Impact of Air Pollutant on Heart Rate Variability in Healthy Young Adults

Abstract

Background:  Air pollution and sex independently affect cardiac autonomic control, which can be assessed by heart rate variability (HRV). The research hypothesis is that individuals exposed to low concentrations of pollution have higher cardiac autonomic modulation compared to those exposed to high concentrations and that women have better cardiac autonomic control than men.

Objective:  To analyze the impact of exposure to air pollutants, specifically smoke, and sex on HRV in healthy young people exposed to different concentrations of pollution over an average period of 22 years.

Methods:  From April to September 2011, 36 participants of both sexes (20-30 years old) were selected, grouped by levels of air pollution exposure according to indices provided by the Environmental Company of São Paulo State. The R-R intervals (R-Ri) of the electrocardiogram were captured using a heart rate monitor during supine rest. HRV was analyzed by spectral analysis and conditional entropy. The Queen's College step test was used to characterize functional capacity. A between-group comparison was performed using the two-way ANOVA statistical test (post hoc Tukey) and p<0.05.

Results:  Significant differences were found in mean R-Ri (p<0.01) and cardiac parasympathetic modulation between sexes in the same city (p=0.02) and between groups exposed to different air pollution concentrations (p<0.01).

Conclusion:  Our results suggest that long-term exposure to air pollutants, specifically smoke, has an unfavorable impact on HRV, with reduced cardiac vagal autonomic modulation in healthy young adults, especially females.

Keywords: Air pollution; sex; autonomic nervous system; heart rate

Introduction

Air pollution is defined as the presence of substances in the atmosphere resulting from human activity or natural processes. When these materials surpass pre-established concentrations, they can be harmful to health, as well as to normal community activities, such as activities of daily living and social participation.¹ , ²

Impacts of air pollution on cardiovascular morbidity and mortality have been described,³ and can be explained by: 1) an increase in fibrinogen and circulating inflammatory factors that lead to an increase in blood viscosity and coagulation disorders; and 2) changes in autonomic nervous system (ANS) modulation to the heart,⁴ – ⁹ which can be evaluated through heart rate variability (HRV).

Studies have shown that some factors like sex and functional capacity can influence ANS modulation.¹⁰ – ¹² Women have a higher resting HRV than men of the same age group, indicating a parasympathetic predominance in females and a sympathetic predominance in males.¹⁰ , ¹² – ¹⁴

Assessment of HRV is a non-invasive, simple and inexpensive method, widely used in the quantification of cardiac sympathetic and parasympathetic modulation using linear and non-linear measures.¹¹ , ¹⁵ – ¹⁹ An individual who exhibits efficient cardiac autonomic control mechanisms has a high resting HRV, characterized by a parasympathetic predominance.⁸ , ¹⁹ , ²⁰ However, some cardiac conditions (e.g., conduction disturbances and atrial fibrillation) can also induce a high HRV.¹⁹

Thus, the research hypothesis is that individuals exposed to low concentrations of air pollution have better cardiac autonomic modulation compared to those exposed to high concentrations, and that women have better cardiac autonomic control than men. Thus, this study aimed to analyze the long-term impact of exposure to pollutants, specifically to smoke, and the influence of sex on cardiac autonomic modulation in healthy young people exposed to two different levels of air pollution.

Methods

Study design

This was an observational, cross-sectional, prospective study.

Sample

Thirty-six participants (20 men and 16 women) between 20 and 30 years old were selected by convenience, and divided into two groups according to the place of residence: a) group 1 – 10 men and 8 women living in a city with a high concentration of air pollution (São Paulo, São Paulo State) and; b) group 2 – 10 men and 8 women living in a city with a low concentration of air pollution (São Carlos, São Paulo State).

All volunteers were considered healthy according to the anamnesis: they did not have any disease of the respiratory, cardiovascular, or skeletal muscle systems. They lived in the same city for at least 15 years, without staying more than six months in other cities that had a concentration of air pollution different from the place of origin. It is known that short-term exposure to pollutants, depending on the concentration and type of pollutant, can acutely increase the risk of respiratory and cardiovascular mortality,³ , ²¹ while exposure to air pollutants for a period between 15 and 30 years can cause medium and long-term impacts on human health.²² Individuals using medications that can alter cardiac autonomic modulation, individuals who consumed alcohol, smokers, hypertensive subjects, those with diabetes mellitus, respiratory or cardiovascular diseases, sedentary lifestyle and/or a body mass index ≥ 30 kg/m² were excluded.

