Skip to main content
Revista Paulista de Pediatria logoLink to Revista Paulista de Pediatria
. 2017 Jul 31;35(3):302–308. doi: 10.1590/1984-0462/;2017;35;3;00007

ASSOCIATION BETWEEN LEISURE TIME AND COMMUTING PHYSICAL ACTIVITIES WITH HEART RATE VARIABILITY IN MALE ADOLESCENTS

ASSOCIAÇÃO ENTRE A ATIVIDADE FÍSICA DE LAZER E DE DESLOCAMENTO COM A VARIABILIDADE DA FREQUÊNCIA CARDÍACA EM ADOLESCENTES DO SEXO MASCULINO

Aline Cabral Palmeira a,*, Breno Quintella Farah b, Antônio Henrique Germano Soares b, Bruno Remígio Cavalcante b, Diego Giulliano Destro Christofaro c, Mauro Virgílio Gomes de Barros b, Raphael Mendes Ritti-Dias d
PMCID: PMC5606175  PMID: 28977292

ABSTRACT

Objective:

To investigate the association between heart rate variability (HRV) parameters with leisure time and commuting physical activities in adolescent boys.

Methods:

The sample included 1152 male adolescents aged 14 to 19 years. The variation of consecutive heart beats (RR intervals) was assessed and HRV parameters in time (SDNN, RMSSD, pNN50) and frequency domains (LF/HF) were calculated. Leisure time and commuting physical activities were obtained using a questionnaire. A binary logistic regression was performed between HRV parameters and physical activity.

Results:

Leisure time physical activity was associated with SDNN, RMSSD, pNN50, while LF/HF was not associated. These associations were stronger when adolescents were also physically active for more than six months. Commuting physical activity was not associated with any HRV parameter. Boys who practiced commuting physical activity and were also physically active for more than six months presented a lower chance of having low SDNN and RMSSD.

Conclusions:

Leisure time physical activity was associated with better HRV and these associations were enhanced when adolescents were physically active for more than six months. Commuting physical activity was not associated with HRV parameters; however, it became associated with better HRV when adolescents were physically active in commuting for more than six months.

Keywords: Physical activity, Autonomic nervous system, Adolescent

INTRODUCTION

Heart rate variability (HRV), which is defined as the variation of consecutive heartbeats, is a marker of cardiac autonomic modulation function. The autonomic nervous system promotes the interaction of the sympathetic pathways, which increase heart rate, contraction force, and vasoconstriction, with the parasympathetic pathways, which have opposite effects. 1 Low HRV is an independent predictor of mortality and incidence of cardiovascular disease in adults. 2 In children and adolescents, low HRV is associated with higher blood pressure levels 3 and abdominal obesity, 4 thus indicating its potential as a tool to screen cardiovascular risk among young people.

An increase in physical activity level has proven to be an effective way to increase HRV parameters in different age groups. In adolescents, although studies have shown a positive association between leisure-time physical activity and better HRV, 5 , 6 , 7 , 8 there are still some gaps. For example, the association between commuting physical activity (walking or cycling to school), which is a common form of physical activity among adolescents, and HRV parameters is unknown.

Adolescent boys have an increased risk for earlier development of cardiovascular disease than girls. 9 The HRV responses can provide subsidies for the implementation of health programs to improve cardiovascular health. This study’s hypothesis is that adolescents who engage in leisure-time and commuting physical activities would have better HRV than their inactive peers and the practice of physical activity for more than six months would bring a positive influence to HRV. Thus, the aim of this study was to investigate the association between the HRV parameters with leisure-time and commuting physical activities in adolescents and the influence of physical activity for more than six months on HRV.

METHOD

This cross-sectional study was approved by the ethical committee in compliance with the Brazilian National Research Ethics System Guidelines. The participants were sampled from among students in the public school system in the state of Pernambuco in northeast Brazil.

