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ORIGINAL ARTICLE
Correlation between heart rate variability indexes and aerobic physiological variables in patients with COPD MARCELI R. LEITE,1 ERCY MARA C. RAMOS,1 CARLOS A. KALVA-FILHO,2 FERNANDA MARIA M. RODRIGUES,1 ANA PAULA C.F. FREIRE,1 GUILHERME Y. TACAO,1 ALESSANDRA C. de TOLEDO,3 MICHEL J. CECÍLIO,4 LUIZ CARLOS M. VANDERLEI1 AND DIONEI RAMOS1 Departments of 1Physiotherapy and 2Physical Education, São Paulo State University (UNESP), 4Department of Medicine, Universidade do Oeste Paulista (UNOESTE), Presidente Prudente, and 3Department of Medicine, São Paulo University (USP), São Paulo, Brazil
ABSTRACT Background and objective: Previous studies have shown a relationship between the level of physical fitness and autonomic variables. However, these relationships have not been investigated in patients with chronic obstructive pulmonary disease (COPD). The objective of this study was to correlate the resting heart rate variability (HRV) indexes with aerobic physiological variables obtained at a maximal exercise test in patients with COPD. Methods: Thirty-seven patients with COPD (63 (59– 70) years; 46 (35.4–63.7) forced expiratory volume in 1 s (FEV1)%) underwent assessment of autonomic modulation at rest for 20 min to determine the HRV indexes in time and frequency domains. Soon after that, the patients performed an incremental exercise test to determine the anaerobic threshold (GET), the peak oxygen uptake (VO2PEAK) and the velocity corresponding to VO2PEAK (vVO2PEAK). Results: The indexes that express parasympathetic component as RMSSD (11.4 [7.5–23.8], HF (ms2) (35 [17–195] and SD1 (8.1 [5.3–16.8]), correlated with GET (r = 0.39; r = 0.43; r = 0.39 respectively). The indexes that represent the overall variability, SDNN (19.5 [13.9– 28.8]), LF (ms2) (111 [38–229]), and SD2 (26.8 [18.6– 35.4]) correlated with vVO2PEAK (r = 0.37; r = 0.38; r = 0.37; r = 0.44; r = 0.43; r = 0.46 respectively). Likewise, the indexes LF (ms2), LF (nu) (63.2 [46–77,9]), HF (nu) (36.8 [22.1–54]), and LF/HF (1.7 [0.9–3.5]) correlated with VO2PEAK (r = 0.35; r = 0.35; r = −0.35; r = 0.40 respectively). Conclusions: This study demonstrated that HRV indexes at rest may become a predictive tool for aerobic capacity in COPD patients after the development of more consistent methods.
Correspondence: Dionei Ramos, Department of Physiotherapy, Universidade Estadual Paulista, Rua Roberto Simonsen 305, Presidente Prudente, São Paulo CEP 19060-900, Brasil. Email: [email protected] Received 26 March 2014; invited to revise 12 May and 7 August 2014; revised 9 June and 18 August 2014; accepted 28 August 2014 (Associate Editor: Melissa Benton). © 2014 Asian Pacific Society of Respirology
SUMMARY AT A GLANCE This study aimed to investigate whether the HRV indexes correlate with aerobic physiological variables obtained in cardiopulmonary exercise testing of patients with COPD. Such correlation would have important clinical implications as it would allow, prior to the realization of the maximal exercise testing, to infer the patient’s aerobic capacity.
Key words: aerobic treatment, autonomic nervous system, parasympathetic nervous system, physical fitness, pulmonary disease. Abbreviations: ANS, autonomic nervous system; COPD, chronic obstructive pulmonary disease; FEV1, forced expiratory volume in 1 s; GET, anaerobic threshold; HF, high frequency; HR, heart rate; HRV, heart rate variability; LF, low frequency; RCP, respiratory compensation point; RMSSD, root mean square of differences between adjacent normal RR intervals in a time interval; SD1, standard deviation of instantaneous beat-to-beat variability; SD2, standard deviation of the long-term continuous RR intervals; SDNN, standard deviation of the mean of all normal RR intervals; VO2PEAK, peak oxygen uptake; vVO2PEAK, velocity corresponding to VO2PEAK.
INTRODUCTION Physical exercise is widely recommended as part of the treatment for patients with chronic obstructive pulmonary disease (COPD) and it is known for bringing evident improvements in quality of life1,2 exercise tolerance,3 functional capacity and relieving the disease’s symptoms.2 In this context, the determination of aerobic physiological variables, such as aerobic power and capacity, obtained during cardiopulmonary exercise testing is fundamental to the treatment of this population and may be used for evaluating, prescribing and monitoring exercise.4,5 Respirology (2014) doi: 10.1111/resp.12424
2 Nevertheless, the cardiopulmonary test for analysis of ventilatory and metabolic responses demands high costs and requires special care with medical accompaniment and emergency apparatus, since patient performs a maximum effort.6,7 During physical exercise, the functioning of the autonomic nervous system (ANS) undergoes changes.8,9 This system can be assessed by analysis of heart rate variability (HRV), a simple and noninvasive method that is gaining increasing importance as a tool for clinical interpretation of the functioning of the ANS due to its wide possibility of use and cost-effectiveness of application.10 Several studies have used HRV analysis in different physiological conditions, especially in acute exercise and in various training phases.11–14 However, this is the first study, to our knowledge, to investigate whether this assessment tool has correlation with aerobic physiological variables obtained in cardiopulmonary exercise testing of patients with COPD. This finding would have important clinical implications once that, if there is correlation between HRV at rest and aerobic physiological variables, we could analyse the feasibility of a new predictive tool for aerobic capacity with future developments of equations for this purpose and this way infer the patient’s aerobic capacity previously to the realization of the maximal exercise testing. Thus, this study would be the first step to the development of a consistent method predictor of aerobic capacity. The study hypothesis is that HRV indexes at rest may present correlation with aerobic physiological variables and may become a predictive tool. This study aimed to assess the degree of correlation between HRV indexes at rest with aerobic physiological variables obtained at maximal exercise test of COPD patients.
