Training & Testing 999

Reliability of Heart Rate Measures during Walking before and after Running Maximal Efforts

Affiliations

Key words ▶ autonomic nervous system ● ▶ parasympathetic ● reactivation ▶ sympathetic withdrawal ● ▶ heart rate complexity ● ▶ detrended fluctuation ● analysis

D. A. Boullosa1, E. S. Barros1, S. del Rosso2, F. Y. Nakamura3, A. S. Leicht4 1

Universidade Católica de Brasília, Pós-Graduação Stricto Sensu em Educação Física, Águas Claras, Brazil Grupo Sobre Entrenamiento (G-SE. com), Córdoba, Argentina 3 Universidade Estadual de Londrina, Departamento de Educação Física, Londrina, Brazil 4 James Cook University, Institute of Sport and Exercise Science, Townsville, Australia 2

Abstract

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Previous studies on HR recovery (HRR) measures have utilized the supine and the seated postures. However, the most common recovery mode in sport and clinical settings after running exercise is active walking. The aim of the current study was to examine the reliability of HR measures during walking (4 km · h − 1) before and following a maximal test. Twelve endurance athletes performed an incremental running test on 2 days separated by 48 h. Absolute (coefficient of variation, CV, %) and relative [Intraclass correlation coefficient, (ICC)] reliability of time domain and non-linear measures of HR variability (HRV)

Introduction

▼ accepted after revision March 03, 2014 Bibliography DOI http://dx.doi.org/ 10.1055/s-0034-1372637 Published online: May 19, 2014 Int J Sports Med 2014; 35: 999–1005 © Georg Thieme Verlag KG Stuttgart · New York ISSN 0172-4622 Correspondence Dr. Daniel A. Boullosa Universidade Católica de Brasília Educação Física Águas Claras Águas Claras Brazil 71966-700 Tel.: + 55/61/3356 9489 Fax: + 55/61/3356 9489 [email protected]

Following exercise, heart rate (HR) returns to resting levels as a result of cardiac parasympathetic reactivation [33] and sympathetic withdrawal [6]. This response is commonly examined to evaluate the impact of training loads on cardiac autonomic control and subsequent training effectiveness [13, 33]. Heart rate recovery (HRR) and HR variability (HRV) have been widely utilized for the simple and non-invasive evaluation of this physiological recovery response [16, 19]. The validity of both HRR and HRV monitoring in different populations including athletes and patients with a number of pathological conditions has been well established [22, 33, 36]. For instance, faster HRR has been reported as a good predictor of lower coronary disease mortality [2] and improved performance after a training period for different sports [8, 27]. Similarly, a faster return to pre-exercise HRV in the acute recovery phase reflects autonomic adaptation to chronic exercise training [33] with the extent of its reduction recently reported to be related to training load variables [24].

from 3 min recordings, and HRR parameters over 5 min were assessed. Moderate to very high reliability was identified for most HRV indices with short-term components of time domain and non-linear HRV measures demonstrating the greatest reliability before (CV: 12–22 %; ICC: 0.73–0.92) and after exercise (CV: 14–32 %; ICC: 0.78–0.91). Most HRR indices and parameters of HRR kinetics demonstrated high to very high reliability with HR values at a given point and the asymptotic value of HR being the most reliable (CV: 2.5–10.6 %; ICC: 0.81–0.97). These findings demonstrate these measures as reliable tools for the assessment of autonomic control of HR during walking before and after maximal efforts.

While the validity of these HRR and HRV measures has been well documented, previous studies have reported only low to moderate reliability for these autonomic indices [3, 7, 18]. One important consideration may be that the level of reliability is strongly dependent on the examined parameter. For example, HRR indices and spectral HRV indices present greater coefficients of variation (CV) than time domain HRV indices [3]. Consequently, the sensitivity of both HRR and HRV for detecting different autonomic responses and adaptations following exercise and chronic training may be dependent on the specific HRR or HRV measure examined. The use of measures with low reliability would not identify the influence of a longitudinal intervention if the measurement error was larger than the expected change. Therefore, selecting appropriate methods of post-exercise HRR and HRV analyses is a key issue for guaranteeing the appropriateness of these monitoring tools. Similarly, the selection of the most appropriate recovery setting including posture (seated vs. supine vs. stand) [11, 39] and mode (active vs. passive) [5, 12] may also significantly impact the

Boullosa DA et al. Reliability of Heart Rate … Int J Sports Med 2014; 35: 999–1005

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Authors

1000 Training & Testing Methods

magnitude of post-exercise HR measures. In this regard, it is interesting to note that previous studies investigating reliability of post-exercise HR measures have examined R-R intervals only in the seated position [3, 7, 18], while most studies on cardiac parasympathetic reactivation after exercise have utilized the supine posture [33]. Furthermore, studies on reliability of postexercise HR measures [3, 7, 18] described a change in posture from the standing position at the end of a running exercise to seated position in a very short period of time ( < 5 s). In this regard, it is well known that changes in posture represent important autonomic challenges [23] and therefore post-exercise changes in posture may affect cardiac parasympathetic reaction and sympathetic withdrawal. As the most common recovery mode in sport and clinical settings after running exercises is active walking [8, 10, 15], further examination of this mode is warranted. Walking recovery is widely used as it allows a safe, fast and easy cooling down after intense or exhausting exercise. Moreover, this recovery mode has been preferred by endurance athletes during interval training sessions [32] and after maximal testing in the field [10]. Paradoxically, the reliability of autonomic indices during this widely used recovery paradigm (which does not incorporate any sudden change in posture) has, to our knowledge, not been examined yet. The aim of the current study was therefore to examine the reliability of both HRR and HRV during acute recovery following an incremental running test in well trained athletes. Additionally, we assessed the reliability of HRV before exercise as baseline condition. It was hypothesized that HRR and HRV during walking recovery in athletes would be highly reproducible and therefore provide a reliable monitoring tool for training and assessing athletes in a more ecological state.

