A pilot study on quantification of training load: The use of HRV in training practice

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1 European Journal of Sport Science, ORIGINAL ARTICLE A pilot study on quantification of training load: The use of HRV in training practice DAMIEN SABOUL 1,2, PASCAL BALDUCCI 1, GRÉGOIRE MILLET 3, VINCENT PIALOUX 1, & CHRISTOPHE HAUTIER 1 1 CRIS, Center of Research and Innovation on Sport, University Claude Bernard Lyon 1, France, 2 Almerys, 46, rue du Ressort Clermont Ferrand, France, 3 ISSUL, Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland Downloaded by [UQ Library] at 07:32 08 February 2015 Abstract Recent laboratory studies have suggested that heart rate variability (HRV) may be an appropriate criterion for training load (TL) quantification. The aim of this study was to validate a novel HRV index that may be used to assess TL in field conditions. Eleven well-trained long-distance male runners performed four exercises of different duration and intensity. TL was evaluated using Foster and Banister methods. In addition, HRV measurements were performed 5 minutes before exercise and 5 and 30 minutes after exercise. We calculated HRV index (TL HRV ) based on the ratio between HRV decrease during exercise and HRV increase during recovery. HRV decrease during exercise was strongly correlated with exercise intensity (R = 0.70; p < 0.01) but not with exercise duration or training volume. TL HRV index was correlated with Foster (R = 0.61; p = 0.01) and Banister (R = 0.57; p = 0.01) methods. This study confirms that HRV changes during exercise and recovery phase are affected by both intensity and physiological impact of the exercise. Since the TL HRV formula takes into account the disturbance and the return to homeostatic balance induced by exercise, this new method provides an objective and rational TL index. However, some simplification of the protocol measurement could be envisaged for field use. Keywords: Heart rate variability, monitoring, fatigue, athletes, autonomic nervous system Introduction Quantification of training load (TL) is considered as an important topic in sport sciences since it is a major tool for training follow-up (Borresen & Lambert, 2009). Initially, questionnaires and diaries were used to measure TL but they were replaced by more objective methods based on physiological measurements (Borresen & Lambert, 2009). Some studies suggested evaluating TL through biological markers like oxygen uptake or blood lactate concentration (Hopkins, 1991). However, these measures require specific equipment and are mainly used in the context of scientific research. Conversely, other methods based on heart rate (HR) or rating of perceived exertion (RPE) appear more suitable for daily use and practical application (Karvonen & Vuorimaa, 1988). From these markers, several indices of training stress, like training impulse (TRIMP) or session RPE, have been developed to assess TL (Banister, Good, Holman, & Hamilton, 1986; Foster, 1998). Especially, Banister method consists in calculating the ratio of HR means during exercise and HR reserve multiplied by weighting coefficient and exercise duration (equation 1; Banister et al., 1986; Borresen & Lambert, 2009). Conversely, Foster method is based on the multiplication of a score (from 1 to 10) representing exercise difficulty perceived by athlete by exercise duration (Borresen & Lambert, 2009; Foster, 1998). However, these two methods present some limitations. For instance, TRIMP method cannot discriminate intermittent or continuous training sessions (TSs) of same duration since mean HR is equivalent. In addition, this method is not valid for high-intensity interval training, especially during shorter repetitions with strong anaerobic contribution because of the shifted kinetics of HR in response to Correspondence: D. Saboul, Centre de Recherche et d Innovation sur le Sport, Université Claude Bernard Lyon 1, Villeurbanne Cedex, Lyon, France. dsaboul@free.fr 2015 European College of Sport Science

