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Clinical Science (2004) 107, 29–35 (Printed in Great Britain) Does the level of chronic physical activity alter heart rate variability in healthy older women? Sylvia RELAND∗ , Nathalie S. VILLE†‡, Sara WONG§, Lotfi SENHADJϧ and François CARRÉ∗ ‡ ∗ Groupe de Recherche Cardiovasculaire, Université de Rennes 1, 35042 Rennes cedex, France, †Laboratoire de Physiologie et Biomécanique de l’Exercice Musculaire, UFR STAPS Université de Rennes 2, 35044 Rennes, France, ‡Groupement d’Intérêt Scientifique Sciences du Mouvement, Université de Rennes 2, 35044 Rennes, France, and §Laboratoire Traitement du Signal et de l’Image, INSERM Université de Rennes 1, 35042 Rennes cedex, France A B S T R A C T In the present study, we investigated the effects of three levels of chronic physical activity on HRV (heart rate variability) in healthy older women. ECG recordings were taken in three (low-, moderate- and high-) activity groups in supine position with free and with controlled breathing, and during orthostatic stress. Temporal and spectral HRV indices were obtained from the ECG signal processing. The main results showed that, in supine position with free and controlled breathing, the HF (high-frequency) spectral component (P < 0.01) and the rMSSD (square root of the mean squared differences) between two adjacent RR intervals (P < 0.05 and P < 0.01 respectively) were elevated in the high-activity group compared with the low-activity group. No significant difference was observed between groups during the orthostatic test. Within groups, in the supine position, the change from free to controlled breathing produced a decrease in the LF (low-frequency) spectral component in all three groups (P < 0.01). The change from supine to standing position produced a decrease in RR in all three groups (P < 0.05 in low- and moderate-activity groups, and P < 0.01 in high-activity group); the rMSSD and the HF spectral component decreased only in the high-activity group (P < 0.01). In conclusion, this study performed on older women showed that parasympathetic indices of resting HRV were significantly elevated in a high physical activity group compared with in a low physical activity group. Furthermore, parasympathetic indices of HRV decreased during an orthostatic test only in the high-activity group. The influence of chronic moderate physical activity on HRV in older women was small in the present study. INTRODUCTION HR (heart rate) is mainly controlled by autonomic nerve activity to the sinoatrial node. Sympathetic and parasympathetic drive can be non-invasively investigated using HRV (HR variability) analysis [1]. A low level of HRV associated with low vagal parasympathetic activity has been identified as a risk marker for all causes of mortality [2]. HRV can be altered by physiological factors, such as aging, gender and physical fitness. The aging process decreases HRV towards a lower parasympathetic modulation [3,4]. Concerning gender, parasympathetic modulation of HRV seems to be generally higher in women than in men [5,6]; however, aging tends to attenuate this difference [6], the change apparently beginning at the menopause [7]. It is widely accepted that a high cardiorespiratory fitness is associated with an Key words: aerobic fitness level, aging, autonomic nervous system, female, orthostatic stress, parasympathetic modulation. Abbreviations: HF, high-frequency; HR, heart rate; HRmax, maximum HR; HRV, HR variability; LF, low-frequency; MET, metabolic equivalent task; rMSSD, square root of the mean squared differences; SDRR, S.D. of all RR intervals; TVAR, time-varying auto-regressive; TP, total power; V̇o2 max, maximal oxygen uptake; Wmax, maximal exercise power. Correspondence: Dr Sylvia Reland, present address: Unité de Biologie et Médecine du sport, Centre Hospitalier Universitaire Pontchaillou, 35000 Rennes, France (e-mail sylvia [email protected]). C 2004 The Biochemical Society 29 30 S. Reland and others elevated level of HRV and this has been shown in endurance-trained young and older men [8–12]. Indeed, a moderate level of physical activity is recommended in the older population [13], which is known to progressively decrease its level of physical activity [14]. In older women, few studies have concerned the effects of physical activity on HRV [15–17]. No alteration [17] or an increase [15,16] of HRV with a high level of physical activity