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EPIDEMIOLOGY

Fitness, Fatness, Physical Activity, and Autonomic Function in Midlife

KIVINIEMI, ANTTI M.1,2; PERKIÖMÄKI, NELLI1,2; AUVINEN, JUHA3,4; NIEMELÄ, MAISA5; TAMMELIN, TUIJA6; PUUKKA, KATRI7; RUOKONEN, AIMO7; KEINÄNEN-KIUKAANNIEMI, SIRKKA3,4; TULPPO, MIKKO P.1,2; JÄRVELIN, MARJO-RIITTA3,4,8,9; JÄMSÄ, TIMO2,5,10; HUIKURI, HEIKKI V.1,2; KORPELAINEN, RAIJA2,3,11

Author Information
Medicine & Science in Sports & Exercise: December 2017 - Volume 49 - Issue 12 - p 2459-2468
doi: 10.1249/MSS.0000000000001387

Abstract

Impaired cardiorespiratory fitness (CRF), physical inactivity, and obesity are important risk factors for many cardiometabolic diseases (28). One potential mechanism for the increased risk related to these factors is impaired autonomic function, manifested as decreased vagal and elevated sympathetic activity. Higher CRF (4,10,13,18,35) and physical activity (PA) (4,12,13,18,22,31,35) and more optimal body weight and composition (10,12,13,35) have been associated with better cardiac autonomic function as measured by heart rate (HR) variability (HRV) and postexercise HR recovery (HRR). Autonomic function is known to be related to several cardiometabolic risk factors (39), and its enhancement with improved CRF and PA is beneficial in reducing cardiovascular risk, independently of traditional risk markers (17).

Several studies have assessed the individual contributions of CRF, PA, and anthropometric measures to autonomic regulation of the activity of the sinoatrial node (4,10,12,13,18,22,31,35). Although these factors display evident interrelationships, there are rather few studies examining their association with autonomic function independently of each other and, as far as we are aware, none conducted in population-based samples of adults. Knowledge about the independent relationship of these factors with autonomic function could help in targeting lifestyle interventions and be important in the primary prevention of autonomic dysfunction and related cardiometabolic diseases. Methodologically, objective PA measurements and detailed measures of body composition have been less extensively reported in large-scale epidemiological studies. Finally, despite well-known sex differences in autonomic function (16), few studies have assessed sex differences in the relationship between cardiac autonomic activity and CRF, PA, and body composition (12,13,31). Previously, Rennie et al. (31) identified a significant association between HRV and PA in men, but this relationship was lacking in women.

We aimed here to assess the extent to which CRF, PA, and body fat proportion (Fat%) would be associated with cardiac autonomic function independently of each other and established cardiometabolic risk factors in men and women. We hypothesized that CRF could be the most important factor underlying the variation in cardiac autonomic function regardless of PA and Fat%, although these factors may have independent associations with autonomic function. Furthermore, we tested the hypothesis that sex would modify the association of autonomic function to CRF, PA, and Fat%.

METHODS

Subjects

All those individuals living in northern Finland whose expected date of birth fell between January 1 and December 31, 1966 (96.3% of all 1966 births, n = 12,058 live births), were included in the prospective Northern Finland Birth Cohort 1966 (NFBC1966) study. Since their mother’s recruitment during her first visit to the maternity health centers, data on their health, lifestyle, and socioeconomic status have been collected. The study was conducted according to the Declaration of Helsinki and approved by the Ethical Committee of the Northern Ostrobothnia Hospital District in Oulu, Finland. The study participants provided their written informed consent for the study.

