A Cycling Movement Based System for Real-Time Muscle Fatigue and Cardiac Stress Monitoring and Analysis

Myoelectric, Myo-Oxygenation, and Myotonometry Changes during Robot-Assisted Bilateral Arm Exercises with Varying Resistances

Robot-assisted bilateral arm training has demonstrated its effectiveness in improving motor function in individuals post-stroke, showing significant enhancements with increased repetitions. However, prolonged training sessions may lead to both mental and muscle fatigue. We conducted two types of robot-assisted bimanual wrist exercises on 16 healthy adults, separated by one week: long-duration, low-resistance workouts and short-duration, high-resistance exercises. Various measures, including surface electromyograms, near-infrared spectroscopy, heart rate, and the Borg Rating of Perceived Exertion scale, were employed to assess fatigue levels and the impacts of exercise intensity. High-resistance exercise resulted in a more pronounced decline in electromyogram median frequency and recruited a greater amount of hemoglobin, indicating increased muscle fatigue and a higher metabolic demand to cope with the intensified workload. Additionally, high-resistance exercise led to increased sympathetic activation and a greater sense of exertion. Conversely, engaging in low-resistance exercises proved beneficial for reducing post-exercise muscle stiffness and enhancing muscle elasticity. Choosing a low-resistance setting for robot-assisted wrist movements offers advantages by alleviating mental and physiological loads. The reduced training intensity can be further optimized by enabling extended exercise periods while maintaining an approximate dosage compared to high-resistance exercises.

Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review

Heart rate time series are widely used to characterize physiological states and athletic performance. Among the main indicators of metabolic and physiological states, the detection of metabolic thresholds is an important tool in establishing training protocols in both sport and clinical fields. This paper reviews the most common methods, applied to heart rate (HR) time series, aiming to detect metabolic thresholds. These methodologies have been largely used to assess energy metabolism and to identify the appropriate intensity of physical exercise which can reduce body weight and improve physical fitness. Specifically, we focused on the main nonlinear signal evaluation methods using HR to identify metabolic thresholds with the purpose of identifying a method which can represent a useful tool for the real-time settings of wearable devices in sport activities. While the advantages and disadvantages of each method, and the possible applications, are presented, this review confirms that the nonlinear analysis of HR time series represents a solid, robust and noninvasive approach to assess metabolic thresholds.

Using the NIH symptom science model to understand fatigue and mitochondrial bioenergetics

The symptom of fatigue is prevalent among patients with chronic diseases and conditions such as congestive heart failure and cancer. It has a significant debilitating impact on patients' physical health, quality of life, and well-being. Early detection and appropriate assessment of fatigue is essential for diagnosing, treating, and monitoring disease progression. However, it is often challenging to manage the symptom of fatigue without first investigating the underlying biological mechanisms. In this narrative review, we conceptualize the symptom of fatigue and its relationship with mitochondrial bioenergetics using the National Institute of Health Symptom Science Model (NIH-SSM). In particular, we discuss mental and physical measures to assess fatigue, the importance of adenosine triphosphate (ATP) in cellular and organ functions, and how impaired ATP production contributes to fatigue. Specific methods to measure ATP are described. Recommendations are provided concerning how to integrate biological mechanisms with the symptom of fatigue for future research and clinical practice to help alleviate symptoms and improve patients' quality of life.

Correlation properties of heart rate variability during endurance exercise: A systematic review

Background

Non‐linear measures of heart rate variability (HRV) may provide new opportunities to monitor cardiac autonomic regulation during exercise. In healthy individuals, the HRV signal is mainly composed of quasi‐periodic oscillations, but it also possesses random fluctuations and so‐called fractal structures. One widely applied approach to investigate fractal correlation properties of heart rate (HR) time series is the detrended fluctuation analysis (DFA). DFA is a non‐linear method to quantify the fractal scale and the degree of correlation of a time series. Regarding the HRV analysis, it should be noted that the short‐term scaling exponent alpha1 of DFA has been used not only to assess cardiovascular risk but also to assess prognosis and predict mortality in clinical settings. It has also been proven to be useful for application in exercise settings including higher exercise intensities, non‐stationary data segments, and relatively short recording times.

Method

Therefore, the purpose of this systematic review was to analyze studies that investigated the effects of acute dynamic endurance exercise on DFA‐alpha1 as a proxy of correlation properties in the HR time series.

Results

The initial search identified 442 articles (351 in PubMed, 91 in Scopus), of which 11 met all inclusion criteria.

Conclusions

The included studies show that DFA‐alpha1 of HRV is suitable for distinguishing between different organismic demands during endurance exercise and may prove helpful to monitor responses to different exercise intensities, movement frequencies, and exercise durations. Additionally, non‐linear DFA of HRV is a suitable analytical approach, providing a differentiated and qualitative view of exercise physiology.

