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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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... Prior to each training session, participants rested for 1 minute in a seated position in a quiet room before starting the recording to reduce the influence of movement on HRV. 23 We then measured resting HR and HRV for 1 minute using the root mean square of successive differences between normal heart beats (RMSSD) with the HRV4training app (https:///www.hrv4training.com) connected to the chest strap (Polar H10, Polar Electro Oy), following the instructions of Plews et al. 23 In addition, jump height was measured during countermovement Jump (OptoGait, Microgate Srl). ...
... 23 We then measured resting HR and HRV for 1 minute using the root mean square of successive differences between normal heart beats (RMSSD) with the HRV4training app (https:///www.hrv4training.com) connected to the chest strap (Polar H10, Polar Electro Oy), following the instructions of Plews et al. 23 In addition, jump height was measured during countermovement Jump (OptoGait, Microgate Srl). 24 At the end of each training session, we collected mixed venous blood from the earlobe and measured BLC, and then the countermovement jump was performed. ...
Article
Purpose : This study investigated the relationship between training-load (TL) metrics and the acute performance decrement (APD) measured immediately after 4 training sessions performed by well-trained runners. Methods : On a treadmill, 12 well-trained runners (10 men and 2 women) performed an incremental test, a baseline time-to-exhaustion (TTE) test at maximal aerobic speed, and 4 randomized training sessions followed by a TTE test to measure APD. The training sessions were matched for external load (60 arbitrary units) but differed in the time spent in the 3 intensity domains. The TL metrics used were based on training impulse, heart-rate variability, ratings of perceived exertion, and the NASA Task Load Index (NASA-TLX) rating scale. Results : TTE was significantly shorter after all the training sessions compared with baseline ( P < .001). While APD was higher (+16%, P = .035) for long-duration high-intensity training (HIT long ) compared with low-intensity training (LIT), most TL metrics showed higher values ( P < .001) in LIT than in HIT long . Conversely, NASA-TLX values were higher ( P < .001) in HIT long than in LIT and were significantly associated with APD values ( P < .001, β = 0.54). Physiological parameters showed that less time was spent above 90% of O 2 max during LIT compared with the other training sessions ( P < .01), while average respiratory frequency and mean respiratory exchange ratio were higher during HIT long compared to LIT ( P < .01). Conclusion : APD was observed after the 4 running training sessions, and it was not associated with most of the TL metrics. Only NASA-TLX was associated with APD, suggesting that this TL metric could be leveraged for training monitoring.
... HRV was assessed through photoplethysmography administered through smartphone technology on the participant's device. Photoplethysmography uses illumination through one's skin from the LED light shown through the smartphone and captured by the nearby camera, resulting in a slow and direct current measurement (Plews et al., 2017). The validity of using smartphone photoplethysmography as a method of HRV data collection is largely comparable to heart rate monitoring using a chest strap and an electrocardiogram (ECG), with no significant differences in timedomain or frequency-domain indices (Heathers, 2013;Peng et al., 2015;Perrotta et al., 2017;Selvaraj et al., 2008). ...
... 10 RAVER, GEVIRTZ, MCCLAIN, ROTH, AND PEREZ as HRV data were collected using the Camera HRV smartphone application due to COVID-19 restrictions, there is a margin of error implicit in the uncontrolled effects of remote data collection versus in-person sensor equipment of HRV metrics. However, previous literature showed that noncontact, camera-based measurements of HRV (Pai et al., 2021), and Camera HRV in particular that uses a scientifically validated camera-based technology (Plews et al., 2017), are practical and robust. Additionally, HRV indices may be impacted by cardiovascular conditions and medication use, which were not objectively measured here but may be in future investigations. ...