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Participants ▶ Table 1) volunteered for this 12 endurance-trained athletes (● study. Before any test or data collection, the athletes were briefed about the aims of the study and signed an informed consent form. All study protocols were in accordance with the Helsinki Declaration, were conducted according to international ethical standards [20] and were approved by the Local Ethics Committee.

The athletes were required to visit the laboratory on 3 occasions with each separated by 48 h. The first visit was devoted to anthropometric measures and familiarization with the instruments and procedures. Thereafter, the same procedures were applied for the following 2 visits (i. e. Test 1 and Test 2) to examine the reliability of HRR and HRV measures. Firstly, participants undertook quiet standing for 3 min (basal HR) followed by 5 min of slow walking at 4 km · h − 1. Thereafter, they performed an incremental maximal running test for the recording of maximum oxygen uptake (VO2max), peak running speed (Speak) and maximum heart rate (HRmax). Immediately after the maximal running test, athletes walked for 5 min at the same pre-exercise speed (4 km · h − 1) for HRR and HRV analyses. The transition from maximal running to walking was ~15–20 s due to the time required for the treadmill (Super ATLMillenium, Imbramed, Brazil) to achieve the target speed after the end of the test. The ▶ Fig. 1. study design is presented in ●

Incremental maximal running test The test protocol used for this study was the same as described by Di Prampero et al. [17]. Briefly, the test started at a speed of 8 km · h − 1 for 4 min with the speed being increased by 0.5 km · h − 1 every 30 s until the athletes reached volitional exhaustion. During the exercise test, gas exchange was monitored breath-bybreath using a metabolic cart (Cortex Metalyzer 3B, Cortex, Leipzig, Germany). Prior to each test, volume and gas calibrations of the metabolic cart were performed according to manufacturer’s instructions. Oxygen uptake recorded during the whole exercise test was subsequently examined to determine VO2max, gas exchange threshold (GET) and cost of running (CR). VO2max was defined as the mean value of oxygen consumption (VO2) recorded during the last 20 s before exhaustion with a respiratory exchange ratio (RER) ≥ 1.10. Before GET and CR determination, breath-by-breath

Table 1 Participants’ characteristics and values of maximum oxygen uptake (VO2max), peak running speed (Speak), gas exchange threshold (GET), and energy cost of running (CR) for Test 1 and Test 2.

age (y) weight (kg) height (cm) %body fat VO2max (mL · kg − 1 · min − 1) Speak (km · h − 1) GET (mL · kg − 1 · min − 1) GET ( %) CR [mL O2/(kg · m)]

Test 1

Test 2

64.5 ± 4.8 21.5 ± 0.9 52.0 ± 5.3 81.6 ± 5.7 0.16 ± 0.02

64.7 ± 6.4 21.8 ± 1.1 51.4 ± 6.1 81.0 ± 7.0 0.16 ± 0.02

29.5 ± 4.7 64.2 ± 4.3 173.7 ± 5.1 6.77 ± 1.73

Values are means ± SD

200 180

Standing Pre-Test HRV & HRC

Walking Pre-Test HRV & HRC

5’ Walking Post-Test HRR

160

HR (bpm)

140 120 100 80 Last 3’ Walking Post-Test HRV & HRC

60 40 20 00:00:00

00:05:00

00:10:00

00:15:00

00:20:00

00:25:00

Time (min)

Boullosa DA et al. Reliability of Heart Rate … Int J Sports Med 2014; 35: 999–1005

00:30:00

Fig. 1 Experimental design. HRV: heart rate variability; HRC: heart rate complexity; HRR: heart rate recovery.

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Study protocol

(VO2 and carbon dioxide production – VCO2) data were smoothed by means of a running average with a sampling proportion of 0.100. GET was then determined by means of the V-slope method while the CR was assessed as previously described by Di Prampero et al. [17].

