Literature DB >> 27243912

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

Tania F Gallo1, Stuart J Cormack, Tim J Gabbett, Christian H Lorenzen.   

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.

Entities:  

Mesh:

Year:  2017        PMID: 27243912     DOI: 10.1519/JSC.0000000000001515

Source DB:  PubMed          Journal:  J Strength Cond Res        ISSN: 1064-8011            Impact factor:   3.775


  10 in total

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3.  Predicting Youth Athlete Sleep Quality and the Development of a Translational Tool to Inform Practitioner Decision Making.

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6.  Association between Training Load and Well-Being Measures in Young Soccer Players during a Season.

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Review 8.  Training, Wellbeing and Recovery Load Monitoring in Female Youth Athletes.

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10.  Keeping Athletes Healthy at the 2020 Tokyo Summer Games: Considerations and Illness Prevention Strategies.

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Journal:  Front Physiol       Date:  2019-04-17       Impact factor: 4.566

  10 in total

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