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Daniel J. Plews, Paul B. Laursen, Andrew E. Kilding, and Martin Buchheit

The aim of this study was to compare 2 different methodological assessments when analyzing the relationship between performance and heart-rate (HR) -derived indices (resting HR [RHR] and HR variability [HRV]) to evaluate positive adaptation to training. The relative change in estimated maximum aerobic speed (MAS) and 10-km-running performance was correlated to the relative change in RHR and the natural logarithm of the square root of the mean sum of the squared differences between R-R intervals on an isolated day (RHRday; Ln rMSSDday) or when averaged over 1 wk (RHRweek; Ln rMSSDweek) in 10 runners who responded to a 9-wk training intervention. Moderate and small correlations existed between changes in MAS and 10-km-running performance and RHRday (r = .35, 90%CI [–.35, .76] and r = –.21 [–.68, .39]), compared with large and very large correlations for RHRweek (r = –.62 [–.87, –.11] and r = .73 [.30, .91]). While a trivial correlation was observed for MAS vs Ln rMSSDday (r = –.06 [–.59, .51]), a very large correlation existed with Ln rMSSDweek (r = .72 [.28, .91]). Similarly, changes in 10-km-running performance revealed a small correlation with Ln rMSSDday (r = –.17 [–.66, .42]), vs a very large correlation for Ln rMSSDweek (r = –.76 [–.92, –.36]). In conclusion, the averaging of RHR and HRV values over a 1-wk period appears to be a superior method for evaluating positive adaption to training compared with assessing its value on a single isolated day.

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Robin T. Thorpe, Anthony J. Strudwick, Martin Buchheit, Greg Atkinson, Barry Drust, and Warren Gregson

Purpose:

To determine the sensitivity of a range of potential fatigue measures to daily training load accumulated over the previous 2, 3, and 4 d during a short in-season competitive period in elite senior soccer players (N = 10).

Methods:

Total highspeed-running distance, perceived ratings of wellness (fatigue, muscle soreness, sleep quality), countermovement-jump height (CMJ), submaximal heart rate (HRex), postexercise heart-rate recovery (HRR), and heart-rate variability (HRV: Ln rMSSD) were analyzed during an in-season competitive period (17 d). General linear models were used to evaluate the influence of 2-, 3-, and 4-d total high-speed-running-distance accumulation on fatigue measures.

Results:

Fluctuations in perceived ratings of fatigue were correlated with fluctuations in total high-speed-running-distance accumulation covered on the previous 2 d (r = –.31; small), 3 d (r = –.42; moderate), and 4 d (r = –.28; small) (P < .05). Changes in HRex (r = .28; small; P = .02) were correlated with changes in 4-d total high-speed-running-distance accumulation only. Correlations between variability in muscle soreness, sleep quality, CMJ, HRR%, and HRV and total high-speed-running distance were negligible and not statistically significant for all accumulation training loads.

Conclusions:

Perceived ratings of fatigue and HRex were sensitive to fluctuations in acute total high-speed-running-distance accumulation, although sensitivity was not systematically influenced by the number of previous days over which the training load was accumulated. The present findings indicate that the sensitivity of morning-measured fatigue variables to changes in training load is generally not improved when compared with training loads beyond the previous day’s training.

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Dean Ritchie, Will G. Hopkins, Martin Buchheit, Justin Cordy, and Jonathan D. Bartlett

Context:

Training volume, intensity, and distribution are important factors during periods of return to play.

Purpose:

To quantify the effect of injury on training load (TL) before and after return to play (RTP) in professional Australian Rules football.

Methods:

Perceived training load (RPE-TL) for 44 players was obtained for all indoor and outdoor training sessions, while field-based training was monitored via GPS (total distance, high-speed running, mean speed). When a player sustained a competition time-loss injury, weekly TL was quantified for 3 wk before and after RTP. General linear mixed models, with inference about magnitudes standardized by between-players SDs, were used to quantify effects of lower- and upper-body injury on TL compared with the team.