METHODS Subjects This was a cross-sectional study and 36 patients (22 men and 14 women) with a diagnosis of COPD, according to the Global Initiative for Chronic Obstructive Lung Disease, were included.15 Exclusion criteria were smoking, pathological disabling conditions (limiting orthopaedic conditions) and/or unstable heart disease that could influence the physical performance (presenting cardiac pacemaker, heart transplant, arrhythmias or heart failure),10 presence of diseases that could interfere with the systemic inflammation, unstable COPD (exacerbations and changes in medication in the last 30 days), use of home oxygen therapy and realization of any physical training programme before participating in this study. The patients were informed about the procedures and objectives of the study and, after agreeing, signed a consent form. All procedures used in this study were approved by the Ethics Committee of the Institution (CAAE: 01114912.0.0000.5402.). Respirology (2014)
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Study design All procedures were performed individually in acclimatized room (temperature range from 21 to 23°C and relative humidity range from 50% to 60%),14 always in the morning from 8:00 a.m. to 12:00 pm, to minimize interference from the circadian rhythm. Initially, patients underwent an assessment that included identification, medical history and anthropometric assessment. The weight was measured on a digital scale accurate to 0.1 kg (Welmy R/I 200; Welmy Ind Com Ltda, São Paulo, Brazil) and height using a stadiometer accurate to 1 cm (Sanny, São Paulo, Brazil). Body mass index was calculated using the formula: weight (kg)/height (m).2 Then patients underwent the pulmonary function evaluations conducted according to the guidelines of the American Thoracic Society and European Respiratory Society16 using a portable spirometer (SpirobankMIR3.6; MIR Medical International Research S.r.l., Roma, Italy). Reference values were those specific to the Brazilian population.17 On the second day of assessments, patients were instructed to not consume alcohol and/or stimulant drinks for a period of 24 h, consume light meal 2 h before the measurements, avoid intense physical efforts for at least 24 h and maintain their usual medications. Medications used by patients during this study were bronchodilators, calcium ion antagonists, angiotensin receptor antagonists, diuretics, cholesterol lowering agents, anxiolytics, analgesics and antiinflammatory agents. Regarding medication use, it is believed that there may be influence of certain types of medication on HRV. However, Bédard et al.18 suggested that the reduction of HRV in patients with COPD is not associated with the use of anticholinergic or adrenergic drugs, the main medications used by our patients. Furthermore, this medication is customarily used by these patients, and therefore it is understood that their actual daily condition was evaluated. After orientation on all procedures, assessment of autonomic modulation was performed. For that matter, an elastic strap for capture was positioned on the patient’s chest, above the xiphoid process and in his fist, the heart rate (HR) receiver Polar S810i (Polar Electro, Kempele, Finland) equipment previously validated for capturing HR beat to beat and their use for analysis of indexes of HRV.19 To capture the HRV, patients were seated and instructed to remain silent for 20 min with spontaneous breathing. Afterwards, the patients performed a progressive treadmill test (Inbrasport ATL 2000; Inbrasport, Rio Grande do Sul, Brazil), escorted by a physician cardiologist, to determine the metabolic thresholds, the peak oxygen uptake (VO2PEAK) and the velocity corresponding to VO2PEAK (vVO2PEAK). Heart rate variability evaluation For analysis of HRV indexes, 256 RR intervals (interval between consecutive heartbeats) of the most stable period from the 20 min of collection were used after digital filtering was performed by the software Polar Precision Performance SW (version 4.01.029) supplemented by manual filtering for disposal of prema© 2014 Asian Pacific Society of Respirology
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ture ectopic beats and artefacts. Only series with more than 95% of sinus beats were included in the study.20 HRV analysis was performed by linear methods in time and frequency domains. The calculated indexes in time domain were the following: SDNN (ms) (standard deviation of the mean of all normal RR intervals) and RMSSD (ms) (root mean square of differences between adjacent normal RR intervals in a time interval). For HRV analysis in the frequency domain, we used the spectral components of low frequency (LF: 0.04– 0.15 Hz) and high frequency (HF: 0.15–0.4 Hz), in normalized units (nu) and milliseconds (ms), besides the LF/HF ratio.21 The indexes of SD1 (standard deviation of instantaneous beat-to-beat variability) and SD2 (standard deviation of the long-term continuous RR intervals)22 were also analysed. Kubios HRV software (version 2.0) was used to calculate these indexes.23
