Eur J Appl Physiol DOI 10.1007/s00421-009-1317-x
ORIGINAL ARTICLE
Monitoring endurance running performance using cardiac parasympathetic function Martin Buchheit • A. Chivot • J. Parouty • D. Mercier • H. Al Haddad • P. B. Laursen S. Ahmaidi
•
Accepted: 1 December 2009 Ó Springer-Verlag 2009
M. Buchheit (&) A. Chivot J. Parouty H. Al Haddad S. Ahmaidi Research Laboratory, EA 3300 ‘‘Laboratory of Exercise Physiology and Rehabilitation’’, Faculty of Sport Sciences, University of Picardie, Jules Verne, 80025 Amiens, France e-mail:
[email protected]
intervention. Correlations (r [ 0.60, P \ 0.01) were observed between changes in vagal-related indices and changes in MAS and 10 km running time. Exercise HR decreased progressively during the training period (P \ 0.01). In the 11 subjects who lowered their 10 km running time [0.5% (responders), resting vagal-related indices showed a progressively increasing trend (time effect P = 0.03) and qualitative indications of possibly and likely higher values during week 7 [?7% (90% CI -3.7;17.0)] and week 9 [?10% (90% CI -1.5;23)] compared with pre-training values, respectively. Post-exercise HRV showed similar changes, despite less pronounced between-group differences. HRR showed a relatively early possible decrease at week 3 [-20% (90% CI -42;10)], with only slight reductions near the end of the program. The results illustrate the potential of resting, exercise and post-exercise HR measurements for both assessing and predicting the impact of aerobic training on endurance running performance.
M. Buchheit A. Chivot J. Parouty H. Al Haddad Sport Development and Analysis, Myorobie Association, Montvalezan, France
Keywords Heart rate variability Heart rate recovery Predicting performance Field tests
Abstract The aims of the present study were to (1) assess relationships between running performance and parasympathetic function both at rest and following exercise, and (2) examine changes in heart rate (HR)-derived indices throughout an 8-week period training program in runners. In 14 moderately trained runners (36 ± 7 years), resting vagal-related HR variability (HRV) indices were measured daily, while exercise HR and post-exercise HR recovery (HRR) and HRV indices were measured fortnightly. Maximal aerobic speed (MAS) and 10 km running performance were assessed before and after the training Communicated by Niels Secher.
M. Buchheit Performance Enhancement and Talent Identification Section, Aspire, Academy for Sports Excellence, Doha, Qatar D. Mercier Cyclide, Montreal, QC, Canada P. B. Laursen New Zealand Academy of Sport North Island, Auckland, New Zealand P. B. Laursen School of Sport and Recreation, Auckland University of Technology, Auckland, New Zealand
Introduction Successful endurance training programs require an appropriate training stimulus relative to the physical status of the individual, coupled with adequate recovery periods. Failure to maintain this equilibrium can rapidly lead to undesirable consequences, including either a stagnation or decrease in performance capacity, or development of chronic fatigue and overtraining (Kuipers and Keizer 1988). Exercise prescriptions based on the general recommendations have proved effective at improving cardiorespiratory levels in
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healthy sedentary or active individuals (American College of Sports Medicine 1998). However, the adaptation response to similar relative endurance training loads vary (Bouchard and Rankinen 2001; Hautala et al. 2003, 2009; Rankinen et al. 2003; Vollaard et al. 2009). For example, large individual differences are observed in the change of peak oxygen _ 2 peak) after an individualized endurance trainuptake (VO ing program, with values ranging from ?2 to ?19% (Hautala et al. 2003). Thus, the ability to identify a priori an individual’s level of ‘trainability’ (Hautala et al. 2003), or to define and adjust training according to an individual’s rate of adaptation, would enhance the optimization of endurance training programs (Kiviniemi et al. 2007). Borresen and Lambert (2008) stated that a single measure that could assess the adaptation response to an endurance training stimulus is yet to be identified. Studies that have consistently tracked autonomic nervous system (ANS) function during training show promising results (Borresen and Lambert 2008; Hautala et al. 2009; Lamberts et al. 2009a, c). The ANS is likely to play an important role in the training response (Hautala et al. 2003, 2004, 2009) and may provide information regarding the functional adaptations occurring to a given training stimulus (Pichot et al. 2002; Iwasaki et al. 2003; Buchheit et al. 2008; Lamberts et al. 2009a, c). Cardiac parasympathetic activity can be measured using either resting heart rate (HR) variability (HRV), post-exercise HR recovery (HRR) or postexercise HRV (Buchheit et al. 2007; Borresen and Lambert 2008). Resting vagal-related HRV indices that are related _ 2 peak to cardiorespiratory fitness [as assessed by VO (Aubert et al. 2003; Buchheit and Gindre 2006)] may be _ 2 peak coupled to the training-induced changes in VO among both sedentary subjects and athletes (Hedelin et al. 2001; Hautala et al. 2003, 2009). Repeated measures of resting diurnal (Hautala et al. 2003; Gamelin et al. 2007) or nocturnal (Pichot et al. 2002; Hautala et al. 2003) vagalrelated HRV indices, as well as exercise HR (ScharhagRosenberger et al. 2009), post-exercise HRR (Sugawara et al. 2001; Buchheit et al. 2008; Lamberts et al. 2009a, b) and HRV (Yamamoto et al. 2001; Buchheit et al. 2008) demonstrate that the cardiac ANS responds and promotes adaption to changes in training loads (Pichot et al. 2002; Iwasaki et al. 2003; Manzi et al. 2009) and fitness (Scharhag-Rosenberger et al. 2009). Thus, these vagal-related measurements can predict exercise performance (Buchheit et al. 2008; Lamberts et al. 2009a, b, c). A marker to assess physiological adaptation to training, would, ideally be easy to administer so that frequent monitoring is possible (Borresen and Lambert 2008). The longterm application of daily resting or nocturnal HRV measurements can be fastidious. An alternate solution might be the collection of post-exercise HRR and HRV recordings because these can be implemented during the warm-up of a
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training session (Buchheit et al. 2008; Lamberts et al. 2009c). However, whether daily, weekly daily average or fortnight measurements can provide equivalent levels of information is not known. Furthermore, whether the time course for change in these variables is comparable during a training intervention has yet to be examined (Yamamoto et al. 2001). Finally, although studies have focused on relationships _ 2 peak (Aubert et al. 2003; between HRV indices and VO Buchheit and Gindre 2006), examining field-based performances (Gratze et al. 2005, 2008; Buchheit et al. 2008; Lamberts et al. 2009a, b; Manzi et al. 2009) are of more interest (Bosquet et al. 2002). The ability of these indices to predict endurance performance in the field and monitor positive or negative adaptations could help in the design of individualized and HRR- or HRV-guided training programs in athletes (Kiviniemi et al. 2007). The aims of this study were to (1) assess relationships between daily resting HRV, fortnightly exercise HR, postexercise HRR and HRV indices and running performance, as assessed by maximal aerobic speed (MAS) and 10 km running time, and (2) examine the respective change in these indices during an 8-week period training program in moderately-trained runners. Based on the previous research (Buchheit et al. 2006, 2008), we hypothesized that the relationship between vagal-related indices and performance would be stronger for HRV as compared to HRR. Further, we expected that exercise HR would decrease progressively during the training period as a consequence of an increase in cardiorespiratory fitness (Borresen and Lambert 2008; Scharhag-Rosenberger et al. 2009). We also hypothesized that both HRR and HRV indices would show changes indicative of greater parasympathetic activity throughout 8-week training period, but that the adaptation would occur earlier for post-exercise HRV (Yamamoto et al. 2001).
