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Citation: Schaffarczyk, M.; Rogers, B.;
Reer, R.; Gronwald, T. Validity of the
Polar H10 Sensor for Heart Rate
Variability Analysis during Resting
State and Incremental Exercise in
Recreational Men and Women.
Sensors 2022,22, 6536. https://
doi.org/10.3390/s22176536
Academic Editor: Juan
Pablo Martínez
Received: 1 August 2022
Accepted: 25 August 2022
Published: 30 August 2022
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sensors
Article
Validity of the Polar H10 Sensor for Heart Rate Variability
Analysis during Resting State and Incremental Exercise in
Recreational Men and Women
Marcelle Schaffarczyk 1,2, * , Bruce Rogers 3, Rüdiger Reer 1and Thomas Gronwald 2
1Department Sports and Exercise Medicine, Institute of Human Movement Science, University of Hamburg,
20148 Hamburg, Germany
2Institute of Interdisciplinary Exercise Science and Sports Medicine, MSH Medical School Hamburg,
20457 Hamburg, Germany
3
Department of Internal Medicine, College of Medicine, University of Central Florida, Orlando, FL 32827, USA
*Correspondence: marcelle.schaffarczyk@medicalschool-hamburg.de
Abstract:
Heart rate variability (HRV) is frequently applied in sport-specific settings. The rising use
of freely accessible applications for its recording requires validation processes to ensure accurate
data. It is the aim of this study to compare the HRV data obtained by the Polar H10 sensor chest
strap device and an electrocardiogram (ECG) with the focus on RR intervals and short-term scaling
exponent alpha 1 of Detrended Fluctuation Analysis (DFA a1) as non-linear metric of HRV analysis. A
group of 25 participants performed an exhaustive cycling ramp with measurements of HRV with both
recording systems. Average time between heartbeats (RR), heart rate (HR) and DFA a1 were recorded
before (PRE), during, and after (POST) the exercise test. High correlations were found for the resting
conditions (PRE: r = 0.95, r
c
= 0.95, ICC
3,1
= 0.95, POST: r = 0.86, r
c
= 0.84, ICC
3,1
= 0.85) and for the
incremental exercise (r > 0.93, r
c
> 0.93, ICC
3,1
> 0.93). While PRE and POST comparisons revealed
no differences, significant bias could be found during the exercise test for all variables (
p< 0.001
).
For RR and HR, bias and limits of agreement (LoA) in the Bland–Altman analysis were minimal
(RR: bias of 0.7 to 0.4 ms with LoA of 4.3 to
−
2.8 ms during low intensity and 1.3 to
−
0.5 ms during
high intensity, HR: bias of
−
0.1 to
−
0.2 ms with LoA of 0.3 to
−
0.5 ms during low intensity and 0.4
to
−0.7 ms
during high intensity). DFA a1 showed wider bias and LoAs (bias of 0.9 to 8.6% with
LoA of 11.6 to
−
9.9% during low intensity and 58.1 to
−
40.9% during high intensity). Linear HRV
measurements derived from the Polar H10 chest strap device show strong agreement and small bias
compared with ECG recordings and can be recommended for practitioners. However, with respect to
DFA a1, values in the uncorrelated range and during higher exercise intensities tend to elicit higher
bias and wider LoA.
Keywords: HRV; RR intervals; DFA a1; chest strap; wearable; endurance exercise
1. Introduction
Heart rate variability (HRV) is believed to reflect autonomic nervous system activity
by non-invasively measuring the time and pattern between consecutive R-waves in the
electrocardiogram (ECG) [
1
,
2
]. It encompasses a wide range of application fields including
medical and sport-specific settings [
3
,
4
]. Here, studies mainly apply HRV in the context
of physical exercise and training monitoring purposes and for the optimization of the
training process, mostly by taking resting measurements with focus on standard linear
parameters like the root mean square of successive differences (RMSSD) [
4
–
6
]. The gold
standard measure for the quantification of RR intervals and assessment of HRV is the
electrocardiogram (ECG) [
7
]. However, it has become common practice to use mobile
systems (e.g., apps, wearables) and chest straps for data recording, storage, analysis and/or
export which offers superior practicability with respect to cost, ease to use, portability and
Sensors 2022,22, 6536. https://doi.org/10.3390/s22176536 https://www.mdpi.com/journal/sensors
Sensors 2022,22, 6536 2 of 13
interpretation [
8
–
10
]. However, an evaluation of these applications and sensor devices
in different populations is required in order to ensure the validity of the RR interval
measurements for the subsequent HRV interpretation [9,11,12].
A frequently used chest strap device is the Polar H10 (Polar Electro Oy, Kempele,
Finland) which has already proven validity to assess RR intervals correctly during rest
and physical exercise conditions [
13
]. In this report, there was a substantially better signal
quality during high-intensity activities for the Polar H10 in comparison to a 3-lead ECG
Holter monitor which led the authors to the conclusion that simple chest strap devices
might be recommended as the gold standard for RR interval assessments when strong body
movements are present [
13
]. However, the investigated sample was very homogeneous
in its characteristics (healthy, lean, and physically fit volunteers with an average age of
25 years
) and the sample size was small (N = 10), so that a generalization of this conclusion
should be regarded with caution. A more recent study evaluated the Polar H10 against a
photoplethysmography technology (Welltory, New York, NY, USA) and a 12-channel ECG
during resting condition in supine and in seated positions [10]. There were no differences
observed in the results of the Polar H10 in comparison to the ECG when Kubios HRV
software was used to obtain HRV data. Nevertheless, the comparison focused solely on
RMSSD and the sample consisted of professional road cyclists, confirming the validity only
for these specificities.
In addition to the requirement to perform population-based validations, to date no
validation has been conducted for non-linear measures of HRV. It is possible that agreement
with ECG measurement is also dependent on the particular index investigated, which
is why there is also the need for metric-specific validations [
9
]. The non-linear short-
term scaling exponent alpha 1 of Detrended Fluctuation Analysis (DFA a1) has shown its
suitability to describe the complex cardiac autonomic regulation during various exercise
intensities, modalities, and environmental conditions while possessing a wide dynamic
range throughout all intensity zones in contrast to standard linear parameters [
14
,
15
]. In
this context, recent studies show the potential of this parameter to demarcate physiological
threshold boundaries by means of fixed DFA a1 values at the aerobic (~0.75) and anaerobic
threshold (~0.5) for usage in performance monitoring and exercise intensity distribution in
endurance sports [
16
,
17
]. Additionally, exercise associated DFA a1 dynamics may also give
insights about physiological status in terms of monitoring of fatigue during low-intensity
exercise [
18
]. Therefore, the importance of a validation study with focus on the recording
method and DFA a1 becomes evident, since researchers and recreational athletes frequently
use this device for data collection and therefore rely on appropriate measurements.
The present study compared selected HRV data (with inclusion of DFA a1) obtained
by the Polar H10 chest strap device using the Elite HRV application for data recording,
storage and export vs. a 12-channel ECG and analyzed by Kubios HRV Software version
3.5.0 (Biosignal Analysis and Medical Imaging Group, Department of Physics, University of
Kuopio, Kuopio, Finland [
19
]) during resting and exercise conditions in a group of female
and male recreational athletes.
