Author(s): NELSON, M., SAUNDERS, S., CHALMERS, S., GREGORACE, J., GREENHAM, G., STANLEY, J., WALLACE, J., BUCKLEY, J., Institution: UNIVERSITY OF SOUTH AUSTRALIA, Country: AUSTRALIA, Abstract-ID: 1162

INTRODUCTION:
Many sports - including Australian Football (AF) - conduct training sessions in varying environmental conditions, which due to a range of acute physiological effects may alter the resultant internal training load for an athlete independent of the external load completed. Being able to predict changes in internal load based on the combination of environmental conditions and expected external training loads would allow for practitioners to plan and potentially alter training sessions to ensure the optimal exposure to training stimulus. This study aimed to determine the effect of differing environmental conditions on internal training load at a given external load in elite AF athletes to create a model to predict internal training load based.
METHODS:
Professional AF athletes (n=52) undertook 18 weeks of training across two pre-seasons. Environmental conditions, internal training load (training impulse (TRIMP) and TRIMP per minute) and external load from Global Positioning System (GPS) data (total distance, very high-intensity running (VHIR) distance and duration) were recorded at every training session. Multilevel mixed-effects linear models were used to produce a predictive model for internal training load which was tested for validity using a holdback sample
RESULTS:
Prediction models were created which resulted in no differences between measured and predicted TRIMP and TRIMP per minute values (p > 0.05). For TRIMP and TRIMP per minute there was good absolute agreement (bias of -9.7 arbitrary units [AU] and -0.08 AU/min, no significant difference between measured and predicted p = 0.59 and p = 0.38 respectively). There was also good relative agreement between measured and predicted values (r = 0.66 and 0.46, p = < 0.001 and p = < 0.001).
CONCLUSION:
Internal training load can be predicted from external load and environmental conditions during team sport training sessions. These predictions could be used to inform adjustments to training load prescription under differing environmental conditions.