Results:

While total RPE-TL was similar to the team 2 wk before RTP, training distribution was different, whereby skills RPE-TL was likely and most likely lower for upper- and lower-body injury, respectively, and most likely replaced with small to very large increases in running and other conditioning load. Weekly total distance and high-speed running were most likely moderately to largely reduced for lower- and upper-body injury until after RTP, at which point total RPE-TL, training distribution, total distance, and high-speed running were similar to the team. Mean speed of field-based training was similar before and after RTP compared with the team.

Conclusions:

Despite injured athletes’ obtaining comparable TLs to uninjured players, training distribution is different until after RTP, indicating the importance of monitoring all types of training that athletes complete.

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Robin T. Thorpe, Anthony J. Strudwick, Martin Buchheit, Greg Atkinson, Barry Drust, and Warren Gregson

Purpose:

To quantify the mean daily changes in training and match load and any parallel changes in indicators of morningmeasured fatigue across in-season training weeks in elite soccer players.

Methods:

After each training session and match (TL), session ratings of perceived exertion (s-RPE) were recorded to calculate overall session load (RPE-TL) in 29 English Premier League players from the same team. Morning ratings of fatigue, sleep quality, and delayed-onset muscle soreness (DOMS), as well as submaximal exercise heart rate (HRex), postexercise heart-rate recovery (HRR%), and heart-rate variability (HRV) were recorded before match day and 1, 2, and 4 d postmatch. Data were collected for a median duration of 3 wk (range 1–13) and reduced to a typical weekly cycle including no midweek match and a weekend match day. Data were analyzed using withinsubject linear mixed models.

Results:

RPE-TL was approximately 600 arbitrary units (AU) (95% confidence interval 546–644) higher on match day than following day (P < .001). RPE-TL progressively decreased by »60 AU per day over the 3 days before a match (P < .05). Morning-measured fatigue, sleep quality, and DOMS tracked the changes in RPE-TL, being 35–40% worse on postmatch day vs prematch day (P < .001). Perceived fatigue, sleep quality, and DOMS improved by 17–26% from postmatch day to 3 d postmatch, with further smaller (7%–14%) improvements occurring between 4 d postmatch and prematch day (P < .01). There were no substantial or statistically significant changes in HRex, HRR%, or HRV over the weekly cycle (P > .05).

Conclusions:

Morning-measured ratings of fatigue, sleep quality, and DOMS are clearly more sensitive than HR-derived indices to the daily fluctuations in session load experienced by elite soccer players in a standard in-season week.

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Hani Al Haddad, Ben M. Simpson, Martin Buchheit, Valter Di Salvo, and Alberto Mendez-Villanueva

This study assessed the relationship between peak match speed (PMS) and maximal sprinting speed (MSS) in regard to age and playing positions. MSS and absolute PMS (PMSAbs) were collected from 180 male youth soccer players (U13–U17, 15.0 ± 1.2 y, 161.5 ± 9.2 cm, and 48.3 ± 8.7 kg). The fastest 10-m split over a 40-m sprint was used to determine MSS. PMSAbs was recorded using a global positioning system and was also expressed as a percentage of MSS (PMSRel). Sprint data were compared between age groups and between playing positions. Results showed that regardless of age and playing positions, faster players were likely to reach higher PMSAbs and possibly lower PMSRel. Despite a lower PMSAbs than in older groups (eg, 23.4 ± 1.8 vs 26.8 ± 1.9 km/h for U13 and U17, respectively, ES = 1.9 90%, confidence limits [1.6;2.1]), younger players reached a greater PMSRel (92.0% ± 6.3% vs. 87.2% ± 5.7% for U13 and U17, respectively, ES = –0.8 90% CL [–1.0;–0.5]). Playing position also affected PMSAbs and PMSRel, as strikers were likely to reach higher PMSAbs (eg, 27.0 ± 2.7 vs 23.6 ± 2.2 km/h for strikers and central midfielders, respectively, ES = 2.0 [1.7;2.2]) and PMSRel (eg, 93.6% ± 5.2% vs 85.3% ± 6.5% for strikers and central midfielders, respectively, ES = 1.0 [0.7;1.3]) than all other positions. The findings confirm that age and playing position affect the absolute and relative intensity of speed-related actions during matches.