2. Materials and Methods
2.1. Participants
Twenty-five participants (men: n = 14, age: 40
±
14 years, height: 178.1
±
9.0 cm,
body weight: 82.2
±
14.8 kg; women: n = 11, age: 34
±
10 years, height: 169.1
±
4.3 cm,
body weight: 67.8
±
9.5 kg) volunteered to take part in this study after being recruited via
word of mouth as via the internet. Entry criteria included adults (>18 years) of either sex
and of any fitness level without previous medical history, current medications or recent
illness. Participants were asked to abstain from caffeine, alcohol, tobacco, and vigorous
exercise
24 h
before testing and provided written informed consent. Ethical approval for
the study was obtained by the University of Hamburg, Department of Psychology and
Movement Science, Germany (reference no.: 2021_400) and is in line with the principles of
the Declaration of Helsinki. The complete methodical procedure can be seen in Figure 1.
Sensors 2022,22, 6536 3 of 13
Sensors 2022, 22, x FOR PEER REVIEW 3 of 15
Participants were asked to abstain from caffeine, alcohol, tobacco, and vigorous exercise
24 h before testing and provided written informed consent. Ethical approval for the study
was obtained by the University of Hamburg, Department of Psychology and Movement
Science, Germany (reference no.: 2021_400) and is in line with the principles of the Decla-
ration of Helsinki. The complete methodical procedure can be seen in Figure 1.
Figure 1. Flow chart of the methodical procedure.
2.2. Exercise Protocol and Data Acquisition
An incremental ramp was performed on a mechanically braked cycle (Ergoselect 4
SN, Ergoline GmbG, Bitz, Germany) with cadence kept between 60–80 rpm. The protocol
consisted of a three-minute initial workload of 50 Watts followed by a 1 watt increase
every 3.6 s (equivalent to 50 Watts/3 min) until the volunteer’s voluntary exhaustion. Re-
cordings of heart rate (HR) and RR intervals were taken continuously during the exercise
test, as well as prior (PRE; after a period of habituation and session preparation) and post
(POST) exercise by means of 3-min supine rest condition measurement intervals with two
devices at the same time: (1) 12-channel ECG CardioPart 12 Blue (AMEDTEC Mediz-
intechnik Aue GmbH, Germany; sampling rate: 500 Hz; desktop software: AMEDTEC
ECGpro version 5.10.002), and (2) Polar H10 sensor chest strap device (Polar Electro Oy,
Kempele, Finland; sampling rate: 1000 Hz; app software: Elite HRV App, Version 5.5.1).
Placement of the ECG electrodes and chest strap device is pictured in Figure 2.
Figure 1. Flow chart of the methodical procedure.
2.2. Exercise Protocol and Data Acquisition
An incremental ramp was performed on a mechanically braked cycle (Ergoselect 4
SN, Ergoline GmbG, Bitz, Germany) with cadence kept between 60–80 rpm. The protocol
consisted of a three-minute initial workload of 50 Watts followed by a 1 watt increase every
3.6 s (equivalent to 50 Watts/3 min) until the volunteer’s voluntary exhaustion. Recordings
of heart rate (HR) and RR intervals were taken continuously during the exercise test, as
well as prior (PRE; after a period of habituation and session preparation) and post (POST)
exercise by means of 3-min supine rest condition measurement intervals with two devices
at the same time: (1) 12-channel ECG CardioPart 12 Blue (AMEDTEC Medizintechnik Aue
GmbH, Germany; sampling rate: 500 Hz; desktop software: AMEDTEC ECGpro version
5.10.002), and (2) Polar H10 sensor chest strap device (Polar Electro Oy, Kempele, Finland;
sampling rate: 1000 Hz; app software: Elite HRV App, Version 5.5.1). Placement of the ECG
electrodes and chest strap device is pictured in Figure 2.
Sensors 2022, 22, x FOR PEER REVIEW 4 of 15
Figure 2. Placement of 12-channel ECG electrodes and Polar H10 chest strap device. V1 = 4th inter-
costal space at the right border of the sternum, V2 = 4th intercostal space at the left border of the
sternum, V3 = midway between locations V2 and V4, V4 = at the mid-clavicular line in the 5th inter-
costal space, V5 = at the anterior axillary line in the same horizontal level as V4, V6 = at the mid-
axillary line on the same horizontal level as V4 and V5. Limb leads of the right arm (RA) and left
arm (LA) outwardly on the shoulders and right leg (RL) and left leg (LL) leads at the lower edge of
the ribcage.
Breath-by-breath pulmonary gas exchanges were recorded throughout the ramp us-
ing a metabolic analyzer (Quark CPET, module A-67-100-02, COSMED Deutschland
GmbH, Fridolfing, Germany; desktop software: Omnia version 1.6.5). Prior to testing, the
gas analyzers were calibrated according to the manufacturer’s instructions. The protocol
was terminated when the participant fell below a cadence of 60 rpm or due to self-deter-
mination. The following criteria served for the assumption of exhaustion: (A) heart rate
>90% of the maximum predicted heart rate (prediction model according to [20]: 208 − (0.7
× age) and (B) respiratory quotient > 1.1. Maximum oxygen uptake (VO
2max
) and maximum
HR (HR
max
) were defined as the average VO
2
and HR over the last 30 s of the test.
2.3. Data Processing
After extraction of the 12-channel ECG data (converted from exported.xml files) and
RR data (exported from the Elite HRV app) as text files, import into Kubios HRV Premium
Software version 3.5.0 (Biosignal Analysis and Medical Imaging Group, Department of
Physics, University of Kuopio, Kuopio, Finland, [19]) was conducted. For the 12-channel
ECG, lead 2 was used as the comparable lead to the chest strap device [21]. Preprocessing
settings were set to the default values including the RR detrending method which was
kept at “smoothness priors” (Lambda = 500). The RR series was then corrected by the Ku-
bios HRV Premium “automatic method” [22]. For DFA a1 calculation window width was
set to 4 ≤ n ≤ 16 beats [23]. During rest conditions a 2-min time window (00:30–02:30 min:s)
was chosen for the analysis. During the incremental exercise time-varying analysis was
adjusted to a 2-min window width and 20-s grid interval for the moving window, so that
the exported.csv files from Kubios HRV Premium Software contained the HRV metrics of
interest (RR, HR, DFA a1) recalculated every 20 s. Data sets with artefacts >5% were ex-
cluded from analysis based on [24].
2.4. Statistics
Figure 2.
Placement of 12-channel ECG electrodes and Polar H10 chest strap device. V1 = 4th intercostal
space at the right border of the sternum, V2 = 4th intercostal space at the left border of the sternum,
V3 = midway between locations V2 and V4, V4 = at the mid-clavicular line in the 5th intercostal space,
V5 = at the anterior axillary line in the same horizontal level as V4, V6 = at the mid-axillary line on the
same horizontal level as V4 and V5. Limb leads of the right arm (RA) and left arm (LA) outwardly on
the shoulders and right leg (RL) and left leg (LL) leads at the lower edge of the ribcage.
Sensors 2022,22, 6536 4 of 13
Breath-by-breath pulmonary gas exchanges were recorded throughout the ramp using
a metabolic analyzer (Quark CPET, module A-67-100-02, COSMED Deutschland GmbH,
Fridolfing, Germany; desktop software: Omnia version 1.6.5). Prior to testing, the gas
analyzers were calibrated according to the manufacturer’s instructions. The protocol was
terminated when the participant fell below a cadence of 60 rpm or due to self-determination.
