original article
http://dx.doi.org/10.1590/bjpt-rbf.2014.0165
289 
Braz J Phys Ther. 2016 July-Aug; 20(4):289-297
Is heart rate variability a feasible method to determine 
anaerobic threshold in progressive resistance exercise in 
coronary artery disease?
Milena P. R. Sperling1,2, Rodrigo P. Simões2, Flávia C. R. Caruso2,  
Renata G. Mendes2, Ross Arena3, Audrey Borghi-Silva1,2,3
ABSTRACT | Background: Recent studies have shown that the magnitude of the metabolic and autonomic responses 
during progressive resistance exercise (PRE) is associated with the determination of the anaerobic threshold (AT). 
AT is an important parameter to determine intensity in dynamic exercise. Objectives: To investigate the metabolic 
and cardiac autonomic responses during dynamic resistance exercise in patients with Coronary Artery Disease (CAD). 
Method: Twenty men (age = 63±7 years) with CAD [Left Ventricular Ejection Fraction (LVEF) = 60±10%] underwent 
a PRE protocol on a leg press until maximal exertion. The protocol began at 10% of One Repetition Maximum Test 
(1‑RM), with subsequent increases of 10% until maximal exhaustion. Heart Rate Variability (HRV) indices from 
Poincaré plots (SD1, SD2, SD1/SD2) and time domain (rMSSD and RMSM), and blood lactate were determined at 
rest and during PRE. Results: Significant alterations in HRV and blood lactate were observed starting at 30% of 1-RM 
(p<0.05). Bland‑Altman plots revealed a consistent agreement between blood lactate threshold (LT) and rMSSD threshold 
(rMSSDT) and between LT and SD1 threshold (SD1T). Relative values of 1‑RM in all LT, rMSSDT and SD1T did not 
differ (29%±5 vs 28%±5 vs 29%±5 Kg, respectively). Conclusion: HRV during PRE could be a feasible noninvasive 
method of determining AT in CAD patients to plan intensities during cardiac rehabilitation. 
Keywords: autonomic nervous system; anaerobic threshold; blood lactate; cardiac rehabilitation; cardiac disease; 1‑RM test.
BULLET POINTS
•  Parasympathetic modulation was reduced during lower extremity resistance exercise.
•  Anaerobic Threshold occurred at ≈30% of 1-RM in patients with CAD.
•  HRV may prove to be a feasible tool in clinical practice to determine Anaerobic Threshold.
•  HRV can be safe and appropriate method to determine exercise intensity in patients with CAD.
HOW TO CITE THIS ARTICLE
Sperling MPR, Simões RP, Caruso FCR, Mendes RG, Arena R, Borghi‑Silva A. Is heart rate variability a feasible method to determine 
anaerobic threshold in progressive resistance exercise in coronary artery disease? Braz J Phys Ther. 2016 July-Aug; 20(4):289-297 . 
http://dx.doi.org/10.1590/bjpt‑rbf.2014.0165
1 Interunidades Bioengenharia (EESC/FMRP/IQSC), Universidade de São Paulo (USP), São Carlos, SP, Brazil
2 Laboratório de Fisioterapia Cardiopulmonar, Departamento de Fisioterapia, Universidade Federal de São Carlos (UFSCar), São Carlos, SP, Brazil
3 Integrative Physiology Laboratory, Department of Physical Therapy, College of Applied Health Sciences, University of Illinois at Chicago (UIC), 
Chicago, USA
Received: Mar 02, 2015 Revised: Aug 25, 2015 Accepted: Jan 28, 2016
Introduction
It is known that the combination of aerobic and 
resistance exercise for cardiac patients synergistically 
improves muscular strength and endurance, functional 
capacity, quality of life, cardiovascular function, 
metabolism, and cardiovascular risk profile1. In addition, 
resistance exercise is considered safe for both healthy 
elderly individuals and cardiac patients1‑4.
The magnitude of the cardiovascular and ventilatory 
responses to exertional demands depends on the 
type of physical exercise and the intensity of effort1. 
With respect to exercise intensity, the anaerobic 
threshold (AT) is defined as a point above a given power 
value when the production of lactic acid is greater 
than the capacity for its utilization by body tissues5‑7. 
The point past which blood lactate concentration 
increases progressively5 is an important parameter in 
determining submaximal exercise tolerance. The use of 
discontinuous protocols to assess functional capacity 
Sperling MPR, Simões RP, Caruso FCR, Mendes RG, Arena R, Borghi-Silva A
 290
Braz J Phys Ther. 2016 July-Aug; 20(4):289-297
and determine AT are potentially advantageous as 
they reduce the inherent added risks incurred during 
maximum stress intensities2.
The ability of Heart Rate Variability (HRV) to 
determine changes in blood lactate and AT during 
resistance and aerobic exercise in healthy individuals 
has already been investigated8,9. Other studies have 
also examined the behavior of HRV indices during 
exercise in diabetic10, heart failure11, and elderly12‑14 
cohorts. However, parameters that indicate safe training 
intensities with resistance exercise, particularly in 
patients with cardiac conditions, remain unclear.