Classification of locations regarding air quality

The indices for estimating air quality and classifying the cities as “highly polluted” and “low polluted” were consulted on the website of the Environmental Company of São Paulo State (CETESB).²³ Primary standards for high pollutant concentrations were those that, when exceeded, can affect the health of the population, that is, the maximum tolerable levels. Secondary standards were defined as concentrations below which have the least impact on the population's well-being, that is, the desired levels of pollutant concentrations. In this study, we adopted 150μg/m³ and 100μg/m³ per day for primary and secondary standards, respectively.²⁴

To obtain the annual averages of 2011, the averages of the indices from the seven air quality monitoring stations in the metropolitan region of São Paulo and from the single monitoring station in São Carlos in that same year, provided by CETESB, were calculated.²³ These data are shown in Table 1 .

Table 1
Primary and secondary smoke index standards of the cities of São Paulo and São Carlos, Brazil, in 2011

	Reference value	São Paulo, SP	São Carlos, SP
Primary standard (μg/m3)	150	158,85	46
Secondary standard (μg/m3)	100	109,71	38

• NB: μg/m³ = micrograms/cubic meter.
²³

However, for the purposes of this research, only smoke concentrations were considered for estimation of air pollution of the two cities mentioned above (São Paulo and São Carlos), which represent only a fraction of the particulate matter that influences this type of pollution. This decision was made based on the information available on the website of CETESB,²³ which provides only smoke indices as measurements in the city of São Carlos.

Procedures

Volunteer recruitment and data collection were carried out from April to September 2011 at the Physical Education and Sport School of the University of São Paulo and the Cardiovascular Physiotherapy Laboratory of the Federal University of São Carlos.

Initially, volunteers were informed about the purposes of the study and experimental procedures and signed an informed consent form. The study was approved by the Research Ethics Committee (EEFE-USP 2011/36) and complied with the 466/12 Resolution of the National Health Council 12/12/2012.

The subjects were instructed to abstain from caffeine and alcoholic beverage intake, and from strenuous physical exercise for at least 24 hours before the HRV measurement, and to have a good night's sleep the night before the tests. The experimental procedure was carried out in the morning period (between 7 am and 12 pm), considering circadian variations.¹⁸ , ²⁵

On the day of the experiment, the volunteers were asked whether they had followed the instructions correctly and if they were in good health. Then, the heart rate monitor (Polar Electro, Finland, S810®) was placed on the volunteer's chest, according to the manufacturer's specifications.¹⁸ Afterwards, the RR intervals (R-Ri) of each participant at supine rest were recorded (in ms), for 15 minutes, in a silent and climate-controlled environment, at a temperature of around 22 °C and relative humidity between 40 and 60%.

Next, the submaximal exercise test was performed with a 40.9 cm step, following the Queen's College protocol²⁶ for people between 18 and 30 years old. The protocol consisted of stepping up and down on the step for three minutes, in cadence with a metronome set at 96 beats per minute (bpm). This test aimed to indirectly estimate the maximum oxygen consumption (VO₂max) reached by the volunteer, considering heart rate recovery (from the 5th to the 20th second heartbeat after the test), and the following equations proposed by the protocol:

$$\mathrm{Men}\to {\mathrm{VO}}_2\max (\mathrm{mL}/\mathrm{Kg}/\min )=111.33-(0.42\times \mathrm{heart\; rate\; recovery})$$

$$\mathrm{Women}\to {\mathrm{VO}}_2\max (\mathrm{mL}/\mathrm{Kg}/\min )=65.81-(0.1847\times \mathrm{heart\; rate\; recovery})$$

Men → VO₂max (mL/Kg/min) = 111.33 – (0.42 x heart rate recovery)

Women → VO₂max (mL/Kg/min) = 65.81 – (0.1847 x heart rate recovery)

The estimated VO₂max values were used considering the indices established by the American Heart Association (AHA)²⁷ to characterize the sample and ensure that the studied groups had similar aerobic functional classification, as this is also a factor that can affect HRV values.¹⁰ , ¹⁸ , ²⁵

Data processing

R-Ri analysis

For HRV analyses, the R-Ri sequence with the highest stability, with a length of 256 points, was selected for each volunteer, excluding the initial and final stretches.¹⁸ , ¹⁹

