Cortisol, DHEA, performance and training in elite swimmers

Salivary cortisol (C) and DHEA concentrations were measured in 9 elite swimmers (4 female and 5 male) over a 37-week period, 5 to 12 times per swimmer, before 68 competitions. For female and male swimmers, no significant relationship was found between C, DHEA and performance. For the whole group, C was negatively correlated with week number of training (r = -0.31, p < 0.01). The incorporation of the cumulated distance swum as a second variable in the regression increased r to 0.56 (p < 0.01). The higher the cumulated distance swum, the higher C. No significant relationship was found between DHEA and distance swum. For individual swimmers, 3 of 4 females showed a significant negative relationship between C and cumulated dry-land training. No equivalent relationship was found for DHEA. The 2 males practicing dry-land training showed a significant and negative relationship between DHEA and cumulated dry-land training. No equivalent relationship was found for C. Thus, C and DHEA were not good predictors of swimming performance. C for individual females, and DHEA for individual males were considered useful markers for dry-land training stress.

Tennis, incidence of URTI and salivary IgA

Tennis played at an elite level requires intensive training characterized by repeated bouts of brief intermittent high intensity exercise over relatively long periods of time (1 - 3 h or more). Competition can place additional stress on players. The purpose of this study was to investigate the temporal association between specific components of tennis training and competition, the incidence of upper respiratory tract infections (URT1), and salivary IgA, in a cohort of seventeen elite female tennis players. Timed, whole unstimulated saliva samples were collected before and after selected 1-h training sessions at 2 weekly intervals, over 12 weeks. Salivary IgA concentration was measured by ELISA and IgA secretion rate calculated (mug IgA x ml(-1) x ml saliva x min(-1)). Players reported URTI symptoms and recorded training and competition in daily logs. Data analysis showed that higher incidence of URTI was significantly associated with increased training duration and load, and competition level, on a weekly basis. Salivary IgA secretion rate (S-IgA) dropped significantly after 1 hour of tennis play. Over the 12-week period, pre-exercise salivary IgA concentration and secretion rate were directly associated with the amount of training undertaken during the previous day and week (p < 0.05). However, the decline in S-IgA after 1 h of intense tennis play was also positively related to the duration and load of training undertaken during the previous day and week (p < 0.05). Although exercise-induced suppression of salivary IgA may be a risk factor, it could not accurately predict the occurrence of URTI in this cohort of athletes.

Single-subject research designs and data analyses for assessing elite athletes' conditioning

Research in conditioning (all the processes of preparation for competition) has used group research designs, where multiple athletes are observed at one or more points in time. However, empirical reports of large inter-individual differences in response to conditioning regimens suggest that applied conditioning research would greatly benefit from single-subject research designs. Single-subject research designs allow us to find out the extent to which a specific conditioning regimen works for a specific athlete, as opposed to the average athlete, who is the focal point of group research designs. The aim of the following review is to outline the strategies and procedures of single-subject research as they pertain to.. the assessment of conditioning for individual athletes. The four main experimental designs in single-subject research are: the AB design, reversal (withdrawal) designs and their extensions, multiple baseline designs and alternating treatment designs. Visual and statistical analyses commonly used to analyse single-subject data, and advantages and limitations are discussed. Modelling of multivariate single-subject data using techniques such as dynamic factor analysis and structural equation modelling may identify individualised models of conditioning leading to better prediction of performance. Despite problems associated with data analyses in single-subject research (e.g. serial dependency), sports scientists should use single-subject research designs in applied conditioning research to understand how well an intervention (e.g. a training method) works and to predict performance for a particular athlete.

Anthropometry profiles of elite rugby players: quantifying changes in lean mass

Objective: To demonstrate the utility of a practical measure of lean mass for monitoring changes in the body composition of athletes. Methods: Between 1999 and 2003 body mass and sum of seven skinfolds were recorded for 40 forwards and 32 backs from one Super 12 rugby union franchise. Players were assessed on 13 (7) occasions ( mean (SD)) over 1.9 (1.3) years. Mixed modelling of log transformed variables provided a lean mass index (LMI) of the form mass/skinfolds(x), for monitoring changes in mass controlled for changes in skinfold thickness. Mean effects of phase of season and time in programme were modelled as percentage changes. Effects were standardised for interpretation of magnitudes. Results: The exponent x was 0.13 for forwards and 0.14 for backs ( 90% confidence limits +/- 0.03). The forwards had a small decrease in skinfolds ( 5.3%, 90% confidence limits +/- 2.2%) between preseason and competition phases, and a small increase ( 7.8%, 90% confidence limits +/- 3.1%) during the club season. A small decrease in LMI (similar to 1.5%) occurred after one year in the programme for forwards and backs, whereas increases in skinfolds for forwards became substantial (4.3%, 90% confidence limits +/- 2.2%) after three years. Individual variation in body composition was small within a season (within subject SD: body mass, 1.6%; skinfolds, 6.8%; LMI, 1.1%) and somewhat greater for body mass (2.1%) and LMI (1.7%) between seasons. Conclusions: Despite a lack of substantial mean changes, there was substantial individual variation in lean mass within and between seasons. An index of lean mass based