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Handling and reporting missing data in training load and injury risk research. | LitMetric
Purpose : To map the current practice of handling missing data in the field of training load and injury risk and to determine how missing data in training load should be handled.Methods : A systematic review of the training load and injury risk literature was performed to determine how missing data are reported and handled. We ran simulations to compare the accuracy of modelling a predetermined relationship between training load and injury risk following handling missing data with different methods. The simulations were based on a Norwegian Premier League men's football dataset (n = 39). Internal training load was measured with the session Rating of Perceived Exertion (sRPE). External training load was the total distance covered measured by a global positioning systems (GPS) device.Results : Only 37 (34%) of 108 studies reported whether training load had any missing observations. Multiple Imputation using Predicted Mean Matching was the best method of handling missing data across multiple scenarios.Conclusion : Studies of training load and injury risk should report the extent of missing data, and how they are handled. Multiple Imputation with Predicted Mean Matching should be used when imputing sRPE and GPS variables.
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