The role of clinical neurophysiology in the definition and assessment of fatigue and fatigability

Effects of a symptom-titrated exercise program on fatigue and quality of life in people with post-COVID condition - a randomized controlled trial

Fatigue is the most prevalent symptom within the post-COVID condition (PCC). Furthermore, many patients suffer from decreased physical performance capacity and post-exertional malaise. Although exercise has been proposed as an effective therapeutic strategy for PCC, there is limited evidence on individualised and symptom-titrated exercise interventions in patients with fatigue and PEM. Therefore, we conducted a multi-centre randomised controlled trial to investigate the effectiveness of an individualised and symptom-titrated exercise program. We measured fatigue, health-related quality of life, hand-grip strength, endurance capacity and PEM before and after the 10-week intervention. A total of 118 individuals with PCC were included in the final intention-to-treat analysis. All tests and training sessions took place in commercial fitness and health facilities. We found significant effects on fatigue severity, health-related quality of life and physical performance capacity. Adjusting the individual exercise load to daily fatigue has proven to be an effective and safe strategy in PCC patients with fatigue. Under the guidance of qualified professionals and by utilising symptom-titrated training recommendations, commercial fitness and health facilities present an appropriate setting for outpatient exercise rehabilitation in PCC.

Non-Hospitalized Patients With Post-COVID Condition and Myopathic Electromyography Findings Show no Difference in Symptom Severity and Clinical Manifestations Compared to Those Without Myopathic Findings

INTRODUCTION

The COVID-19 pandemic has resulted in a post-infectious syndrome designated as long-COVID or post-COVID condition (PCC) that presents with numerous symptoms including fatigue and myalgias. This study evaluated myopathic electromyography (EMG) findings in non-hospitalized PCC patients in relation to symptom severity, quality of life (QoL), and physical function.

METHODS

Twenty-nine PCC patients with persistent symptoms ≥ 3 months after laboratory-confirmed SARS-CoV-2 infection, without hospitalization or comorbidities, were included. EMG, nerve conduction studies (NCS), and quantitative sensory testing (QST) were performed. Symptom severity was measured with visual analog scales, QoL with validated questionnaires, and physical function with the 6-min walk test, cardiopulmonary exercise testing, handgrip strength, and isokinetic dynamometry.

RESULTS

Myopathic findings on EMG were present in 62% of PCC patients (n = 18). Symptom severity (muscle pain and fatigue) and QoL (physical function and fatigue) were similar between patients with and without myopathic EMG findings. The 6-min walk test (457 ± 81 vs. 459 ± 86 m) and peak VO2 (29 ± 9 vs. 28 ± 6 mL/kg/min) were similar between patients with and without myopathic EMG findings. Handgrip strength (32 [29-43] vs. 33 [29-50] kg) and quadriceps muscle strength (136 [111-191] vs. 136 [114-184] Nm) were comparable between the groups. NCS and QST results were normal in all patients.

DISCUSSION

Myopathic findings on EMG are common in PCC patients, but no significant differences in symptom severity, QoL, or physical function were found between those with and without myopathic EMG findings. Myopathic EMG changes in PCC patients should be interpreted with caution, considering the overall clinical context.

Post-stroke fatigue - a multidimensional problem or a cluster of disorders? A case for phenotyping post-stroke fatigue

Post-stroke fatigue is a chronic problem with significant impact on morbidity and mortality, which urgently needs effective treatments. The last decade has seen a considerable increase in interest in understanding the pathophysiology of fatigue and developing treatments. In this review, following a summary of theoretical frameworks to understand chronic fatigue, I make a case for why phenotyping fatigue is a necessary step to fully understand pathophysiology, which in turn is essential for the development of robust treatments. I then appraise current post-stroke fatigue literature with the view of identifying post-stroke fatigue phenotypes.

Relationship between fatigue and quantitative electromyography findings in patients with myasthenia gravis

Background

Fatigue is a common complaint among patients with myasthenia gravis (MG). In this study, we investigated the alterations in muscle morphology in patients with MG experiencing fatigue using quantitative electromyography (QEMG), and explored the relationship between electrophysiological findings and the severity of both fatigue and disease.

Methods

We performed QEMG of the biceps brachii muscle using the peak ratio method and multi-motor unit potential (MUP) analysis across three groups: 18 MG patients with fatigue, 34 MG patients without fatigue, and 33 healthy subjects. Stimulated single-fiber EMG was performed on the frontalis muscle. The severity of perceived fatigue and disease was subsequently assessed using the quantitative myasthenia gravis (QMG) score, the MG-activities of daily living (MG-ADL) profile, self-reported fatigue questionnaires, and handgrip strength measurements.

Results

The QEMG study revealed a reduced mean MUP duration and size index (SI), in addition to an increased peak ratio in patients with MG (p < 0.05), which tended to be more pronounced in those experiencing fatigue. Compared to healthy subjects, MG patients with fatigue displayed a myopathic pattern characterised by a high peak ratio, short duration, and small-amplitude MUPs, without any increase in the number of phases or small time intervals. The mean peak ratio was positively correlated with the QMG, MG-ADL, and Fatigue Impact Scale total and physical subscores (p < 0.05). Further, MG patients with fatigue exhibited reduced maximum grip strength, which was positively correlated with the mean MUP duration, amplitude, SI, and thickness, and negatively correlated with the mean peak ratio (p < 0.05). No significant differences were observed in the jitter or block measurements (p > 0.05).

Conclusions

The present study investigated electrophysiological findings that were not considered or theorised in prior studies on patients with MG experiencing fatigue. The results of this study suggest that myopathic changes may be a critical pathophysiological component underlying the fatigue associated with MG.

Reduced TMS-evoked EEG oscillatory activity in cortical motor regions in patients with post-COVID fatigue

OBJECTIVE

Persistent fatigue is a major symptom of the so-called 'long-COVID syndrome', but the pathophysiological processes that cause it remain unclear. We hypothesized that fatigue after COVID-19 would be associated with altered cortical activity in premotor and motor regions.

METHODS

We used transcranial magnetic stimulation combined with EEG (TMS-EEG) to explore the neural oscillatory activity of the left primary motor area (l-M1) and supplementary motor area (SMA) in a group of sixteen post-COVID patients complaining of lingering fatigue as compared to a sample of age-matched healthy controls. Perceived fatigue was assessed with the Fatigue Severity Scale (FSS) and Fatigue Rating Scale (FRS).

RESULTS

Post-COVID patients showed a remarkable reduction of beta frequency in both areas. Correlation analysis exploring linear relation between neurophysiological and clinical measures revealed a significant inverse correlation between the individual level of beta oscillations evoked by TMS of SMA with the individual scores in the FRS (r(15) = -0.596; p = 0.012).

CONCLUSIONS

Post-COVID fatigue is associated with a reduction of TMS-evoked beta oscillatory activity in SMA.

SIGNIFICANCE

TMS-EEG could be used to identify early alterations of cortical oscillatory activity that could be related to the COVID impact in central fatigue.

Revealing the Complexity of Fatigue: A Review of the Persistent Challenges and Promises of Artificial Intelligence

Part I reviews persistent challenges obstructing progress in understanding complex fatigue's biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and deep learning techniques can help address limitations through pattern recognition of complex physiological signatures as more objective biomarkers, predictive modeling to capture individual differences, consolidation of disjointed findings via data mining, and simulation to explore interventions. Conversational agents like Claude and ChatGPT also have potential to accelerate human fatigue research, but they currently lack capacities for robust autonomous contributions. Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades.