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Purpose: To establish the validity of smartphone photoplethysmography (PPG) and heart rate sensor in the measurement of heart rate variability (HRV). Methods: 29 healthy subjects were measured at rest during 5 min of guided breathing (GB) and normal breathing (NB) using Smartphone PPG, heart rate chest strap and electrocardiography (ECG). The root mean sum of the squared differences between R-R intervals (rMSSD) was determined from each device. Results: Compared to ECG, the technical error of estimate (TEE) was acceptable for all conditions (average TEE CV% (90% CI) = 6.35 (5.13; 8.5)). When assessed as a standardised difference, all differences were deemed "Trivial" (average std. diff (90% CI) = 0.10 (0.08; 0.13). Both PPG and HR sensor derived measures had almost perfect correlations with ECG (R = 1.00 (0.99; 1:00). Conclusion: Both PPG and heart rate sensor provide an acceptable agreement for the measurement of rMSSD when compared with ECG. Smartphone PPG technology may be a preferred method of HRV data collection for athletes due to its practicality and ease of use in the field.
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Article Type: Original report
Title: Comparison of heart rate variability recording with smart phone photoplethysmographic,
Polar H7 chest strap and electrocardiogram methods.
Author: Daniel J. Plews1, 2, 3, Ben Scott1,4, Marco Altini5, Matt Wood2, Andrew E. Kilding2
and Paul B. Laursen1, 2
Affiliations:
1. High Performance Sport New Zealand, Auckland, New Zealand
2. Sports Performance Research Institute New Zealand (SPRINZ), Auckland University of
Technology, Auckland, New Zealand
3. University of Waikato, Hamilton, New Zealand
4. Loughborough University, Loughborough, United Kingdom
5. ACTLab, University of Passau, Germany
Contact Information:
Daniel Plews
High Performance Sport New Zealand
Millennium Institute of Sport & Health,
17 Antares Place,
Mairangi Bay, 0632, New Zealand
Ph: +64 21 250 9591
Fax: +64 9 479 1486
Corresponding Author: Daniel Plews: daniel.plews@hpsnz.org.nz
Abstract: 174
Main text: 2745
Figures and tables: 2 Figures, 1 Tables
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Abstract
Purpose: To establish the validity of smartphone photoplethysmography (PPG) and heart rate
sensor in the measurement of heart rate variability (HRV). Methods: 29 healthy subjects were
measured at rest during 5 min of guided breathing (GB) and normal breathing (NB) using
Smartphone PPG, heart rate chest strap and electrocardiography (ECG). The root mean sum of
the squared differences between RR intervals (rMSSD) was determined from each device.
Results: Compared to ECG, the technical error of estimate (TEE) was acceptable for all
conditions (average TEE CV% (90% CI) = 6.35 (5.13; 8.5)). When assessed as a standardised
difference, all differences were deemed “Trivial” (average std. diff (90% CI) = 0.10 (0.08;
0.13). Both PPG and HR sensor derived measures had almost perfect correlations with ECG
(R = 1.00 (0.99; 1:00). Conclusion: Both PPG and heart rate sensor provide an acceptable
agreement for the measurement of rMSSD when compared with ECG. Smartphone PPG
technology may be a preferred method of HRV data collection for athletes due to its practicality
and ease of use in the field.
Keywords: Cardiac parasympathetic, monitoring, athletic performance
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Introduction
The ability to monitor human movement and physiological state has advanced rapidly
in recent years. As one example, “Smart” devices, which use technologies such as
accelerometry, actigraphy and photoplethysmography can measure many aspects of human
performance and movement.1 For athletes striving for peak performances, the need to
effectively monitor human movement and physiological state are important so that more
objective decisions around training can be made.2 The regular assessment of heart rate
variability (HRV) has immerged as one measure of “physiological state” that has grown in its
popularity and is used by many sporting teams and athletes on a day-to-day basis.3 HRV
involves measurement of the variation between individual heart beats across consecutive
cardiac cycles, and this variation can provide an estimate of a person’s autonomic nervous
system (ANS) activity.4
Several aspects can converge to reduce daily athlete measurement compliance,
including the convenience of having the appropriate equipment available each morning, a
consistent morning room temperature to enable ease of putting on a chest strap, and other
factors. Moreover, due to the natural relative variability or noise of daily HRV recordings,
multiple daily recordings are required, with weekly and rolling averages needed to gain a true
representation of an athlete’s physiological state.5-7 As such, ways by which HRV recording
can be improved would be advantageous to both coaches and practitioners wishing to use HRV
in the field.
Photoplethysmography (PPG) is one technological advancement that may allow HRV
to be measured simply via a smartphone device. PPG is measured via reflection through the
illumination of the skin using an LED (e.g. the smartphone’s flash) and through detection of
the amount of light that is reflected by a photodetector or a camera located next to the light
source. The resulting PPG signal is composed of a direct current (DC) component, which varies
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
slowly depending on tissue properties and blood volume. The alternating current (AC)
component varies more rapidly to detect the pulsatile factor. After cardiac systole, local blood
volume increases acutely, reducing the received light intensity. During diastole, blood volume
decreases, and light reflection increases.8 Compared with other HRV measurement devices
used by athletes (e.g. heart rate monitor sensors), PPG can then be considered a more user-
friendly model of HRV attainment, as no additional apparatus is required other than a
smartphone device that can easily transfer acquired data via Wi-Fi or 3/4G transfer to the
internet. Together, these combined innovations have the capacity to greatly improve athlete
compliance via enhanced ease of daily recording.
The aim of this study was to compare the accuracy and validity of HRV recordings
attained via a PPG smartphone application (HRV4Training), and via the Polar H7 (a device
more traditionally used by athletes to record HRV in a practical setting), alongside gold
standard electrocardiography (ECG).
Methods
Participants
Twenty-nine subjects were initially recruited for this study. From this data set 2 subjects
were removed, as they were unable to complete an entire 60 s of usable PPG data. Another
subject was removed due to a suspected heart arrhythmia. This left 26 complete data sets to be
used in the final analysis (♂ = 22, ♀= 7, age = 31 ± 10 years; Height = 175 ± 9 cm; weight =
73 ± 11; BMI = 23.7 ± 2.3). Of these 26 subjects, 3 were elite athletes, 13 were well-trained
athletes and 10 were recreationally-trained athletes. Prior to taking part in the study, all
participants completed a standardised medical screening form and provided written informed
consent. The study was approved by the Human Research Ethics Committee of AUT
University. Participants were provided with a demonstration of how to use the PPG smartphone
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
application before they completed 5 min of guided learning time where they could become
familiar with how to use the app, including how to apply appropriate finger pressure as well as
use an entrained breathing setting.
Data acquisition and processing
Camera and HR chest strap data were acquired and processed using an in-house built
smartphone application. This application could acquire simultaneous RR intervals from a Polar
H7 Bluetooth heart rate monitor and a phone camera.9 ECG data was acquired using a
diagnostic quality 12-lead system (Cosmed, Quark T12x, USA).
Prior to electrode placement, the skin of participants was prepped at the appropriate
sites by way of shaving, abrading and swabbing with alcohol wipes. A standard 12-lead
electrode placement was used for ECG recording. The six chest leads were placed as follows:
V1 in the fourth intercostal space to the right of the sternum, V2 in the fourth intercostal space
to the left of the sternum, V3 between V2 and V4, V4 in the fifth intercostal space in the
midclavicular line, V5 between V4 and V6 and V6 in the fifth intercostal space in the
midaxillary line. Finally, arm electrodes were placed 2 cm below the anterior deltoids in the
midclavicular line and leg electrodes were placed medially from the suprailiac crest in the
midclavicular line. Once the ECG had been attached, participants were given a Polar H7 heart
rate monitor, which was fitted just below V6.
Photoplethysmography
Photoplethysmography (PPG) was acquired via a commercially available smartphone
application known as “HRV4training” (see http://www.hrv4training.com/). Given the low
frame rate of mobile phone cameras, different signal processing techniques should be
employed to derive HRV from the phone video stream.10 HRV4Training acquires a video
stream at a frame rate of 30 Hz, where red, green, and blue (RGB) channels are averaged over
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
the entire frame, before converting between the RGB and the hue, saturation, and value (HSV)
colour space. The intensity component of the HSV colour space is filtered using a Butterworth
band pass filter of order 4 and frequency pass band between 0.1 and 10 Hz, to remove the DC
component of the signal, as well as any high-frequency noise while maintaining the AC
component. Finally, cubic spline interpolation is used to up-sample the signal between 30 and
180 Hz. Up-sampling of the data is a necessary requirement for sufficient resolution of HRV
feature computation.11
RR interval extraction, data synchronization, and features computation.
HRV4Training implements a peak detection algorithm to determine peak-to-peak
intervals from up-sampled PPG data. Peak detection is based on a slope inversion
algorithm,9 where peak-to-peak intervals are corrected for artefacts according to two criteria.
First, consecutive RR intervals extracted with PPG are removed when they differ by more than
75% from the previous one. Additionally, outliers are removed by including only RR intervals
that are within less than 25% of the 1st quartile and within more than 25% of the 3rd quartile.
This technique avoids over-correcting, a problem of the widely employed removal of
consecutive RR intervals differing by more than 25%12 for individuals with very high beat-to-
beat variability. Finally, the first or second minute of data were discarded when the PPG signal
was disrupted by excessive noise, e.g. due to the participant’s movement or other unidentified
causes beyond the scope of this comparison.
The Polar H7, as with other Bluetooth low energy chest straps, already provides RR
intervals, and therefore RR data does not require additional processing.
ECG data was exported from the Cosmed Quark T12x system. A continuous wavelet
transform based beat detection algorithm was used to extract RR intervals from lead 3 of
the ECG data. A custom software was then used to display ECG and detected peaks so that the
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
experimenter could manually edit detected peaks to ensure correctness of the algorithm output.
The same lead configuration and processing procedure was used for all subjects.
RR intervals for PPG and H7 data were acquired using an in-house built smartphone
application so that RR interval data could be almost perfectly synchronized. However, some
limitations did apply. First, Bluetooth low energy radio packets have priority over camera
acquisition and therefore could from time to time introduce small (order of milliseconds) delays
in PPG data acquisition. As a result, this setup is a worst case scenario for time sensitive
operations such as RR interval extraction from a camera-based data stream. Second, data could
not be synchronized automatically as the Bluetooth low energy protocol does not provide
timestamped RR intervals, but sends RR intervals appended to the average heart rate of the
past second. Hence, RR intervals gathered over the relevant 60 s window were appended and
visual synchronization was necessary before HRV computation. Similalry, manual
synchronization was necessary for ECG data, as these data was acquired from a separate system
(Cosmed Quark T12x). Manual synchronization was performed by visually aligning the RR
interval time series (see for example Fig. 1), as RR interval oscillations due to breathing allow
for visual synchronization regardless of small time delays due to the unlikelihood of starting
the different systems at exactly the same time.
