The popularity of youth basketball has increased over the past few decades at both competitive and recreational levels. 1-3 While high participation rates are essential for improved health and wellness in youth, injuries are a common occurrence in youth basketball. 4-6 Injuries in youth basketball players also have short and long term consequences that are

Objective To examine the differences in external and internal workload in players with and without patellar tendinopathy. Design Nested case-control study. Methods Workload was monitored in 152 players (aged 13–18 years) for a 1-week period, including all practices, games, and conditioning sessions. Players were prescreened into patellar tendinopathy cases and controls without patellar tendinopathy, using the previously validated Oslo Sports Trauma Research Center-patellar tendinopathy questionnaire. Simple linear regression analysis, with adjustment for clustering by team and Bonferroni correction, was used to examine mean differences in measures of external workload (cumulative jump counts and sessions completed) and internal workload (session rating of perceived exertion in arbitrary units) between cases and controls. Results A total of 144 players (19 cases, 125 controls) met the inclusion criteria for final analysis. No significant differences were found between players with patellar tendinopathy and those without patellar tendinopathy in the 3 outcomes: jump count (mean difference, 45 jumps; 98.3% confidence interval [CI]: −41, 130; P = .177), basketball sessions completed (mean difference, 0.9; 98.3% CI: −0.3, 2.2; P = .067), and session rating of perceived exertion (mean difference, 346 arbitrary units; 98.3% CI: −459, 1151; P = .260). Conclusion In the current study, a significant difference in workload was not detected between youth basketball players with patellar tendinopathy and players without patellar tendinopathy. Efforts toward identifying players at early stages of patellar tendinopathy and applying relevant interventions are warranted. J Orthop Sports Phys Ther 2020;50(7):402–408. Epub 6 Sep 2019. doi:10.2519/jospt.2020.9094

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Missing data can influence calculations of accumulated athlete workload. The objectives were to identify the best single imputation methods and examine workload trends using multiple imputation. External (jumps per hour) and internal (rating of perceived exertion; RPE) workload were recorded for 93 (45 females, 48 males) high school basketball players throughout a season. Recorded data were simulated as missing and imputed using ten imputation methods based on the context of the individual, team and session. Both single imputation and machine learning methods were used to impute the simulated missing data. The difference between the imputed data and the actual workload values was computed as root mean squared error (RMSE). A generalized estimating equation determined the effect of imputation method on RMSE. Multiple imputation of the original dataset, with all known and actual missing workload data, was used to examine trends in longitudinal workload data. Following multiple imputation, a Pearson correlation evaluated the longitudinal association between jump count and sRPE over the season. A single imputation method based on the specific context of the session for which data are missing (team mean) was only outperformed by methods that combine information about the session and the individual (machine learning models). There was a significant and strong association between jump count and sRPE in the original data and imputed datasets using multiple imputation. The amount and nature of the missing data should be considered when choosing a method for single imputation of workload data in youth basketball. Multiple imputation using several predictor variables in a regression model can be used for analyses where workload is accumulated across an entire season.

Overuse injuries are common in basketball. Wearable technology enables the workload to be monitored in sport settings. However, workload–injury models lack a biological basis both in the metrics recorded and how workload is accumulated. We introduce a new metric for monitoring workload: weighted jump height, where each jump height is weighted to represent the expected effect of the jump magnitude on damage to the tendon. The objectives of this study were to use principal components analysis to identify distinct modes of variation in all workload metrics accumulated over 1, 2, 3, and 4 weeks and to examine differences among the modes of variation in workload metrics between participants before the injury and uninjured participants. Forty-nine youth basketball players participated in their typical basketball practices and games, and lower extremity injuries were classified as patellar or Achilles tendinopathy, other overuse, or acute. An inertial measurement unit recorded the number and height of all jumps, and session rating of perceived exertion was recorded. The previous 1-, 2-, 3-, and 4-week workloads of jump count, jump height, weighted jump height, and session rating of perceived exertion were summed for each participant-week. Principal components analysis explained the variance in the accumulated workload variables. Using the retained principal components, the difference between the workload of injured participants in the week before the injury and the mean workload of uninjured participants was described for patellar or Achilles tendinopathy, overuse lower extremity injury, and any lower extremity injury. Participants with patellar or Achilles tendinopathy and overuse lower extremity injuries had a low workload magnitude for all variables in the 1, 2, 3, and 4 weeks before injury compared with the weeks before no injury. Participants with overuse lower extremity injuries and any lower extremity injury had a high previous 1-week workload for all variables along with a low previous 3- and 4-week jump count, jump height, and weighted jump height before injury compared with the weeks before no injury. Weighted jump height represents the cumulative damage experienced by tissues due to repetitive loads. Injured youth basketball athletes had a low previous 3- and 4-week workloads coupled with a high previous 1-week workload.