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

Kinugasa, Taisuke, Cerin, Ester and Hooper, Sue (2004) Single-subject research designs and data analyses for assessing elite athletes' conditioning. Sports Medicine, 34 15: 1035-1050. doi:10.2165/00007256-200434150-00003


Author Kinugasa, Taisuke
Cerin, Ester
Hooper, Sue
Title Single-subject research designs and data analyses for assessing elite athletes' conditioning
Journal name Sports Medicine   Check publisher's open access policy
ISSN 0112-1642
Publication date 2004-01-01
Sub-type Article (original research)
DOI 10.2165/00007256-200434150-00003
Volume 34
Issue 15
Start page 1035
End page 1050
Total pages 16
Editor J. N. Shanahan
Place of publication Auckland, New Zealand
Publisher Adis International
Language eng
Subject C1
321401 Exercise Physiology
750203 Organised sports
Abstract 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.
Keyword Sport Sciences
Time-series Analysis
Evaluating Treatment Interventions
Statistical-analysis
Randomization Tests
Performance
Exercise
Power
Methodology
Reliability
Intensity
Q-Index Code C1

 
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Created: Wed, 15 Aug 2007, 13:27:39 EST