While HRV indices are important predictors of 
cardiovascular risk and risk of sudden cardiac death 
and may be used as potential indices of relative risk15, 
the use of HRV to determine the point of transition 
between aerobic and anaerobic metabolism (i.e., AT) 
during incremental resistance exercise in patients with 
cardiac disease is unknown. Therefore, the objectives 
of this study were to: 1) evaluate the behavior of 
HRV and blood lactate; 2) determine the AT during 
an incremental leg‑press protocol with an incremental 
percentage of One Repetition Maximum Test (1‑RM); 
and 3) evaluate the degree of agreement between HRV 
indices and blood lactate in relation to the AT in a 
cohort diagnosed with coronary artery disease (CAD).
Method
Study design and population
This is an observational cross‑sectional study 
involving 20 males with clinically stable CAD (sample 
of convenience) participating in an outpatient cardiac 
rehabilitation program. Inclusion criteria consisted of 
1) being at least 12 months post an acute event (i.e., 
myocardial infarction) or 12 months after a surgical or 
percutaneous revascularization procedure and 2) being 
clinically stable on a regular pharmacologic regimen.
The experimental protocol was approved by the 
Research Ethics Committee of Centro Universitário 
de Araraquara, Araraquara, SP, Brazil (n. 1331‑11). 
All procedures were conducted in accordance with 
the Declaration of Helsinki. All participants signed 
an informed consent form.
Experimental procedures
Subjects did not ingest caffeine or alcohol during 
the 24‑hour period prior to any of the testing protocols 
and did not perform any rigorous physical activity 
during the 48 hours prior to testing. All trials were 
performed at the same period of the day to avoid 
any influence of circadian rhythm on cardiovascular 
variables. The experiments were carried out in a 
climate‑controlled room (21‑24 °C) with a relative 
air humidity of 40‑60%.
Clinical examination was performed by a physician 
(cardiologist) before study initiation. This examination 
consisted of anamnesis and resting 12‑lead 
electrocardiography. A transthoracic echocardiogram 
was also performed for all patients.
Cardiopulmonary exercise testing – CPX
A symptom‑limited incremental exercise test 
(CPX) was performed on a recumbent cycle ergometer 
(Corival, Lode, Groningen, The Netherlands) with the 
collection of gas exchange and ventilatory variables 
using a calibrated computer‑based exercise system 
(Oxycon Mobile, JaegerTM, Hoechberg, Germany).
The workload (W) was continuously increased in a 
linear “ramp” pattern of 15 W.min–1. The test finished 
when subjects reached physical exhaustion or when 
abnormal test responses warranted test termination16,17. 
The incremental exercise testing duration was between 
8 and 12 minutes18.
Peak VO2 was defined as the highest value during 
the last 15 seconds of exercise and peak respiratory 
exchange ratio (RER) was the 15‑second averaged 
VCO2 divided by VO2 at peak exercise16.
One Repetition Maximum test – (1-RM - leg 
press)
This test was applied by gradually increasing the 
resistance until the patients succeeded in performing no 
more than one repetition on the leg press at a 45 degree 
angle (Pro‑Fitness, São Paulo, Brazil). The resistance 
load for 1‑RM was estimated (1‑RM‑E) before the 
test by multiplying subject body weight by 3.5, based 
on pilot testing. The details of this test protocol have 
been described previously13.
Discontinuous resistance exercise testing 
(DRET-leg press)
72 hours after the 1‑RM test, Discontinuous 
Resistance Exercise Testing (DRET–leg press) was 
performed, starting at a load of 10% of 1‑RM with 
subsequent increases of 10% until exhaustion. At each 
percentage of effort, patients underwent 2 minutes of 
exercise at a movement rhythm of 12 repetitions/minute, 
maintaining respiratory cadence. The period between 
trials was 5 minutes. The details of this test protocol 
have been described previously13.
HRV in resistance exercise in CAD patients
291 
Braz J Phys Ther. 2016 July-Aug; 20(4):289-297
Heart Rate (HR) and R‑R intervals were recorded 
with a wireless HR monitor (Polar S810i, Kempele, 
Finland) and blood samples (via earlobe puncture) 
were taken at rest and immediately after the final 
repetition completed at each load (i.e., % of 1‑RM). 
Blood samples were analyzed using a YSI 1500 
Sports Lactate Analyzer (YSI Inc., Yellow Springs, 
OH, USA).
Measurement of HRV
The R‑R intervals were recorded continuously 
with the wireless HR monitor (Polar S810i) during 
all exercise protocols. The R‑Ri captured with the 
monitor can be analyzed with both linear and nonlinear 
models. After data capture, the signals were transmitted 
to a receiver and interface connected to a computer 
for subsequent analysis. The details of this technique 
have been described previously13.
Safety during exercise protocols (CPX, 
1-RM–leg press and DRET-leg press)
During all exercise protocols, HR was recorded 
with the HR monitor (Polar S810i) and the ECG was 
constantly monitored using a USB electrocardiogram 
(WinCardio, Micromed Biotecnologia, Brasilia, 
Brazil) to detect any potential arrhythmias or signs 
of ischemia that would indicate the protocol should 
be interrupted. Blood pressure (auscultation) and 
symptoms (muscle fatigue, chest pain, and breathing/
dyspnea), assessed by means of the 10-point modified 
Borg Scale Rating16,19‑21, were measured and recorded 
immediately after each effort.