Testing procedure
5-min recordings were taken under two conditions; sitting guided breathing (GB) and
sitting normal breathing (NB). Sitting was chosen to reduce any possible parasympathetic
saturation which is often observed in individuals with low resting heart rates.15 Recordings
were taken in the same order as listed before duplicate measures were taken in the identical
order. Participants rested in each position for 1 min before beginning a recording to reduce the
influence of movement on HRV. As 1-min HRV data has been shown to be as valid a measure
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
as longer time frames,16 data were measured during the first 1 min of recordings after discarding
the first 5 s. As such, data were included from 5 s to 1 min 5 s (60 s total duration), thereby
allowing for a 5 sec stabilization period. Furthermore, as an aim of this study was to investigate
ways by which to increase the practicality and ease of HRV data capturing, only 1-min
durations were investigated.
Importantly, erroneous data were discarded from any recording. The in-house built
data-capturing application was designed to inform the user whether data were of sufficient
quality or not. For this, the first minute of data was discarded in two circumstances for LB1
and in three circumstances for LNB1. Periods of high noise were identified by analyzing the
percentage of discarded RR intervals over a given time-period, as RR intervals are discarded
when timing differences are outside of expected or normal values, typically due to underlying
noise or ectopic beats. In cases where the rMSSD data attained were inappropriate due to user
error (e.g. movement of the finger over the camera), the subject would be informed and data
would be discarded. The subject would then be asked to make another recording until it was
deemed successful.
Statistical analysis
All data are presented as mean ± 90% confidence limits (CL) unless otherwise stated.
Comparisons to rMSSD values derived from ECG to Polar H7 and PPG were achieved using
a Pearson product-moment correlation analysis, standard linear regression, typical error of
estimate (TEE) and mean bias (%). Inspection of the slope and intercept of the linear regression
was examined to characterize the level of agreement between PPG to ECG and Polar H7 to
PPG. The TEE and mean bias between PPG and the other methods were determined using an
excel spreadsheet,17 with the TEE expressed both in raw units and as a percentage. To assess
differences between measures, standardised differences were also calculated using the same
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
spreadsheet. The following threshold values for standardized differences were ≤0.2 (trivial),
>0.2 (small), >0.6 (moderate), >1.2 (large), and >2.0 (very large). The magnitude of the
correlation between PPG/Polar H7 to ECG was assessed with the following thresholds <0.1,
trivial; <0.10.3, small; <0.30.5, moderate ;< 0.50.7, large ;< 0.70.9, very large; and <0.9
1.0, almost perfect. If the 90 % confidence intervals (CI) overlapped small positive and
negative values, the magnitude of correlation was deemed ‘unclear’.
Results
An example of raw R-R data from one subject across the recording period for all capture
methods is shown in Figure 1. The TEE for all four conditions are presented in Table 1.
Compared with ECG, PPG GB had the lowest TEE (CV% (90% CI) = 3.8 (3.1; 5.0)) whereas
Polar H7 NB had the highest CV% (90% CI) = 8.6 (6.9; 11.6)). When assessed as a standardised
difference (PPG/Polar H7 vs. ECG), all differences were deemed “Trivial”.
The magnitudes of the correlation between PPG/Polar H7 and ECG are shown in Figure
2. All methods of HRV assessment displayed almost perfect correlations compared with ECG.
PPG vs. ECG GB displayed the clearest correlation (r = 1.00 (1.00; 1.00), whereas the Polar
H7 NB showed the slightly lower correlation (r = 0.99 (0.98; 1.00). However all correlations
were deemed “almost perfect”.
Discussion
We have previously shown that in order to effectively monitor an athletes HRV,
weekly rolling-averaged values tend to be more useful than values taken on an isolated day.5
As such, daily monitoring and clean” data is paramount for practitioners and coaches to
effectively monitor an athlete’s training adaptation via the use of morning resting HRV3.
However, in some athletes, achieving daily compliance can often be difficult.5 The main
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
finding of this study is that both PPG recorded via smartphone technology and the Polar HR
sensor has acceptable levels of agreement with ECG for recording the rMSSD index of HRV.
The ability to effectively record HRV via an athlete’s own smartphone and PPG
technology is one method that would indeed simplify data acquisition. This method removes
the necessity to acquire and fit the HR strap (methods which have been traditionally used by
athletes collecting HRV5,13,18) with now just one device needed (i.e. a smartphone). This also
happens to be a device that is now traditionally positioned at bedside by most individuals and
used as their alarm clock, etc., which makes the early morning routine of HRV data capture
relatively seamless.
Although all methods of HRV assessment used in the present study were shown to be
acceptable, the PPG method, using GB, showed the lowest TEE (CV% = 3.8 (3.1; 5.0) and
standardized difference (Std diff = 0.06 (0.05; 0.08) “trivial”). Furthermore, the mean bias in
raw units was ≤ 2.0 ms (Table 1). Considering that rMSSD values typically range from ~50-
250 ms, this is a very small bias. When we contrast other studies that have compared HRV
values measured through PPG against ECG it is currently difficult, as differences between
experimental settings and/or methods of analysis are apparent.19 Furthermore, many of these
studies have been carried out using a variety of “clip-on” devices (e.g. devices clipped onto the
finger or earlobe) rather than the smartphone camera per se. For example, Esco et al20 recently
compared PPG smartphone with ECG and similarly found “trivial” HRV differences (Std diff
= 0.15). Interestingly, during their supine recordings without breathing control, these authors
found the same negligible differences (Std diff = 0.15 vs 0.14). Similarly, Esco et al.20 found
almost a perfect correlation between PPG and ECG HRV recordings.
