Explore the vast range of titles available.

Exercise-induced fatigue causes changes within the central nervous system that decrease force production capacity in fatigued muscles. The impact on unrelated, non-exercised muscle performance is still unclear. The primary aim of this study was to examine the impact of a bilateral forearm muscle contraction on the motor function of the distal and unrelated ankle plantar-flexor muscles. The secondary aim was to compare the impact of maximal and submaximal forearm contractions on the non-fatigued ankle plantar-flexor muscles. Maximal voluntary contractions (MVC) of the forearm and ankle plantar-flexor muscles as well as voluntary activation (VA) and twitch torque of the ankle plantar-flexor muscles were assessed pre-fatigue and throughout a 10-min recovery period. Maximal (100 % MVC) and submaximal (30 % MVC) sustained isometric handgrip contractions caused a decreased handgrip MVC (to 49.3 ± 15.4 and 45.4 ± 11.4 % of the initial MVC for maximal and submaximal contraction, respectively) that remained throughout the 10-min recovery period. The fatigue protocols also caused a decreased ankle plantar-flexor MVC (to 77 ± 8.3 and 92.4 ± 6.2 % of pre-fatigue MVC for maximal and submaximal contraction, respectively) and VA (to 84.3 ± 15.7 and 97.7 ± 16.1 % of pre-fatigue VA for maximal and submaximal contraction, respectively). These results suggest central fatigue created by the fatiguing handgrip contraction translated to the performance of the non-exercised ankle muscles. Our results also show that the maximal fatigue protocol affected ankle plantar-flexor MVC and VA more severely than the submaximal protocol, highlighting the task-specificity of neuromuscular fatigue.

High-intensity interval training (HIT), in a variety of forms, is today one of the most effective means of improving cardiorespiratory and metabolic function and, in turn, the physical performance of athletes. HIT involves repeated short-to-long bouts of rather high-intensity exercise interspersed with recovery periods. For team and racquet sport players, the inclusion of sprints and all-out efforts into HIT programmes has also been shown to be an effective practice. It is believed that an optimal stimulus to elicit both maximal cardiovascular and peripheral adaptations is one where athletes spend at least several minutes per session in their ‘red zone,’ which generally means reaching at least 90 % of their maximal oxygen uptake ( O 2max ). While use of HIT is not the only approach to improve physiological parameters and performance, there has been a growth in interest by the sport science community for characterizing training protocols that allow athletes to maintain long periods of time above 90 % of O 2max (T@ O 2max ). In addition to T@ O 2max , other physiological variables should also be considered to fully characterize the training stimulus when programming HIT, including cardiovascular work, anaerobic glycolytic energy contribution and acute neuromuscular load and musculoskeletal strain. Prescription for HIT consists of the manipulation of up to nine variables, which include the work interval intensity and duration, relief interval intensity and duration, exercise modality, number of repetitions, number of series, as well as the between-series recovery duration and intensity. The manipulation of any of these variables can affect the acute physiological responses to HIT. This article is Part I of a subsequent II-part review and will discuss the different aspects of HIT programming, from work/relief interval manipulation to the selection of exercise mode, using different examples of training cycles from different sports, with continued reference to T@ O 2max and cardiovascular responses. Additional programming and periodization considerations will also be discussed with respect to other variables such as anaerobic glycolytic system contribution (as inferred from blood lactate accumulation), neuromuscular load and musculoskeletal strain (Part II).

Objective The purpose of these experiments was to develop a rating-of-fatigue (ROF) scale capable of tracking the intensity of perceived fatigue in a variety of contexts. Methods Four experiments were carried out. The first provided the evidential basis for the construction of the ROF scale. The second tested the face validity of the ROF, and the third tested the convergent and divergent validity of the ROF scale during ramped cycling to exhaustion and 30 min of resting recovery. The final experiment tested the convergent validity of the ROF scale with time of day and physical activity (accelerometer counts) across a whole week. Results Modal selections of descriptions and diagrams at different levels of exertion and recovery were found during Experiment 1 upon which the ROF scale was constructed and finalised. In Experiment 2, a high level of face validity was indicated, in that ROF was reported to represent fatigue rather than exertion. Descriptor and diagrammatic elements of ROF reportedly added to the coherence and ease of use of the scale. In Experiment 3, high convergence between ROF and various physiological measures were found during exercise and recovery (heart rate, blood lactate concentration, oxygen uptake, carbon dioxide production, respiratory exchange ratio and ventilation rate were all P  < 0.001). During ramped cycling to exhaustion ROF and RPE did correspond ( P  < 0.0001) but not during recovery, demonstrating discriminant validity. Experiment 4 found ROF to correspond with waking time during each day (Mon–Sun all P  < 0.0001) and with physical activity (accelerometer count) (Mon–Sun all P  < 0.001). Conclusions The ROF scale has good face validity and high levels of convergent validity during ramped cycling to exhaustion, resting recovery and daily living activities. The ROF scale has both theoretical and applied potential in understanding changes in fatigue in a variety of contexts.