has been reported. Thus these data highlight that, in older women, the effects of physical training on HRV are still a matter of debate and need to be clarified. The cardiovascular adaptations induced by physical training are known to be more detectable during physical stress, such as orthostasis [18]; however, the relationship between HRV and physical training has been studied more in the older population at rest in the supine position with free or controlled breathing [11,19–21]. In addition, older people are known to have a restricted range of sympathetic and parasympathetic responses to orthostasis [3,4], which may have important clinical implications given the high risk of falls observed in elderly subjects during the postural change to a standing position [22]. Thus it is of particular interest to establish the effects of training on HRV during a postural change, as recommended by some authors [23]. Thus the aim of the present study was to assess the effects of three levels of chronic physical activity on HRV in healthy older women at rest in the supine position with both free or controlled breathing and during an orthostatic test. We hypothesize that, in older women, the level of physical training performed influences HRV at rest and the change in HRV induced by the physiological stress of standing, and that the parasympathetic modulation of HRV is particularly affected. MATERIALS AND METHODS Subjects Forty-five healthy postmenopausal women (aged 60– 70 years) participated in the present investigation. The nature, purpose and risks of the study were explained to each subject and written informed consent was obtained. This study was approved by the Ethics Committee of the Faculty of Medicine of the Rennes University. The participants were all non-smokers, normotensive, free of cardiovascular disease by history and clinical examination, which included resting blood pressure measurement and ECG. None was taking cardioactive medication. Self-reported usual physical activity levels were assessed using a physical activity questionnaire [24]. A MET (metabolic equivalent task) value was assigned to each activity (1 MET = 4.185 kJ · kg−1 · h−1 ). The number of hours spent at each activity/week was multiplied by the appropriate MET value and the subject’s body mass to obtain a value of energy expenditure (kJ · week−1 ). The sum of the activity values was an estimation of C 2004 The Biochemical Society weekly exercise energy expenditure. Three groups were then identified. Subjects who were not engaged in any regular physical activity and reported less than 4185 kJ · week−1 (1000 kcal · week−1 ) constituted the low-activity group (n = 15). This limit corresponded to the recommendations for minimal activity required to provide substantial health benefits [25]. Subjects who reported more than 8370 kJ · week−1 (2000 kcal · week−1 ) constitute the high-activity group. This corresponds to a physical activity level above which there is a reduced age-adjusted relative risk of myocardial infarction [26]. Subjects came from a cycle touring club and had cycled for more than 4 years regularly in excess of 80 km/ week (n = 15). Subjects who reported physical activity levels between 4185–8370 kJ · week−1 (1000–2000 kcal · week−1 ) constituted the moderate-activity group. This group had regularly practiced voluntary gymnastics for more than 4 years (n = 15). Aerobic fitness was assessed from a V̇o2 max (maximal oxygen uptake) measurement, as V̇o2 max represents the usual index of maximal cardiovascular function. V̇o2 max in the low-activity group was lower than predicted [27]. In the moderateactivity group, V̇o2 max was between 100–120 % of that predicted, whereas in the high-activity group it was > 130 % of predicted uptake. Body fat percentage was measured using the skin fold thickness method [28]. The physical characteristics of the three groups are described in Table 1. Experimental procedure All subjects performed a graded maximal exercise test to measure V̇o2 max (ml · min−1 · kg−1 ), HRmax (maximum HR; beats/min), Wmax (maximal exercise power, measured in Watts) and to reveal medical exclusion criteria for the study. The graded exercise test was performed on an ergocycle (ERG 900, Marquette Hellige; Milwaukee, WI, U.S.A.) with continuous recording 12lead ECG (Cardio System, Marquette Hellige) and breath-by-breath gas exchanges analysis (Oxycon Delta; Jaeger, Hoechberg, Germany). The