The criteria for protocol termination were a systolic 
blood pressure >200 mmHg, symptoms of lower limb 
fatigue, angina or shortness of breath, development 
of any cardiac arrhythmias, or achieving maximum 
voluntary exhaustion13.
Data analysis
To evaluate the responses of HR and R‑Ri during 
DRET–leg press, the first step in the data analysis 
involved a visual inspection of R‑Ri (ms) distribution 
in the ECG in order to select the sections corresponding 
to the final minute of each load (second minute) of 
resistance exercise maneuvers, as this was considered 
to be a more stable phase for analysis22‑24.
Ectopic beats, arrhythmic events, missing data, and 
noise effects that might alter the estimation of HRV 
were excluded15. HRV analysis was carried out using 
the following linear and nonlinear methods: 1) Linear 
methods ‑ RMSSD (square root of the mean of the sum 
of the squares of differences between adjacent RRi 
divided by the number of RRi minus one, expressed in 
ms) and RMSM (square root of the sum of the squares 
of differences of individual values compared to the 
mean value, divided by the number of RRi in a period 
for the time domain); and 2) nonlinear method ‑ SD1 
(instantaneous R‑R interval variability from Poincaré 
plots), SD2 (standard deviation of continuous long‑
term R‑R interval variability), and the SD1/SD2 ratio 
carried out by the Poincaré method of quantitative 
two‑dimensional vector analysis15.
The Poincaré plots were analyzed quantitatively, 
based on the premise of different temporal effects 
of changes in vagal and sympathetic modulation of 
HR on the R‑R intervals without a requirement for 
a stationary quality of the data24. RR‑interval series 
were processed using Kubios HRV 2.0 (University of 
Kuopio, Finland). The details of this technique have 
been described previously24.
Determination of Anaerobic Threshold (AT) 
in resistance exercise
To determine AT, changes in blood lactate curves 
were generated for each subject and the AT was 
defined as the exercise intensity at which the blood 
lactate concentration began to increase exponentially, 
i.e., breakpoint8,12‑14.
To determine the HRV threshold, the rMSSD and 
SD1 for each stage of exercise were plotted against 
work rate. This HRV deflection point was defined as 
the HRV threshold9. The point at which there was 
an initial decline in indices during exercise, thus 
indicating vagal withdrawal.
The determination of the lactate and HRV threshold 
occurred through visual inspection of lactate and HRV 
curves, respectively, by two independent experienced 
examiners. When there was no agreement between 
the two evaluators, a third evaluator was called to 
give the casting vote.
Statistical analysis
The sample size for the current study was estimated 
considering previous studies with the same resistance 
exercise protocol for healthy and elderly13,14 subjects 
and also different resistance exercise protocols for 
coronary artery disease22,25. Considering the presence 
of coronary artery disease, we doubled the sample 
size (n=20) to increase the chance of having less 
variability of the resulting data.
Initially, we used the Kolmogorov‑Smirnov test 
to verify the normality of the data and subsequently 
Sperling MPR, Simões RP, Caruso FCR, Mendes RG, Arena R, Borghi-Silva A
 292
Braz J Phys Ther. 2016 July-Aug; 20(4):289-297
one‑way ANOVA with repeated measures was used 
to analyze the behavior of the HRV indices, blood 
lactate curves, R‑Ri and RPP responses during the 
DRET–leg press (at different percentages of 1‑RM), 
and the different methods of identifying AT (blood 
lactate curves, rMSSD, and SD1 threshold). When 
appropriate, post‑hoc analyses were performed using 
the Tukey test. The degree of agreement between the 
methods used to determine AT was verified using 
Bland‑Altman plots26‑28.
Data are reported as mean and standard deviation 
and the significance level was set at 5%. The statistical 
analysis was carried out using Sigma Plot for Windows 
version 11.0 (Sigma Plot, San Jose, CA, USA) and 
MedCalc version 12.6.1.0 (MedCalc Software, 
Ostend, Belgium).
Results
Over a one‑year period, 42 patients were assessed 
for eligibility, 26 were recruited, five did not meet the 
inclusion criteria, and one was excluded for having 
an inadequate blood pressure response during CPX. 
Among the remaining subjects, 20 completed the 
protocol successfully with no abnormalities that 
would contraindicate enrollment in the present study 
and were included in the final analysis.
The clinical characteristics of the subjects are 
summarized in Table 1. All subjects had normal left 
ventricular systolic function (and mild left ventricle 
diastolic dysfunction in 45% of the study population) 
measured by echocardiography. The majority of 
patients had hypertension, history of smoking, and a 
family history of CAD. Myocardial infarction was the 
predominant clinical diagnosis and all patients were 
NYHA class I. Pharmacologic treatment commonly 
included antiplatelets, statins, beta‑blockers, ACE 
inhibitors, and hypoglycemic agents. Mean CPX 
values indicate this sample had a well‑preserved 
functional capacity and exerted maximal effort 
during the exercise test, according to American Heart 
Association (AHA) standards16.
