Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players

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Original research

Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players M. Buchheit a,∗ , S. Racinais b , J.C. Bilsborough c,d , P.C. Bourdon a , S.C. Voss a , J. Hocking c , J. Cordy c , A. Mendez-Villanueva a , A.J. Coutts c,d a

ASPIRE, Academy for Sports Excellence, Doha, Qatar Aspetar, Qatar Orthopaedic and Sports Medicine Hospital, Doha, Qatar c Carlton Football Club, Carlton, Australia d Sport & Exercise Discipline Group, UTS: Health, University of Technology, Sydney, Australia b

a r t i c l e

i n f o

Article history: Received 4 October 2012 Received in revised form 19 November 2012 Accepted 1 December 2012 Available online xxx Keywords: Heart rate variability Psychometric measures Saliva cortisol Standardized drills High-intensity intermittent running performance GPS

a b s t r a c t Objectives: To examine the usefulness of selected physiological and perceptual measures to monitor fitness, fatigue and running performance during a pre-season, 2-week training camp in eighteen professional Australian Rules Football players (21.9 ± 2.0 years). Design: Observational. Methods: Training load, perceived ratings of wellness (e.g. fatigue, sleep quality) and salivary cortisol were collected daily. Submaximal exercise heart rate (HRex) and a vagal-related heart rate variability index (LnSD1) were also collected at the start of each training session. Yo-Yo Intermittent Recovery level 2 test (Yo-YoIR2, assessed pre-, mid- and post-camp, temperate conditions) and high-speed running distance during standardized drills (HSR, >14.4 km h−1 , 4 times throughout, outdoor) were used as performance measures. Results: There were significant (P < 0.001 for all) day-to-day variations in training load (coefficient of variation, CV: 66%), wellness measures (6–18%), HRex (3.3%), LnSD1 (19.0%), but not cortisol (20.0%, P = 0.60). While the overall wellness (+0.06, 90% CL (−0.14; 0.02) AU day−1 ) did not change substantially throughout the camp, HRex decreased (−0.51 (−0.58; −0.45) % day−1 ), and cortisol (+0.31 (0.06; 0.57) nmol L−1 day−1 ), LnSD1 (+0.1 (0.04; 0.06) ms day−1 ), Yo-YoIR2 performance (+23.7 (20.8; 26.6) m day−1 , P < 0.001), and HSR (+4.1 (1.5; 6.6) m day−1 , P < 0.001) increased. Day-to-day HRex (r = 0.80, 90% CL (0.75; 0.85)), LnSD1 (0.51 (r = 0.40; 0.62)) and all wellness measures (0.28 (−0.39; −0.17) < r < 0.25 (0.14; 0.36)) were related to training load. There was however no clear relationship between cortisol and training load. Yo-YoIR2 was correlated with HRex (r = 0.88 (0.84; 0.92)), LnSD1 (r = 0.78 (0.67; 0.89)), wellness (r = 0.58 (0.41; 0.75), but not cortisol. HSR was correlated with HRex (r = −0.27 (−0.48; −0.06)) and wellness (r = 0.65 (0.49; 0.81)), but neither with LnSD1 nor cortisol. Conclusion: Training load, HRex and wellness measures are the best simple measures for monitoring training responses to an intensified training camp; cortisol post-exercise and LnSD1 did not show practical efficacy here. © 2013 Published by Elsevier Ltd on behalf of Sports Medicine Australia.

1. Introduction One of the main goals of the pre-season training phase in team sports is to develop fitness in preparation for the impending competition season.1 Compared with the in-season, training loads (TL) are generally increased up to 2–4 times during the pre-season period.1 Programming training during the pre-season can be challenging for coaches, since they are required to prescribe TLs that both maximize positive physiological adaptations, while avoiding

∗ Corresponding author. E-mail address: [email protected] (M. Buchheit).