A novel inclusion in the present study is that we also compared the Polar H7 to ECG as
this heart rate sensor is currently the method being adopted by most athletes when measuring
HRV in the field.5,13,18 Indeed, all methods of HRV assessment compared to ECG were
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
statistically the same. For example, a correlation of 0.97, 0.99 or 1 are all practically identical,
and any small discrepency of value is likely the result of a small differences in the rMSSD
value (Figure 2.). These correlations are indeed very high, but due to the wide range of rMSSD
values (20-300 ms), statistical artifacts may have pushed the correlations to extremely high
values. Conversely, if all the rMSSD values had been lower (e.g. 40-60 ms), correlations may
not have been as high. The equivalency of the results obtained via all methods examined in the
present study is further supported by the “trivial” standardised differences shown.
Practical Application
Daily athlete compliance to complete HRV recordings can often be difficult. Due to the
relative noise of HRV recordings, daily recordings are required, with weekly and rolling
averages needed to gain a true representation of an athlete’s physiological state.5-7 Although all
methods we compared to gold standard ECG were acceptable, HRV recorded via PPG
smartphone technology with guided breathing showed the strongest validity compared with
ECG measures. Given the ease and practicality of use, such a data-capturing and analysis
system may be more advantageous than other methods of daily HRV assessment as daily
compliance is likely to be enhanced.
Conclusion
Measures of rMSSD derived via PPG and Polar H7 during guided and normal breathing
both shared acceptable agreement to HRV recorded via ECG. Given the superior practicality
and strong validity of HRV recorded via PPG with guided breathing, this method may be the
most sensible choice to select when assessing HRV on athletes in the field.
Conflicts of interest
Marco Altini is the owner and developer of HRV4Training.
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
15. Sacknoff D, Gleim G, Stachenfeld N, Glace B, Coplan N. Suppression of high-
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Figure 1: Simultaneous R-R interval of an individual subject during 60 seconds of recording
for photoplethysmographic (PPG), Polar chest strap (H7) and electrocardiogram (ECG).
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Figure 2: Correlation plots (±90 % confidence intervals expressed by dashed lines) and linear
regression equations for photoplethysmography (PPG) and Polar H7 heart rate sensor during
guided and normal breathing. Solid black line represents line of equivalence (r = 1.0).
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Comparison of Heart Rate Variability Recording With Smart Phone Photoplethysmographic, Polar H7 Chest Strap and
Electrocardiogram Methods” by Plews DJ et al.
International Journal of Sports Physiology and Performance
© 2017 Human Kinetics, Inc.
Table 1:
1-min
measure
TEE
as a
CV
%
90% CI
Std.
differen
ce
Qualitativ
e inference
Mean
bias
(ms)
90% CI
PPG vs. ECG,
GB
3.8
3.1; 5.0
0.06
Trivial
2.0
1.3; 2,7
H7 vs. ECG,
GB
6.1
4.9; 8.1
0.10
Trivial
-0.4
-0.6; 1.4
PPG vs. ECG,
NB
6.9
5.6; 9.3
0.11
Trivial
1.4
0.2; 2.6
H7 vs. ECG,
NB
8.6
6.9; 11.6
0.14
Trivial
-1.5
-3.3; 0.4
PPG = Photoplethysmographic; ECG = Electrocardiogram; H7 =heart rate sensor; GB =
Guided breathing; NB = Normal breathing; TEE = Technical error of estimate; CV =
Coefficient variation; CI = Confidence interval.
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... The natural logarithm of RMSSD (LnRMSSD) is taken to reduce the skewness of the RMSSD [29]. HRV measurements were obtained using photoplethysmography (PPG) through a smartphone application (app) "HRV4Training", previously validated against the Polar H7 Heart Rate Strap and Electrocardiography [30]. This app was chosen as all that is required is a smartphone with a camera and a flash. ...
... Due to remotely monitoring the athletes the research team felt that this was the best solution as the PPG technology has previously been validated against techniques which require more equipment and is readily available in the Apple and Android App Store. PPG is technique that detects changes in blood volume during a cardiac cycle by illuminating the skin and measuring changes in light absorption [30]. Sampling rate is conducted at 30 frames per second and filtered using a Butterworth band pass filter [31]. ...
... Sampling rate is conducted at 30 frames per second and filtered using a Butterworth band pass filter [31]. Instructions on how to place the finger over the camera and to hold still while recording for the minute duration were provided by the HRV4Training smartphone app upon first use [30].To avoid circadian fluctuations due to time of day and measurement position, subjects were instructed to complete measurements every morning after waking and emptying their bladder; they did so in a seated position and the total assessment time was about one-minute in length [32], [33]. Athletes were blinded to their data displayed both on the mobile and desktop application to prevent them from modulating their training protocols based on data feedback and interpretation. ...