The following criteria served for the assumption of exhaustion: (A) heart rate >90% of
the maximum predicted heart rate (prediction model according to [
20
]: 208
−
(0.7
×
age)
and (B) respiratory quotient > 1.1. Maximum oxygen uptake (VO
2max
) and maximum HR
(HRmax) were defined as the average VO2and HR over the last 30 s of the test.
2.3. Data Processing
After extraction of the 12-channel ECG data (converted from exported.xml files) and
RR data (exported from the Elite HRV app) as text files, import into Kubios HRV Premium
Software version 3.5.0 (Biosignal Analysis and Medical Imaging Group, Department of
Physics, University of Kuopio, Kuopio, Finland, [
19
]) was conducted. For the 12-channel
ECG, lead 2 was used as the comparable lead to the chest strap device [
21
]. Preprocessing
settings were set to the default values including the RR detrending method which was kept
at “smoothness priors” (Lambda = 500). The RR series was then corrected by the Kubios
HRV Premium “automatic method” [
22
]. For DFA a1 calculation window width was set
to 4
≤
n
≤
16 beats [
23
]. During rest conditions a 2-min time window (00:30–02:30 min:s)
was chosen for the analysis. During the incremental exercise time-varying analysis was
adjusted to a 2-min window width and 20-s grid interval for the moving window, so that
the exported.csv files from Kubios HRV Premium Software contained the HRV metrics
of interest (RR, HR, DFA a1) recalculated every 20 s. Data sets with artefacts >5% were
excluded from analysis based on [24].
2.4. Statistics
Results are presented as mean
±
standard deviation (SD). Normal distribution of data
was checked by Shapiro–Wilk testing and visual inspection of data histograms. Agreement
of the variables obtained by means of the two devices during the three conditions (PRE,
incremental exercise, POST) was evaluated using linear regression, Pearson’s r correla-
tion coefficient (r), Lin’s Concordance Correlation Coefficient (r
c
), Intraclass Correlation
Coefficient (ICC
3,1
), coefficient of determination (R
2
), standard error of estimate (SEE)
and Bland–Altman plots with limits of agreement (LoA) [
25
]. The size of Pearson’s r
correlation coefficient was evaluated as follows; 0.3
≤
r < 0.5 low; 0.6
≤
r < 0.8 moder-
ate and r
≥
0.8 high [
26
]. Computation of r
c
was conducted using a S1Macro for SPSS
(https://doi.org/10.1371/journal.pone.0239931.s002 (accessed on 5 July 2022). It repre-
sents a modification of Pearson’s r correlation coefficient, in that it assesses not merely the
distance of data points to the line of best fit, but also how far this line deviates from the
line of perfect agreement, as represented by the 45-degree line through the origin. Sizes of
≥
0.8 are rated at almost perfect agreement [
27
]. Bland–Altman mean differences for data
comparisons were expressed as either absolute or percentage bias (difference/
mean ×100
).
Analyze-it software (Version 5.66) was used for its automatic computation. In the case of
normal distribution, paired t-test was used for the comparison of data, whereas Wilcoxon
Signed Ranks Test was applied in case of violation of the precondition. In the case of non-
normally distributed data, medians and estimates of the median differences along with the
95% confidence intervals (Hodges-Lehmann estimator) were additionally calculated using a
SPSS Syntax code (Hodges-Lehmann Confidence Interval for Median difference|Raynald’s
SPSS Tools; [
28
]). Effect sizes were calculated with Cohen’s d (d) and its respective thresh-
olds (small effect = 0.20, medium effect = 0.50, large effect = 0.80; [
29
]. For all tests, the
statistical significance was accepted as p
≤
0.05. Analysis was performed using IBM
®
SPSS
®
Statistics 25 and Microsoft Excel 365 (Version 2204).
Sensors 2022,22, 6536 5 of 13
3. Results
The participants reached maximal power (P
max
) values of 258
±
49 watts, a VO
2max
of
40.6
±
7.6 mL/kg/min anda HR
max
of 178
±
14 bpm (men: 279
±
51 watts,
42.1 ±8.8 mL/kg/min
,
180
±
17 bpm; women: 230
±
31 watts, 38.7
±
5.6 mL/kg/min, 177
±
10 bpm). Five participants
were excluded from exercise analysis and two participants from resting POST measurement due to
artefacts >5%.
3.1. PRE and POST Analysis
The comparisons during resting state conditions PRE and POST showed nearly perfect
correlations for the linear parameters RR and HR (r = 1.00, r
c
= 1.00, ICC
3,1
= 1.00). Although
correlations were also high for DFA a1 (r > 0.86, r
c
> 0.84, ICC
3,1
> 0.85), they were
comparatively weaker (see Table 1and Figure 3). There were no significant differences
for both conditions and the three variables of interest (p> 0.05) (Table 1). Bland–Altman
analysis revealed a mean difference of 0.2 to 0.1 ms for RR with upper LoA of 2.6 to
1.3 ms
and lower LoA of
−
2.2 to
−
1.1 ms in PRE, and a bias of 0.2 to 0.0 ms with upper
and lower LoA of 1.6 to 0.5 ms and
−
1.2 to
−
0.6 ms in POST, respectively. With respect
to DFA a1 there was bias of
−
2.1% (upper LoA of 14.7 to 16.4% and lower LoA of
−
18.9
to
−
20.5%) and 2.5% for POST (upper LoA of 15.6 to 23.7% and lower LoA of
−
10.2 to
−19.1%). Detailed plot analysis for all variables can be found in Figure 4.
Sensors 2022, 22, x FOR PEER REVIEW 6 of 15
mean 987.6 987.5 63.2 63.2 0.94 0.96 686.4 686.3 88.9 88.9 1.38 1.35
SD 201.1 201.1 13.1 13.1 0.31 0.32 90.5 90.4 11.9 11.9 0.23 0.22
p-value 0.56 0.74 0.35 0.50 0.50 0.18
d 0.12 0.07 0.19 0.14 0.14 0.29
r 1.00 1.00 0.95 1.00 1.00 0.86
r
c
1.00 1.00 0.95 1.00 1.00 0.84
ICC
3,1
1.00 1.00 0.95 1.00 1.00 0.85
Figure 3. Regression plots for the comparison of the ECG (ECG) and the Polar H10 sensor chest
strap device (H10) during PRE and POST the incremental exercise test for RR (A), HR (B) and DFA
a1 (C). Slope, coefficient of determination (R
2
), standard error of estimate (SEE), and p-value shown
in the bottom right plot.
Figure 3.
Regression plots for the comparison of the ECG (ECG) and the Polar H10 sensor chest strap
device (H10) during PRE and POST the incremental exercise test for RR (
A
), HR (
B
) and DFA a1 (
C
).
Slope, coefficient of determination (R
2
), standard error of estimate (SEE), and p-value shown in the
bottom right plot.
Sensors 2022,22, 6536 6 of 13
Table 1.
Mean, standard deviation (SD), Minimum and Maximum for RR, HR and DFA a1 obtained
from the ECG (ECG) and the Polar H10 sensor chest strap device (H10) during resting conditions.
Statistics are represented by means of p-value, Cohen’s d (d), Pearson’s r (r), Lin’s Concordance
Correlation Coefficient (rc) and Intraclass Correlation Coefficient (ICC3,1).
