Bayesian Estimation of Correlation Between Measures of Blood Pressure Indices, Aerobic Capacity and Resting Heart Rate Variability Using Markov Chain Monte Carlo Simulation and 95% High Density Interval in Female School Teachers

Overview

Journal Int J Environ Res Public Health

Publisher MDPI

Specialties Environmental Health
Public Health

Date 2020 Sep 19

PMID 32947985

Citations 2

Authors

Shaher A I Shalfawi

Affiliations

Soon will be listed here.

Abstract

Background: Several explanations regarding the disparity observed in the literature with regard to heart rate variability (HRV) and its association with performance parameters have been proposed: the time of day when the recording was conducted, the condition (i.e., rest, active, post activity) and the mathematical and physiological relationships that could have influenced the results. A notable observation about early studies is that they all followed the frequentist approach to data analyses. Therefore, in an attempt to explain the disparity observed in the literature, the primary purpose of this study was to estimate the association between measures of HRV indices, aerobic performance parameters and blood pressure indices using the Bayesian estimation of correlation on simulated data using Markov Chain Monte Carlo (MCMC) and the equal probability of the 95% high density interval (95% HDI).

Methods: The within-subjects with a one-group pretest experimental design was chosen to investigate the relationship between baseline measures of HRV (rest; independent variable), myocardial work (rate-pressure product (RPP)), mean arterial pressure (MAP) and aerobic performance parameters. The study participants were eight local female schoolteachers aged 54.1 ± 6.5 years (mean ± SD), with a body mass of 70.6 ± 11.5 kg and a height of 164.5 ± 6.5 cm. Their HRV data were analyzed in R package, and the Bayesian estimation of correlation was calculated employing the Bayesian hierarchical model that uses MCMC simulation integrated in the JAGS package.

Results: The Bayesian estimation of correlation using MCMC simulation reproduced and supported the findings reported regarding norms and the within-HRV-indices associations. The results of the Bayesian estimation showed a possible association (regardless of the strength) between pNN50% and MAP ( = 0.671; 95% HDI = 0.928-0.004), MeanRR (ms) and RPP ( = -0.68; 95% HDI = -0.064--0.935), SDNN (ms) and RPP ( = 0.672; 95% HDI = 0.918-0.001), LF (ms) and RPP ( = 0.733; 95% HDI = 0.935-0.118) and SD2 and RPP ( = 0.692; 95% HDI = 0.939-0.055).

Conclusions: The Bayesian estimation of correlation with 95% HDI on MCMC simulated data is a new technique for data analysis in sport science and seems to provide a more robust approach to allocating credibility through a meaningful mathematical model. However, the 95% HDI found in this study, accompanied by the theoretical explanations regarding the dynamics between the parasympathetic nervous system and the sympathetic nervous system in relation to different recording conditions (supine, reactivation, rest), recording systems, time of day (morning, evening, sleep etc.) and age of participants, suggests that the association between measures of HRV indices and aerobic performance parameters has yet to be explicated.

Citing Articles

Bayesian Estimation of the Variation in Strength and Aerobic Physical Performances in Young Eumenorrheic Female College Students during a Menstrual Cycle.

Shalfawi S, El Kailani G Sports (Basel). 2021; 9(9).

PMID: 34564335 PMC: 8472434. DOI: 10.3390/sports9090130.

Validity of the Polar V800 Monitor for Assessing Heart Rate Variability in Elderly Adults under Mental Stress and Dual Task Conditions.

Huang C, Chan H, Chang Y, Chen S, Hsu M Int J Environ Res Public Health. 2021; 18(3).

PMID: 33498381 PMC: 7908342. DOI: 10.3390/ijerph18030869.

References

Cassirame J, Vanhaesebrouck R, Chevrolat S, Mourot L . Accuracy of the Garmin 920 XT HRM to perform HRV analysis. Australas Phys Eng Sci Med. 2017; 40(4):831-839. DOI: 10.1007/s13246-017-0593-8. View

Marocolo M, Nadal J, Benchimol Barbosa P . The effect of an aerobic training program on the electrical remodeling of heart high-frequency components of the signal-averaged electrocardiogram is a predictor of the maximal aerobic power. Braz J Med Biol Res. 2007; 40(2):199-208. DOI: 10.1590/s0100-879x2007000200006. View

Stein P . Assessing heart rate variability from real-world Holter reports. Card Electrophysiol Rev. 2002; 6(3):239-44. DOI: 10.1023/a:1016376924850. View

Berntson G, BIGGER Jr J, Eckberg D, Grossman P, Kaufmann P, Malik M . Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology. 1997; 34(6):623-48. DOI: 10.1111/j.1469-8986.1997.tb02140.x. View