Physiological variables determination To determine the physiological variables related to aerobic metabolism, participants underwent an incremental test with 2.0 km/h as initial velocity, constant slope of 3% and 0.5 km/h increments every 2 min. The test was performed until voluntary exhaustion.24 No patient had clinical and/or electrocardiographic abnormalities that prevented completion of the test. During the test, oxygen uptake (VO2) and CO2 output (VCO2) were obtained for each cycle of three breaths (VO2000; MGC Diagnostics Corporation, Saint Paul, MN, USA). The VO2PEAK was assumed as the highest average of VO2 in the last 30 s of exercise when at least two of the following three criteria were met: (i) HR >90% of agepredicted maximum (220—age); (ii) respiratory quotient >1.10; and (iii) variation in VO2 between the penultimate and final exercise stage lower than 2.1 mL/kg/min. vVO2PEAK was assumed as the highest intensity reached during the incremental test. In the case of the patient coming into exhaustion before the end of the stage, vVO2PEAK was adjusted by the equation proposed by Kuipers et al.25 The anaerobic threshold (GET) was determined by the V-Slope method, described by Sue et al.26 for COPD patients. This method used the behaviour of VCO2 in function of VO2, and the GET was assumed by the breakpoint of this relationship. In the present study, the breakpoint VO2 was determined and the GET was expressed by the relative intensity where this value was observed during incremental test. Statistical analysis The normality of the data was verified by the Shapiro– Wilk test. For variables that did not display a normal distribution, a logarithmic transformation was performed before the parametric analysis. Therefore, the data were represented as median, interquartile range (Q25%–Q75%), and minimum and maximum values. Correlations between HRV variables and aerobic parameters were evidenced by the Pearson correlation test. For all analyses, the Statistical Package for Social Science, version 17.0 (SPSS Inc, Chicago, IL, USA) was used and the significance level was set at P < 0.05. © 2014 Asian Pacific Society of Respirology
RESULTS Thirty-seven patients with COPD were included in this study (stage II, n = 11; stage III, n = 11; stage IV, n = 3). The characteristics and spirometric variables of the patients are described in Table 1. Table 2 describes the values of HRV indexes at rest and aerobic physiological variables measured during the progressive test. Correlation coefficients between indexes of HRV with aerobic parameters are described in Table 3.
Table 1 Characterization spirometry of patients
Characteristics Age (years) Height (cm) Body weight (kg) BMI (kg/cm2) Spirometry FEV1 (%pred) FEV1 (L) FVC (%pred) FVC (L) FEV1/FVC ratio (%)
and
measures
related
to
Median (IQR)
MinimumMaximum
63 (59–70) 163 (154–171) 71 (61–80) 26.5 (22.7–30.5)
48–80 146–187 45–106 19.4–38.7
46 (35.4–63.7) 1.12 (0.92–1.79) 74 (65.2–91.8) 2.26 (1.83–3.55) 50 (44.7–60.1)
25–87.5 0.54–2.45 37–120 1.03–4.27 31–70
BMI, body mass índex; FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity; IQR, interquartile range.
Table 2 Values of HRV indexes and aerobic physiological variables from patients
HRV SDNN RMSSD LF (ms) HF (ms) LF (nu) HF (nu) LF/HF SD1 SD2 Aerobic variables GET (km/h) VO2PEAK (L/min) VO2PEAK (mL/kg/min) vVO2PEAK (km/h)
Median (IQR)
MinimumMaximum
19.5 (13.9–28.8) 11.4 (7.5–23.8) 111 (38–229) 35 (17–195) 63.2 (46–77.9) 36.8 (22.1–54) 1.7 (0.9–3.5) 8.1 (5.3–16.8) 26.8 (18.6–35.4)
5.3–55.7 2.9–51.1 4–2107 2–538 19.0–96.5 3.5–81 0.2–28 2.1–36.2 7.2–75
3.5 (3–4) 0.6 (0.5–0.9) 9.3 (6.7–12.8) 5.1 (4.5–6.2)
2.5–5.5 0.2–1.5 3.7–18.2 3.4–7.5
GET, anaerobic threshold; HF, high-frequency spectral component; HRV, heart rate variability; IQR, interquartile range; LF, lowfrequency spectral component; RMSSD, root mean square of differences between adjacent normal RR intervals in a time interval; SDNN, standard deviation of the mean of all normal RR intervals; VO2PEAK, peak oxygen uptake; vVO2PEAK, velocity corresponding to VO2PEAK. Respirology (2014)
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Table 3 Correlation coefficients between indexes of heart rate variability and aerobic parameters
SDNN RMSSD LF (ms2) HF (ms2) LF (nu) HF (nu) LF/HF SD1 SD2
GET (km/h)
VO2PEAK (L/min)
VO2PEAK (mL/kg/min)
vVO2PEAK (km/h)
0.34 0.39* 0.31 0.43* −0.14 0.14 −0.17 0.39* 0.31
0.3 0.25 0.31 0.21 0.11 −0.11 0.12 0.25 0.29
0.25 0.10 0.35* 0.04 0.35* −0.35* 0.40* 0.10 0.27
0.44* 0.37* 0.43* 0.38* 0.05 −0.05 0.03 0.37* 0.46*
*Significant difference in the correlation analysis (P < 0.05). GET, anaerobic threshold; HF, high-frequency spectral component; LF, low-frequency spectral component; LF/HF, ratio between spectral components of low frequency and high frequency; RMSSD, root mean square of differences between adjacent normal RR intervals in a time interval; SD1, standard deviation of instantaneous beat-to-beat variability; SD2, standard deviation of the long-term continuous RR intervals; SDNN, standard deviation of mean of all normal RR intervals; VO2PEAK, peak oxygen uptake; vVO2PEAK, velocity corresponding to VO2PEAK.