Methods Subject recruitment Forty-nine healthy males responded to a newspaper advertisement for participating in this study. Participants needed to be medically cleared to exercise, to have trained at least two times per week during the previous year and to have participated in at least one 10 km running race during the previous year. Thirty-nine runners entered the study (mean ± SD): 37 ± 7 years, 1.78 ± 0.1 cm, 77.0 ± 8.3 kg and body fat 14.2 ± 4.6%. The study that was approved by the local research ethics committee conformed to the recommendations of the Declaration of Helsinki and participants gave voluntary written informed consent to participate in the experiment.
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Experimental protocol Participants were first familiarized with the test procedures. Tests were performed on 3 days of the week preceding the commencement of training (W0), and the week immediately following the 8-week training period (W9). Tests included graded maximal running to determine MAS (day 1, at 7 p.m.), lower limb power assessment, a 15-s all-out shuttle run (day 2) and a 10 km run (day 3, at 3 p.m.). Subjects were also told not to perform intense exercise on the prior day, and to consume their usual last meal at least 3 h before the scheduled test time. On day 2, body mass, height and skinfold thickness from four skinfolds (subscapular, biceps brachii, triceps brachii and iliac crest) were measured. A S810 HR monitor (Polar Electro, Kempele, Finland) and an electrode transmitter belt (T61, Polar Electro) were provided. Subjects were asked to monitor their HR during all testing and training sessions (5-s average), to collect beat-by-beat data for 8 min every morning (in bed, immediately after awakening) and during and after a standardized sub-maximal warm-up [50 -50 (Buchheit et al. 2009)] every second week under supervision.
The participants indicated their rating of perceived exertion (RPE 0–10 Borg’s scale) immediately upon completion of each test. Training intervention Subjects performed two supervised training sessions per week, in addition to 1–2 self-regulated moderate-to lowintensity sessions during the weekend, for 8 weeks. Training programs followed a periodic plan that incorporated progression and used a short tapering period to maximize final performance (PROFILDM training concept, Cyclide, QC, Canada, Table 1).
Procedures Lower limb explosive power This was assessed using a vertical countermovement jump (CMJ; cm) and a hopping test [Hop; kN m-1 (Dalleau et al.
Table 1 Outline of the 8-week training program Weeks
Intervalsa
Continuous runs Low intensity
Moderate intensity
1
450 at 70 ± 4%
2 9 400 at 73 ± 4%
2
1 h at 73 ± 4%
400 at 76 ± 4%
Race pace runs
Short
Long
4(4 9 2000 at 120 ± 2%)
6 9 20 3000 at 85 ± 2%
r = 10 3000
r = 10 4500
R = 50 3
1 h at 74 ± 4%
400 at 77 ± 4%
2(6 9 30’’ at 116 ± 2%) 0
00
R = 1 45 r=5 4
450 at 74 ± 4%
200 at 82 ± 3%
450 at 73 ± 4% 300 at 73 ± 4%
r = 20 1500
0
2(7 9 3000 at 112 ± 2%)
4 9 50 at 90 ± 2%
r = 5000
r = 20 3000
R=5 5
4 9 40 at 92 ± 2%
0
2(4 9 4500 at 107 ± 2%) r = 10
3 9 70 3000 at 85 ± 2% r = 30
R = 50 6
450 at 74 ± 4%
450 at 78 ± 4%
300 at 83 ± 3%
3 9 100 at 87 ± 2% r = 40
0
7
45 at 75 ± 4%
0
3 9 150 at 87 ± 2%
25 at 85 ± 2%
0
r = 50
45 at 75 ± 4% 8
0
30 at 73 ± 4% 300 at 73 ± 4%
00
7 9 30 at 111 ± 2%
3 9 50 at 90 ± 2%
r = 5000
r = 50
R = 50 Exercise intensity for all training sessions are expressed as a percentage of maximal aerobic speed Additional (non-reported) HR-based recommendations were provided to each participants for long duration runs (i.e., targeted HR ranges) All sessions started with a 10-min warm-up consisting of slow jogging, short accelerations and active stretching, which were followed by a 10 min cool down of slow jogging a
All interval sessions were followed by a recovery day usually consisting of no exercise or 20–30 min of walking or slow jogging
r and R represent recovery time between sets and series, respectively
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2004)] with flight time measured by Optojump (Optojump, Microgate, Bolzano, Italy) to calculate jump height. Each trial was validated by inspection to ensure each landing was without significant leg flexion and participants were instructed to keep their hands on their hips during both CMJ and Hop with the depth of the CMJ self selected. For the Hop test, runners were asked to perform 7 plyometric calf jumps in a row (i.e., bouncing 7 times) at a selfselected frequency between 1.5 and 2 Hz (Dalleau et al. 2004). The last six jumps were retained and jump height was averaged. During post-testing, if hopping frequency was different from that of the pre-test by more than 0.05 Hz, the runners were asked to perform the test again after 45 s. Athletes were encouraged to perform maximally during both jumping tests. Each test was performed three times, separated by 45 s of passive recovery, and the best performance was recorded. Anaerobic power Following a standardized warm-up, subjects sprinted as fast as possible for 15 s on a 20-m shuttle track and the distance covered was taken as an index of the subject’s anaerobic power. MAS _ 2 peak, since MAS is Focus was on MAS rather on VO thought to be a superior predictor of endurance performance (Paavolainen et al. 1999; Bosquet et al. 2002). _ 2 peak, there is a Furthermore, when compared with VO stronger link between changes in HRV indices and both MAS (Kiviniemi et al. 2007) and endurance performance (Hautala et al. 2004). The field test (PROFILDM 3-3 test) was adapted from Leger and Boucher (1980), where subjects ran for 3 min at 8 km h-1 for the first stage, with speed increasing by 1.5 km h-1 for every 3 min thereafter; each stage separated by 3 min of passive recovery. Participants ran at a pace governed by a pre-recorded beep, with auditory cues coinciding with cones placed 50-m intervals along a 400-m athletics track. Termination of the test was determined by the subject, or when participants failed to reach the required cone in the necessary time on three consecutive occasions. Runners were encouraged and the velocity of the last stage completed was retained as MAS. If the last stage was not completed, the MAS was calculated as V ? (t/180 9 1.5), where V was the last completed speed (km h-1) and t the time (s) of the uncompleted step. The maximal HR was considered to be the participant’s HRpeak. In 17 athletes of a similar age and fitness level, this test has a typical error of measurement of 0.23 km h-1, or a CV of 1.3% (unpublished data).
