Self-Reported Wellness Profiles of Professional Australian Football Players During the Competition Phase of the Season

Abstract

Gallo, TF, Cormack, SJ, Gabbett, TJ, and Lorenzen, CH. Self-reported wellness profiles of professional Australian football players during the competition phase of the season. J Strength Cond Res 31(2): 495-502, 2017-With the prevalence of customized self-report measures in high-performance sport, and the incomplete understanding of athletes' perceived wellness in response to matches and training load, the objective of this study was to explore weekly wellness profiles within the context of the competitive season of professional Australian football. Internal match load, measured through the session-rating of perceived exertion method, match-to-match microcycle, stage of the season, and training load were included in multivariate linear models to determine their effect on weekly wellness profile (n = 1,835). There was a lower weekly training load on a 6-day microcycle compared with a 7-day and 8-day microcycle. Match load had no significant impact on weekly wellness profile, while there was an interaction between microcycle and days postmatch. There was a likely moderately lower wellness Z-score 1 day postmatch for an 8-day microcycle (mean; 95% confidence interval: -1.79; -2.02 to -1.56) compared with a 6-day (-1.19; -1.30 to -1.08) and 7-day (-1.22; -1.34 to -1.09) cycle (d; 95% confidence interval: -0.82; -1.3 to -0.36, -0.78; -1.3 to -0.28, respectively). The second half of the season saw a possibly small reduction in overall wellness Z-score than the first half of the season (0.22; 0.12-0.32). Finally, training load had no effect on wellness Z-score when controlled for days postmatch, microcycle, and stage of the season. These results provide information on the status of players in response to matches and fixed conditions. Knowing when wellness Z-score returns to baseline relative to the length of the microcycle may lead practitioners to prescribe the heaviest load of the week accordingly. Furthermore, wellness "red flags" should be made relative to the microcycles and stage of the season to determine an athlete's status relative to their typical weekly profile.

Multivariate modelling of subjective and objective monitoring data improve the detection of non-contact injury risk in elite Australian footballers

“…As previously reported (11), a clear relationship between independently modelled very low cumulative chronic loads and increased injury risk in the subsequent week was also identified. However, both screening (14) and wellness 'red flags' (13) did not improve injury model predictive accuracy here, despite applying an objective, individualized criterion (a 1 SD decline from the norm). Manipulating training loads in response to wellness (13) and/or screening profiles is common in elite sport, possibly explaining the null predictive value, presenting a potential limitation to research designs in these settings.…”

“…Additional to load monitoring, weekly perceived wellness responses are commonly collected, with irregularities in player profiles warning of potential over-reaching (13). Regular musculoskeletal screening also occurs to determine any significant deviations from baseline scores, to assess the progress of injury rehabilitation programs, and establish future return-to-play status for healthy players (14).…”

Monitoring Fatigue Status in Elite Team-Sport Athletes: Implications for Practice

“…22,24 Similarly, ASRM were sensitive to changes in training load during typical weeks in AFL and EPL players across the course of the season. 21,23,25 Further importance of ASRM and relationship with injury/illness has been observed in Rugby League, in this study fluctuations in ASRM between macrocycles were shown to provide useful insights into possible illness risk in players. 26 Further work is required to examine the relationships between ASRM and injury/illness risk in team-sport athletes.…”

“…20 In team sports, for example contemporary Australian Football League (AFL) and English Premier League (EPL) research has shown custom psychometric scales to be sensitive to daily, within-weekly and seasonal changes in training load. [21][22][23] Indeed, daily ASRM (fatigue, sleep quality, stress, mood, and muscle soreness) were significantly correlated with daily training load in a pre-season camp and competitive period in AFL and EPL players, respectively. 22,24 Similarly, ASRM were sensitive to changes in training load during typical weeks in AFL and EPL players across the course of the season.…”

“…From a performance perspective, establishing pretraining or prematch MCID for fatigue measures would theoretically offer an indication on the quality of the external output that might be produced. 23 This would provide coaches with the ability to make adjustments to the scheduled training or rotate the players from a match perspective. However, application to match play is unlikely to be feasible due to the high inherent variability of match-play physical performance in team sports.…”