DISCUSSION This study describes, for the first time, associations between HRV of COPD patients at rest with the main aerobic physiological indexes. We can observe that the indexes that express action with predominance of one component of autonomic modulation— parasympathetic (RMSSD, SD1 and HF) as well as indexes that represent the global HRV (SDNN, LF and SD2)—correlate with the vVO2PEAK. Therefore, parasympathetic values were correlated with an indicator of physiological transition threshold during exercise (i.e. GET). Thereby, it can be inferred that individuals with high values of RMSSD, SD1 and HF indexes in resting condition tend to reach the GET in higher exercise intensities, reflecting a more prolonged action of parasympathetic activity during progressive exercise and, therefore, tend to have better aerobic fitness. Likewise, patients with high values of LF (ms), LF (nu) and LF/HF indexes also tend to have better aerobic fitness, seen their correlation with VO2PEAK and vVO2PEAK. Previous studies have already indicated correlation between autonomic function and improved physical performance at different ages, supporting the concept that HRV is strongly linked to aerobic fitness.27 The GET and VO2PEAK are traditionally indicated for the prescription and monitoring of physical training.28,29 These variables have sensitivity according to the degree of physical training30 and chronological age, while with its advancement occur a progressive decrease in physical capacity, by both physiological processes such as secondary factors, as sedentary lifestyles.31 However, for its determination, individuals must be driven to exhaustion, which is one of the disadvantRespirology (2014)
ages to getting these physiological variables, especially when the test is performed in risk populations, as is the case of patients with COPD. Therefore, investigations to establish inferences regarding the physical capacity by methods with less risk are fundamental. In this context, the analysis of HRV deserves to be highlighted; it has been considered as an important tool of ANS assessment and has established itself as a predictor of the internal functions of the body.10 Its use is diverse for being a strong risk indicator related to adverse events in normal subjects and in patients with various disease types.32 Despite the widespread use of HRV analysis in understanding the phenomena involved with the ANS in normal33 and pathological conditions34,35 and after exercise,36–38 studies correlating its analysis at rest with aerobic indexes in patients with COPD were not found in the literature. It was recently demonstrated the association of HRV with some indicators of aerobic fitness. Fronchetti et al.39 found a significant association between HRV indexes at rest with a variable indicating aerobic fitness, represented by the threshold of HRV in apparently healthy young men. These results showed that individuals who both have low values of resting HR and increased HRV at rest tend to achieve HRV threshold at greater intensities effort, indicating that a high resting vagal activity suggests a good cardiovascular condition function and also appears to be related to aerobic capacity. Furthermore, SD1, SD2, RMSSD, pNN50, HF and LF/HF indexes at rest showed no strong correlation with the HRV threshold, r = (0.51), r = (0.46), r = (0.48), r = (0.55), r = (0.50) and r = (0.56), respectively, which corroborates with the present study although this study used a different methodology. Still, the authors suggest that from resting and exercise variables that were analysed, it is possible to make inferences about the cardiac autonomic regulation and aerobic capacity of the subjects. Similar outcome was found in the study of Atlaoui et al.40 that found a direct relationship between the level of aerobic performance and HRV, particularly at HF index, which reflects parasympathetic activity in swimmers. The correlations found in this study indicate that there is an association between HRV indexes at rest and aerobic physiological variables, showing that by this method it may be possible to infer the aerobic capacity in individuals with COPD. However, this analysis alone is not able to predict aerobic capacity, the development of specific equations for this purpose is required. Thus, we highlight the clinical relevance of this study which is the first one to demonstrate that HRV indexes at rest may become a predictive tool for aerobic capacity in COPD patients after the development of more consistent methods. Through a simple HRV assessment at rest, the therapist would be able to get a preview perception of the performance that the patient can achieve in cardiopulmonary test, so better HRV indexes may be related to a better performance in a maximum test, just as patients with worse HRV indexes at rest would have an inferior performance in the test. However, to © 2014 Asian Pacific Society of Respirology
Heart rate variability in COPD
achieve this concept, we suggest that further studies are needed to establish cut-off values for HRV. In addition, future studies require a bigger sample size for more consistent results. It can be pointed out as a limitation the medication use prior to the assessments of autonomic function and physical fitness, which may have influenced the results of these tests and also the lack of statistical adjustment for possible fixes of these influences. In summary, these results indicate that there is a correlation, although slight, between autonomic function at rest and aerobic physiological variables, which suggests that the response of HRV indexes may reflect the aerobic capacity of patients with COPD prior to cardiopulmonary exercise testing. Given these results, mores studies are needed to establish possible cut-off values for HRV indexes that may indicate the level of fitness of this population. In conclusion, the findings suggest that patients with COPD that have higher values of SDNN, RMSSD, LF (ms2), HF (ms2), SD1 and SD2 at rest tend to achieve GET and vVO2PEAK at higher exercise intensity, and the indexes LF (ms2), LF (nu), HF (nu) and LF/HF ratio at rest have a slightly correlation with variable that indicates aerobic fitness, represented by VO2PEAK. This study demonstrated that HRV indexes at rest may become a predictive tool for aerobic capacity in COPD patients after the development of more consistent methods.
Acknowledgements This work was supported by the following Brazilian scientific agency: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP).