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10 km Participants performed pre- and post-10 km trials on the same outdoor course (three 3.330 km laps). Participants were asked to cover the 10 km distance as fast as possible, with the competition of approximately 50 runners. The temperature was 3°C higher during the post-testing day. Time was measured with a stop watch and rounded at the nearest second. The reliability of the 10 km run in five athletes of a similar age and fitness level has a typical error of measurement of 0.28 s, or 1.0% expressed as a CV (unpublished data). Sub-maximal running As a part of the warm-up for the first supervised sessions of the week, runners performed a 5-min sub-maximal exercise bout [50 –50 , 60% MAS (Buchheit et al. 2009)] followed by 5 min of passive (standing) recovery, every 2 weeks. Running pace was governed by a prerecorded beep that sounded at appropriate intervals to allow participants to adjust their running speed as they passed through specific parts of the field. At the end of exercise, participants stopped their effort within 3 s and stood standing for 5 min. Runners were also not allowed to drink or talk during this period. The standing posture was chosen to avoid any eventual ‘saturation’ of HRV indices (Kiviniemi et al. 2004). Short-term resting and post-exercise HRV R–R series recorded by the S810 were extracted weekly on PC (Polar Precision Performance SW 5.20, Polar Electro). Ectopic beats were identified and replaced with interpolated adjacent R–R interval values. HRV indices were calculated during the last 5 min of the resting 8-min period in the morning (rest) and during the last 3 min of the 5-min recovery period following the 5-min sub-maximal exercise test (post-ex) (Buchheit et al. 2009). We chose not to control for respiratory rate because the measurements were implemented in the field. Similarly, we did not control breathing rate during morning recordings. Respiratory rate was, however, in the high frequency range ([0.15 Hz) and vagal-related HRV indices during spontaneous and metronome-guided breathing differ little (Bloomfield et al. 2001). Only vagal-related HRV indices were calculated, i.e., the mean HR (HRrest and HRpost-ex) and the natural logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R–R intervals (Ln rMSSDrest and Ln rMSSDpost-ex) (Buchheit et al. 2009). Because of the uncertainty in the physiological meaning of low frequency (LF) oscillations during such short recording periods (Task Force 1996), we did not
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examine the LF power spectral density. Moreover, given the correlations between the power spectral density in the high frequencies and rMSSD values (Task Force 1996), data were restricted to time domain indices. All calculations were computed with the accompanying Polar software (Nunan et al. 2009). Short-term resting time domain HRV indices demonstrated coefficients of variations ranging from 5 to 10% (Sandercock et al. 2005b). Reproducibility data on post-exercise HRV measurements are lacking in adults, but those in adolescents have shown good reliability, with an SEM of 14.2 ms for (non-transformed) post-exercise rMSSD (Buchheit et al. 2008). HR recovery Post-exercise HR was assessed during the 5 min following the 5-min sub-maximal exercise tests (Buchheit et al. 2009). Mean HR during the last 30 s of exercise was computed and termed HRex. HR recovery was calculated via the time constant of the HR decay by a first-order exponential decay curve (HRRs, s). HRRs has an SEM of 12.6 s in adults (Bosquet et al. 2007b). Weekly training load Calculations of the training impulse (TRIMP) (Banister and Hamilton 1985) included the duration of any training bout (TD, and not the total training session) multiplied by the average HR achieved during this exercise. Exercise HR were expressed as a fraction of the HR reserve (%HRRES), that was derived from HRrest and HRpeak. Training impulse was determined as: TD %HRRES 0:64 eð1:92%HRRES Þ :
ð1Þ
Statistical analyses Data in the text and figures are presented as mean ± SD. Each variable was examined with the Kolmogorov–Smirnov normality test. Because HRV indices were skewed, logtransformed data were assumed to be normality distributed. Pearson’s product–moment correlation analysis was used to compare association between running performance and HRderived indices. All correlations were adjusted by age using partial correlations due to effects of age on baseline HRV and the training-induced HRV response (Sandercock et al. 2005a). In addition to measures of statistical significance, the following criteria were adopted to interpret the magnitude of the correlation (r) between test 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% confidence intervals overlapped, small positive and
negative values for the magnitude were deemed unclear; otherwise that magnitude was deemed to be the observed magnitude (Hopkins et al. 2009). As the focus of the present study was also to investigate individual responses to training, subjects were divided into groups, post hoc (Vollaard et al. 2009), based on their change in 10 km run performance after training; subjects who improved their 10 km performance time by more than 0.5% were included in the responders group, while the others were included in the non-responders group. A 0.5% reduction in running time is reported to be representative of a meaningful difference in cross-country and road race performance in male runners (Hopkins and Hewson 2001). This value also corresponds to half of the between-athlete CV reported for 10 km races performed by this population (CV of 1.0%). A cut-off point of half of the CV in performance has been suggested as being important for detecting the smallest worthwhile performance enhancement in athletes, irrespective of the level of group homogeneity (Hopkins et al. 1999). Changes in running performance and HRV indices as a result of the training were first analyzed using a two-factor repeated measure ANOVA, with one within factor (time; week or pre-training versus post-training) and one between factor (group; responders versus non-responders). Each of these analyses was carried out with Minitab 14.1 Software (Minitab Inc., Paris, France) and the level of significance was set at P B 0.05. In addition, data were assessed for significance using an approach based on the magnitudes of change (Hopkins et al. 2009). The magnitude of change after training, or difference between groups, were first expressed as standardized mean differences (Cohen effect sizes, ES), which were calculated using the pooled pretraining standard deviations (Cohen 1988) and adjusted for age (Hopkins spreadsheet). Threshold values for Cohen ES statistics were [0.2 (small), 0.5 (moderate) and [0.8 (large). Confidence intervals (90%) for the (true) mean changes or between-group differences in the training response were estimated (Hopkins et al. 2009). For withinand between-group comparisons, the chances that the (true) changes in performance or HR-derived indices for responders were greater (i.e., greater than the smallest practically important effect, or the smallest worthwhile change, SWC [0.2 multiplied by the between-subject standard deviation, based on Cohen’s effect size principle (Cohen 1988)], unclear or smaller than these for the nonresponders group were calculated. Quantitative chances of higher or smaller training effects were assessed qualitatively as follows: \1%, almost certainly not; 1–5%, very unlikely; 5–25%, unlikely; 25–75%, possible; 75–95%, likely; 95–99, very likely; [99%, almost certain. If the chance of having beneficial/better or detrimental/poorer performances were both [5%, the true difference was assessed as unclear (Hopkins et al. 2009).