PRE POST
RR [ms] HR [bpm] DFA a1 RR [ms] HR [bpm] DFA a1
H10 ECG H10 ECG H10 ECG H10 ECG H10 ECG H10 ECG
mean 987.6 987.5 63.2 63.2 0.94 0.96 686.4 686.3 88.9 88.9 1.38 1.35
SD 201.1 201.1 13.1 13.1 0.31 0.32 90.5 90.4 11.9 11.9 0.23 0.22
p-value 0.56 0.74 0.35 0.50 0.50 0.18
d 0.12 0.07 0.19 0.14 0.14 0.29
r 1.00 1.00 0.95 1.00 1.00 0.86
rc1.00 1.00 0.95 1.00 1.00 0.84
ICC3,1 1.00 1.00 0.95 1.00 1.00 0.85
Sensors 2022, 22, x FOR PEER REVIEW 7 of 15
Figure 4. Bland–Altman analysis for the comparison of the ECG (ECG) and the Polar H10 sensor
chest strap device (H10) during PRE and POST the incremental exercise test for RR (A), HR (B) and
DFA a1 (C). Center solid line in each plot represents the mean bias (difference) between each paired
value as absolute (RR, HR) or relative values (DFA a1). The top and bottom dashed lines are 1.96
standard deviations from the mean difference.
3.2. Incremental Exercise Analysis
High correlations could be found during the incremental exercise test for the linear
parameters (r = 1.00. r
c
= 1.00, ICC
3,1
= 1.00), and DFA a1 (r > 0.93. r
c
> 0.93, ICC
3,1
> 0.93),
although with a statistically significant difference (p < 0.001) (see Figure 5 and Table 2).
Bland–Altman plot for RR showed a mean difference of 0.7 to 0.4 ms with upper and lower
LoA of 4.3 to −2.8 ms during low intensity and 1.3 to −0.5 ms during high intensity exer-
cise. DFA a1 showed wider bias (0.9 to 8.6%) and LoAs of 11.6 to −9.9% during low inten-
sity and 58.1 to −40.9% during high intensity. Further results are depicted in Figure 6.
Figure 4.
Bland–Altman analysis for the comparison of the ECG (ECG) and the Polar H10 sensor
chest strap device (H10) during PRE and POST the incremental exercise test for RR (
A
), HR (
B
)
and DFA a1 (
C
). Center solid line in each plot represents the mean bias (difference) between each
paired value as absolute (RR, HR) or relative values (DFA a1). The top and bottom dashed lines are
1.96 standard deviations from the mean difference.
Sensors 2022,22, 6536 7 of 13
3.2. Incremental Exercise Analysis
High correlations could be found during the incremental exercise test for the linear
parameters (r = 1.00. r
c
= 1.00, ICC
3,1
= 1.00), and DFA a1 (r > 0.93. r
c
> 0.93, ICC
3,1
> 0.93),
although with a statistically significant difference (p< 0.001) (see Figure 5and Table 2).
Bland–Altman plot for RR showed a mean difference of 0.7 to 0.4 ms with upper and
lower LoA of 4.3 to
−
2.8 ms during low intensity and 1.3 to
−
0.5 ms during high intensity
exercise. DFA a1 showed wider bias (0.9 to 8.6%) and LoAs of 11.6 to
−
9.9% during low
intensity and 58.1 to
−
40.9% during high intensity. Further results are depicted in Figure 6.
Sensors 2022, 22, x FOR PEER REVIEW 8 of 15
Table 2. Mean, standard deviation (SD) and medians for RR, HR and DFA a1 obtained from the
ECG (ECG) and the Polar H10 sensor chest strap device (H10) during the incremental exercise test.
Paired data statistics are represented by means of Hodges-Lehmann estimator, p-value, Cohen’s d
(d), Pearson’s r (r), Lin’s Concordance Correlation Coefficient (r
c
) and Intraclass Correlation Coeffi-
cient (ICC
3,1
).
RR [ms] HR [bpm] DFA a1
H10 ECG H10 ECG H10 ECG
mean 485.5 485.0 130.1 130.2 1.05 1.01
SD 112.4 112.3 28.7 28.7 0.40 0.40
median 470.0 470.3 127.7 127.6 1.07 1.01
Hodges-Lehmann estimator −0.48 (−10.75 to 9.74) −0.14 (−2.75 to 3.04) −0.04 (−0.08 to 0.00)
p-value <0.001 <0.001 <0.001
d 0.48 0.51 0.28
r 1.00 1.00 0.93
r
c
1.00 1.00 0.93
ICC
3,1
1.00 1.00 0.93
Figure 5. Regression Plots for the comparison of the ECG (ECG) and the Polar H10 sensor chest
strap device (H10) during the incremental exercise test for RR (A), HR (B) and DFA a1 (C). Slope,
coefficient of determination (R
2
), standard error of estimate (SEE), and p-value are shown in the
bottom right of each plot.
Figure 5.
Regression Plots for the comparison of the ECG (ECG) and the Polar H10 sensor chest strap
device (H10) during the incremental exercise test for RR (
A
), HR (
B
) and DFA a1 (
C
). Slope, coefficient of
determination (R
2
), standard error of estimate (SEE), and p-value are shown in the bottom right of each plot.
Table 2.
Mean, standard deviation (SD) and medians for RR, HR and DFA a1 obtained from the ECG
(ECG) and the Polar H10 sensor chest strap device (H10) during the incremental exercise test. Paired
data statistics are represented by means of Hodges-Lehmann estimator, p-value, Cohen’s d (d), Pearson’s
r (r), Lin’s Concordance Correlation Coefficient (rc) and Intraclass Correlation Coefficient (ICC3,1).
RR [ms] HR [bpm] DFA a1
H10 ECG H10 ECG H10 ECG
mean 485.5 485.0 130.1 130.2 1.05 1.01
SD 112.4 112.3 28.7 28.7 0.40 0.40
median 470.0 470.3 127.7 127.6 1.07 1.01
Hodges-Lehmann estimator −0.48 (−10.75 to 9.74) −0.14 (−2.75 to 3.04) −0.04 (−0.08 to 0.00)
p-value <0.001 <0.001 <0.001
d 0.48 0.51 0.28
r 1.00 1.00 0.93
rc1.00 1.00 0.93
ICC3,1 1.00 1.00 0.93
Sensors 2022,22, 6536 8 of 13
Sensors 2022, 22, x FOR PEER REVIEW 9 of 15
Figure 6. Bland–Altman analysis for the comparison of the ECG (ECG) and the Polar H10 sensor
chest strap device (H10) during the incremental exercise test for RR (A), HR (B) and DFA a1 (C).
Center solid line in each plot represents the mean bias (difference) between each paired value as
absolute (RR, HR) or relative values (DFA a1). The top and bottom dashed lines are 1.96 standard
deviations from the mean difference.