overtraining and injury. Therefore, the precise control of TL and individual responses to training is essential for maximizing training adaptations.2 Although multiple indices for monitoring both TL and training status have been suggested,2 their invasive (e.g. blood markers3 ) and/or exhaustive (e.g. (supra)maximal tests4 ) nature makes their frequent use difficult with elite athletes. Additionally, while TL assessment via heart rate (HR) measures is well accepted in endurance sports, this method is questionable in team sports since the overall TL often comprises of workouts that do not include a significant cardiorespiratory component (e.g. strength/speed training).2 For this reason, the use of the rating of perceived exertion (RPE) based method has emerged as a practical and

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Please cite this article in press as: Buchheit M, et al. Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players. J Sci Med Sport (2013), http://dx.doi.org/10.1016/j.jsams.2012.12.003

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valid method of estimating TL in team sports.5 With respect to non-invasive and non-exhaustive measures of assessing fitness, wellness (e.g. stress, fatigue), recovery status and physical performance, submaximal exercise HR (HRex) and post-exercise cardiac autonomic activity as inferred from heart rate variability (HRV) measures have recently received increased interest.6 Despite some limitations,7 HRex is considered an index of cardiorespiratory fitness which is strongly correlated with running performance.7,8 HRV measures have been shown to reflect acute fatigue (i.e. homeostasis perturbation) following exercise,9 and have been used to make inferences about appropriate training periodization.10 The use of salivary hormones such as cortisol, a stress hormone that mediates catabolic activity,4 has also increased in team sports, largely because of its non-invasive nature.11,12 Finally, psychological monitoring is also purported to be an effective means of assessing players’ responses to training,2,4,13 and responds well to subtle TL variations.11 However, despite the possible advantages of the aforementioned variables, it is still unclear how useful these measures are for monitoring changes in wellness, recovery status and in turn, fitness, during an intense training period in elite team sport players. Importantly, the relationship of these variables with physical performance has only been assessed under standardized exercise conditions (i.e. HR-derived measures vs. incremental test,7,8 10km run8 or Yo-Yo Intermittent Recovery test14 ). Whether these measures can also track changes in running performance during less controlled but more sport-specific conditions such as during outdoor ball games is unknown. The purpose of this study was therefore to (1) document the daily variations of selected physiological and psychometric variables during an intense pre-season training camp in professional football players, and (2) examine their usefulness for monitoring training responses (i.e. fatigue status, fitness, and high-speed running performance during both a Yo-Yo Intermittent Recovery test15 and standardized playing drills).

2. Methods Eighteen professional Australian Rules Football (ARF) players (21.9 ± 2.0 years, 189 ± 8 cm and 87.8 ± 9.1 kg) participated in this study, which was approved by the University of Technology, Sydney (UTS) Human Research Ethics Committee. All players provided written informed consent. Prior to inclusion into the study, players were examined by a sports physician and were deemed to be free from illness/injury. The data analyzed in the present study are part of a larger study,16 where half of the team was exposed to simulated altitude overnight and during some ‘interval cycling’ sessions (≈14 ± 1 h altitude exposure/day) while the other half of the team had no altitude exposure. However, since there was no effect of altitude on any of the variables examined,16 data from both groups were pooled together for the present study. The study was conducted in Qatar immediately following the off-season period (October 2011). During the 2-week training camp, all players took part in normal team training as prescribed by the coaches and strength and conditioning staff. Training content was not altered for the purposes of this study. Players participated in 10 outdoor ARF-specific skills sessions (32 ± 1 ◦ C [range: 33–29 ◦ C], 39 ± 5% RH [range: 37–50%], total exposure time = 11.5 h), 7 ‘interval cycling’ sessions ([10–15 maximal effort repetitions of 15–30 s] × 3–5 sets, 22 ± 1 ◦ C, 58 ± 2% RH, total session time = 4.3 h) and 8 indoor strength sessions (23 ± 1 ◦ C, 57 ± 2% RH, total session time = 9.3 h). Players were provided with a post-training nutrition plan developed by a nutritionist to ensure adequate fluid and nutrient intake between the sessions. Training load (arbitrary units, AU) was estimated for all players as follows: total training session duration (min) × session RPE.5