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Results Participants 10 out of the 39 subjects did not complete the study due to work or family time constraints (n = 3), injury (n = 2) or lack of training program participation (i.e., \85% of all training sessions, n = 5), and were not included in the analysis. However, due to the imposed 95% cutoffs for acceptable R–R intervals in every recording and completed recording days, complete data sets were only available from 14 subjects. The anthropometric attributes of these 14 runners (Table 2) were not significantly different from those who either dropped out or were omitted from analysis. Training loads Training impulses computed for both responders and nonresponders are illustrated in Fig. 4. TRIMPs during weeks 3 and 6 were higher than during weeks 1, 2, 4, 8 and 9 (all P \ 0.05), but neither a group (P = 0.63) nor an interaction effect was noted (P = 0.09). These results were confirmed by the qualitative analysis, with training loads being possibly and likely higher than week 1 during weeks 3, 6 and 7 (see Fig. 4 for details). During week 3, nonresponders presented very likely higher TRIMPs than responders, with chances that the true values were higher/ trivial/lower for non-responders being 98/1/0. Anthropometric measurements and athletic performances There was neither a training nor group effect for mass, percentage body fat, CMJ, Hop and 15-s shuttle run performance (all P [ 0.15, Table 2). A training effect was, however, observed for MAS (P \ 0.001), but neither a significant group nor interaction effect was noted (all P [ 0.50). For 10 km run time, neither a training (P = 0.10) nor group (P = 0.35) effect was observed; an interaction effect was however close to significance (P = 0.07). However qualitative analyses enabled a more precise tracking of changes within and between the two groups (Table 2; Fig. 4). Non-responders tended to have better pre-training MAS and endurance performance time when compared with responders (differences rated as moderate). CMJ, 15-s shuttle sprint distance and 10 km performance time showed small improvements, whereas increases for MAS were moderate. When considering responders and non-responders separately, as expected, responders showed very likely higher improvements in 10 km run time as compared to nonresponders. CMJ and Hop also showed likely greater improvements in responders when compared with nonresponders. Absolute changes in MAS were neither related to
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age [r = 0.07 (-0.40;0.51), P = 0.80] nor percentage body fat [0.26 (-0.23;-0.64), P = 0.38], but a large correlation with pre-training MAS values was noted [r = -0.59 (-0.83;-0.18), P = 0.025]. Similarly, absolute changes in 10 km run times were neither related to age [r = -0.38 (-0.71;-0.10), P = 0.19] nor percentage body fat [r = -0.28 (-0.65;-0.21), P = 0.33]. A very large correlation was however observed between 10 km performance improvements and pre-training 10 km run times [r = -0.89 (-0.96;-0.73), P \ 0.001]. Anthropometric attributes and HR-derived indices Baseline Ln rMSSDrest tended to be inversely related to age [r = -0.50 (-0.78;-0.05), P = 0.07, correlation rated as large] but not percentage body fat [-0.45 (-0.75;0.01), P = 0.10, correlation rated as unclear]. Change in Ln rMSSDrest was largely related to age [r = -0.67 (-0.86;-0.30), P \ 0.01] but not percentage body fat [0.22 (-0.27;0.66), P = 0.46]. Conversely, there was no association between baseline Ln rMSSDpost-ex and either age [r = -0.13 (-0.56;0.35), P = 0.65] or percentage body fat [-0.14 (-0.56;0.34), P = 0.63]. Neither there was a relationship between baseline HRRs and age [r = 0.01 (-0.45;0.47), P = 0.97] or percentage body fat [0.23 (-0.26;0.62), P = 0.43]. Changes in Ln rMSSDpost-ex and HRRs were not related to age or percentage body fat (all P [ 0.52, with correlations rated as unclear). Resting HRV measures and running performance Relationships between resting HRV indices at the start of the training program and running performance, adjusted by age, are presented in Table 3. Most basal resting HRV indices were well correlated (i.e., large to very large correlations) with both MAS and 10 km run time before training, whereas they were only correlated with changes in 10 km run time, but not post-training times. Figure 1 illustrates the large and very large correlations between Ln rMSSDrest and MAS (upper panel), and Ln rMSSDrest and 10 km running time (lower panel), with data from pre- and post-training assessments pooled (n = 28). As shown in Fig. 3, moderate and very large relationships were observed between relative changes in Ln rMSSDrest (i.e., D week 9-week 1, expressed as a percentage of baseline values) and changes in MAS and 10 km run performance time, respectively. HRR and post-exercise HRV measures and running performance Except for HRRs that was negatively related to 10 km run time before the training period, none of the post-exercise
2/98/0 Unclear
Percentage chances of better/trivial/ poorer for resp. versus non-resp.
Outcome
Unclear
8/91/1
-2.1 (-8.9;4.3)
Likely
91/8/1
7.7 (1.8;13.3)
0.39 (small)
-0.08 (trivial)
28.8 ± 3.1
29.2 ± 3.7
0.41 (small)
33.1 ± 4.5
31.1 ± 5.1
0.32 (small)
32.3 ± 4.5
30.7 ± 4.8
CMJ (cm)
Likely
86/10/4
9.5 (2.3;19.9)
-2.9 (large)
-0.57 (moderate)
23.4 ± 1.7
25.4 ± 1.9
0.14 (trivial)
23.2 ± 4.8
22.5 ± 2.7
0.07 (trivial)
23.3 ± 4.3
23.0 ± 2.7
Hop (kN m-1)
Unclear
29/18/54
-2.3 (-16.3;10.1)
0.90 (large)
0.50 (moderate)
74.8 ± 2.8
72.1 ± 7.2
0.22 (small)
74.2 ± 3.8
73.0 ± 5.0
0.37 (small)
74.4 ± 3.5
72.8 ± 5.3
15-sec Shuttle run (m)
Unclear
19/52/29
-0.6 (-6.7;5.2)
-0.90 (large)
0.82 (large)
17.9 ± 0.8
16.5 ± 0.8
0.78 (moderate)
17.1 ± 1.7
15.6 ± 2.1
0.86 (large)
17.3 ± 1.6*
15.8 ± 1.9
MAS (km h-1)
Very likely
97/3/0
-8.6 (-12.7;4.6)
0.83 (large)
0.05 (trivial)
45:27 ± 2:57
45:06 ± 3:19
-0.40 (small)
48:25 ± 7:23a
52:28 ± 10:20
-0.37 (small)
47:47 ± 6:42
50:53 ± 9:41
10 km (min:s)
Significant training effect revealed by the two-factor ANOVA (P \ 0.05)
ES effect size (qualitative outcome, see ‘‘Methods’’ for thresholds used)
a
Magnitudes of the between-group difference in response to training are expressed as percentage chances (90% CI) and qualitative outcome for responders to have better/trivial/poorer responses than the non-responders
Values are mean ± SD for body mass and percentage body fat, counter movement jump (CMJ), leg stiffness (Hop), distance covered during the 15-s shuttle sprint, maximal aerobic speed (MAS) and 10 km running performance in participants having improved their 10 km performance (responders) or not (non-responders)
-0.8 (-2.0;0.4)
-0.47 (small)
-0.01 (trivial)
-0.03 (trivial)
-0.47 (small)
15.6 ± 2.9
15.7 ± 3.0
-0.07 (trivial)
13.4 ± 3.6
79.7 ± 5.9
Percentage difference (90% CI) for resp. versus non-resp.