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Daniel J. Plews, Paul B. Laursen, Andrew E. Kilding, and Martin Buchheit

Purpose:

Elite endurance athletes may train in a polarized fashion, such that their training-intensity distribution preserves autonomic balance. However, field data supporting this are limited.

Methods:

The authors examined the relationship between heart-rate variability and training-intensity distribution in 9 elite rowers during the 26-wk build-up to the 2012 Olympic Games (2 won gold and 2 won bronze medals). Weekly averaged log-transformed square root of the mean sum of the squared differences between R-R intervals (Ln rMSSD) was examined, with respect to changes in total training time (TTT) and training time below the first lactate threshold (>LT1), above the second lactate threshold (LT2), and between LT1 and LT2 (LT1–LT2).

Results:

After substantial increases in training time in a particular training zone or load, standardized changes in Ln rMSSD were +0.13 (unclear) for TTT, +0.20 (51% chance increase) for time >LT1, –0.02 (trivial) for time LT1–LT2, and –0.20 (53% chance decrease) for time >LT2. Correlations (±90% confidence limits) for Ln rMSSD were small vs TTT (r = .37 ± .80), moderate vs time >LT1 (r = .43 ± .10), unclear vs LT1–LT2 (r = .01 ± .17), and small vs >LT2 (r = –.22 ± .50).

Conclusion:

These data provide supportive rationale for the polarized model of training, showing that training phases with increased time spent at high intensity suppress parasympathetic activity, while low-intensity training preserves and increases it. As such, periodized low-intensity training may be beneficial for optimal training programming.

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Dean Ritchie, Will G. Hopkins, Martin Buchheit, Justin Cordy, and Jonathan D. Bartlett

Purpose:

Load monitoring in Australian football (AF) has been widely adopted, yet team-sport periodization strategies are relatively unknown. The authors aimed to quantify training and competition load across a season in an elite AF team, using rating of perceived exertion (RPE) and GPS tracking.

Methods:

Weekly totals for RPE and GPS loads (including accelerometer data; PlayerLoad) were obtained for 44 players across a full season for each training modality and for competition. General linear mixed models compared mean weekly load between 3 preseason and 4 in-season blocks. Effects were assessed with inferences about magnitudes standardized with between-players SD.

Results:

Total RPE load was most likely greater during preseason, where the majority of load was obtained via skills and conditioning. There was a large reduction in RPE load in the last preseason block. During in-season, half the total load came from games and the remaining half from training, predominantly skills and upper-body weights. Total distance, high-intensity running, and PlayerLoad showed large to very large reductions from preseason to in-season, whereas changes in mean speed were trivial across all blocks. All these effects were clear at the 99% level.

Conclusions:

These data provide useful information about targeted periods of loading and unloading across different stages of a season. The study also provides a framework for further investigation of training periodization in AF teams.

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Robin T. Thorpe, Anthony J. Strudwick, Martin Buchheit, Greg Atkinson, Barry Drust, and Warren Gregson

Purpose:

To quantify the relationship between daily training load and a range of potential measures of fatigue in elite soccer players during an in-season competitive phase (17 d).

Methods:

Total high-intensity-running (THIR) distance, perceived ratings of wellness (fatigue, muscle soreness, sleep quality), countermovement-jump height (CMJ), postexercise heart-rate recovery (HRR), and heart-rate variability (Ln rMSSD) were analyzed during an in-season competitive period (17 d). General linear models were used to evaluate the influence of daily fluctuation in THIR distance on potential fatigue variables.

