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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.
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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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... High-intensity remote physical exercise program guided daily by HRV Although all participants will daily measure their HRV, only in this intervention group each day's results will influence their exercise intensity. From the outcomes reported with smartphone photoplethysmography, explained the measurement process in the outcomes part, the root means squared differences of successive RR intervals (rMSSD) will be chosen as a reflection of vagal activity to prescribe the workout intensity [50]. In this regard, the natural logarithm of rMSSD will be calculated to make parametric statistical comparisons assuming a normal distribution. ...
... Heart rate variability and heart rate in rest measurements The assessment of heart rate in rest and HRV will be performed by the Polar H10 chest strap and by photoplethysmography (PPG) with the validated mobile app of HRV4training [50,66]. The participants will remain in the supine position for 8 min to obtain 5 min of a stable signal. ...
... On the other hand, daily heart rate variability (HRV) is measured using the HRV4Training application, a validated mobile application [50,66] that allows HRV values to be obtained by PPG (rMSSD, LF, HF, SDNN, SDNN, AVNN, pNN50 heart rate and recovery points). The morning measurement will be performed every day, in the supine decubitus position upon awakening, for 1 min. ...
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Background Breast cancer is a chronic disease with a large growth in its treatments, prognosis, improvements, side effects and rehabilitation therapies research. These advances have also highlighted the need to use physical exercise as a countermeasure to reduce the cardiotoxicity of pharmacological treatments, increase patients' strength and quality of life and improve body composition, physical condition and mental health. However, new investigations show the need for a closed exercise individualisation to produce higher physiological, physical and psychological benefits in remote exercise programs. To this end, the present study will use, in a novel way in this population, heart rate variability (HRV) as a measure for prescribing high-intensity training. Thus, the primary objective of this randomised clinical trial is to analyse the effects of a high-intensity exercise program daily guided by HRV, a preplanned moderate to high-intensity exercise intervention and a usual care group, in breast cancer patients after chemotherapy and radiotherapy treatments. Methods For this purpose, a 16-week intervention will be carried out with 90 breast cancer patients distributed in 3 groups (a control group, a moderate to high-intensity preplanned exercise group and a high-intensity exercise group guided by HRV). Both physical exercise interventions will be developed remotely and supervised including strength and cardiovascular exercises. Physiological variables, such as cardiotoxicity, biomarkers, lipid profile, glucose, heart rate and blood pressure; physical measures like cardiorespiratory capacity, strength, flexibility, agility, balance and body composition; and psychosocial variables, as health-related quality of life, fatigue, functionality, self-esteem, movement fear, physical exercise level, anxiety and depression will be measure before, after the intervention and 3 and 6 months follow up. Discussion Personalized high-intensity exercise could be a promising exercise intervention in contrast to moderate-intensity or usual care in breast cancer patients to reach higher clinical, physical and mental effects. In addition, the novelty of controlling HRV measures daily may reflect exercise effects and patients' adaptation in the preplanned exercise group and a new opportunity to adjust intensity. Moreover, findings may support the effectiveness and security of physical exercise remotely supervised, although with high-intensity exercise, to reach cardiotoxicity improvements and increase physical and psychosocial variables after breast cancer treatments. Trial registration ClinicalTrials.gov nº NCT05040867 (https://clinicaltrials.gov/ct2/show/record/NCT05040867).
... The premise of these day-to-day models is that training is modulated according to the organism's status (based on HRV), performing vigorous physical exercise when the individual is prepared (i.e., normal values in HRV) and, conversely, performing light physical exercise or resting when there is a sign of excessive fatigue or stress (i.e., alteration in HRV values). Currently, there are some apps that have been validated for HRV measurement using photoplethysmography, such as HRV4Training [23] and Welltory [24], and using a HR monitor with the placement of a Bluetooth-connected chest strap like Elite HRV [25], finding acceptable agreement compared to an electrocardiogram (ECG) as the gold standard. However, these HRV apps indicate the value and whether the subject is in normal condition or not, but they do not recommend physical exercise based on the HRV value. ...