The sample was selected by the application of a stratified two-stage (school and class) random sampling procedure. The schools were selected according to size (small, medium, and large) and the classes according to the shift (day or night). The draws were conducted by random number generation through www.randomizer.org site. Participants who were eligible for the study included male adolescents between the ages of 14 and 19 years who were in the classroom on the collection day and provided prior parental or guardian consent. Volunteers with known diabetes mellitus, cardiovascular disease, and neurological or mental disabilities were excluded, as well as those with a low-quality HRV signal or poorly completed questionnaire. The exclusion criteria also included consumption of caffeinated beverages 12 hours prior to the HRV evaluation, the use of alcohol, any form of tobacco and/or other illicit drugs, and participation in any physical exercise training 24 hours before the evaluations. 10

Data were collected from May to October 2011 during the volunteers’ class period (morning, afternoon, and evening). To obtain data on physical activity level, age, ethnicity, housing area (rural or urban), and issues related to their economic condition, an adapted version of the Global School-based Student Health Survey (GSSHS) was used. This questionnaire has been widely applied in epidemiological studies with adolescents and has a reported concordance coefficient (kappa test) between 0.52 and 1.00. 3 , 11 For this sample, leisure-time physical activity, commuting activity, and practice time presented a reproducibility indicator (i.e., test-retest consistency 1-week apart) respectively of 0.63, 0.59, and 0.57.

Leisure-time physical activity was assessed by the question: “Do you regularly perform some sort of physical activity in your free time, such as exercise, sports, dance or martial arts?” The adolescents were classified as active (if the answer was yes) or not active. Commuting physical activity was assessed by the question: ‘During the past seven days, how many days have you walked or cycled to and from school?’ The adolescents who responded that they got to and from school on foot or by bicycle three days or more were considered active. The time of regular practice of physical activity (i.e. physical activity for more than six months; PA>6 months) was assessed with the question “A young man is considered to be physically active if he engages in at least 60 minutes of daily physical activity on five or more days of the week. In relation to your physical activity habits, you say…” Adolescents were considered PA>6 months if they answered “I have been physically active for more than 6 months.”

Adolescents were weighed without shoes and coats on an automatic scale, and the height was measured with a stadiometer. Waist circumference was measured in the standing position at the level of the umbilicus using a constant tension tape. Overweight was determined by a body mass index measurement above the 85th percentile for age. 12 Abdominal obesity was determined by waist circumference above the 80th percentile for age. 13 Blood pressure was measured by using the Omron HEM 742 14 (Omron, Shangai, China) after the adolescents rested and remained seated with legs uncrossed for 5 minutes. All blood pressure measurements were performed 3 times on the right arm, which was placed at heart level in a seated position. The mean value of the least two measurements was used for analysis. High blood pressure was defined as systolic and/or diastolic blood pressure equal or higher than the reference sex, age, and height-specific 95th percentile. 15

Heart rate variability was obtained by analysis of RR intervals by using a heart rate monitor (POLAR, RS 800 CX; Polar Electro OyInc, Kempele, Finland). Adolescents were in a supine position for 10 minutes after approximately 30 minutes of rest. Analyses were performed using Kubios HRV software (Biosignal Analysis and Medical Imaging Group, Joensuu, Finland) by a single evaluator blind to the other variables and following the recommendations of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. 16

The time domain variables consisted of the standard deviation of all RR intervals (SDNN), the root mean square of the squared differences between adjacent normal RR intervals (RMSSD), and the percentage of adjacent intervals over 50 ms (pNN50). The frequency domain was analyzed using the spectral analysis of HRV. Stationary periods of the tachogram of at least five minutes were broken down into bands of low (LF) and high (HF) frequencies by using the autoregressive method with a model order of 12 according to Akaike’s information criterion. Frequencies were considered physiologically significant when they ranged between 0.04 and 0.4 Hz. Oscillations between 0.04 and 0.15 Hz were considered as an LF component, whereas oscillations between 0.15 and 0.4 Hz represented the HF component. For analysis, we used the LF/HF component as an indicator of the sympathovagal balance on the heart. The reliability of the measurements was assessed using the intraclass correlation coefficient (ICC) and the values ranged from 0.70 to 0.91. 17

The HRV parameters were classified in quartiles and then dichotomized for analysis (1st quartile versus 2nd to 4th for SDNN, RMSSD, pNN50 and 4th quartile versus 1st to 3rd quartiles for LF/HF). For the SDNN, RMSSD e pNN50 parameters, the 1st quartile was considered the best and, for LF/HF parameters, the 4th quartile was considered optimal.