2 2 D. Saboul et al. exercise (Buchheit & Laursen, 2013). Similarly, Foster method is mainly based on a self-evaluation of the TS intensity which is likely a limitation for subjects not familiarised with the RPE scale and also for expert subjects which may under or over evaluate the results (Foster, Heimann, Esten, Brice, & Porcari, 2001). Nonetheless, for practical reasons, the Banister and Foster methods are widely used by coaches and athletes to assess TL and monitor endurance training but it is important to note that there is no real gold standard for TL evaluation (Hellard et al., 2006; Kaikkonen, Hynynen, Mann, Rusko, & Nummela, 2010). For 20 years, heart rate variability (HRV) has been widely used as a non-invasive method to estimate cardiac autonomic regulation, which may reflect the activity of the autonomic nervous system (ANS; Task-Force, 1996). This indicator is sensitive to homeostatic perturbations like fatigue, physiological and psychological stress (Aubert, Seps, & Beckers, 2003; Chandola, Heraclides, & Kumari, 2010). After stimulus, ANS regulates homeostatic function of the body (Aubert et al., 2003; Buchheit, Laursen, & Ahmaidi, 2007) and therefore plays an important role in the individual exercise training responses (Chalencon et al., 2012; Hautala, Kiviniemi, & Tulppo, 2009). More specifically, it was shown that exercise induced parasympathetic withdrawal and sympathetic excitation and that these effects were reversed during recovery phase (Buchheit et al., 2007). Recent studies focused on the relationship between training content (e.g. intensity, duration, etc.) and post-exercise HRV changes (Casties, Mottet, & Le Gallais, 2006; Kaikkonen, Hynynen, Mann, Rusko, & Nummela, 2012; S. Seiler, Haugen, & Kuffel, 2007). It is now clearly established that intensity is related to immediate post-exercise HRV (Buchheit et al., 2007; Kaikkonen, Rusko, & Martinmäki, 2008; Kaikkonen et al., 2010). On the other hand, it has been shown that immediate post-exercise HRV was not affected by the increase of exercise duration up to twice the baseline (Kaikkonen, Nummela, & Rusko, 2007; Kaikkonen et al., 2010; S. Seiler et al., 2007). Moreover, the time course of HRV markers during recovery has been studied by several authors to quantify vagal reactivation after exercise (Kaikkonen et al., 2010, 2012; S. Seiler et al., 2007). Recent studies showed that training above the lactate threshold (LT) intensity delayed HRV recovery compared with training below LT (Plews, Laursen, Kilding, & Buchheit, 2014). Other studies presented different post-exercise HRV kinetics between continuous and intermittent running sessions (Kaikkonen et al., 2008). In summary, the duration for post-exercise HRV to return to baseline values seems longer after exercise inducing a greater metabolic demand (Stanley, Peake, & Buchheit, 2013). Thereby, authors suggested that post-exercise HRV may enable an objective TL evaluation (Kaikkonen et al., 2010, 2012). However, most studies in line with post-exercise HRV recovery and TL have been performed on subjects moderately trained, in laboratory conditions (i.e. on treadmill or ergocycle) and during exercise protocols far removed from usual TSs (Kaikkonen et al., 2007, 2010; Martinmäki & Rusko, 2008). In addition, to our knowledge, there exists no method or tool based on HRV measurements for the rational evaluation of TL in field conditions (Kaikkonen et al., 2012). The aim of the present study was therefore to propose a new HRV-based method for quantifying TL. This method was tested in highly trained athletes in field conditions during their usual TSs. Finally, this new method was compared to two previous methods commonly used by coaches and athletes (i.e. Banister and Foster). Methods Subjects Eleven well-trained long-distance male runners were recruited from local running teams [competition level: seven regional, three national and one international; age = 32 ± 6 year; height = 182 ± 5 cm; body mass = 76.3 ± 10.2 kg; maximal aerobic speed (MAS) = 18.9 ± 1.2 km/h; resting HR = 44 ± 4 bpm; maximal HR = 187 ± 8 bpm; training = 450 ± 139 h/year]. Subjects receiving medical treatment, or with asthma or cardiovascular disorders, were excluded. The subjects, volunteers, gave written informed consent to participate in this study. In addition, throughout the experiment, the subjects agreed not to change their living routine including sleep duration, diet and professional occupation. The protocol was approved by the ethical committee of Lyon Sud-Est II and was in accordance with the guidelines set by the Declaration of Helsinki. Experimental design The total duration of the study was two weeks. MAS was first measured in a preliminary session using a validated continuous multi stage track test: the Université de Montréal track test (Leger & Boucher, 1980). Resting HR (HR rest ) was measured with the subject in a sitting position before the MAS test. HR was recorded during the test (Suunto T6d heart rate monitor, Suunto Oy, Finland) and the maximal HR (5-s average) was considered to be the participant s HR max (Buchheit et al., 2010). The subjects performed four different TSs throughout the experiment. Exercises were performed at the same time of day in a random order

3 on four different days, separated by at least three days. The subjects were asked to refrain from intense physical exercise for two days and from alcohol and caffeine consumption for one day prior to any experimental session. HRV measurements For each session, HRV measurements were performed in three different five-minute periods: [ 5; 0] minutes before warm-up for all TS (Pre5), [5; 10] minutes after TS (Post5) and [30; 35] minutes after TS (Post30), respectively. HRV measurement consisted of a five-minute R R interval recording in supine position in a quiet environment (Buchheit et al., 2010; Plews, Laursen, Stanley, Kilding, & Buchheit, 2013). Data were collected and recorded using a validated HR monitor (Suunto T6d 1000 Hz). Data analyses were restricted to time domain indices and the only HRV marker used was the root mean square difference of successive normal R R intervals (RMSSD; Buchheit et al., 2010; Plews, Laursen, Stanley, et al., 2013). RMSSD was chosen because it represents short-term HRV variability and especially vagal modulation (Buchheit et al., 2007, 2010; Task-Force, 1996). Consequently, this HRV marker is widely used in the field of exercise physiology (Buchheit et al., 2010; Plews, Laursen, Kilding, & Buchheit, 2012). On the contrary, the spectral HRV markers like LF, HF and LF/HF ratio are less reliable in the context of well-trained athletes (Saboul, Pialoux, & Hautier, 2013, 2014). Between Post5 and Post30 HRV measurements (i.e. recovery phase), subjects had to stay seated alone, in a locker room, in quiet and comfortable environment ( C). They were asked to drink up to a maximum of 500 ml of water but no food. Training sessions The experimental TSs were designed first to represent the usual TSs regularly undertaken during the season by these well-trained athletes (Stepto, Hawley, Dennis, & Hopkins, 1999) and second to cover a representative range of track sessions in terms of intensity and duration. Consequently by design the workloads were not matched for intensity volume. Each session was performed on an athletic track for a similar duration for each subject. To control exercise intensity, the speeds were individualised as a percentage of each athlete s MAS. TS 1 (S 70% ) consisted of a 10-minute warm-up run followed by active endurance running at 70% of MAS for 34 minutes. The session ended with 10 minutes of cool down at low speed (50% MAS) for a total training duration of 54 minutes (total volume = 3580 a.u.). Training load quantification with HRV 3 TS 2 (S 85% ) consisted of a 20-minute warm-up run followed by three 10-minute bouts at 85% of MAS with three minutes of passive recovery. The session ended with 10 minutes of cool down at low speed (50% MAS) for a total training duration of 69 minutes (total volume = 4350 a.u.). TS 3 (S 95% ) consisted of a 20-minute warm-up run followed by eight 2-minute bouts at 95% of MAS with 1 minute