Heart rate monitoring in basketball: Applications, player responses, and practical recommendations - Berkelmans, D. M.; Dalbo, V. J.; Kean, C. O.; Milanovic, Z.; Stojanovic, E.; Stojiljkovic, N.; Scanlan, A. T. (The Journal of Strength and Conditioning Research, 2018)
The aims of this review were to collate the existing literature encompassing heart rate (HR) monitoring in basketball to (a) identify the applications of HR measurement; (b) report HR responses in male and female players during training and game-play; (c) evaluate use of current HR-based training load models; and (d) provide recommendations for future research and best practice approaches for basketball practitioners. Heart rate monitoring in basketball carries 3 primary applications: (a) monitoring exercise intensity; (b) assessing player fatigue status; and (c) quantifying internal training load. When interpreting the available training and game-play HR data in basketball players, key differences have been observed between playing positions and playing levels. Sex- and age-based differences in HR responses during basketball training and game-play are apparent across separate studies; however, further research exploring HR responses in wider player groups is needed, especially in female and junior players. There is also a lack of research directly comparing player HR responses during training and game-play to ascertain the effectiveness of different drills in preparing players for competition. Heart rate-based models have been frequently used to quantify the internal training load in basketball players, including Banister's Training Impulse (TRIMP), Lucia's TRIMP, and Edwards' Summated-Heart-Rate-Zones (SHRZ). The SHRZ model seems to hold practical advantages and better detect changes in player responses across training cycles compared with other approaches. Practical outcomes of this review center on recommendations for position-specific training plans, drills to promote desired cardiovascular stress, analysis of HR outcome measures, and ideal training load monitoring approaches.