Identifying mediators of training effects on performance-related psychobiosocial states: A single-case observational study in an elite female triathlete

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• 作者：Anthony Barnett^a ; ^{abarnett@hku.hk} ; Ester Cerin^a ; ^{ecerin@hku.hk} ; Peter Reaburn^b ; ^{p.reaburn@cqu.edu.au} ; Sue Hooper^c ; ^{Sue.Hooper@communities.qld.gov.au}
• 关键词：Dynamic linear model ; Mediation ; Single-case study ; Sport ; Self-reported psychobiosocial states ; Triathlon
• 刊名：Psychology of Sport and Exercise
• 出版年：2012
• 期刊代码：227_14690292
• 类别：med
• 出版时间：September, 2012
• 卷：13
• 期：5
• 页码：541-549
• 文件大小：595 K

摘要

Objectives

Relationships between training load, psychobiosocial (PBS) states and performance are dynamic and individual-specific. The nature of these relationships can be investigated using a combination of dynamic linear models (DLMs) and mediating variable analysis, potentially assisting applied sports psychologists in planning and monitoring of individual elite athletes鈥?intervention programmes.

Design

We illustrate this approach by examining the relationships of training loads with a performance-related state (鈥榮elf-efficacy鈥? and the role of potential mediating PBS variables (鈥榝atigue/lack of energy鈥?and 鈥榖eing in shape鈥? in explaining these relationships in an elite triathlete across time.

Method

Self-reports of PBS states (twice weekly) and training data were collected over 137 days. Using DLMs and mediating variable analysis, direct (unmediated) and indirect (mediated) short-term associations of training load with 鈥榮elf-efficacy鈥?were examined.

Results

In this triathlete, we found evidence for positive effects of training on 鈥榮elf-efficacy鈥? which were partly explained by feelings of 鈥榖eing in shape鈥?and suppressed by feelings of 鈥榝atigue/lack of energy鈥? Changes in the relationship between lagged training load and 鈥榝atigue/lack of energy鈥?were observed across time and were particularly pronounced in temporal proximity of an injury.

Conclusion

Strengths of the presented approach are its dynamic nature enabling the observation of changes occurring over time, use of statistical inference rather than visual data interpretation, and quantification of mediating effects to identify potential pathways of intervention. Additionally, the DLM method can identify complex nonlinear associations by examining correspondence between changes in levels of predictors and changes in magnitude and direction of predictor-outcome associations.