Search Results

Purpose: To assess and compare the validity of internal and external Australian football (AF) training-load measures for predicting preseason variation of match-play exercise intensity (MEI sim/min) using a variable dose–response model. Methods: A total of 21 professional male AF players completed an 18-wk preseason macrocycle. Preseason internal training load was quntified using the session rating-of-perceived-exertion method (sRPE) and external load from satellite (as distance [Dist] and high-speed distance [HS Dist]) and accelerometer (Player Load [PL]) data. Using a training-impulse (TRIMPs) calculation, external load expressed in arbitrary units was represented as TRIMPs_Dist, TRIMPs_HSDist, and TRIMPs_PL. Preseason training load and MEI sim/min data were applied to a variable dose–response model, which provided estimates of MEI sim/min. Model estimates of MEI sim/min were correlated with actual measures from each match-play drill performed during the preseason macrocycle. Magnitude-based inferences (effect size [90% confidence interval]) were calculated to determine practical differences in the precision of MEI sim/min estimates using each of the internal- and external-load inputs. Results: Estimates of MEI sim/min demonstrated very large and large associations with actual MEI sim/min with models constructed from external and internal training inputs (r [90% confidence interval]; TRIMPs_Dist .73 [.72–.74], TRIMPs_PL .72 [.71–.73], and sRPE_Skills .67 [.56–.78]). There were trivial differences in the precision of MEI sim/min estimates between models constructed from TRIMPs_Dist and TRIMPs_PL and between internal input methods. Conclusions: Variable dose-response models from multiple training-load inputs can predict the within-individual variation of MEI sim/min across an entire preseason macrocycle. Models informed by external training inputs (TRIMPs_Dist and TRIMPs_PL) exhibited predictive power comparable to those of sRPE_Skills models.

Purpose: To assess and compare the validity of internal and external Australian football (AF) training-load measures for predicting match exercise intensity (MEI/min) and player-rank score (PR_Score) using a variable dose-response model. Methods: A cohort of 25 professional AF players (23 ± 3 y, 188.3 ± 7.2 cm, 87.7 ± 8.4 kg) completed a 24-wk in-season macrocycle. In-season internal training and match load were quantified using session rating of perceived exertion (sRPE) and external load from satellite and accelerometer data. Using a training-impulse (TRIMP) calculation, external load (au) was represented as distance (TRIMP_Dist), distance ≥4.16 m/s (TRIMP_HSDist), and PlayerLoad (TRIMP_PL). In-season training load, MEI/min, and PR_Score were applied to a variable dose-response model, which provided estimates of MEI/min and PR_Score. Predicted MEI/min and PR_Score were correlated with actual measures from each match. The magnitude of the difference between MEI/min and PR_Score estimates for each model input and the difference between the precision of internal and external load measures to predict MEI/min and PR_Score were calculated using the effect size ± 90% confidence interval (CI). Results: Estimates of MEI/min demonstrated very large associations with actual MEI/min (r, 90% CI) (eg, TRIMP_Dist .76 ± .13, and sRPE_Skills .73 ± .14). Estimates of PR_Score demonstrated associations of large magnitude with actual PR_Score using the same inputs. Precision of actual MEI/min was lowest using sRPE compared with (ES ± 90% CI) TRIMP_Dist, −.67 ± .34, and TRIMP_PL, −.91 ± .39. There were trivial and unclear differences in the precision of PR_Score estimates between TRIMP and sRPE inputs. Conclusions: Dose-response models from multiple training-load inputs can predict within-individual variation of MEI/min and PR_Score. Internal and external training-input methods exhibited comparable predictive power.