Heart rate data analysis Heart rate was continuously monitored beat-by-beat with a telemetric device (RS800 CX, Polar Electro Oy, Finland) during the whole protocol. Recorded HR data were downloaded to a computer using an infrared interface and Polar software (Polar ProTrainer 5, Polar Electro, Kempele, Finland). Raw HR data were visually inspected and where necessary, automatically filtered using a moderate filter (minimum beat protection zone of 6 beats [29]) with all irregular heartbeats and artefacts replaced with interpolated adjacent R-R interval values using the Polar Software (Pro Trainer 5, Polar Electro, Finland). HRR assessment included both raw and relative parameters as well as HRR kinetics. Raw HR was defined as the HR value at a given point of recovery period (i. e., 30 s, 1 min, 2 min, 3 min, and 5 min), and relative HR recovery was defined as the difference between the HR registered at the end of maximal exercise (HRend) and after 30 s, 1 min, 2 min, 3 min, and 5 min (i. e., Δ30’’, Δ1’, Δ2’, Δ3’ and Δ5’) [7]. For the assessment of HRR kinetics over the entire recovery period, individual data were modeled with a monoexponential fit (Sigmaplot 12; SPSS Science, Chicago, IL) using the following equation [18, 30]: HR(t) = HR0 + HRamp × e( − t/τ), where, HR(t) is the HR at a given time; HR0 is the asymptotic value of HR (bpm); HRamp is the amplitude of HR decrement from HRend to HR0 for t = ∞ (bpm); and τ is the time constant (i. e. time to reach 1–1/e or ~63.2 % of the total HRR, in s). Heart rate data from the pre-test period were used to assess basal HRV with spontaneous breathing. Short-term HRV indices were calculated for the 3-min standing period and for the final 3 min of the 5 min walking period before the incremental test ▶ Fig. 1). Time domain indices included the standard deviation (● of the R-R series (SDNN) and the square root of the mean of the sum of the squares of differences between adjacent R-R intervals (RMSSD) [35]. Non-linear analyses of HRV included: the long (SD1) and the short (SD2) axes from Poincarè Plots reflecting the short- and the long-term modulation, respectively; HR complexity analysis via sample entropy (SampEn), which measures the irregularity of the signal; and the detrended fluctuations of short- (α1) and long-term (α2) fractal scaling as determined from a double log graph that assesses the correlation within the signal [9, 28, 34, 38]. Complexity analyses have recently been demonstrated to better detect training adaptations when obtained from steady state exercises [25]. During recovery, both time domain indices and non-linear HR analyses were obtained ▶ Fig. 1) to ensure stability of data as previfrom the last 3 min (● ously suggested [11, 18]. All HRV indices were derived using the Kubios software (University of Kuopio, Kuopio, Finland) following standard procedures with default values of the software [34].

Statistical analyses Descriptive statistics were used to present means, standard deviations ( ± SD) and 90 % confidence interval (90 % Cl). Normality was assessed by Shapiro-Wilk test and visual inspection of the Q-Q plots of both raw and transformed data. Homoscedasticity of variance was assessed by a modified Levene test (Sigmaplot 12; SPSS Science, Chicago, IL). The variables associated with HRV analysis deviated from normality and showing hetero-

scedasticity were log-transformed (i. e. natural logarithm) to allow for parametric statistical analysis. Comparisons between Test 1 and Test 2 values of VO2max, Speak, GET (absolute and relative), CR, HRV and HRR indices, and HRR kinetics (i. e. HR0, HRamp and τ) were compared using paired t-tests (SPSS, version 19.0, SPSS inc., Chicago, IL). Relative reliability was assessed via the intra-class correlation coefficient (ICC). Reliability was considered to be very high with an ICC ≥ 0.90, high with an ICC between 0.70 and 0.89, and moderate with an ICC between 0.50 and 0.69 [18]. Absolute reliability was assessed via the typical error of measurement (TEM) and coefficient of variation (CV, %). The ICC, TEM and CV % were calculated using a reliability spreadsheet [21] that provided reliability statistics for consecutive pairs of trials. All reliability analyses were performed with the log-transformed variables as it has been suggested that this procedure substantially reduces the non-uniform errors [3]. For all analyses the level of significance was set at p ≤ 0.05.

Results

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The results obtained from the maximal incremental running ▶ Table 1. There were no significant differtests are presented in ● ences between Test 1 and Test 2 for all variables analyzed. Descriptive values and reliability of time domain and non-linear ▶ Table 2. There were no signifiHRV indices are presented in ● cant differences between Test 1 and Test 2 for HRV parameters. High to very high relative reliability was identified for time domain HRV indices for pre-test and recovery walking conditions, with the exception of SDNN that exhibited a moderate relative reliability during active walking recovery (ICC = 0.58). Non-linear HRV parameters were less variable and more reproducible for both walking conditions than the standing condition. Moreover, TEM and CV values tended to be lower for both walking conditions than the standing condition. Descriptive and reliability data for raw and relative HRR and ▶ Table 3. No signifiparameters of HRR kinetics are reported in ● cant differences were observed between days for raw and relative HRR values, as well as all parameters of HRR kinetics. Mean coefficients of determination (R2) of the exponential fits of HRR were 0.98 ± 0.01 for both tests. HRR indices and parameters of HRR kinetics were identified as highly reliable (ICC > 0.78) with the exception of Δ1’ that displayed moderate relative reliability (ICC = 0.64). The largest variation of relative HRR indices was noted for Δ30’’ and Δ1’ which exhibited CVs of 16.5–38.3 %. The TEM and CV values for raw and relative HRR tended to be lower with increasing recovery time.