of active recovery at 60% of MAS. The session ended with 10 minutes of cool down at low speed (50% MAS) for a total training duration of 54 minutes (total volume = 3800 a.u.). TS 4 (S 100% ) consisted of a 25-minute warm-up run followed by one bout of 6-minute duration at 100% of MAS. The session ended with 10 minutes of cool down at low speed (50% MAS) for a total training duration of 41 minutes (total volume = 2700 a.u.). TL estimation Three methods were used in order to quantify the TL of each session. First, we used the HR recorded during exercise. TRIMP was calculated according to equation 1 (Banister et al., 1986). TRIMP ¼ T HR exe HR rest HR max HR rest 0:64 e 1:92 HRexe HR rest HRmax HR rest TRIMP: training impulse T: duration of TS (min) HR exe : mean HR of the TS (bpm) HR rest : HR at rest (bpm) HR max : maximal HR (bpm) e: Naperian logarithm of ð1þ Second, RPE (scale 0 10) was obtained 30 minutes after the exercise and multiplied by the duration of the TS (min) according to Foster method (Borresen & Lambert, 2009; Foster, 1998). Third, we defined a new index (TL HRV ) for quantifying the TL from the change from pre- to post-exercise RMSSD. TL HRV index was calculated according to equation 2. Pre5 Post5 TL HRV ¼ In T Post30 Post5 ð2þ TL HRV : TL index T: duration of the TS (min) Pre5: RMSSD value before TS (ms) Post5: RMSSD value five minutes after TS (ms) Post30: RMSSD value 30 minutes after TS (ms)

4 4 D. Saboul et al. This equation was designed to reflect the kinetics of HRV recovery with both the disturbance (i.e. Pre5 Post5) and the return to homeostatic state of rest (i.e. Post30 Post5). The first part of the calculation was designed to take into account training intensity through RMSSD decrease (Pre5 Post5) since previous results demonstrated that HRV modifications were more sensitive to exercise intensity than exercise duration (Kaikkonen et al., 2007). Moreover, post-exercise RMSSD increase (Post30 Post5) was included in the formula to evaluate exercise effects on vagal reactivation (Buchheit et al., 2007). We chose to assess the second measurement 30 minutes after the end of exercise: first, since previous results reported that 30 minutes of HRV recovery is a mid-point and a good compromise to investigate recovery processes (Kaikkonen et al., 2007, 2008; S. Seiler et al., 2007) and second, in comparison with the Foster method that also uses feedback recorded after 30 minutes (Borresen & Lambert, 2009; Foster, 1998). Finally, the method included a ratio between RMSSD decrease (Pre5 Post5) and post-exercise increase (Post30 Post5) which normalised HRV changes, lowering the influence of day-to-day baseline HRV fluctuations linked to sleep, diet or stress. Post-HRV measure being not significantly influenced by training duration (Kaikkonen et al., 2010), according to the Banister and Foster methods, our (Pre5 Post5)/(Post30 Post5) ratio was also multiplied by the TS duration (i.e. T). Finally, due to the skewed nature of HRV recordings, these data were log transformed by taking the natural logarithm (ln; equation 2; Buchheit et al., 2010; Plews et al., 2012). Statistical analysis All values were expressed as means ± 90% confidence limits. The normality of data was tested with the Shapiro Wilk test. Data were not normally distributed. Thus, the Friedman test is used for one-way repeated measures analysis of variance in order (1) to examine the difference in HRV value (Pre5 vs. Post5 vs. Post30) for each TS, (2) to compare the difference between all sessions (S 70% vs. S 85% vs. S 95% vs. S 100% ) at each HRV recovery time and (3) to compare TL of all TSs (TL-S 70% vs. TL- S 85% vs. TL-S 95% vs. TL-S 100% ) calculated with the three TL methods. Post hoc analyses were performed with the Wilcoxon signed rank test. In addition, the magnitudes of change between HRV values (Pre5 vs. Post5 vs. Post30) for each TS and between all TSs calculated with the three TL methods where expressed as the standardised mean differences [effect size (ES)], using Hopkins spreadsheet. The following threshold values for