subjects sat quietly on the ergocycle for 2 min, connected to the gas analyser. Following a 3 min warming-up period at 20 Watts, the work rate was increased by 10 Watts every minute until exhaustion. The mean V̇o2 and HR measured during the last 30 s of each stage were taken into account. The exercise test was stopped when at least three classic criteria of V̇o2 max were attained [29]. ECG recordings for HRV measurement were always performed 1 week after the maximal exercise test. The subjects performed two trials, one familiarization and one experimental, at an interval of 1 week. Subjects were instructed to refrain from any excessive physical activity and from ingesting beverages containing caffeine or alcohol for at least 24 h before testing. All trials were held in the morning, 3 h after a light meal, at the same time of the day and in the same room. In all cases, room temperature Fitness level and heart rate variability Table 1 Physical characteristics of low-, moderate- and high-activity groups ∗∗ ∗∗∗ Values are means + − S.E.M. P < 0.01 and P < 0.001 compared with the high-activity group; ††P < 0.01 and †††P < 0.001 compared with the low-activity group; ‡P < 0.05 and ‡‡P < 0.01 compared with the moderate-activity group. Age (years) Weight (kg) Height (cm) Body mass index (kg · m−2 ) Body fat (%) Energy expenditure (kJ · week−1 ) V˙o2 max (ml · min−1 · kg−1 ) Percentage predicted V˙o2 max HRmax (beats/min) W max (Watts) Low activity (n = 14) Moderate activity (n = 13) High activity (n = 14) 67.2 + − 1.1 57.5 + − 1.8 159.5 + − 1.2 22.5 + − 0.5 32.6 + − 0.5 2963 + − 400 (1464−4018)‡ 19.3 + − 0.7‡ 92.8 + − 2.3‡‡ 151.6 + − 2.7 77.8 + − 3.7‡‡ 65.4 + − 3.0 57.7 + − 2.2 157.4 + − 1.6 23.2 + − 0.7 32.9 + − 0.7 ∗∗∗ 6260 + − 381 (5800−8110) ∗∗∗ + 22.4 − 0.6 ∗∗∗ 112.4 + − 1.8 + 149.1 − 3.4∗∗ ∗∗∗ 95.4 + − 2.9 64.6 + − 1.0 59.8 + − 1.6 159.8 + − 1.0 23.4 + − 0.7 33.0 + − 1.6 14 417 + − 1714 (10 923−21 970)††† 30.1 + − 1.1††† 146.5 + − 4.3††† 165.2 + − 3.3†† 137.1 + − 4.2††† was between 20 and 22 ◦ C, room lighting and the number of technicians present were kept constant. Noise level was minimized during the trial. At the beginning of each trial, subjects rested for 10 min in a supine position. The subjects were not informed that ECG recording had begun. A three-lead ECG was recorded under three different conditions defined as usual clinical tests to study HRV [20,30]: free breathing in the supine position (Test 1), controlled frequency breathing (20 breaths/min) in the supine position (Test 2) and orthostatic stress (Test 3). During a pilot study, we tested various controlled breathing protocols at 12, 15 and 20 breaths/min. Since this latter rate seemed the most practical to maintain in our subjects, it was retained for this present study. To achieve 20 breaths/min, subjects inspired (1.5 s) and expired (1.5 s) in synchronization with an audible signal. No attempt was made to influence tidal volume. After controlled breathing, the subjects remained in an undisturbed supine resting position for 10 min. During Test 3, subjects were instructed to stand up abruptly, as quickly as possible, and to remain standing unsupported for 6 min. The ECG recording started from the beginning of the postural change. In each test, ECG and breathing frequency were recorded over a 6 min period. ECG data analysis The ECG was sampled at 1000 Hz with the PowerLab® acquisition system (ADInstruments Pty Ltd, Castle Hill, Australia) installed on a Macintosh computer (Power Mac). Thus the accuracy of the measurements was 1 ms. The first minute of each ECG recording was disregarded to allow for stabilization of the data prior to analysis. The detection of the QRS complex was conducted using the Gritzali’s algorithm [31]. RR interval sequence was defined by the duration between two consecutive R-peaks. These data were edited to eliminate any glitches, due to premature cardiac contraction, using the procedure reported by Bruggeman and Andersen [32]. Each RR interval was visually validated by two experts before temporal and spectral analysis. For each RR sequence, three classical temporal parameters were then extracted [33]: the mean RR, which represents mean HR; S.D. of all RR intervals (SDRR), which reflects all the cyclic components responsible for variability in the period of recording, and rMSSD (square root of the mean squared differences) between adjacent RR intervals, which is considered as an