Magnitude of between-groups differences in responses to training
ES (rating) for resp. versus non-resp.
Magnitude of between-group differences at baseline
ES (rating)
Post
80.0 ± 6.4
-0.10 (trivial)
ES (rating)
Non-responders (n = 3) Pre
75.9 ± 8.8 74.9 ± 7.4
Pre
Post
13.9 ± 3.9
-0.11 (trivial)
-0.10 (trivial)
Responders (n = 11)
ES (rating)
13.8 ± 3.4
76.8 ± 8.2 75.9 ± 7.2
Pre
14.3 ± 3.7
Body fat (%)
Post
All pooled (n = 14)
Body mass (kg)
Table 2 Subjects characteristics and performances before and after the 8-week training program
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Eur J Appl Physiol Table 3 Relationships between baseline HR-derived indices measured at rest and after sub-maximal exercise and running performance Absolute D
Pre-training MAS
10 km
MAS
10 km
-0.23 (-0.62;0.26)
-0.72 (-0.89;-0.39)**,b
Resta HRrest
-0.77 (-0.91;-0.48)**,b 0.73 (0.41;0.89)**,b
Ln rMSSDrest
0.76 (0.46;0.90)**,b -0.77 (-0.91;-0.48)**,b
0.23 (-0.26;0.62)
0.76 (0.46;0.90)**,b
Post-exercise HRex
-0.08 (-0.52;0.39)
0.15 (-0.33;0.57)
0.36 (-0.12;0.70)
-0.42 (-0.74;0.05)
HRRs
-0.29 (-0.66;0.19)
0.54 (0.11;0.80)*,c
0.39 (-0.08;0.22)
-0.53 (-0.80;-0.09)*,c
HRpost-ex Ln rMSSDpost-ex
0.05 (-0.42;0.50) 0.06 (-0.41;0.51)
0.12 (-0.36;0.55) -0.16 (-0.58;0.32)
,c
0.66 (0.29;0.86)* -0.65 (-0.86;-0.29)*,c
-0.72 (-0.89;-0.39)**,b 0.82 (0.58;0.93)***,b
Pearson’s correlations are performed for baseline (pre-training) HR-derived parameters versus absolute changes (D) in maximal aerobic speed (MAS) and 10 km running performance (10 km) Heart rate (HRrest) and logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R–R intervals (Ln rMSSDrest) calculated at rest (weekly averaged values of daily recordings during the first week of training) HR during the 5-min sub-maximal exercise bout (HRex), time constant of full 5-min HR recovery (HRRs), and post-exercise HR (HRpost-ex) and Ln rMSSD (Ln rMSSDpost-ex) as calculated during the last 3 min of the 5-min recovery period after the sub-maximal exercise performed during the first week of training (n = 14) * P \ 0.05 ** P \ 0.01 *** P \ 0.001 a
Note that all correlations for resting values are adjusted for age
b
Clear, very large correlation
c
Clear, large correlation
HR-derived indices measured prior to training were correlated with either MAS or 10 km performance (Table 3). However, most of the indices at baseline were well correlated with both relative and absolute changes in either MAS or 10 km time after training. Figure 2 shows the large correlations between basal Ln rMSSDpost-ex and relative changes in MAS (upper panel) and 10 km running time (lower panel). Relative changes in post-exercise vagal-related indices were also correlated with changes in running performance (all r [ 0.59 and P \ 0.01, with large correlations). HRV and HRR indices during the 9-week period Figure 4 illustrates the time course response of Ln rMSSDrest, Ln rMSSDpost-ex, HRRs and HRex for the responder and non-responder groups during the 8-week period. Table 4 summarizes pre- and post-training values for subjects, both pooled or within their respective groups (with ES and inferences adjusted by age). Non-responders tended to have worse pre-training parasympathetic function indices compared with responders (e.g., differences rated as moderate for Ln rMSSDrest and HRRs). When considering the analysis of either the nine or five repeated measurements, very few between- and within-group differences were apparent. Furthermore, with the exception of HRex (P \ 0.01), no time effect was observed for these analyses
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(all P [ 0.09). Analysis of pre- and post-training results only (week 1 vs. 9) showed a main training effect for most parameters (e.g., P \ 0.01 for HRrest or P = 0.03 for Ln rMSSDrest), as well as a group 9 time interaction (e.g., P = 0.04 for HRrest or P = 0.03 for Ln rMSSDrest). Again, these trends were confirmed by the qualitative analyses. Ln rMSSDrest did not show any meaningful changes in the non-responder group, whereas it showed a tendency for a gradual increase throughout the training program in the responder group, to reach possibly (with chances that the values were higher/trivial/lower than week 1 of 64/30/5) and likely (with chances of 84/13/3) higher values compared with week 1 during weeks 7 and 9, respectively. Mean values for the non-responders were also very likely higher than those for the responders during the first 4 weeks (e.g., with chances of 97/2/1 at week 1), and likely higher from weeks 5 to 7 (e.g., with chances of 84/13/3 at week 6). HRrest displayed similar time course and betweengroup differences compared with Ln rMSSDrest. HRex was similar in both groups and reached possibly lower values during weeks 7 and 9 (e.g., with chances of 5/16/79 for week 7 for non-responders). HRRs displayed different changes compared with HRV values: in responders, compared with week 1, HRRs was likely shortened at weeks 3 and 7 (e.g., with chances of 73/22/5 for week 3) and possibly at weeks 5 and 9 (e.g., with chances of 57/43/0 for week 5), respectively. Again, there was no change for
Eur J Appl Physiol 22
-1
18 16 14 12 n = 28 r = 0.68 (0.46; 0.82), P < 0.001
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
20 15 10 5 n = 14 r = -0.76 (-0.90; -0.46), P < 0.01
0
10 2.8
Responders Non Responders
25
Changes in MAS (%)
20
MAS (km.h )
30
Responders (pre) Non Responders (pre) Responders (post) Non Responders (post)
4.8
-5 1.2
1.6
2.0
2.4
2.8
3.2
3.6
4.0
2.8
3.2
3.6
4.0
10 01:10:00
01:00:00
00:50:00
00:40:00 n = 28 r = -0.70 (-0.83; -0.49), P < 0.001
00:30:00 2.8
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
Changes in 10 km time (%)
10 km running time (min:sec)
01:20:00
5
n = 14 r = 0.77 (0.48; 0.91), P < 0.01
0 -5 -10 -15 -20
Ln rMSSDrest (ms) -25
Fig. 1 Relationships between pre- and post-training weekly average of the logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R–R intervals measured daily at rest after awakening, Ln rMSSDrest) and maximal aerobic speed (MAS, top) and 10 km running performance (bottom) in participants having improved their 10 km performance more than 0.5% (responders) or not (non-responders). Correlation coefficients after adjustment on age were r = 0.63 (0.39;0.79), P \ 0.001 for MAS and r = 0.65 (0.42;0.80), P \ 0.001 for 10 km running performance
non-responders (Fig. 4). Ln rMSSDpost-ex showed similar trends compared with Ln rMSSDrest for both groups. There was however no noticeable between-group differences, but responders reached possibly higher values when compared with week 1 during week 9 (with chances of 64/31/4). No change was apparent in the non-responder group. At last, HRpost-ex showed similar trends compared with Ln rMSSDrest and Ln rMSSDpost-ex for both groups. HR-derived data collected at rest and following exercise We observed a large correlation between mean Ln rMSSDrest and Ln rMSSDpost-ex [n = 10, r = 0.61 (0.09;0.87), P = 0.05], as well as a very large correlation between mean Ln rMSSDrest and HRRs computed for each group [n = 10, r = -0.74 (-0.92;-0.32), P = 0.015].
