This study aimed to 1) identify the impact of external load variables on changes in wellness
and 2) identify the impact of age, training/playing history, strength levels and pre-season loads on changes in wellness
in elite Australian footballers.
Data were collected from one team (45 athletes) during the 2017 season. Self-reported wellness
was collected daily (4=best score possible, 28=worst score possible). External load/session availability variables were calculated using global positioning systems/session availability data from every training session and match. Additional variables included demographic data, pre-season external loads and strength/power measures. Linear mixed models were built and compared using root mean square error (RMSE) to determine the impact of variables on wellness
The external load variables explained wellness
to a large degree (RMSE=1.55, 95% confidence intervals=1.52 to 1.57). Modelling athlete ID as a random effect appeared to have the largest impact on wellness
, improving the RMSE by 1.06 points. Aside from athlete ID, the variable that had the largest (albeit negligible) impact on wellness
was sprint distance covered across pre-season. Every additional 2.1 km covered across pre-season worsened athletes’ in-season wellness
scores by 1.2 points (95% confidence intervals=0.0 to 2.3).
The isolated impact of the individual variables on wellness
was negligible. However, after accounting for the individual athlete variability, the external load variables examined collectively were were able to explain wellness
to a large extent. These results validate the sensitivity of wellness
to monitor individual athletes’ responses to the external loads imposed on them.