4. Discussions
The objective of the present study was to evaluate the extent of agreement of HRV
data obtained via the Polar H10 sensor chest strap device with that recorded by means of
a reference laboratory grade ECG. Comparisons were done for RR intervals, HR and DFA
a1 both at resting conditions (PRE and POST) and during incremental cycling exercise (see
Figure A1 for exemplary ECG waveforms). Data for linear HRV showed excellent, near
perfect agreement during resting conditions with the divergences being minimal and not
clinically relevant. Considering DFA a1 comparison, while mean bias was −2.1% for PRE
(upper LoA of 14.7 to 16.4% and lower LoA of −18.9 to −20.5%) and 2.5% for POST (upper
LoA of 15.6 to 23.7% and lower LoA of −10.2 to −19.1%) in the present study, it was 1.0%
(LoA: 20.5 to −18.5 %)) and −0.7% (LoA: 14.2 to −15.7%) in a comparable chest strap device
validation with the Movesense Medical Sensor [30]. Although comparisons where statis-
tically different during the incremental exercise test, absolute mean bias in RR was very
low (under 1 ms), LoA were close (upper and lower LoA of 4.3 to −2.8 ms during low
intensity and 1.3 to −0.5 ms during high intensity exercise) and correlation coefficients
Figure 6.
Bland–Altman analysis for the comparison of the ECG (ECG) and the Polar H10 sensor
chest strap device (H10) during the incremental exercise test for RR (
A
), HR (
B
) and DFA a1 (
C
).
Center solid line in each plot represents the mean bias (difference) between each paired value as
absolute (RR, HR) or relative values (DFA a1). The top and bottom dashed lines are 1.96 standard
deviations from the mean difference.
4. Discussions
The objective of the present study was to evaluate the extent of agreement of HRV
data obtained via the Polar H10 sensor chest strap device with that recorded by means
of a reference laboratory grade ECG. Comparisons were done for RR intervals, HR and
DFA a1 both at resting conditions (PRE and POST) and during incremental cycling exercise
(see Figure A1 for exemplary ECG waveforms). Data for linear HRV showed excellent,
near perfect agreement during resting conditions with the divergences being minimal and
not clinically relevant. Considering DFA a1 comparison, while mean bias was
−
2.1% for
PRE (upper LoA of 14.7 to 16.4% and lower LoA of
−
18.9 to
−
20.5%) and 2.5% for POST
(upper LoA of 15.6 to 23.7% and lower LoA of
−
10.2 to
−
19.1%) in the present study, it
was 1.0% (LoA: 20.5 to
−
18.5 %)) and
−
0.7% (LoA: 14.2 to
−
15.7%) in a comparable chest
strap device validation with the Movesense Medical Sensor [
30
]. Although comparisons
where statistically different during the incremental exercise test, absolute mean bias in
RR was very low (under 1 ms), LoA were close (upper and lower LoA of 4.3 to
−
2.8 ms
during low intensity and 1.3 to
−
0.5 ms during high intensity exercise) and correlation
coefficients were high (from 0.93 to 1.0). Magnitude of DFA a1 bias was small with low
intensity showing a percentage difference of 0.9% (LoA: 11.6 to
−
9.9%) and an increased
difference of 8.6% (LoA: 58.1 to
−
40.9%) during high intensity exercise, comparable to the
study by [30] with the Movesense Medical sensor device.
Sensors 2022,22, 6536 9 of 13
Since DFA a1 has been employed as a marker of both physiological thresholds and
autonomic fatigue during exercise [
15
,
18
], the clinical consequence of DFA a1 divergence
between devices should be examined. Index values tended to be approximately 6% and
7.5% higher at the fixed values of 0.75 and 0.5 for Polar H10 in comparison to the ECG
(Figure 5C), with expected consequences for the proposed HRV threshold boundaries [
15
].
Both threshold boundaries were crossed at higher HRs when using the Polar H10 which
although are relatively minor, could have consequences for exercise and training pre-
scription and detection of fatigue. However, the amount of deviation was variable, with
some participants showing very close agreement between devices. The degree of bias
may simply relate to the discordance between actual ECG waveform signal strength and
morphology [
21
]. As previously noted with the Movesense Medical sensor as a single
channel ECG module [
30
], some individuals will exhibit variation in DFA a1 through an
exercise ramp simply based on the difference between a chest strap sensor vs. a lead 2
ECG waveform. Hence, it is certainly plausible that some participants had either differ-
ent or sub optimal signal strength using a chest strap device as opposed to ECG lead
2. In fact, Polar’s own documentation recommends that both strap placement and even
module inversion be considered if results appear unreliable [
31
]. Therefore, it is recom-
mended to visually evaluate an initial baseline of the Polar H10 ECG waveform before
usage for exercise intensity investigation. This can help one optimize QRS wave morphol-
ogy by chest strap rotation and/or module inversion [
32
] for best signal to noise ratio.
Several smartphone apps are available providing ECG waveform display and recording
including Fatmaxxer (https://github.com/IanPeake/FatMaxxer) and the Polar Equine app
(https://play.google.com/store/apps/details?id=fi.polar.equine&gl=US).
Other factors may also affect RR time series accuracy, thereby impacting DFA a1 with
its reliance on actual patterns of RR intervals over time [
33
]. Unfortunately, hardware
and software technicalities are often just known by the manufacturers themselves, despite
the widespread knowledge of the effects of, e.g., sampling frequency, RR resolution or
the preprocessing algorithms used for filtering background noise or muscular contractile
activity on RR intervals [
1
,
12
,
34
,
35
]. Although both the Polar H10 and ECG based RR mea-
surements are computed with signal processing algorithms resembling the Pan-Tompkins
method, the exact rules and procedures are likely different, leading to some degree of end
result deviation. In addition, factors involving the individual characteristics of the male
and female participants (ventricular size or mass, subcutaneous fat, skin characteristics),
electrode positioning, choice of lead for analysis or cardiac disease pathology might affect
the QRS complex and thus the timing of the RR detection [
1
,
11
,
21
,
36
–
39
]. As discussed
above, the recording systems compared here not only differ in device type, but as men-
tioned above also in lead placement, therefore it is impossible to attribute a single variable
for the data discrepancy.
Although not directly related to the issue of HRV agreement, mention should be made
of the disadvantages of just recording RR intervals without continuous ECG tracings. Since
the Polar H10 only stores RR intervals and not ECG waveform recordings, it is not possible
to identify or potentially correct signals with excessive artefacts leading to a data loss. In
this context, it is also impossible to differentiate whether abundant artifacts originate from
noise or due to cardiac arrhythmia [
30
]. Figure 7illustrates this with an example of a female
participant from the present study whose data during the incremental cycling ramp was
excluded from validation analysis since artifact exceeded >5%. On closer examination of
the parallel recorded ECG, these “artifacts” were classified as frequent atrial premature
complexes (APCs) which can be a risk factor for atrial fibrillation [
40
]. In terms of DFA a1
bias during the incremental tests, artifact correction can lead to variable results [24].
Even though this study attempted to exclude recordings exceeding 5% artifact, it
cannot be guaranteed that sporadic DFA a1 variation did not occur. This can be observed
in Figure 8where DFA a1 courses of four study participants are depicted. The effects of
high degrees of artifact correction are demonstrated, showing either data loss or HRV bias.
Sensors 2022,22, 6536 10 of 13
The potential effect of lead placement (chest strap electrodes vs. ECG lead 2) is also notable
in terms of DFA a1 determination.
Sensors 2022, 22, x FOR PEER REVIEW 11 of 15
Figure 7. (A) Modified Kubios HRV Premium software output of the raw data of the Polar H10 in
one female participant during incremental cycling ramp; the measurement window from minute 12
to 14 indicated artifacts over 6%. (B) Evaluation of the lead 2 ECG waveform recordings during the
same measurement window, there could be found some runs of atrial premature complexes (APC,
red circles) pointing to the artifacts not really being artifacts and the pitfall of a mere RR recording.
