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 preseason 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 and session availability data from every training session and match. Additional variables included demographic data, preseason 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). Modeling 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 preseason. Every additional 2.1 km covered across preseason worsened athletes’ in-season wellness scores by 1.2 points (95% confidence intervals = 0.0–2.3).
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 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.