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© 2014 Asian Pacific Society of Respirology
ORIGINAL ARTICLE
Correlation between heart rate variability indexes and aerobic physiological variables in patients with COPD MARCELI R. LEITE,1 ERCY MARA C. RAMOS,1 CARLOS A. KALVA-FILHO,2 FERNANDA MARIA M. RODRIGUES,1 ANA PAULA C.F. FREIRE,1 GUILHERME Y. TACAO,1 ALESSANDRA C. de TOLEDO,3 MICHEL J. CECÍLIO,4 LUIZ CARLOS M. VANDERLEI1 AND DIONEI RAMOS1 Departments of 1Physiotherapy and 2Physical Education, São Paulo State University (UNESP), 4Department of Medicine, Universidade do Oeste Paulista (UNOESTE), Presidente Prudente, and 3Department of Medicine, São Paulo University (USP), São Paulo, Brazil
ABSTRACT Background and objective: Previous studies have shown a relationship between the level of physical fitness and autonomic variables. However, these relationships have not been investigated in patients with chronic obstructive pulmonary disease (COPD). The objective of this study was to correlate the resting heart rate variability (HRV) indexes with aerobic physiological variables obtained at a maximal exercise test in patients with COPD. Methods: Thirty-seven patients with COPD (63 (59– 70) years; 46 (35.4–63.7) forced expiratory volume in 1 s (FEV1)%) underwent assessment of autonomic modulation at rest for 20 min to determine the HRV indexes in time and frequency domains. Soon after that, the patients performed an incremental exercise test to determine the anaerobic threshold (GET), the peak oxygen uptake (VO2PEAK) and the velocity corresponding to VO2PEAK (vVO2PEAK). Results: The indexes that express parasympathetic component as RMSSD (11.4 [7.5–23.8], HF (ms2) (35 [17–195] and SD1 (8.1 [5.3–16.8]), correlated with GET (r = 0.39; r = 0.43; r = 0.39 respectively). The indexes that represent the overall variability, SDNN (19.5 [13.9– 28.8]), LF (ms2) (111 [38–229]), and SD2 (26.8 [18.6– 35.4]) correlated with vVO2PEAK (r = 0.37; r = 0.38; r = 0.37; r = 0.44; r = 0.43; r = 0.46 respectively). Likewise, the indexes LF (ms2), LF (nu) (63.2 [46–77,9]), HF (nu) (36.8 [22.1–54]), and LF/HF (1.7 [0.9–3.5]) correlated with VO2PEAK (r = 0.35; r = 0.35; r = −0.35; r = 0.40 respectively). Conclusions: This study demonstrated that HRV indexes at rest may become a predictive tool for aerobic capacity in COPD patients after the development of more consistent methods.
Correspondence: Dionei Ramos, Department of Physiotherapy, Universidade Estadual Paulista, Rua Roberto Simonsen 305, Presidente Prudente, São Paulo CEP 19060-900, Brasil. Email: [email protected] Received 26 March 2014; invited to revise 12 May and 7 August 2014; revised 9 June and 18 August 2014; accepted 28 August 2014 (Associate Editor: Melissa Benton). © 2014 Asian Pacific Society of Respirology
SUMMARY AT A GLANCE This study aimed to investigate whether the HRV indexes correlate with aerobic physiological variables obtained in cardiopulmonary exercise testing of patients with COPD. Such correlation would have important clinical implications as it would allow, prior to the realization of the maximal exercise testing, to infer the patient’s aerobic capacity.
Key words: aerobic treatment, autonomic nervous system, parasympathetic nervous system, physical fitness, pulmonary disease. Abbreviations: ANS, autonomic nervous system; COPD, chronic obstructive pulmonary disease; FEV1, forced expiratory volume in 1 s; GET, anaerobic threshold; HF, high frequency; HR, heart rate; HRV, heart rate variability; LF, low frequency; RCP, respiratory compensation point; RMSSD, root mean square of differences between adjacent normal RR intervals in a time interval; SD1, standard deviation of instantaneous beat-to-beat variability; SD2, standard deviation of the long-term continuous RR intervals; SDNN, standard deviation of the mean of all normal RR intervals; VO2PEAK, peak oxygen uptake; vVO2PEAK, velocity corresponding to VO2PEAK.
INTRODUCTION Physical exercise is widely recommended as part of the treatment for patients with chronic obstructive pulmonary disease (COPD) and it is known for bringing evident improvements in quality of life1,2 exercise tolerance,3 functional capacity and relieving the disease’s symptoms.2 In this context, the determination of aerobic physiological variables, such as aerobic power and capacity, obtained during cardiopulmonary exercise testing is fundamental to the treatment of this population and may be used for evaluating, prescribing and monitoring exercise.4,5 Respirology (2014) doi: 10.1111/resp.12424
2 Nevertheless, the cardiopulmonary test for analysis of ventilatory and metabolic responses demands high costs and requires special care with medical accompaniment and emergency apparatus, since patient performs a maximum effort.6,7 During physical exercise, the functioning of the autonomic nervous system (ANS) undergoes changes.8,9 This system can be assessed by analysis of heart rate variability (HRV), a simple and noninvasive method that is gaining increasing importance as a tool for clinical interpretation of the functioning of the ANS due to its wide possibility of use and cost-effectiveness of application.10 Several studies have used HRV analysis in different physiological conditions, especially in acute exercise and in various training phases.11–14 However, this is the first study, to our knowledge, to investigate whether this assessment tool has correlation with aerobic physiological variables obtained in cardiopulmonary exercise testing of patients with COPD. This finding would have important clinical implications once that, if there is correlation between HRV at rest and aerobic physiological variables, we could analyse the feasibility of a new predictive tool for aerobic capacity with future developments of equations for this purpose and this way infer the patient’s aerobic capacity previously to the realization of the maximal exercise testing. Thus, this study would be the first step to the development of a consistent method predictor of aerobic capacity. The study hypothesis is that HRV indexes at rest may present correlation with aerobic physiological variables and may become a predictive tool. This study aimed to assess the degree of correlation between HRV indexes at rest with aerobic physiological variables obtained at maximal exercise test of COPD patients.