Discussion

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The main finding of this study was that most HRV and HRR measures during walking recovery were highly reliable in a group of well-trained endurance runners after an incremental running test until exhaustion. Previous studies have examined HRV and HRR during passive recovery using predominantly linear HRV measures, whereas the current study expanded the range to include non-linear analyses of HRV that demonstrated greater reliability, especially for short-term modulation measures (i. e. SD1, α1). These findings have important practical consequences as walking recovery is the most utilized recovery mode in both field and laboratory conditions as it allows a fast Boullosa DA et al. Reliability of Heart Rate … Int J Sports Med 2014; 35: 999–1005

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Training & Testing 1001

1002 Training & Testing

Standing Ln SDNN Ln RMSSD SD1 SD2 SampEn α1 α2 Walking (pre-test) Ln SDNN Ln RMSSD SD1 SD2 SampEn α1 α2 Walking (recovery) Ln SDNN Ln RMSSD SD1 SD2 SampEn α1 α2

Test 1

Test 2

TEM (90 % Cl)

CV, % (90 % Cl)

ICC (90 % Cl)

4.32 ± 0.32 3.87 ± 0.70 42.89 ± 29.87 108.32 ± 46.51 1.543 ± 0.544 1.248 ± 0.353 0.982 ± 0.222

4.31 ± 0.36 3.74 ± 0.54 33.44 ± 14.72 106.01 ± 37.01 1.267 ± 0.330 1.318 ± 0.310 0.863 ± 0.167

0.60 (0.45–0.93) 0.50 (0.37–0.78) 0.58 (0.43–0.89) 0.85 (0.63–1.31) 1.02 (0.76–1.59) 0.73 (0.55–1.14) 1.04 (0.78–1.61)

27.3 (19.8–45.4) 35.9 (25.8–61.0) 35.9 (25.8–60.9) 28.7 (20.7–47.8) 34.8 (25.1–59.0) 25.1 (18.2–41.5) 25.7 (18.6–42.6)

0.68 (0.30–0.87) 0.79 (0.49–0.92) 0.79 (0.50–0.92) 0.63 (0.21–0.85) − 0.05 ( − 0.51–0.44) 0.50 (0.04–0.79) − 0.08 ( − 0.54–0.41)

4.08 ± 0.38 3.85 ± 0.62 40.62 ± 28.85 79.21 ± 27.72 1.698 ± 0.325 1.190 ± 0.270 0.739 ± 0.232

4.07 ± 0.42 3.69 ± 0.62 33.55 ± 18.58 81.79 ± 34.63 1.531 ± 0.345 1.209 ± 0.213 0.818 ± 0.262

0.36 (0.27–0.56) 0.32 (0.24–0.50) 0.32 (0.24–0.50) 0.38 (0.28–0.58) 0.47 (0.35–0.72) 0.56 (0.42–0.87) 0.87 (0.65–1.35)

15.7 (11.5–25.4) 22.4 (16.3–36.7) 22.4 (16.3–36.8) 15.6 (11.4–25.2) 10.3 (7.6–16.4) 12.4 (9.1–19.8) 38.4 (25.7–65.4)

0.89 (0.73–0.96) 0.92 (0.78–0.97) 0.92 (0.78–0.97) 0.88 (0.70–0.96) 0.82 (0.56–0.93) 0.73 (0.38–0.89) 0.27 ( − 0.24–0.66)

3.05 ± 0.36 1.99 ± 0.49 5.85 ± 3.34 31.33 ± 12.95 1.057 ± 0.337 1.087 ± 0.257 1.200 ± 0.173

3.02 ± 0.30 1.92 ± 0.56 5.58 ± 3.34 29.47 ± 9.23 0.939 ± 0.325 1.114 ± 0.270 1.202 ± 0.178

0.68 (0.51–1.06) 0.34 (0.25–0.52) 0.34 (0.25–0.53) 0.72 (0.54–1.12) 0.59 (0.44–0.91) 0.50 (0.39–0.78) 0.81 (0.60–1.25)

25.7 (18.7–42.6) 19.4 (14.2–31.6) 19.7 (14.4–32.1) 27.4 (19.8–45.5) 28.0 (20.3–46.7) 13.2 (9.7–21.2) 13.1 (9.6–20.9)

0.58 (0.14–0.83) 0.91 (0.76–0.97) 0.91 (0.76–0.97) 0.52 (0.06–0.80) 0.70 (0.33–0.88) 0.78 (0.49–0.92) 0.39 ( − 0.11–0.73)

Data are mean ± SD. TEM = Typical Error of measurement; CV, % = TEM expressed as the coefficient of variation; ICC = intra-class correlation coefficient. TEM, CV % and ICC were calculated for the log-transformed variables. Ln SDNN = Natural log of the standard deviation of the R-R series. Ln RMSSD = Natural log of the square root of the mean of the sum of the squares of differences between adjacent R-R intervals. SD1 = Short term variability from Poincaré Plots. SD2 = Long term variability from Poincaré Plots. SampEn = Sample entropy; α1 = short-term detrended fluctuations; α2 = long-term detrended fluctuations

Table 3 Heart rate recovery analyses for Test 1 and Test 2 and their corresponding reliability measures. Test 1 Raw & relative HR recovery HRmax (bpm) 188 ± 11 HRend (bpm) 185 ± 11 Time at HRmax (s) 1 519 ± 77 Time at HRend (s) 1 530 ± 77 HR 30’’ (bpm) 167 ± 20 HR 1’ (bpm) 144 ± 20 HR 2’ (bpm) 115 ± 20 HR 3’ (bpm) 106 ± 19 HR 5’ (bpm) 103 ± 14 Δ 30’’ (bpm) 17 ± 12 Δ 1’ (bpm) 40 ± 13 Δ 2’ (bpm) 68 ± 13 Δ 3’ (bpm) 77 ± 12 Δ 5’ (bpm) 80 ± 9 HR recovery kinetics HR0 (bpm) 97 ± 16 HRamp (bpm) 94 ± 15 HRτ (s) 80 ± 23