A submaximal 5-min running/5-min recovery test17 was performed at the start of every training/testing session (i.e. indoor before the Yo-YoIR2 tests and outdoor before training sessions) to assess training status. All players were tested together with the intensity of the exercise bout fixed at 13 km h−1 over 40-m shuttles. HRex and post-exercise HRV (standard deviation of instantaneous beat-to-beat R–R interval variability measured from Poincaré plots, SD118 ) were assessed as previously described17 using a Polar Team 2 system (1.4.1, Polar Electro Oy, Kempele, Finland). Day-to-day variations in HRex and HRV during a competitive period in soccer players are on average 3.4 and 10.7%, respectively.17 Plasma volume (PV) was indirectly assessed on day 1 and 14 by the optimized CO rebreathing procedure for the measurement of haemoglobin mass.19 Endocrine responses to training were measured by salivary cortisol (cortisol, nmol L−1 ). Samples were collected before breakfast (6:00 AM) with players passively drooling directly into a 15-mL Falcon tube (Becton Dickinson, Plymouth, UK). Within 30 min of collection, the samples were centrifuged (2000 × g force) for 10 min at 4 ◦ C. Analyses were performed on an automated bench-top analyser (Roche Elecsys 2010, Mannheim, Germany) by the corresponding Cortisol kit (Roche, Mannheim, Germany). The coefficient of variation of the assays was 1.5%. A psychometric questionnaire, based on previous recommendations,13 was used to assess general indicators of player wellness.11 The questionnaire comprised of 5 questions relating to perceived fatigue, sleep quality, general muscle soreness, stress levels and mood with each question scored on a five-point scale (scores of 1–5, with 1 and 5 representing poor and very good wellness ratings, respectively, 0.5 point increments).11 The questionnaire was completed daily upon awakening immediately after giving the saliva sample and was reflective of the response to the preceding daily TL. Overall wellness (Wellness) was then determined by summing the five scores. High-intensity intermittent running performance was evaluated using the Yo-Yo Intermittent Recovery Level 2 test (Yo-YoIR215 ) on an indoor field (temperate conditions, 23 ± 1 ◦ C, 57 ± 2% RH) on the mornings of days 1, 7 and 14. All outdoor training sessions (32 ± 1 ◦ C, 39 ± 5% RH) were monitored using GPS units (10-Hz, minimaxX, Catapult Innovations, Australia). Sport-specific running performance was assessed using the total distance (TD) covered and distance ran at high speed (HSR, >14.4 km h−1 ), during standardized training drills completed on days 2, 5, 9 and 11. These drills consisted of three separate exercises: (1) 4 min of a 5-star handball drill (i.e. players were split into 5 even groups positioned around a 5 star course marked with cones. Players then proceeded to pass the ball only using handball to every second group around the course. The player receiving the ball led from the cone to receive a handball and then followed their handball to the next group. The distance between cones was 8 m; (2) 5 min of a 5-star ‘stationary’ kicking drill (i.e. same pattern as the previous drill except players remained on their groups cone to receive a pass. Once they caught the ball the players would kick it to the next group and then follow their kick to that group. The distance between the cones was 15 m; and (3) 5-star kicking drill with leads (i.e. same as previous drills except that players would lead from cones to receive the pass. Distance between cones was 25 m. All of the standardized drills were completed at the same time of day and at the start of the technical/tactical training sessions. All players completed the drills under standard instructions, with a strong encouragement from the coaches. These drills are common training drills in ARF, and the players were highly familiarized with the protocols. Data are presented as means (±SD) and correlations as means (90% confidence limits, CL). The distribution of each variable was

Please cite this article in press as: Buchheit M, et al. Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players. J Sci Med Sport (2013), http://dx.doi.org/10.1016/j.jsams.2012.12.003

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Fig. 1. Changes in mean (SD) training load, submaximal exercise heart rate (HRex), natural logarithm of standard deviation of instantaneous beat-to-beat R–R interval variability, measured from Poincaré plots during the last 3 min of recovery following exercise (LnSD1), overall wellness rating (wellness) and saliva cortisol (cortisol) during the 2-week training camp. Circles indicate HR measures taken indoor (23 ± 1 ◦ C, 57 ± 2% RH). *Significant difference vs. day 1 (or 2 for wellness) with P < 0.05. **Significant difference vs. day 1 (or 2 for wellness) with P < 0.01. ***Significant difference vs. day 1 (or 2 for wellness) with P < 0.001. TL was significantly different (all P < 0.05) between all days except for day 2 vs. 8, 3 vs. 11 and 10 vs. 12.

