprised an open-ended concept elicitation part and a more structured cognitive part. Concept elicitation was used to identify relevant themes describing the patients' experiences. Patients were asked to describe their LN-related symptoms, the severity and impact of those symptoms and their satisfaction with treatment. A cognitive interview approach evaluated the appropriate understanding of the items, instructions, and response options and asked patients about their understanding of the FACIT-Fatigue or LupusQoL measures, their relevance to the condition, and any aspects of confusion or need for better clarity of the questionnaires. All interviews were recorded and transcribed. The concept elicitation data were coded, while the cognitive interview data were tabulated to present the participants' responses next to the interview questions to support the evaluation of their understanding of the questionnaire items. RESULTS: Overall, 10 patients participated in FACIT-Fatigue and another 10 in LupusQoL interviews; 18 patients were female, 10 were Black (self-reported) and 17 were receiving maintenance treatment for LN with stable disease activity. When patients recalled their symptoms, 670 expressions of varying symptoms were reported. All patients described pain, discomfort, and energy-related symptoms. Urinary frequency and non-joint swelling were most frequently attributed to LN rather than SLE. Patients felt the questions asked in the FACIT-Fatigue and LupusQoL surveys were relevant to their LN experience. CONCLUSIONS: The symptoms reported by patients with LN were consistent with symptoms reported by the overall SLE population. However, patients indicated that some symptoms of LN were more profound than symptoms of SLE alone, affecting a broad range of areas of daily life activity and resulting in a higher burden on their HRQoL. FACIT-Fatigue and LupusQoL demonstrated content relevance as meaningful tools for patients with LN. However, further quantitative data collection is needed to ensure that these patient-reported outcome tools demonstrate good measurement properties in an LN population.

Long COVID or Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) and the Urgent Need to Identify Diagnostic Biomarkers and Risk Factors.

Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), also known as post-COVID-19 condition or post-COVID syndrome, can affect anyone infected with SARS-CoV-2, regardless of age or the severity of the initial symptoms of COVID-19. Long COVID/PASC is the continuation or development of new symptoms after three months from the initial SARS-CoV-2 infection, which lasts for at least two months and has no other identifiable cause. Long COVID/PASC occurs in 10-20% of patients infected with SARS-CoV-2. The most common symptoms include fatigue, cognitive impairment (brain fog), and shortness of breath. However, more than 200 symptoms have been reported. No phenotypic or diagnostic biomarkers have been identified for developing long COVID/PASC, which is a multisystem disorder that can present with isolated or combined respiratory, hematological, immunological, cardiovascular, and neuropsychiatric symptoms. There is no cure. Therefore, individualized patient management requires a multidisciplinary clinical approach. Because millions of people have had and continue to have COVID-19, even in the era of vaccination and antiviral therapies, long COVID/PASC is now and will increasingly become a health and economic burden that the world must prepare for. Almost five years from the beginning of the COVID-19 pandemic, this article aims to review what is currently known about long COVID/PASC, the anticipated increasing global health burden, and why there is still an urgent need to identify diagnostic biomarkers and risk factors to improve prevention and treatment.

Research on fatigue detection of flight trainees based on face EMF feature model combination with PSO-CNN algorithm.

Even though the capability of aircraft manufacturing has improved, human factors still play a pivotal role in flight accidents. For example, fatigue-related accidents are a common factor in human-led accidents. Hence, pilots' precise fatigue detections could help increase the flight safety of airplanes. The article suggests a model to recognize fatigue by implementing the convolutional neural network (CNN) by implementing flight trainees' face attributions. First, the flight trainees' face attributions are derived by a method called the land-air call process when the flight simulation is run. Then, sixty-eight points of face attributions are detected by employing the Dlib package. Fatigue attribution points were derived based on the face attribution points to construct a model called EMF to detect face fatigue. Finally, the proposed PSO-CNN algorithm is implemented to learn and train the dataset, and the network algorithm achieves a recognition ratio of 93.9% on the test set, which can efficiently pinpoint the flight trainees' fatigue level. Also, the reliability of the proposed algorithm is validated by comparing two machine learning models.

Designing a practical fatigue detection system: A review on recent developments and challenges.

