Heart Rate Variability, Training Variation and ...

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Satzbetrieb Ziegler + Müller Verlag Thieme/Hentze Datum 14.10.2006

D. Atlaoui1 V. Pichot1 L. Lacoste2 F. Barale2

Heart Rate Variability, Training Variation and Performance in Elite Swimmers

J.-R. Lacour3 J.-C. Chatard1

The aim of the present study was to investigate the relationships between heart rate variability (HRV) changes and both training variations and performances in elite swimmers. A secondary purpose was to measure catecholamine urinary excretion in elite swimmers to validate the HRV indices of sympathetic activity during training. Thirteen swimmers (4 females and 9 males) were tested before and after 4 weeks of intense training (IT) and 3 weeks of reduced training (RT). At the end of each period, the swimmers participated in an official competition of their best event. Individual performances were expressed as percentage of the previous seasons best performance. Spectral analysis was used to investigate RR interval variability. HRV indices failed to show any significant changes between the study periods (p > 0.05). Pre-IT HF was correlated with performance (r = 0.45; p = 0.05) and HFnu (r = 0.59; p < 0.05) during RT. On the other hand, once RT was completed, HFnu was correlated positively to

Introduction Periods of heavy overload training are intentionally used in the training process of elite athletes to induce an overreaching state (ORS). The ORS has been characterized by transient performance decrements and acute feelings of fatigue [8, 23]. This state, followed by an appropriate period of reduced training, can result in performance increases exceeding the level previously achieved [23]. Recent reports provide evidence of alterations in the auto-

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performance (r = 0.81; p < 0.01) and negatively to fatigue (r = ± 0.63; p < 0.03). Conversely, the indices of sympathetic activity, i.e., LFnu and LF/HF ratio were inversely related to performance (both r = ± 0.81; p < 0.01); total fatigue score was correlated to the changes in HFnu (r = ± 0.63; p < 0.03) and in the LF/HF ratio (r = 0.58; p < 0.05). Changes in the adrenaline/noradrenaline ratio over the follow-up period were related to the changes in the LF/ HF ratio (r = 0.45; p < 0.03). In highly trained swimmers coping well with a training program, including 4 weeks of IT followed by 3 weeks of RT, HRV indices were unaltered. On the other hand, after the 3 weeks of RT, HFnu was positively related to performance and inversely related to the fatigue score. Thus, elevated initial HF levels could be important in the parasympathetic activity increases during taper and, hence, in swimming performance improvement. Key words Autonomic nervous system ´ overreaching ´ tapering ´ swimming

nomic nervous system (ANS), as reflected by changes in heart rate variability (HRV) during prolonged periods of intense training in elite athletes [14, 29, 30, 35]. Thus, HRV has been proposed as a tool for monitoring the stress of training to avoid the imbalance between intense training and recovery periods [16, 29, 35]. HRV is higher in trained individuals. It has been evidenced by higher cardiac vagal tone in trained individuals as compared to controls [1, 4,10, 37]. Daily sessions of prolonged and/or intense

Affiliation Laboratory of Physiology, PPEH EA (3062), Jean Monnet University, Saint-Etienne, France 2 Dauphins of Toulouse Olympic Employee Club, Toulouse, France 3 Faculty of Medicine Lyon Sud, Lyon 1 University, Lyon, France

Correspondence Djamila Atlaoui ´ Service de MØdecine du Sport ´ Pavillon 9, Bellevue ´ CHU de Saint-Etienne ´ 42055 Saint-Etienne Cedex 2 ´ France ´ Fax: + 33 47712 72 29 ´ E-mail: [email protected] Accepted after revision: June 20, 2006 Bibliography Int J Sports Med 2006; 27: 1 ± 7 Georg Thieme Verlag KG ´ Stuttgart ´ New York ´ DOI 10.1055/s-2006-924490 ´ ISSN 0172-4622