1‑RM testing and DRET (30% and 50%) responses 
are summarized in Table 2. Regarding the response 
to the 1‑RM testing, the criterion for termination 
for almost all subjects was muscle fatigue (rate of 
perceived exertion – RPE = 9.2±2.0), with only one test 
interrupted due to chest pain. No test was interrupted 
due to ECG alterations or an excessive rise in SBP 
(>200 mmHg). In relation to the resistance load 
achieved during 1‑RM, values were similar to those 
Table 1. Baseline characteristics of the study population.
CAD, n=20
Demographics/anthropometrics
Age, years
63±7
Height, m
1.7±0.1
Body mass, kg
75.7±12.7
BMI, kg/m2
26.6±2.9
Transthoracic echocardiography
LVEF, %
60±10
LV diastolic diameter, cm
5.3±0.6
LV diastolic volume, ml
143±40
Septal thickness, cm
0.9±0.2
Posterior wall thickness, cm
0.9±0.1
Doppler echocardiography
LV diastolic function*:
Normal
11 (55)
Mild dysfunction
9 (45)
Risk Factors, n (%)
Diabetes
5 (25)
Hypertension
13 (65)
History of smoking
12 (60)
Family history of CAD
18 (90)
Functional Class (NYHA): I, n (%)
20 (100)
History of Myocardial infarction, n (%)
15 (75)
Intervention, n (%)
CABG
9 (45)
PCI
18 (90)
Medications, n (%)
Antiplatelet (aspirin)
20 (100)
Statin
20 (100)
Beta‑blocker
14 (70)
ACE inhibitor
7 (35)
Hypoglycemic
5 (25)
CPX
Peak VO2, ml.Kg–1.min–1
24±5
PredictiveVO2, ml.Kg–1.min–1 (%)*
85±14
Peak workload, W
134±23
Data are presented as mean±SD or number (percentage) of subjects. CAD: 
coronary artery disease; BMI: body mass index; NYHA: New York Heart 
Association; CABG: coronary artery bypass grafting; PCI: percutaneous 
coronary intervention; LVEF: left ventricular ejection fraction; ACE: 
angiotensin‑converting enzyme; CPX: cardiopulmonary exercise 
testing; VO2: oxygen uptake; W: watts. *Clinical recommendations 
for Cardiopulmonary Exercise Testing data assessment in specific 
patient populations16.
HRV in resistance exercise in CAD patients
293 
Braz J Phys Ther. 2016 July-Aug; 20(4):289-297
stipulated previously during the pilot test (3.5 times the 
body weight of the patient). Regarding the response 
to the DRET, the criterion for termination for almost 
all subjects was muscle fatigue (RPE = 6.6±2.8) or 
an excessive rise in SBP (>200 mHg) and only one 
test was interrupted due to chest pain.
Figure 1 illustrates the behavior of HRV indices, 
blood lactate, and R‑Ri and RPP at rest and with 
the increasing resistance exercise loads through the 
common maximum load achieved by all patients (i.e., 
50% of 1‑RM). Both rMSSD and SD1 indices, which 
are representative of parasympathetic modulation, 
demonstrated a significant decrease at peak load 
compared to resting values, with a significant drop at 
30% of 1‑RM (Figure 1A, C) with a parallel significant 
increase in blood lactate at 30% of 1‑RM (Figure 1B).
The SD1/SD2 ratio had a significant decrease from 
40% of 1‑RM (Figure 1E). The RMSM and SD2 
indices (Figure 1A, C) were significantly increased 
at 50% of 1‑RM, although there was an increasing 
trend starting in 30% of 1‑RM, perceived visually. 
R‑Ri showed a progressive reduction (Figure 1D) 
and RPP showed a progressive increase concurrently 
(Figure 1F), reflecting the progressive increase in the 
intensity of effort at 50% of 1‑RM.
The AT was determined for each patient through 
the analysis of blood lactate curves, rMSSD, and 
SD1, expressed in both absolute and relative values 
(Table 3). There were no significant differences in 
relation to different methods for identifying absolute 
values in Kg (lactate threshold ‑ LT: 81±19, rMSSD 
threshold ‑ rMSSDT: 78 ± 14; SD1 threshold ‑ SD1T: 
79±13) and relative AT values at ≈30% of 1-RM (29±5; 
28±5; 29±5; respectively), as presented in Table 3.
The analysis of agreement between methods of 
determining the AT was carried out using Bland‑Altman 
plots, considering the blood lactate analyses as the 
“gold standard“. LT vs. rMSSDT and LT vs. SD1T were 
plotted. The mean of the differences for identifying AT 
using the LT and rMSSDT methods was 2.7±20 Kg 
(Figure 2A), and the mean difference between LT and 
SD1T was 1.3±19.1 Kg (Figure 2B), demonstrating 
good agreement.
Discussion
In this observational cross‑sectional study, we were 
able to demonstrate that the fall in parasympathetic 
indices is associated with an increase in blood lactate, 
starting at ≈30% of 1-RM using a leg-press maneuver. 