Protocol

Postal surveys inquiring about the participant’s health status and lifestyle, including an invitation to attend a clinical examination, were sent in 2012–2014 to subjects who were living at known addresses in Finland (n = 10,321). The response rate to the postal surveys was 66% (n = 6825). A total of 5861 (57%) subjects participated in the clinical examinations in one of the three laboratory units (Oulu, southern and northern Finland) between April 2012 and March 2014 (Fig. 1). The subjects entered the laboratory between 7:00 and 11:00 AM after overnight fasting (12 h) and abstained from smoking and drinking coffee during the examination day. Venous blood samples were drawn for the analysis of glycemic and lipid status. Serum glucose was analyzed using an enzymatic hexokinase/glucose-6-phosphate dehydrogenase method. Total cholesterol, HDL and LDL cholesterol, and triglycerides were determined using an enzymatic assay method. The concentrations of glycated and total hemoglobin were measured using immunochemical assay methods. The ratio is reported as percent hemoglobin A1c (national glycohemoglobin standardization program). The samples were analyzed in NordLab Oulu, a testing laboratory (T113) accredited by the Finnish Accreditation Service (EN ISO 15189; all methods: Advia 1800; Siemens Healthcare Diagnostics Inc., Tarrytown, NY). Seated systolic (SBP) and diastolic blood pressures (DBP) were measured three times (the two lowest values averaged; Omron M10; Omron Healthcare, Kyoto, Japan) after 15 min of rest. After the anthropometric measurements, including body composition (Fat%) by bioimpedance (InBody720; InBody, Seoul, Korea), and other examinations, the participants had a light meal 60–90 min before the assessments of cardiovascular autonomic function and performance of the submaximal exercise test. Subsequently, the 2-wk monitoring of PA was initiated. On a separate day, an oral glucose tolerance test was conducted according to the recommendations of the World Health Organization in those participants without medication for diabetes.

FIGURE 1
FIGURE 1:
The selection of the study population from the NFBC1966. Antihypertensive medication included β-blockers, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, diuretics, and calcium-channel blockers.

Inclusions/exclusions

A total of 4537 subjects successfully underwent HRV recording, submaximal exercise test with HRR assessment, PA, and bioimpedance measurements. Further exclusions are described in Figure 1. The final population included 1383 men and 1761 women for HRV and HRR, and 709 men and 805 women for BRS. On the basis of the questionnaire, approximately 4% of women did not have an active menstrual cycle, whereas 28% had undergone hysterectomy and/or were on hormone therapy.

Lifestyle factors

On the basis of the questionnaire, subjects were defined as nonsmokers, ex-smokers, and current smokers. The amount of alcohol consumed per day was estimated from the questions concerning the frequency and the usual amount of beverage consumed on one occasion. Total sitting time during waking hours was established by asking the subjects how many hours on average they sat on weekdays (at work, at home, in a vehicle, and elsewhere) and the total sum of sitting hours was used. Finally, the subjects were asked how tired they typically felt in the morning during the first half hour after awakening (very tired, somewhat tired, somewhat rested, or well rested).

PA monitoring

PA was objectively measured with a wrist-worn Polar Active device (Polar Electro Oy, Kempele, Finland). Participants were asked to wear the Polar Active monitor for 24 h every day for at least 14 d and while sleeping on the nondominant wrist. The first day when activity monitors were given was excluded from the analysis. An eligible day was considered as at least 600 min·d−1 of wearing time during waking hours. Participants with four or more eligible days were included in the analyses. In the final data set, mean (SD) for eligible days was 13.6 (1.2), ranging from 4 to 19 d and including weekends. Polar Active provides daily PA on the basis of estimated MET values every half minute (26). Daily averages of duration spent in different PA levels (min·d−1) were calculated in all participants using the cutoff values provided by the manufacturer (very light, 1–2 METs; light, 2–3.5 METs; moderate, 3.5–5 METs; vigorous, 5–8 METs; and very vigorous, >8 METs). The three highest activity levels were combined as moderate-to-vigorous PA (MVPA), which was the primary PA variable, and the two highest as vigorous PA.

Values obtained from the wrist-worn PA monitor have been shown to correlate (R2 = 0.74) with a doubly labeled water technique when assessing energy expenditure during exercise training intervention (21). The amount of MVPA measured by the wrist-worn Polar Active is higher compared with that by hip-worn accelerometers when using standard cutoffs of 3 and 6 METs for moderate and vigorous PA, respectively (26). However, the differences between Polar Active and hip-worn monitors decline when using the cutoffs values provided by the Polar Active manufacturer (26).

