Monitoring training to assess changes in fitness and fatigue: The ...

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1Faculty of Health, University of Technology, Sydney, New South Wales, Australia, 2The New South Wales Institute of Sport, Sydney,. New South Wales ...
© 2015 John Wiley & Sons A/S.

Scand J Med Sci Sports 2015: 25 (Suppl. 1): 287–295 doi: 10.1111/sms.12364

Published by John Wiley & Sons Ltd

Monitoring training to assess changes in fitness and fatigue: The effects of training in heat and hypoxia S. Crowcroft1, R. Duffield1, E. McCleave1,2, K. Slattery2, L. K. Wallace1, A. J. Coutts1 Faculty of Health, University of Technology, Sydney, New South Wales, Australia, 2The New South Wales Institute of Sport, Sydney, New South Wales, Australia Corresponding author: Stephen Crowcroft, Faculty of Health, University of Technology, Sydney, Eton Road, Lindfield, NSW, 2070 Australia. Tel: +61 408 986 917, Fax: +61 2 9514 5195, E-mail: [email protected]

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Accepted for publication 11 October 2014

This study examined the association between monitoring tools, training loads, and performance in concurrent heat and hypoxia (H + H) compared with temperate training environments. A randomized parallel matched-group design involved 18 well-trained male cyclists. Participants performed 12 interval sessions (3 weeks) in either H + H (32 ± 1 °C, 50% RH, 16.6% O2 normobaric hypoxia) or control (21 °C, 50% RH, 21% O2), followed by a sevensession taper (3 weeks; 21 °C, 50% RH, 21% O2), while also maintaining external training (∼ 6–10 h/week). A 20-km time trial (TT) was completed pre- and posttraining block (21 °C, 50% RH, 21% O2). Before each TT and once weekly, a 4-min cycle warm-up (70% 4-min mean maximum power) was completed. Visual analog scale rating for pain, recovery, and fatigue was recorded before the warm-up, with heart rate (HREx), heart rate recovery (HRR), and rating of perceived exertion

(RPEWU) recorded following. Training load was quantified using the session rating of perceived exertion (sRPE) method throughout. Overall TT improved 35 ± 47 s with moderate correlations to HRR (r = 0.49) and recovery (r = −0.55). H + H group had a likely greater reduction in HREx [ES = −0.50 (90% CL) (−0.88; 0.12)] throughout and a greater sRPE (ES = 1.20 [0.41; 1.99]), and reduction in HRR [ES = −0.37 (−0.70;−0.04)] through the overload. RPEWU was associated with weekly training load (r = 0.37). These findings suggest that recovery and HRR in a temperate environment may be used as simple measures to identify an athlete’s readiness to perform. Alternatively, the relationship of RPEWU and training load suggests that perception of effort following a standardized warm-up may be a valid measure when monitoring an athlete’s training response, irrespective of the training environment.

Training in heat or hypoxic exposure has been shown as an efficacious approach in potentiating physiological and performance adaptations, particularly during intensified training camps (Hue et al., 2007; Lorenzo et al., 2010; Buchheit et al., 2011, 2013b). Although it is well understood that heat or hypoxia in isolation can improve performance and accelerate physiological adaptations, when combined, the benefits remain largely unknown (Buchheit et al., 2013c). Recently, the combination of heat training and 14 h/day of hypoxic exposure was shown to facilitate positive physiological responses and performance outcomes in team sport athletes (Buchheit et al., 2013a). However, heat and hypoxic exposure (H + H) may also exacerbate fatigue and compromise the ability for athletes to maintain training intensities (Girard & Racinais, 2014). Therefore, when training in these extreme environments, athletes should be monitored frequently to ensure they are coping with the prescribed training loads and prevent maladaptation (Borresen & Lambert, 2009). Training in heat and/or hypoxia has been shown to exacerbate the perceptual and physiological responses

from exercise, particularly in athletes who complete long periods of intensified endurance training (Buchheit et al., 2013b). When cycling at a constant power output, exercise in H + H has been shown to decrease time to exhaustion, elicit a faster peak heart rate, and increase rating of perceived exertion (RPE) compared with heat or hypoxia alone (Girard & Racinais, 2014). Therefore, athletes training in these environments are at an increased risk of unplanned accumulated fatigue, illness, and potentially overreaching (St Clair Gibson & Noakes, 2004; Peiffer & Abbiss, 2011; Buchheit et al., 2012). To assist coaches to identify how athletes are coping, common monitoring tools to assess fatigue and recovery status such as resting heart rate (RHR), submaximal heart rate (HREx), heart rate recovery (HRR), and perceptual measures are used (Buchheit et al., 2011, 2013c). These measures can assist coaches to make decisions on future training prescription and to control the training dose at the individual level. For example, decreases in HR during a standardized warm-up have been related to improvements in a Yo-Yo intermittent recovery test (Buchheit et al., 2011), while RHR has been associated to 10-km running performance