All statistical analyses were performed by using the software SPSS/PASW version 20.0 (IBM Corp, Armonk, New York). Data normality was verified by the Kolmogorov-Smirnov test. The HRV parameters data are presented in mean, standard deviation, and confidence intervals (CI) of 95%.

The binary logistic regression analysis was performed to analyze the association between the leisure-time and commuting physical activities with HRV. A cluster analysis was also conducted and included a binary logistic regression between the parameters of HRV with leisure-time physical activity, along with the regular practice of physical activity (LPA+PA>6) and commuting physical activity along with regular practice of physical activity (CPA+PA>6). These findings were adjusted for the period of the day, obesity, and hypertension. The Hosmer-Lemeshow test was used to evaluate the goodness of fit. The significance level for all analysis was p<0.05.

RESULTS

A total of 1212 male adolescents were enrolled in the study; 60 boys were excluded due to low signal quality (stationary periods of the tachogram lengths lower than 5 minutes). Thus, the final analysis consists of data from 1152 male adolescents with a mean age of 16.6±1.2 years. When asked which leisure activity they preferred, 43.8% of adolescents said playing sports. Table 1 describes the characteristics of the sample and the percentage of activity in each type of physical activity. Table 2 presents the criteria used for the stratification of HRV parameters in statistical analysis.

Table 1: Characteristics of the studied adolescents (n=1152).

graphic file with name 0103-0582-rpp-2017-35-3-00007-gt1.jpg

CI: confidence interval.

Table 2: Criterion used for stratification of heart rate variability parameters in statistical analysis (n=1152).

graphic file with name 0103-0582-rpp-2017-35-3-00007-gt2.jpg

SD: standard deviation; SDNN: standard deviation of the RR interval; RMSSD: root mean square of the squared differences between adjacent normal RR intervals; pNN50: the percentage of adjacent intervals over 50 ms; LF/HF: sympathovagal balance; HRV: heart rate variability.

The association between leisure-time and commuting physical activities with HRV parameters are shown in Figure 1. There were significant associations between leisure-time physical activity and HRV parameters in the time domain (SDNN (OR 0.57; 95%CI 0.42-0.78), RMSSD (OR 0.59; 95%CI 0.43-0.80), pNN50 (OR 0.60; 95%CI 0.44-0.81)). Commuting physical activity was not associated with any HRV parameter. The total volume of commuting physical activity was not associated with any HRV parameter (SDNN (p=0.937), RMSSD (p=0.664), pNN50 (p=0.323)).

Figure 1: Association between the types of physical activity and the heart rate variability parameters.

Figure 1:

The cluster of leisure-time physical activity and PA>6 months (Table 3) revealed significant associations with HRV in the time domain (p<0.05). The cluster of commuting physical activity and PA>6 months was significant (p<0.05) only in the SDNN and RMSSD parameters.

Table 3: Association between the cluster the types of physical activity and heart rate variability parameters.

graphic file with name 0103-0582-rpp-2017-35-3-00007-gt3.jpg

LPA: leisure-time physical activity; PA: physical activity; CPA: commuting physical activity. Models adjusted by hypertension, obesity and period of the day.

DISCUSSION

The main results of this study were that:

  1. leisure-time physical activity is associated with higher HRV;

  2. the associations between leisure-time physical activity were enhanced when physical activity is practiced for more than 6 months;

  3. commuting physical activity is not associated with HRV in adolescents;

  4. however, the adolescent active in commuting physical activity for more than six months had higher HRV.

The strengths of this study include its large sample size, since there are no epidemiological studies with a sample of this size that analyzes HRV. We assessed blood pressure and obesity, which are important confounding variables and closely linked to HRV. 3 , 18 Moreover, because the HRV was analyzed by only one investigator blind to all other study variables, the results are highly reproducible and reliable. Finally, the study examined the relationship of HRV and different physical activity, which has not been done previously.

Sports practice is the main frequent form of leisure-time physical activity among adolescent boys. 19 The association between leisure-time physical activity and cardiovascular function, 20 , 21 including HRV parameters, was previously described in adolescents. 7 In this study, adolescents active during their leisure-time also presented better HRV. However, this study expands the current knowledge by indicating that adolescents active during leisure-time and who practiced physical activity for more than six months presented an even better HRV, suggesting that the accumulation of leisure-time physical activity has a positive impact on cardiovascular health in boys.

