Edited by: Jerzy Sacha, Opole University of Technology, Poland
Reviewed by: Juha Perkiömäki, University of Oulu, Finland; Rafal Baranowski, Ministry of Defence, Brazil
*Correspondence: Susi Kriemler
This article was submitted to Clinical and Translational Physiology, a section of the journal Frontiers in Physiology
†These authors have contributed equally to this work.
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Cardiovascular (CV) risk factors develop in early childhood. They may alter autonomic balance which seems to be associated with reduced heart rate variability (HRV; Zhou et al.,
Previously performed studies in children have used short measurements of 5–10 min during the day (Michels et al.,
Pediatric studies that have reported HRV data have generally reported an increase in HRV markers of vagal tone with increasing age up to age 6 (Finley and Nugent,
The main aim of the present study was to assess the effect of PA, anthropometric parameters and body composition on linear and non-linear HRV parameters while correcting for the growth associated decline of HR. A secondary aim was to compare different methodologies to measure HRV during sleep in young children.
The present study was conducted under the umbrella of the Swiss Preschoolers Health Study (SPLASHY, Current Controlled Trials Registry: ISRCTN41045021). SPLASHY is a prospective, multi-center, national study to investigate the effect of stress and physical activity on health in preschool children. The present study is based on cross-sectional data of the baseline assessments in 2014 including healthy preschoolers aged 2–6 years recruited from randomly selected childcare centers in Switzerland. Ethical approval was obtained from the responsible ethical committees of the respective cantons, and the children's parents provided written informed consent in accordance with the Declaration of Helsinki.
The children were fitted with two chest electrodes and a small device (e-Motion, Mega Electronics, Kuopio, Finland) which records interbeat duration (R-R intervals) at a sampling rate of 1,000 Hz, and a three-dimensional accelerometer validated for preschool children (Pate et al.,
R-R intervals were further analyzed using Matlab (2014a, The Mathworks, Natick, MA) with a procedure developed specifically for this study. R-R interval recordings and accelerometer data of the same time period were synchronized (Figure
This 20-min time segment was selected 15 min after sleep onset (SO). SO times were automatically determined for each night based on (1) no accelerometer activity and (2) a clear shift toward a lower HR. When no simultaneous valid accelerometer recording was available, SO was determined based on the sudden constant decrease in HR only. Two researchers from the team visually and independently validated the R-R interval signal from the automatically computed SO times. If the automatic SO detection was obviously wrong, the SO time was changed manually. In a further step, all manually adjusted SO times and unclear data were discussed in a group of four researchers and a decision was made by agreement of all. ECG signal artifacts (i.e., due to removal of the device or loss of electrodes during sleep) and data files with unidentifiable SO time were excluded.
By means of a custom built Matlab procedure, percentage HF power of total power (TP) (for explanation see chapter “HRV analysis” below) was calculated from 5-min windows moved by 30 s over the whole night. Previous studies have shown specific HRV characteristics in deep sleep with stationary and uncorrelated successive R-R intervals in deep sleep and a high percentage of HF power (Brandenberger et al.,
A
The following time domain parameters were used for analysis: HR (beats.min−1), the square root of the mean squared differences of adjacent R-R intervals (RMSSD, ms) and the standard deviation of all R-R intervals (SDNN, ms). For spectral analysis, R-R intervals were interpolated using a cubic spline interpolation method and then resampled at 4 Hz. We applied an advanced smoothness prior approach for detrending of R-R intervals with a smoothing parameter of λ = 500, which corresponds to a cut-off frequency of 0.035 Hz (Tarvainen et al.,
Accelerometery data was recorded at a sampling rate of 30 Hz. Periods of 20 min of continuous zero values were interpreted as not worn and removed. A minimum of 4 days with 10 h of wearing time on each day were required for inclusion in the data analysis. PA data recorded between 7 a.m. and 9 p.m. were included in the analysis that defined total daily PA (counts.min−1), light PA (LPA, min.day−1), moderate-to-vigorous PA (MVPA, min.day−1), vigorous PA (VPA, min.day−1), total PA (TPA, min.day−1), and sedentary time (ST, min.day−1) using the cutpoints by Butte et al. (