Results:

Fluctuations in fatigue (r = −.51, large, P < .001), Ln rMSSD (r = −.24, small, P = .04), and CMJ (r = .23, small, P = .04) were significantly correlated with fluctuations in THIR distance. Correlations between variability in muscle soreness, sleep quality, and HRR and THIR distance were negligible and not statistically significant.

Conclusions:

Perceived ratings of fatigue and Ln rMSSD were sensitive to daily fluctuations in THIR distance in a sample of elite soccer players. Therefore, these particular markers show promise as simple, noninvasive assessments of fatigue status in elite soccer players during a short in-season competitive phase.

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Yann Le Meur, Martin Buchheit, Anaël Aubry, Aaron J Coutts, and Christophe Hausswirth

Purpose:

Faster heart-rate recovery (HRR) after high to maximal exercise (≥90% of maximal heart rate) has been reported in athletes suspected of functional overreaching (f-OR). This study investigated whether this response would also occur at lower exercise intensity.

Methods:

Responses of HRR and rating of perceived exertion (RPE) were compared during an incremental intermittent running protocol to exhaustion in 20 experienced male triathletes (8 control subjects and 13 overload subjects led to f-OR) before and immediately after an overload training period and after a 1-wk taper.

Results:

Both groups demonstrated an increase in HRR values immediately after the training period, but this change was very likely to almost certainly larger in the f-OR group at all running intensities (large to very large differences, eg, +16 ± 7 vs +3 ± 5 beats/min, in the f-OR and control groups at 11 km/h, respectively). The highest between-groups differences in changes in HRR were reported at 11 km/h (13 ± 4 beats/min) and 12 km/h (10 ± 6 beats/min). A concomitant increase in RPE at all intensities was reported only in the f-OR group (large to extremely large differences, +2.1 ± 1.5 to +0.7 ± 1.5 arbitrary units).

Conclusion:

These findings confirm that faster HRR does not systematically predict better physical performance. However, when interpreted in the context of the athletes’ fatigue state and training phase, HRR after submaximal exercise may be more discriminant than HRR measures taken after maximal exercise for monitoring f-OR. These findings may be applied in practice by regularly assessing HRR after submaximal exercise (ie, warm-up) for monitoring endurance athletes’ responses to training.

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Laurent Mourot, Nicolas Fabre, Erik Andersson, Sarah Willis, Martin Buchheit, and Hans-Christer Holmberg

Postexercise heart-rate (HR) recovery (HRR) indices have been associated with running and cycling endurance-exercise performance. The current study was designed (1) to test whether such a relationship also exists in the case of cross-country skiing (XCS) and (2) to determine whether the magnitude of any such relationship is related to the intensity of exercise before obtaining HRR indices. Ten elite male cross-country skiers (mean ± SD; 28.2 ± 5.4 y, 181 ± 8 cm, 77.9 ± 9.4 kg, 69.5 ± 4.3 mL · min−1 · kg−1 maximal oxygen uptake [VO2max]) performed 2 sessions of roller-skiing on a treadmill: a 2 × 3-km time trial and the same 6-km at an imposed submaximal speed followed by a final 800-m time trial. VO2 and HR were monitored continuously, while HRR and blood lactate (BLa) were assessed during 2 min immediately after each 6-km and the 800-m time trial. The 6-km time-trial time was largely negatively correlated with VO2max and BLa. On the contrary, there was no clear correlation between the 800-m time-trial time and VO2, HR, or BLa. In addition, in no case was any clear correlation between any of the HRR indices and performance time or VO2max observed. These findings confirm that XCS performance is largely correlated with VO2max and the ability to tolerate high levels of BLa; however, postexercise HRR showed no clear association with performance. The homogeneity of the group of athletes involved and the contribution of the arms and upper body to the exercise preceding determination of HRR may explain this absence of a relationship.