index of parasympathetic modulation of HR. Prior to power spectrum density estimation, the RR sequence, which is intrinsically non-evenly spaced data, was linearly interpolated in order to obtain a series of uniformly sampled data. An interpretation of frequency contents of HRV was therefore possible independently of the mean RR value. The retained sampling rate was then set to 2 Hz. Using a sliding window of 64 s duration, time-varying auto-regressive (TVAR) modelling of the interpolated RR sequence was performed to estimate its power spectrum (ms2 ) in order to eliminate the slight non-stationarities of the sequence. On the basis of the well-known Akaike information criteria, the order of the TVAR model was set to 12 [34]. The frequency component of HRV were therefore the LF (lowfrequency) component, which was defined between 0.04–0.15 Hz, and the HF (high-frequency) component, which was defined as a narrow frequency band (0.06 Hz) centred around the breathing frequency of each individual [33,35]. TP (total power) was defined as being the area under the curve of the whole power spectrum from 0.04– 1 Hz. Because of the short recording duration, the very LF component (0.00–0.04 Hz) was not taken into account as recommended [33]. The spectral power of the RR series in these frequency bands was then calculated and averaged over the last 5 min of each recording [36]. The HF component of HRV corresponds to respiratory sinus arrhythmia and represents vagally mediated modulations in HR. The LF component is influenced by both sympathetic and parasympathetic activity. LF/HF is described as a classical marker of sympathovagal balance [33]. C 2004 The Biochemical Society 31 32 S. Reland and others Table 2 HRV indices in the low-, moderate- and high-activity groups during the three tests Values are means + − S.E.M. Test 1, supine position with free breathing; Test 2, supine position with controlled breathing frequency (20 breaths/min); and Test 3, orthostatic stress. ∗ P 0.05 and ∗∗ P < 0.01 compared with Test 2; †P < 0.05 and ††P < 0.01 compared with Test 1; ‡P < 0.05 and ‡‡P < 0.01 compared with high-activity group; §P < 0.05 compared with high-activity group. Groups Test RR (ms) SDRR (ms) rMSSD (ms) HF (ms2 ) LF (ms2 ) TP (ms2 ) LF/HF Low 1 2 3 1 2 3 1 2 3 847 + − 29 829 + − 29 805 + − 24† 903 + − 37 892 + − 33 871 + − 33† 924 + − 25 910 + − 97 845 + − 38†† 25.0 + − 2.6‡ 23.7 + − 2.4 24.0 + − 1.8 25.5 + − 2.0§ 24.6 + − 2.0 26.3 + − 2.6 ∗∗ 34.1 + − 3.3 + 25.0 − 2.0 29.0 + − 3.1† 15.2 + − 1.9‡‡ 15.5 + − 1.8‡ 14.3 + − 1.7 19.0 + − 2.7 17.8 + − 2.6 15.9 + − 1.8 24.4 + − 2.9 24.0 + − 3.1 17.5 + − 3.5†† 25.9 + − 6.4‡‡ 32.7 + − 7.6‡‡ 26.6 + − 10.4 54.4 + − 13.0 52.2 + − 12.8 39.6 + − 10.4 127.7 + − 50.0 96.8 + − 24.8 49.7 + − 18.2†† ∗∗ 238.8 + − 72.0 126.63 + − 38.3 141.8 + − 21.1 ∗∗ 175.6 + − 35.7 111.1 + − 23.0 201.3 + − 59.9 ∗∗ 296.7 + − 55.2 + 129.1 − 20.2 226.1 + − 58.9 ∗ 270.3 + − 38.7 ‡‡ 193.8 + − 36.1‡‡ 253.7 + − 88.8 ∗ 345.9 + − 122.1 228.2 + − 47.2 299.4 + − 69.4 ∗ 558.2 + − 109.1 + 342.3 − 79.2 380.1 + − 117.4† 10.9 + − 2.3 6.0 + − 1.6 12.4 + − 2.7 7.7 + − 2.6 4.6 + − 1.6 13.2 + − 6.2 ∗ 5.5 + − 1.6 + 2.4 − 0.6 25.0 + − 11.0† Moderate High Statistical analysis Data are reported as means + − S.E.M. A one-way ANOVA was performed to compare the physical characteristics between the three groups. HRV indices obtained during the experimental trial were tested for normality. As the data were not normally distributed, the Kruskall–Wallis test, followed by the Mann–Whitney test, were performed to assess the difference between groups. The difference within groups was tested using a Wilcoxon test. The three tests for the trial were the following: free breathing in supine position (Test 1), controlled breathing in supine position (Test 2) and orthostatic stress (Test 3). Because of the lack of controlled breathing during Test 3, Test 2 with Test 3 were not compared. The relationship between V̇o2 max and parasympathetic indices of HRV, rMSSD of the RR interval and the HF component of HRV were assessed in the pooled population using Spearman’s correlation coefficient. A P value < 0.05 was considered as significant. Statistical analyses were performed using Statistica software version 5. 