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Shift work is now common in society and is not restricted to heavy industry or emergency services, but is increasingly found amongst 'white collar' occupations and the growing number of service industries. Participation in shift work is associated with increased body mass index, prevalence of obesity and other health problems. We review the behavioural and biological disturbances that occur during shift work and discuss their impact on leisure-time physical activity and energy balance. Shift work generally decreases opportunities for physical activity and participation in sports. For those shift workers who are able to exercise, subjective and biological responses can be altered if the exercise is taken at unusual times of day and/or if the shift worker is sleep deprived. These altered responses may in turn impact on the longer-term adherence to an exercise programme. The favourable effects of exercise on body mass control and sleep quality in shift workers have not been confirmed. Similarly, recent reports of relationships between sleep duration and obesity have not been examined in a shift work context. There is no evidence that exercise can mediate certain circadian rhythm characteristics (e.g. amplitude or timing) for improved tolerance to shift work. Total energy intake and meal composition do not seem to be affected by participation in shift work. Meal frequency is generally reduced but snacking is increased on the night shift. Unavailability of preferred foods in the workplace, a lack of time, and a reduced desire to eat at night explain these findings. 'Normal' eating habits with the family are also disrupted. The metabolic responses to food are also altered by shift work-mediated disruptions to sleep and circadian rhythms. Whether any interactions on human metabolism exist between timing or content of food intake and physical activity during shift work is not known at present. There are very few randomized controlled studies on the efficacy of physical activity or dietary interventions during shift work. Some favourable effects of such interventions on fatigue levels at work have been reported, but biological and behavioural outcomes relevant to long-term health and energy balance have not been studied adequately. In addition, recruitment and retention of research participants for randomized controlled trials of physical activity or dietary interventions have been very difficult. We present a model of the various behavioural and biological factors relevant to exercise and energy balance during shift work as a framework for future research.

Soccer (association football) is a team sport that incorporates frequent fluctuations between high and low exercise intensities. These unpredictable changes may be accompanied by unorthodox patterns of movements and the performance of specific skills. The individual activity profiles are highly variable and include elements of self-pacing, since decision making about opportunities to become engaged in play dictates individual activities. Approaches utilised to investigate the demands placed on players during competitive performances include behavioural observations during games, physiological evaluations in matches and assessments of the physical capacity of players. Observations made during games to determine the work-rate patterns of individual players are highly variable and make generalisations based on individual activity patterns conditional, unless the sample sizes are large and data are collected on a number of occasions. The data may also be affected by the diverse methodological approaches to their collection and analysis and a failure to determine the reliability and objectivity of the relevant measuring tools. Techniques that can be used to collect data in matches are limited as the sports rules and regulations restrict some approaches. The validity of applying data from non-competitive matches to the competitive situation must, therefore, be subject to formal scrutiny. There is also a concern as to the degree to which principles of steady-state are applicable to dynamically changing exercise intensities. In the evaluation of the physical capacities of players, the variability in overall soccer performance is reduced to fitness statistics, whereas in reality, soccer performance is a construct based on many different performance components and their interaction at the level of both player and team. Despite these caveats, valuable insights have been acquired into the physiological requirements of the game that have subsequently informed both research projects and impacted upon practice. The challenge for future researchers is to overcome remaining research design hurdles and devise ways to understand more fully the complexities of invasive field games such as soccer. The interactions between individuals within a team require investigation and there is a need to refine and develop methods that employ sophisticated measurement techniques and yet possess both internal and external validity, such as laboratory-based simulations.