Article
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Powerlifting competition is comprised of three barbell lifts: squat, bench press, and deadlift that are all completed in a single day and summed together, ultimately normalized to the lifter’s body weight via the Wilks Coefficient. This figure is then subsequently employed to determine the “best” athlete in that meet. During the competition preparation, powerlifters often undergo peaking protocols which include physiologically taxing overreach and low-volume, recovery-focused taper phases to collectively induce super-compensatory strength adaptations. Heart rate variability (HRV) has emerged as an easily accessible, user-friendly biomarker for autonomic nervous system-associated fatigue and readiness. Therefore, the purpose of this observational study was to investigate the potential impact of a peaking protocol on fatigue/readiness via HRV measurements and its possible relationship with competitive powerlifting performance. Daily measurements of HRV were taken, each morning, using the HRV4Trainning smartphone application by nineteen competitive powerlifters (26.16±4.56 years) from 14-days prior to a peaking protocol, throughout individual peaking phases, on meet day, and 14- days following competition. A quadratic regression was used to determine the predictability of HRV measurements and powerlifting performance. The change in HRV from competition day to baseline was found to be a significant predictor of Wilks coefficient (p=0.038, R2=0.336; mean±SE log- transformed root mean square of successive R-R intervals [lnRMSSD] = -51.98±22.23). Although extrapolations of the present study are limited by inherent subject peaking protocol variability, these data suggest HRV may nonetheless represent a viable means to modulate individual athlete training programs to promote recovery.
... This sensor enables real-time HRV analysis by providing raw ECG, HR, RRi with precision of milliseconds [50]. Besides, it is relatively low-cost (about C80) compared to other ECG devices with similar precision [48]. ...
... This high correlation between signals suggests that the wearable sensor Polar H10 proposed to record cardiac activity (ECG/heartbeats) yields similar precision than the BrainProducts LiveAmp, which can be considered a "gold standard" in research [48]. The differences in the values could be caused by the noise inherent to physiological data collection. ...
... We understand that the interested reader may desire fieldbased HRV measurements, and we therefore provide some suggestions for further reading around additional devices based on PPG beyond the Oura ring that would also enable large scale vmHRV assessment, in particular via smartphone apps using chest belts (e.g. Polar, Garmin, Suunto), smartwatches (e.g., Apple Watch, Fitbit), or smartphone apps like Elite HRV, Kenkou, and HRV4Training (Plews et al., 2017;Altini and Plews, 2021). An overview of the accuracy of popular commercial technologies that measure resting HRV can be found in Stone et al. (2021). ...
... Likewise, decreases in HRV are associated with an increased risk for new cardiac events and all-cause mortality [22]. Several studies validate photoplethysmography-based HRV compared to the gold standard of ECG, in both healthy adults, as well as athletes [23,24]. It is difficult to identify normal and abnormal values, as age and gender need to be considered and there are no current studies that establish norms. ...
Article
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Purpose of Review Wearable technology is rapidly evolving and the data that it can provide regarding an individual’s health is becoming increasingly important for clinicians to consider. The purpose of this review is to help inform health care providers of the benefits of smartwatch interrogation, with a focus on reviewing the various parameters and how to apply the data in a meaningful way. Recent Findings This review details interpretation of various parameters found commonly in newer smartwatches such as heart rate, step count, ECG, heart rate recovery (HRR), and heart rate variability (HRV), while also discussing potential pitfalls that a clinician should be aware of. Summary Smartwatch interrogation is becoming increasingly relevant as the continuous data it provides helps health care providers make more informed decisions regarding diagnosis and treatment. For this reason, we recommend health care providers familiarize themselves with the technology and integrate it into clinical practice.
... RMSSD is the primary time-domain measure to estimate parasympathetic activity, preferred over the rest of the features due to its stability and low sensitivity to breathing patterns across different measuring conditions (Shaffer, McCraty, & Zerr, 2014). Moreover, the logarithm of the RMSSD (LnRMSSD) is the gold standard to monitor training conditioning in professional athletes (Buchheit, 2014;Plews et al., 2017). ...
Thesis
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Alleviating the burden of breast cancer has become in one of the biggest challenges of our times. The advances in surgery, radiotherapy, and systemic therapy have improved the survival rates of patients with breast cancer, but have also produced a higher number of patients suffering short- and long-term side effects, with high the risk of recurrence, developing comorbidities, and death. Therapeutic exercise poses a means to address this issues; however, exercise interventions in patients with cancer are often adhered to the same therapeutic exercise guidelines. This results in one-size-fits-all exercise prescriptions for all adults, regardless their individual exercise capabilities and needs, which may lead to inadequate training adaptation. The mobile health (mHealth) paradigm has enabled the remote and individual monitoring of health through wearable sensors and smartphones. Personalizing training adaptation with an mHealth approach has already been successfully conducted in sports settings, and the literature suggests that similar strategies may translated to patients with chronic conditions such as breast cancer. However, recent works do not target the adjustment of training doses to the individual needs of the patients. This thesis presents three contributions to support the personalization of therapeutic exercise intervention in patients with breast cancer. First, ATOPE+, an mHealth system to support the remote monitoring of patients’ training load through heart rate variability (HRV), self-reported wellness, and Fitbit physical activity and sleep data. ATOPE+ also integrates a decision-support system with expert rules that automatically trigger daily exercise recommendations for patients. Second, the ATOPE+Breast dataset, an open dataset describing the continuous evolution of training load during therapeutic exercise intervention for 23 patients with breast cancer. Third, a clustering approach to assess training needs in patients with breast cancer. Data science and artificial intelligence (AI) are leveraged in this approach to better understand the different states of the patient throughout an exercise intervention, and eventually serve as a tool to make more informed decisions when prescribing an exercise dose. The potential of these contributions may lead to new research directions in the personalization of therapeutic exercise interventions in real-life scenarios, specially regarding the application of mHealth and AI to improve chronic conditions.