Table 2. Cardiovascular, metabolic, and cardiac autonomic variables obtained during peak 1‑RM testing and DRET (30% and 50% of 
the 1‑RM testing).
CAD, n=20
1-RM testing
DRET 30%
DRET 50%
SBP, mmHg
137±24
156±25
172±23
F=9.07
DBP, mmHg
76±14
87±11
89±18
F=4.47
HR, bpm
95±13
88±11
104a±16
F=6.15
RPE, 0‑10
Lower limb fatigue
9.2±2.0
3.0±2.3
6.6±2.8
F=35.79
Chest pain (angina)
*
−−
*
F=9.22
Breathing (dyspnea)
5.9±3.2
2.8±2.4
5.9±2.9
Load, Kg
282±46
85±13c
144a,b±23
F=211
Load 1RM/total body mass
3.8±0.9
−−
−−
Lactate, mlmol.L–1
−−
1.5±0.8
3.4±1.7a
F=28.60
HRV indices
rMSSD
−−
8.6±4.1
7.1±4.7
F=23.61
RMSM
−−
15.8±7.3
25.5a±16.0
F=2.53
SD1
−−
6.1±2.9
5.1±3.3
F=23.66
SD2
−−
21.2±10.2
35.5a±22.4
F=3.24
SD1/SD2
−−
0.3±0.2
0.2±0.1
F=21.14
Data are presented as mean ± SD. SBP: systolic blood pressure; DBP: diastolic blood pressure; HR: heart rate; 1‑RM: one repetition maximum; 
DRET: discontinuous resistance exercise testing; RPE: rate of perceived exertion. *Only one patient had chest pain. a: difference between DRET 
50% and DRET 30%. b: difference between DRET 50% and 1‑RM testing. c: difference between DRET 30% and 1‑RM testing (p value <0.05, 
one‑way ANOVA with repeated measures).
Sperling MPR, Simões RP, Caruso FCR, Mendes RG, Arena R, Borghi-Silva A
 294
Braz J Phys Ther. 2016 July-Aug; 20(4):289-297
The good agreement between HRV indices and blood 
lactate curves may represent the importance and value 
of HRV in CAD patients for exercise prescription 
and monitoring.
Responses during discontinuous resistance 
exercise
The rMSSD and SD1 indices reflect parasympathetic 
heart activity15,24,28 and they both demonstrated a 
significant drop from ≈30% of 1-RM (Figure 1A, C). 
The total HRV, represented by RMSM and SD2 
indices (Figure 1A, C) were significantly increased 
from ≈50%of 1-RM, although the increasing trend 
started at ≈30% of 1-RM, observed visually. Lastly, 
the SD1/SD2 ratio appears stable up to ≈30% of 
1‑RM, followed by changes thereafter (Figure 1E). 
All of these changes indicate a shift in sympathovagal 
balance towards sympathetic predominance and 
reduced vagal tone29,30. This increase in sympathetic 
tone appears to correspond with AT, which in this 
study, corresponded to ≈30% of 1-RM.
Figure 1. Behavior of variables in Discontinuous Resistance Exercise Testing (DRET) in percentage of 1 repetition maximum (1‑RM; 
x axis), starting from rest until the load in common for all patients (50% of 1‑RM). Data are presented as mean±SD. (A) rMSSD (square 
root of the difference in the sum of squares between R‑R interval on the recording, divided by the determined time minus one) and 
RMSM (root mean square of the differences from the mean interval); (B) Blood Lactate; (C) SD1 (standard deviation of instantaneous 
beat‑to‑beat R‑R interval variability) and SD2 (the standard deviation of continuous long‑term R‑R interval. &: difference in relation 
to rest. +: difference in relation to 10% of 1‑RM. ‡: difference in relation to 20% of 1‑RM. #: difference in relation to 30% of 1‑RM. 
•: difference in relation to 40% of 1-RM. *: difference in relation to 50% of 1-RM (one-way ANOVA with repeated measures; p<0.05).
HRV in resistance exercise in CAD patients
295 
Braz J Phys Ther. 2016 July-Aug; 20(4):289-297
Anaerobic threshold determination by HRV 
and blood lactate
The determination of AT through indices of HRV 
was effective and associated with blood lactate 
responses in patients with CAD who are receiving 
standard pharmacological therapy. This is an important 
topic, since these results can be more representative 
of the CAD population seen clinically. In this context, 
Machado et al.25 assessed HRV indices during 
progressive upper limb exercise in CAD patients and 
found medications did not influence the HRV response.
In the present study, the load corresponding to 
the AT, considering the blood lactate threshold as a 
parameter during DRET‑leg press was obtained at 
≈30% of the peak load reached during the 1-RM test 
(Table 3), which is in accordance with other studies 
in assessing apparently healthy subjects14,31.
Figure 2 demonstrates that, although there were 
agreements among the methods for determining the 
AT (the mean of the differences was close to zero), 
the limits of agreement were clinically wide. Other 
studies have shown the potential use of HRV for 
the determination of AT/ventilator threshold on a 
cycle ergometer using rMSSD and SD1 in healthy 
adults9 and in patients with type‑2 diabetes10. To our 
knowledge, this is the first study to analyze the behavior 
of metabolic and autonomic responses during lower 
limb resistance exercise in CAD patients.