97 (StatSoft Inc, Maisons-Alfort, France). RESULTS Two subjects did not complete the study (after the maximal exercise test) for personal reasons and two subjects were excluded because of technical problems. Thus 41 subjects finally completed the study (lowactivity group, n = 14; moderate-activity group, n = 13; high-activity group, n = 14). A few subjects (three in the low-, four in the moderate- and four in the highactivity groups) were receiving hormone-replacement therapy, whose effect on HRV is controversial [37,38]. All populations would therefore have been affected similarly by potential influences of hormone therapy. Moreover, individual data of the subjects receiving this therapy were dispersed within each group. C 2004 The Biochemical Society No significant difference was observed between the three groups with regard to age, height, weight, body mass index or body fat percentage. As expected, the score for physical activity questionnaire, V̇o2 max, the percentage of V̇o2 max predicted and Wmax were the highest in the high-activity group (P < 0.001), and were higher in the moderate- than the low- activity groups (P < 0.05 for physical activity questionnaire and Wmax, and P < 0.01 for V̇o2 max and the percentage of V̇o2 max predicted). HRmax was significantly elevated in the highactivity group compared with both other groups (P < 0.01). Concerning HRV indices, significant differences were observed between and within groups (Table 2). Betweengroups comparisons showed that, during Test 1, the high-activity group had the highest SDRR (P < 0.05). During Test 1 and Test 2, rMSSD (P < 0.01 for Test 1, and P < 0.05 for Test 2), the HF component (P < 0.01) and TP (P < 0.01) were elevated in the high-activity group compared with the low-activity group. No other significant differences were observed. Within groups, from Test 1 to Test 2, the mean frequency peak of the HF component shifted to the right (0.34 + − 0.005 Hz), without any significant change in its spectral power. LF component (P < 0.01) and TP (P < 0.05) decreased in all three groups. The LF/HF ratio decreased in the high-activity group (P < 0.05) and tended to be lower in the low- and moderate- activity groups (P = 0.05 and P = 0.09 respectively). Moreover SDRR decreased only in the highly active subjects (P < 0.01). From Test 1 to Test 3, RR decreased in the low- (P < 0.05), moderate- (P < 0.05) and high-activity groups (P < 0.01). However, SDRR (P < 0.05), rMSSD (P < 0.01), HF component (P < 0.01), TP (P < 0.05) and LF/HF ratio (P < 0.05) decreased only in the highactivity group. Figures 1 and 2 illustrate the tachogram of RR values and HRV spectra respectively, in a highly active subject during the three Tests. Fitness level and heart rate variability Figure 1 Interval tachogram of 100 RR values in a highly active subject in Test 1 (top), Test 2 (middle) and Test 3 (bottom) Figure 3 Relationship between rMSSD between adjacent RR intervals and V̇o2 max in all subjects in the supine position with controlled breathing n = 41. Correlation, r = 0.40, P < 0.01. Figure 2 HRV spectra in a highly active subject in Test 1 (top), Test 2 (middle) and Test 3 (bottom) The highly active subject was the same as in Figure 1. Positive relationships were observed in all the subjects pooled between V̇o2 max and rMSSD in Test 1 (r = 0.45, P < 0.01) and Test 2 (r = 0.40, P < 0.01; Figure 3) and between V̇o2 max and HF in Test 1 (r = 0.42, P < 0.01) and Test 2 (r = 0.45, P < 0.01, Figure 4). Figure 4 Relationship between the HF component and V̇o2 max in all subjects in the supine position with controlled breathing DISCUSSION n = 41. Correlation, r = 0.45, P < 0.01. The major findings of the present study performed in older women are that (i) a high level of chronic physical activity increases parasympathetic indices of HRV in the supine position with free and controlled breathing, and (ii) orthostatic stress induces a significant decrease in these indices only in the highly trained women. HRV analysis is used to non-invasively assess the influences of autonomic activity on the sino-atrial node. In the present study, HRV values obtained in the supine position with free and controlled breathing in the lowactivity group are in agreement with previous data obtained in a similar population [17]. They are lower than those reported in young women of low physical activity [16]. These results are consistent with evidence that HRV decreases with age, as reported previously [3,4]. Despite the loss of HRV with age, when the older women in the present study were considered together, two indices of parasympathetic modulation of HRV showed a