Statistical analysis was performed using the software R (Version 3.2.3, R Core Team, 2015). Normality of the data was visually assessed using QQ-plots. Differences between boys and girls in anthropometric parameters and physical activity were assessed using unpaired
We collected data from 476 children attending 84 different childcare centers in Switzerland. Of these, 402 children had overnight ECG measurements of which 325 could be analyzed. Valid accelerometry data were obtained from 435 children. Only children with both, valid overnight ECG and valid daytime accelerometry data, i.e., 309 children, were included in the statistical analysis. The sample included 162 boys and 147 girls with a mean age of 3.9 ± 0.7 years, height of 102.8 ± 6.6 cm, weight of 17.1 ± 2.5 kg, BMI
N [m,f] | 19 (5; 14) | 158 (58; 49) | 108 (58; 50) | 23 (13; 10) |
Age [years] | 2.8 (2.6; 2.9) | 3.5 (3.3; 3.7) | 4.3 (4.1; 4.6) | 5.5 (5.2; 5.9) |
Height [cm] | 94 (91; 95) | 100 (97; 107) | 106 (103; 108) | 117 (110; 118) |
Weight [kg] | 14.1 (12.9; 15.8) | 16.0 (15.0; 17.5) | 17.8 (16.2; 18.9) | 20.9 (18.8; 22.9) |
BMI [kg.m−2] | 16.4 (15.7; 17.1) | 16.1 (15.4; 16.9) | 15.9 (15.3; 16.6) | 15.6 (15.0; 16.8) |
TPA [counts.min−1] | 1,321 (1,102; 1,401) | 1,357 (1,160; 1,554) | 1,489 (1,289; 1,680) | 1,571 (1,367; 1,705) |
MVPA [min.day−1] | 54.8 (35.6; 78.2) | 60.6 (45.0; 87.4) | 83.6 (62.0; 110.0) | 101.7(84.3; 114.3) |
HR [beats.min−1] | 87.6 (85.2; 93.26) | 86.9 (80.7; 93.4) | 84.9 (78.0; 90.2) | 80.0 (74.1; 85.8) |
RMSSD [ms] | 62.3 (36.5; 86.5) | 56.7 (35.6; 94.8) | 57.3 (37.8; 85.2) | 66.0 (38.9; 124.8) |
SDNN [ms] | 58.9 (32.6; 72.1) | 47.2 (31.6; 75.6) | 49.9 (33.3; 69.3) | 53.5 (32.0; 95.2) |
HF [ms2] | 2,526 (754; 3,699) | 1,499 (630; 3,983) | 1,559 (719; 3,292) | 2,051 (718; 6,309) |
LF [ms2] | 228 (172; 464) | 242 (102; 579) | 230 (111; 620) | 415 (144; 723) |
LF/HF | 0.14 (0.08; 0.27) | 0.16 (0.10; 0.25) | 0.16 (0.10; 0.30) | 0.17 (0.10; 0.26) |
Total Power [ms2] | 2,961 (902; 4,538) | 1,764 (742; 4,652) | 1,284 (869; 3,961) | 2,474 (824; 7,684) |
DFA alpha 1 | 0.60 (0.52; 0.69) | 0.55 (0.46; 0.63) | 0.55 (0.44; 0.63) | 0.52 (0.41; 0.59) |
Out of 309 measured children, 309
HR [beats.min−1] | 87.4 (81.7; 93.4) | 85.7 (79.5; 91.7) |
85.6 (80.3; 85.6) | −1.9 |
RMSSD [ms] | 57.4 (37.2; 87.9) | 58.5 (39.9; 91.2) |
61.5 (42.8; 85.0) | 1.9 |
SDNN [ms] | 53.2 (37.2; 72.7) | 49.4 (32.5; 72.5) |
86.2 (68.0; 106.3) |
−7.1 |
HF power [ms2] | 1,558 (658; 3,324) | 1,721 (705; 3,691) |
1,936 (985; 3,729) |
10.5 |
LF power [ms2] | 405 (231; 947) | 250 (106; 608) |
1,320 (816; 2,036) |
−38.1 |
LF/HF | 0.25 (0.15; 0.45) | 0.16 (0.10; 0.27) |
0.68 (0.49; 0.93) |
−36.0 |
Total Power [ms2] | 2,259 (993; 4,397) | 2,028 (826; 4,599) |
7,366 (5,759; 9,625) |
−10.2 |
DFA alpha 1 | 0.58 (0.46; 0.69) | 0.55 (0.45; 0.64) | 0.62 (0.47; 0.81) | −5.2% |
HRV data from the
Models were conducted for HRV parameters of all three segments. Results of the
Age | − |
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0.09 |
HR | – | − |
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Height | − |
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HR | – | − |
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Weight | − |
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0.02 |
HR | – | − |
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BMI | 0.06 | −0.04 | −0.06 | 0.01 |
HR | – | − |
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BMIZ−score | 0.04 | −0.05 | −0.06 | 0.02 |
HR | – | − |
− |
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Skinfolds | −0.01 | −0.06 | −0.08 | |
HR | – | − |
− |
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Sex | 0.09 | 0.01 | 0.02 | 0.08 |
HR | – | − |
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TPA | − |
−0.04 | 0.03 | 0.01 |
HR | – | − |
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Age | − |
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0.01 |
MVPA | − |
−0.05 | −0.03 | 0.01 |
HR | – | − |
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Age | − |
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0.08 |
HR | – | |||||||||||
ln(RMSSD) | − |
– | ||||||||||
ln(SDNN) | − |
– | ||||||||||
DFA | − |
− |
– | |||||||||
Age | − |
0.03 | 0.03 | −0.05 | – | |||||||
Height | − |
0.01 | −0.01 | −0.10 | – | |||||||
Weight | − |
−0.03 | −0.05 | −0.07 | 0.83 | – | ||||||
BMI | 0.06 | −0.05 | −0.05 | 0.03 | − |
−0.07 | 0.49 | – | ||||
Sex | −0.06 | −0.06 | −0.01 | − |
− |
− |
0.01 | – | ||||
TPA | − |
0.10 | 0.09 | − |
0.06 | − |
– | |||||
MVPA | − |
0.10 | − |
0.01 | − |
– | ||||||
Skinfolds | − |
− |
−0.03 | −0.09 | 0.04 | − |
− |
– |