... Besides ECG, which is used as the gold standard to measure HRV, nowadays with the development of science and technology, many new methods have been developed to measure HRV such as photoplethysmography (PPG) through smartphone, smartwatch, ear strap, chest strap, or wrist strap devices. ese methods allow for more convenient and cost-effective HRV monitoring [12]. Studies have suggested that the PPG method is equivalent to ECG, with a high correlation coefficient [13,14]. ...
Article
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Heart rate variability (HRV) is the variation in time between each heartbeat. Increasing HRV may contribute to improving autonomic nervous system dysfunctions. Acupuncture stimulation through the vagus plexus in the ear is considered as a method that can improve HRV. In this pilot study, we examined 114 healthy volunteers at the Faculty of Traditional Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, from January to May 2020. During a 20-minute interval, participants were stimulated two times at the acupoint in the left ear with Semen seed. The heart rate and HRV values were monitored before, during, and after acupressure every 5 minutes. When we compared the experimental group with the control group, HRV significantly increased in the stage of ear-stimulated acupressure compared with the stage before and after the auricular acupressure ( p = 0.01 , p = 0.04 , p = 0.04 and p = 0.02 ) and the difference was not statistically significant compared with the phase of nonstimulated ( p = 0.15 , p = 0.28 ). The changes in other values including SDNN (standard deviation of the average NN), RMSSD (root mean square of successive RR interval differences), LF (low-frequency power), and HF (high-frequency power) in all stages were not statistically significant p = > 0.05 between groups. Based on the results, we can determine the increase in HRV when conducting auricular acupressure with stimulation at the heart acupoint on the left ear. This leads to a direction in further studies for clinical application for patients with autonomic nervous disorder.
... Plews et al showed the validity of this approach. (10,11) They used rMSSD compared with ECG. The company promotes its product as a valid tool to guide the training of elite sportspeople. ...
Research
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Objective: To present a pragmatic narrative of the measurements of my heart rate variability (HRV) gathered over a 6-week period. I propose both a meaning and a role for HRV in the clinical practice of chiropractic. Methods: A consumer-wearable was used to gather convenience recordings of HRV (cHRV) for 3 weeks leading up to a week in which chiropractic spinal correction was received, and then for 3 weeks after. Data management and analysis occurred via an electronic spreadsheet (Numbers, Apple). Results: The weekly mean of daily means for 3 weeks following 1 instance of chiropractic spinal correction showed a chance difference to the weekly mean of daily means over 3 weeks beforehand. The variance (VAR) of cHRV measurements taken over any 24h period show a high value of VAR, as do the weekly day-by-day means. Conclusion: The statistical mean of a gathering of measurements holds more clinical meaning than any single instantaneous measure. It is pointless to take a single reading of HRV in a patient at the time of consultation, whereas considering the mean value of a series of measures provides useful values that may be a marker of change associated with chiropractic spinal correction. The convenience of taking measurements of HRV by a consumer wearable must not overshadow the importance of a reliable method of gathering and interpreting the data it provides. (J Contemporary Chiropr 2022;5:85-96)
Chapter
Con el prólogo del gran Zaid Ait Malek (ganador de carreras copa del mundo CxM), el autor realiza un repaso desde dentro de las mejores carreras del mundo en trail y Ultra-Trail (Transvulcania, Zegama, Campeonatos del Mundo...) así como su paso por carreras en ruta hasta cosechar marcas personales como 1h 05´ en media maratón y 2h 22´ en maratón. Por otro lado, presenta algunos capítulos de interés para cualquier persona y/o deportista que quiera sentir en primera persona lo que es vivir carreras de más de 18 horas corriendo por lugares inverosímiles o conocer que es lo que pensamos mientras pensamos en correr, así como habla sobre las nuevas metodologías de entrenamiento científicas para corredores profesionales que ha tratado el autor en su tesis doctoral (HRV + rMSSD). Un libro autobiográfico que ofrece lecciones de vida por medio del deporte de resistencia; y una vida dedicada a ello, compaginando el deporte, la familia y la vida personal y laboral, logrando un equilibrio excelso entre ellas. "Nunca es tarde para luchar por tus sueños corriendo en busca de la felicidad". Gracias por formar parte de esta historia
Chapter
Con el prólogo del gran Zaid Ait Malek (ganador de carreras copa del mundo CxM), el autor realiza un repaso desde dentro de las mejores carreras del mundo en trail y Ultra-Trail (Transvulcania, Zegama, Campeonatos del Mundo...) así como su paso por carreras en ruta hasta cosechar marcas personales como 1h 05´ en media maratón y 2h 22´ en maratón. Por otro lado, presenta algunos capítulos de interés para cualquier persona y/o deportista que quiera sentir en primera persona lo que es vivir carreras de más de 18 horas corriendo por lugares inverosímiles o conocer que es lo que pensamos mientras pensamos en correr, así como habla sobre las nuevas metodologías de entrenamiento científicas para corredores profesionales que ha tratado el autor en su tesis doctoral (HRV + rMSSD). Un libro autobiográfico que ofrece lecciones de vida por medio del deporte de resistencia; y una vida dedicada a ello, compaginando el deporte, la familia y la vida personal y laboral, logrando un equilibrio excelso entre ellas. "Nunca es tarde para luchar por tus sueños corriendo en busca de la felicidad". Gracias por formar parte de esta historia
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The purpose of this study was to determine the agreement between a smartphone pulse finger sensor (SPFS) and electrocardiography (ECG) for determining ultra-short-term heart rate variability (HRV) in three different positions. Thirty college-aged men (n = 15) and women (n = 15) volunteered to participate in this study. Sixty second heart rate measures were simultaneously taken with the SPFS and ECG in supine, seated and standing positions. lnRMSSD was calculated from the SPFS and ECG. The lnRMSSD values were 81.5 ± 11.7 via ECG and 81.6 ± 11.3 via SPFS (p = 0.63, Cohen's d = 0.01) in the supine position, 76.5 ± 8.2 via ECG and 77.5 ± 8.2 via SPFS (p = 0.007, Cohen's d = 0.11) in the seated position, and 66.5 ± 9.2 via ECG and 67.8 ± 9.1 via SPFS (p < 0.001, Cohen's d = 0.15) in the standing positions. The SPFS showed a possibly strong correlation to the ECG in all three positions (r values from 0.98 to 0.99). In addition, the limits of agreement (CE ± 1.98 SD) were -0.13 ± 2.83 for the supine values, -0.94± 3.47 for the seated values, and -1.37 ± 3.56 for the standing values. The results of the study suggest good agreement between the SPFS and ECG for measuring lnRMSSD in supine, seated, and standing positions. Though significant differences were noted between the two methods in the seated and standing positions, the effect sizes were trivial.