METHODS Subjects This was a cross-sectional study and 36 patients (22 men and 14 women) with a diagnosis of COPD, according to the Global Initiative for Chronic Obstructive Lung Disease, were included.15 Exclusion criteria were smoking, pathological disabling conditions (limiting orthopaedic conditions) and/or unstable heart disease that could influence the physical performance (presenting cardiac pacemaker, heart transplant, arrhythmias or heart failure),10 presence of diseases that could interfere with the systemic inflammation, unstable COPD (exacerbations and changes in medication in the last 30 days), use of home oxygen therapy and realization of any physical training programme before participating in this study. The patients were informed about the procedures and objectives of the study and, after agreeing, signed a consent form. All procedures used in this study were approved by the Ethics Committee of the Institution (CAAE: 01114912.0.0000.5402.). Respirology (2014)
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Study design All procedures were performed individually in acclimatized room (temperature range from 21 to 23°C and relative humidity range from 50% to 60%),14 always in the morning from 8:00 a.m. to 12:00 pm, to minimize interference from the circadian rhythm. Initially, patients underwent an assessment that included identification, medical history and anthropometric assessment. The weight was measured on a digital scale accurate to 0.1 kg (Welmy R/I 200; Welmy Ind Com Ltda, São Paulo, Brazil) and height using a stadiometer accurate to 1 cm (Sanny, São Paulo, Brazil). Body mass index was calculated using the formula: weight (kg)/height (m).2 Then patients underwent the pulmonary function evaluations conducted according to the guidelines of the American Thoracic Society and European Respiratory Society16 using a portable spirometer (SpirobankMIR3.6; MIR Medical International Research S.r.l., Roma, Italy). Reference values were those specific to the Brazilian population.17 On the second day of assessments, patients were instructed to not consume alcohol and/or stimulant drinks for a period of 24 h, consume light meal 2 h before the measurements, avoid intense physical efforts for at least 24 h and maintain their usual medications. Medications used by patients during this study were bronchodilators, calcium ion antagonists, angiotensin receptor antagonists, diuretics, cholesterol lowering agents, anxiolytics, analgesics and antiinflammatory agents. Regarding medication use, it is believed that there may be influence of certain types of medication on HRV. However, Bédard et al.18 suggested that the reduction of HRV in patients with COPD is not associated with the use of anticholinergic or adrenergic drugs, the main medications used by our patients. Furthermore, this medication is customarily used by these patients, and therefore it is understood that their actual daily condition was evaluated. After orientation on all procedures, assessment of autonomic modulation was performed. For that matter, an elastic strap for capture was positioned on the patient’s chest, above the xiphoid process and in his fist, the heart rate (HR) receiver Polar S810i (Polar Electro, Kempele, Finland) equipment previously validated for capturing HR beat to beat and their use for analysis of indexes of HRV.19 To capture the HRV, patients were seated and instructed to remain silent for 20 min with spontaneous breathing. Afterwards, the patients performed a progressive treadmill test (Inbrasport ATL 2000; Inbrasport, Rio Grande do Sul, Brazil), escorted by a physician cardiologist, to determine the metabolic thresholds, the peak oxygen uptake (VO2PEAK) and the velocity corresponding to VO2PEAK (vVO2PEAK). Heart rate variability evaluation For analysis of HRV indexes, 256 RR intervals (interval between consecutive heartbeats) of the most stable period from the 20 min of collection were used after digital filtering was performed by the software Polar Precision Performance SW (version 4.01.029) supplemented by manual filtering for disposal of prema© 2014 Asian Pacific Society of Respirology
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ture ectopic beats and artefacts. Only series with more than 95% of sinus beats were included in the study.20 HRV analysis was performed by linear methods in time and frequency domains. The calculated indexes in time domain were the following: SDNN (ms) (standard deviation of the mean of all normal RR intervals) and RMSSD (ms) (root mean square of differences between adjacent normal RR intervals in a time interval). For HRV analysis in the frequency domain, we used the spectral components of low frequency (LF: 0.04– 0.15 Hz) and high frequency (HF: 0.15–0.4 Hz), in normalized units (nu) and milliseconds (ms), besides the LF/HF ratio.21 The indexes of SD1 (standard deviation of instantaneous beat-to-beat variability) and SD2 (standard deviation of the long-term continuous RR intervals)22 were also analysed. Kubios HRV software (version 2.0) was used to calculate these indexes.23