Although the mechanisms responsible for causing such effects are not clear, it is believed that regular physical activity brings changes that are evident at rest, such as a decrease in HR, improvements in the neuro-hormonal modulation caused by the decrease in catecholamine levels 22 and angiotensin II. 22 , 23 The practice of physical activity is related to the modulation of cardiorespiratory capacity, which may slow the reduction of parasympathetic activity. 21 , 24 , 25 These adaptations alter the autonomic nervous system, increasing vagal tone and decreasing sympathetic activity in the heart. 26 , 27

Commuting activity, such as walking or cycling, was not associated with HRV parameters and the main hypothesis for this outcome is related to the intensity of these activities. Active commuting is an option primarily when destinations are no more than 20 minutes 26 , 28 and generally performed at a low-to-moderate intensity. Therefore, the intensity of commuting physical activities is probably not enough to promote adaptations in cardiac autonomic modulation. 3 , 20 , 29 , 30 , 31 Interestingly, the commuting physical activity, along with the PA>6 months, was associated with the HRV parameters, which indicates that, for a long period, this kind of activity seems to be a beneficial influence to the cardiovascular system.

Some limitations of this study should be considered. The cross-sectional design is the main concern, as it limits the establishment of causal relationships. The sample included only male adolescents, and extrapolation for female adolescents is limited. Although the ages of participants were tightly controlled, we could not determine the Tanner stage of the participants. Thus, possible influences of biological maturation on the results were not controlled. 6 The physical activity level was assessed subjectively by a questionnaire. Although the questionnaire is an indirect measure, it has a good reproducibility and allows for measuring the level of physical activity in different domains (leisure, commuting), which is not possible with direct measures such as pedometer and accelerometer are applied. 32 The leisure-time physical activity included all leisurely physical activities and it was not possible to determine the influence of their different types. Also, commuting physical activity considered only the route to and from the school, without assessing other commuting physical activities.

It can be concluded that leisure-time physical active adolescents presented better HRV indices. The parameters of HRV presented better indices in adolescents who were active for more than six months in commuting physical activity.

Footnotes

Funding: All phases of this study were supported by ‘Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPQ’, ‘Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES’ and ‘Fundação de Amparo a Pesquisa do Estado de Pernambuco - FACEPE’.