We aim to summarise the impact and mechanisms of work-rate pacing during individual cycling time trials (TTs). Unlike time-to-exhaustion tests, a TT provides an externally valid model for examining how an initial work rate is chosen and maintained by an athlete during self-selected exercise. The selection and distribution of work rate is one of many factors that influence cycling speed. Mathematical models are available to predict the impact of factors such as gradient and wind velocity on cycling speed, but only a few researchers have examined the inter-relationships between these factors and work-rate distribution within a TT. When environmental conditions are relatively stable (e.g. in a velodrome) and the TT is10 minutes, then an even distribution of work rate is optimal. For a shorter TT (or = 10 minutes), work rate should be increased during the starting effort because this proportion of total race time is significant. For a very short TT (or = 2 minutes), the starting effort should be maximal, since the time saved during the starting phase is predicted to outweigh any time lost during the final metres because of fatigue. A similar 'time saving' rationale underpins the advice that work rate should vary in parallel with any changes in gradient or wind speed during a road TT. Increasing work rate in headwind and uphill sections, and vice versa, decreases the variability in speed and, therefore, the total race time. It seems that even experienced cyclists naturally select a supraoptimal work rate at the start of a longer TT. Whether such a start can be 'blunted' through coaching or the monitoring of psychophysiological variables is unknown. Similarly, the extent to which cyclists can vary and monitor work rate during a TT is unclear. There is evidence that sub-elite cyclists can vary work rate by +/-5% the average for a TT lasting 25-60 minutes, but such variability might be difficult with high-performance cyclists whose average work rate during a TT is already extremely high (350 watts). During a TT, pacing strategy is regulated in a complex anticipatory system that monitors afferent feedback from various physiological systems, and then regulates the work rate so that potentially limiting changes do not occur before the endpoint of exercise is reached. It is critical that the endpoint of exercise is known by the cyclist so that adjustments to exercise work rate can be made within the context of an estimated finish time. Pacing strategies are thus the consequence of complex regulation and serve a dual role: they are both the result of homeostatic regulation by the brain, as well as being the means by which such regulation is achieved. The pacing strategy 'algorithm' is sited in the brain and would need afferent input from interoceptors, such as heart rate and respiratory rate, as well as exteroceptors providing information on local environmental conditions. Such inputs have been shown to induce activity in the thalamus, hypothalamus and the parietal somatosensory cortex. Knowledge of time, modulated by the cerebellum, basal ganglia and primary somatosensory cortex, would also input to the pacing algorithm as would information stored in memory about previous similar exercise bouts. How all this information is assimilated by the different regions of the brain is not known at present.

Although regular physical activity is beneficial for many clinical conditions, an acute bout of exercise might increase the risk of an adverse clinical event, such as sudden cardiac death or myocardial infarction, particularly in vulnerable individuals. Since it is also known that the incidence of these events peaks in the morning and that some cardiac patients prefer to schedule leisure-time physical activity before lunch, the question arises as to whether morning exercise is 'inherently' more risky than physical activity performed at other times of day. We attempt to answer this question by reviewing the relevant epidemiological data as well as the results of chronobiological and exercise-related studies that have concentrated on the pathophysiological mechanisms for sudden cardiac events. We also consider generally how chronobiology might impact on exercise prescription in heart disease. We performed a structured literature search in the PubMed and WEBofSCIENCE databases for relevant studies published between 1981 and 2004. The limited amount of published epidemiological data did not allow us to conclude that a bout of vigorous exercise in the morning increases the relative risk of either primary cardiac events in apparently healthy individuals, or secondary events in cardiac patients enrolled in supervised exercise programmes. Nevertheless, these data are not directly relevant to individuals who have a history of heart disease and perform uncontrolled habitual activities. It appears as though the influence of time of day on the cardiovascular safety of this type of exercise has not been examined in this population. There is evidence that several pathophysiological variables (e.g. blood pressure, endothelial function, fibrinolysis) vary in parallel with typical diurnal changes in freely chosen activity. Nevertheless, few studies have been designed to examine specifically whether such variables respond differently to a 'set' level of exercise in the morning compared with the afternoon or evening. Even fewer researchers have adequately separated the influences of waking from sleep, adopting an upright posture and physical exertion per se on these pathophysiological responses at different times of day. In healthy individuals, exercise is generally perceived as more difficult and functional performance is decreased in the morning hours. These observations have been confirmed for patients with heart disease in only one small study. It has also not been confirmed, using an adequately powered study involving cardiac patients, that the responses of heart rate and oxygen consumption (VO(2)) to a set bout of exercise show the highest reactivity in the afternoon and evening, which is the case with healthy individuals. Confirmation of this circadian variation would be important, since it would mean that exercise might be prescribed at too high an intensity in the morning if heart rate or VO(2) responses are employed as markers of exercise load. We conclude that there is some parallelism between the diurnal changes in physical activity and those in the pathophysiological mechanisms associated with acute cardiac events. Nevertheless, more studies are needed to ascertain whether the responses of endothelial function, fibrinolysis and blood pressure to a set exercise regimen differ according to time of day. The results of epidemiological studies suggest that morning exercise is just as safe as afternoon exercise for cardiac patients enrolled in a supervised rehabilitation programme. Nevertheless, it is unclear whether time of day alters the risk of a cardiac event occurring during spontaneous physical activity performed by individuals with established risk factors for heart disease.