Physiological variables determination To determine the physiological variables related to aerobic metabolism, participants underwent an incremental test with 2.0 km/h as initial velocity, constant slope of 3% and 0.5 km/h increments every 2 min. The test was performed until voluntary exhaustion.24 No patient had clinical and/or electrocardiographic abnormalities that prevented completion of the test. During the test, oxygen uptake (VO2) and CO2 output (VCO2) were obtained for each cycle of three breaths (VO2000; MGC Diagnostics Corporation, Saint Paul, MN, USA). The VO2PEAK was assumed as the highest average of VO2 in the last 30 s of exercise when at least two of the following three criteria were met: (i) HR >90% of agepredicted maximum (220—age); (ii) respiratory quotient >1.10; and (iii) variation in VO2 between the penultimate and final exercise stage lower than 2.1 mL/kg/min. vVO2PEAK was assumed as the highest intensity reached during the incremental test. In the case of the patient coming into exhaustion before the end of the stage, vVO2PEAK was adjusted by the equation proposed by Kuipers et al.25 The anaerobic threshold (GET) was determined by the V-Slope method, described by Sue et al.26 for COPD patients. This method used the behaviour of VCO2 in function of VO2, and the GET was assumed by the breakpoint of this relationship. In the present study, the breakpoint VO2 was determined and the GET was expressed by the relative intensity where this value was observed during incremental test. Statistical analysis The normality of the data was verified by the Shapiro– Wilk test. For variables that did not display a normal distribution, a logarithmic transformation was performed before the parametric analysis. Therefore, the data were represented as median, interquartile range (Q25%–Q75%), and minimum and maximum values. Correlations between HRV variables and aerobic parameters were evidenced by the Pearson correlation test. For all analyses, the Statistical Package for Social Science, version 17.0 (SPSS Inc, Chicago, IL, USA) was used and the significance level was set at P < 0.05. © 2014 Asian Pacific Society of Respirology
RESULTS Thirty-seven patients with COPD were included in this study (stage II, n = 11; stage III, n = 11; stage IV, n = 3). The characteristics and spirometric variables of the patients are described in Table 1. Table 2 describes the values of HRV indexes at rest and aerobic physiological variables measured during the progressive test. Correlation coefficients between indexes of HRV with aerobic parameters are described in Table 3.
Table 1 Characterization spirometry of patients
Characteristics Age (years) Height (cm) Body weight (kg) BMI (kg/cm2) Spirometry FEV1 (%pred) FEV1 (L) FVC (%pred) FVC (L) FEV1/FVC ratio (%)
and
measures
related
to
Median (IQR)
MinimumMaximum
63 (59–70) 163 (154–171) 71 (61–80) 26.5 (22.7–30.5)
48–80 146–187 45–106 19.4–38.7
46 (35.4–63.7) 1.12 (0.92–1.79) 74 (65.2–91.8) 2.26 (1.83–3.55) 50 (44.7–60.1)
25–87.5 0.54–2.45 37–120 1.03–4.27 31–70
BMI, body mass índex; FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity; IQR, interquartile range.
Table 2 Values of HRV indexes and aerobic physiological variables from patients
HRV SDNN RMSSD LF (ms) HF (ms) LF (nu) HF (nu) LF/HF SD1 SD2 Aerobic variables GET (km/h) VO2PEAK (L/min) VO2PEAK (mL/kg/min) vVO2PEAK (km/h)
Median (IQR)
MinimumMaximum
19.5 (13.9–28.8) 11.4 (7.5–23.8) 111 (38–229) 35 (17–195) 63.2 (46–77.9) 36.8 (22.1–54) 1.7 (0.9–3.5) 8.1 (5.3–16.8) 26.8 (18.6–35.4)
5.3–55.7 2.9–51.1 4–2107 2–538 19.0–96.5 3.5–81 0.2–28 2.1–36.2 7.2–75
3.5 (3–4) 0.6 (0.5–0.9) 9.3 (6.7–12.8) 5.1 (4.5–6.2)
2.5–5.5 0.2–1.5 3.7–18.2 3.4–7.5
GET, anaerobic threshold; HF, high-frequency spectral component; HRV, heart rate variability; IQR, interquartile range; LF, lowfrequency spectral component; RMSSD, root mean square of differences between adjacent normal RR intervals in a time interval; SDNN, standard deviation of the mean of all normal RR intervals; VO2PEAK, peak oxygen uptake; vVO2PEAK, velocity corresponding to VO2PEAK. Respirology (2014)
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Table 3 Correlation coefficients between indexes of heart rate variability and aerobic parameters
SDNN RMSSD LF (ms2) HF (ms2) LF (nu) HF (nu) LF/HF SD1 SD2
GET (km/h)
VO2PEAK (L/min)
VO2PEAK (mL/kg/min)
vVO2PEAK (km/h)
0.34 0.39* 0.31 0.43* −0.14 0.14 −0.17 0.39* 0.31
0.3 0.25 0.31 0.21 0.11 −0.11 0.12 0.25 0.29
0.25 0.10 0.35* 0.04 0.35* −0.35* 0.40* 0.10 0.27
0.44* 0.37* 0.43* 0.38* 0.05 −0.05 0.03 0.37* 0.46*
*Significant difference in the correlation analysis (P < 0.05). GET, anaerobic threshold; HF, high-frequency spectral component; LF, low-frequency spectral component; LF/HF, ratio between spectral components of low frequency and high frequency; RMSSD, root mean square of differences between adjacent normal RR intervals in a time interval; SD1, standard deviation of instantaneous beat-to-beat variability; SD2, standard deviation of the long-term continuous RR intervals; SDNN, standard deviation of mean of all normal RR intervals; VO2PEAK, peak oxygen uptake; vVO2PEAK, velocity corresponding to VO2PEAK.