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Crowcroft et al. (Plews et al., 2013). Likewise, others have suggested that HRR following a submaximal cycle test may be useful to individualize an athlete’s training prescription and optimize the balance between training load and recovery (Capostagno et al., 2014). However, at present, the suitability of these common athlete monitoring tools when training in H + H remains largely unknown. Perceptual measures can also be used to monitor how athletes are responding to training. For example, Lamberts et al. (2011b) identified the RPE following a standardized warm-up may be a simple perceptual measure to predict changes in a cyclist’s endurance performance. Similarly, wellness questionnaires have been used to identify changes in the fatigue and recovery status of endurances athletes, through both intensified training periods and tapering (Halson et al., 2002; Coutts et al., 2007). It is well known that exercising in extreme environments such as heat and/or hypoxia places additional stress on athletes and could alter the perceptual and physiological responses (Girard & Racinais, 2014). However, it is not known if the relationships between training load, perceived recovery, fatigue, and performance remain when training in these environments. Indeed, when athletes change training environments, it may be difficult to delineate the influence of the environment from changes in the athlete’s state of fatigue and recovery. These effects may reduce the efficacy of these measures to monitor an athlete’s training stress, especially during periods of intensified training in extreme environments. Therefore, the purpose of the present study was to (a) investigate the relationship between common monitoring tools of fatigue and recovery to training load and performance in both H + H and temperate training environments; and (b) assess the efficacy of simple monitoring tools to identify changes in fitness and fatigue following a submaximal cycle test in H + H and temperate training environments. Methods Participants Eighteen well-trained male cyclists, whose characteristics are shown in Fig. 1, met the requirements for eligibility [i.e., maximal oxygen consumption (VO2peak) > 50 mL/kg/min, training history in cycling greater than 18 months and more than 10 h/week] and were recruited to participate in the study. Participants provided informed written consent and were deemed to be free of any illness or injury prior to commencement. Ethical clearance was obtained from the Australian Institute of Sport Human Research Ethics Committee before the commencement of this study.

normobaric hypoxia) or control (21 °C, 50% RH, 21% O2), while maintaining their typical low-intensity training external to the laboratory-based sessions. After the overload, both H + H and control completed a 3-week taper (seven sessions) in temperate normoxic conditions (21 °C, 50% RH, 21% O2), while continuing external training (Fig. 1). Following preliminary testing, participants were matched by VO2peak and 4-min mean maximum power output, then randomly assigned to either H + H or control group. Prior to the overload (pre), and again following the taper (post), participants completed a 20-km laboratory-based time trial (TT) in temperate normoxic conditions (21 °C, 50% RH, 21% O2). Prior to each TT and once weekly throughout the study, participants performed a standardized submaximal cycle warm-up test to monitor training responses. Prior to each TT, participants were asked to present in a rested state at the same time of day, with standardized food and fluid consumption for 24 h prior to each test, including no caffeine 3 h or alcohol 24 h preceding testing. Prior to post-testing TT, cyclists were provided with a copy of their 24-h dietary recall for participants to replicate nutritional intake. All training external to the laboratory sessions was recorded from 3 weeks prior to the controlled training period to quantify baseline training loads and match training between groups. The participants were blinded to the laboratory training environmental conditions and were lead to believe that the environment was manipulated for both groups throughout the overload phase.

Power profile (PP) test During initial testing, participants completed a PP test consisting of repeated efforts of 6 s, 6 s, 15 s, 30 s, 60 s, 4 min, and 10 min according to the methods described by Quod et al. (2010). The PP was completed on an electromagnetically braked cycle ergometer (Velotron Racermate, Seattle, Washington, USA) and mean maximum power (MMP) for the 4-min effort was used to prescribe ensuing training and warm-up intensities (typical error: 3.6 ± 0.8%; Quod et al., 2010). The MMP was calculated using the Velotron Coaching Software (version 1.6.458, Racermate). During the 4-min and 10-min efforts, pulmonary gas exchange was recorded continuously using Moxus Modular Metabolic System (AEI Technologies, Pittsburgh, Pennsylvania, USA) to determine VO2peak.

Training phases The training protocol was divided into two distinct training phases: an overload and taper, respectively. All laboratory-based training sessions were completed on a cycle ergometer (Wattbike Ltd., Nottingham, UK). Both experimental groups were matched for external work and individualized training intensities were prescribed relative to participants’ 4-min MMP as determined from the PP (Fig. 1). Throughout the overload phase, all warm-up and laboratory training sessions were conducted in a customized heat and normobaric hypoxic chamber (Altitude Training Systems, Sydney, Australia). Following the overload phase, both groups completed a taper that included seven laboratory-based sessions (21oC, 50% RH, 21% O2,) and had an exponential reduction in external training (Thomas et al., 2008).

