Author Identifier
Date of Award
2026
Keywords
biomechanics, resistance training, cluster set, weightlifting
Document Type
Thesis - ECU Access Only
Publisher
Edith Cowan University
Degree Name
Doctor of Philosophy
School
School of Medical and Health Sciences
First Supervisor
G. Gregory Haff
Second Supervisor
Kristina Kendall
Third Supervisor
Shayne Vial
Fourth Supervisor
Paul Comfort
Abstract
Weightlifting movements and their derivatives like the power clean are commonly included in many athletes’ resistance training programmes. Therefore, understanding the biomechanical factors that underpin technical efficiency is crucial for optimising performance and training adaptations. While the most common approach used for analysing biomechanical data from weightlifting movements is discrete data analysis, this approach does not allow the entire time-series data to be retained and analysed. Advanced data analytic approaches, such as functional data analysis techniques and statistical parametric mapping, have been proposed to address this issue, although their application to weightlifting movements remains limited. In addition to analytical considerations, training structure may influence technical performance in weightlifting movements, as traditional sets, where repetitions are performed continuously without rest within each set, have been shown to result in fatigue-induced alterations in lifting technique during the power clean. Cluster set structures have been proposed as an alternative set structure that may mitigate these fatigue-induced technical changes. Despite growing interest in this programming strategy, it remains unknown the extent to which cluster sets are implemented in applied settings, whether they are more effective at preserving movement velocity than other alternative set structures, and whether training intensity can be manipulated when performing high-volume cluster sets with the power clean. As such, this thesis was designed to investigate the perceptions and applications of the cluster set and to synthesise the existing literature on the acute effects of these set structures. This thesis also aimed to explore the relationships between various kinematic factors and power clean performance. Furthermore, the study aimed to investigate the cluster set’s effectiveness in preserving these kinematic characteristics and analyse the impact of varying external loads within a cluster set.
The primary finding of this thesis is that although previous research has highlighted the importance of horizontal barbell displacement for successful execution of weightlifting movements, this was not evidenced for the power clean in the present thesis (Study Ⅲ). Instead, successful power clean performances were characterised by greater maximal vertical barbell displacement and higher peak velocities (Study Ⅲ). Importantly, these key performance characteristics were better maintained when performing cluster sets during high-volume power clean sessions (Study VI). Additionally, fatigue induced changes in horizontal barbell displacement observed during traditional sets were minimised when performing cluster sets (Study VI), even when greater loads were programmed (Study VII). Furthermore, a large proportion of strength and conditioning practitioners reported using cluster sets in their athletes’ resistance training programs (Study I), and cluster sets appeared to be the most effective alternative set structure for maintaining movement velocity during resistance training (Study II). As such, cluster sets are a practical, effective alternative set structure that can be strategically designed to not only modulate exercise-induced fatigue and maintain the key performance characteristics associated with the power clean but also increase external loads during high-volume power clean sessions.
Access Note
Access to this thesis is embargoed until 28th July 2031
Recommended Citation
Nagatani, T. (2026). Kinematic determinants of weightlifting performance and the effectiveness of cluster set training in mitigating fatigue-induced performance decline. Edith Cowan University. https://doi.org/10.25958/q1a3-vt27