Physiology & Biochemistry

Abstract

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Physiology & Biochemistry 2

training load have been shown to alter sympathovagal balance [14,18, 24, 29, 30, 35]. However, controversial results have been reported. Indeed, overload training periods have been shown to be associated with decreased [9,18,19, 29] or increased [30] HRV indices. Inversely, some studies have failed to show any connection between overload training and HRV changes [5,15]. The inferences that have been drawn from these studies are difficult to reconcile owing to: 1) the differences in recording methods, diurnal short-term (about 10 min) versus nocturnal long-term recording (over 24 h), and the time elapsed between the last exercise sessions and collection of HRV data. For example, it has been shown that sympathetic activity remained elevated (i.e., high LF) for up to 24 h after an exercise bout [9] and; 2) the lack of HRV measures during periods of reduced training loads (i.e., tapering) concomitant with performance assessment, so that it is difficult to differentiate between ANS adaptations and/or disturbances in response to training stress. However, a recent investigation [9] has shown a correlation between HRV changes and performance changes in teenage swimmers. In these young swimmers, the rebound in ANS activity after a 2-week recovery has been associated with performance enhancement, suggesting HRV measurements could be a useful tool for monitoring individual training and tapering-induced adaptation. However, no study has related HRV changes during taper with performance in elite swimmers, undergoing larger amounts of high-intensity training and competitions than young swimmers. The purpose of this study was to determine the relationship between HRV changes and both training variations and performances in elite swimmers. Urinary 24-h CA determination provides a non-stressful integrated measurement of the whole-body daily CA secretion and mirrors the average sympatho-adrenomedullar activity [6]. High noradrenaline (NA) levels have been reported to influence cardiovascular regulatory mechanism [33]. According to these authors [33], exercising conditions that represent a potent physiological stimulus on CA increases could influence HRV indices. Moreover, HRV, when measured in the morning before exercise, was reported to reflect basal sympathetic activity as CA [18]. However, studies combining catecholamine measures and diurnal short-term HRV recording during training are lacking. Thus, a further aim of the current study was to test the correlation between catecholamine urinary excretion and HRV indices during training.

Materials and Methods Subjects The subjects of this study were 13 French swimmers competing nationally and internationally. The group included 4 females (21 2 years, 173 5 cm, 60 5 kg) and 9 males (23 4 years, 186 7 cm, 80 7 kg). All were members of the same team and had a background in competitive swimming that averaged 15 3 years. They were 50-m, 100-m, 200-m, 400-m, or 1500-m specialists in different strokes. All the swimmers trained 6 days per week, usually twice a day, with a rest on Sunday. They also practiced regular dryland training, one hour per day. Approval for the project was obtained from the Local Committee on Human Research. After being informed of the nature of the study, swimmers gave their written consent to participate in this study. Atlaoui D et al. HRV Changes in Elite Swimmers ¼ Int J Sports Med 2006; 27: 1 ± 7



Fig. 1 Study design: training load and timing of performance assessment, TSF measures, electrocardiographic recording, and urinary sampling.

Testing procedures Over a 7-week period (Fig. 1), between March and May 2001, swimmers were tested before and after a 4-week intense training period (IT) and a 3-week reduced training period (RT). The swimmers were tested before the IT period (Pre-IT values), week 27. Week 1 was the beginning of the training season (at the beginning of September). The Pre-IT training parameters were averaged over 27 weeks (from week 1 to week 27). At the end of IT and RT periods, the swimmers performed a competition and answered an 8-item questionnaire on fatigue [2] the day of the competition. The heart rate recordings were also performed the day of the competition, and urinary samples were collected over a 24-h period. Performance assessment At the end of each period, the swimmers performed in their best event at an official competition, as planned in their normal training season program. Individual performances were expressed as a percentage of the previous seasons best performance. A decrease in the performance time corresponded to an improvement in the performance percentage. During the follow-up period, they were asked to avoid any medication and maintain their usual diet. Fatigue questionnaire On the morning of each competition day, the swimmers answered the 8-item questionnaire on fatigue [2]. The responses of each question were collated to obtain the total score of fatigue (TSF). TSF corresponds to a self-report of the perception of training, sleep, leg pain, infection, concentration, efficacy, anxiety, irritability, and general stress. Intra-subject variability of the TSF,


assessed in 20 swimmers from coefficient of variation of difference between double measurements within half a day, was 2.3 %.