Test 2

TEM (90 % Cl)

CV, % (90 % Cl)

ICC (90 % Cl)

188 ± 11 184 ± 11 1 540 ± 70 1 551 ± 65 171 ± 13 149 ± 20 119 ± 21 110 ± 19 105 ± 16 12 ± 6 35 ± 12 65 ± 15 74 ± 14 78 ± 12

0.29 (0.21–0.45) 0.33 (0.25–0.52) 0.43 (0.57–0.89) 0.46 (0.35–0.72) 0.49 (0.37–0.76) 0.54 (0.40–0.83) 0.31 (0.23–0.48) 0.18 (0.14–0.28) 0.33 (0.25–0.51) 0.37 (0.28–0.57) 0.82 (0.61–1.27) 0.39 (0.29–0.60) 0.34 (0.25–0.52) 0.59 (0.44–0.91)

1.6 (1.2–2.5) 2.0 (1.5–3.1) 2.8 (2.1–4.4) 2.2 (1.6–3.4) 4.8 (3.5–7.5) 6.7 (5.0–10.6) 5.5 (4.1–8.6) 3.3 (2.5–5.2) 4.7 (3.5–7.4) 22.6 (16.5–37.2) 23.3 (16.9–38.3) 8.4 (6.2–13.3) 5.9 (4.4–9.2) 7.0 (5.2–11.1)

0.93 (0.82–0.98) 0.91 (0.76–0.97) 0.71 (0.36–0.89) 0.82 (0.56–0.93) 0.84 (0.60–0.94) 0.81 (0.55–0.93) 0.93 (0.81–0.97) 0.97 (0.93–0.99) 0.92 (0.79–0.97) 0.89 (0.71–0.96) 0.64 (0.24–0.86) 0.89 (0.72–0.96) 0.92 (0.78–0.97) 0.78 (0.48–0.92)

99 ± 15 91 ± 17 82 ± 20

0.28 (0.21–0.43) 0.44 (0.33–0.68) 0.49 (0.37–0.77)

4.5 (3.3–7.0) 7.36 (5.4–11.5) 13.2 (9.7–21.1)

0.94 (0.84–0.98) 0.87 (0.67–0.95) 0.84 (0.60–0.94)

Data are mean ± SD. TEM = Typical Error of measurement; CV, % = TEM expressed as the coefficient of variation; ICC = intra-class correlation coefficient. TEM, CV % and ICC were calculated for the log-transformed variables. Time at HRmax = Time at which HRmax was recorded. Time at HRend = Time at which HRend was recorded. HRmax = Maximum Heart Rate; HRend = Heart rate at the end of the maximal exercise test. HR 30 s – 5’ = HR at 30 s, 1 min, 2 min, 3 min and 5 min post-test. Δ 30 s – 5’ = Difference between HRend and HR at 30 s, 1 min, 2 min, 3 min and 5 min post-test, respectively. HR0 = the asymptotic value of heart rate (bpm) from the exponential fit of HR recovery. HRamp = amplitude of HR decrement from HRend to HR0 for t = ∞ (bpm). HRτ = time constant

and easy transition to rest after incremental running exercises. This is an important consideration as the simultaneous evaluation of metabolic and autonomic adaptations in a single session has the advantage of saving time for athletes’ evaluations [10]. Therefore, most of the HR measures analyzed in the current Boullosa DA et al. Reliability of Heart Rate … Int J Sports Med 2014; 35: 999–1005

study could be utilized for ascertaining the extent of autonomic adaptations after a training period due to the relatively low CVs and high ICCs. Overall, HRV measures exhibited moderate to very high reliability in the current study. For example, ICCs for time domain and

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Table 2 Heart rate variability values and their corresponding reliability measures.