The pineal hormone, melatonin, has widespread effects on the body. The aim of this review is to consider the specific interactions between melatonin and human physiological functions associated with sport and exercise medicine. Separate researchers have reported that melatonin concentrations increase, decrease and remain unaffected by bouts of exercise. Such conflicting findings may be explained by inter-study differences in lighting conditions and the time of day the study participants have exercised. Age and fitness status have also been identified as intervening factors in exercise-mediated changes in melatonin concentration. The administration of exogenous melatonin leads to hypnotic and hypothermic responses in humans, which can be linked to immediate reductions in short-term mental and physical performance. Depending on the dose of melatonin, these effects may still be apparent 3-5 hours after administration for some types of cognitive performance, but effects on physical performance seem more short-lived. The hypothesis that the hypothermic effects of melatonin lead to improved endurance performance in hot environments is not supported by evidence from studies involving military recruits who exercised at relatively low intensities. Nevertheless, no research group has examined such a hypothesis with athletes as study participants and with the associated more intense levels of exercise. The fact that melatonin has also been found to preserve muscle and liver glycogen in exercised rats adds weight to the notion that melatonin might affect endurance exercise in humans. Melatonin has been successfully used to alleviate jet lag symptoms of travellers and there is also a smaller amount of evidence that the hormone helps shiftworkers adjust to nocturnal regimens. Nevertheless, the symptoms of jet lag and shiftwork problems have primarily included sleep characteristics rather than performance variables. The few studies that have involved athletes and performance-related symptoms have produced equivocal results. Melatonin has also been found to be useful for treating some sleeping disorders, but interactions between sleep, melatonin and exercise have not been studied extensively with trained study participants. It is unknown whether melatonin plays a role in some exercise training-related problems such as amenorrhoea and over-training syndrome.

We have shown that the SEM (typical error) and LOA are very similar when defined at the same level of abstraction. The calculation of these sample statistics does not depend on sample size, but the precision of their estimate for the population parameter does. Only the latter concept involves the t-statistic. They do differ in the type of measurement error that is described (true score error versus test-retest error) and the coverage probability of the reference interval (0.6 versus 0.95). Bland and Altman and Atdinson and Nevill have promoted the citation of either the SEM or the LOA to help researchers in their discussion of the impact of error to real uses of the measurement tol. What is vital in this discussion is the researcher having a thorough understanding of the underlying theory behind the measurement error statistic(s) that is/are employed, especially the definition of error and the coverage probability that is selected. Such issues have been built into the title of the 95% LOA statistic, and are also an inherent part of SEM. Whilst the concept of typical error appears to be easy to understand and teach, this is only because the underlying theory and definition of what the statistic actually represents is not communicated. Only if all researchers adopt one single statistic (e.g. typical error) for measurement error is it remotely possible to push underlying theory into the background, since there would be a baseline of comparison for all. Because this scenario is highly unlikely, it is important that any measurement error statistic is well defined and understood by researchers.

Minimal measurement error (reliability) during the collection of interval- and ratio-type data is critically important to sports medicine research. The main components of measurement error are systematic bias (e.g. general learning or fatigue effects on the tests) and random error due to biological or mechanical variation. Both error components should be meaningfully quantified for the sports physician to relate the described error to judgements regarding ‘analytical goals’ (the requirements of the measurement tool for effective practical use) rather than the statistical significance of any reliability indicators.