The mean CPX values (peak VO2 and predictive 
VO2) indicate that these patients had a well‑preserved 
functional capacity and confirm a maximal effort 
during the exercise test according to AHA standards16. 
Regarding the response to the DRET, the criteria for 
interrupting the test was muscle fatigue or excessive 
rise in SBP (>200 mmHg). All of these patients were 
included in the data analysis.
Study perspectives
Our results suggest that HRV may also be considered 
a useful tool in clinical practice to determine the intensity 
corresponding to AT. AT was approximately 30% of 
1‑RM testing for CAD patients with well‑preserved 
Table 3. Comparison of relative and absolute resistance values for anaerobic threshold measured with different methods of identification 
during discontinuous resistance exercise testing (DRET).
LT
rMSSDT
SD1T
DRET
Absolute values, Kg
81±19
78±14
79±13
p=0.43; F=0.94
Relative values, %
29±5
28±5
29±5
p=0.52; F=0.76
Data are presented as mean±SD. LT: Lactate threshold; rMSSDT: rMSSD threshold; SD1T: SD1 threshold. No significant differences among 
the three methods of identifying the anaerobic threshold (one‑way ANOVA with repeated measures).
Figure 2. Bland‑Altman plot showing the agreement between LT and rMSSDT (A) and LT and SD1T (B). BIAS = mean of the differences 
among the averages; ± 1.96 SD = 95% limits of agreement. LT = lactate threshold; rMSSDT = rMSSD threshold (rMSSD: square root 
of the mean of the sum of the squares of differences between adjacent RR‑intervals on the recording, divided by the determined time 
minus one); SD1T = SD1 threshold (SD1: standard deviation of instantaneous R‑R interval variability). Horizontal lines indicate mean 
(solid lines) and 95% confidence intervals (dashed lines) of differences between two measurements.
Sperling MPR, Simões RP, Caruso FCR, Mendes RG, Arena R, Borghi-Silva A
 296
Braz J Phys Ther. 2016 July-Aug; 20(4):289-297
functional capacity. HRV analysis using linear and 
nonlinear methods could be considered an important 
method for evaluating and understanding cardiac 
autonomic modulation in CAD patients during dynamic 
resistance exercise.
In order to establish the correct intensity, it is 
important to consider that the same exercise may 
lead to different levels of stress in different patient 
populations. Several factors, such as body weight, 
coordination, intention, and perception of the level 
of effort during resistance exercise, directly interfere 
with measurements of effort2.
Limitations of this study
The current study has limitations that should be 
recognized. RMSM and SD2 HRV indices reflect both 
sympathetic and parasympathetic influences15,29 and a 
pure index representative of the sympathetic modulation 
was not assessed in this study. Moreover, during the 
exercise protocol with increased load increments every 
2 minutes, HRV indices reached steady state in the last 
minute of each stage of exercise up to AT. However, 
after AT, this equilibrium condition was not maintained, 
which is inherent to high exercise intensities. Thus, it 
is possible that exercise intensities after AT may have 
affected HRV data capture. Even so, clear trends were 
apparent in the current investigation. The results found 
in this study may be protocol‑dependent, considering 
the duration of each load and rest periods between 
them. The leg press was chosen because it induces 
more changes in cardiac, ventilatory, and metabolic 
parameters, but it is necessary to investigate other kinds 
of resistance exercise. Once the resistance activity 
stops, the blood pressure decreases quite rapidly so 
that measuring by auscultation at the end of exercise 
would do not give a reliable estimation of the blood 
pressure during exercise. The evaluation of the blood 
pressure response was limited to the evaluation of 
discontinuous blood pressure monitoring, measured 
at the end of the exercise. However, this is still the 
most widely used method in clinical practice.
Conclusion
Our results suggest that parasympathetic modulation 
was reduced during lower extremity resistance exercise, 
beginning at AT, which occurred at ≈30% of 1-RM. 
Moreover, HRV may prove to be a feasible tool in 
clinical practice to determine AT, aiding in setting 
safe and appropriate exercise intensity parameters 
in patients with CAD.
Acknowledgements
This work was funded by Coordenação de 
Aperfeiçoamento de Pessoal de Nível Superior ‑ CAPES 
(grant no. 00011/07‑0) and Fundação de Amparo à 
Pesquisa do Estado de São Paulo ‑ FAPESP (grant 
no. 2009/01842‑0).
References
1. Williams MA, Haskell WL, Ades PA, Amsterdam EA, Bittner 
V, Franklin BA, et al. Resistance exercise in individuals with 
and without cardiovascular disease: 2007 update: a scientific 
statement from the American Heart Association Council 
on Clinical Cardiology and Council on Nutrition, Physical 
Activity, and Metabolism. Circulation. 2007;116(5):572‑84. 
http://dx.doi.org/10.1161/CIRCULATIONAHA.107.185214. 
PMid:17638929.
