For the elite athletes, conditioning includes all the processes of preparation for major competitions. Previous conditioning research has used group designs where multiple athletes have been observed at one or more points in time and studied as a group. In these designs, the sample mean is used as the representative value of the group, whilst individual data departures from the mean are treated as ÅgerrorsÅh. Thus, group data mask important individual characteristics and changes which need to be taken into consideration when altering the conditioning regimes of elite athletes. It is well known that individual differences exist in response to conditioning. These can be assessed through single-case research designs and statistical methods for longitudinal data. Three case studies (two rowers and one track athlete) will be presented to show that data (ie training, recovery, and the response to these) recorded in a daily self-monitoring log can be statistically analyzed by the randomization test and interrupted time series analysis correlation procedure (ITSACORR) to provide useful information about how the athlete is progressing. Two rowers showed statistically significant improvements in level of recovery and fatigue in the period leading to their major competition. Through appropriate statistical methods for longitudinal data the effects of conditioning on individual athletes can be evaluated fortnightly to provide quick and reliable feedback. In conclusion, the new approach using the randomization test and ITSACORR was useful to identify positive and negative responses of conditioning for individual elite athletes. 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.
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