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measures (6–18%, P < 0.001 for all), HRex (3.3%, P < 0.001), LnSD1 (19.0%, P < 0.001), but not cortisol (20.0%, P = 0.60). Whilst wellness (0.06, 90% CL (−0.14; 0.02) AU day−1 ) did not change substantially throughout the camp, HRex decreased (−0.51 (−0.58; −0.45) % day−1 ), and cortisol (+0.31 (0.06; 0.57) nmol L−1 day−1 ) and LnSD1 (+0.1 (0.04; 0.06) ms day−1 ) increased. Yo-YoIR2 performance (+23.7 (20.8; 26.6) m day−1 , P < 0.001), TD (+5.8 (2.2; 9.5) m day−1 , P < 0.001) and HSR (+4.1 (1.5;

Standarized drills high speed running distance (m)

examined with the Kolmogorov–Smirnov normality test. When data were skewed or heteroscedastic (i.e. SD1), data were logtransformed. A one-way ANOVA for repeated measures with Bonferroni’s post hoc tests was used to assess the time-course of the changes in TL, fitness, fatigue/wellness and running performance measures throughout the camp. The overall change in the different variables throughout the camp was also assessed with within-individual linear regressions (%/day, with 90% CL). A substantial trend was considered if the 90% CL did not overlap zero. Pearson’s product–moment correlation analysis was also used to assess the associations between within-player daily changes in TL, fitness, fatigue/wellness and running performance measures. To isolate the possible effect of fatigue/wellness on changes in running performance, these relationships were adjusted for changes in fitness (i.e. HRex) with partial correlations. Correlations including changes in HR-derived measures were also adjusted for ambient temperature with partial correlations. The following criteria were adopted to interpret the magnitude of the correlation (r) between the different measures: ≤0.1, trivial; >0.1–0.3, small; >0.3–0.5, moderate; >0.5–0.7, large; >0.7–0.9, very large; and >0.9–1.0, almost perfect. If the 90% CL overlapped positive and negative values, the magnitude was deemed unclear; otherwise that magnitude was deemed to be the observed magnitude.20

9 10 11 12 13 14 15

Days 3. Results Changes in TL, HR-derived measures, wellness and cortisol are shown in Fig. 1. There were significant day-to-day variations in TL (coefficient of variation, CV: 66%, P < 0.001), all wellness

Fig. 2. Running performance changes during and after the camp as measured by total distance during the Yo-Yo Intermittent Recovery Level 2 (Yo-YoIR2, performed indoor in temperature conditions) and total (TD) and high-speed running (HSR) distance during standardized drills (performed outdoor in hot environmental conditions). *Significant difference vs. initial test with P < 0.05. **Significant difference vs. initial test with P < 0.01. ***Significant difference vs. initial test with P < 0.001.

Please cite this article in press as: Buchheit M, et al. Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players. J Sci Med Sport (2013), http://dx.doi.org/10.1016/j.jsams.2012.12.003

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ΔHRex ΔLnSD1 ΔWellness ΔCortisol Fig. 3. Upper panel: correlation coefficients (90% confidence intervals, CI) between daily (i.e. session-to-session) changes in training load (TL) and daily (i.e. session-to-session) changes in submaximal exercise heart rate (HRex), natural logarithm of standard deviation of instantaneous beat-to-beat R–R interval variability, measured from Poincaré plots during the last 3 min of recovery following exercise (LnSD1), perceived fatigue (Fatigue), sleep quality (Sleep), muscle soreness (Soreness), stress (Stress), mood (Mood) and salivary cortisol (Cortisol) during the 2-week training camp. Lower panel: correlation coefficients (90% CI) between daily individual changes in submaximal exercise heart rate (HRex), natural logarithm of standard deviation of instantaneous beat-to-beat R–R interval variability, measured from Poincaré plots during the last 3 min of recovery following exercise (LnSD1), overall wellness (Wellness), saliva cortisol (Cortisol) and running performance changes during and after the camp as measured by total distance during the Yo-Yo Intermittent Recovery Level 2 (Yo-YoIR2) performed indoor and total (TD) and high-speed running (HSR) distance standardized drills performed outdoor in hot environmental conditions.