... Furthermore, our study showed an almost perfect correlation (>0.90) in both body positions in resting conditions compared to the ECG in the results analysed with Kubios and with the app (Figures 1 and 2). The results found in the present study are very similar to previous studies with other smartphone applications using a HR monitor with the placement of a Bluetooth-connected chest strap [23][24][25]48]. However, in other studies the correlation coefficient was lower than those shown in this study [24,25,48] and one study showed the same coefficient as in the present study using a HR monitor [23]. ...
... The results found in the present study are very similar to previous studies with other smartphone applications using a HR monitor with the placement of a Bluetooth-connected chest strap [23][24][25]48]. However, in other studies the correlation coefficient was lower than those shown in this study [24,25,48] and one study showed the same coefficient as in the present study using a HR monitor [23]. Importantly, our study includes a 1 min stabilisation period to obtain valid and reliable HRV measurement data [49] in the time domain, situation which has only been used in one previous study [24]. ...
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Heart rate variability (HRV) has allowed the implementation of a methodology for daily decision making called day-to-day training, which allows data to be recorded by anyone with a smartphone. The purpose of the present work was to evaluate the validity and reliability of HRV measurements with a new mobile app (Selftraining UMH) in two resting conditions. Twenty healthy people (10 male and 10 female) were measured at rest in supine and seated positions with an electrocardiogram and an application for smartphones at the same time (Selftraining UMH) using recordings obtained through an already validated chest-worn heart rate monitor (Polar H10). The Selftraining UMH app showed no significant differences compared to an electrocardiogram, neither in supine nor in sitting position (p > 0.05) and they presented almost perfect correlation levels (r ≥ 0.99). Furthermore, no significant differences were found between ultra-short (1-min) and short (5-min) length measurements. The intraclass correlation coefficient showed excellent reliability (>0.90) and the standard error of measurement remained below 5%. The Selftraining UMH smartphone app connected via Bluetooth to the Polar H10 chest strap can be used to register daily HRV recordings in healthy sedentary people.
... The fatigue monitoring system includes B) a 'toolbox' of items that achieved consensus and rated highly (�3/5) for importance and feasibility to implement in the rugby football codes for objective (i.e. neuromuscular performance [32][33][34], cardio-autonomic [35,36]), and subjective (i.e. self-report assessment [5,37,38]) domains. ...
... Items which achieved consensus and rated highly for importance but not for feasibility to implement are represented by the arrow linking to the box to the right of the Venn diagram. Included within the graphical representation are C) considerations for testing and analysing data for the domains of these measures and metrics (see [5,32,33,[35][36][37][38][39][40] for further assessment considerations. ECG-echocardiogram, PPGphotoplethysmography, rMSSD-square root of the mean sum of the squared differences in R-R intervals, RFD-rate of force development, RSImod-reactive strength index modified, and SDNN-standard deviation of all normal-to-normal intervals. ...
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The rugby codes (i.e., rugby union, rugby league, rugby sevens [termed 'rugby']) are team-sports that impose multiple complex physical, perceptual, and technical demands on players which leads to substantial player fatigue post-match. In the post-match period, fatigue manifests through multiple domains and negatively influences recovery. There is, however, currently no definition of fatigue contextualised to the unique characteristics of rugby (e.g., locomotor and collision loads). Similarly, the methods and metrics which practitioners consider when quantifying the components of post-match fatigue and subsequent recovery are not known. The aims of this study were to develop a definition of fatigue in rugby, to determine agreement with this common definition of fatigue, and to outline which methods and metrics are considered important and feasible to implement to quantify post-match fatigue. Subject matter experts (SME) undertook a two-round online Delphi questionnaire (round one; n = 42, round two; n = 23). SME responses in round one were analysed to derive a definition of fatigue, which after discussion and agreement by the investigators, obtained 96% agreement in round two. The SME agreed that fatigue in rugby refers to a reduction in performance-related task ability which is underpinned by time-dependent negative changes within and between cognitive, neuromuscular, perceptual, physiological, emotional, and technical/tactical domains. Further, there were 33 items in the neuromuscular performance, cardio-autonomic, or self-report domains achieved consensus for importance and/or feasibility to implement. Highly rated methods and metrics included countermovement jump force/power (neuromuscular performance), heart rate variability (cardio-autonomic measures), and soreness, mood, stress, and sleep quality (self-reported assessments). A monitoring system including highly-rated fatigue monitoring objective and subjective methods and metrics in rugby is presented. Practical recommendations of objective and subjective measures, and broader considerations for testing and analysing the resulting data in relation to monitoring fatigue are provided.