DISCUSSION This study describes, for the first time, associations between HRV of COPD patients at rest with the main aerobic physiological indexes. We can observe that the indexes that express action with predominance of one component of autonomic modulation— parasympathetic (RMSSD, SD1 and HF) as well as indexes that represent the global HRV (SDNN, LF and SD2)—correlate with the vVO2PEAK. Therefore, parasympathetic values were correlated with an indicator of physiological transition threshold during exercise (i.e. GET). Thereby, it can be inferred that individuals with high values of RMSSD, SD1 and HF indexes in resting condition tend to reach the GET in higher exercise intensities, reflecting a more prolonged action of parasympathetic activity during progressive exercise and, therefore, tend to have better aerobic fitness. Likewise, patients with high values of LF (ms), LF (nu) and LF/HF indexes also tend to have better aerobic fitness, seen their correlation with VO2PEAK and vVO2PEAK. Previous studies have already indicated correlation between autonomic function and improved physical performance at different ages, supporting the concept that HRV is strongly linked to aerobic fitness.27 The GET and VO2PEAK are traditionally indicated for the prescription and monitoring of physical training.28,29 These variables have sensitivity according to the degree of physical training30 and chronological age, while with its advancement occur a progressive decrease in physical capacity, by both physiological processes such as secondary factors, as sedentary lifestyles.31 However, for its determination, individuals must be driven to exhaustion, which is one of the disadvantRespirology (2014)
ages to getting these physiological variables, especially when the test is performed in risk populations, as is the case of patients with COPD. Therefore, investigations to establish inferences regarding the physical capacity by methods with less risk are fundamental. In this context, the analysis of HRV deserves to be highlighted; it has been considered as an important tool of ANS assessment and has established itself as a predictor of the internal functions of the body.10 Its use is diverse for being a strong risk indicator related to adverse events in normal subjects and in patients with various disease types.32 Despite the widespread use of HRV analysis in understanding the phenomena involved with the ANS in normal33 and pathological conditions34,35 and after exercise,36–38 studies correlating its analysis at rest with aerobic indexes in patients with COPD were not found in the literature. It was recently demonstrated the association of HRV with some indicators of aerobic fitness. Fronchetti et al.39 found a significant association between HRV indexes at rest with a variable indicating aerobic fitness, represented by the threshold of HRV in apparently healthy young men. These results showed that individuals who both have low values of resting HR and increased HRV at rest tend to achieve HRV threshold at greater intensities effort, indicating that a high resting vagal activity suggests a good cardiovascular condition function and also appears to be related to aerobic capacity. Furthermore, SD1, SD2, RMSSD, pNN50, HF and LF/HF indexes at rest showed no strong correlation with the HRV threshold, r = (0.51), r = (0.46), r = (0.48), r = (0.55), r = (0.50) and r = (0.56), respectively, which corroborates with the present study although this study used a different methodology. Still, the authors suggest that from resting and exercise variables that were analysed, it is possible to make inferences about the cardiac autonomic regulation and aerobic capacity of the subjects. Similar outcome was found in the study of Atlaoui et al.40 that found a direct relationship between the level of aerobic performance and HRV, particularly at HF index, which reflects parasympathetic activity in swimmers. The correlations found in this study indicate that there is an association between HRV indexes at rest and aerobic physiological variables, showing that by this method it may be possible to infer the aerobic capacity in individuals with COPD. However, this analysis alone is not able to predict aerobic capacity, the development of specific equations for this purpose is required. Thus, we highlight the clinical relevance of this study which is the first one to demonstrate that HRV indexes at rest may become a predictive tool for aerobic capacity in COPD patients after the development of more consistent methods. Through a simple HRV assessment at rest, the therapist would be able to get a preview perception of the performance that the patient can achieve in cardiopulmonary test, so better HRV indexes may be related to a better performance in a maximum test, just as patients with worse HRV indexes at rest would have an inferior performance in the test. However, to © 2014 Asian Pacific Society of Respirology
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achieve this concept, we suggest that further studies are needed to establish cut-off values for HRV. In addition, future studies require a bigger sample size for more consistent results. It can be pointed out as a limitation the medication use prior to the assessments of autonomic function and physical fitness, which may have influenced the results of these tests and also the lack of statistical adjustment for possible fixes of these influences. In summary, these results indicate that there is a correlation, although slight, between autonomic function at rest and aerobic physiological variables, which suggests that the response of HRV indexes may reflect the aerobic capacity of patients with COPD prior to cardiopulmonary exercise testing. Given these results, mores studies are needed to establish possible cut-off values for HRV indexes that may indicate the level of fitness of this population. In conclusion, the findings suggest that patients with COPD that have higher values of SDNN, RMSSD, LF (ms2), HF (ms2), SD1 and SD2 at rest tend to achieve GET and vVO2PEAK at higher exercise intensity, and the indexes LF (ms2), LF (nu), HF (nu) and LF/HF ratio at rest have a slightly correlation with variable that indicates aerobic fitness, represented by VO2PEAK. This study demonstrated that HRV indexes at rest may become a predictive tool for aerobic capacity in COPD patients after the development of more consistent methods.
Acknowledgements This work was supported by the following Brazilian scientific agency: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP).
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