6.6) m day−1 , P < 0.001) during the standardized drills improved progressively (Fig. 2). Day-to-day HRex, LnSD1 and changes in individual wellness measures were related to TL (Fig. 3, upper panel). There was no clear relationship with Cortisol. The LnSD1 and Soreness were largely correlated (r = −0.53 (−0.63; −0.41)). Similarly, the HRex correlated largely with PV (r = −0.57 (−0.80; −0.20)); the correlation with LnSD1 was unclear (r = −0.19 (−0.56; 0.24)). HRex was correlated with Yo-YoIR2 and HSR, but not TD during standardized drills (Fig. 3, lower panel). LnSD1 was correlated with Yo-YoIR2, but not with running performance during drills. There were large correlations between Wellness and YoYoIR2, TD and HSR. Cortisol was no correlated to any variable.

We examined the usefulness of selected physiological and psychometric measures for monitoring fitness, physiological adaptations, wellness and high-intensity running performance during a pre-season camp in professional players. The main results were: (1) Yo-YoIR2 performance and HSR during standardized drills increased substantially throughout the camp; (2) HRex, LnSD1 and all wellness measures, but not cortisol, were sensitive to subtle changes in daily TL; (3) changes in HRex were largely correlated with changes in PV and (4) changes in HRex and wellness, but not cortisol, were slightly to very-largely correlated with changes in Yo-YoIR2 performance and HSR during the standardized training drills. In the present study, the average weekly TL was greater than 10,000 AU (Fig. 1), which is considerably greater than that generally reported in-season in ARF11 and other team sports (i.e. 2000–4000 AU5,21 ). Despite this high TL, the players appeared to cope with the training demands of the camp. This is supported by the increased physical performance (Fig. 2), the absence of injury, and with the exception of a slight increase in cortisol (+0.31 (0.06; 0.57) nmol L−1 day−1 ), the stable wellness scores throughout the camp (Fig. 1). Moreover, LnSD1, as a cardiac autonomic marker that generally decreases with physiological stress,9 improved over the camp. There are a myriad of likely reasons for this high level of training tolerance and this cannot be fully examined with the present study design, but the training background of the players, the careful design and implementation of each training session, as well as the fact that the skills sessions were all performed in the heat should be considered.14 In fact, when playing/training in the heat, average running intensity and distance are generally decreased,22,23 which can have a prophylactic effect. In support of this, players in a soccer match played in the heat recorded less muscle damage (inferred from blood creatine kinase) than in a game played in temperate conditions.14 Further research is needed to examine the impact of heat exposure on both external and internal training loads with respect to training adaptations, musculoskeletal injury risk and fatigue management. Another interesting finding was that the daily variations in TL (CV = 66%, P < 0.001) systematically affected some physiological (HRex, 3.3%, P < 0.001 and LnSD1, 19.0%, P < 0.001) and all wellness measures (CV = 6–18%, P < 0.001 for all) the following day (Fig. 1), which highlights the importance of quantifying and managing TL properly. These observations provide evidence supporting the sensitivity of these simple measures to changes in TL. They also show that TL data, even considered in isolation, provide meaningful indirect information on the acute training responses and recovery status the following day. The negative correlations between TL and wellness measures were expected (i.e. the greater the load, the worse the wellness scores the following day) (Fig. 3, upper panel), and confirm the usefulness of these simple measures to monitor acute fatigue, stress and wellness in an applied setting.11 Surprisingly however, correlation analyses (Fig. 3, upper panel) suggested that an increased TL was associated with a decreased HRex and an increased LnSD1, which contrasts with what may be expected. For instance, an acute training-induced fatigue is generally associated with an increased sympathetic activity,9 which may increase HRex and decrease LnSD1. In contrast, an improved fitness level and/or complete recovery may be reflected by decreased HRex and increased LnSD1.9,24 Since fitness is unlikely to be substantially improved after only one day of intense training, and considering the decreased wellness scores, these changes in HR measures are more likely a consequence of an exercise-induced plasma expansion,24 which might be potentiated by the heat.25 Previous14,24 and present data showed large correlations between changes in submaximal HR and PV, with the higher the PV, the