Quantifying training loads Experimental overview The 6-week training study was conducted following a single-blind, randomized parallel matched pairs design. Participants completed a 3-week simulated intensified training camp (overload phase), which included 12 sessions (four sessions per week) in either H + H (32 ± 1°C, 50% Relative Humidity (RH), 16.6% O2,

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All training loads for laboratory and outside training sessions were calculated using the session RPE method (sRPE; Foster et al., 2001). This method uses RPE (CR-10) multiplied by duration (min), presented as total training load in arbitrary units (AU). RPE was recorded ∼ 30 min following all training sessions to quantify internal training load (sRPE). All participants were familiarized with the RPE scale prior to the commencement of the study.

Monitoring training in heat and hypoxia Participants completed a daily training diary starting from 3 weeks prior to the first 20 km TT, which documented the sRPE and duration (min) for training sessions completed outside the laboratory. Cyclists were instructed to avoid any additional intense training sessions outside of the prescribed laboratory sessions throughout the 6 weeks of training. During the taper, the cyclists were provided training guidelines to ensure recommended tapering strategies (i.e., maintenance of training intensity and reduced training duration) and a 52–54% reduction in training load was completed as suggested by Bosquet et al. (2007).

Time trial Prior to the overload and following the taper, all participants completed a 20-km TT. All TTs were completed on an electromagnetically braked cycle ergometer (Velotron Racermate) and the data were then downloaded for analysis of time using Velotron Coaching Software (version 1.6.458, Racermate). Participants received no feedback apart from distance covered throughout and were allowed to drink water, ad libitum. Rating of perceived exertion was recorded every 5 km and within 30 min of completing the TT.

Standardized warm-up Before each TT and once weekly throughout, participants completed a standardized 4-min warm-up. The warm-up consisted of a 4-min submaximal exercise bout corresponding to 70% of their 4-min MMP followed by 2 min of passive seated upright recovery. Following the 4-min cycle, athletes were required to be stationary, without talking or drinking. Throughout all warm-up tests, heart rate (HR) was monitored continuously using a heart rate monitor (T6, Suunto, Vantaa, Finland). At the end of the 4-min cycle, HREx and HR at 1-min post were recorded from a mean 10 s preceding each respective time point. The difference in beats between HREx and HR at 1-min post was recorded as HRR and analyzed relative to change from the pre-warm-up testing value. The HR data were downloaded and analyzed after visual examination for outliers on Suunto Team Manager (version 2.3.0.15). The pre- and post-warm-up was completed in a temperate normoxic training environment (21 °C, 50% RH, 21% O2; Fig. 1), while the weekly warm-up was completed in the respective experimental training environments, i.e., overload (H + H or control) and taper (temperate normoxic). Perceptual measures were recorded prior to commencing each warm-up test when seated in the training environment. Cyclists were required to quantify their current level of perceived pain,

Fig. 1. Experimental overview, participant characteristics, training prescription, and average weekly phase training load. MMP, mean maximal power; HAP, highest average power.

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Crowcroft et al.

Fig. 2. Results of common monitoring tools from once weekly measures and session RPE values (± SD). Shaded area represents the overload phase (OL) when groups were trained in different environments before completing a similar reduction of training load in the taper (T). HREx, submaximal heart rate; HRR, heart rate recovery; RPEWU, rating of perceived exertion following warm-up; Pre, pretraining time trial; Post, post-training time trial. recovery, and fatigue using a visual analog scale (VAS; 0–100) to identify their physical state. Zero represented no pain/fatigue and worst possible recovery, while 100 represented worst possible pain/fatigue and best possible recovery (Impellizzeri & Maffiuletti, 2007; Le Meur et al., 2013). Immediately following the passive recovery from the warm-up, RPE (CR-10) was recorded as a measure of exercise tolerance and fatigue (RPEWU).

effect sizes (ES) were > 0.2 (small), > 0.6 (moderate), > 1.2 (large), and > 2.0 (very large). The criteria adopted to interpret the magnitude of the correlation (r) between variables were < 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 (Hopkins et al., 2009).

Results Statistical analysis All data are presented as means with 90% confidence limits (CL) unless otherwise stated. All data were log transformed to reduce bias arising from nonuniformity of error. A magnitude-based approach was used to assess the chances of true differences [i.e., greater than the smallest worthwhile change (SWC), between groups, phases, and overall changes in variables]. The SWC was calculated by multiplying 0.2 by the between athlete standard deviation (SD; Hopkins et al., 2009). Quantitative changes to the variables were assessed qualitatively as < 1%, almost certainly not; 1–5%, very unlikely; > 5–25%, unlikely; > 25–75%, possible; > 75–95%, likely; > 95–99%, very likely; and > 99%, almost certain. If the chance of having beneficial/better or detrimental/poorer performances were both > 5%, the true difference was assessed as unclear. Threshold values for standardized