effects size statistics were adopted: 0.1 (trivial), 0.2 (small), 0.6 (moderate), 1.2 (large) and 2.0 (very large). Spearman s correlation coefficient was used to study the relationships between exercise intensity vs. normalised Post5 HRV values (i.e. relative to Pre5 HRV values), total exercise volume vs. normalised Post5 HRV values and exercise duration vs. normalised Post5 HRV values. In addition, Spearman s correlation coefficient was used to study the relationships between the three TL indexes (Foster vs. Banister vs. TL HRV ). The R 2 values were converted to R values in order to use the following adapted criteria to interpret the magnitude of the relationship, where is small, is moderate, is large, is very large and is almost perfect. Agreement between the three methods was examined by Bland and Altman plots. Since the three methods do not have the same unit, the individual TL calculated by each method was expressed as a percentage of the total TL of the four TSs in order to construct Bland and Alman plots; for example, % TL-S 70% = 100 [TL-S 70% /(TL-S 70% + TL-S 85% + TL-S 95% + TL-S 100% )]. The differences between the measurements of TL performed with the three methods (expressed as a percentage) were devised in relation to the mean values; 95% of the differences were expected to lie between the two limits of agreement that were the mean difference ± 1.96 SD of the differences, expressed as bias ± random error. In addition, heteroscedasticity was tested. Because all data have been normalised (i.e. expressed as a percentage), all bias of Bland and Altman plots are equal to zero. The data were analysed using StatSoft software (Statistica 7.1, StatSoft, Inc., USA) and the statistical significance was set at p < Results The RMSSD values were significantly different between Pre5, Post5 and Post30 within each session (p < 0.05). As shown in Table I, Post5 RMSSD values were largely lower than Pre5 values in all sessions (ES ± 90% confidence limits: 1.26 ± 0.10). Conversely, Post30 RMSSD values were moderately greater than at Post5 values (ES ± 90% CL: 0.96 ± 0.11). RMSSD values between the four sessions were not significantly different (p = 0.16) in baseline (Pre5) where as they were significantly different in Post5 (p = ) and Post30 (p = ; Table I). As presented in the last column of Table I, there was a significant difference (p = ) between the TL HRV index calculated from the three RMSSD values of each TS. A significant correlation [R = 0.70 (very large); p < ] was observed between exercise intensity and normalised Post5

5 Training load quantification with HRV 5 Table I. Mean ± 90% confidence limits of RMSSD (ms), HR exe (bpm) and RPE score for each TSs Pre5 RMSSD Post5 RMSSD Post30 RMSSD Pre5 Post5 ES Post5 Post30 ES TL HRV HR exe RPE score S 70% 78 ± ± 12* 119 ± 59 # Moderate Moderate 3.7 ± ± ± 0.8 S 85% 89 ± ± 9* 65 ± 42 #a Large Moderate 5.5 ± 0.7 a ± ± 0.7 a S 95% 73 ± ± 3* ab 36 ± 15 # * ab Large Moderate 5.0 ± 0.5 a ± ± 0.8 a S 100% 83±23 9±2* ab 76 ± 30 #c Large Large 4.0 ± 0.3 bc ± 2.1 bc 7.5 ± 0.7 a S 70% /S 85% /S 95% /S 100% : TS number 1, 2, 3 or 4. Pre5: RMSSD before TS (ms). Post5: RMSSD five minutes after TS (ms). Post30: RMSSD 30 minutes after TS (ms). RMSSD: root mean-square difference of successive normal R R intervals (ms). TL HRV : HRV coefficient for TL estimation (a.u.). HRexe: mean HR of the TS (bpm). RPE score: RPE for TS (1 10). *p < 0.05: different from Pre5; # p < 0.05: different from Post5; a p < 0.05: different from S 70% ; b p < 0.05: different from S 85% ; c p < 0.05: different from S 95%. Downloaded by [UQ Library] at 07:32 08 February 2015 HRV values (expressed as a percentage of Pre5; Figure 1). By contrast, no correlation was observed between exercise duration and normalised Post5 HRV values [R = 0.22 (small); p = 0.15] and between total training volume and Post5 HRV values [R = 0.20 (small); p = 0.19]. In addition, no significant correlations were found between Post30 HRV values and exercise intensity, exercise duration or training volume [respectively, R = 0.27 (small), R = 0.04 (small) and R = 0.08 (small) with p > 0.05 for all correlations]. TL of each session was evaluated by the three different methods. Results are presented in Figure 2 for Banister (top), Foster (middle) and TL HRV (bottom) methods. Banister s method revealed significant TL differences for all TS except between S 70% and S 95%. ES is large ( 1.25) for S 70% vs. S 85%, small ( 0.25) for S 70% vs. S 95%, large (1.49) for S 70% vs. S 100%, moderate ( 1.17) for S 85% vs. S 95%, large ( 1.84) for S 85% vs. S 100% and large (1.61) for S 95% vs. S 100%.Using Foster s method, we observed significant differences for TL of all TS except between S 85% and S 95%. ES is large ( 1.51) for S 70% vs. S 85%, large ( 1.36) for S 70% vs. S 95%, moderate ( 1.00) for S 70% vs. S 100%, small ( 0.52) for S 85% vs. S 95%, large ( 1.25) for S 85% vs. S 100% and moderate (0.93) for S 95% vs. S 100%. Finally, TL HRV provided significant differences for TL of all TS except between S 85% and S 95% and between S 70% and S 100%.ESis large ( 1.28) for S 70% vs. S 85%, large ( 1.28) for S 70% vs. S 95%, small ( 0.48) for S 70% vs. S 100%, small ( 0.42) for S 85% vs. S 95%, moderate ( 1.14) for S 85% vs. S 100% and moderate (1.06) for S 95% vs. S 100%. More generally, TL HRV and Foster values were significantly correlated [R = 0.61 (large); p = ]. In addition, correlation between TL HRV and Banister values and between Foster and Banister values was also significant [respectively, R = 0.57 (large); p = and R = 0.43 (moderate); p = 0.004]. The Bland and Altman plots presented in Figure 3 showed that x-axis values of all methods are heterogeneously distributed and the differences (i.e. y-axis values) are normally distributed. In the middle graph (i.e. Foster vs. TL HRV ), all the differences are comprised between the 95% limits of agreement (mean ± 1.96 SD). In the top and bottom graphs (i.e. Banister vs. TL HRV and Foster vs. Banister), only one point is not included between the 95% limits of agreement (less than 5%). According to heteroscedasticity results, there is a positive relationship between the mean values and difference values of TL HRV vs. Foster methods (R = 0.45; p < 0.01). Figure 1. Linear regression between exercise intensity and normalised Post5 HRV values. o: Individual values of each subject. : Mean values of each training intensity. Curving lines represent 90% of confidence interval. Discussion The aim of the present study was to examine the relationship between TL and HRV variations induced by aerobic exercise in field conditions on highly trained athletes. The results of this work can be summarised by two main findings. First, as

6 6 D. Saboul et al. Downloaded by [UQ Library] at 07:32 08 February 2015 Figure 2. TL quantification of each TS calculated with the methods of Banister, Foster and HRV (data are presented as means and 90% confidence intervals). S 70%,S 85%,S 95% and S 100% : TSs. a p < 0.05: different from S 70% ; b p < 0.05: different from S 85% ; c p < 0.05: different from S 95%. *(stars) represents a large ES of this significant difference. (square) represents a moderate ES of this significant difference. reported by previous studies in laboratory conditions, HRV decrease immediately after the exercise session (Post5) performed in field conditions is closely related to, and enables evaluation of, exercise intensity. Second, the present TL HRV index can reflect the TL of aerobic exercise performed in field conditions similarly to previous validated methods. Immediate post-exercise HRV is related to exercise intensity During all TSs, from Pre5 to Post5, we observed a significant decrease in RMSSD values. As observed Figure 3. Bland and Altman plots for assessing agreement between the three methods of TL quantification. Since the three methods do not provide the same unit of TL, the individual TL calculated by each method was expressed as a percentage of the total TL corresponding to the some of the four TSs; for example, % TL-S 70% = 100 [TL-S 70% /(TL-S 70% + TL-S 85% + TL-S 95% + TL-S 100% )].

