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Canberra, Australia

Recovery after strenuous exercise involves processes that are dependent on fl uid and food intake. Current sports nutrition guidelines provide recommendations for the quantity and timing of consumption of nutrients to optimise recovery issues such as refuelling, rehydration and protein synthesis for repair and adaptation. Recovery of immune and antioxidant systems is important but less well documented. In some cases, there is little effective recovery until nutrients are supplied, while in others, the stimulus for recovery is strongest in the period immediately after exercise. Lack of appropriate nutritional support will reduce adaption to exercise and impair preparation for future bouts. Ramadan represents a special case of intermittent fasting undertaken by many athletes during periods of training as well as important competitive events. The avoidance of fl uid and food intake from sunrise to sundown involves prolonged periods without intake of nutrients, infl exibility with the timing of eating and drinking over the day and around an exercise session, and changes to usual dietary choices due to the special foods involved with various rituals. These outcomes will all challenge the athlete's ability to recover optimally between exercise sessions undertaken during the fast or from day to day. Source


Halson S.L.,Australian Institute of Sport
Sports Medicine | Year: 2014

Sleep has numerous important physiological and cognitive functions that may be particularly important to elite athletes. Recent evidence, as well as anecdotal information, suggests that athletes may experience a reduced quality and/or quantity of sleep. Sleep deprivation can have significant effects on athletic performance, especially submaximal, prolonged exercise. Compromised sleep may also influence learning, memory, cognition, pain perception, immunity and inflammation. Furthermore, changes in glucose metabolism and neuroendocrine function as a result of chronic, partial sleep deprivation may result in alterations in carbohydrate metabolism, appetite, food intake and protein synthesis. These factors can ultimately have a negative influence on an athlete's nutritional, metabolic and endocrine status and hence potentially reduce athletic performance. Research has identified a number of neurotransmitters associated with the sleep-wake cycle. These include serotonin, gamma-aminobutyric acid, orexin, melanin-concentrating hormone, cholinergic, galanin, noradrenaline, and histamine. Therefore, nutritional interventions that may act on these neurotransmitters in the brain may also influence sleep. Carbohydrate, tryptophan, valerian, melatonin and other nutritional interventions have been investigated as possible sleep inducers and represent promising potential interventions. In this review, the factors influencing sleep quality and quantity in athletic populations are examined and the potential impact of nutritional interventions is considered. While there is some research investigating the effects of nutritional interventions on sleep, future research may highlight the importance of nutritional and dietary interventions to enhance sleep. © The Author(s) 2014. Source


Halson S.L.,Australian Institute of Sport
International Journal of Sports Physiology and Performance | Year: 2011

An increase in research investigating recovery strategies has occurred alongside the increase in usage of recovery by elite athletes. Because there is inconsistent evidence regarding the benefits of recovery on performance, it is necessary to examine research design to identify possible strategies that enhance performance in different athlete settings. The purpose of this review is to examine available recovery literature specifically related to the time frame between performance assessments to identify considerations for both research design and practical use of recovery techniques. © 2011 Human Kinetics Inc. Source


Cook J.L.,Monash University | Purdam C.,Australian Institute of Sport
British Journal of Sports Medicine | Year: 2012

Tendons are designed to take tensile load, but excessive load can cause overuse tendinopathy. Overuse tendinopathy Results: in extensive changes to the cells and extracellular matrix, resulting in activated cells, increase in large proteoglycans and a breakdown of the collagen structure. Within these pathological changes, there are areas of fibrocartilaginous metaplasia, and mechanotransduction models suggest that this response could be due to compressive load. As load management is a cornerstone of treating overuse tendinopathy, defining the effect of tensile and compressive loads is important in optimising the clinical management of tendinopathy. This paper examines the potential role of compressive loads in the onset and perpetuation of tendinopathy, and reviews the anatomical, epidemiological and clinical evidence that supports consideration of compressive loads in overuse tendinopathy. Source


Welsh A.H.,Australian National University | Knight E.J.,Australian Institute of Sport
Medicine and Science in Sports and Exercise | Year: 2014

Purpose: We consider "magnitude-based inference" and its interpretation by examining in detail its use in the problem of comparing two means. Methods: We extract from the spreadsheets, which are provided to users of the analysis (http:// www.sportsci.org/), a precise description of how "magnitude-based inference" is implemented.We compare the implemented version of the method with general descriptions of it and interpret the method in familiar statistical terms. Results and Conclusions: We show that "magnitude-based inference" is not a progressive improvement on modern statistics. The additional probabilities introduced are not directly related to the confidence interval but, rather, are interpretable either as P values for two different nonstandard tests (for different null hypotheses) or as approximate Bayesian calculations, which also lead to a type of test. We also discuss sample size calculations associated with "magnitude-based inference" and show that the substantial reduction in sample sizes claimed for the method (30% of the sample size obtained from standard frequentist calculations) is not justifiable so the sample size calculations should not be used. Rather than using "magnitude-based inference," a better solution is to be realistic about the limitations of the data and use either confidence intervals or a fully Bayesian analysis. © 2014 by the American College of Sports Medicine. Source

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