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Data and change in mean (± SD) between variables are presented in Fig. 2. Effects sizes ( ± 90% CL) of training loads and variables for differences between groups overall and in the respective overload and taper phases are presented in Table 1. The correlation between each monitoring tool to weekly training load and performance is shown in Fig. 3. From pre- to post-testing, overall, there was a 35 ± 47 s improvement in TT performance. From the pre- to post-warm-up, there were unclear differences in HREx [ES = 0.10 (−0.74; 0.93)], HRR, [ES = −0.09 (−0.88; 0.70)], recovery [ES = −0.28 (−1.11; 0.55)], and fatigue [ES = −0.06 (−0.97; 0.84)] between groups. The control group had a likely greater

Monitoring training in heat and hypoxia Table 1. Difference of change in effect size (90% confidence limits) of variables between groups (H + H – control)

Outcomes in standardized (Cohen) units

HREx HRR RPEWU Pain Recovery Fatigue sRPE Total training loads

Pre to post

Between phases

Overload

Taper

0.10 (−0.74; 0.93) −0.09 (−0.88; 0.70) 0.82 (0.10; 1.54)* 1.49 (−0.04; 3.02)* −0.28 (−1.11; 0.55) −0.06 (−0.97; 0.84)

−0.50 (−0.88; −0.12)* 0.00 (−0.35; −0.36) 0.04 (−0.33; 0.42) 0.40 (−0.06; 0.86)* 0.04 (−0.35; 0.43) −0.09 (−0.57; 0.39) −0.80 (−1.03; −0.56)* 0.06 (−0.25; 0.38)

0.03 (−0.22; 0.28) −0.37 (−0.70; −0.04)* 0.38 (0.01; 0.75)* 0.00 (−0.51; 0.51) 0.19 (−0.31; 0.70) −0.04 (−0.45; 0.38) 1.20 (0.41; 1.99)* 0.58 (−0.21; 1.37)

−0.08 (−0.34; 0.18) −0.45 (−0.86; −0.05)* −0.03 (−0.28; 0.22) 0.40 (−0.21; 1.00) −0.04 (−0.47; 0.39) 0.31 (−0.26; 0.88) −0.38 (−1.17; 0.41) 0.66 (−0.12; 1.45)

*> 95% quantitative change to variable. Pre to post (post-taper–pre-overload); between phases (post-taper–week 1); overload (end of week 3–week 1); taper (pos-taper–week 4). HREx, submaximal heart rate; HRR, heart rate recovery; RPEWU, rating of perceived exertion following warm-up; sRPE; session rating of perceived exertion.

Fig. 3. Correlates of variables to time trial performance and weekly training load (90% CL) throughout the 6-week study (overall), the overload, and taper. HREx, submaximal heart rate; HRR, heart rate recovery; RPEWU, rating of perceived exertion following warm-up.

decrease in RPEWU [ES = 0.82 (0.10; 1.54)], whereas H + H had a likely greater increase in pain throughout [ES = 1.49 (−0.04; 3.02)]. From the 6-week training block (overload and taper), there were no clear differences between groups for total training loads (H + H: 8092 ± 2355 to 4858 ± 2001 AU; control: 6689 ± 2861 to 3486 ± 1215 AU) [ES = 0.06 (−0.25; 0.38)]. From the beginning of the overload to the end of the taper, H + H had a most

likely greater reduction in sRPE [ES = −0.80 (−1.03; −0.56)] and a likely greater reduction in HREx [ES = −0.50 (−0.88; 0.12)]. Pain had a small likely greater decrease in the control [ES = 0.40 (−0.06, 0.86)]; however, there were no clear differences between groups from the overload to the taper for HRR [ES = 0.00 (−0.35; 0.36)], recovery [ES = 0.04 (−0.35, 0.43)], fatigue [ES = −0.09 (−0.57, 0.39)], or RPEWU [ES = 0.04 (−0.33, 0.42)].

