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Citation: Bellenger, C.R.; Miller, D.;
Halson, S.L.; Roach, G.D.; Maclennan,
M.; Sargent, C. Evaluating the Typical
Day-to-Day Variability of WHOOP-
Derived Heart Rate Variability in
Olympic Water Polo Athletes. Sensors
2022,22, 6723. https://doi.org/
10.3390/s22186723
Academic Editor: Yvonne Tran
Received: 29 July 2022
Accepted: 3 September 2022
Published: 6 September 2022
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sensors
Article
Evaluating the Typical Day-to-Day Variability of WHOOP-Derived
Heart Rate Variability in Olympic Water Polo Athletes
Clint R. Bellenger 1, *, Dean Miller 2, Shona L. Halson 3, Gregory D. Roach 2, Michael Maclennan 4
and Charli Sargent 2
1Alliance for Research in Exercise, Nutrition and Activity (ARENA), Allied Health and Human Performance,
University of South Australia, Adelaide 5000, Australia
2The Appleton Institute for Behavioural Science, Central Queensland University, Adelaide 5034, Australia
3School of Behavioural and Health Sciences, Australian Catholic University, Brisbane 4014, Australia
4Water Polo Australia, Sydney Olympic Park, Sydney 2127, Australia
*Correspondence: clint.bellenger@unisa.edu.au; Tel.: +61-8-8302-2060
Abstract:
Heart rate (HR) and HR variability (HRV) can be used to infer readiness to perform
exercise in athletic populations. Advancements in the photoplethysmography technology of wearable
devices such as WHOOP allow for the frequent and convenient measurement of HR and HRV,
and therefore enhanced application in athletes. However, it is important that the reliability of
such technology is acceptable prior to its application in practical settings. Eleven elite male water
polo players (age 28.8
±
5.3 years [mean
±
standard deviation]; height 190.3
±
3.8 cm; body mass
95.0 ±6.9 kg
; international matches 117.9
±
92.1) collected their HR and HRV daily via a WHOOP
strap (WHOOP 3.0, CB Rank, Boston, MA, USA) over 16 weeks ahead of the 2021 Tokyo Olympic
Games. The WHOOP strap quantified HR and HRV via wrist-based photoplethysmography during
overnight sleep periods. The weekly (i.e., 7-day) coefficient of variation in lnRMSSD (lnRMSSD
CV
)
and HR (HR
CV
) was calculated as a measure of day-to-day variability in lnRMSSD and HR, and
presented as a mean of the entire recording period. The mean weekly lnRMSSD
CV
and HR
CV
over
the 16-week period was 5.4
±
0.7% (mean
±
95% confidence intervals) and 7.6
±
1.3%, respectively.
The day-to-day variability in WHOOP-derived lnRMSSD and HR is within or below the range of
day-to-day variability in alternative lnRMSSD (~3–13%) and HR (~10–11%) assessment protocols,
indicating that the assessment of HR and HRV by WHOOP does not introduce any more variability
than that which is naturally present in these variables.
Keywords:
autonomic nervous system; reliability; photoplethysmography; readiness to perform;
coefficient of variation
1. Introduction
The accurate assessment of readiness to perform exercise in athletes is important since
it facilitates subtle manipulations in training load to optimise physiological adaptation and
subsequent exercise performance [
1
]. For example, the accurate assessment of excessive
fatigue during training periods allows coaches and sport science practitioners to prioritise
recovery, thereby avoiding the undesired training states of non-functional overreaching
and overtraining [1].
Assessment of autonomic nervous system function by heart rate (HR) and HR variabil-
ity (HRV) are popular and sensitive measures of readiness to perform exercise in athletes [
2
].
Specifically, HRV is a sensitive marker of the physiological response to acute training ses-
sions [
3
,
4
], and a sensitive marker of improvements [
5
] and decrements [
6
–
8
] in exercise
performance following longitudinal training programs. Consequently, endurance training
guided exclusively by HRV assessment has been shown to effectively improve exercise
performance [9–11].