Measurement of resting cardiovascular autonomic function

Each participant sat in a chair to allow for instrumentation and was provided with a review of the protocol. An HR monitor (RS800CX; Polar Electro Oy) was used to record R-R intervals (RRi). In half of the participants (Oulu laboratory unit), spontaneous baroreflex sensitivity (BRS) was also assessed. Standard lead II ECG (Cardiolife; Nihon Kohden, Tokyo, Japan), breathing frequency (MLT415/D, Nasal Temperature Probe; ADInstruments, Bella Vista, New South Wales, Australia), and blood pressure (BP) by finger photoplethysmography (Nexfin; BMEYE Medical Systems, Amsterdam, the Netherlands) were recorded with a sampling frequency of 1000 Hz (PowerLab 8/35; ADInstruments). The finger cuff was adjusted so that SBP and DBP assessed by finger photoplethysmography (left arm, supported by an arm sling) did not differ by more than 10 mm Hg from the values measured by the automated sphygmomanometer (right arm; Omron M10). Physiological calibration of finger BP was then turned off. After these procedures (5–10 min), there was at least a 1-min period allowing for stabilization of HR before the recording of 3 min in the seated position while breathing spontaneously. A 1-min stabilization period has been documented to suffice for robust HRV measurements from even as short as a 1-min recording (11). The first 150 s of 3-min recording was analyzed. Spontaneous breathing was allowed because it requires less familiarization and cooperation with the participant, and breathing frequency has been reported to exert only a modest impact on the present main HRV variable, root mean square of successive differences in RRi (rMSSD; ms), has been reported (29). Conversely, a low breathing frequency may overestimate BRS (36) despite its good reproducibility during spontaneous breathing (27).

HRV

Artifacts and ectopic beats were removed and replaced by the local average (Hearts 1.2; University of Oulu, Oulu, Finland). Sequences with ≥10 consecutive beats of noise or ectopic beats were deleted. The RRi series with ≥80% accepted data were included in the analyses. A total of 5679 subjects took part in the RRi recordings, and of these, 5473 (96%) had eligible HRV data. Mean HR (HRREST) and rMSSD (ms), a robust measure of cardiac vagal activity (29), were analyzed.

BRS

Continuous ECG, BP, and respiration signals were imported to a custom-made stand-alone Matlab-based software (Biosignal Processing Team, University of Oulu) where RRi and SBP values were extracted. Artifacts and ectopic beats were replaced using linear interpolation (<5% for accepted recording) and, thereafter, resampled at 2 Hz and detrended (<0.04 Hz, Savitzky–Golay method). A fast Fourier transform (Welch method, segments of 128 samples with 50% overlap) was performed to analyze low-frequency (LF; 0.04–0.15 Hz) power of RRi and SBP oscillations for subsequent analysis of BRS by the alpha method, if sufficient coherence (≥0.5) between LF oscillations in RRi and SBP was verified. Of 2726 recordings, BRS was successfully calculated in 2599 subjects (95%).

CRF

CRF was measured by a submaximal 4-min single-step test with a stepping rate of 23 ascents per minute paced by metronome and expressed as peak HR during the step test (HRSTEP) (33). In a previous substudy (n = 124) of NFBC1966 at the age of 31 yr, the correlation between HRSTEP and directly measured maximal oxygen uptake during a maximal cycle ergometer test was −0.52 (33). Stepping was performed without shoes on a bench adjusted to a height of 33 cm for women and 40 cm for men. HR was measured during and 90 s after the stepping in a seated position (RS800CX). The population was divided into CRF sex-wise tertiles and percentiles according to HRSTEP. The participants who terminated the test because of exhaustion were placed in the lowest tertile or percentile. Of 5861 participants, 5019 successfully performed the test, 237 terminated the test because of exhaustion (test duration >60 s), 40 terminated the test because of some reason other than exhaustion, 534 did not perform the test because of impaired health status (e.g., musculoskeletal problems, elevated BP, or exercise-induced angina pectoris) or unwillingness, and 31 had technical problems with HR recording.

HRR after exercise

The HR recording was transformed into moving 10-beat median data that was visually inspected for noise and ectopic beats. The peak HR of the test was determined as 10-beat median at the time of cessation of the stepping. Subsequently, the median HR at 60 s after the stepping was registered and HRR calculated (peak HR − HR at 60 s after exercise). In addition, the steepest 30-s slope during 60 s of recovery was calculated from the median HR data. The HRR at 60 s (bpm) and the HRR slope (bpm·s−1) were also normalized by peak HR.