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Crowcroft et al. Throughout the overload training period, there were unclear differences between groups in HREx [ES = 0.03 (−0.22; 0.28)], pain [ES = 0.00 (−0.51, 0.51)], recovery [ES = 0.19 (−0.31, 0.70)], and fatigue [ES = −0.04 (−0.45, 0.38)]. In contrast, the H + H group demonstrated a likely greater increase in RPEWU [ES = 0.38 (0.01, 0.75)] and a very likely greater sRPE [ES = 1.20 (0.41; 1.99)], and the control had a likely greater increase in HRR [ES = −0.37 (−0.70; 0.04)]. Throughout the taper, HREx had an unlikely greater difference in the taper [ES = −0.08 (−0.34; 0.18)], and the control had a likely greater increase in HRR [ES = −0.45 (−0.86; −0.05)]. Furthermore, there were unclear differences between groups in the taper for pain [ES = 0.40 (−0.21, 1.00)], recovery [ES = −0.04 (−0.47, 0.39)], fatigue [ES = 0.31 (−0.26, 0.88)], RPEWU [ES = −0.03 (−0.28, 0.22)], or sRPE [ES = −0.38 (−1.17; 0.41]). Discussion The purpose of this study was to examine the relationship of common monitoring tools to training loads and performance during an overload and taper phase in H + H and temperate training environments. The main findings showed HRR and perceptions of recovery to have a moderate to large correlation with performance, when used in a temperate environment. However, from the beginning of the overload to the taper, the H + H group had a “likely” greater reduction in HR measures, but had only trivial to small correlations to training load throughout the overload phase. These changes were most likely a consequence of altered HR responses caused by the plasma volume expansion and environmental exposure induced by training in the heat (Buchheit et al., 2009). Furthermore, H + H had a “very likely” greater sRPE for the same external load in the overload phase, highlighting the influence of the environment on the athlete’s perceptions of load. While HRR and recovery were correlated to performance in a temperate environment, RPEWU was correlated to weekly training load during the 6-week period and had a moderate correlation throughout the overload phase. This suggests the usefulness of RPEWU as a monitoring tool independent of the training environment. This study demonstrated that despite the matching for external load, the H + H group perceived training to be more intense (higher sRPE) during the overload. This is in agreement with previous research that has shown a greater perceived effort in either heat or hypoxia to the same external load (Nybo & Nielsen, 2001; Tucker et al., 2004, 2006; Peiffer & Abbiss, 2011). For example, Nybo and Nielsen (2001) investigated the relationship of cerebral activity, core temperature, and sRPE from a standardized workload or to volitional fatigue in 18 and 40 °C for 1 h. There was a reported linear correlation between core temperature, electroencephalography, and sRPE implying an association between perceived effort

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and hyperthermia-induced fatigue in the heat. Similar mechanisms are likely present in the current study and these probably underlie the elevated perceived exertion in the H + H conditions. Indeed, previous research using high-intensity intermittent training has identified hypoxia may induce a greater peripheral and central RPE than when exercise is performed at a higher relative intensity in normoxia (Buchheit et al., 2012). The increased RPE may be due to the low brain oxygenation, inhibitory feedback from the peripheral effects of low oxygen availability, or that independent of exerciserelated stress, hypoxia may affect perceived exertion (Amann & Calbet, 2008; Buchheit et al., 2012). While this research has identified that sRPE is exacerbated in either heat or hypoxia in isolation, the present study identifies that despite being matched for external load, sRPE is exacerbated in H + H compared with a control condition. The increased HREx in the H + H group, compared with control during the overload, most likely reflects the increased cardiovascular strain that occurs when training in heat and/or hypoxia (Mazzeo, 2008; Cheuvront et al., 2010; Periard et al., 2011; Girard & Racinais, 2014). Furthermore, this is magnified with a greater reduction of HREx in the H + H group from the overload to the taper. These responses may be due to changes in both plasma volume and the autonomic control associated with load reduction (Buchheit et al., 2009). However, in the present study, pre- to post-training changes in HREx had similar or unclear differences between groups and small to moderate correlations to training load and TT performance in a temperate normoxic training environment. These findings are weaker than previous research that has identified large to very large correlations between changes in HREx and high-intensity performance tests (Lamberts et al., 2011b; Buchheit, 2014). A possible explanation for the weaker relationship in the current study was the lower exercise intensity prescribed in the warm-up. It has been suggested that the higher intensities of 86–93% maximal HR may be most appropriate for a monitoring warm-up test, as the HR responses are more reliable and there is a decrease in the day-to-day variability compared with lower intensity tests (Lamberts et al., 2011a). However, in the present study, a lower warm-up intensity was set at ∼ 70% of 4-min MMP, as higher intensities may have been too strenuous for the athletes exercising in H + H. Similar to HREx, HRR has been used to identify fitness adaptations as it is indicative of changes in blood pressure regulation and the metaboreflex response, which reflect both sympathetic withdrawal and parasympathetic reactivation following exercise (Daanen et al., 2012; Buchheit, 2014). Indeed, as this study and previous research have suggested, HRR may be a useful tool to identify a response to training and improvements in TT performance when in a standardized training environment (Lamberts et al., 2009; Mann et al., 2014).