INTRODUCTION: Fatigue is considered to have a life-threatening effect on human health and it has been an active field of research in different sectors. Deploying wearable physiological sensors helps to detect the level of fatigue objectively without any concern of bias in subjective assessment and interfering with work. METHODS: This paper provides an in-depth review of fatigue detection approaches using physiological signals to pinpoint their main achievements, identify research gaps, and recommend avenues for future research. The review results are presented under three headings, including: signal modality, experimental environments, and fatigue detection models. Fatigue detection studies are first divided based on signal modality into uni-modal and multi-modal approaches. Then, the experimental environments utilized for fatigue data collection are critically analyzed. At the end, the machine learning models used for the classification of fatigue state are reviewed. PRACTICAL APPLICATIONS: The directions for future research are provided based on critical analysis of past studies. Finally, the challenges of objective fatigue detection in the real-world scenario are discussed.

[Fatigue feature extraction and classification algorithm of forehead single-channel electroencephalography signals].

Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which is greatly improved compared with other advanced algorithms, and the accuracy and feasibility of fatigue detection by single-channel EEG signals are significantly improved. The results provide strong support for the application of single-channel EEG signals, and also provide a new idea for fatigue detection research.

Urdu translation and cross-cultural validation of neurological fatigue index on post stroke.

BACKGROUND: The Neurological Fatigue Index (NFI) is the instrument used to evaluate stroke patients' fatigue. There was no Urdu version of NFI available officially. OBJECTIVE: This study aimed to translate the Neurological Fatigue Index into Urdu and to determine the validity and reliability of Urdu NFI among stroke patients. METHODOLOGY: It is a cross-cultural validation study. According to international guidelines in phase I, a process of translation was carried out. In phase II, using the sample of 120 participants, validity and reliability of the Urdu version of the Neurological Fatigue Index scale was conducted. The Urdu version's content validity, convergent/concurrent validity, test-retest reliability, and internal consistency were determined. The latest version of SPSS was used for the data analysis. RESULTS: The Urdu version of NFI was drafted after the expert's review. The content validity index was used to analyze the content validity. The reliability and validity of the Urdu version NFI were evaluated by calculating Cronbach's alpha (α = 0.86), and intra-class correlation coefficient (ICC = 0.823). Correlations with other scales were the fatigue Severity Scale (FSS) (r = 0.76), Mental Fatigue Scale (MFS) (r = 0.68), Beck Depression Inventory (BDI) (r = 0.53) and Epworth Sleepiness Scale (ESS) (r = 0.47). CONCLUSION: The Urdu Version was linguistically acceptable for the fatigue assessment in post-stroke patients. It showed good content validity, convergent/concurrent validity, internal consistency, and test-retest reliability.

Development and validation of a nomogram to estimate fatigue probability in hemodialysis patients.

OBJECTIVE: This study aims to investigate the factors that influence fatigue in hemodialysis (HD) patients and to develop and validate a nomogram to estimate the probability of fatigue in this population. METHODS: This cross-sectional study collected 453 patients who underwent HD at the tertiary hospital in Hubei, China, from April to December 2023. They were randomly divided into a 70% training group (n = 316) and a 30% validation group (n = 137). In the training set, factors influencing fatigue were screened using multivariate logistic regression analysis, and a nomogram was developed to estimate fatigue probability in HD patients. The discrimination and calibration of the nomogram were validated in both the training and validation sets through the area under the receiver operating characteristic (ROC) curve (AUC) and the Hosmer-Lemeshow (H-L) test. RESULTS: In the training group, logistic regression showed that age, dialysis vintage, inter-dialysis weight gain, hemoglobin, depression, insomnia, and social support were variables associated with fatigue in HD patients. Based on these factors, a nomogram for assessing fatigue probability in HD patients was developed. The AUC was 0.955 (95% CI: 0.932-0.977) and 0.979 (95% CI: 0.961-0.997) in the training and validation sets. The results from the H-L test indicated a good fit. CONCLUSION: The nomogram can evaluate fatigue probability in HD patients and may serve as a convenient clinical tool.

Automated Speech Analysis for Risk Detection of Depression, Anxiety, Insomnia, and Fatigue: Algorithm Development and Validation Study.

