![]() Comparisons suggest that the HR-based models were less effective in detecting periodized increases in training load, particularly during court-based, intermittent, multidirectional drills. Conclusions: While the training-load models were significantly correlated during each training mode, weaker relationships were observed during specific conditioning. Furthermore, the sRPE model detected greater increases (126–429 AU) in training load than the TRIMP (15–65 AU) and SHRZ models (27–170 AU) transitioning between training modes. 05) than during specific conditioning (sRPE-TRIMP r =. 05) and tactical/game-play conditioning (sRPE-TRIMP r =. Results: Stronger relationships between perceptual and physiological models were evident during base (sRPE-TRIMP r =. ![]() One-way ANOVAs were used to compare training loads between training modes for each model. Pearson correlations were used to determine the relationships between the sRPE model and 2 HR-based models: the training impulse (TRIMP) and summated HR zones (SHRZ). Player session ratings of perceived exertion (sRPE) and heart-rate (HR) responses were gathered across base, specific, and tactical/game-play training modes. Methods: Eight semiprofessional male basketball players (age 26.3 ± 6.7 y, height 188.1 ± 6.2 cm, body mass 92.0 ± 13.8 kg) were monitored across a 10-wk period in the preparatory phase of their training plan. Dalbo Purpose: To compare perceptual and physiological training-load responses during various basketball training modes. Training Mode’s Influence on the Relationships Between Training-Load Models During Basketball Conditioning Aaron T. International Journal of Sports Physiology and Performance, 2014, 9, 851-856 © 2014 Human Kinetics, Inc.
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