Even though this study attempted to exclude recordings exceeding 5% artifact, it can-
not be guaranteed that sporadic DFA a1 variation did not occur. This can be observed in
Figure 8 where DFA a1 courses of four study participants are depicted. The effects of high
degrees of artifact correction are demonstrated, showing either data loss or HRV bias. The
potential effect of lead placement (chest strap electrodes vs. ECG lead 2) is also notable in
terms of DFA a1 determination.
Figure 7.
(
A
) Modified Kubios HRV Premium software output of the raw data of the Polar H10 in
one female participant during incremental cycling ramp; the measurement window from minute 12
to 14 indicated artifacts over 6%. (
B
) Evaluation of the lead 2 ECG waveform recordings during the
same measurement window, there could be found some runs of atrial premature complexes (APC,
red circles) pointing to the artifacts not really being artifacts and the pitfall of a mere RR recording.
Sensors 2022, 22, x FOR PEER REVIEW 11 of 15
Figure 7. (A) Modified Kubios HRV Premium software output of the raw data of the Polar H10 in
one female participant during incremental cycling ramp; the measurement window from minute 12
to 14 indicated artifacts over 6%. (B) Evaluation of the lead 2 ECG waveform recordings during the
same measurement window, there could be found some runs of atrial premature complexes (APC,
red circles) pointing to the artifacts not really being artifacts and the pitfall of a mere RR recording.
Even though this study attempted to exclude recordings exceeding 5% artifact, it can-
not be guaranteed that sporadic DFA a1 variation did not occur. This can be observed in
Figure 8 where DFA a1 courses of four study participants are depicted. The effects of high
degrees of artifact correction are demonstrated, showing either data loss or HRV bias. The
potential effect of lead placement (chest strap electrodes vs. ECG lead 2) is also notable in
terms of DFA a1 determination.
Figure 8.
Course of DFA a1 during incremental cycling ramp until voluntary exhaustion, including
3 min of warm-up at 50 W and 5 min cool-down of unloaded pedaling (vertical lines mark the start of
the incremental test and voluntary exhaustion) wearing a 12-channel ECG (lead 2 and 3 depicted), a
Polar H10 sensor chest strap device, and a Movesense Medical sensor single channel ECG chest strap
device. (A) Good agreement between both chest strap devices and ECG lead 3 in a male participant
with ECG lead 2 showing up to 50% divergence from the Polar H10 sensor (artifacts 2% during the
marked range; black line). (
B
) Female participant with excellent agreement in all four signal sources.
(
C
) Female participant showing high artifacts (>5%) in the marked range (black line) and deviations
between all 4 devices. (
D
) Obese participant with artifacts >5% in the marked range (black line) for
ECG Lead 2 and 3 with a subsequent complete loss of the signals. However, minimal artefacts in
Polar H10 and Movesense Medical sensor (<1%).
Sensors 2022,22, 6536 11 of 13
It can be considered as a possible limitation that this study did not group the sam-
ples by their individual personal characteristics. Future studies could elucidate if body
composition, gender, age or ventricular characteristics influence study outcomes. For recre-
ational athletes with the aim of using the Polar H10 chest strap device with the Elite HRV
App, it should be emphasized that raw data output assuming no data loss or corrections
was analyzed via Kubios HRV Premium software containing the feature of automatic
artifact correction. The basic, cost-free “standard” version of Kubios HRV with an alternate
threshold-based artifact correction method could lead to slightly different results than
presented [24].
5. Conclusions
Linear HRV measurements derived from the Polar H10 sensor chest strap device
recorded with the Elite HRV app correspond closely with measurements taken with a
reference ECG in terms of RR intervals and HR. However, with respect to DFA a1, values
in the uncorrelated range and during higher exercise intensities tend to elicit higher bias
and wider LoA. This may partly be related to the expected differences in non-linear HRV
associated with ECG lead placement. Nevertheless, since this mobile-based HRV recording
setup displays superior practicability with generally comparable results, its commercial use
for the monitoring of HRV data during resting and endurance exercise conditions could
be justified.
Author Contributions:
M.S. and T.G. conceived the study. M.S. requested ethical approval, con-
ducted the participant recruitment and performed the physiologic testing. M.S. and B.R. performed
the data analysis. M.S. wrote the first draft of the article. M.S., B.R., R.R. and T.G. revised it critically
for important intellectual content. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
The studies involving human participants were reviewed
and approved by the University of Hamburg, Department of Psychology and Movement Science,
Germany (reference no.: 2021_400) and conformed to the principles of the Declaration of Helsinki.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the
study. Written informed consent has been obtained from the patient(s) to publish this paper.
Data Availability Statement:
The datasets analyzed during the current study are available from the
corresponding author on reasonable request.
Acknowledgments:
The authors would like to thank the collaboration of all the volunteers who
participated in the study.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
Abbreviations
HRV = heart rate variability, ECG = electrocardiogram, DFA a1 = short-term scaling exponent
alpha1 of Detrended Fluctuation Analysis, RR = average time between heartbeats, HR = heart rate,
RMSSD = root mean square of successive differences, VO
2max
= maximum oxygen uptake, r = Pear-
son’s r correlation coefficient, r
c
= Lin’s Concordance Correlation Coefficient,
ICC3,10 = Intraclass
Cor-
relation Coefficient, R
2
= coefficient of determination, SEE = standard error of estimate;
LoA = limits
of agreement, SD = standard deviation (SD), H10 =Polar H10 sensor chest strap (H10), d = Cohen’s d,
APC = atrial premature complex.
Sensors 2022,22, 6536 12 of 13
Appendix A
Sensors 2022, 22, x FOR PEER REVIEW 13 of 15
HRV = heart rate variability, ECG = electrocardiogram, DFA a1 = short-term scaling exponent
alpha1 of Detrended Fluctuation Analysis, RR = average time between heartbeats, HR = heart rate,
RMSSD = root mean square of successive differences, VO2max = maximum oxygen uptake, r = Pear-
son’s r correlation coefficient, rc = Lin’s Concordance Correlation Coefficient, ICC3,10 = Intraclass Cor-
relation Coefficient, R2 = coefficient of determination, SEE = standard error of estimate; LoA = limits
of agreement, SD = standard deviation (SD), H10 =Polar H10 sensor chest strap (H10), d = Cohen’s
d, APC = atrial premature complex
Appendix A
Figure A1. Representative participant ECG waveforms and background noise during 6 s windows
using Kubios HRV software Premium. (A) Pre incremental ramp, HR of 65 bpm. (B) During incre-
mental ramp just prior to exhaustion, HR of 180 bpm. (C) Post incremental ramp, HR of 92 bpm.
References
1. Weippert, M.; Kumar, M.; Kreuzfeld, S.; Arndt, D.; Rieger, A.; Stoll, R. Comparison of Three Mobile Devices for Measuring R–
R Intervals and Heart Rate Variability: Polar S810i, Suunto T6 and an Ambulatory ECG System. Eur. J. Appl. Physiol. 2010, 109,
779–786. https://doi.org/10.1007/s00421-010-1415-9.