Linear analysis of HRV

The spectral analysis of the HRV was performed by the autoregressive method²⁸ , ²⁹ in two bands: low frequency (LF) and high frequency (HF).¹³ , ¹⁸ , ²⁵ Their values were expressed in normalized units – LFnu for cardiac sympathetic modulation and HFnu for parasympathetic modulation – ⁶ , ⁷ calculated as follows: $\mathrm{LFnu}=\mathrm{LF\; power}/\mathrm{TP}–\mathrm{VLF\; power}\times 100$ ; $\mathrm{HFnu}=\mathrm{HF\; power}/\mathrm{TP}–\mathrm{VLF\; power}\times 100$ .²⁸ , ²⁹ Mean R-Ri and variance were also calculated.

Nonlinear HRV analysis (conditional entropy)

The study of entropy qualifies and quantifies HRV complexity.³⁰ Conditional entropy based on symbolic dynamics¹⁷ shows information about the regularity of HRV, that is, it is able to clarify whether the amount of information provided from a new sample can be determined by analyzing previous values.¹⁵ , ¹⁶ This analysis provides the complexity index (CI), which was normalized by Shannon entropy, resulting in the normalized complexity index (NCI).¹⁷ According to Porta et al.,¹⁷ the NCI can range from 0 (null information) to 1 (maximum information). This means that the higher the NCI, the lower the regularity of the series and the greater the complexity.

Statistical analysis

The Shapiro-Wilk test was used to verify data normality. The impact of exposure to pollutants and of sex on HRV were analyzed by the two-way ANOVA statistical test, taking sex (male and female) and pollution (most polluted location – São Paulo and least polluted location – São Carlos) as factors, and analyzing the interaction between them using the post hoc Tukey's Test. Data were expressed as mean ± standard deviation. A significance level of 5% (p<0.05) was considered. For this purpose, the Sigma Plot 11.0 for Windows software was used.

Results

Anthropometric characteristics and age of participants are described in Table 2 . There were statistical differences in body mass, height and estimated VO₂max between the sexes, where women showed lower values compared to men (as expected), regardless of the city in which they lived ( Table 2 ). Group 1 volunteers lived in São Paulo for 21.94 ± 1.43 years and group 2 volunteers in São Carlos for 22.55 ± 4.40 years.

Table 2
Age, anthropometric characteristics, and functional capacity of residents of São Paulo and São Carlos, included in the study

	Group 1 (São Paulo, SP)		Group 2 (São Carlos, SP)		P values
	Men (n = 10)	Women (n = 8)	Men (n = 10)	Women (n = 8)	Impact of air pollution exposure	Impact of sex	Interaction between the impacts
Age (years)	22.2 ± 1.7	21.6 ± 1,1	22.8 ± 3.2	23.8 ± 3.2	0.109	0.822	0.363
Body mass (Kg)	74.1 ± 7.9	61.6 ± 5.8	76.4 ± 11.5	64.7 ± 16.4	0.473	0.003 *	0.918
Height (m)	1.8 ± 0.1	1.6 ± 0.1	1.8 ± 0.1	1.6 ± 0.1	0.395	< 0.001 *	0.938
BMI (Kg/m²)	23.4 ± 1.5	22.7 ± 1.9	24.7 ± 2.9	24.3 ± 6.2	0.228	0.640	0.886
Estimated VO2max	50.5 ± 3.8	36.2 ± 3.1	50.8 ± 5.5	34.4 ± 2.7	0.559	< 0.001 *	0.443

• NB: values expressed as mean ± standard deviation; BMI: body mass index; VO₂max = maximum oxygen consumption;
• ^*
  significant difference according to the two-way ANOVA statistical test, with post hoc Tukey test.
Source: research data.

Table 3 shows the results of linear (time and frequency domain) and non-linear (conditional entropy) HRV analysis in supine rest of the evaluated groups. Mean R-Ri showed the impact of sex and its interaction with air pollution exposure, which was higher in men compared to women in the city of São Paulo. Likely, HFnu (cardiac parasympathetic modulation) showed an interaction between sex and air pollution exposure, which was higher in women compared to men living in the city of São Carlos.