Purpose: To assess the impact of match-start time and days relative to match compared with the habitual sleep characteristics of elite Australian Football (AF) players. Methods: 45 elite male AF players were assessed during the preseason (habitual) and across 4 home matches during the season. Players wore an activity monitor the night before (−1), night of (0), 1 night after (+1), and 2 nights (+2) after each match and completed a self-reported rating of sleep quality. A 2-way ANOVA with Tukey post hoc was used to determine differences in sleep characteristics between match-start times and days relative to the match. Two-way nested ANOVA was conducted to examine differences between competition and habitual phases. Effect size ± 90% confidence interval (ES ± 90% CI) was calculated to quantify the magnitude of pairwise differences. Results: Differences observed in sleep-onset latency (ES = 0.11 ± 0.16), sleep rating (ES = 0.08 ± 0.14), and sleep duration (ES = 0.08 ± 0.01) between competition and habitual periods were trivial. Sleep efficiency was almost certainly higher during competition than habitual, but this was not reflected in the subjective rating of sleep quality. Conclusions: Elite AF competition does not cause substantial disruption to sleep characteristics compared with habitual sleep. While match-start time has some impact on sleep variables, it appears that the match itself is more of a disruption than the start time. Subjective ratings of sleep from well-being questionnaires appear limited in their ability to accurately provide an indication of sleep quality.

Purpose: To determine the impact of the quality and quantity of sleep during an international flight on subsequent objective sleep characteristics, training and match-day load, self-reported well-being, and perceptions of jet lag of elite female cricketers during an International Cricket Council Women’s T20 World Cup. Methods: In-flight and tournament objective sleep characteristics of 11 elite female cricketers were assessed using activity monitors. Seated in business class, players traveled west from Melbourne, Australia, to Chennai, India. The outbound flight departed Melbourne at 3:30 AM with a stopover in Dubai for 2 hours. The arrival time in Chennai was 8:10 PM local time (1:40 AM in Melbourne). The total travel time was 19 hours 35 minutes. Perceptual ratings of jet lag, well-being, and training and competition load were collected. To determine the impact of in-flight sleep on tournament measures, a median split was used to create subsamples based on (1) in-flight sleep quantity and (2) in-flight sleep quality (2 groups: higher vs lower). Spearman correlation coefficients were calculated to assess the bivariate associations between sleep measures, self-reported well-being, perceptual measures of jet lag, and internal training and match-day load. Results: Mean duration and efficiency of in-flight sleep bouts were 4.72 hours and 87.45%, respectively. Aggregated in-flight sleep duration was 14.64 + 3.56 hours. Players with higher in-flight sleep efficiency reported higher ratings for fatigue (ie, lower perceived fatigue) during the tournament period. Tournament sleep duration was longer, and bed and wake times were earlier compared with habitual. Compared with other nights during the tournament, sleep duration was shorter following matches. Conclusions: Maximizing in-flight sleep quality and quantity appears to have implications for recovery and sleep exhibited during competition. Sleep duration was longer than habitual except for the night of a match, which suggests that T20 matches may disrupt sleep duration.

Purpose: To assess relationships between objective sleep characteristics, external training loads, and subjective ratings of well-being in elite Australian football (AF) players. Methods: A total of 38 elite male AF players recorded objective sleep characteristics over a 15-day period using an activity monitor. External load was assessed during main field sessions, and ratings of well-being were provided each morning. Canonical correlation analysis was used to create canonical dimensions for each variable set (sleep, well-being, and external load). Relationships between dimensions representing sleep, external load, and well-being were quantified using Pearson r. Results: Canonical correlations were moderate between pretraining sleep and external training load (r = .32–.49), pretraining sleep and well-being (r = .32), and well-being and posttraining sleep (r = .36). Moderate to strong correlations were observed between dimensions representing external training load and posttraining sleep (r = .31–.67), and well-being and external training load (r = .32–.67). Player load and Player load 2D (PL2D) showed the greatest association to pretraining and posttraining objective sleep characteristics and well-being. Fragmented sleep was associated with players completing the following training with a higher PL2D. Conclusions: Maximum speed, player load, and PL2D were the common associations between objective sleep characteristics and well-being in AF players. Improving pretraining sleep quality and quantity may have a positive impact on AF players’ well-being and movement strategy during field sessions. Following training sessions that have high maximum speed and PL2D, the increased requirement for sleep should be considered by ensuring that subsequent sessions do not start earlier than required.