non-linear measures were high ( ≥ 0.70) to very high ( ≥ 0.90) for most HRV variables analyzed. Comparison with previous studies [3, 7, 18] is limited because of differences in protocols (i. e. initial speed, stages speed and duration in incremental tests) [7, 18], exercise intensities (i. e. submaximal, supramaximal) [3] and mode (i. e. cycling vs. running) [3], statistical analyses (e. g. non prior log-transformation of data) [7, 18], and HRV time window (e. g. from the 5th to the 10th min of recovery) [18] and measures employed (i. e. power spectral indices) [3, 18]. One interesting result was that the reliability of HRV measures before exercise in the standing position was substantially lower than the values observed during walking prior to and following the maximal run ▶ Table 2). Furthermore, the absolute and relative reliability of (● the short-term components of HRV during walking pre-test and walking recovery were very similar despite being recorded before and after exhausting exercise. This was a surprising finding that reinforces the reliability of walking HRV before and after running exercises. Previously, Tulppo et al. [37] reported that vagal modulation of HR is more reproducible during cycling exercise than at supine rest in men and suggested that this would be due to the effect of exercise itself on autonomic function that may minimize the influence of other confounding factors. Although speculative, we would also suggest the possible influence of the exercise pressor reflex [31] for these different responses. Accordingly, the exercise pressor reflex may reduce autonomic control variation during walking via resetting of the baroreflex gain. In contrast, cardiac afferent, regulation during the standing position may be more varied (and hence less reliable) because of the increased venous pooling of the lower limbs and stimulated baroreflex. While this study was not designed for comparing the cardiovascular responses between static and dynamic evaluations of different postures, further studies are needed for a better understanding for the greater reliability of walking HRV measures compared to static positions that may encourage venous pooling (e. g. standing and seated). Additionally, it should be pointed out that other factors such as chemoreceptor activity and circulating hormones influence cardiovascular control and have yet to be examined with regard to HRV analyses [4]. To our knowledge, the current study is the first to report the reliability of non-linear measures of HRV after maximal efforts, as previous studies have analyzed time and frequency domain parameters. We decided not to examine frequency domain parameters because these variables could be influenced by the non-stationarity of recovery HR data and breathing patterns, thereby biasing results [3, 24]. Moreover, the recent study of Karavirta et al. [25] suggests the appropriateness of HR complexity (HRC) measures for detecting autonomic adaptations after training periods when evaluated during constant submaximal cycling exercise at 50 W starting after 2 min of the initiation of exercise. Thus, the relative and absolute reliability values of the non-linear measures (i. e. Poincaré plots, HRC and detrended fluctuation analyses) included in the current study further support the usefulness of these measures for evaluating autonomic responses during constant submaximal exercise (i. e. walking) before and after maximal efforts. Additionally, our results confirm that the short-term components (i. e. RMSSD, SD1, α1) were more reliable (i. e. greater ICC) than longer-term components of HRV (i. e. SDNN, SD2, α2). Moreover, when compared to a previous study that analyzed the reliability of RMSSD from the 5th to the 10th min of passive recovery after an incremental test [18], we obtained a lower CV (19.4 vs. 141 %) and a greater ICC (0.91

vs. 0.14) for this index (3–5 min of walking recovery). However, it should be pointed out that the maximal running test utilized in the current study presented an important difference in design (i. e. initial velocity and stage increments and duration) when compared to this previous study [18] as well as a different HRV analysis (i. e. log-transformed in our study) that may have improved its reliability. Based on the current results, we would therefore suggest the analysis of the short-term components of linear and non-linear HRV measures and SampEn as better monitoring tools for the evaluation of autonomic adaptations during walking. It remains to be established to what extent these indices can be modified in response to interventions such as training and detraining under the condition analyzed (i. e. walking during pre- and post-maximal exercise) and if the reliability of longterm components of HRV would be improved with longer recordings [38]. Regarding HRR, both relative and absolute reliability values were similar to those reported in previous studies after incremental maximal exercises [7, 18] while HRR kinetic parameters were even better [7, 18]. Interestingly, the raw HRR exhibited a slightly better reliability than the relative values, while the absolute reliability was improved with increasing recovery duration as previously reported [7]. Therefore, the recording of HRR during the first 5 min of walking recovery could be a useful monitoring tool for athletes, allowing an easier and safe cooling down after incremental exhausting exercises. Our findings also confirm the strong association (r > 0.7) previously reported between HRR values at 1 and 2 min of walking recovery after 2 different field running tests [10]. As post-exercise walking is preferred by athletes [10, 32], monitoring of HRR during walking recovery after exercise could be widely used in both laboratory and field settings. However, it remains to be seen whether our results for post-maximal exercise are reproducible following other incremental running protocols or interval and constant-load running exercises at different intensities (e. g. submaximal and supramaximal), which are commonly used by athletes [14, 26]. Additionally, it should be pointed out that in the current study the treadmill needed some time (e. g. 15–20 s) to slow down at the end of the incremental test to achieve a recovery walking pace mainly because of the very high speed achieved by athletes at the end of the incremental protocol. In this regard, athletes may have benefited from the influence of the exercise pressor reflex previously commented upon during this slowing down phase. Further studies should be conducted to verify if the times required by different treadmills to slow down post-exercise would affect HRR values and whether different HRR values exist following field-based exercise where this transition time is avoided. This consideration is also important as ultra-short-term HRR (e. g. HRR at 10–20 s) has recently been reported to be an important monitoring tool for elite soccer players [8]. Further studies are therefore needed to assess the reliability of ultra-short-term HRR in both laboratory and field conditions.

Study Limitations

▼

The current study presents some possible limitations that should be brought to attention. Firstly, the small number of participants and their homogeneous characteristics may limit the application of the current results to other populations. Moreover, our sample was composed of highly trained endurance runners with high VO2max values (~64 mL · kg − 1 · min − 1) – values Boullosa DA et al. Reliability of Heart Rate … Int J Sports Med 2014; 35: 999–1005

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Future perspectives While we evaluated the reliability of HR measures after an incremental maximal running test, further studies are needed for evaluating the impact of different exercise intensities (e. g. submaximal, supramaximal) and longer recovery periods (e. g. 10 min) on the reliability of these measures. The current results should be confirmed in other samples of various fitness levels including clinical populations. Attention should be drawn in this regard to treadmill velocity during recovery, as 4 km · h − 1 could be too high for clinical populations with future studies also being needed to reduce the transition time between the end of the test and the target speed during walking recovery. Additionally, as this recovery paradigm is widely utilized in the field, further studies are needed for ascertaining the potential influence of environmental factors [1] on HRV and HRR results.