Equation 1: 3 5 km4 8 km5 dryland equivalent MI 1 km1 2 km2 3 kmtraining volume For each period, the training volume (km), MI, and TT were expressed as a weekly average (Table 1). Heart rate variability recording On each competition day, the HRV recordings were achieved between 9:00 and 12:00 a. m., using the Holter system (Polar R-R recorder, Polar Electro Oy, Kempele, Finland). The Polar RR recorder, used in the current study, has been reported as a reliable and valid tool when compared to an ECG [31]. After 5 min of supine rest, HRV recordings were performed in the lying position for 10 min. Before the data collection, swimmers were made familiar with the equipment. During the recording, they were asked to be as relaxed as possible, to not sleep, and to keep their eyes open. Each recording was performed in a room with a quiet atmosphere of ambient temperature and with dimmed lighting. The swimmers were alone with the experimenter, who was as quiet as possible so as not to disturb the subjects. The mean RR interval, time domain analysis, and Fourier transform indices of heart rate variability were standardized and calculated as previously described [32]. Time domain analysis On each recording, the following indices were calculated (Table 2): the percentage of differences between adjacent normal RR intervals more than 50 ms (PNN50, short-term HRV); the standard deviation of all normal RR intervals (SDNN, global HRV); the square root of the mean of the sum of the squared differences between adjacent normal RR intervals (RMSSD, short-term HRV); the standard deviations of the mean of all normal RR intervals for 5-min segments (SDANN, long-term HRV); and the mean of the standard deviation of all normal RR intervals for all 5-min segments (SDNNIDX, global HRV). Fourier transform analysis For each record, the Fourier transform analysis was conducted over periods of 256 consecutive RR intervals. It is generally assumed that the indices of heart rate variability reflect the autonomic nervous activity (Table 3). The high frequency peak of the spectrum expressed in absolute (HF) mirrors the parasympathetic activity; the low frequency power expressed in absolute


Table 1 Mean SD of performance, training variables, and total score of fatigue (TSF) over the follow-up period for the 13 swimmers Week of the season Variables

Week 27 Pre-IT values

Week 31 Intensified training (IT) 98.5 1.1

99.5* 0.8

Training volume (km)

39.1 13.0

47.1 12.6

34.7* 7.2

Mean intensity (a. u.)

2.05 0.2

2.14 0.18

1.89* 0.06

Dryland training (km)

8.3 4.9

13.2 5.3

4.1* 2.5

Training load (a. u.)

80.5 23.0

100.3 22.3

66.3* 13.5

TSF (points)

29.7 7.3

24.6 5.0

19.2* 6.9

Performance (%)

Week 34 Reduced training (RT)

*p < 0.05 from IT

Table 2 Mean SD of supine HR and time domain indices over the follow-up period for the 13 swimmers Week of the season Variables


Week 31 Intensified training (IT)


HR (bpm)

59.97 9.7

62.23 10.6

61.95 9.1

PNN50 (%)

17.02 9.6

16.59 12.7

16.48 11.2

SDNN (ms)

77.24 37.9

81.20 44.5

79.80 40.3

rMSSD (ms)

76.95 50.2

76.32 58.6

70.88 47.3

SDNNIDX (ms)

77.12 38.4

80.68 46.6

79.51 42.1

Table 3 Mean SD of the supine HRV Fourrier analyses over the follow-up period for the 13 swimmers Week of the season Variables


Week 31 Intensified training (IT)


Ptot (ms2)

3625.61 3977.1

3896.1 4818.8

3436.5 3470.1

LF (ms2)

1482.4 2266.0

1503.7 3191.6

1305.4 1556.3

LFnu (%)

46.3 15.6

49.9 23.0

54.4 20.6

HF (ms2)