This book surveys the research evidence into exercise-induced fatigue and discusses how knowledge of fatigue can be applied in sport and exercise contexts. The book examines the different \"types\" of fatigue and the difficulties of identifying which types are prevalent during different types of exercise. It introduces the fundamental science of fatigue, focusing predominantly on physiological and neuromuscular aspects, and explores key topics in detail, such as energy depletion, lactic acid, dehydration, electrolytes and minerals, and the perception of fatigue.-- From publisher's description.

Background Although the acquisition of heat acclimation (HA) is well-documented, less is known about HA decay (HAD) and heat re-acclimation (HRA). The available literature suggests 1 day of HA is lost following 2 days of HAD. Understanding this relationship has the potential to impact upon the manner in which athletes prepare for major competitions, as a HA regimen may be disruptive during final preparations (i.e., taper). Objective The aim of this systematic review and meta-analysis was to determine the rate of HAD and HRA in three of the main physiological adaptations occurring during HA: heart rate (HR), core temperature ( T c ), and sweat rate (SR). Data Sources Data for this systematic review were retrieved from Scopus and critical review of the cited references. Study Selection Studies were included when they met the following criteria: HA, HAD, and HRA (when available) were quantified in terms of exposure and duration. HA had to be for at least 5 days and HAD for at least 7 days for longitudinal studies. HR, T c , or SR had to be monitored in human participants. Study Appraisal The level of bias in each study was assessed using the McMaster critical review form. Multiple linear regression techniques were used to determine the dependency of HAD in HR, T c , and SR from the number of HA and HAD days, daily HA exposure duration, and intensity. Results Twelve studies met the criteria and were systematically reviewed. HAD was quantified as a percentage change relative to HA (0% = HA, 100% = unacclimated state). Adaptations in end-exercise HR decreased by 2.3% ( P  < 0.001) for every day of HAD. For end-exercise T c , the daily decrease was 2.6% ( P  < 0.01). The adaptations in T c during the HA period were more sustainable when the daily heat exposure duration was increased and heat exposure intensity decreased. The decay in SR was not related to the number of decay days. However, protracted HA-regimens seem to induce longer-lasting adaptations in SR. High heat exposure intensities during HA seem to evoke more sustained adaptations in SR than lower heat stress. Only eight studies investigated HRA. HRA was 8–12 times faster than HAD at inducing adaptations in HR and T c , but no differences could be established for SR. Limitations The available studies lacked standardization in the protocols for HA and HAD. Conclusions HAD and HRA differ considerably between physiological systems. Five or more HA days are sufficient to cause adaptations in HR and T c ; however, extending the daily heat exposure duration enhances T c adaptations. For every decay day, ~ 2.5% of the adaptations in HR and T c are lost. For SR, longer HA periods are related to better adaptations. High heat exposure intensities seem beneficial for adaptations in SR, but not in T c . HRA induces adaptations in HR and T c at a faster rate than HA. HRA may thus provide a practical and less disruptive means of maintaining and optimizing HA prior to competition.

This book provides fundamental concepts in human thermal physiology and their applications in general public, occupational, military, and athletics settings from the biometeorological perspective. The book includes a section on human physiology, epidemiology and special considerations in aforementioned populations, and behavioral and technological adjustments people may take to combat thermal environmental stress and safeguard their health. The book is the first of its kind to compile multiple disciplines--human physiology, climatology, and medicine--in one to provide fundamental concepts in human thermal physiology and their applications in general public, occupational, military, and athletics settings from the biometeorological perspective; Developed by experts, scientists, and physicians from exercise physiology, climatology, public health, sports medicine, and military medicine; Highlights special considerations and applications of thermal physiology to general public, occupational, military, and athletics settings.

BackgroundThe temporal and insular cortex (TC, IC) have been associated with autonomic nervous system (ANS) control and the awareness of emotional feelings from the body. Evidence shows that the ANS and rating of perceived exertion (RPE) regulate exercise performance. Non-invasive brain stimulation can modulate the cortical area directly beneath the electrode related to ANS and RPE, but it could also affect subcortical areas by connection within the cortico-cortical neural networks. This study evaluated the effects of transcranial direct current stimulation (tDCS) over the TC on the ANS, RPE and performance during a maximal dynamic exercise.MethodsTen trained cyclists participated in this study (33±9 years; 171.5±5.8 cm; 72.8±9.5 kg; 10–11 training years). After 20-min of receiving either anodal tDCS applied over the left TC (T3) or sham stimulation, subjects completed a maximal incremental cycling exercise test. RPE, heart rate (HR) and R–R intervals (as a measure of ANS function) were recorded continuously throughout the tests. Peak power output (PPO) was recorded at the end of the tests.ResultsWith anodal tDCS, PPO improved by ∼4% (anodal tDCS: 313.2±29.9 vs 301.0±19.8 watts: sham tDCS; p=0.043), parasympathetic vagal withdrawal was delayed (anodal tDCS: 147.5±53.3 vs 125.0±35.4 watts: sham tDCS; p=0.041) and HR was reduced at submaximal workloads. RPE also increased more slowly during exercise following anodal tDCS application, but maximal RPE and HR values were not affected by cortical stimulation.ConclusionsThe findings suggest that non-invasive brain stimulation over the TC modulates the ANS activity and the sensory perception of effort and exercise performance, indicating that the brain plays a crucial role in the exercise performance regulation.