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We describe an approach to support athletes at various fitness levels in their training load analysis using heart rate (HR) and heart rate variability (HRV). A smartphone-based application (HRV4Training) was developed that captures heart activity over one to five minutes using photoplethysmog-raphy (PPG) and derives HR and HRV features. HRV4Training integrated a guide for an early morning spot measurement protocol and a questionnaire to capture self-reported training activity. The smartphone application was made publicly available for interested users to quantify training effect. Here we analyze data acquired over a period of 3 weeks to 5 months, including 797 users, breaking down results by gender and age group. Our results suggest a strong relation between HR, HRV and self-reported training load independent of gender and age group. HRV changes due to training were larger than those of HR. We conclude that smartphone-based training monitoring is feasible and a can be used as a practical tool to support large populations outside controlled laboratory environments.
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Background Innovations in mobile and electronic healthcare are revolutionizing the involvement of both doctors and patients in the modern healthcare system by extending the capabilities of physiological monitoring devices. Despite significant progress within the monitoring device industry, the widespread integration of this technology into medical practice remains limited. The purpose of this review is to summarize the developments and clinical utility of smart wearable body sensors. Methods We reviewed the literature for connected device, sensor, trackers, telemonitoring, wireless technology and real time home tracking devices and their application for clinicians. Results Smart wearable sensors are effective and reliable for preventative methods in many different facets of medicine such as, cardiopulmonary, vascular, endocrine, neurological function and rehabilitation medicine. These sensors have also been shown to be accurate and useful for perioperative monitoring and rehabilitation medicine. Conclusion Although these devices have been shown to be accurate and have clinical utility, they continue to be underutilized in the healthcare industry. Incorporating smart wearable sensors into routine care of patients could augment physician-patient relationships, increase the autonomy and involvement of patients in regards to their healthcare and will provide for novel remote monitoring techniques which will revolutionize healthcare management and spending.
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Elite endurance athletes may train in a 'polarised' fashion, such that their training intensity distribution preserves autonomic balance. However, field data supporting this is limited. We examined the relationship between heart rate variability and training intensity distribution in 9 elite rowers during the 26-week build-up to the 2012 Olympic Games (2 won gold and 2 won bronze medals). Weekly-averaged log-transformed square root of the mean sum of the squared differences between R-R intervals (Ln rMSSD) were examined, with respect to changes in total training time (TTT) and training time below the first lactate threshold (<LT1), above the second lactate threshold (LT2), and between LT1 and LT2 (LT1-LT2). After substantial increases in training time in a particular training zone/load, standardized changes in Ln rMSSD were +0.13 (unclear) for TTT, +0.20 (51% chance increase) for time <LT1, -0.02 (trivial) for time LT1-LT2, and -0.20 (53% chance decrease) for time >LT2. Correlations (±90% confidence limits) for Ln rMSSD were small vs. TTT (r = 0.37 ±0.8), moderate vs. time <LT1 (r =0.43 ±0.10)), unclear vs. LT1-LT2 (r = 0.01 ±0.17)) and small vs. >LT2 (r = -0.22 ±0.5). These data provide supportive rationale for the polarised model of training, showing that training phases with increased time spent at high-intensity suppress parasympathetic activity, whilst low-intensity training preserves and increases it. As such, periodised low-intensity training may be beneficial for optimal training programming.
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Measures of resting, exercise, and recovery heart rate are receiving increasing interest for monitoring fatigue, fitness and endurance performance responses, which has direct implications for adjusting training load (1) daily during specific training blocks and (2) throughout the competitive season. However, these measures are still not widely implemented to monitor athletes' responses to training load, probably because of apparent contradictory findings in the literature. In this review I contend that most of the contradictory findings are related to methodological inconsistencies and/or misinterpretation of the data rather than to limitations of heart rate measures to accurately inform on training status. I also provide evidence that measures derived from 5-min (almost daily) recordings of resting (indices capturing beat-to-beat changes in heart rate, reflecting cardiac parasympathetic activity) and submaximal exercise (30- to 60-s average) heart rate are likely the most useful monitoring tools. For appropriate interpretation at the individual level, changes in a given measure should be interpreted by taking into account the error of measurement and the smallest important change of the measure, as well as the training context (training phase, load, and intensity distribution). The decision to use a given measure should be based upon the level of information that is required by the athlete, the marker's sensitivity to changes in training status and the practical constrains required for the measurements. However, measures of heart rate cannot inform on all aspects of wellness, fatigue, and performance, so their use in combination with daily training logs, psychometric questionnaires and non-invasive, cost-effective performance tests such as a countermovement jump may offer a complete solution to monitor training status in athletes participating in aerobic-oriented sports.