1.2
1.6
2.0
2.4
Baseline Ln rMSSDPost-ex (ms)
Fig. 2 Relationships between pre-training logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R–R intervals measured following the sub-maximal exercise, Ln rMSSDpost-ex) and relative changes in maximal aerobic speed (MAS, top) and 10 km running performance (bottom) in participants having improved their 10 km performance more than 0.5% (responders) or not (non-responders)
Discussion We examined relationships between cardiac autonomic regulation and both maximal aerobic power and aerobic endurance capacity, as evaluated via field running tests. We also assessed the respective time course of changes in submaximal exercise HR and vagal-related HR-derived indices during a 8-week training program in moderately trained runners. We observed large correlations between indices of parasympathetic function and both MAS and 10 km running time, as well as large correlations between the changes in theses parameters with training. Large interindividual responses were observed for both running performance (i.e., ?2.5 to ?25% and -2.1 to ?17.5% for MAS and 10 km run time, respectively) and vagal-related indices (e.g., -4.5 to ?23.3% for Ln rMSSDrest). Exercise HR decreased continuously during the training period in all participants.
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Eur J Appl Physiol 30
Ln rMSSDrest (ms)
n = 14 r = 0.52 (0.08; 0.79), P = 0.05
20 15 10 5 0
Responders Non Responders
-5
10
-4
-2
0
2
4
6
8
10
12
14
16
18
3
n = 14 r = -0.73 (-0.89; -0.41), P < 0.01
‡
‡
‡
†
†
†
5
6
7
4 3 2 60
HRR (s)
-6
†
45 30
5
15
HR ex (beats.min )
0
-1
Changes in 10 km time (%)
4
‡
Ln rMSSDPost-ex (ms)
Changes in MAS (%)
25
Non Responders (n = 3)
Responders (n = 11)
5
-5 -10
165 150 135 120 600
-15 -20 -6
-4
-2
0
2
4
6
8
10
12
14
16
18
Changes in Ln rMSSDrest (%)
Fig. 3 Relationships between relative changes in the weekly average of the logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R–R intervals measured daily at rest after awakening, Ln rMSSDrest) and relative changes in maximal aerobic speed (MAS, top) and 10 km running performance (bottom) in participants having improved their 10 km performance more than 0.5% (responders) or not (non-responders). Correlation coefficients after adjustment on age were r = 0.57 (0.15;0.82), P \ 0.05 for MAS and r = -0.71(1) (-0.88;-0.37), P \ 0.01 for 10 km running performance
Vagal-related indices showed trends throughout the training intervention indicating progressively higher parasympathetic levels, but only in subjects who presented with[0.5% reductions in 10 km running time. Despite these large relationships (all r C 0.61 and P B 0.05), most measured HRderived indices presented with different adaptation times. Run performance and HR-derived indices The number of runners who dropped out of the study due to family time constraints, injury or lack of training program participation, is within similar ranges reported in longitudinal studies (Impellizzeri et al. 2006). Nevertheless, due jointly to the noise involved with home-based HRV recording, along with our stringently imposed cutoff limits for completed HRV data sets (95%), only 50% of the
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TRIMPs
‡
450 300 150 0 1
2
3
4
8
9
Weeks
Fig. 4 Values are mean ± SD. Logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R–R intervals measured daily at rest after awakening (weekly average value, Ln rMSSDrest) or during recovery following the 5-min exercise (Ln rMSSDpost-ex), heart rate recovery time constant (HRRs) during the 5-min post-exercise recovery period following the 5-min sub-maximal exercise, average HR during the last 30 s during the sub-maximal exercise (HRex) and weekly training impulses (TRIMPs) in participants having improved their 10 km performance more than 0.5% (responders) or not (non-responders). Shaded areas represent the smallest worthwhile change (i.e., within group SD 9 0.2, see ‘‘Methods’’). Circles around symbols denote possible within-condition difference from week 1 (i.e., 25–75% chances that the true value of the statistic is practically meaningful). Squares around symbols denote likely within-condition difference from week 1 (i.e., [75% chances that the true value of the statistic is practically meaningful). Between-group difference rated as likely. àBetween-group difference rated as ‘very likely’ (i.e., [95% chances that the true value of the statistic is practically meaningful)
remaining runners were included in our final analysis. We felt the matching of both resting and exercise data sets were important, so that the complete autonomic picture could be analyzed. Although missed data from one day was not likely to affect individual weekly average values, the
0.49 (small)
-0.60 (moderate)
0.54b (small)
-0.58b (moderate)
ES (rating)
ES (rating) for resp. versus non-resp.
67/31/2 Possibly
Percentage chances of better/trivial/ poorer for resp. versus non-resp.