Monitoring training in heat and hypoxia Interestingly, in the present study, there was minimal change between groups in HRR and a trivial to small correlation to training load. These findings may have occurred because of the increased HR response and slowed HRR reflective of the climatic strain and vasodilation of blood vessels, resulting in a reduced venous return when training in heat and a greater parasympathetic reactivation and lower HREx following training in a normoxic and temperate environment (Kilgour et al., 1993; Daanen et al., 2012; Buchheit, 2014). However, this poor relationship to training load may not be uncommon within well-trained athletes. For example, Hug et al. (2014) have shown that a slowed HRR following a 10-day taper had a very strong relationship with improved marathon running performance. These results support a more balanced sympathovagal activity reflective of a slower HRR in contrast to a high parasympathetic dominance to enhance aerobic performance. As such, the authors suggested that the use and interpretation of changes to HRR should be monitored closely with an understanding of how the change in HRR relate to both the training phase and an athlete’s readiness to perform. Furthermore, Buchheit (2014) reported that the effectiveness of HRR as a measure of fitness or fatigue is dependent upon the relative intensity of exercise, with higher intensity exercise resulting in a higher HR and allowing a larger decrease in HRR. Nonetheless, while these findings support the use of HR-derived measures in a temperate environment to identify improvements in TT performance, this study highlights the limitations of HRR to identify changes in fitness or fatigue in H + H. In the current study, the relationship between RPEWU and total weekly training load showed that perceived exertion from a standardized exercise bout may be used as a simple tool to assess how an athlete is coping with the prescribed training loads, independent of the training environment. The additional training stress placed upon the H + H group because of the altered environmental conditions was demonstrated by a greater RPE for both the warm-up and subsequent training session during the overload period when compared with the control. Similar to these findings, previous research has demonstrated the use of RPE following a standardized warm-up as a reliable measure to identify changes in peak power output, endurance performance, or changes in wellbeing (Lamberts, 2009; Buchheit et al., 2013c). For example, Buchheit et al. (2013b) reported that RPE was higher from a standardized submaximal run during initial exposure to altitude in both Australian (native sea level) and Bolivian (native altitude) soccer players (3600 m – La Paz). However, it has been suggested that during initial acclimation, the RPE may take a few days to normalize (Buchheit et al., 2013a). Therefore, sensitivity and technical error of measurement, when used as a monitoring tool throughout a training camp, particularly when changing between environments, may require further investigation. Nonetheless, changes in RPE

following a set workload have also been used as a marker of potential performance capacity. For example, Lamberts et al. (2011b) have reported that RPE recorded following a submaximal cycle warm-up test at 90% of maximal HR has a low technical error of the mean (0.7 [0.6–0.8]) and is an indicator of an athlete’s level of recovery, accumulated fatigue, and predict performance change. Yet in the current study, there were only trivial to small relationships of RPEWU to performance. This may have been due to the lower warm-up intensity previously discussed, or the use of the CR 1–10 RPE scale, compared with the 6–20 Borg scale used by Lamberts et al. (2011a). Based on these results, RPEWU may be a useful tool to measure training tolerance irrespective of environmental conditions. However, further research is required to establish the feasibility of using RPE in altered training environments to assess performance. Similar to the RPE, the VAS is a noninvasive and non-fatiguing tool to provide readily available information on an athlete’s response to training (Impellizzeri & Maffiuletti, 2007). In the present study, the VAS scales for pain and recovery both had a “moderate” and “large” correlation to performance. Further to this, the small and “unclear” difference in pain and fatigue VAS between groups through the overload and taper provide some support for its use to monitor an athlete’s response to training as it is not influenced through the change of environments. This is may be due to VAS measures being recorded prior to exercise, limiting the influence of the environment upon perceptual measures. Recently, Le Meur et al. (2013) reported that perceived fatigue from the VAS was greater in triathletes who were deliberately overreached by increasing their training volume by 40% from their previous 3 weeks mean training load. These athletes recorded a “very likely” greater fatigue and an acute decrement in performance, providing evidence for the use of the VAS to identify an athlete’s state of accumulated training fatigue. Interestingly, in the present study, there were no clear group differences in fatigue or recovery despite an observed higher sRPE in H + H throughout the overload phase. Although both studies had similar percentage increases in training loads throughout the overload phase, the discrepancies between fatigue responses may have been due to an increased intensity in training from the current study, rather than the discussed increased training volume as noted by Le Meur et al. (2013). Previously, Impellizzeri and Maffiuletti (2007) observed that the VAS was a sensitive and valid measure compared with a pain Likert scale of perceived muscle soreness for up to 96 h following plyometric and eccentric muscle damage in soccer players. Although the current results suggest that there were only trivial correlations for pain, recovery, and fatigue in association to training load, a greater correlation between the VAS and weekly training loads may have taken place with more frequent measures. This lack of relationship may have also occurred as cycling

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Crowcroft et al. exercise does not induce the same eccentric muscle damage as plyometric training and there was no deliberate increase in external training loads to have elicited symptoms of overreaching in the cyclists. Alternatively, the small difference between groups may have arisen from the formula used to quantify the magnitude of change. Indeed, recent research has now identified that the method chosen to identify the SWC may influence the magnitude of change when using a progressive statistical approach (Buchheit et al., 2014). To improve accuracy in data sets with a large inter-subject variability, as was observed in this study, a separate SWC may be required for each individual. However, the moderate and large relationships of pain and recovery to TT performance do provide support for the use in these measures to identify an athlete’s readiness to perform. Therefore, future research may attempt to compare the differences in an individual coefficient of variance to the group variance when determining the SWC to investigate the day-to-day variance, sensitivity, and frequency of perceptual-based measures for monitoring an athlete’s training response. A limitation of the present study is that a match pair parallel research design was used in an attempt to replicate the overload and taper in a practical sport setting. This model does not allow for comparison of individual training responses between environments. In addition, with measures recorded once weekly, there is a relatively small number of individual measures throughout the study. This low number may consequently influence the correlations derived from this research. Further investigation with more frequent monitoring and with greater than 20 measurements per participant is warranted.