7 by other authors, each exercise induced a disturbance of the homeostatic balance with an alteration of autonomic cardiac control (Buchheit et al., 2007). We did not find correlation between immediate postexercise HRV and exercise volume or between immediate post-exercise HRV and exercise durations. Although this study was not designed to test the influence of exercise duration on HRV modifications, previous works reported that increases exercise duration did not affect immediate or acute HRV recovery (Stanley et al., 2013). Thus with respect to our study conditions including TSs lasting less than 70 minutes, we can assume that exercise duration impacted less Post5 HRV than intensity. Conversely, whatever exercise type (i.e. continuous or intermittent), exercise intensity was the main factor of the HRV decrease observed between baseline and immediate post-exercise values (Figure 1). As reported by several laboratory studies, Post5 HRV value may reflect the blood lactate concentration and thus exercise intensity (Kaikkonen et al., 2010, 2012; S. Seiler et al., 2007). Unfortunately, blood lactate was not measured in the present study; however, it can be assumed that HRV measurement performed immediately after exercise could be a relevant and objective tool to assess training intensity in field conditions. Post-training HRV increase Between Post5 and Post30 (acute recovery phase), the significant RMSSD increase observed in all four TSs may be explained by a reduction in cardiac sympathetic activity with a simultaneous increase in vagal nerve activation (Buchheit et al., 2007; Kaikkonen et al., 2010). It seems that RMSSD reactivation reflects the individual subject training response in relation to the specificities and contents of the exercise performed (Kaikkonen et al., 2012; S. Seiler et al., 2007). Indeed, Post30 in S 100% did not present significant difference with Post30 in S 85% while it was significantly lower in Post5. In addition, Post30 in S 100% was significantly greater than Post30 in S 95%. These results suggest that the reactivation of vagal modulation is a complex process and does not only depend on exercise intensity. Indeed, despite that Post5 was strongly linked to exercise intensity it is important to note that Post30 HRV is not correlated to either intensity or duration and training volume (p > 0.05 for all). These findings are in accordance with recent study who reported significant differences between Post30 HRV values of two TSs performed at the same intensity (85% of MAS) but with different methods (continuous vs. intermittent; Kaikkonen et al., 2008). Finally, the postexercise RMSSD increase (i.e. from Post5 to Training load quantification with HRV 7 Post30) can be delayed depending on several parameters, such as intensity, muscular and cardiovascular demands of exercise, as well as the fatigue status or even the training level of the subject (Aubert et al., 2003; Kaikkonen et al., 2012; S. Seiler et al., 2007). TL HRV calculation Several parameters have to be taken into account when assessing TL (Borresen & Lambert, 2009). As described above, the different information provided by the RMSSD measures appears to be closely related to the nature of the exercise. Therefore, we propose a new TL HRV formula (equation 2) that includes pre- and post-exercise HRV data. Interestingly, none of the three HRV measurements of our TL HRV marker taken individually is correlated with either Banister or Foster TL. This suggests that the inclusion of the three measures is important for the validity of TL HRV. In addition, the ratio between RMSSD decrease and post-exercise RMSSD increase provides normalised values with respect to inter-individual differences. The main interest of our method includes the three-point HRV measures which provide a wide range of information. First, Pre5 HRV measurement performed at rest before exercise represents baseline states that reflect current or short-term fitness/fatigue status of athlete and was generally use during athlete monitoring (i.e. day-to-day HRV follow-up; Bosquet, Merkari, Arvisais, & Aubert, 2008; Kiviniemi et al., 2010; Plews, Laursen, Kilding, & Buchheit, 2013). Second, Post5 HRV measurement which is strongly linked to exercise intensity (Kaikkonen et al., 2010; S. Seiler et al., 2007). Finally, Post30 HRV measurement potentially reflects the acute athlete s recovery ability of ANS (S. Seiler et al., 2007). TL HRV index vs. other TL methods As shown in Figure 2, the new TL HRV provided TL repartition between the four TS similar and the two other methods. This visual observation is corroborated with the significant correlations obtained between TL HRV indices and the two other methods. However, Bland and Altman plots (Figure 3) showed that the three methods have assessed different training impacts for each TS. For example, Foster underestimated S 70% compared to TL HRV and Banister methods whereas Banister underestimated S 100% compared to TL HRV and Foster methods. These findings can be explained by the characteristics of each method. Indeed, TRIMP may underestimate the energetic and sympathetic stress of short high-