Outcome
Very likely
97/3/0
11.4 (4.7;18.5)b
-1.01 (large)
Unclear
22/24/54
2.4%(-7.6;13.6)
-0.28 (small)
-0.84 (moderate)
149 ± 13 138 ± 17
-0.58 (small)
137 ± 12a
145 ± 12
-0.66 (moderate)
138 ± 13
146 ± 12
HRex (beats min-1)
Almost certain
100/0/0
-38.9% (-40.7;-37.1)
0.88 (large)
0.18 (trivial)
28 ± 9 29 ± 5
-1.02 (large)
27 ± 9
41 ± 14
-0.88 (large)
27 ± 8
37 ± 14
HRRs (s)
Almost certain
97/2/0
-23.9% (-40.7;-9.1)
-0.39 (small)
-0.16 (trivial)
2.97 ± 0.69 2.85 ± 0.73
0.59 (moderate)
3.12 ± 0.59
2.66 ± 0.64
0.50 (small)
3.05 ± 0.60
2.74 ± 0.64
Ln rMSSDpost-ex (ms)
Unclear
27/36/37
-0.9% (-11.8;11.4)
0.07 (trivial)
-0.67 (large)
88 ± 12 80 ± 14
-0.73 (moderate)
80 ± 10
89 ± 13
-0.80 (large)
80 ± 14a
89 ± 12
HRpost-ex (beats min-1)
b
a
Statistics for resting HRV values are adjusted for age
Significant training effect revealed by the two-factor ANOVA (P \ 0.05)
Magnitude of the between-group differences in responses to training are expressed as % chances (90% CI) and qualitative outcome for responders to have better/trivial/poorer responses than non-responders
ES effect size (qualitative outcome, see ‘‘Methods’’ for thresholds used)
Values are mean ± SD for resting heart rate (HRrest) and the logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R–R intervals (weekly average value, Ln rMSSDrest) measured daily at rest after awakening, HR during exercise (HRex), time constant of HR recovery (HRRs), Ln rMSSD (Ln rMSSDpost-ex) and HR (HRpost-ex) measured during the last 5-min of the recovery period following the 5-min sub-maximal exercise in participants having improved their 10 km performance (responders) or not (non-responders)
-4.3 (-9.3;1)b
Percentage difference (90% CI) for resp. versus non-resp.
Magnitude of between-groups differences in responses to training
1.23 (large)
-0.14b (trivial)
-0.30b (small)
ES (rating)
Magnitude of between-groups differences at baseline
4.07 ± 0.17 4.05 ± 0.19
50 ± 5 49 ± 6
Pre Post
Non-responders (n = 3)
3.58 ± 0.58 3.90 ± 0.54a
59 ± 8 54 ± 6a
Pre
Post
Responders (n = 11)
ES (rating)
3.68 ± 0.55 3.94 ± 0.49a
57 ± 8 54 ± 6a
Pre
Ln rMSSDrest (ms)
Post
All pooled (n = 14)
HRrest (beats min-1)
Table 4 Heart rate-derived indices before and after the 8-week program
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absence of (post-) exercise HR indices would have been problematic, because these values were independent of others. Regardless, in the 14 runners remaining with complete data sets, large to very large correlations were observed between resting cardiac ANS indices and running performance (Table 3; Fig. 1). The correlations were also consistently larger for 10 km running time than for MAS, suggesting that cardiac autonomic activity might be a better predictor of, or might share more common determinants with aerobic endurance capacity than aerobic power. Correcting the correlations for age, which adversely affects baseline autonomic control and autonomic response to exercise (Sandercock et al. 2005a), did not affect the results. Although the association between vagal-related _ 2 peak has been well documented (e.g., indices and VO Aubert et al. 2003; Buchheit and Gindre 2006), their relationship with field endurance capacity has received less attention. However, our results are in line with the recent studies of Manzi et al. (2009) and Gratze et al. (2005, 2008), who reported associations between pre- and postrace markers of parasympathetic and sympathetic cardiac autonomic activity and marathon or triathlon finishing time. Our results contrast however the study of Bosquet et al. (2007a), who showed no differences in resting HRV or post-exercise HRR indices between groups of subjects with different ventilatory thresholds (VT, 88 vs. 82% of _ 2 peak), which the authors used as indirect markers of VO endurance capacity. Limitations to the validity of VT determination and its capacity to predict long-distance field running performance may explain these discrepancies (Bosquet et al. 2002). Although interindividual differences in both baseline training status and age are known to affect both cardiac ANS function and fitness levels (Aubert et al. 2003; Sandercock et al. 2005a), it is also possible that a common genetic denominator might explain both aerobic (endurance) performance and ANS function. For instance, genetic factors may determine a large proportion of the interindividual variation in either HRV (Singh et al. 2001; Uusitalo et al. 2007) or aerobic function (Bouchard and Rankinen 2001; Rankinen et al. 2003). The lack of a relationship between baseline post-exercise HRV indices and running performance (Table 3) could be explained by the individualized sub-maximal exercise intensity (i.e., 60% of MAS), which might have normalized the level of post-exercise cardiac autonomic activity. Finally, pre-training HRR was not correlated with MAS (r = -0.29), but was largely correlated with 10 km running time (r = 0.54). This is consistent with the assump_ 2 peak tion that HRR is not likely to be related to VO (Buchheit et al. 2006, 2008), but may share more association with habitual training loads (i.e., the best endurance performers in the group were also likely more trained at commencement of the experimentation).