Please cite this article in press as: Buchheit M, et al. Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players. J Sci Med Sport (2013), http://dx.doi.org/10.1016/j.jsams.2012.12.003

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greater the stroke volume, and, in turn, the lower the HR. These findings show that the use of isolated HR-derived values to predict fitness changes may be limited over short periods. As previously recommended8,12 we suggest monitoring HR (and other variables) trends over at least a week before fitness changes can be confidently interpreted. In contrast to HR and wellness scores, cortisol was less sensitive to TL variations (20.0%, P = 0.60), and did not show a clear correlation with TL. This was surprising, since significant changes in cortisol have previously been reported following both ARF training sessions11 and games 11,12 . Differences in players’ training status, TL, competition period (pre-season vs. in-season) and methods of analysis may explain the observed discrepancies. Finally, throughout the camp, high-intensity running performance increased both during the Yo-YoIR2 and standardized drills (Fig. 2). While a lack of a control group prevents us from drawing definitive conclusions, these results still confirm the efficacy of such a camp to improve physical performance. These results have direct practical applications for coaches searching for efficient pre-season training strategies and/or preparing players for games in hot environments.26 While the improvement in running performance during both performance tests is likely related to general training-induced improvements in fitness and wellness, the improvements during the standardized training drills might also be the consequence of player’s acclimatization through their daily training sessions in the heat.26 Interestingly, we observed large to very-large correlations between HRex, LnSD1, changes in all wellness scores and high-intensity running performance (Fig. 3, lower panel), which confirms the potential of these simple, non-invasive and non-fatiguing measures for monitoring (at least positive7 ) training responses in highly-trained team-sport players. Present results are in agreement with previous findings showing large associations between changes in submaximal HR and peak incremental running speed7 and high-intensity intermittent running performance.14 Importantly, the increase in HSR distance during the drills (+20%, standardized difference [Cohen’s d] = +1) was much lower than that observed for the Yo-YoIR2 (+44%, d = +2.3). Similarly, the correlations between HSR and HR-derived measures were of smaller amplitude when compared with Yo-YoIR2 (Fig. 3, lower panel). This confirms that factors other than physical fitness/readiness to perform (i.e. tactical and technical constrains) may restrict players’ running activity during sport-specific tasks.27 Consequently, predicting changes in running performance during training/games from physiological measures appears more difficult than in highly-controlled tests such as the Yo-YoIR2. Finally, in agreement with a previous study on overreached rugby players,28 we did not observe any clear correlation between cortisol and running performance (Fig. 3, lower panel). Together with its lack of sensitivity to acute TL variations, the present data questions the usefulness of cortisol in monitoring training responses, at least during short training camps.

5. Conclusion HRex and all wellness measures, but not cortisol, are highly sensitive to subtle daily changes in TL and are well correlated with positive changes in high-intensity running performance during both Yo-YoIR2 in temperate conditions and standardized drills in the heat. Additionally, changes in HRex are also associated with changes in plasma volume. The present results suggest that RPEbased TL quantification, HRex and wellness measures are useful variables for monitoring positive training responses during intense pre-season training.