2. Bjarnason‑Wehrens B, Mayer‑Berger W, Meister ER, Baum 
K, Hambrecht R, Gielen S. Recommendations for resistance 
exercise in cardiac rehabilitation. Recommendations of 
the German Federation for Cardiovascular Prevention and 
Rehabilitation. Eur J Cardiovasc Prev Rehabil. 2004;11(4):352‑
61. http://dx.doi.org/10.1097/01.hjr.0000137692.36013.27. 
PMid:15292771.
3. Delagardelle C, Feiereisen P, Autier P, Shita R, Krecke R, 
Beissel J. Strength/endurance training versus endurance 
training in congestive heart failure. Med Sci Sports Exerc. 
2002;34(12):1868‑72. http://dx.doi.org/10.1097/00005768‑
200212000‑00002. PMid:12471289.
4. Pollock ML, Franklin BA, Balady GJ, Chaitman BL, Fleg 
JL, Fletcher B, et al. Resistance exercise in individuals with 
and without cardiovascular disease: benefits, rationale, 
safety, and prescription: an advisory from the Committee 
on Exercise, Rehabilitation, and Prevention, Council 
on Clinical Cardiology, American Heart Association. 
Circulation. 2000;101(7):828‑33. http://dx.doi.org/10.1161/01.
CIR.101.7.828. PMid:10683360.
5. Davis JA, Rozenek R, DeCicco DM, Carizzi MT, Pham PH. 
Comparison of three methods for detection of the lactate 
threshold. Clin Physiol Funct Imaging. 2007;27(6):381‑
4. http://dx.doi.org/10.1111/j.1475‑097X.2007.00762.x. 
PMid:17944661.
6. Wasserman K, Hansen JE, Sue D, Whipp BJ, Casaburi R. 
Principles of exercise testing and interpretation. 3rd ed. 
Philadelphia: Lea & Febiger; 1999.
7. Ekkekakis P, Hall EE, Petruzzello S. Practical markers of 
the transition from aerobic to anaerobic metabolism during 
exercise: rationale and a case for affect‑based exercise 
prescription. Prev Med. 2004;38(2):149‑59. http://dx.doi.
org/10.1016/j.ypmed.2003.09.038. PMid:14715206.
8. Souza NM, Magosso RF, Pereira GB, Leite RD, Arakelian 
VM, Montagnolli AN, et al. The measurement of lactate 
threshold in resistance exercise: a comparison of methods. 
Clin Physiol Funct Imaging. 2011;31(5):376‑81. http://dx.doi.
org/10.1111/j.1475‑097X.2011.01027.x. PMid:21771257.
9. Karapetian GK, Engels HJ, Gretebeck RJ. Use of heart 
rate variability to estimate LT and VT. Int J Sports Med. 
HRV in resistance exercise in CAD patients
297 
Braz J Phys Ther. 2016 July-Aug; 20(4):289-297
2008;29(8):652‑7. http://dx.doi.org/10.1055/s‑2007‑989423. 
PMid:18213538.
10. Sales MM, Campbell CS, Morais PK, Ernesto C, Soares‑
Caldeira LF, Russo P, et al. Noninvasive method to 
estimate anaerobic threshold in individuals with type 2 
diabetes. Diabetol Metab Syndr. 2011;3(1):1. http://dx.doi.
org/10.1186/1758‑5996‑3‑1. PMid:21226946.
11. Leprêtre PM, Bulvestre M, Ghannem M, Ahmaidi S, Weissland 
T, Lopes P. Determination of ventilatory threshold using 
heart rate variability in patients with heart failure. Surgery. 
2013; S12:003. http://dx.doi.org/10.4172/2161‑1076.S12‑003.
12. Simões RP, Castello‑Simões V, Mendes RG, Archiza B, Santos 
DA, Bonjorno JC Jr, et al. Identification of anaerobic threshold 
by analysis of heart rate variability during discontinuous 
dynamic and resistance exercise protocols in healthy older 
men. Clin Physiol Funct Imaging. 2014;34(2):98‑108. http://
dx.doi.org/10.1111/cpf.12070. PMid:23879324.
13. Simões RP, Castello‑Simões V, Mendes RG, Machado 
HG, Archisa B, Santos DA, et al. Lactate and Heart Rate 
Variability Threshold during Resistance Exercise in the 
Young and Elderly. Int J Sports Med. 2013;34(11):991‑6. 
http://dx.doi.org/10.1055/s‑0033‑1337946. PMid:23606341.
14. Simões RP, Mendes RG, Castello V, Machado HG, Almeida 
LB, Baldissera V, et al. Heart rate variability and blood‑lactate 
threshold interaction during progressive resistance exercise 
in healthy older men. J Strength Cond Res. 2010;24(5):1313‑
20. http://dx.doi.org/10.1519/JSC.0b013e3181d2c0fe. 
PMid:20393353.
15. Electrophysiology TFESCNAS, and the 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(5):1043‑65. http://dx.doi.
org/10.1161/01.CIR.93.5.1043. PMid:8598068.
16. Guazzi M, Adams V, Conraads V, Halle M, Mezzani 
A, Vanhees L, et al. Clinical recommendations for 
Cardiopulmonary Exercise Testing data assessment in 
specific patient populations: EACPR/AHA scientific 
statement. Circulation. 2012;126(18):2261‑74. http://dx.doi.
org/10.1161/CIR.0b013e31826fb946. PMid:22952317.