1464.9 1657.5

1358.8 1576.1

1308.7 1711.7

HFnu %

53.7 15.6

50.1 23.0

45.6 20.6

1.1 0.9

1.5 1.4

1.7 1.4

LF/HF

(LF) represents both parasympathetic and sympathetic activities. The high and low frequency indices were also calculated in normalized units (HFnu and LFnu) as 100 ´ HF/(total power [Ptot] ± very low frequency [VLF] and 100 ´ LF/(Ptot ± VLF), respectively. The normalization procedure has been shown to be useful. It allows comparisons between subjects or experimental conditions characterized by large differences in total power or VLF noise [20, 28]. The total power of the spectrum (Ptot) denotes the overAtlaoui D et al. HRV Changes in Elite Swimmers ¼ Int J Sports Med 2006; 27: 1 ± 7


Training program The swimmers followed the training program set by their two coaches. The training volume, intensity, and dryland work times were recorded by the coaches each day for each swimmer and then averaged per week and period. The different swimming intensities ranged from 1 to 5 and were evaluated after lactate testing as described by Mujika et al. [26]. Dryland training was calculated and expressed in km according to the recommendations of Mujika et al. [26]. The mean intensity of training periods (MI) was calculated and expressed in arbitrary units (equation 1) [26]. The total training load (TT) corresponds to the sum of the distance covered and the dryland training and was averaged for each period.


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all state of the autonomic nervous system; and the LF/HF ratio mirrors the autonomic nervous system balance [32].


Urinary sampling During each competition day, urine was collected over a 24-h period, the diuresis was noted and 100 ml samples were immediately frozen at ± 80 8C until analysis. Concentrations of urinary Ad and NA were determined by high-performance liquid chromatography (HPLC) with electrochemical detection based on the method of Hay and Mormde [13] with some adaptations. Urine was centrifuged for 15 min at 4 8C at 4000 g and filtered (1/20). Internal standards (50, 100 l) and EDTA (8 ml) were added to the urine. The pH was adjusted at 6.5 using hydrochloric acid. Samples were applied to ion exchange columns (size: 150 4.6 mm; type: HS-1546-M185; Higgins Analytical, CA, USA) and catecholamines were eluted from the columns and with 8 ml boric acid (0.065 Mol ´ l±1). Boric acid eluates were injected into the HPLC system. Quantification was performed with electrochemical detection as previously described [13]. Urinary CA output is generally related to urine creatinine concentration to adjust for dilution. However, for the present study, CA output was not related to urine creatinine concentration to avoid differences related to fluctuations in creatinine production, which can unpredictably increase during intense exercise [22]. Hence, both Ad and NA concentrations were expressed in g per 24-h period and determined in duplicate. The intra-assay and inter-assay coefficients of variations were < 1%. Statistical analysis Means and standard deviations were calculated for all variables. One-way repeated ANOVA tests were performed to analyze the mean differences between the three training periods. When the differences were significant, the F-test was followed by post hoc procedures (Fishers PLSD test). Correlations between HRV indices, performance, TSF and catecholamines were calculated from linear regression. Correlations were retained only when the significances were reconfirmed using a Spearman nonparametric test. The StatView program (Brain Power, Inc., Calabasas, CA, USA) was used in all statistical analyses. A probability level of 0.05 was selected as a criterion for statistical significance.

Results HRV and training For the whole swimmer group, supine HR, time domain indices, and relative HRV indices are shown in Table 2 and 3, respectively. The HR and HRV indices did not show any significant change between the three studied periods. No relationship was found with the training variables. HRV, performances and TSF Basal HF values were correlated with performance during RT (r = 0.57; p = 0.05). At the end of IT, neither the HRV indices nor the TSF were significantly related to the performances. However, at the end of RT, performances were significantly negatively related to LF/HF (r = ± 0.81; p < 0.01; Fig. 2 A) and LFnu (r = 0.81; p < 0.01; Fig. 2 B), and positively related to HFnu (r = 0.81; p < 0.01; Fig. 2 C). Between IT and RT periods, the changes in TSF

Atlaoui D et al. HRV Changes in Elite Swimmers ¼ Int J Sports Med 2006; 27: 1 ± 7

Fig. 2 A to C Relationship between (A) LF/HF ratio, (B) normalized value of low frequency power (LFnu), and (C) normalized value of high frequency power (HFnu) and performance after the 3 weeks of reduced training (RT).