Conclusion

▼

The current results indicated high to very high reliability of most HRR and HRV measures during walking before and 5 min after maximal efforts in well trained runners. Short-term components of HRV (e. g. RMSSD, SD1) and longer-term HRR (e. g. 2–5 min) and HRR kinetics provided the highest reliability and therefore would be accurate for detecting autonomic adaptations after training periods. As walking recovery is the preferred recovery mode of athletes, the current findings support the usefulness of these monitoring tools in laboratory and field conditions for assessing acute cardiac parasympathetic reactivation and sympathetic withdrawal. Further studies are needed to

Boullosa DA et al. Reliability of Heart Rate … Int J Sports Med 2014; 35: 999–1005

assess the influence of different exercise stimuli on the reliability of HR measures during walking recovery in similar and other populations.

Acknowledgements

▼

We thank MSc. Luis Beltrame for his help during data collection and the athletes for their enthusiastic participation.

Conflict of interest: There is no conflict of interest declared. References 1 Achten J, Jeukendrup AE. Heart rate monitoring. Applications and limitations. Sports Med 2003; 33: 517–538 2 Aijaz B, Squires RW, Thomas RJ, Johnson BD, Allison TG. Predictive value of heart rate recovery and peak oxygen consumption for long-term mortality in patients with coronary heart disease. Am J Cardiol 2009; 103: 1641–1646 3 Al Haddad H, Laursen PB, Chollet D, Ahmaidi S, Buchheit M. Reliability of resting and postexercise heart rate measures. Int J Sports Med 2011; 32: 598–605 4 Aubert AE, Seps B, Beckers F. Heart rate variability in athletes. Sports Med 2003; 33: 889–919 5 Barak OF, Obvcin ZB, Jakovljevic DG, Lozanov-Crvenkovic Z, Brodie DA, Grujic NG. Heart rate recovery after submaximal exercise in four different recovery protocols in male athletes and non-athletes. J Sports Sci Med 2011; 10: 369–375 6 Borresen J, Lambert MI. Autonomic control of heart rate during and after exercise. Measurements and implications for monitoring training status. Sports Med 2008; 38: 633–646 7 Bosquet L, Gamelin FX, Berthoin S. Reliability of postexercise heart rate recovery. Int J Sports Med 2008; 29: 238–243 8 Boullosa DA, Abreu L, Nakamura FY, Munoz VE, Dominguez E, Leicht AS. Cardiac autonomic adaptations in elite Spanish soccer players during preseason. Int J Sports Physiol Perform 2013; 8: 400–409 9 Boullosa DA, Abreu L, Tonello L, Hofmann P, Leicht AS. Exercise is medicine: case report of a woman with smoldering multiple myeloma. Med Sci Sports Exerc 2013; 45: 1223–1228 10 Boullosa DA, Tuimil JL, Leicht AS, Crespo-Salgado JJ. Parasympathetic modulation and running performance in distance runners. J Strength Cond Res 2009; 23: 626–631 11 Buchheit M, Al Haddad H, Laursen PB, Ahmaidi S. Effect of body posture on postexercise parasympathetic reactivation in men. Exp Physiol 2009; 94: 795–804 12 Buchheit M, Cormie P, Abbiss CR, Ahmaidi S, Nosaka KK, Laursen PB. Muscle deoxygenation during repeated sprint running: Effect of active vs. passive recovery. Int J Sports Med 2009; 30: 418–425 13 Buchheit M, Gindre C. Cardiac parasympathetic regulation: respective associations with cardiorespiratory fitness and training load. Am J Physiol 2006; 291: H451–H458 14 Buchheit M, Simpson MB, Al Haddad H, Bourdon PC, Mendez-Villanueva A. Monitoring changes in physical performance with heart rate measures in young soccer players. Eur J Appl Physiol 2012; 112: 711–723 15 Carnethon MR, Sternfeld B, Liu K, Jacobs DR Jr, Schreiner PJ, Williams OD, Lewis CE, Sidney S. Correlates of heart rate recovery over 20 years in a healthy population sample. Med Sci Sports Exerc 2012; 44: 273–279 16 Daanen HA, Lamberts RP, Kallen VL, Jin A, Van Meeteren NL. A systematic review on heart-rate recovery to monitor changes in training status in athletes. Int J Sports Physiol Perform 2012; 7: 251–260 17 di Prampero PE, Salvadego D, Fusi S, Grassi B. A simple method for assessing the energy cost of running during incremental tests. J Appl Physiol 2009; 107: 1068–1075 18 Dupuy O, Mekary S, Berryman N, Bherer L, Audiffren M, Bosquet L. Reliability of heart rate measures used to assess post-exercise parasympathetic reactivation. Clin Physiol Funct Imaging 2012; 32: 296–304 19 Goldberger JJ, Le FK, Lahiri M, Kannankeril PJ, Ng J, Kadish AH. Assessment of parasympathetic reactivation after exercise. Am J Physiol 2006; 290: H2446–H2452 20 Harriss DJ, Atkinson G. Ethical standards in sport and exercise science research: 2014 Update. Int J Sports Med 2013; 34: 1025–1028 21 Hopkins WG. Reliability from consecutive pairs of trials (Excel spreadsheet). Sportscience 2000 http://sportsci.org/