... Throughout the day, HRV is continuously influenced by factors such as stress (Kim et al., 2018) and emotions (McCraty et al., 1995), body posture (Buchheit et al., 2009), exercise (Michael et al., 2017) and intake of caffeine (Koenig et al., 2013) or alcohol (Romanowicz et al., 2011). HRV measurements are therefore context-dependent and fluctuate throughout the day, but when measured in a similar resting state where confounders are minimized (e.g., during sleep or upon awakening), accurate measurement of resting HRV is possible, even with consumer-available wearables or the camera of a smartphone (Plews et al., 2017;Stone et al., 2021). ...
... The HRV was then calculated for each sleep episode. Since motion artefacts are common in real-life wearablebased measurements and can influence the accuracy of the HRV estimation, an artefact detection algorithm that has been used in prior research was used (Plews et al., 2017). This method consists of two steps. ...
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The emergence of wearable sensor technology may provide opportunities for automated measurement of psychophysiological markers of mental and physical fitness, which can be used for personalized feedback. This study explores to what extent within-subject changes in resting heart rate variability (HRV) during sleep predict the perceived mental and physical fitness of military personnel on the subsequent morning. Participants wore a Garmin wrist-worn wearable and filled in a short morning questionnaire on their perceived mental and physical fitness during a period of up to 46 days. A custom-built smartphone app was used to directly retrieve heart rate and accelerometer data from the wearable, on which open-source algorithms for sleep detection and artefact filtering were applied. A sample of 571 complete observations in 63 participants were analyzed using linear mixed models. Resting HRV during sleep was a small predictor of perceived physical fitness (marginal R² = .031), but not of mental fitness. The items on perceived mental and physical fitness were strongly correlated (r = .77). Based on the current findings, resting HRV during sleep appears to be more related to the physical component of perceived fitness than its mental component. Recommendations for future studies include improvements in the measurement of sleep and resting HRV, as well as further investigation of the potential impact of resting HRV as a buffer on stress-related outcomes.
... The predictive performance was good. Today, resting heart rate and HRV could be measured with reasonable accuracy via mobile apps [25], rings [26], wristwatches [27,28], and sensors placed under mattresses [29]. The benefit in terms of feasibility was that many of these devices can collect data automatically during the night [26,29], requiring no additional effort compared to traditional morning HR recordings. ...
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Angina pectoris is associated with adverse cardiovascular events. In this study, a Bi-directional Long Short-Term Memory (Bi-LSTM) prediction model with the Attention layer was established to explore the predictive value of the resting-state RR interval time series on the occurrence of angina pectoris. The data of this cohort study were from the Sleep Heart Health Study database, 2,977 people were included with the follow-up of 15 years. We used the RR interval time series of electrocardiogram signals in the resting state. The outcome variables were any angina events during the follow-up. We randomly divided 2,977 participants into training ( n = 2680) and testing sets ( n = 297) with a partition ratio of 9:1. The prediction model of Bi-LSTM with Attention layer was developed and the predictive performance was assessed. 1,236 had angina pectoris and 1,741 patients did not have angina pectoris during the follow-up period. The predictive performance of the Bi-LSTM model was great with the value of accuracy = 0.913, area under the curve (AUC) = 0.922, precision = 0.913 in the testing set. RR intervals may be the potential predictors of angina events. It is more and more convenient to obtain heart rate with the development of wearable devices; the Bi-LSTM prediction model established in this study is expected to provide support for the intelligent prediction of angina pectoris events.
