Melanie Lewis1. 1. Department of Psychology, University of Oklahoma, Norman.
Abstract
CONTEXT: National Basketball Association (NBA) athletes experience a high rate of injuries. Injury prevention requires identifying observable and controllable risk factors. OBJECTIVE: To examine the relationship among game load, fatigue, and injuries in NBA athletes. DESIGN: Cross-sectional study. SETTING: Game statistics and injury reports over 3 NBA seasons (2012-2015). PATIENTS OR OTHER PARTICIPANTS: Data represented 627 players (height = 200.7 ± 8.9 cm, mass = 100.6 ± 12.1 kg, NBA experience = 4.8 ± 4.2 years, pre-NBA experience = 3.2 ± 1.9 years), 73 209 games, and 1663 injury events. MAIN OUTCOME MEASURE(S): An injury event was defined as a player missing or leaving a game due to injury. Logistic multilevel regression was used to predict injuries from time-lagged fatigue and game load with between-subjects differences explained by demographic variables. RESULTS: The odds of injury increased by 2.87% ( P < .001) for each 96 minutes played and decreased by 15.96% ( P < .001) for each day of rest. Increases in game load increased injury odds by 8.23% ( P < .001) for every additional 3 rebounds and 9.87% ( P < .001) for every additional 3 field-goal attempts. When fatigue and game load were held constant, injury odds increased by 3.03% ( P = .04) for each year of NBA experience and 10.59% ( P = .02) for a 6-cm decrease in height. I observed variability in the intercepts ( P < .001) and the slopes for minutes, rest, field-goal attempts, and rebounds (all P < .001). CONCLUSIONS: Injuries were associated with greater fatigue and game load, more years of NBA experience, and being shorter than average. Both baseline injury risk and the magnitude of the load-injury and fatigue-injury associations varied across individuals. Researchers should explore the nature of these relationships.
CONTEXT: National Basketball Association (NBA) athletes experience a high rate of injuries. Injury prevention requires identifying observable and controllable risk factors. OBJECTIVE: To examine the relationship among game load, fatigue, and injuries in NBA athletes. DESIGN: Cross-sectional study. SETTING: Game statistics and injury reports over 3 NBA seasons (2012-2015). PATIENTS OR OTHER PARTICIPANTS: Data represented 627 players (height = 200.7 ± 8.9 cm, mass = 100.6 ± 12.1 kg, NBA experience = 4.8 ± 4.2 years, pre-NBA experience = 3.2 ± 1.9 years), 73 209 games, and 1663 injury events. MAIN OUTCOME MEASURE(S): An injury event was defined as a player missing or leaving a game due to injury. Logistic multilevel regression was used to predict injuries from time-lagged fatigue and game load with between-subjects differences explained by demographic variables. RESULTS: The odds of injury increased by 2.87% ( P < .001) for each 96 minutes played and decreased by 15.96% ( P < .001) for each day of rest. Increases in game load increased injury odds by 8.23% ( P < .001) for every additional 3 rebounds and 9.87% ( P < .001) for every additional 3 field-goal attempts. When fatigue and game load were held constant, injury odds increased by 3.03% ( P = .04) for each year of NBA experience and 10.59% ( P = .02) for a 6-cm decrease in height. I observed variability in the intercepts ( P < .001) and the slopes for minutes, rest, field-goal attempts, and rebounds (all P < .001). CONCLUSIONS: Injuries were associated with greater fatigue and game load, more years of NBA experience, and being shorter than average. Both baseline injury risk and the magnitude of the load-injury and fatigue-injury associations varied across individuals. Researchers should explore the nature of these relationships.
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