HR (beats.min−1) | 85.8 (80.7; 91.6) | 86.7 (79.3; 92.0) | 85.5 (78.0; 90.9) | 0.49 |
RMSSD (ms) | 61.9 (35.8; 95.0) | 64.6 (40.4; 97.5) | 53.9 (33.3; 82.6) | 0.33 |
SDNN (ms) | 46.9 (30.6; 74.3) | 53.2. (36.5; 81.2) | 44.8 (30.4; 67.7) | 0.33 |
HF power (ms2) | 1,479 (570; 3,684) | 1,679 (818; 3,524) | 1,350 (665; 2,775) | 0.59 |
LF power (ms2) | 236 (106; 557) | 296 (121; 751) | 221 (100; 574) | 0.30 |
LF/HF | 0.19 (0.11; 0.26) | 0.17 (0.11; 0.30) | 0.15 (0.10; 0.27) | 0.48 |
Total Power (ms2) | 1,947 (711; 4,668) | 2,225 (974; 5,100) | 1,711 (766; 3,781) | 0.71 |
DFA alpha 1 | 0.55 (0.45; 0.63) | 0.54 (0.46; 0.63) | 0.55 (0.45; 0.64) | 0.81 |
The results of the present study show a decrease in HR with increasing age and PA, an increase with skinfold thickness, and no increase in HRV parameters including the commonly used markers of vagal tone. When HRV parameters were adjusted for HR there was a decrease in RMSSD and SDNN with age. This is in accordance with one recent study (Gasior et al.,
There are a number of previous studies that have assessed HRV in young children. However, only some of them have reported nocturnal HRV measurements (Finley and Nugent,
It is likely that in growing children, HR decreases as a consequence of different factors such as the growing heart which grows proportionally to height; (St John Sutton et al.,
When HRV is used as a marker of the autonomic nervous activity, correction for HR has to be considered. HRV parameters have been shown to reflect autonomic nervous activity in many previous studies (Pomeranz et al.,
We found physical activity to be inversely related to HR but not to HRV parameters in our models adjusted for HR. This is in accordance with what was found in a comparable study in boys, but not in girls (Michels et al.,
The close correspondence of deep sleep segments identified by two different methods, one semi-automatically (by decrease in HR und cessation of activity) and one by an algorithm based on high percentage of HF power indicates a reliable identification of deep sleep. The systematic difference between the two segments was most likely due to the later occurrence of the automatically detected segment with an already lower HR. In contrast, HRV during the
A limitation of the present study is the missing information on true intrinsic HR of our subjects. Our hypothesis that there is a growth-related decrease in HR is based on the only existing study that has determined intrinsic HR under vagal and sympathetic blockade in children, and more general literature that has related HR to heart size. Therefore, we cannot define the development of vagal activity with age but we stress that interpretation of cardiac autonomic nervous system activity greatly depends on the assumption of the origin of HR decline with age (i.e., that either there is a growth-related decrease of intrinsic HR or that HR declines as a consequence of increased vagal activity). Further, the present results are based on cross-sectional data. Longitudinal data over this age range would have reduced the data variance, however, longitudinal data would most likely also show a reduction in HR and the directly linked increase in vagal markers of HRV, without giving any further explanation as to what the origin of HR decline is. Measurements of skinfold thickness only provide an approximation of body composition. However, they were chosen because of non-invasiveness and higher precision than BMI.
Strengths of the present study are the automatic identification of a deep sleep segment providing stationary HRV data undisturbed by environmental stimulants and with regular respiration frequency which could be an optimal method to assess HRV in young children, and secondly, the adjustment for HR in models relating anthropometric and physical activity parameters to HRV markers of vagal tone.
In conclusion, we found no increase of standard HRV parameters with age, however, when adjusted for HR, there was a significant decrease of HRV parameters with increasing age, in accordance with one previous study (Gasior et al.,
DH, PE, SK, TRa, PA, MW, JP, OJ, SM: designed research of the substudy; DH, PE, TRa, TRu, AW, PA, SK performed data analyses; DH, PE: performed statistical analyses; DH, PE, TRa, SK, PA: wrote and commented the manuscript; NM, TK, KS, CL, ES, AZ, AA, AW, TRa: contributed to data collection. All authors approved the final version of the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank all children, families, and day care centers that contributed data to SPLASHY. We also thank all students and the research team for their valuable contribution. The study was funded through a Sinergia grant from the Swiss National Foundation (Grant Number: CRSII3_147673) (