17. Balady GJ, Arena R, Sietsema K, Miers J, Coke L, Fletcher 
GF, et al. Clinician’s guide to cardiopulmonary exercise 
testing in adults: a cientific statement from the American 
Heart Association. Circulation. 2010;122(2):191‑225. http://
dx.doi.org/10.1161/CIR.0b013e3181e52e69. PMid:20585013.
18. Buchfuhrer MJ, Hansen JE, Robinson TE, Sue DY, Wasserman 
K, Whipp BJ. Optimizing the exercise protocol for 
cardiopulmonary assessment. J Appl Physiol. 1983;55(5):1558‑
64. PMid:6643191.
19. Myers J, Arena R, Franklin B, Pina I, Kraus WE, Mcinnis 
K, et al. Recommendations for clinical exercise laboratories: 
a scientific statement from the American Heart Association. 
Circulation. 2009;119(24):3144‑61. http://dx.doi.org/10.1161/
CIRCULATIONAHA.109.192520. PMid:19487589.
20. Myers JN. Perception of chest pain during exercise testing 
in patients with coronary artery disease. Med Sci Sports 
Exerc. 1994;26(9):1082‑6. http://dx.doi.org/10.1249/00005768‑
199409000‑00003. PMid:7808240.
21. Borg GA. Psychophysical bases of perceived exertion. 
Med Sci Sports Exerc. 1982;14(5):377‑81. http://dx.doi.
org/10.1249/00005768‑198205000‑00012. PMid:7154893.
22. Machado‑Vidotti HG, Mendes RG, Simões RP, Castello‑
Simões V, Catai AM, Borghi‑Silva A. Cardiac autonomic 
responses during upper versus lower limb resistance exercise 
in healthy elderly men. Braz J Phys Ther. 2014;18(1):9‑
18. http://dx.doi.org/10.1590/S1413‑35552012005000140. 
PMid:24675908.
23. Moreira SR, Arsa G, Oliveira HB, Lima LC, Campbell CS, 
Simões HG. Methods to identify the lactate and glucose 
thresholds during resistance exercise for individuals with type 
2 diabetes. J Strength Cond Res. 2008;22(4):1108‑15. http://
dx.doi.org/10.1519/JSC.0b013e31816eb47c. PMid:18545200.
24. Tulppo MP, Makikallio TH, Takala TES, Seppanen T, Huikuri 
HV. Quantitative beat‑to‑beat analysis of heart rate dynamics 
during exercise. Am J Physiol 1996;271:H244‑H252.
25. Machado HG, Simões RP, Mendes RG, Castello V, Di 
Thommazo L, Almeida LB, et al. Cardiac autonomic 
modulation during progressive upper limb exercise by 
patients with coronary artery disease. Braz J Med Biol 
Res. 2011;44(12):1276‑84. http://dx.doi.org/10.1590/S0100‑
879X2011007500134. PMid:22002089.
26. Bland JM, Altman DG. Agreed statistics: measurement 
method comparison. Anesthesiology. 2012;116(1):182‑
5. http://dx.doi.org/10.1097/ALN.0b013e31823d7784. 
PMid:22129533.
27. Bland JM, Altman DG. Measuring agreement in method 
comparison studies. Stat Methods Med Res. 1999;8(2):135‑
60. http://dx.doi.org/10.1191/096228099673819272. 
PMid:10501650.
28. Bland JM, Altman DG. Statistical methods for assessing 
agreement between two methods of clinical measurement. 
Lancet. 1986;8(1):307‑10. http://dx.doi.org/10.1016/S0140‑
6736(86)90837‑8. PMid:2868172.
29. Tulppo MP, Makikallio TH, Seppanen T, Laukkanen RT, 
Heikki V, Huikuri HV. Vagal modulation of heart rate 
during exercise: effects of age and physical fitness. Am J 
Physiol. 1998;274:H424‑H429.
30. Mitchell JHJB. Wolffe memorial lecture. Neural control 
of the circulation during exercise. Med Sci Sports Exerc. 
1990;22(2):141‑54. PMid:2192221.
31. Souza NM, Magosso RF, Pereira GB, Sousa MV, Vieira A, 
Marine DA, et al. Acute cardiorrespiratory and metabolic 
responses during resistance exercise in the lactate threshold 
intensity. Int J Sports Med. 2012;33(2):108‑13. http://dx.doi.
org/10.1055/s‑0031‑1286315. PMid:22127560.
Correspondence 
Milena Pelosi Rizk Sperling 
Universidade Federal de São Carlos 
Departamento de Fisioterapia 
Laboratório de Fisioterapia Cardiopulmonar 
Rodovia Washington Luis, Km 235 
CEP 13565‑905, São Carlos, SP, Brazil 
e‑mail: milenasperling@yahoo.com.br

Is heart rate variability a feasible method to determine anaerobic threshold in progressive resistance exercise in coronary artery disease?
Sperling, Milena P R,Simões, Rodrigo P,Caruso, Flávia C R,Mendes, Renata G,Arena, Ross,Borghi-Silva, Audrey