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Thesis
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Stress has a major impact on both an individual and a societal level. Early recognition of the negative impact of stress or reduced resilience can be used in personalized interventions that enable the user to break the identified pattern through timely feedback, and thus limit the emergence of stress-related problems. The emergence of wearable sensor technology makes it possible to continuously monitor relevant behavioral and physical parameters such as sleep and heart rate variability (HRV). Sleep and HRV have been linked to stress and resilience in population studies, but knowledge on whether these relationships also apply within individuals, which is necessary for the aforementioned personalization, is lacking. This thesis introduces a cyclical conceptual model for resilience and four observational studies that test relationships between sleep, HRV and subjective resilience-related outcomes within participants using different types of data analysis at different timeframes. The relationships from the conceptual model and the related hypotheses are broadly confirmed in these studies. Participants tended to have more favorable subjective stress- and resilience-related outcomes on days with a relatively high resting HRV or long total sleep duration. Also, having a resting HRV that fluctuates relatively little from day to day was related to less stress and somatization. However, the strength of the relationships found was modest. The current findings can therefore not yet be directly implemented to initiate meaningful feedback, but they do provide starting points for future research and take a relevant step towards the possible future development of automated resilience interventions.
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Background: Cervical spine mobilizations may differentially modulate both components of the stress response, consisting of the autonomic nervous system and hypothalamic pituitary adrenal-axis, depending on whether the target location is the upper or lower cervical spine. To date, no study has investigated this. Methods: A randomized, crossover trial investigated the effects of upper versus lower cervical mobilization on both components of the stress response simultaneously. The primary outcome was salivary cortisol (sCOR) concentration. The secondary outcome was heart rate variability measured with a smartphone application. Twenty healthy males, aged 21-35, were included. Participants were randomly assigned to block-AB (upper then lower cervical mobilization, n = 10) or block-BA (lower than upper cervical mobilization, n = 10), separated by a one-week washout period. All interventions were performed in the same room (University clinic) under controlled conditions. Statistical analyses were performed with a Friedman's Two-Way ANOVA and Wilcoxon Signed Rank Test. Results: Within groups, sCOR concentration reduced thirty-minutes following lower cervical mobilization (p = 0.049). Between groups, sCOR concentration was different at thirty-minutes following the intervention (p = 0.018). Conclusion: There was a statistically significant reduction in sCOR concentration following lower cervical spine mobilization, and between-group difference, 30 min following the intervention. This indicates that mobilizations applied to separate target locations within the cervical spine can differentially modulate the stress response.
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Heart rate variability (HRV) extracted from the electrocardiogram (ECG) is an essential indicator for assessing the autonomic nervous system in clinical. Some scholars have studied the feasibility of pulse rate variability (PRV) instead of HRV. However, there is little qualitative research in different body states. In this paper, the photoplethysmography (PPG) of postauricular and finger and the ECG of fifteen subjects were synchronously collected for comparative analysis. The eleven experiments were designed according to the daily living state, including the stationary state, limb movement state, and facial movement state. The substitutability of nine variables was investigated in the time, frequency, and nonlinearity domain by Passing Bablok regression and Bland Altman analysis. The results showed that the PPG of the finger was destroyed in the limb movement state. There were six variables of postauricular PRV, which showed a positive linear relationship and good agreement (p > 0.05, ratio ≤0.2) with HRV in all experiments. Our study suggests that the postauricular PPG could retain the necessary information of the pulse signal under the limb movement state and facial movement state. Therefore, postauricular PPG could be a better substitute for HRV, daily PPG detection, and mobile health than finger PPG.
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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.
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.