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The aim of this study was to establish the minimum number of days that HRV (i.e., the natural logarithm of square root of the mean sum of the squared differences between R-R intervals, Ln rMSSD) data should be averaged in order to achieve correspondingly equivalent results as data averaged over a 1-week period. Standardised changes in Ln rMSSD between different phases of training (normal training, functional overreaching (FOR), overall training and taper) and the correlation coefficients of percentage changes in performance vs. changes in Ln rMSSD were compared when averaging Ln rMSSD from 1 to 7 days, randomly selected within the week. Standardised Ln rMSSD changes (90% confidence limits, CL) from baseline to overload (FOR) were 0.20 ±0.28; 0.33 ±0.26; 0.49 ±0.33; 0.48 ±0.28; 0.47 ±0.26; 0.45 ±0.26 and 0.43 ±0.29 using from 1 to 7 days, respectively. Correlations (90% confidence limits (CL)) over the same time sequence and training phase were: -0.02 ±0.23; -0.07 ± 0.23; -0.17 ±0.22; -0.25 ±0.22; -0.26 ±0.22; -0.28 ±0.21 and -0.25 ±0.22 from 1 to 7 days, respectively. There were almost perfect quadratic relationships between standardised changes/r values vs. the number of days Ln rMSSD was averaged (r2 = 0.92 and 0.97, respectively) in trained triathletes during FOR. This indicates a plateau in the increase in standardised changes/r values magnitude after 3 and 4 respectively days in trained triathletes. This suggests that practitioners using HRV to monitor training adaptation should use a minimum of 3 (randomly selected) valid data points per week.
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The measurement of heart rate variability (HRV) is often considered a convenient non-invasive assessment tool for monitoring individual adaptation to training. Decreases and increases in vagal-derived indices of HRV have been suggested to indicate negative and positive adaptations, respectively, to endurance training regimens. However, much of the research in this area has involved recreational and well-trained athletes, with the small number of studies conducted in elite athletes revealing equivocal outcomes. For example, in elite athletes, studies have revealed both increases and decreases in HRV to be associated with negative adaptation. Additionally, signs of positive adaptation, such as increases in cardiorespiratory fitness, have been observed with atypical concomitant decreases in HRV. As such, practical ways by which HRV can be used to monitor training status in elites are yet to be established. This article addresses the current literature that has assessed changes in HRV in response to training loads and the likely positive and negative adaptations shown. We reveal limitations with respect to how the measurement of HRV has been interpreted to assess positive and negative adaptation to endurance training regimens and subsequent physical performance. We offer solutions to some of the methodological issues associated with using HRV as a day-to-day monitoring tool. These include the use of appropriate averaging techniques, and the use of specific HRV indices to overcome the issue of HRV saturation in elite athletes (i.e., reductions in HRV despite decreases in resting heart rate). Finally, we provide examples in Olympic and World Champion athletes showing how these indices can be practically applied to assess training status and readiness to perform in the period leading up to a pinnacle event. The paper reveals how longitudinal HRV monitoring in elites is required to understand their unique individual HRV fingerprint. For the first time, we demonstrate how increases and decreases in HRV relate to changes in fitness and freshness, respectively, in elite athletes.
Article
Resting heart rate variability (HRV) is a potentially useful marker to consider for monitoring training status in athletes. However, traditional HRV data collection methodology requires a 5-min recording period preceded by a 5-min stabilization period. This lengthy process may limit HRV monitoring in the field due to time constraints and high compliance demands of athletes. Investigation into more practical methodology for HRV data acquisitions is required. The aim of this study was to determine the time course for stabilization of ECG-derived lnRMSSD from traditional HRV recordings. Ten-minute supine ECG measures were obtained in ten male and ten female collegiate cross-country athletes. The first 5 min for each ECG was separately analysed in successive 1-min intervals as follows: minutes 0-1 (lnRMSSD0-1 ), 1-2 (lnRMSSD1-2 ), 2-3 (lnRMSSD2-3 ), 3-4 (lnRMSSD3-4 ) and 4-5 (lnRMSSD4-5 ). Each 1-min lnRMSSD segment was then sequentially compared to lnRMSSD of the 5- to 10-min ECG segment, which was considered the criterion (lnRMSSDC riterion ). There were no significant differences between each 1-min lnRMSSD segment and lnRMSSDC riterion , and the effect sizes were considered trivial (ES ranged from 0·07 to 0·12). In addition, the ICC for each 1-min segment compared to the criterion was near perfect (ICC values ranged from 0·92 to 0·97). The limits of agreement between the prerecording values and lnRMSSDC riterion ranged from ±0·28 to ±0·45 ms. These results lend support to shorter, more convenient ECG recording procedures for lnRMSSD assessment in athletes by reducing the prerecording stabilization period to 1 min. © 2015 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.