2. Singh, N.; Moneghetti, K.J.; Christle, J.W.; Hadley, D.; Plews, D.; Froelicher, V. Heart Rate Variability: An Old Metric with New
Meaning in the Era of Using MHealth Technologies for Health and Exercise Training Guidance. Part One: Physiology and
Methods. Arrhythm. Electrophysiol. Rev. 2018, 7, 193. https://doi.org/10.15420/aer.2018.27.2.
3. Buchheit, M. Monitoring Training Status with HR Measures: Do All Roads Lead to Rome? Front. Physiol. 2014, 5, 73.
https://doi.org/10.3389/fphys.2014.00073.
4. Plews, D.J.; Laursen, P.B.; Buchheit, M. Day-to-Day Heart-Rate Variability Recordings in World-Champion Rowers: Appreciat-
ing Unique Athlete Characteristics. Int. J. Sports Physiol. Perform. 2017, 12, 697–703. https://doi.org/10.1123/ijspp.2016-0343.
5. Stanley, J.; Peake, J.M.; Buchheit, M. Cardiac Parasympathetic Reactivation Following Exercise: Implications for Training Pre-
scription. Sports Med. 2013, 43, 1259–1277. https://doi.org/10.1007/s40279-013-0083-4.
6. Vesterinen, V.; Nummela, A.; Heikura, I.; Laine, T.; Hynynen, E.; Botella, J.; Häkkinen, K. Individual Endurance Training Pre-
scription with Heart Rate Variability. Med. Sci. Sports Exerc. 2016, 48, 1347–1354. https://doi.org/10.1249/MSS.0000000000000910.
7. Sammito, S.; Böckelmann, I. Reference Values for Time- and Frequency-Domain Heart Rate Variability Measures. Heart Rhythm.
2016, 13, 1309–1316. https://doi.org/10.1016/j.hrthm.2016.02.006.
8. Perrotta, A.S.; Jeklin, A.T.; Hives, B.A.; Meanwell, L.E.; Warburton, D.E.R. Validity of the Elite HRV Smartphone Application
for Examining Heart Rate Variability in a Field-Based Setting. J. Strength Cond. Res. 2017, 31, 2296–2302.
https://doi.org/10.1519/JSC.0000000000001841.
9. Dobbs, W.C.; Fedewa, M.V.; MacDonald, H.V.; Holmes, C.J.; Cicone, Z.S.; Plews, D.J.; Esco, M.R. The Accuracy of Acquiring
Heart Rate Variability from Portable Devices: A Systematic Review and Meta-Analysis. Sports Med. 2019, 49, 417–435.
https://doi.org/10.1007/s40279-019-01061-5.
10. Moya-Ramon, M.; Mateo-March, M.; Peña-González, I.; Zabala, M.; Javaloyes, A. Validity and Reliability of Different
Smartphones Applications to Measure HRV during Short and Ultra-Short Measurements in Elite Athletes. Comput. Methods
Programs Biomed. 2022, 217, 106696. https://doi.org/10.1016/j.cmpb.2022.106696.
Figure A1.
Representative participant ECG waveforms and background noise during 6 s windows
using Kubios HRV software Premium. (
A
) Pre incremental ramp, HR of 65 bpm. (
B
) During
incremental ramp just prior to exhaustion, HR of 180 bpm. (
C
) Post incremental ramp, HR of 92 bpm.
References
1.
Weippert, M.; Kumar, M.; Kreuzfeld, S.; Arndt, D.; Rieger, A.; Stoll, R. Comparison of Three Mobile Devices for Measuring R–R
Intervals and Heart Rate Variability: Polar S810i, Suunto T6 and an Ambulatory ECG System. Eur. J. Appl. Physiol.
2010
,109,
779–786. [CrossRef] [PubMed]
2.
Singh, N.; Moneghetti, K.J.; Christle, J.W.; Hadley, D.; Plews, D.; Froelicher, V. Heart Rate Variability: An Old Metric with New
Meaning in the Era of Using MHealth Technologies for Health and Exercise Training Guidance. Part One: Physiology and
Methods. Arrhythm. Electrophysiol. Rev. 2018,7, 193. [CrossRef] [PubMed]
3.
Buchheit, M. Monitoring Training Status with HR Measures: Do All Roads Lead to Rome? Front. Physiol.
2014
,5, 73. [CrossRef]
[PubMed]
4.
Plews, D.J.; Laursen, P.B.; Buchheit, M. Day-to-Day Heart-Rate Variability Recordings in World-Champion Rowers: Appreciating
Unique Athlete Characteristics. Int. J. Sports Physiol. Perform. 2017,12, 697–703. [CrossRef] [PubMed]
5.
Stanley, J.; Peake, J.M.; Buchheit, M. Cardiac Parasympathetic Reactivation Following Exercise: Implications for Training
Prescription. Sports Med. 2013,43, 1259–1277. [CrossRef]
6.
Vesterinen, V.; Nummela, A.; Heikura, I.; Laine, T.; Hynynen, E.; Botella, J.; Häkkinen, K. Individual Endurance Training
Prescription with Heart Rate Variability. Med. Sci. Sports Exerc. 2016,48, 1347–1354. [CrossRef]
7. Sammito, S.; Böckelmann, I. Reference Values for Time- and Frequency-Domain Heart Rate Variability Measures. Heart Rhythm.
2016,13, 1309–1316. [CrossRef]
8.
Perrotta, A.S.; Jeklin, A.T.; Hives, B.A.; Meanwell, L.E.; Warburton, D.E.R. Validity of the Elite HRV Smartphone Application for
Examining Heart Rate Variability in a Field-Based Setting. J. Strength Cond. Res. 2017,31, 2296–2302. [CrossRef]
9.
Dobbs, W.C.; Fedewa, M.V.; MacDonald, H.V.; Holmes, C.J.; Cicone, Z.S.; Plews, D.J.; Esco, M.R. The Accuracy of Acquiring Heart
Rate Variability from Portable Devices: A Systematic Review and Meta-Analysis. Sports Med. 2019,49, 417–435. [CrossRef]
10.
Moya-Ramon, M.; Mateo-March, M.; Peña-González, I.; Zabala, M.; Javaloyes, A. Validity and Reliability of Different Smartphones
Applications to Measure HRV during Short and Ultra-Short Measurements in Elite Athletes. Comput. Methods Programs Biomed.
2022,217, 106696. [CrossRef]
11.
Mühlen, J.M.; Stang, J.; Lykke Skovgaard, E.; Judice, P.B.; Molina-Garcia, P.; Johnston, W.; Sardinha, L.B.; Ortega, F.B.; Caulfield,
B.; Bloch, W.; et al. Recommendations for Determining the Validity of Consumer Wearable Heart Rate Devices: Expert Statement
and Checklist of the INTERLIVE Network. Br. J. Sports Med. 2021,55, 767–779. [CrossRef] [PubMed]
12.
Stone, J.D.; Ulman, H.K.; Tran, K.; Thompson, A.G.; Halter, M.D.; Ramadan, J.H.; Stephenson, M.; Finomore, V.S.; Galster, S.M.;
Rezai, A.R.; et al. Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate
Variability. Front. Sports Act. Living 2021,3, 585870. [CrossRef] [PubMed]
13.
Gilgen-Ammann, R.; Schweizer, T.; Wyss, T. RR Interval Signal Quality of a Heart Rate Monitor and an ECG Holter at Rest and
during Exercise. Eur. J. Appl. Physiol. 2019,119, 1525–1532. [CrossRef] [PubMed]
14.