Table 3
Heart rate variability assessed in the time, frequency and conditional entropy domains

	Group 1 (São Paulo, SP)		Group 2 (São Carlos, SP)		P values
	Men (n = 10)	Women (n = 8)	Men (n = 10)	Women (n = 8)	Impact of air pollution exposure	Impact of sex	Interaction between the impacts
Mean R-Ri (ms)	1078.6 ± 138.7	816.6 ± 89.8	1005.1 ± 135.5	909.9 ± 57.4	0.798	< 0.001 *	0.0037 *
R-Ri Variance (ms²)	6373.5 ± 7263.5	2921.8 ± 2425.1	3998.4 ± 2626.7	3876.9 ± 2128.8	0.631	0.231	0.264
Spectral analysis
LFnu (nu)	42.0 ± 20.5	46.1 ± 11.9	48.2 ± 18.3	30.2 ± 18.9	0.424	0.254	0.075
HFnu (nu)	55.8 ± 18.1	49.3 ± 12.2	47.5 ± 17.3	68.4 ± 19.1	0.351	0.217	0.022 *
Conditional Entropy
NCI CI	0.8 ± 0.1 1.2 ± 0.1	0.8 ± 0.0 1.1 ± 0.1	0.8 ± 0.1 1.2 ± 0.1	0.8 ± 0.1 1.2 ± 0.2	0.589 0.917	0.291 0.504	0.472 0.169

• Note: values expressed as mean ± standard deviation. R-Ri: R-R intervals; LFnu: low frequency in normalized units; HFnu: high frequency in normalized units; NCI: normalized complexity index; CI: complexity index;
• ^*
  significant difference according to the two-way ANOVA statistical test, with post hoc Tukey test.
Source: research data.

Moreover, HFnu was higher in women living in São Carlos, compared to those living in São Paulo. On the other hand, LFnu (cardiac sympathetic modulation) and variables related to the HRV complexity (conditional entropy) did not show an impact from pollution or sex.

The main findings of the present study were: 1) there was an impact of sex on mean R-Ri in subjects living the city with the highest concentration of air pollution (São Paulo/SP), as men had higher values than women; 2) there was an impact of sex on cardiac parasympathetic modulation in the city with the lowest concentration of air pollution (São Carlos, SP), as women had greater parasympathetic modulation compared to men; and 3) there was an impact of air pollution on cardiac parasympathetic modulation among women, as women from São Carlos, SP had greater modulation than those from São Paulo, SP.³¹ , ³²

Discussion

The main finding of the present study was that long-term exposure to pollutants, specifically smoke, interferes with cardiac autonomic modulation, and female residents of the city with lower air pollution levels showed greater parasympathetic modulation compared to male residents.

Mean R-Ri at rest was higher in men than in women only in group 1, i.e ., among individuals exposed to a higher concentration of pollution, indicating an impact of sex on R-Ri regardless of air pollution levels. This may be explained by the fact that male individuals have a larger heart size³³ and, consequently, greater cardiac output and blood volume ejected by systole, presenting a lower basal heart rate compared to women.³⁴ A possible adaptive process related to long-term exposure to pollutants in both sexes should also be considered. Furthermore, men had a better functional capacity than women according to the AHA functional classification.²⁷

Parasympathetic modulation was greater in women than in men only in group 2 (lower concentration of air pollution). The association of resting HRV with sex, with higher vagal modulation at rest among women, has been observed in several studies,¹⁰ , ¹² , ³⁵ corroborating our finding. This may be due to a higher resting HRV and greater vagal modulation in women than men.¹⁰ , ¹² , ¹³ However, pollution causes a decline in parasympathetic modulation and an increment in sympathetic activity,³⁶ , ³⁷ suggesting that a greater exposure to pollution did not equally affect the expected responses in HRV in men and women.

Concerning parasympathetic modulation, the within-sex analysis revealed an impact of air pollution exposure on females, as women in group 2 showed greater modulation than those in group 1. Thus, exposure to a higher concentration of pollution had an unfavorable impact on cardiac autonomic modulation in the study women. This indicates that females are more susceptible to the damage caused by pollution, as the parasympathetic modulation of men in the two cities was not significantly different. Other studies have also reported a reduction in cardiac vagal tone in individuals exposed to pollution, but they did not carry out an analysis by sex, which makes it difficult to make a comparison with the findings of the present study.⁸ , ²⁰

As previously stated, we observed different HRV between sexes and, therefore, men and women may suffer different effects of the long-term exposure to air pollution on the ANS. This finding may guide future studies regarding the association among these variables.