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greater than those of previous studies [3, 7, 18]. We do not know of the impact of aerobic fitness (VO2max) on the current results, as a better fitness and exercise tolerance for trained individuals may result in a lower cardiac autonomic disturbance after maximal efforts. Secondly, the repeat assessments were separated by 2 days with reliability potentially different with a greater number of days between evaluations. However, inclusion of greater time between evaluations may be impacted by changes in fitness as a consequence of training with this impact yet to be determined for highly trained athletes. Thirdly, the current study examined recovery following an incremental, maximal effort with reliability potentially influenced by exercise protocol and intensity. Reliability of HR measures after submaximal efforts are warranted as a previous study [26] found greater sensitivity of HRR after running intensities ranging between 86 and 93 % of HRmax. This is an important issue as the interplay between parasympathetic and sympathetic modulation during recovery may depend on the intensity of previous exercise [6]. Nevertheless, the use of an incremental running test until exhaustion provides a standardized condition in which all participants run until their maximal effort that avoids individual differences in relative exercise intensities experienced with other protocols. Fourthly, HR recordings were obtained via HR monitors instead of ECG recordings that may limit the accuracy of analyses, though the instruments used in the current study have been previously demonstrated to be valid and highly reliable for HRV analysis [29]. Finally, the transition between exercise and recovery was greater than expected due to the treadmill operations from high Speak values and may have impacted HRR and HRV measures.

22 Imai K, Sato H, Hori M, Kusuoka H, Ozaki H, Yokoyama H, Takeda H, Inoue M, Kamada T. Vagally mediated heart rate recovery after exercise is accelerated in athletes but blunted in patients with chronic heart failure. J Am Coll Cardiol 1994; 15: 1529–1535 23 Johar P, Grover V, Disanto MC, Button DC, Behm DG. A rapid rotation to an inverted seated posture inhibits muscle force, activation, heart rate and blood pressure. Eur J Appl Physiol 2013; 113: 2005–2013 24 Kaikkonen P, Hynynen E, Mann T, Rusko H, Nummela A. Heart rate variability is related to training load variables in interval running exercises. Eur J Appl Physiol 2012; 112: 829–838 25 Karavirta L, Costa MD, Goldberger AL, Tulppo MP, Laaksonen DE, Nyman K, Keskitalo M, Hakkinen A, Hakkinen K. Heart rate dynamics after combined strength and endurance training in middle-aged women: heterogeneity of responses. PLoS One 2013; 8: e72664 26 Lamberts RP, Maskell S, Borresen J, Lambert MI. Adapting workload improves the measurement of heart rate recovery. Int J Sports Med 2011; 32: 698–702 27 Lamberts RP, Swart J, Noakes TD, Lambert MI. Changes in heart rate recovery after high-intensity training in well-trained cyclists. Eur J Appl Physiol 2009; 105: 705–713 28 Leicht AS, Sinclair WH, Patterson MJ, Rudzki S, Tulppo MP, Fogarty AL, Winter S. Influence of postexercise cooling techniques on heart rate variability in men. Exp Physiol 2009; 94: 695–703 29 Nunan D, Donovan G, Jakovljevic DG, Hodges LD, Sandercock GR, Brodie DA. Validity and reliability of short-term heart-rate variability from the Polar S810. Med Sci Sports Exerc 2009; 41: 243–250 30 Perini R, Orizio C, Cornande A, Castellano M, Beschi M, Veicsteinas A. Plasma norepinephrine and heart-rate dynamics during recovery from submaximal exercise in men. Eur J Appl Physiol 1989; 58: 879–883

31 Secher NH, Amann M. Human investigations into the exercise pressor reflex. Exp Physiol 2012; 97: 59–69 32 Seiler S, Hetlelid KJ. The impact of rest duration on work intensity and RPE during interval training. Med Sci Sports Exerc 2005; 37: 1601–1607 33 Stanley J, Peake JM, Buchheit M. Cardiac parasympathetic reactivation following exercise: implications for training prescription. Sports Med 2013; 43: 1259–1277 34 Tarvainen MP, Niskanen J, Kubios HRV. User’s Guide. Version 2.0. University of Kuopio, Kuopio (Finland) 2008; http://kubios.uef.fi/media/ Kubios_HRV_Users_Guide.pdf 35 Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 1996; 93: 1043–1065 36 Tiukinhoy S, Beohar N, Hsie M. Improvement in heart rate recovery after cardiac rehabilitation. J Cardiopulm Rehabil 2003; 23: 84–87 37 Tulppo MP, Mäkikallio TH, Seppänem T, Laukkanen RT, Huikuri HV. Vagal modulation of heart rate during exercise: effects of age and physical fitness. Am J Physiol 1998; 274: H424–H429 38 Yeh RG, Chen G-Y, Shieh J-S, Kuo CD. Parameter investigation of detrended fluctuation analysis for short-term human heart rate variability. J Med Biol Eng 2009; 30: 277–282 39 Young FL, Leicht AS. Short-term stability of resting heart rate variability: influence of position and gender. Appl Physiol Nutr Metab 2011; 36: 210–218

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