8 8 D. Saboul et al. intensity bouts (especially interval training; Borresen & Lambert, 2009; K. S. Seiler & Kjerland, 2006) as demonstrated by the similar TL given for S 70% and S 95%. Indeed, these two sessions expressed the same TRIMP since they have the same duration and HR means (Borresen & Lambert, 2009; Lucía, Pardo, Durántez, Hoyos, & Chicharro, 1998) whereas it is obvious that S 95% had a higher physiological impact than S 70% as reported by TL HRV and RPE methods. TRIMP method is not valid for high-intensity interval training (HIT) with short repetitions and high anaerobic contribution because of the shifted kinetic of HR make totally ineffective (Buchheit & Laursen, 2013). On the contrary, during such HIT, the acidosis resulting from anaerobic metabolism solicitation induces metaboreflex stimulation that can be quantified by HRV. Using RPE scale, athletes may evaluate only the difficulty of the body of the session while the Foster method takes into account the total duration of the TS (including warm-up and cool down at lower intensities). This may lead to an overestimation of high-intensity TSs assessed by Foster (Borresen & Lambert, 2008, 2009). In addition, because the Foster method is subjective, it is also possible that athletes may change their choice according to the coach s expectations (Borresen & Lambert, 2009; Foster et al., 2001). Conversely, TL HRV was built to assess TL with objective parameters like current fitness/fatigue status (Pre5; Plews, Laursen, Kilding, et al., 2013), exercise intensity (Post5; Kaikkonen et al., 2010) and acute athlete s recovery ability (Post30; S. Seiler et al., 2007). The fact that TL HRV does not discriminate S 70% vs. S 100% and S 85% vs. S 95% suggested that, despite different content, the results of the homeostatic perturbations induced by these TSs may be similar. Indeed, TL is modulated by both intensity and duration of exercise and despite different content (high intensity/short time or low intensity/long time), TL of these sessions may be identical. Limitations, perspectives and practical applications We should acknowledge that TL HRV method is more complex than the Foster method for daily use. Consequently, this study represents the first step of a more global work aiming to demonstrate that HRV could be used to quantify TL on the field. Obviously, the future studies should rule out the limitations that emerge from the present study more particularly the limited sensitivity of intensity measured by Post5 HRV (see Figure 1). In addition, the future experimentation should aim to test the HRV measurement in standing position during an active walking phase (Boullosa, Barros, Del Rosso, Nakamura, & Leicht, 2014) in the context of TL HRV. Then, other studies should shorten the protocol to obtain a more simple method for daily monitoring. In this context, the linearity of the post- HRV reactivation observed during the first hour may justify a post-exercise recording reduced to 10 minutes (Casties et al., 2006). Lastly, the TL HRV will have to be validated on a larger range of training modalities [e.g. resistance training that may also be assessed by post-hrv measurements where nonlinear indexes have been shown to be relevant (Caruso et al., in press)]. From a practical point of view, this new TL HRV marker may also be used by elite athletes during routine TSs (performed at the end of each month or training cycle) to objectively and simply measure their current fitness/fatigue status. Indeed, considering that the parasympathetic reactivation is closely related to the current athlete level and/or fatigue status (Buchheit et al., 2007; S. Seiler et al., 2007), we can assume that TL HRV measurement performed regularly with exactly the same TS may provide information on the current fitness level of an athlete. In this sense, future investigations will be conducted to verify this relation. Conclusion The main purpose of the present study was to define a new method for quantifying TL by using pre- and post-exercise RMSSD measurements in field conditions. TL HRV may provide objective and rational information about the current intensity of the exercise but also on the TL in line with the two main validated methods (i.e. Foster and Banister). It is also the first study to test HRV tools (i.e. with formula that provide numeric and rational data) in relation to TL. Acknowledgement The authors would like to thank the SUUNTO company for its material support (Hervé Riffault). 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