Regarding the complexity of HRV, assessed by the conditional entropy, no statistical differences were found between the groups (São Paulo vs. São Carlos residents) or between men and women. This is in accordance with the findings of Catai et al.,¹¹ who assessed various age groups and, for the age range similar to our study, there were no differences between sexes, as women showed lower HRV complexity only after menopause, due to the sharp decline in the estrogen hormone, while men showed a linear decrease throughout the aging process. On the other hand, Voss et al.,³⁸ who also assessed different age groups, found that in the 25–34-year age range, the complexity indices varied according to sex and were higher in women than in men.

If, on the one hand, the impact of sex on the variables related to HRV is well documented in the literature, on the other hand, the relationship of these same variables with exposure to atmospheric pollution is still poorly known. Existing studies mainly cover short-term pollution,³ , ⁴ , ⁶ , ⁷ , ⁹ , ³⁶ , ³⁷ making it difficult to compare their results with ours. In a meta-analysis of epidemiological studies carried out by Pieters et al.,³⁹ the authors found a reduction in HRV with an increase in air pollution exposure. However, when the literature search encompassed HRV, sex and long-term air pollution, we did not find any studies.

This study has some limitations. The first is that it involved only two cities in São Paulo State and, therefore, the results cannot be generalized; although there are differences in the concentrations of smoke pollution between the cities of São Paulo and São Carlos, these may not have been enough to cause other significant changes in HRV. It is important to consider that São Carlos is a medium-sized city, with many industries and a large vehicle fleet. If the comparison had been made between São Paulo and a smaller city (and thus with lower air pollution concentrations), perhaps greater differences would have been observed; however, smaller cities usually do not have air quality control, which could lead to spurious conclusions. Also, even though the municipality of São Carlos performs the measurement of smoke indices, these represents only a fraction of the particulate-matter air pollution. Another potential limitation is the small sample size, which may have influenced the results in some of the analyses, such as of HRV complexity (conditional entropy).

Therefore, our study suggests that long-term exposure to air pollution reduces HRV in healthy young adults, especially females, with a reduction in cardiac parasympathetic modulation. Thus, our findings can be considered as a springboard for further studies with a larger sample size, focusing on the assessment of data from different cities and age groups and the relationship between HRV, long-term pollution and sex, in order to confirm and expand our findings.

Conclusion

The results suggest that long-term exposure to pollutants, specifically to smoke, has an unfavorable impact on HRV, with a reduction in cardiac vagal autonomic modulation, in healthy young adults, mainly females.

• Sources of Funding
  This study was funded by Conselho Nacional de desenvolvimento Científico e Tecnológico (CNPq – 310612/2019-5) and Fundação de Amparo à Pesquisa doestado de São Paulo (FAPESP – scholarship 2011/18919-5).
• Study Association
  This study is not associated with any graduation program.
• Ethics approval and consent to participate
  This study was approved by the Ethics Committee of the EEFE – ESP under the protocol number 2011/36. All the procedures in this study were in accordance with the 1975 Helsinki Declaration, updated in 2013. Informed consent was obtained from all participants included in the study.

Acknowledgments

The authors would like to acknowledge the National Council for Scientific and Technological Development (CNPq-Grant 310612/2019-5) and São Paulo Research Foundation (FAPESP- scholarship grant 2011/18919-5) for funding this study.