Statistical analysis

The distributions of the dependent variables were first assessed by analyzing the skewness of the data by visual inspection of histograms. In the case of skewed distributions (|skewness| > 1; rMSSD and BRS) (14), the variable was transformed into its natural logarithm (ln), which eliminated skewness in the dependent variables. Thereafter, these transformed variables were verified to be Gaussian. One-way ANOVA was used to compare the groups and sexes, and Bonferroni post hoc test was used to account for multiple testing. Sex differences in categorical variables were analyzed using chi-square test. Interactions of CRF, MVPA, and Fat%, in tertiles, with sex in their associations with cardiac autonomic function were assessed by using ANCOVA. The linearity and collinearity of the associations were assessed by using the linear and quadratic regression models with continuous and by using contrasts estimated by ANOVA with categorical independent variables. The main explanatory variables (CRF, MVPA, and Fat%) were transformed into categorical (tertiles) or percentiles (continuous) for each sex before ANOVA and Pearson correlation analyses. Subsequently, multivariate linear regression analysis (enter method) was used where the association analyses of CRF, MVPA, Fat%, all of them together (in percentiles) with autonomic function were adjusted for the potential contributing factors (enter method: smoking, alcohol consumption, sitting time, tiredness in the morning, brachial SBP and DBP, glycated hemoglobin, fasting plasma glucose, serum total and HDL cholesterol, and triglycerides). LDL cholesterol was excluded from the covariates because of its significant collinearity with total cholesterol (variance inflation factor >5). No significant collinearity was observed between CRF, MVPA, and Fat%. ANCOVA was used to assess interactions between the tertiles of CRF, MVPA, and Fat%. The data were analyzed using SPSS software (IBM SPSS Statistics 21; IBM Corp., New York, NY). A P value of <0.05 was considered significant.

RESULTS

The characteristics of the study population are presented in Table 1. In the univariate analysis, CRF, MVPA, and Fat% were linearly associated with cardiac autonomic function (Figs. 2, 3; also see Tables, Supplemental Digital Content 1, Correlations between cardiovascular autonomic function, CRF by HRSTEP, MVPA, and Fat%, http://links.lww.com/MSS/B9; Supplemental Digital Content 2, Cardiovascular autonomic function according to tertiles of CRF by HRSTEP, MVPA, and Fat% in men, http://links.lww.com/MSS/B10; Supplemental Digital Content 3, Cardiovascular autonomic function according to tertiles of CRF by HRSTEP, MVPA, and Fat% in women, http://links.lww.com/MSS/B11). In both sexes, CRF (Figs. 2A–E, 3A–E) and MVPA (Figs. 2F–J, 3F–J) were significantly and positively associated with rMSSD, BRS, and HRR and inversely related to HRREST. Similarly, Fat% was significantly and inversely associated with rMSSD, BRS, and HRR and positively associated with HRREST in both sexes (Figs. 2K–O, 3K–O). Significant interactions between CRF and sex were observed in their associations with HRREST (P < 0.001) and rMSSD (P < 0.002) and between Fat% and sex with HRREST (P < 0.001), rMSSD (P < 0.001), HRR60s (P < 0.001), and HRRSLOPE (P = 0.004), with men manifesting a clearer trend across the tertiles (see Figure, Supplemental Digital Content 4, Sex interactions in the associations of autonomic function to CRF, MVPA, and Fat%, http://links.lww.com/MSS/B12).

TABLE 1
TABLE 1:
Characteristics of the study population.
FIGURE 2
FIGURE 2:
Correlations of CRF (A–E) as evaluated by HRSTEP, daily amount of MVPA (F–J), and Fat% (K–O) with cardiac autonomic function in men. Percentiles of HRSTEP, MVPA, and Fat% and natural logarithm of BRS and rMSSD were used in Pearson correlation analyses.
FIGURE 3
FIGURE 3:
Correlations of CRF (A–E) as evaluated by HRSTEP, daily amount of MVPA (F–J), and Fat% (K–O) with cardiac autonomic function in women. Percentiles of HRSTEP, MVPA, and Fat% and natural logarithm of BRS and rMSSD were used in Pearson correlation analyses.

In men, when assessing the contributions of CRF, MVPA, and Fat% to autonomic function separately after adjustments for covariates, all associations remained significant, except for the association of MVPA with rMSSD and BRS (Table 2). The standardized β values were consistently greater with CRF and autonomic function than with MVPA or Fat% (Table 2), and remained greater also when including all CRF, MVPA, and Fat% together in the initial block of regression (Table 2). After further adjustment for covariates, CRF was associated with all cardiac autonomic function variables (Table 2), with MVPA being significantly related only to HRR variables but not to HRREST or BRS (Table 2). An unexpected but statistically significant negative association was observed between MVPA and rMSSD when including CRF, MVPA, and Fat% in the same regression model. However, no significant interactions or collinearity were present in the associations of these variables to HRV.