Fig. 3 A and B Relationship between (A) the normalized value of high frequency power (HFnu) and (B) the LF/HF ratio changes between IT and RT and total score of fatigue (TSF) changes.

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Fig. 4 A and B Relationship between (A) the differences in normalized values of low-frequency power (LFnu), and (B) LF/HF ratio and the differences in adrenaline/noradrenaline (Ad/NA) ratio between two competitions over the studied period.

were related to the changes in HFnu (r = ± 0.58; p < 0.05; Fig. 3 A), LFnu (r = 0.61; p < 0.04) and to LF/HF (r = 0.64; p < 0.03; Fig. 3 B). HRV and CA No relationship was found between noradrenaline (NA) and HRV indices. However, the differences in the adrenaline/noradrenaline (Ad/NA) ratio between two competitions were related to the differences in LFnu (r = 0.53; p < 0.01; Fig. 4 A) and LF/HF (r = 0.45; p < 0.03; Fig. 4 B).

Discussion The main findings of the present study are that in the whole group, after the 3 weeks of taper, HFnu, LFnu and LF/HF were sig-

nificantly related to the swimming performance. On the other hand, the swimmers with higher HFnu and lower LF/HF, after the 3 weeks of RT, showed a lower total score of fatigue. HRV and training The results of the current study revealed no significant changes in HRV with training load variations. The 4 weeks of IT followed by the 3 weeks of taper did not result in any significant change in ANS of the swimmers, whatever the recording position. The lack of variations in the HRV indices observed in the present study is in line with some previous reports [5,15] but in contradiction with some other studies [16, 29, 35]. Indeed, studies investigating HRV after prolonged periods of overload training or in overreached athletes have reported either no changes [5,15] or inconsistent changes [16, 29, 35]. Heightened sympathetic activity (i.e., Atlaoui D et al. HRV Changes in Elite Swimmers ¼ Int J Sports Med 2006; 27: 1 ± 7



elevated LF) has been detected after an intensified training period [9,18,19, 29, 36], whereas declines in LF powers have also been reported [14, 30]. However, in the aforementioned studies, parasympathetic activity has been found to be unchanged [14, 36], to increase [30] and to decrease [9,19, 29] in response to intensified training that resulted in an overreached state. The recording conditions could be responsible for this difference. For instance, in some studies, HRV measures were achieved after at least 3 days of recovery [30, 35], that would allow the possible vagal rebound during tilting [14, 30]. Contradictory findings have also been reported among studies measuring HRV during the night [5, 9, 29] and close to the training sessions [5, 9,18,19, 29]. This may be of importance. Indeed, Fortrat et al. [7] reported the cardiovascular dynamics to be frequently altered in the waking state. Considering such heterogeneity in the methods, it is difficult to collate data from the literature. Hence, harmonizations of the methods are needed to draw conclusive inferences. HRV, performance and TSF The normalized units of spectral power indexes or LF/HF have been found to be more related to the sympathetic activity than to the spectral power indexes in absolute units studied by MSNA recording from peroneal nerve under pharmacological blockade [25, 28]. Tulppo et al. [34] have stressed the major influence of the average RR interval itself on all the time and frequency HR measurements, when analysed in absolute units. According to these authors, the normalized units of spectral indexes or LF/HF are less sensitive to the average HR itself and, therefore, these indexes are more suitable. In the present study, higher basal HF were related to the swimming performance during RT. The swimmers with high Pre-IT HF improved their performance during RT. It is noteworthy that the swimmers showing elevated Pre-IT HF evidenced higher HF and HFnu levels during RT. Furthermore, the results of the current study showed that elevated HFnu during RT was associated with an increase in performance. Such relationships have only been studied in teenage swimmers [9]. In these young swimmers, elevated performances were achieved when overall autonomic activity and parasympathetic activity were higher, whereas lower performances were reported with decreasing overall autonomic activity and parasympathetic activity. These results corroborate the assumption that the initial level of autonomic modulation might be a determinant of the responses to aerobic training [12,14]. Thus, elevated initial HF levels could be important in the parasympathetic activity increases during taper and, hence, in swimming performance improvement. Likewise, significant increases in HFnu and marked decreases in LFnu component during a 3-week period of reduction in training load were seen to be associated with an improvement in swimming performance [9]. In a longitudinal designed investigation, Pichot et al. [29] found a shift toward parasympathetic dominance after a week of recovery following 3 weeks of elevated training in middle distance runners. In the present study, a negative and significant correlation was found between the changes in TSF score measured after the 3 weeks of RT and the variations of normalized high-frequency power (i.e., HFnu). These results suggest that the increase in parasympathetic activity with training load reduction argues in favor of swimmers aptitude for recovery. Atlaoui D et al. HRV Changes in Elite Swimmers ¼ Int J Sports Med 2006; 27: 1 ± 7