References

• ¹ Cançado JED, Braga A,Pereira LA, Arbex ML A, Saldiva PHN, Santos UP. Repercussões clínicas da exposição à poluição atmosférica* Clinical repercussions of exposure to atmospheric pollution. j Bras Pneumol. 2006;32(Supl 1):S5-S11.
• ² Brasil. Ministério do Meio Ambiente/Conselho Nacional do Meio Ambiente. RESOLUÇÃO No 491 de 19 de Novembro de 2018. Dispõe sobre a qualidade doar. Diário Oficicial da União. 2018;edição 223, Seção 1, p. 155.
• ³ Liu C, Chen R, Sera F, Vicedo-Cabrera AM, Guo Y, Tong S, et al. Ambient Particulate Air Pollution and Daily Mortality in 652 Cities. N Engl J Med. 2019;381(8):705-15. Doi: https://www.nejm.org/doi/full/10.1056/nejmoa1817364
  » https://www.nejm.org/doi/full/10.1056/nejmoa1817364
• ⁴ Warburton DER, Bredin SSD, Shellington EM, Cole C, de Faye A, Harris J, et al. A Systematic Review of the Short-Term Health Effects of Air Pollution in Persons Living with Coronary Heart Disease. J Clin Med. 2019 Feb 24;8(2):274. doi: 10.3390/jcm8020274.
  » https://doi.org/10.3390/jcm8020274
• ⁵ Meier-Girard D, Delgado-Eckert E, Schaffner E, Schindler C, Künzli N, Adam M, et al. Association of long-term exposure to traffic-related PM10 with heart rate variability and heart rate dynamics in healthy subjects. Environ Int. 2019 Apr 1;125:107–16. Doi: 10.1016/j.envint.2019.01.03.
  » https://doi.org/10.1016/j.envint.2019.01.03
• ⁶ Shutt RH, Kauri LM, Weichenthal S, Kumarathasan P, Vincent R, Thomson EM, et al. Exposure to air pollution near a steel plant is associated with reduced heart rate variability: A randomised crossover study. Environ Health. 2017 Jan 28;16(1):4. Doi: 10.1186/s12940-016-0206-0.
  » https://doi.org/10.1186/s12940-016-0206-0
• ⁷ Mirowsky JE, Peltier RE, Lippmann M, Thurston G, Chen LC, Neas L, et al. Repeated measures of inflammation, blood pressure, and heart rate variability associated with traffic exposures in healthy adults. Environ Health; 2015;14(1):66. Doi: 10.1186/s12940-015-0049-0.
  » https://doi.org/10.1186/s12940-015-0049-0
• ⁸ Pumprla J, Howorka K, Groves D, Chester M, Nolan J. Functional assessment of heart rate variability: Physiological basis and practical applications. Int J Cardiol .2002;84(1):1-14. Doi: 10.1016/s0167-5273(02)00057-8.
  » https://doi.org/10.1016/s0167-5273(02)00057-8
• ⁹ Paoin K, Ueda K, Seposo XT, Hayano J, Kiyono K, Ueda N, et al. Association between PM2.5 exposure and heart rate variability for the patients with cardiac problems in Japan. Air Qual Atmos Health. 2020;13(3):339-47. Doi: 10.1007/s11869-020-00797-8.
  » https://doi.org/10.1007/s11869-020-00797-8
• ¹⁰ Antelmi I, De Paula RS, Shinzato AR, Peres CA, Mansur AJ, Grupi CJ. Influence of age, sex, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. Am J Cardiol. 2004 Feb 1;93(3):381–5. Doi: 10.1016/j.amjcard.2003.09.065.
  » https://doi.org/10.1016/j.amjcard.2003.09.065
• ¹¹ Catai A, Takahashi A, Perseguini N, Milan J, Minatel V, Rehder-Santos P, et al. Effect of the Postural Challenge on the Dependence of the Cardiovascular Control Complexity on Age. Entropy 2014, 16(12), 6686 6704. Doi: https://doi.org/10.3390/e161266
  » https://doi.org/10.3390/e161266
• ¹² Ramaekers D, Ector H, Aubert AE, Rubens A, Van De Werf F. Heart rate variability and heart rate in healthy volunteers Is the female autonomic nervous system cardioprotective? Eur Heart J. 1998;19(9):1334-41. Doi:10.1053/euhj.1998.1084.
  » https://doi.org/10.1053/euhj.1998.1084
• ¹³ Acharya UR, Joseph KP, Kannathal N, Lim CM, Suri JS. Heart rate variability: A review. Med Biol Eng Comput. 2006;44(12):1031-51. Doi: 10.1007/s11517-006-0119-0.
  » https://doi.org/10.1007/s11517-006-0119-0