BACKGROUND: While speech analysis holds promise for mental health assessment, research often focuses on single symptoms, despite symptom co-occurrences and interactions. In addition, predictive models in mental health do not properly assess the limitations of speech-based systems, such as uncertainty, or fairness for a safe clinical deployment. OBJECTIVE: We investigated the predictive potential of mobile-collected speech data for detecting and estimating depression, anxiety, fatigue, and insomnia, focusing on other factors than mere accuracy, in the general population. METHODS: We included 865 healthy adults and recorded their answers regarding their perceived mental and sleep states. We asked how they felt and if they had slept well lately. Clinically validated questionnaires measuring depression, anxiety, insomnia, and fatigue severity were also used. We developed a novel speech and machine learning pipeline involving voice activity detection, feature extraction, and model training. We automatically modeled speech with pretrained deep learning models that were pretrained on a large, open, and free database, and we selected the best one on the validation set. Based on the best speech modeling approach, clinical threshold detection, individual score prediction, model uncertainty estimation, and performance fairness across demographics (age, sex, and education) were evaluated. We used a train-validation-test split for all evaluations: to develop our models, select the best ones, and assess the generalizability of held-out data. RESULTS: The best model was Whisper M with a max pooling and oversampling method. Our methods achieved good detection performance for all symptoms, depression (Patient Health Questionnaire-9: area under the curve [AUC]=0.76; F1-score=0.49 and Beck Depression Inventory: AUC=0.78; F1-score=0.65), anxiety (Generalized Anxiety Disorder 7-item scale: AUC=0.77; F1-score=0.50), insomnia (Athens Insomnia Scale: AUC=0.73; F1-score=0.62), and fatigue (Multidimensional Fatigue Inventory total score: AUC=0.68; F1-score=0.88). The system performed well when it needed to abstain from making predictions, as demonstrated by low abstention rates in depression detection with the Beck Depression Inventory and fatigue, with risk-coverage AUCs below 0.4. Individual symptom scores were accurately predicted (correlations were all significant with Pearson strengths between 0.31 and 0.49). Fairness analysis revealed that models were consistent for sex (average disparity ratio [DR] 0.86, SD 0.13), to a lesser extent for education level (average DR 0.47, SD 0.30), and worse for age groups (average DR 0.33, SD 0.30). CONCLUSIONS: This study demonstrates the potential of speech-based systems for multifaceted mental health assessment in the general population, not only for detecting clinical thresholds but also for estimating their severity. Addressing fairness and incorporating uncertainty estimation with selective classification are key contributions that can enhance the clinical utility and responsible implementation of such systems.

A systematic literature review of the measurement of patient-reported fatigue in studies of disease modifying therapies for multiple sclerosis.

BACKGROUND: Fatigue is the most commonly experienced symptom among people with multiple sclerosis (MS) and has the greatest impact in reducing quality of life. It is important to measure change in MS-related fatigue (MS-fatigue) in response to treatment, particularly the more recent disease modifying therapies (DMTs). To date there has been no systematic literature review of the patient reported outcome (PRO) tools used to measure MS- fatigue specifically in the context of DMTs. METHODS: MEDLINE, Embase and Clinicaltrials.gov were searched from 01 January 2000 to 13 April 2021 to identify published studies of the treatment of MS with DMTs. Studies where MS-fatigue was measured as an outcome using a PRO tool were included in the review. Further literature searches were undertaken to provide information about the development and validation of each PRO tool. RESULTS: 739 abstracts and 96 clinical trials were manually screened resulting in 68 articles for full text screening. 48 studies were identified for the review; 10 of these were RCTs that considered MS-fatigue as a secondary outcome (4 were Phase 3 trials). The PRO instruments used in the 10 RCTs were the Fatigue Scale for Motor and Cognitive Functions, Fatigue Impact Scale, Modified Fatigue Impact Scale, Fatigue Severity Scale, and Fatigue Symptoms and Impacts Questionnaire - Relapsing Multiple Sclerosis. The other 38 studies were all open-label, longitudinal, non-randomized studies and used the following PRO instruments in addition to those listed above: the Visual Analogue Scale for Fatigue, the Fatigue Descriptive Scale, Modified Fatigue Impact Scale (5 items) and the Würzburger Fatigue Inventory for MS. All these PRO tools were specifically developed for MS-fatigue. Of these 9 PRO tools, 7 were of good methodological quality according to the existing validation studies using the Consensus-based standards for the selection of health measurement instruments (COSMIN) check list and were used in the majority of the MS DMT studies (44/48, 92%). The median follow-up time from baseline to PRO measurement was 12 months (range 1-36 months). Most studies reported on MS fatigue in terms of its change from baseline and whether the change was statistically significant. 5 studies also reported what they considered to be a clinically meaningful difference. CONCLUSIONS: Although fatigue has the greatest impact on quality of life in people with MS, few studies have rigorously investigated the impact of DMTs on fatigue. Comparisons between study outcomes using different PRO tools is challenging due to the variety of psychometric constructs addressed by the questionnaires and differences in the recall period for fatigue symptoms and the measurement scale. Furthermore most of the PRO tools used to quantify MS-fatigue in studies of DMTs are descended from PRO tools developed during the 1990s before DMTs emerged and before widespread patient involvement in PRO development. New PRO tools should involve patients in their development as recommended by the US Food and Drug Administration and the validation process should consider the sensitivity of the PRO tool to change in fatigue over time or between groups.

