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Nathan W. Pitchford, Sam J. Robertson, Charli Sargent, Justin Cordy, David J. Bishop, and Jonathan D. Bartlett

Purpose:

To assess the effects of a change in training environment on the sleep characteristics of elite Australian Rules football (AF) players.

Methods:

In an observational crossover trial, 19 elite AF players had time in bed (TIB), total sleep time (TST), sleep efficiency (SE), and wake after sleep onset (WASO) assessed using wristwatch activity devices and subjective sleep diaries across 8-d home and camp periods. Repeated-measures ANOVA determined mean differences in sleep, training load (session rating of perceived exertion [RPE]), and environment. Pearson product–moment correlations, controlling for repeated observations on individuals, were used to assess the relationship between changes in sleep characteristics at home and camp. Cohen effect sizes (d) were calculated using individual means.

Results:

On camp TIB (+34 min) and WASO (+26 min) increased compared with home. However, TST was similar between home and camp, significantly reducing camp SE (–5.82%). Individually, there were strong negative correlations for TIB and WASO (r = -.75 and r = -.72, respectively) and a moderate negative correlation for SE (r = -.46) between home and relative changes on camp. Camp increased the relationship between individual s-RPE variation and TST variation compared with home (increased load r = -.367 vs .051, reduced load r = .319 vs –.033, camp vs home respectively).

Conclusions:

Camp compromised sleep quality due to significantly increased TIB without increased TST. Individually, AF players with higher home SE experienced greater reductions in SE on camp. Together, this emphasizes the importance of individualized interventions for elite team-sport athletes when traveling and/or changing environments.

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Jonathan D. Bartlett, Fergus O’Connor, Nathan Pitchford, Lorena Torres-Ronda, and Samuel J. Robertson

Purpose:

The aim of this study was to quantify and predict relationships between rating of perceived exertion (RPE) and GPS training-load (TL) variables in professional Australian football (AF) players using group and individualized modeling approaches.

Methods:

TL data (GPS and RPE) for 41 professional AF players were obtained over a period of 27 wk. A total of 2711 training observations were analyzed with a total of 66 ± 13 sessions/player (range 39–89). Separate generalized estimating equations (GEEs) and artificial-neural-network analyses (ANNs) were conducted to determine the ability to predict RPE from TL variables (ie, session distance, high-speed running [HSR], HSR %, m/min) on a group and individual basis.

Results:

Prediction error for the individualized ANN (root-mean-square error [RMSE] 1.24 ± 0.41) was lower than the group ANN (RMSE 1.42 ± 0.44), individualized GEE (RMSE 1.58 ± 0.41), and group GEE (RMSE 1.85 ± 0.49). Both the GEE and ANN models determined session distance as the most important predictor of RPE. Furthermore, importance plots generated from the ANN revealed session distance as most predictive of RPE in 36 of the 41 players, whereas HSR was predictive of RPE in just 3 players and m/min was predictive of RPE in just 2 players.

Conclusions:

This study demonstrates that machine learning approaches may outperform more traditional methodologies with respect to predicting athlete responses to TL. These approaches enable further individualization of load monitoring, leading to more accurate training prescription and evaluation.

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Heidi R. Thornton, Grant M. Duthie, Nathan W. Pitchford, Jace A. Delaney, Dean T. Benton, and Ben J. Dascombe

Purpose:

To investigate the effects of a training camp on the sleep characteristics of professional rugby league players compared with a home period.

Methods:

During a 7-d home and 13-d camp period, time in bed (TIB), total sleep time (TST), sleep efficiency (SE), and wake after sleep onset were measured using wristwatch actigraphy. Subjective wellness and training loads (TL) were also collected. Differences in sleep and TL between the 2 periods and the effect of daytime naps on nighttime sleep were examined using linear mixed models. Pearson correlations assessed the relationship of changes in TL on individuals’ TST.

Results:

During the training camp, TST (–85 min), TIB (–53 min), and SE (–8%) were reduced compared with home. Those who undertook daytime naps showed increased TIB (+33 min), TST (+30 min), and SE (+0.9%). Increases in daily total distance and training duration above individual baseline means during the training camp shared moderate (r = –.31) and trivial (r = –.04) negative relationships with TST.

Conclusions:

Sleep quality and quantity may be compromised during training camps; however, daytime naps may be beneficial for athletes due to their known benefits, without being detrimental to nighttime sleep.

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Farhan Juhari, Dean Ritchie, Fergus O’Connor, Nathan Pitchford, Matthew Weston, Heidi R. Thornton, and Jonathan D. Bartlett

Context: Team-sport training requires the daily manipulation of intensity, duration, and frequency, with preseason training focusing on meeting the demands of in-season competition and training on maintaining fitness. Purpose: To provide information about daily training in Australian football (AF), this study aimed to quantify session intensity, duration, and intensity distribution across different stages of an entire season. Methods: Intensity (session ratings of perceived exertion; CR-10 scale) and duration were collected from 45 professional male AF players for every training session and game. Each session’s rating of perceived exertion was categorized into a corresponding intensity zone, low (<4.0 arbitrary units), moderate (≥4.0 and <7.0), and high (≥7.0), to categorize session intensity. Linear mixed models were constructed to estimate session duration, intensity, and distribution between the 3 preseason and 4 in-season periods. Effects were assessed using linear mixed models and magnitude-based inferences. Results: The distribution of the mean session intensity across the season was 29% low intensity, 57% moderate intensity, and 14% high intensity. While 96% of games were high intensity, 44% and 49% of skills training sessions were low intensity and moderate intensity, respectively. Running had the highest proportion of high-intensity training sessions (27%). Preseason displayed higher training-session intensity (effect size [ES] = 0.29–0.91) and duration (ES = 0.33–1.44), while in-season game intensity (ES = 0.31–0.51) and duration (ES = 0.51–0.82) were higher. Conclusions: By using a cost-effective monitoring tool, this study provides information about the intensity, duration, and intensity distribution of all training types across different phases of a season, thus allowing a greater understanding of the training and competition demands of Australian footballers.