TABLE 2
TABLE 2:
Multivariate analysis of CRF by HRSTEP, MVPA, and Fat% as determinants of autonomic function in men.

In women, associations of CRF, MVPA, and Fat%, when analyzed separately, remained significant after adjustments for covariates, except for MVPA with rMSSD and BRS (Table 3). Similar to the findings in men, the standardized β values of CRF to autonomic function were higher than those with MVPA and Fat% (Table 3). When including all CRF, MVPA, and Fat% in the same model that adjusted for potential covariates, CRF was still associated with all indexes of autonomic function, whereas MVPA remained a significant determinant of HRR but not HRREST, rMSSD, or BRS (Table 3). Fat% was not significantly related to rMSSD, BRS, or HRR in this model. The relationship between Fat% and HRREST became negative when CRF and MVPA were included in the same model. However, no significant interactions or collinearity between CRF, MVPA, and Fat% were observed in this respect.

TABLE 3
TABLE 3:
Multivariate analysis of CRF by HRSTEP, MVPA, and Fat% as determinants of autonomic function in women.

DISCUSSION

In this study, CRF was the most significant factor accounting for the variation in cardiac autonomic function; its contribution was greater than objectively measured MVPA and body composition in middle-age men and women. However, MVPA was associated with HRR, regardless of CRF, body composition, and several cardiometabolic risk factors in both men and women, whereas no independent contribution of Fat% to autonomic function was observed. The present results suggest that CRF should be the primary target in the prevention of abnormalities in cardiac autonomic function and related cardiometabolic diseases.

Previous studies in different populations have shown that impaired CRF is a more significant cardiovascular risk factor than either overweight or abdominal obesity (6,24) or physical inactivity (25,28,32). One plausible explanation for our finding concerning the strong association between CRF and cardiac autonomic function is that genetic and lifelong environmental effects on autonomic function are better integrated with CRF than with MVPA and body composition in the current cross-sectional setting. First, an important factor underlying CRF is stroke volume; this is known to improve with aerobic training via increased left ventricular dimensions and contractility as well as an increased plasma volume (1,9). These factors are also major determinants of cardiac autonomic function (1,5). Second, although exercise training increases CRF, the adaptations of CRF, that is, central hemodynamics and functional properties of the myocardium to exercise, are individual and may even be absent (3,30). Training-induced improvement in CRF has been suggested to be positively associated with pretraining cardiac vagal activity (15). Therefore, it can be speculated that among those with high CRF, high cardiac vagal activity may have contributed to the CRF response to PA. Whether this explanation is true cannot be determined in the present cross-sectional study.

It has been suggested that up to 50% of CRF is explained by genetic factors (37). Nonetheless, physical exercise remains the most potentially modifiable means of improving CRF, body composition, and cardiometabolic risk factors (19). In the present study, objectively measured MVPA was independently associated with cardiac autonomic function, particularly with HRR. This suggests that the prevailing PA contributes to cardiac autonomic function regardless of CRF. It is noteworthy that PA was measured continuously during a period of approximately 2 wk, and therefore, it can be considered as representative of the overall current PA level. It can also be speculated that PA affects autonomic function via mechanisms not shared with CRF. Our findings on the associations between MVPA and HRR are supported by Buchheit et al. (4), who reported a stronger association between training load and HRR than between CRF and HRR. Methodologically, it is also possible that the measurement error of CRF leaves room for the association between PA and autonomic function. For example, if a subject has high true maximal HR, he/she potentially has a high absolute HR during submaximal step test, and CRF may be underestimated despite high PA. It has been shown that inclusion of PA into the regression model for maximal oxygen uptake significantly improves the accuracy of the CRF estimation by the peak HR during the submaximal stepping test (33).

The present study showed that Fat% was significantly associated with cardiac autonomic function independently of CRF and MVPA. However, these associations disappeared after further adjustments for other lifestyle and cardiometabolic risk factors. This may not nullify the contribution of Fat% to autonomic function but rather emphasizes that there are potent mediators, such as glycemic and lipid profile and BP accompanying obesity (20), that also underlie this relationship. Fat% had a consistently stronger association with these cardiometabolic risk markers than with either CRF or MVPA among both men and women in the present study (data not shown). Our findings support the previous reports stating that CRF and PA seem to provide important prognostic information than can be ascertained from overweight and obesity (2,28)—obesity is not related to increased cardiometabolic risk in the presence of good CRF or PA. In this study, CRF and PA were more strongly associated with cardiac autonomic function than with Fat%. It may be that body fatness alone is not as detrimental as either low CRF or physical inactivity for cardiac autonomic function, which is known to be a significant risk factor for cardiovascular morbidities and mortality in population-based samples (7,23,38).