Perspectives The present results showed that perception of effort following a standardized warm-up may hold as a valid and simple measure that can be used to monitor an athlete’s training response, regardless of the training environment. Moreover, it provides further support for the use of pain, recovery, and HRR to identify an athlete’s training response. The current findings showed that HR-derived measures may be useful for monitoring athletes in a standardized training environment, but clearly demonstrate limitations in using HR to monitor athletes when changing between training environments. Of note, there were no clear changes between groups in VAS measures. Such a finding may be due to the frequency of measures, or the sensitivity of the methods used to quantify the magnitude of change. These findings provide opportunity for future research to examine the use of perceptualbased measures at rest or from a warm-up to identify optimal frequency of measurement, the day-to-day variance, and the most suitable measures to identify both acute and chronic changes in fitness and fatigue. Key words: Athlete monitoring, training load, heart rate, perceptual scales.

Acknowledgements This investigation was supported by a Collaborative Funding Grant from the Australian Institute of Sport and New South Wales Institute of Sport.

Conflicts of interest: The authors of this study declare that they have no conflicts of interest.

References Amann M, Calbet JA. Convective oxygen transport and fatigue. J Appl Physiol 2008: 104: 861–870. Borresen J, Lambert MI. The quantification of training load, the training response and the effect on performance. Sports Med 2009: 9: 779–795. Bosquet L, Montpetit J, Arvisais D, Mujika I. Effects of tapering on performance: a meta-analysis. Med Sci Sports Exerc 2007: 39: 1358–1365. Buchheit M. Monitoring training status with HR measures: do all roads lead to Rome? Front Physiol 2014: 5: 1–12. Buchheit M, Kuitunen S, Voss SC, Williams BK, Mendez-Villanueva A, Bourdon PC. Physiological strain associated with high-intensity hypoxic intervals in highly trained young runners. J Strength Cond Res 2012: 26: 94–105.

294

Buchheit M, Laursen PB, Al Haddad H, Ahmaidi S. Exercise-induced plasma volume expansion and post-exercise parasympathetic reactivation. Eur J Appl Physiol 2009: 105: 471–481. Buchheit M, Rabbani A, Beigi HT. Predicting changes in high-intensity intermittent running performance with acute responses to short jump rope workouts in children. J Sports Sci Med 2014: 13: 476–482. Buchheit M, Racinais S, Bilsborough J, Hocking J, Mendez-Villanueva A, Bourdon PC, Voss S, Livingston S, Christian R, Périard J, Cordy J, Coutts AJ. Adding heat to the live-high train-low altitude model: a practical insight from professional football. Br J Sports Med 2013a: 47: i59–i69. Buchheit M, Racinais S, Bilsborough JC, Bourdon PC, Voss SC, Hocking J, Cordy J, Mendez-Villanueva A, Coutts AJ. Monitoring fitness fatigue and

running performance during pre season training camp in elite football players. J Sci Med Sport 2013b: 16: 550–555. Buchheit M, Simpson MB, Garvican-Lewis L, Hammond K, Kley M, Schmidt W, Aughey R, Soria R, Sargemt C, Roach G, Jimenez JC, Gore CJ. Wellness, fatigue and physical performance acclimatisation to a 2-week soccer camp at 3600m. Br J Sports Med 2013c: 47: i100–i106. Buchheit M, Voss SC, Nybo L, Mohr M, Racinais S. 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: e477–e485. Capostagno B, Lambert MI, Lamberts RP. Standardized versus customized high-intensity training: effects on cycling performance. Int J Sports Physiol Perform 2014: 9: 292–301.