• ¹⁴ Dantas EM, Kemp AH, Andreão RV, da Silva VJD, Brunoni AR, Hoshi RA, et al. Reference values for short-term resting-state heart rate variability in healthy adults: Results from the Brazilian Longitudinal Study of Adult Health - ELSA-Brasil study. Psychophysiology. 2018;55(6):e13052. Doi: 10.1111/psyp.13052.
  » https://doi.org/10.1111/psyp.13052
• ¹⁵ Porta A, Baselli G, Liberati D, Montano N, Cogliati C, Gnecchi-Ruscone T, et al. Measuring regularity by means of a corrected conditional entropy in sympathetic outflow. Biol Cybern. Biol Cybern. 1998;78(1):71-8. Doi: 10.1007/s004220050414.
  » https://doi.org/10.1007/s004220050414
• ¹⁶ Porta A, Guzzetti S, Montano N, Furlan R, Pagani M, Malliani A, et al. Entropy, entropy rate, and pattern classification as tools to typify complexity in short heart period variability series. IEEE Trans Biomed Eng. 2001;48(11):1282–91. Doi: 10.1109/10.959324.
  » https://doi.org/10.1109/10.959324
• ¹⁷ Porta A, Faes L, Masé M, D’Addio G, Pinna GD, Maestri R, et al. An integrated approach based on uniform quantization for the evaluation of complexity of short-term heart period variability: Application to 24 h Holter recordings in healthy and heart failure humans. Chaos. 2007;17(1):0511. Doi:10.1063/1.2404630.
  » https://doi.org/10.1063/1.2404630
• ¹⁸ Electrophysiology TF of the ES. Heart Rate Variability. Standards of measurement, physiologicalinterpretation and clinical use. Circulation. 1996; 93(5):1043-65. Doi: https://www.ahajournals.org/doi/10.1161/01.CIR.93.5.1043
  » https://www.ahajournals.org/doi/10.1161/01.CIR.93.5.1043
• ¹⁹ Catai AM, Pastre CM, Godoy MF de, Silva E da, Takahashi AC de M, Vanderlei LCM. Heart rate variability: are you using it properly? Standardisation checklist of procedures. Braz J Phys Ther. 2020;24(2):91-102. Doi:10.1016/j.bjpt.2019.02.006.
  » https://doi.org/10.1016/j.bjpt.2019.02.006
• ²⁰ Regis da Costa e Oliveira J, Base LH, Maia LCP, Ferreira de Lima Antão JYF, de Abreu LC, Oliveira FR, et al. Geometric indexes of heart rate variability in healthy individuals exposed to long-term air pollution. Environ Sci Pollut Res [Internet]. 2020 Feb 1 [cited 2020 Jun 20];27(4):4170–7. Available from: https://link.springer.com/article/10.1007/s11356-019-06965-3
  » https://link.springer.com/article/10.1007/s11356-019-06965-3
• ²¹ Fajersztajn L, Saldiva P, Pereira LAA, Leite VF, Buehler AM. Short-term effects of fine particulate matter pollution on daily health events in Latin America: a systematic review and meta-analysis. Int J Publ Health. Basel. 2017;62(7):729-38. Doi:10.1007/s00038-017-0960-y
  » https://doi.org/10.1007/s00038-017-0960-y
• ²² Brasil. Ministério da Saúde. Riscos Ambientais e a saúde humana. Fundação Nacional da Saúde (Funasa); 2002. [Internet]. [cited 2020 Jun 20]. Available from: https://www.saude.gov.br/vigilancia-em-saude/vigilancia-ambiental/vigiar/riscos-ambientais-e-a-saude-humana
  » https://www.saude.gov.br/vigilancia-em-saude/vigilancia-ambiental/vigiar/riscos-ambientais-e-a-saude-humana
• ²³ São Paulo(Estado). Companhia Ambiental do Estado de São Paulo (CETESB). Publicações / Relatórios | Qualidade do Ar São Paulo;2011.[Internet]. [cited 2021 Feb 7]. Available from: https://cetesb.sp.gov.br/ar/publicacoes-relatorios/
  » https://cetesb.sp.gov.br/ar/publicacoes-relatorios/
• ²⁴ Brasil. Ministério do Meio Ambiente. Conselho Nacional do Meio Ambiente. Resolução n. 342 de 25 de setembro de 2003. [Internet]. [cited in 2020 Jul 12] Available from: http://conforlab.com.br/legislacao/conama03.pdf
  » http://conforlab.com.br/legislacao/conama03.pdf
• ²⁵ Catai AM, Chacon-Mikahil MPT, Martinelli FS, Forti VAM, Silva E, Golfetti R, et al. Effects of aerobic exercise training on heart rate variability during wakefulness and sleep and cardiorespiratory responses of young and middle-aged healthy men. Brazilian J Med Biol Res. 2002;35(6):741–52.
