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The Relationships Between External and Internal Workloads During Basketball Training and Games

Jordan L. Fox, Cody J. O’Grady, Aaron T. Scanlan

2020International Journal of Sports Physiology and Performance50 citationsDOIOpen Access PDF

Abstract

PURPOSE: To investigate the relationships between external and internal workloads using a comprehensive selection of variables during basketball training and games. METHODS: Eight semiprofessional, male basketball players were monitored during training and games for an entire season. External workload was determined as PlayerLoad™: total and high-intensity accelerations, decelerations, changes of direction, and jumps and total low-intensity, medium-intensity, high-intensity, and overall inertial movement analysis events. Internal workload was determined using the summated-heart-rate zones and session rating of perceived exertion models. The relationships between external and internal workload variables were separately calculated for training and games using repeated-measures correlations with 95% confidence intervals. RESULTS: PlayerLoad was more strongly related to summated-heart-rate zones (r = .88 ± .03, very large [training]; r = .69 ± .09, large [games]) and session rating of perceived exertion (r = .74 ± .06, very large [training]; r = .53 ± .12, large [games]) than other external workload variables (P < .05). Correlations between total and high-intensity accelerations, decelerations, changes of direction, and jumps and total low-intensity, medium-intensity, high-intensity, and overall inertial movement analysis events and internal workloads were stronger during training (r = .44-.88) than during games (r = .15-.69). CONCLUSIONS: PlayerLoad and summated-heart-rate zones possess the strongest dose-response relationship among a comprehensive selection of external and internal workload variables in basketball, particularly during training sessions compared with games. Basketball practitioners may therefore be able to best anticipate player responses when prescribing training drills using these variables for optimal workload management across the season.

Topics & Concepts

BasketballWorkloadRating of perceived exertionSession (web analytics)Intensity (physics)Training (meteorology)Perceived exertionHeart ratePsychologyExercise intensityStatisticsSimulationPhysical medicine and rehabilitationComputer scienceMathematicsMedicineMeteorologyWorld Wide WebQuantum mechanicsOperating systemPhysicsRadiologyHistoryArchaeologyBlood pressureSports Performance and TrainingSport Psychology and PerformanceCardiovascular and exercise physiology