Article
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The social and nonsocial cognitive deficits found in schizophrenia (SZ) and in individuals at risk for the illness are relatively treatment-resistant and yet are the best predictors of real-world functioning. As such, pathophysiological markers that have been shown to be remediable, and associated with cognitive and functional targets, may serve as an indirect approach to improved outcomes. Heart rate variability (HRV), a measure of autonomic adaptability, is suppressed in individuals with SZ and predictive of psychosocial function. Here, we aimed to clarify the relationships between autonomic adaptability, social cognition, and psychosocial dysfunction in individuals who may be at risk for psychosis. HRV was measured before and after a stressor task to assess baseline and recovery, and social cognition was assessed with affective valence recognition in 25 at-risk individuals who report distress to psychotic-like experiences (PLE) and 30 healthy comparisons. PLE demonstrated blunted baseline HRV, worse performance for neutral, but not positive or negative, affective faces, as well as role and social dysfunction. In PLE, significant relationships were found between negative valence accuracy and baseline HRV and role function, as well as between recovery HRV and social and role function. Group classification revealed 70.9% accuracy when using recovery HRV and role function. Results are the first to demonstrate that aberrant autonomic arousal is predictive of maladaptive social cognitive and functional behaviors in individuals who may be at risk for psychosis. Early identification of those at risk may mitigate functional decline.
... Data from the ECG system and prototype (in-ear, fingertip-based) sensors were processed by a single script that extracted the R-R intervals (ECG data) and interbeat intervals (PPG data) from the specified time window and processed them through a series of checks and corrections. Various publications [50][51][52][53] were reviewed to understand different methods used for detecting outliers in the interval series data to complete the ...
... Data from the ECG system and prototype (in-ear, fingertip-based) sensors were processed by a single script that extracted the R-R intervals (ECG data) and interbeat intervals (PPG data) from the specified time window and processed them through a series of checks and corrections. Various publications [50][51][52][53] were reviewed to understand different methods used for detecting outliers in the interval series data to complete the correction stage. Additionally, the in-ear dataset contained a signal quality score for each RR interval logged. ...
Article
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This paper presents a study undertaken to evaluate the sensor systems that were shortlisted to be used in the development of a portable respiratory-gated transcutaneous auricular vagus nerve stimulation (taVNS) system. To date, all published studies assessing respiratory-gated taVNS have been performed in controlled laboratory environments. This limitation arises from the reliance on non-portable sensing equipment, which poses significant logistical challenges. Therefore, we recognised a need to develop a portable sensor system for future research, enabling participants to perform respiratory-gated stimulation conveniently from their homes. This study aimed to measure the accuracy of an in-ear and a fingertip-based photoplethysmography (PPG) sensor in measuring cardiac vagal tone relevant heart rate variability (HRV) parameters of root mean square of successive R-R interval differences (RMSSDs) and the high-frequency (HF) component of HRV. Thirty healthy participants wore the prototype sensor equipment and the gold standard electrocardiogram (ECG) equipment to record beat-to-beat intervals simultaneously during 10 min of normal breathing and 10 min of deep slow breathing (DSB). Additionally, a stretch sensor was evaluated to measure its accuracy in detecting exhalation when compared to the gold standard sensor. We used Bland–Altman analysis to establish the agreement between the prototypes and the ECG system. Intraclass correlation coefficients (ICCs) were calculated to establish consistency between the prototypes and the ECG system. For the stretch sensor, the true positive rate (TPR), false positive rate (FPR), and false negative rate (FNR) were calculated. Results indicate that while ICC values were generally good to excellent, only the fingertip-based sensor had an acceptable level of agreement in measuring RMSSDs during both breathing phases. Only the fingertip-based sensor had an acceptable level of agreement during normal breathing in measuring HF-HRV. The study highlights that a high correlation between sensors does not necessarily translate into a high level of agreement. In the case of the stretch sensor, it had an acceptable level of accuracy with a mean TPR of 85% during normal breathing and 95% during DSB. The results show that the fingertip-based sensor and the stretch sensor had acceptable levels of accuracy for use in the development of the respiratory-gated taVNS system.
... which is validated for this purpose. [19] Participants placed their index finger on the smartphone camera lens for 5 minutes. Alcohol swabs were used to sanitize the camera lens and participants' fingers before measurement. ...