REFERÊNCIAS

  • 1.Kuster DW, Merkus D, Velden J, Verhoeven AJ, Duncker DJ. Integrative Physiology 2.0': integration of systems biology into physiology and its application to cardiovascular homeostasis. J Physiol. 2011;589:1037 45–1037 45. doi: 10.1113/jphysiol.2010.201533. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Wulsin LR, Horn PS, Perry JL, Massaro JM. D'Agostino RB Autonomic imbalance as a predictor of metabolic risks, cardiovascular disease, diabetes, and mortality. J Clin Endocrinol Metab. 2015;100:2443 8–2443 8. doi: 10.1210/jc.2015-1748. [DOI] [PubMed] [Google Scholar]
  • 3.Farah BQ, Barros MV, Balagopal B, Ritti Dias RM. Heart rate variability and cardiovascular risk factors in adolescent boys. J Pediatr. 2014;165:945 50–945 50. doi: 10.1016/j.jpeds.2014.06.065. [DOI] [PubMed] [Google Scholar]
  • 4.Baum P, Petroff D, Classen J, Kiess W, Bluher S. Dysfunction of autonomic nervous system in childhood obesity: a cross sectional study. PloS One. 2013;8:e54546. doi: 10.1371/journal.pone.0054546. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Nagai N, Moritani T. Effect of physical activity on autonomic nervous system function in lean and obese children. Int J Obes (Lond) 2004;28:27 33–27 33. doi: 10.1038/sj.ijo.0802470. [DOI] [PubMed] [Google Scholar]
  • 6.Chen SR, Chiu HW, Lee YJ, Sheen TC, Jeng C. Impact of pubertal development and physical activity on heart rate variability in overweight and obese children in Taiwan. J Sch Nurs. 2012;28:284 90–284 90. doi: 10.1177/1059840511435248. [DOI] [PubMed] [Google Scholar]
  • 7.Cayres SU, Vanderlei LC, Rodrigues AM, Silva MJ, Codogno JS, Barbosa MF, et al. Sports practice is related to parasympathetic activity in adolescents. Rev Paul Pediatr. 2015;33:174 80–174 80. doi: 10.1016/j.rpped.2014.09.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Sharma VK, Subramanian SK, Arunachalam V, Rajendran R. Heart rate variability in adolescents normative data stratified by sex and physical activity. J Clin Diagn Res. 2015;9:CC08 13–CC08 13. doi: 10.7860/JCDR/2015/15373.6662. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Barros MV, Ritti Dias RM, Honda Barros SS, Mota J, Andersen LB. Does self reported physical activity associate with high blood pressure in adolescents when adiposity is adjusted for? J Sports Sci. 2013;31:387 95–387 95. doi: 10.1080/02640414.2012.734631. [DOI] [PubMed] [Google Scholar]
  • 10.Silva DF, Bianchini JA, Antonini VD, Hermoso DA, Lopera CA, Pagan BG, et al. Parasympathetic cardiac activity is associated with cardiorespiratory fitness in overweight and obese adolescents. Pediatr Cardiol. 2014;35:684 90–684 90. doi: 10.1007/s00246-013-0838-6. [DOI] [PubMed] [Google Scholar]
  • 11.Tassitano RM, Barros MV, Tenório MC, Bezerra J, Florindo AA, Reis RS. Enrollment in physical education is associated with health related behavior among high school students. J Sch Health. 2010;80:126 33–126 33. doi: 10.1111/j.1746-1561.2009.00476.x. [DOI] [PubMed] [Google Scholar]
  • 12.Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320:1240 2–1240 2. doi: 10.1136/bmj.320.7244.1240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Taylor RW, Jones IE, Williams SM, Goulding A. Evaluation of waist circumference, waist to hip ratio, and the conicity index as screening tools for high trunk fat mass, as measured by dual energy X ray absorptiometry, in children aged 3 19 y. Am J Clin Nutr. 2000;72:490 5–490 5. doi: 10.1093/ajcn/72.2.490. [DOI] [PubMed] [Google Scholar]
  • 14.Christofaro DG, Fernandes RA, Gerage AM, Alves MJ, Polito MD, Oliveira AR. Validation of the Omron HEM 742 blood pressure monitoring device in adolescents. Arq Bras Cardiol. 2009;92:10 5–10 5. doi: 10.1590/s0066-782x2009000100003. [DOI] [PubMed] [Google Scholar]
  • 15.Falkner B, Daniels SR. The Fourth Report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Hypertension. 2004;44:387 8–387 8. doi: 10.1161/01.HYP.0000143545.54637.af. [DOI] [PubMed] [Google Scholar]
  • 16.No referred authorship Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J. 1996;17:354 81–354 81. [PubMed] [Google Scholar]
  • 17.Farah BQ, Lima AH, Cavalcante BR, Oliveira LM, Brito AL, Barros MV. Intra individuals and inter and intra observer reliability of short term heart rate variability in adolescents. Clin Physiol Funct Imaging. 2016;36:33 9–33 9. doi: 10.1111/cpf.12190. [DOI] [PubMed] [Google Scholar]
