Persian version of the Chalder Fatigue Questionnaire and Multidimensional Fatigue Symptom Inventory-Short Form: psychometric properties in Iranian chronic stroke survivors

Minimal and robust clinically important differences for patient-reported outcome measures of fatigue in chronic stroke survivors after fatigue rehabilitation

PURPOSE

The minimal and robust clinically important difference (MCID and/or RCID) are essential in assessing the clinical significance of multidimensional fatigue inventory-20 and checklist of individual strength-fatigue subscale questionnaires changes scores. This is the first study to determine the MCID and RCID of these questionnaires in chronic stroke survivors.

MATERIALS AND METHODS

A total of 125 participants in an observational cohort study completed MFI-20 and CIS-fs before and after receiving multidisciplinary rehabilitation (cognitive behavioral therapy, graded exercise and adaptive pacing therapy). Anchor-based MCIDs and RCIDs were calculated using the mean change, the mean difference and the receiver operating characteristics methods. To evaluate the accordance between of distribution-based MCIDs (1 SD, ½ SD, SEM, 1.96 SEM and MDC values) with anchored values, the accuracy, sensitivity, specificity and Youden's index were calculated.

RESULTS

The anchored MCIDs were between -5 to -7.33 for MFI-20 and -4.87 to -5.40 for CIS-fs. The anchored RCIDs ranged from -5 to -13.88 and -6 to -9.88 for MFI-20 and CIS-fs, respectively. The values of ½ SD and SEM for CIS-fs were consistent with anchored RCIDs.

CONCLUSIONS

The estimated MCIDs and RCIDs of MFI-20 and CIS-fs can help researchers and clinicians interpret their chronic stroke patient data.

Post-stroke fatigue: a review of development, prevalence, predisposing factors, measurements, and treatments

Background

Post-stroke fatigue (PSF) is a ubiquitous and overwhelming symptom for most stroke survivors. However, there are no effective management strategies for PSF, which is partly due to our limited understanding.

Objective

In this paper, we review the development, prevalence, predisposing factors, measurements, and treatments of PSF.

Results

PSF is an independent symptom after stroke, with a prevalence ranging from 42 to 53%, which depends on the selection of measurement tools and stroke characteristics. It is affected by biological, physical, and psychological factors, among which inflammation may play a key role.

Conclusion

Numerous but non-specific evaluation measurement tools limit the management of PSF. In clinical practice, it may be beneficial to identify PSF by combining scales and objective indexes, such as walking tests and electromyographic examinations. There are no evidence-based interventions to improve PSF. However, increasing evidence suggests that transcranial direct-current stimulation and mindfulness-based interventions may become promising treatments. Further studies are urgently needed to better understand the etiology of PSF, thereby providing the basis for developing new measurement tools and targeted treatments.

Deep Learning for Detecting Multi-Level Driver Fatigue Using Physiological Signals: A Comprehensive Approach

A large share of traffic accidents is related to driver fatigue. In recent years, many studies have been organized in order to diagnose and warn drivers. In this research, a new approach was presented in order to detect multi-level driver fatigue. A multi-level driver tiredness diagnostic database based on physiological signals including ECG, EEG, EMG, and respiratory effort was developed for this aim. The EEG signal was used for processing and other recorded signals were used to confirm the driver's fatigue so that fatigue was not confirmed based on self-report questionnaires. A customized architecture based on adversarial generative networks and convolutional neural networks (end-to-end) was utilized to select/extract features and classify different levels of fatigue. In the customized architecture, with the objective of eliminating uncertainty, type 2 fuzzy sets were used instead of activation functions such as Relu and Leaky Relu, and the performance of each was investigated. The final accuracy obtained in the three scenarios considered, two-level, three-level, and five-level, were 96.8%, 95.1%, and 89.1%, respectively. Given the suggested model's optimal performance, which can identify five various levels of driver fatigue with high accuracy, it can be employed in practical applications of driver fatigue to warn drivers.