The present findings, together with those of the aforementioned studies, highlight the importance of the use of HRV changes as a relevant tool for monitoring the recovery efficiency after the overload period. HRV and CA This study is the first evaluating the effects of a heavy training period followed by a taper period on both HRV indices and CA measurements during real life training season and competition conditions in highly trained athletes. Previously, Nakamura et al. [27] have found significant correlations between plasma CA levels and sympathetic outflow during exercise. Significant relationships between resting plasma NA concentrations and MSNA have also been shown in healthy subjects [11]. However, in the present study, no relationship was found between CA excretion and HRV indices. These results show that CA excretion does not reflect cardiac sympathetic activity. Similarly, Kingwell and coauthors [21] found no significant correlation between myocardial NA spillover and absolute or relative 0.1-Hz RR interval spectral power in healthy supine subjects. Even so, CA excretion represents a noninvasive and non-stressful mean among others commonly used to assess average human sympathetic activity [6]. Nevertheless, the use of this technique has many drawbacks, because 10 ± 20 % of the amount of adrenergic neurotransmitter secreted NA is released to the circulation contrarily to Ad mainly secreted by the adrenal medulla. Moreover, the measurements of free CA in urine account for only 10% of circulating CA concentrations. Thus, the Ad/NA ratio has been suggested as a tool to assess the adrenomedullar response to the SNS activity [38]. Hence, the Ad/NA ratio could be considered as a roundabout mean to assess the relation between the adrenomedullar and the SNA. In the present study, changes in the Ad/NA ratio over the follow-up period were significantly related to the changes in LFnu and LF/ HF. Indeed, it is suggested that the increased Ad/NA ratio observed before competition with improved performance could be an adjustment related to higher sympathetic activity [3]. However, the mechanism of such an adjustment should be clarified. There are some limitations to the present investigation. Indeed, the lack of a control group prevented comparison. However, within the framework of the present study, it would have been difficult to interpret the relevance of HRV changes for different performance and training levels. Indeed, HRV changes have previously been shown to be different between trained and untrained subjects [4]. The fact that the study group included both men and women is also a theoretical limitation [17], but the statistical analysis failed to disclose any gender effect. Another probable limitation is that the short-term HR recordings close to training sessions in competition conditions, with the known increase in CA, could have influenced the ANS changes [33]. In summary, the results of the present study suggest that in highly trained swimmers coping well with their training program, higher levels of HFnu during taper could constitute a favorable condition to increase swimming performance. Thus, HRV changes during taper could be a valuable tool for monitoring the adaptation to the training load variations and, hence, to performance improvement in elite swimmers during RT.


Acknowledgements The authors gratefully acknowledge athletes and members of the Dauphins of Toulouse Olympic Employee Club, and the Research Department of the French Swimming Federation who supported this study. The authors express their thanks to P. Buono (Monitor, Bayonne, France) for providing the recording equipment of the heart rate variability used in this investigation. The authors also thank Prof. AndrØ Geyssant, Prof. Thierry Busso, and Dr. Mohamed Rehalia for their valuable assistance, and Gerald Pope for reviewing the English manuscript.

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