Six-minute walk test as outcome measure of fatigability in adults with spinal muscular atrophy treated with nusinersen.

INTRODUCTION/AIMS: Fatigue (subjective perception) and fatigability (objective motor performance worsening) are relevant aspects of disability in individuals with spinal muscular atrophy (SMA). The effect of nusinersen on fatigability in SMA patients has been investigated with conflicting results. We aimed to evaluate this in adult with SMA3. METHODS: We conducted a multicenter retrospective cohort study, including adult ambulant patients with SMA3, data available on 6-minute walk test (6MWT) and Hammersmith Functional Motor Scale-Expanded (HFMSE) at baseline and at least at 6 months of treatment with nusinersen. We investigated fatigability, estimated as 10% or higher decrease in walked distance between the first and sixth minute of the 6MWT, at baseline and over the 14-month follow-up. RESULTS: Forty-eight patients (56% females) were included. The 6MWT improved after 6, 10, and 14 months of treatment (p < 0.05). Of the 27 patients who completed the entire follow-up, 37% improved (6MWT distance increase ≥30 m), 48.2% remained stable, and 14.8% worsened (6MWT distance decline ≥30 m). Fatigability was found at baseline in 26/38 (68%) patients and confirmed at subsequent time points (p < 0.05) without any significant change over the treatment period. There was no correlation between fatigability and SMN2 copy number, sex, age at disease onset, age at baseline, nor with 6MWT total distance and baseline HFMSE score. DISCUSSION: Fatigability was detected at baseline in approximately 2/3 of SMA3 walker patients, without any correlation with clinical features, included motor performance. No effect on fatigability was observed during the 14-month treatment period with nusinersen.

Research on low-power driving fatigue monitoring method based on spiking neural network.

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21-42.59%.

Construct validity of PROMIS pain interference, fatigue, and physical function as patient-reported outcomes in adults with idiopathic inflammatory myopathies: An international study from the OMERACT myositis working group.