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Changes in endurance performance and HR-derived indices When considering all participants, MAS and 10 km running performance were improved by 10 and 5%, respectively (Table 2). We are not aware of a training study that has used similar performance tests, but changes are within _ 2 peak and endurance performance the range of VO improvements reported in long-distance runners (Midgley et al. 2007). Large heterogeneity occurred in the response to training (i.e., ?2.5 to ?25.0% and -2.1 to ?17.5% for MAS and 10 km run, respectively), despite individualized training contents; such a varied response to training is commonly reported (Bouchard and Rankinen 2001; Hautala et al. 2003, 2006) and has been associated with differences in age, gender, race, baseline fitness level and genetic factors (Bouchard and Rankinen 2001; Rankinen et al. 2003). In the present study, initial performance level was a strong predictor of improvements after training (e.g., r = -0.89 for 10 km run). It also appeared that the nonresponders had better baseline performances and physical qualities than the responder group (e.g., difference in leg stiffness, MAS and 10 km running performance rated as large, Table 2). Although the program was individualized in terms of the exercise training intensity, it is possible that the training stimuli (i.e., training volume or total load), might not have been enough for these individuals with higher exercise capacities. As illustrated in Fig. 4, with the exception of week 3 when the non-responders presented with higher TRIMPs compared with the responders (likely because of different HR responses with the short interval training sessions and the higher maximal running speeds), total training load was similar for both groups. Thus, it is possible that the non-responders were somewhat detrained; the reduction in leg stiffness in this group supporting such a viewpoint (Table 2). A non-exercising control group could have assisted to understand the present results. It was, however, difficult to find volunteers willing to undertake daily HRV measurements without benefit. The response to training has been proposed to be related to baseline cardiac autonomic activity (Hedelin et al. 2001; Hautala et al. 2003, 2009). For example, Hautala et al. (2003) found that pre-training resting cardiac parasympathetic function accounted for 27% of the change in the _ 2 peak. The aerobic training response, as expressed by VO authors suggested a mechanistic link between vagal functioning and the capacity to adapt to aerobic exercise training. However, although these authors found a positive correlation (the higher the vagal modulation, the higher the response), we observed the opposite relationship (the subjects with the lower vagal modulation improved more, r = 0.82 for D10 km running time versus baseline Ln rMSSDpost-ex, Fig. 2). The strong relationships noted
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between absolute levels of cardiac parasympathetic function and running performance (Fig. 1), as well as the large effect of pre-training scores on performance changes, suggest that the correlations observed are more likely to be related to the interdependence of cardiac autonomic control and aerobic performance than to an individual ‘trainability’ component per se (Hautala et al. 2009). This important variance in training responses reported for indices of cardiac parasympathetic function (e.g., -4.5 to ?23.3% for Ln rMSSDrest), together with the correlations between changes in endurance performance and HR-derived indices (Fig. 3), confirms the strong association between endurance running capacity and cardiac autonomic function. Along these same lines, changes in HR-derived indices were only observed in participants who improved their 10 km finish time (Table 3; Fig. 4). Correlations have also been reported between changes in vagal-related indices and time to exhaustion after training (Hautala et al. 2004); changes at the central nervous system level have been proposed to explain this association. Time course of changes in HR-derived indices Although several studies have tracked changes in cardiac parasympathetic function throughout a training program using exercise HR (Scharhag-Rosenberger et al. 2009), resting (Yamamoto et al. 2001; Pichot et al. 2002; Iwasaki et al. 2003; Gamelin et al. 2007; Manzi et al. 2009) or postexercise HRV (Yamamoto et al. 2001), and post-exercise HRR recordings (Sugawara et al. 2001; Lamberts et al. 2009a, b) our study reports the time course of all indices simultaneously. These indices, although all vagally mediated, have been suggested to represent independent aspects of cardiac parasympathetic function (Buchheit et al. 2006, 2007, 2008), and respond differentially to endurance training (Yamamoto et al. 2001; Buchheit et al. 2008). To compare the time course of each index in response to the endurance training program, observations were performed on both groups independently (Fig. 4). Although a priori study design with two groups of equivalent sample size would have been optimal, examining the individual responses to training could only be done by grouping participants a posteriori (Vollaard et al. 2009). This resulted in a small sample size for the non-responder group. However, we used a qualitative statistical analysis approach, which was in part designed to allow for an inferences-based comparison to be made with such a small sample size (Hopkins et al. 2009). In the responders group, resting Ln rMSSD values remained strictly unchanged for the first 4 weeks (Fig. 4), and then showed an increasing trend to reach higher values during weeks 7 and 9. The lack of a change in the vagally mediated resting index during the first 4 weeks is consistent
with the conclusions of a meta-analysis (Sandercock et al. 2005a), which shows that at least 4 weeks of training are needed to observe modifications in cardiac parasympathetic function. The magnitude of changes (all subjects pooled) in either resting HR (ES = 0.60) or Ln rMSSD (ES = 0.49) were within the average range of changes reported (Sandercock et al. 2005a) [ES = 0.79 and 0.49 for mean R–R interval and high frequency (HF) power, respectively, which can be substituted for HR and Ln rMSSD, respectively (Task Force 1996)]. It is difficult to judge whether the time course of postexercise Ln rMSSD differed from that of resting measurements (Fig. 4), but the time course of these changes was somewhat similar. The large correlation (r = 0.61) between mean Ln rMSSDrest and Ln rMSSDpost-ex supports this view, but contrasts with the findings of Yamamoto et al. (2001). These authors reported differences in the adaptabilities of HRV indices obtained at rest or following exercise, with the adaptations of cardiac modulation occurring much faster during the post-exercise period compared with rest (i.e., 7 vs. 42 days needed for changes in post-exercise vs. resting indices, respectively). However, differences in exercise duration (40 vs. 5 min) and intensity _ 2 peak), as well as in post-exercise (80 vs. 60% of VO recovery position [sitting vs. standing (Buchheit et al. 2009)] might explain these discrepancies. Exercise HR was the only index to show similar changes for both groups, with a progressive and continued decrease throughout the program (Fig. 4). The similar changes in both groups can be explained by the fact that exercise HR is thought to be representative of the exercise load (Borresen and Lambert 2007, 2008). Because MAS increased in both groups after training (Table 2), the relative intensity of the sub-maximal exercise might have decreased accordingly, so that exercise HR decreased even in subjects who did not improve their endurance capacity (i.e., 10 km time). The early and continuous decrease in exercise HR was also consistent with the fast changes in sub-maximal exercise HR reported during the first 9 weeks of a 1-year endurance training program (Scharhag-Rosenberger et al. 2009). In the responder group, HRR showed rapid adaptations, with a shortening of the time constant during the third week; a trend that was maintained until week 8. During week 9, which followed the ‘tapering’ week of the program (Table 1), HRR time constant was further shortened. The fast adaptation of HRR within the early weeks of training is in line with the findings of Sugawara et al. (2001) and Lamberts et al. (2009b), because HRR measured after either sub-maximal or high-intensity exercise improved within the first 4 weeks of training, with only small changes occurring during subsequent weeks. The likely improvement in cardiorespiratory fitness after the first few weeks of training (not directly measured, but which could
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be inferred from the decreased HRex) could have led to progressively lower system stress metabolite accumulation in the blood following the sub-maximal exercise bout, with the consequence being a decreased activation of the metaboreflex and therefore a faster HRR (Rowell and O’Leary 1990). It is also possible that post-exercise blood lactate or epinephrine levels after the third week of training had attained levels that would no longer have affected the metaboreflex, thereby explaining the unchanged time course of HRR during the subsequent weeks. Lastly, the final improvements in HRR during week 9 could be related to the final rebound of parasympathetic activity (evidenced by the parallel increases in Ln rMSSD during week 9, Fig. 4) following the tapering period (Pichot et al. 2002). The present study confirms a strong interdependency of cardiac autonomic function and aerobic running performance. Even when performed every second week, a single 5-min sub-maximal exercise bout, followed by 5 min of passive recovery (used as an exercise warm-up), may be an efficient means of assessing autonomic status. Sub-maximal exercise HR may be used to track changes in maximal aerobic power, at least during the first 2 months of training (Scharhag-Rosenberger et al. 2009). Resting, as well postexercise HRV measurements, may be more predictive of changes in aerobic endurance capacity. Acknowledgments The authors thank the runners for their enthusiastic participation and Marc Quod for his help during the revision of the manuscript.
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