• ²⁶ McArdle WD, Katch FI, Pechar GS, Jacobson L, Ruck S. Reliability and interrelationships between maximal oxygen intake, physical work capacity and step-test scores in college women. Med Sci Sports Exerc. 1972;4(4):182–6. PMID: 4648576
• ²⁷ American Heart Association. Committee of Exercise. Exercise testing and training of apparently healthy individuals. A handbook for physicians. New York: AHA; 1972
• ²⁸ Malliani A, Pagani M, Lombardi F, Cerutti S. Cardiovascular neural regulation explored in the frequency domain. Circulation. 1991;84(2):482–92.
• ²⁹ Pagani M, Lombardi F, Guzzetti S, Rimoldi O, Furlan R, Pizzinelli P, et al. Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. Circ Res. 1986;59(2):178–93. Doi: 10.1161/01.res.59.2.178.
  » https://doi.org/10.1161/01.res.59.2.178
• ³⁰ Porta A, Di Rienzo M, Wessel N, Kurths J. Addressing the complexity of cardiovascular regulation. Philos Trans R Soc A Math Phys Eng Sci. 367(1892):1215–8. Doi: 10.1098/rsta.2008.0292.
  » https://doi.org/10.1098/rsta.2008.0292
• ³¹ de Almeida AEM, Stefani C de M, do Nascimento JA, de Almeida NM, Santos A da C, Ribeiro JP, et al. An equation for the prediction of oxygen consumption in a Brazilian population. Arq Bras Cardiol. 2014 Oct 1;103(4):299-307. Doi: 10.5935/abc.20140137.
  » https://doi.org/10.5935/abc.20140137
• ³² Charkoudian N, Joyner MJ. Physiologic considerations for exercise performance in women. Clin Chest Med. 200; 25(2):247-55. Doi: 10.1016/j.ccm.2004.01.001.
  » https://doi.org/10.1016/j.ccm.2004.01.001
• ³³ Olivetti G, Giordano G, Corradi D, Melissari M, Lagrasta C, Gambert SR, et al. Sex differences and aging: Effects on the human heart. J Am Coll Cardiol. 1995;26(4):1068–79. doi: 10.1016/0735-1097(95)00282-8.
  » https://doi.org/10.1016/0735-1097(95)00282-8
• ³⁴ Umetani K, Singer DH, McCraty R, Atkinson M. Twenty-four hour time domain heart rate variability and heart rate: Relations to age and sex over nine decades. J Am Coll Cardiol. 1998 Mar;31(3):593-601. Doi:10.1016/S0735-1097(97)00554-8.
  » https://doi.org/10.1016/S0735-1097(97)00554-8
• ³⁵ Perseguini NM, Takahashi ACM, Rebelatto JR, Silva E, Borghi-Silva A, Porta A, et al. Spectral and symbolic analysis of the effect of sex and postural change on cardiac autonomic modulation in healthy elderly subjects. Braz J Med Biol Res. 2011;44(1):29–37. Doi: 10.1590/S0100-879X2010007500137.
  » https://doi.org/10.1590/S0100-879X2010007500137
• ³⁶ Gold DR, Litonjua A, Schwartz J, Lovett E, Larson A, Nearing B, et al. Ambient pollution and heart rate variability. Circulation. 2000 Mar 21;101(11):1267–73. Doi: 10.1161/01.cir.101.11.1267.
  » https://doi.org/10.1161/01.cir.101.11.1267
• ³⁷ He F, Shaffer ML, Li X, Rodriguez-Colon S, Wolbrette DL, Williams R, et al. Individual-level PM2.5 exposure and the time course of impaired heart rate variability: The APACR Study. J Expo Sci Environ Epidemiol. 2011 Jan;21(1):65–73. Doi: 10.1038/jes.2010.21.
  » https://doi.org/10.1038/jes.2010.21
• ³⁸ Voss A, Schroeder R, Fischer C, Heitmann A, Peters A, Perz S. Influence of age and sex on complexity measures for short term heart rate variability analysis in healthy subjects. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5574-7. Doi: 10.1109/EMBC.2013.6610813.
  » https://doi.org/10.1109/EMBC.2013.6610813
• ³⁹ Pieters N, Plusquin M, Cox B, Kicinski M, Vangronsveld J, Nawrot TS. An epidemiological appraisal of the association between heart rate variability and particulate air pollution: A meta-analysis. Heart. 2012;98(15):1127-35. Doi: 10.1136/heartjnl-2011-301505
  » https://doi.org/10.1136/heartjnl-2011-301505

Publication Dates

• Publication in this collection
  01 Apr 2022
• Date of issue
  2022

History

• Received
  19 Nov 2020
• Reviewed
  30 Aug 2021
• Accepted
  20 Sept 2021