Article
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The COVID-19 pandemic has raised critical concerns about its long-term effects on cardiovascular health, particularly concerning autonomic nervous system (ANS) function. Little is known about the differences in ANS function between physically active and inactive individuals exposed and nonexposed to COVID-19. This study aimed to compare the impact of self-reported physical activity on ANS function using heart rate variability (HRV) metrics in individuals exposed and nonexposed to COVID-19. In total 142 participants from the Riyadh region, Saudi Arabia, were divided into exposed (n = 70) and nonexposed (n = 71) groups based on their COVID-19 exposure. HRV was assessed using photoplethysmography and analyzed using time and frequency domains. Physical activity was assessed using simple yes or no question, and duration was categorized into less than 30 minutes, 30 minutes, and more than 30 minutes. Physically active participants generally exhibited higher HRV metrics, suggesting better autonomic function, although this effect was more pronounced in the nonexposed group. Interestingly, the low-frequency to high-frequency ratio was the only HRV metric that showed a statistically significant difference between active and inactive participants in the nonexposed group ( P = .04). There were no significant differences in HRV metrics based on the duration of reported physical activity in either the COVID-19-exposed or nonexposed groups. The study underscored the importance of monitoring cardiovascular health in post-COVID-19 populations and suggested that while physical activity is beneficial, the virus may blunted its benefits. Further research is needed to explore the long-term implications of COVID-19 on autonomic function and the potential for physical activity to mitigate these effects.
... In an inpatient setting, HRV can be calculated while at rest or during sleep utilizing ECG or telemetry (Montano et al., 2017). In an outpatient setting, patients with commercially available wearable devices utilizing photoplethysmography (PPG), a method of pulse rate variability measurement comparable to HRV measured by ECG during resting conditions (Plews et al., 2017), can bring their data into the clinic for physician evaluation. In both inpatient and outpatient medical settings, it is important for physicians to be familiar with the HRV data that is presented and understand how to interpret it properly. ...
Article
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HRV is clinically considered to be a surrogate measure of the asymmetrical interplay of the sympathetic and parasympathetic nervous system. While HRV has become an increasingly measured variable through commercially-available wearable devices, HRV is not routinely monitored or utilized in healthcare settings at this time. The purpose of this narrative review is to discuss and evaluate the current research and potential future applications of HRV in several medical specialties, including critical care, cardiology, pulmonology, nephrology, gastroenterology, endocrinology, infectious disease, hematology and oncology, neurology and rehabilitation, sports medicine, surgery and anesthesiology, rheumatology and chronic pain, obstetrics and gynecology, pediatrics, and psychiatry/psychology. A narrative literature review was conducted with search terms including HRV and relevant terminology to the medical specialty in question. While HRV has demonstrated promise for some diagnoses as a non-invasive, easy to use, and cost-effective metric for early disease detection, prognosis and mortality prediction, disease monitoring, and biofeedback therapy, several issues plague the current literature. Substantial heterogeneity exists in the current HRV literature which limits its applicability in clinical practice. However, applications of HRV in psychiatry, critical care, and in specific chronic diseases demonstrate sufficient evidence to warrant clinical application regardless of the surmountable research issues. More data is needed to understand the exact impact of standardizing HRV monitoring and treatment protocols on patient outcomes in each of the clinical contexts discussed in this paper.
... The interplay between SNS and PNS activity is dynamic; for instance, during stress, a shift from PNS dominance to SNS activation occurs, affecting HRV patterns [4]. Technological developments in the last decade have allowed us to measure HRV using sports watches or smartphones that operate according to the photoplethysmography (PPG) principle [5][6][7][8]. HRV is widely used in sports science and is a valuable tool for monitoring training responses, detecting signs of overtraining, and evaluating recovery processes [9]. The athlete's physiological response to exercise is influenced by a training stimulus, and a suitable training load promotes optimum performance improvements [10,11]. ...