Leite M, Ramos E, Kalva-Filho C, Rodrigues F, Freire A, Tacao G . Correlation between heart rate variability indexes and aerobic physiological variables in patients with COPD. Respirology. 2014; 20(2):273-8. DOI: 10.1111/resp.12424. View

Nunan D, Donovan G, Jakovljevic D, Hodges L, Sandercock G, Brodie D . Validity and reliability of short-term heart-rate variability from the Polar S810. Med Sci Sports Exerc. 2008; 41(1):243-50. DOI: 10.1249/MSS.0b013e318184a4b1. View

Berntson G, Quigley K, Jang J, Boysen S . An approach to artifact identification: application to heart period data. Psychophysiology. 1990; 27(5):586-98. DOI: 10.1111/j.1469-8986.1990.tb01982.x. View

Kazmi S, Zhang H, Aziz W, Monfredi O, Abbas S, Shah S . Inverse Correlation between Heart Rate Variability and Heart Rate Demonstrated by Linear and Nonlinear Analysis. PLoS One. 2016; 11(6):e0157557. PMC: 4919077. DOI: 10.1371/journal.pone.0157557. View

Castaneda D, Esparza A, Ghamari M, Soltanpur C, Nazeran H . A review on wearable photoplethysmography sensors and their potential future applications in health care. Int J Biosens Bioelectron. 2019; 4(4):195-202. PMC: 6426305. DOI: 10.15406/ijbsbe.2018.04.00125. View

10.

Shaffer F, McCraty R, Zerr C . A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability. Front Psychol. 2014; 5:1040. PMC: 4179748. DOI: 10.3389/fpsyg.2014.01040. View

11.

Kruschke J . Posterior predictive checks can and should be Bayesian: comment on Gelman and Shalizi, 'Philosophy and the practice of Bayesian statistics'. Br J Math Stat Psychol. 2012; 66(1):45-56. DOI: 10.1111/j.2044-8317.2012.02063.x. View

12.

Ansari M, Javadi H, Pourbehi M, Mogharrabi M, Rayzan M, Semnani S . The association of rate pressure product (RPP) and myocardial perfusion imaging (MPI) findings: a preliminary study. Perfusion. 2012; 27(3):207-13. DOI: 10.1177/0267659112436631. View

13.

Laborde S, Mosley E, Thayer J . Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research - Recommendations for Experiment Planning, Data Analysis, and Data Reporting. Front Psychol. 2017; 8:213. PMC: 5316555. DOI: 10.3389/fpsyg.2017.00213. View

14.

Schmitt L, Fouillot J, Millet G, Robach P, Nicolet G, Brugniaux J . Altitude, heart rate variability and aerobic capacities. Int J Sports Med. 2007; 29(4):300-6. DOI: 10.1055/s-2007-965355. View

15.

Phoemsapthawee J, Prasertsri P, Leelayuwat N . Heart rate variability responses to a combined exercise training program: correlation with adiposity and cardiorespiratory fitness changes in obese young men. J Exerc Rehabil. 2019; 15(1):114-122. PMC: 6416511. DOI: 10.12965/jer.1836486.243. View

16.

Flatt A, Esco M . Evaluating Individual Training Adaptation With Smartphone-Derived Heart Rate Variability in a Collegiate Female Soccer Team. J Strength Cond Res. 2015; 30(2):378-85. DOI: 10.1519/JSC.0000000000001095. View

17.

Vitale J, Bonato M, LA Torre A, Banfi G . Heart Rate Variability in Sport Performance: Do Time of Day and Chronotype Play A Role?. J Clin Med. 2019; 8(5). PMC: 6571903. DOI: 10.3390/jcm8050723. View

18.

Flatt A, Esco M, Nakamura F . Individual Heart Rate Variability Responses to Preseason Training in High Level Female Soccer Players. J Strength Cond Res. 2016; 31(2):531-538. DOI: 10.1519/JSC.0000000000001482. View

19.

Kallioinen N, Hill A, Horswill M, Ward H, Watson M . Sources of inaccuracy in the measurement of adult patients' resting blood pressure in clinical settings: a systematic review. J Hypertens. 2016; 35(3):421-441. PMC: 5278896. DOI: 10.1097/HJH.0000000000001197. View

20.

Hernando D, Garatachea N, Almeida R, Casajus J, Bailon R . Validation of Heart Rate Monitor Polar RS800 for Heart Rate Variability Analysis During Exercise. J Strength Cond Res. 2016; 32(3):716-725. DOI: 10.1519/JSC.0000000000001662. View