Monitoring training in heat and hypoxia Cheuvront SN, Kenefick RW, Montain SJ, Sawka MN. Mechanisms of aerobic performance impairment with heat stress and dehydration. J Appl Physiol 2010: 109: 1989–1995. Coutts AJ, Slattery KM, Wallace LK. Practical tests for monitoring performance, fatigue and recovery in triathletes. J Sci Med Sport 2007: 10: 372–381. Daanen HA, Lamberts RP, Kallen VL, Jin A, Van Meeteren N. A systematic review on heart rate recovery to monitor changes in training status in athletes. Int J Sports Physiol Perform 2012: 7: 251–260. Foster C, Florhaug JA, Franklin J, Gottschall L, Hrovatin LA, Parker S, Doleshal P, Dodge C. A new approach to monitoring exercise training. J Strength Cond Res 2001: 15: 109–115. Girard O, Racinais S. Combining heat stress and moderate hypoxia reduces cycling time to exhaustion without modifying neuromuscular fatigue characteristics. Eur J Appl Physiol 2014: 114 (7): 1521–1532. Halson SL, Bridge MW, Meeusen R, Busschaert B, Gleeson M, Jones DA, Jeukendrup AE. Time course of performance changes and fatigue markers during intensified training in trained cyclists. J Appl Physiol 2002: 93: 947–956. Hopkins WG, Batterham AM, Marshall SW, Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc 2009: 41: 3–13. Hue O, Antoine-Jonville S, Sara F. The effect of 8 days of training in tropical environment on performance in neutral climate in swimmers. Int J Sports Med 2007: 28: 48–52. Hug B, Heyer L, Naef N, Buchheit M, Wehrlin JP, Millet GP. Tapering for marathon and cardiac autonomic function. Int J Sports Med 2014: 35: 676–683.

Impellizzeri FM, Maffiuletti NA. Convergent evidence for construct validity of a 7-point Likert scale of lower limb muscle soreness. Clin J Sport Med 2007: 17: 494–496. Kilgour RD, Gariepy P, Rehel R. Cardiovascular responses during recovery from exercise and thermal stress. Aviat Space Environ Med 1993: 64: 224–229. Lamberts RP. The development of an evidence-based submaximal cycle test designed to monitor and predict cycling performance. The Lamberts and Lambert Submaximal Cycle test (LSCT). 1st edn. Enschede, the Netherlands: Ipskamp Drukkers, 2009: 705–713. Lamberts RP, Maskell S, Borresen J, Lambert MI. Adapting workload improves the measurement of heart rate recovery. Int J Sports Med 2011a: 32: 698–702. Lamberts RP, Swart J, Noakes TD, Lambert MI. Changes in heart rate recovery after high-intensity training in well-trained cyclists. Eur J Appl Physiol 2009: 105: 705–713. Lamberts RP, Swart J, Noakes TD, Lambert MI. A novel submaximal cycle test to monitor fatigue and predict cycling performance. Br J Sports Med 2011b: 45: 797–804. Le Meur Y, Pichon A, Schaal K, Schmitt L, Louis J, Gueneron J, Vidal P, Hausswirth C. Evidence of parasympathetic hyperactivity in functionally overreached athletes. Med Sci Sports Exerc 2013: 45: 2061–2071. Lorenzo S, Halliwill JR, Sawka MN, Minson CT. Heat acclimation improves exercise performance. J Appl Physiol 2010: 109: 1140–1147. Mann TN, Webster C, Lamberts RP, Lambert MI. Effect of exercise intensity on post-exercise oxygen consumption and heart rate recovery. Eur J Appl Physiol 2014: 114: 1–12.

Mazzeo RS. Physiological responses to exercise at altitude. Sports Med 2008: 38: 1–8. Nybo L, Nielsen B. Perceived exertion is associated with an altered brain activity during exercise with progressive hyperthermia. J Appl Physiol 2001: 91: 2017–2023. Peiffer JJ, Abbiss CR. Influence of environmental temperature on 40 km cycling time-trial performance. Int J Sports Physiol Perform 2011: 6: 208–220. Periard JD, Cramer MN, Chapman PG, Caillaud C, Thompson MW. Cardiovascular strain impairs prolonged self-paced exercise in the heat. Exp Physiol 2011: 96: 134–144. Plews DJ, Laursen PB, Kilding AE, Buchheit M. Evaluating training adaptation with heart rate measures: a methodological comparison. Int J Sports Physiol Perform 2013: 6: 688–691. Quod MJ, Martin DT, Martin JC, Laursen PB. The power profile predicts road cycling MMP. Int J Sports Med 2010: 31: 397–401. St Clair Gibson A, Noakes TD. Evidence for complex system integration and dynamic neural regulation of skeletal muscle recruitment during exercise in humans. Br J Sports Med 2004: 38: 797–806. Thomas L, Mujika I, Busso T. A model study of optimal training reduction during pre-event taper in elite swimmers. J Sports Sci 2008: 26: 643–652. Tucker R, Rauch L, Harley YX, Noakes TD. Impaired exercise performance in the heat is associated with an anticipatory reduction in skeletal muscle recruitment. Pflugers Arch 2004: 448: 422–430. Tucker R, Marle T, Lambert EV, Noakes TD. The rate of heat storage mediates an anticipatory reduction in exercise intensity during cycling at a fixed rating of perceived exertion. J Physiol 2006: 574: 905–915.

295