Gronwald, T.; Hoos, O. Correlation Properties of Heart Rate Variability during Endurance Exercise: A Systematic Review. Ann.
Noninvasive Electrocardiol. 2020,25, e12697. [CrossRef]
Sensors 2022,22, 6536 13 of 13
15.
Rogers, B.; Gronwald, T. Fractal Correlation Properties of Heart Rate Variability as a Biomarker for Intensity Distribution and
Training Prescription in Endurance Exercise: An Update. Front. Physiol. 2022,13, 879071. [CrossRef]
16.
Rogers, B.; Giles, D.; Draper, N.; Mourot, L.; Gronwald, T. Detection of the Anaerobic Threshold in Endurance Sports: Validation
of a New Method Using Correlation Properties of Heart Rate Variability. J. Funct. Morphol. Kinesiol. 2021,6, 38. [CrossRef]
17.
Mateo-March, M.; Moya-Ramón, M.; Javaloyes, A.; Sánchez-Muñoz, C.; Clemente-Suárez, V.J. Validity of Detrended Fluctuation
Analysis of Heart Rate Variability to Determine Intensity Thresholds in Professional Cyclists. Eur. J. Sport Sci.
2022
, 1–20.
[CrossRef]
18.
Rogers, B.; Mourot, L.; Doucende, G.; Gronwald, T. Fractal Correlation Properties of Heart Rate Variability as a Biomarker of
Endurance Exercise Fatigue in Ultramarathon Runners. Physiol. Rep. 2021,9, e14956. [CrossRef]
19.
Tarvainen, M.P.; Niskanen, J.-P.; Lipponen, J.A.; Ranta-aho, P.O.; Karjalainen, P.A. Kubios HRV—Heart Rate Variability Analysis
Software. Comput. Methods Progr. Biomed. 2014,113, 210–220. [CrossRef]
20.
Tanaka, H.; Monahan, K.D.; Seals, D.R. Age-Predicted Maximal Heart Rate Revisited. J. Am. Coll. Cardiol.
2001
,37, 153–156.
[CrossRef]
21.
Jeyhani, V.; Mantysalo, M.; Noponen, K.; Seppanen, T.; Vehkaoja, A. Effect of Different ECG Leads on Estimated R–R Intervals
and Heart Rate Variability Parameters. In Proceedings of the 41st Annual International Conference of the IEEE Engineering in
Medicine and Biology Society (EMBC), Berlin, Germany, 23–27 July 2019; IEEE: New York, NY, USA, 2019; pp. 3786–3790.
22.
Lipponen, J.A.; Tarvainen, M.P. A Robust Algorithm for Heart Rate Variability Time Series Artefact Correction Using Novel Beat
Classification. J. Med. Eng. Technol. 2019,43, 173–181. [CrossRef] [PubMed]
23.
Peng, C.-K.; Havlin, S.; Stanley, H.E.; Goldberger, A.L. Quantification of Scaling Exponents and Crossover Phenomena in
Nonstationary Heartbeat Time Series. Chaos 1995,5, 82–87. [CrossRef] [PubMed]
24.
Rogers, B.; Giles, D.; Draper, N.; Mourot, L.; Gronwald, T. Influence of Artefact Correction and Recording Device Type on the
Practical Application of a Non-Linear Heart Rate Variability Biomarker for Aerobic Threshold Determination. Sensors
2021
,21,
821. [CrossRef] [PubMed]
25.
Bland, J.M.; Altman, D.G. Measuring Agreement in Method Comparison Studies. Stat. Methods Med. Res.
1999
,8, 135–160.
[CrossRef] [PubMed]
26. Chan, Y.H. Biostatistics 104: Correlational Analysis. Singapore Med. J. 2003,44, 614–619.
27. Lin, L.I.-K. A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics 1989,45, 255. [CrossRef]
28. Hodges, J.R.; Lehmann, E.L. Estimates of Location Based on Rank Tests. Ann. Math. Stat. 1963,34, 598–611. [CrossRef]
29.
Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed.; L. Erlbaum Associates: Hillsdale, NJ, USA, 1988; ISBN
978-0-8058-0283-2.
30.
Rogers, B.; Schaffarczyk, M.; Clauß, M.; Mourot, L.; Gronwald, T. The Movesense Medical Sensor Chest Belt Device as Single
Channel ECG for RR Interval Detection and HRV Analysis during Resting State and Incremental Exercise: A Cross-Sectional
Validation Study. Sensors 2022,22, 2032. [CrossRef]
31.
Polar Electro 2022. Available online: https://support.polar.com/de/support/FAQs/Abnormal_Heart_Rate_Readings_During_
Exercise?product_id= (accessed on 1 July 2022).
32.
Rogers, B. Pitfalls in DFA A1—Polar Belt Position. Muscle Oxygen Training. 2022. Available online: http://www.muscleoxygentr
aining.com/2022/01/pitfalls-in-dfa-a1- polar-belt- position.html (accessed on 10 July 2022).
33.
Cassirame, J.; Chevrolat, S.; Mourot, L. Effects of R-R Time Series Accuracy on Heart Rate Variability Indexes. Mov. Sport Sci. Sci.
Mot. 2019,106, 27–35. [CrossRef]
34. Pan, J.; Tompkins, W.J. A Real-Time QRS Detection Algorithm. IEEE Trans. Biomed. Eng. 1985,32, 230–236. [CrossRef]
35.
Mohd Apandi, Z.F.; Ikeura, R.; Hayakawa, S.; Tsutsumi, S. An Analysis of the Effects of Noisy Electrocardiogram Signal on
Heartbeat Detection Performance. Bioengineering 2020,7, 53. [CrossRef] [PubMed]
36.
Chatterjee, S.; Changawala, N. Fragmented QRS Complex: A Novel Marker of Cardiovascular Disease. Clin. Cardiol.
2010
,33,
68–71. [CrossRef] [PubMed]
37.
Drezner, J.A.; Fischbach, P.; Froelicher, V.; Marek, J.; Pelliccia, A.; Prutkin, J.M.; Schmied, C.M.; Sharma, S.; Wilson, M.G.;
Ackerman, M.J.; et al. Normal Electrocardiographic Findings: Recognising Physiological Adaptations in Athletes. Br. J. Sports
Med. 2013,47, 125–136. [CrossRef] [PubMed]
38.
Pérez-Riera, A.R.; Barbosa-Barros, R.; Daminello-Raimundo, R.; de Abreu, L.C. Main Artifacts in Electrocardiography. Ann.
Noninvasive Electrocardiol. 2018,23, e12494. [CrossRef] [PubMed]
39.
Hernández-Vicente, A.; Hernando, D.; Marín-Puyalto, J.; Vicente-Rodríguez, G.; Garatachea, N.; Pueyo, E.; Bailón, R. Validity of
the Polar H7 Heart Rate Sensor for Heart Rate Variability Analysis during Exercise in Different Age, Body Composition and
Fitness Level Groups. Sensors 2021,21, 902. [CrossRef]
40.
Im, S.I.; Park, D.H.; Kim, B.J.; Cho, K.I.; Kim, H.S.; Heo, J.H. Clinical and Electrocardiographic Characteristics for Prediction
of New-Onset Atrial Fibrillation in Asymptomatic Patients with Atrial Premature Complexes. IJC Heart Vasc.
2018
,19, 70–74.
[CrossRef]
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