strong C 2004 The Biochemical Society 33 34 S. Reland and others correlation with V̇o2 max, as expected in a younger age group [39]. SDRR, rMSSD and HF at rest in the supine position with free breathing were increased in the high- relative to the low-activity group, and SDRR was increased in the high- relative to the moderate-activity group. These findings apparently contrast with reports in the literature concerning the effects of physical training on cardiac autonomic modulation in older people. The duration and the intensity of physical training may be implicated in this discrepancy. Thus Perini et al. [15] showed that a short period (8 weeks) of intense aerobic training increased V̇o2 max in older people without any change in resting HRV [17]. Furthermore, a longer period (12 months) of low-intensity endurance training did not alter resting HRV in middle-aged men [21]. However, in competitive long-distance postmenopausal runners of national standard, who had been exercising for more than 10 years, resting HRV was increased [15,16]. Our highly active subjects regularly participated in relatively long and intense aerobic exercise. Thus, considering these data together, it seems reasonable to propose that a long period of high-intensity physical training is needed to induce significant changes in parasympathetic indices of HRV in an older population when tested in the supine position with free breathing. As recommended, we also performed HRV analysis when breathing frequency was controlled [33,40]. The HF component of HRV represents respiratory sinus arrhythmia and primarily reflects the response of the sinus node to parasympathetic activity [40]. Brown et al. [41] observed a significant decrease in the HF component in young subjects at rest with controlled breathing at 20 cycles · min−1 compared with free breathing. When breathing was controlled, we have shown that, in the high-activity group, there was a decrease in SDRR, an index of HRV that includes that due to parasympathetic modulation. Moreover, there was an apparent trend for the HF component to decrease in this high-activity group, but not in the others. In view of the data of Brown et al. [41], the lack of change observed in HRV in response to controlled breathing in the low- and moderate-activity groups is consistent with a decrease in parasympathetic modulation of HRV with aging. Elderly populations often present orthostatic intolerance with a high risk of falls [22]. Postural changes greatly modify cardiovascular homoeostasis and induce autonomic reflexes that help to maintain arterial pressure. It is therefore recommended that cardiovascular adaptations to orthostatic stress are tested when examining elderly subjects medically [22]. In young male subjects, orthostatic stress increases HR and induces a withdrawal of the parasympathetic influence associated with an increase in the sympathetic influence [42]. However, it has been reported [18,43] that the chronotropic and HRV alterations to orthostasis are decreased by the aging C 2004 The Biochemical Society process. Indeed, in sedentary older men, it was found that the increase in the LF component is blunted or absent and the HF component remains unchanged during orthostatic stress [42]. In our present study, HR increased significantly in all the groups studied. In the high-activity group, rMSSD and the HF component were decreased significantly without any change in the LF component. By contrast, no significant alteration in rMSSD, or in the HF or the LF components of HRV were observed in the low- and moderate-activity groups. Thus, in highly active older women, the increase in HR during orthostatic stress can be explained by a decrease in the parasympathetic modulation of HR; sympathetic modulation was apparently not altered. Thus it seems that the higher activity group have a high parasympathetic tone and can, therefore, show a larger increase in HR by withdrawing this parasympathetic tone; this may prevent orthostatic intolerance in this section of the elderly population. Conclusion Our present study suggests that a chronic high level of physical activity partly counteracts some of the deleterious effects of aging on parasympathetic regulation of HR in healthy women. Resting parasympathetic indices of HRV were significantly higher in a high-physical activity group than in a low-physical activity group. 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