How Physical Activities Affect Mental Fatigue Based on EEG Energy, Connectivity, and Complexity

Many studies have verified that there is an interaction between physical activities and mental fatigue. However, few studies are focused on the effect of physical activities on mental fatigue. This study was to analyze the states of mental fatigue based on electroencephalography (EEG) and investigate how physical activities affect mental fatigue. Fourteen healthy participants participated in an experiment including a 2-back mental task (the control) and the same mental task with cycling simultaneously (physical-mental task). Each experiment consisted of three 20 min fatigue-inducing sessions repeatedly (mental fatigue for mental tasks or mental fatigue plus physical activities for physical-mental tasks). During the evaluation sessions (before and after the fatigue-inducing sessions), the states of the participants were assessed by EEG parameters. Wavelet Packet Energy (WPE), Spectral Coherence Value (SCV), and Lempel-Ziv Complexity (LZC) were used to indicate mental fatigue from the perspectives of activation, functional connectivity, and complexity of the brain. The indices are the beta band energy E_β, the energy ratio E_α/β, inter-hemispheric SCV of beta band SCV_β and LZC. The statistical analysis shows that mental fatigue was detected by E_β, E_α/β, SCV_β, and LZC in physical-mental task. The slopes of the linear fit on these indices verified that the mental fatigue increased more fast during physical-mental task. It is concluded form the result that physical activities can enhance the mental fatigue and speed up the fatigue process based on brain activation, functional connection, and complexity. This result differs from the traditional opinion that physical activities have no influence on mental fatigue, and finds that physical activities can increase mental fatigue. This finding helps fatigue management through exercise instruction.

Validity and reliability of the Traditional Chinese version of the Multidimensional Fatigue Inventory in general population

Background

Fatigue is a common symptom in the general population and has a substantial effect on individuals’ quality of life. The Multidimensional Fatigue Inventory (MFI) has been widely used to quantify the impact of fatigue, but no Traditional Chinese translation has yet been validated. The goal of this study was to translate the MFI from English into Traditional Chinese (‘the MFI-TC’) and subsequently to examine its validity and reliability.

Methods

The study recruited a convenience sample of 123 people from various age groups in Taiwan. The MFI was examined using a two-step process: (1) translation and back-translation of the instrument; and (2) examination of construct validity, convergent validity, internal consistency, test-retest reliability, and measurement error. The validity and reliability of the MFI-TC were assessed by factor analysis, Spearman rho correlation coefficient, Cronbach’s alpha coefficient, intraclass correlation coefficient (ICC), minimal detectable change (MDC), and Bland-Altman analysis. All participants completed the Short-Form-36 Health Survey Taiwan Form (SF-36-T) and the Chinese version of the Pittsburgh Sleep Quality Index (PSQI) concurrently to test the convergent validity of the MFI-TC. Test-retest reliability was assessed by readministration of the MFI-TC after a 1-week interval.

Results

Factor analysis confirmed the four dimensions of fatigue: general/physical fatigue, reduced activity, reduced motivation, and mental fatigue. A four-factor model was extracted, combining general fatigue and physical fatigue as one factor. The results demonstrated moderate convergent validity when correlating fatigue (MFI-TC) with quality of life (SF-36-T) and sleep disturbances (PSQI) (Spearman's rho = 0.68 and 0.47, respectively). Cronbach’s alpha for the MFI-TC total scale and subscales ranged from 0.73 (mental fatigue subscale) to 0.92 (MFI-TC total scale). ICCs ranged from 0.85 (reduced motivation) to 0.94 (MFI-TC total scale), and the MDC ranged from 2.33 points (mental fatigue) to 9.5 points (MFI-TC total scale). The Bland-Altman analyses showed no significant systematic bias between the repeated assessments.

Conclusions

The results support the use of the Traditional Chinese version of the MFI as a comprehensive instrument for measuring specific aspects of fatigue. Clinicians and researchers should consider interpreting general fatigue and physical fatigue as one subscale when measuring fatigue in Traditional Chinese-speaking populations.

Wet, volatile, and dry biomarkers of exercise-induced muscle fatigue

Background

The physiological background of exercise-induced muscle fatigue(EIMUF) is only poorly understood. Thus, monitoring of EIMUF by a single or multiple biomarkers(BMs) is under debate.

After a systematic literature review 91 papers were included.

Results

EIMUF is mainly due to depletion of substrates, increased oxidative stress, muscle membrane depolarisation following potassium depletion, muscle hyperthermia, muscle damage, impaired oxygen supply to the muscle, activation of an inflammatory response, or impaired calcium-handling. Dehydration, hyperammonemia, mitochondrial biogenesis, and genetic responses are also discussed. Since EIMUF is dependent on age, sex, degree of fatigue, type, intensity, and duration of exercise, energy supply during exercise, climate, training status (physical fitness), and health status, BMs currently available for monitoring EIMUF have limited reliability. Generally, wet, volatile, and dry BMs are differentiated. Among dry BMs of EIMUF the most promising include power output measures, electrophysiological measures, cardiologic measures, and questionnaires. Among wet BMs of EIMUF those most applicable include markers of ATP-metabolism, of oxidative stress, muscle damage, and inflammation. VO₂-kinetics are used as a volatile BM.

Conclusions

Though the physiology of EIMUF remains to be fully elucidated, some promising BMs have been recently introduced, which together with other BMs, could be useful in monitoring EIMUF. The combination of biomarkers seems to be more efficient than a single biomarker to monitor EIMUF. However, it is essential that efficacy, reliability, and applicability of each BM candidate is validated in appropriate studies.