  • 18.Gutin B, Howe C, Johnson MH, Humphries MC, Snieder H, Barbeau P. Heart rate variability in adolescents: relations to physical activity, fitness, and adiposity. Med Sci Sports Exerc. 2005;37:1856 63–1856 63. doi: 10.1249/01.mss.0000175867.98628.27. [DOI] [PubMed] [Google Scholar]
  • 19.Azevedo MR, Araujo CL, Cozzensa da Silva M, Hallal PC. Tracking of physical activity from adolescence to adulthood: a population based study. Rev Saude Publica. 2007;41:69 75–69 75. doi: 10.1590/s0034-89102007000100010. [DOI] [PubMed] [Google Scholar]
  • 20.Buchheit M, Platat C, Oujaa M, Simon C. Habitual physical activity, physical fitness and heart rate variability in preadolescents. Int J Sports Med. 2007;28:204 10–204 10. doi: 10.1055/s-2006-924296. [DOI] [PubMed] [Google Scholar]
  • 21.DeFina LF, Haskell WL, Willis BL, Barlow CE, Finley CE, Levine BD. Physical activity versus cardiorespiratory fitness: two (partly) distinct components of cardiovascular health? Prog Cardiovasc Dis. 2015;57:324 9–324 9. doi: 10.1016/j.pcad.2014.09.008. [DOI] [PubMed] [Google Scholar]
  • 22.Vanhees L, Fagard R, Lijnen P, Moerman E, De Geest H, Amery A. Influence of physical training on blood pressure, plasma renin, angiotensin and catecholamines in patients with ischaemic heart disease. Eur J Appl Physiol Occup Physiol. 1984;53:219 24–219 24. doi: 10.1007/BF00776593. [DOI] [PubMed] [Google Scholar]
  • 23.Fernandes T, Hashimoto NY, Magalhaes FC, Fernandes FB, Casarini DE, Carmona AK. Aerobic exercise training induced left ventricular hypertrophy involves regulatory MicroRNAs, decreased angiotensin converting enzyme angiotensin ii, and synergistic regulation of angiotensin converting enzyme 2 angiotensin (1 7) Hypertension. 2011;58:182 9–182 9. doi: 10.1161/HYPERTENSIONAHA.110.168252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.De Meersman RE, Stein PK. Vagal modulation and aging. Biol Psychol. 2007;74:165 73–165 73. doi: 10.1016/j.biopsycho.2006.04.008. [DOI] [PubMed] [Google Scholar]
  • 25.Fernandes RA, Ronque ER, Venturini D, Barbosa DS, Silva DP, Cogo CT. Resting heart rate: its correlations and potential for screening metabolic dysfunctions in adolescents. BMC Pediatr. 2013;13:48–48. doi: 10.1186/1471-2431-13-48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Silva KS, Ada SL, Hardman CM, Cabral LG, Silva SG, Nahas MV. Commuting to school and to work among high school students in Santa Catarina state, Brazil: a comparative analysis between 2001 and 2011. J Phys Act Health. 2014;11:1458 67–1458 67. doi: 10.1123/jpah.2012-0410. [DOI] [PubMed] [Google Scholar]
  • 27.Panda K, Krishna P. Physical exercise and cardiac autonomic activity in healthy adult men. Indian J Physiol Pharmacol. 2014;58:365 70–365 70. [PubMed] [Google Scholar]
  • 28.Silva KS, Vasques DG, Martins CO, Williams LA, Lopes AS. Active commuting: prevalence, barriers, and associated variables. J Phys Act Health. 2011;8:750 7–750 7. doi: 10.1123/jpah.8.6.750. [DOI] [PubMed] [Google Scholar]
  • 29.Rennie KL, Hemingway H, Kumari M, Brunner E, Malik M, Marmot M. Effects of moderate and vigorous physical activity on heart rate variability in a British study of civil servants. Am J Epidemiol. 2003;158:135 43–135 43. doi: 10.1093/aje/kwg120. [DOI] [PubMed] [Google Scholar]
  • 30.Gutin B, Barbeau P, Litaker MS, Ferguson M, Owens S. Heart rate variability in obese children: relations to total body and visceral adiposity, and changes with physical training and detraining. Obes Res. 2000;8:12 9–12 9. doi: 10.1038/oby.2000.3. [DOI] [PubMed] [Google Scholar]
  • 31.Sandercock GR, Hardy Shepherd D, Nunan D, Brodie D. The relationships between self assessed habitual physical activity and non invasive measures of cardiac autonomic modulation in young healthy volunteers. J Sports Sci. 2008;26:1171 7–1171 7. doi: 10.1080/02640410802004930. [DOI] [PubMed] [Google Scholar]
  • 32.Skender S, Ose J, Chang Claude J, Paskow M, Bruhmann B, Siegel EM. Accelerometry and physical activity questionnaires a systematic review. BMC Public Health. 2016;16:515–515. doi: 10.1186/s12889-016-3172-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Revista Paulista de Pediatria are provided here courtesy of Sociedade De Pediatria De Sao Paulo

RESOURCES