Article
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This study aimed to examine the influence of training load, performance, sleep, and menstrual parameters on heart rate variability (HRV) and to evaluate its potential as a predictor of sports performance. A four-year longitudinal case study was conducted on a female elite kayak athlete, involving daily monitoring of HRV, sleep quality and duration, menstrual cycles, illnesses, and acute training loads. Over this period, 1394 measurements were taken each morning immediately after waking up and before getting up. The results of four competitive seasons were analyzed using a performance index and were statistically processed with a linear mixed model. The analysis revealed a statistically significant positive association between rMSSD and both sleep quality (p < 0.001) and the follicular phase of the menstrual cycle (p = 0.003). In contrast, the training load (p = 0.94), sleep duration (p = 0.27), and illness (p > 0.05) showed no statistically significant effect on rMSSD. Additionally, neither rMSSD (p = 0.82) nor its trend (p = 0.70) were significant predictors of the performance index. Despite the lack of a statistically significant correlation between HRV and sports performance, the findings suggest that the pre-competition decrease in HRV observed in this case study may reflect anticipatory physiological changes, potentially linked to increased sympathetic activation, as suggested in the existing literature.
... It is also one of the most accessible and affordable parameters for the public who are just beginning to exercise. Many sensors are available to measure heart rate, ranging from chest straps placed on the diaphragm under the chest, upper arm straps, and smartwatch-integrated sensors to those integrated with specific exercise equipment such as treadmills and stationary bikes (Gordon et al., 2016;Plews et al., 2017). Heart rate sensors have also been widely used in research, including studies on students (Martinez-López et al., 2018). ...
Article
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Introduction: Long-term exercise induces physiological adaptations that differ between aerobic and anaerobic training, affecting heart rate responses requiring further exploration. Objective: This Study compares the heart rate responses of individuals who specialized in aerobic or anaerobic training when subjected to the same physical activity trial. Method: This study conducted a stationary bike test to investigate heart rate fluctuations in 16 anaerobic (AN) and 25 aerobic (AE) male exercise enthusiasts who regularly exercise. The stationary bike test started at 50W, increased gradually by 30W until reaching 230W (peak), and then gradually decreased to 50W while the heart rate was recorded every 5 seconds. Result: The results indicated no significant difference (p>0.05) in heart rate between the two groups at low intensity. However, after reaching 200W (moderate intensity), the heart rate of the AN group appeared to be lower than that of the AE group. Additionally, the heart rate during the descending phase after reaching 230W was significantly higher (p<0.05) compared to the ascending phase before the peak, and the heart rate did not return to baseline, even during the resting period after the stationary bike test. Conclusion: Anaerobic-trained individuals maintain a lower heart rate than aerobic-trained individuals at moderate or higher intensity. The post-peak heart rate is always higher than the pre-peak heart rate and does not immediately return to baseline.
... Although technology appears to hold multiple potential communication and intervention delivery solutions and opportunities for athletes and practitioners, proceeding with an agnostic view may be best suited to the rapidly evolving digital landscape. How, when and where an individual's physiological data can now be captured, interpreted and returned is no longer limited to lab-based settings (Plews et al., 2017;Falter et al., 2019;Miller et al, 2021). Instead, mobile phones, smart watches and biometric rings (e.g. ...