BACKGROUND: Validated patient-reported outcome measures to assess disease impact in patients with adult idiopathic inflammatory myopathies (IIMs) are needed. The objective of this study was to assess the construct validity of PROMIS Pain Interference, Fatigue, and Physical Function measures in comparison with core disease activity measures. METHODS: Adults with IIM, excluding inclusion body myositis, from OMERACT Myositis Working Group (MWG) clinic sites completed PROMIS Short Form v1.0-Pain Interference 6a, PROMIS Short Form v1.0-Fatigue 7a, and PROMIS Short Form v2.0-Physical Function 8b measures. Core disease activity measures including patient and physician global disease activity assessments, manual muscle testing, serum creatine kinase activity, and Health Assessment Questionnaire Disability Index (HAQ-DI) were simultaneously assessed. To evaluate construct validity, a priori hypotheses for the expected correlations between PROMIS measures, age, and core disease measures were determined by >70 % agreement among MWG members and were compared against observed Pearson's correlations. Internal consistency of items and floor or ceiling effects for the PROMIS measures were also assessed. Subgroup analysis according to IIM subtype (dermatomyositis vs. non-dermatomyositis IIM) was performed. RESULTS: 135 adults with IIM from 5 countries across North America, Europe, Asia, and Australia were included. For construct validity, a priori hypotheses were confirmed for 5 of 6 (83 %) PROMIS Pain Interference, 4 of 5 (80 %) PROMIS Fatigue, and 3 of 4 (75 %) PROMIS Physical Function correlations. Internal consistency was high for each PROMIS measure (Cronbach's alpha >0.9). Ceiling effects were observed only for PROMIS Pain Interference, with low/no pain in 29 % of patients. Subgroup analysis between dermatomyositis (n = 65) and non-dermatomyositis (n = 70) subtypes demonstrated similar correlations between PROMIS measures and disease activity measures. CONCLUSIONS: PROMIS Short Form v1.0-Pain Interference 6a, PROMIS Short Form v1.0-Fatigue 7a, and PROMIS Short Form v2.0-Physical Function 8b measures demonstrate strong construct validity when compared to core disease activity measures in IIM, with consistent results across IIM subtypes. These findings support the use of these selected PROMIS measures to assess core domains of interest for measuring life impact in IIMs.

A bio-behavioral model of systemic inflammation at breast cancer diagnosis and fatigue of clinical importance 2 years later.

BACKGROUND: We aimed to generate a model of cancer-related fatigue (CRF) of clinical importance 2 years after diagnosis of breast cancer building on clinical and behavioral factors and integrating pre-treatment markers of systemic inflammation. PATIENTS AND METHODS: Women with stage I-III hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer were included from the multimodal, prospective CANTO cohort (NCT01993498). The primary outcome was global CRF of clinical importance [European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ)-C30 ≥40/100] 2 years after diagnosis (year 2). Secondary outcomes included physical, emotional, and cognitive CRF (EORTC QLQ-FA12). All pre-treatment candidate variables were assessed at diagnosis, including inflammatory markers [interleukin (IL)-1α, IL-1ß, IL-2, IL-4, IL-6, IL-8, IL-10, interferon γ, IL-1 receptor antagonist, tumor necrosis factor-α, and C-reactive protein], and were tested in multivariable logistic regression models implementing multiple imputation and validation by 100-fold bootstrap resampling. RESULTS: Among 1208 patients, 415 (34.4%) reported global CRF of clinical importance at year 2. High pre-treatment levels of IL-6 (quartile 4 versus 1) were associated with global CRF at year 2 [adjusted odds ratio (aOR): 2.06 (95% confidence interval [CI] 1.40-3.03); P = 0.0002; area under the receiver operating characteristic curve = 0.74]. Patients with high pre-treatment IL-6 had unhealthier behaviors, including being frequently either overweight or obese [62.4%; mean body mass index 28.0 (standard deviation 6.3 kg/m2)] and physically inactive (53.5% did not meet World Health Organization recommendations). Clinical and behavioral associations with CRF at year 2 included pre-treatment CRF [aOR versus no pre-treatment CRF: 3.99 (95% CI 2.81-5.66)], younger age [aOR per 1-year decrement: 1.02 (95% CI 1.01-1.03)], current tobacco smoking [aOR versus never: 1.81 (95% CI 1.26-2.58)], and worse insomnia or pain [aOR per 10-unit increment: 1.08 (95% CI 1.04-1.13), and 1.12 (95% CI 1.04-1.21), respectively]. Secondary analyses indicated additional associations of IL-2 [aOR per log-unit increment: 1.32 (95% CI 1.03-1.70)] and IL-10 [0.73 (95% CI 0.57-0.93)] with global CRF and of C-reactive protein [1.42 (95% CI 1.13-1.78)] with cognitive CRF at year 2. Emotional distress was consistently associated with physical, emotional, and cognitive CRF. CONCLUSIONS: This study proposes a bio-behavioral framework linking pre-treatment systemic inflammation with CRF of clinical importance 2 years later among a large prospective sample of survivors of breast cancer.

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 .