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Practical implications • Managing daily TL is important, since subtle TL variations modify the physiological and wellness status of highly-trained football players during intense training phases. • TL, HRex and wellness measures, but not salivary cortisol, can be used to monitor training-induced changes in recovery status and fatigue, as well as positive changes in both generic and sportspecific running performance. In the context of the present study however, post-exercise HRV measures (i.e. LnSD1) may add little practically. • Monitoring HSR during ball games (e.g. GPS measures) is valuable to confirm potential transfers from generic to sport-specific running performance. Acknowledgements No external financial support was received for this study. We thank S. Livingston and R. Christian for their help in data collection. References 1. Jeong TS, Reilly T, Morton J et al. Quantification of the physiological loading of one week of “pre-season” and one week of “in-season” training in professional soccer players. J Sports Sci 2011; 29(11):1161–1166. 2. Borresen J, Lambert MI. The quantification of training load, the training response and the effect on performance. Sports Med 2009; 39(9):779–795. 3. Heisterberg MF, Fahrenkrug J, Krustrup P et al. Extensive monitoring through multiple blood samples in professional soccer players. J Strength Cond Res 2012. 4. Meeusen R, Duclos M, Gleeson M et al. Prevention, diagnosis and treatment of the overtraining syndrome. Eur J Sport Sci 2006; 6(1):1–14. 5. Impellizzeri FM, Rampinini E, Coutts AJ et al. Use of RPE-based training load in soccer. Med Sci Sports Exerc 2004; 36(6):1042–1047. 6. Borresen J, Lambert MI. Autonomic control of heart rate during and after exercise: measurements and implications for monitoring training status. Sports Med 2008; 38(8):633–646. 7. Buchheit M, Simpson MB, Al Haddad H et al. Monitoring changes in physical performance with heart rate measures in young soccer players. Eur J Appl Physiol 2012; 112(2):711–723. 8. Buchheit M, Chivot A, Parouty J et al. Monitoring endurance running performance using cardiac parasympathetic function. Eur J Appl Physiol 2010; 108:1153–1167. 9. Mourot L, Bouhaddi M, Tordi N et al. Short- and long-term effects of a single bout of exercise on heart rate variability: comparison between constant and interval training exercises. Eur J Appl Physiol 2004; 92(4–5):508–517. 10. Kiviniemi AM, Hautala AJ, Kinnunen H et al. Endurance training guided individually by daily heart rate variability measurements. Eur J Appl Physiol 2007; 101(6):743–751. 11. McLean BD, Coutts AJ, Kelly V et al. Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players. Int J Sports Physiol Perform 2010; 5(3):367–383. 12. Cormack SJ, Newton RU, McGuigan MR et al. Neuromuscular and endocrine responses of elite players during an Australian rules football season. Int J Sports Physiol Perform 2008; 3(4):439–453. 13. Hooper SL, Mackinnon LT. Monitoring overtraining in athletes. Recommendations. Sports Med 1995; 20(5):321–327. 14. Buchheit M, Voss SC, Nybo L et al. Physiological and performance adaptations to an in-season soccer camp in the heat: associations with heart rate and heart rate variability. Scand J Med Sci Sports 2011; 21(6):e477–e485. 15. Bangsbo J, Iaia FM, Krustrup P. The Yo-Yo intermittent recovery test: a useful tool for evaluation of physical performance in intermittent sports. Sports Med 2008; 38(1):37–51. 16. Buchheit M, Racinais S, Bilsborough J et al. Live high-train low in the heat: an efficient new training model? 17th Annual Congress of the European College of Sport Sciences, 2012. 17. Buchheit M, Mendez-Villanueva A, Quod M et al. Determinants of the variability of heart rate measures during a competitive period in young soccer players. Eur J Appl Physiol 2010; 109:869–878. 18. Huikuri HV, Seppanen T, Koistinen MJ et al. Abnormalities in beat-to-beat dynamics of heart rate before the spontaneous onset of life-threatening ventricular tachyarrhythmias in patients with prior myocardial infarction. Circulation 1996; 93(10):1836–1844. 19. Schmidt W, Prommer N. The optimised CO-rebreathing method: a new tool to determine total haemoglobin mass routinely. Eur J Appl Physiol 2005; 95(5–6):486–495. 20. Hopkins WG, Marshall SW, Batterham AM et al. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009; 41(1):3–13. 21. Gabbett TJ, Jenkins DG. Relationship between training load and injury in professional rugby league players. J Sci Med Sports 2011; 14(3):204–209.

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Please cite this article in press as: Buchheit M, et al. Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players. J Sci Med Sport (2013), http://dx.doi.org/10.1016/j.jsams.2012.12.003