Thesis
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Since the early 1900’s sports nutrition research has focused almost exclusively on increasing our understanding of nutrition’s impact on metabolism, physiology and physical performance, facilitating the development of more robust fuelling, recover and performance strategies. The last 20 years in particular has played host to the most rapid period of growth and knowledge creation in the history of the discipline. However, the translation of this knowledge into practice, and ultimately athlete behaviours, remains slow. In parallel to the more recent rise of sports nutrition, the popularity and uptake of smartphones and mobile apps has exploded globally and been ubiquitously accepted as the norm. The need to understand and integrate these advancements in technology to support and enhance service provision, as well as accelerate the translation of knowledge to practice, in sports nutrition has been cited for its potential and is in need of development. Through a pragmatic paradigm and utilising innovation research methodologies, as well as behaviour change theory and design thinking, this thesis aimed to develop and pilot a mobile app digital intervention that caters to the needs of both the athlete and the practitioner in applied sports nutrition. Study 1 explored, via a sequential mixed methods approach; how social media mobile apps are being used by sports nutritionists (n = 44) as part of their service provision to athletes, as well as capture their experiences and opinions of its use. Survey responses were reported as descriptive statistics. Findings indicated social media was used by 89% of sports nutritionists to support practice, of which 97% perceived its use to be beneficial. Interviews were thematically analysed and the findings demonstrated that, despite sports nutritionists embracing digital technology as an extension of practice, they reported both a lack of time and digital intervention training as challenges to using these technology tools in practice. Study 2 explored, via a qualitative approach, athletes’ (n = 41) experiences and opinions of communication strategies in applied sports nutrition, as well as their suggestions for future mobile app supportive solutions. Data was analysed using reflexive thematic analysis. Athletes were dissatisfied with the levels of personalisation in the nutrition support they receive. Limited practitioner contact time was a contributing factor to this problem. Athletes cited the usefulness of online remote nutrition support and reported a desire for more personalised technology that can tailor support to their individual needs. Study 3 explored the design and pilot of an industry specific mobile app intervention implementing behaviour change theory and design thinking methodologies. A 5-step Behavioural Design Thinking approach was utilised. This included a 14-day pilot testing period and with national level athletes (n = 26). Empathy mapping in step 1 identified a fundamental mismatch between what practitioners report they are currently capable of delivering and what athletes describe they need. The behaviour change requirements and solution designed from steps 2 to 4 was a digital behaviour change intervention (DBCI), that enables athletes to create personalised and periodised daily nutrition plans. Pilot-testing, conducted at step 5, revealed participants planned 78.80% (SD = 29.24) of their scheduled training sessions in the app. The app was utilised on 85.96% (SD = 28.26) of the participants planned training days and 62.73% (SD = 32.53) of their non-training days. The average number of engagement sessions per day was 2.53 (SD = 1.84). The mean amount of time each participant spent on the app per day was 3.68 minutes (SD = 2.54). This thesis provides a proof of concept that the piloted industry specific mobile app DBCI has the potential to address the problems of time and training being experienced by sports nutritionists, whilst also delivering on the personalisation expectations of athletes with a scalable and autonomy supportive solution. Future research should focus on understanding the longer-term trends in the effectiveness, usage and uptake of the developed mobile app DBCI on a larger scale and across both male and female populations. This will facilitate a more representative picture of the longer-term impact of the technology on the nutrition planning behaviours of athletes.
Article
Background Autonomic nervous system activation plays an important role in the pathophysiology of atrial fibrillation. It can be determined using heart rate variability. We aimed to evaluate the feasibility of using photoplethysmography recordings for the assessment of the ultra-short-term heart rate variability. Methods TeleCheck-AF is a structured mobile health approach, comprising the on-demand use of a photoplethysmography-based smartphone application prior to a scheduled teleconsultation to ensure comprehensive remote atrial fibrillation management. Participants with at least one photoplethysmography recording in sinus rhythm were included to assess resting heart rate, RMSSD, patient compliance and data consistency. Results In total, 855 patients [39.4% women] with 13,465 recordings were included. Patient compliance was 95.2% (IQR 76.2%-114.3%). Median heart rate per patient was 66.5 (IQR 60.0-74.0) bpm, median RMSSD per patient was 40 (IQR 33-50) ms and median recording consistency was ±5.2 (IQR 3.8-7.0) bpm and ±14.8 (IQR 9.3-21.1) ms, respectively. RMSSD was lower in men than women, in patients with CHA2D2-VA-Score ≥2, with a history of atrial fibrillation, and following ablation of atrial fibrillation. Older age and lower body mass index were associated with higher RMSSD. Conclusion The ultra-short-term heart rate variability can be determined in one-minute photoplethysmography recordings with high user compliance and high inter-recording consistency within a structured mobile health atrial fibrillation management approach. The strategy used in this study may also be feasible for management of other conditions in which the heart rate variability plays a role for diagnostics and therapy.
Article
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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.
Article
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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.
Article
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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.
Article
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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.
Article
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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.
Article
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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.
Conference Paper
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 photoplethysmography (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.
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.