In this study, a significant interaction with sex was observed in the associations of CRF and Fat%, but not MVPA, with cardiac autonomic function (see Figure, Supplemental Digital Content 4, Sex interactions in the associations of autonomic function to CRF, MVPA, and Fat%, http://links.lww.com/MSS/B12). The associations of these factors with autonomic function were linear but stronger in men than in women. Although the sex differences in autonomic function have been well documented (16,22), the between-tertile differences were greater in men than in women, which was reflected also in the correlation coefficients (see Table, Supplemental Digital Content 1, Correlations between cardiovascular autonomic function, CRF by HRSTEP, MVPA, and Fat%, http://links.lww.com/MSS/B9). The reason why men seem to benefit more than women from greater CRF and lower Fat% in terms of autonomic regulation remains unknown. For instance, Gutin et al. (13) reported more deleterious effects of adiposity on autonomic function in adolescent girls than in boys. It remains unclear why this association seems to reverse opposite at midlife. The previous findings by Rennie et al. (31) show that sex differences affect the relationship between PA and autonomic function, but this was not confirmed in the present study. Differences in PA assessments (questionnaire vs accelerometer) may partly explain these contrasting findings.

Study limitations

The HRV analysis is considered less reproducible from short-term laboratory measurements than longer-term ambulatory recordings (8). For example, the time elapsing since the previous meal may affect the quantification of autonomic function, which was relatively short but controlled and optimized, taking into account the other competing objectives of the NFBC1966 study. Spontaneous breathing may confound the spectral analysis of BRS, whereas rMSSD is a more robust measure of cardiac vagal activity regardless of the breathing pattern (29). The objective PA measurements were based on wrist-worn accelerometry with known limitations regarding PA without arm movement and arm movement without significant PA (26). However, the ability present PA method to identify the fulfillment of daily PA recommendation is comparable to hip-worn devices (26). Also, it remains unclear how well does the current PA level represent longer-term PA, which may have contributed more to the current CRF. This may be one factor explaining the stronger association of autonomic function with CRF than with PA. The CRF was estimated by the submaximal step test HR, which includes bias caused by individual differences in maximal HR (34) and does not fully concur with the direct measurement of maximal oxygen uptake (33). In addition, HR in the step test per se reflects cardiac autonomic function during submaximal exercise; this may partly explain the strong association between autonomic measures at rest and estimated CRF (40). Furthermore, we cannot establish the causality in the present observations because of the study’s cross-sectional design. More detailed information about diet and clinical status, especially concerning disorders other than those used for exclusions, would have strengthened the interpretation of the findings. Finally, the population does not fully represent the whole NFBC1966 because of incomplete attendance to the measurements at the age of 46 yr and the exclusions of individuals with cardiorespiratory and metabolic diseases and medications affecting autonomic function.

CONCLUSIONS

CRF was a stronger determinant of cardiac autonomic function than MVPA and Fat%. Nonetheless, MVPA but not Fat% was independently associated with cardiac autonomic function in men and women. The present results suggest that primary prevention of abnormalities in autonomic function and related cardiometabolic risk should focus on improving CRF.

We thank the late professor Paula Rantakallio (launch of NFBC1966), the participants in the 46-yr study, and the NFBC project center. Meri-Maija Ollila, BM, is gratefully acknowledged for her help with processing of HRV data. This work was supported by the University of Oulu (Grant No. 24000692); Oulu University Hospital (Grant No. 24301140); European Regional Development Fund (Grant No. 539/2010 A31592); the Academy of Finland (Grant Nos. 267435 and 285547); the Ministry of Education and Culture (Grant No. 86/686/2014); the Sigrid Juselius Foundation; the Finnish Foundation for Cardiovascular Research, Helsinki, Finland; and the European Union’s Horizon 2020 research and innovation program (Grant No. 633595). The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.

The authors declare no conflicts of interest. The results of the present study do not constitute endorsement by the American College of Sports Medicine.

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Keywords:

EXERCISE; BODY COMPOSITION; HEART RATE VARIABILITY; HEART RATE RECOVERY; BAROREFLEX

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