Sensors 2022,22, 6723. https://doi.org/10.3390/s22186723 https://www.mdpi.com/journal/sensors
Sensors 2022,22, 6723 2 of 7
The quantification of typical day-to-day variability in any physiological variable
(including HR and HRV) is an important process in the application of this variable for
inferring readiness to perform exercise. Day-to-day variability concerns the reproducibility
of an observed value when a measurement is repeated [
12
], and is important to quantify as
it allows sport and exercise science practitioners to separate a “true” change in a variable
of interest from the inherent “noise” in the variable. Regarding HRV assessment, typical
day-to-day variability in the natural logarithm of the root mean square of successive R
wave to R wave differences (lnRMSSD) ranges between 3 and 13% measured via coefficient
of variation [
2
,
13
–
19
]. The range in variability reported is likely attributable to assessment
nuances, such as the timing of assessment (i.e., morning waking versus nocturnal), posture
(i.e., supine versus sitting versus standing), recording device, and the training load applied
to the athletes/participants at the time of assessment (i.e., no training/minimal training
versus typical/baseline training versus overload training, etc.). Additionally, the typical
day-to-day variability in HR is ~10–11% [19,20].
Technological advancements in wearable HR monitor technology have facilitated a
number of novel recording devices for quantifying HR and HRV. WHOOP is one such
recording device, with several assessment nuances. Specifically, the WHOOP3.0 unit
quantifies HR and HRV via wrist-based photoplethysmography during overnight sleep
periods [
21
]. The validity of WHOOP3.0-derived HR and HRV, and its determination of
sleep, has been previously demonstrated [
22
,
23
], however the typical day-to-day variability
(i.e., reliability) in WHOOP3.0-derived HR and HRV has yet to be determined.
Consequently, given the novelty of WHOOP and its assessment nuances for quantifying
HR and HRV, the primary aim of this study was to determine the typical day-to-day variability
in WHOOP3.0-derived HR and HRV in Olympic water polo athletes during a period of habitual
training. The impact of training load on day-to-day variability in WHOOP3.0-derived HR and
HRV was also of interest, with the specific focus of determining day-to-day variability during
weeks corresponding to typical or baseline training load.
2. Materials and Methods
2.1. Experimental Overview and Participants
This study concerns a retrospective analysis of data collected from 11 elite water polo
athletes (age 28.8
±
5.3 years [mean
±
standard deviation]; height 190.3
±
3.8 cm; body
mass 95.0
±
6.9 kg; international matches 117.9
±
92.1) during a 16-week period of routine
training in preparation for the 2021 Tokyo Olympic Games. The sample size was fixed
given the retrospective analysis study design. Athletes provided written informed consent
for the inclusion of their de-identified data, and the University of South Australia’s Human
Research Ethics Committee approved the retrospective analysis of these de-identified data.
2.2. Data Collection and Analysis
Athletes collected their HR and HRV (i.e., RMSSD) daily via WHOOP strap (WHOOP 3.0,
CB Rank, Boston, MA, USA) use during overnight sleep periods [
21
]. WHOOP3.0-derived HR
and HRV data from the 16-week recording period were extracted into a customised Microsoft
Excel spreadsheet for analysis.
Natural logarithm transformation of RMSSD data (i.e., lnRMSSD) were performed
to reduce bias from heteroscedasticity [
24
], as has become standard practice for the lon-
gitudinal monitoring of training status by HRV [
2
]. For each 7-day period (i.e., Monday
to Sunday) during the 16-week recording period, a coefficient of variation (CV%; 7-day
standard deviation as a percentage of 7-day mean) was calculated for each athlete as a
measure of day-to-day variability in lnRMSSD (lnRMSSD
CV
) and HR (HR
CV
). A 7-day
CV% has previously been used to quantify day-to-day variability in HRV [
13
,
14
,
16
,
17
].
Additionally, given that 7-day averages of HRV have been advocated in the longitudinal
monitoring of HRV [
6
,
8
,
14
,
25
,
26
], it is intuitive that the day-to-day variability in HRV (and
HR) be quantified over a 7-day period. To account for compliance issues in data collected in
the routine training environment, a minimum of three measurements in any 7-day period
Sensors 2022,22, 6723 3 of 7
was required for valid calculation [
14
]. The weekly values of lnRMSSD
CV
and HR
CV
reflect
the range of day-to-day variability in WHOOP3.0-derived HRV and HR over the 16-week
period of habitual training. Mean lnRMSSD
CV
and mean HR
CV
were also calculated from
the weekly values to reflect the mean day-to-day variability in WHOOP3.0-derived HRV
and HR over the 16-week period of habitual training.
To contextualise the weekly values of lnRMSSD
CV
and HR
CV
by training load, daily
training load was quantified via WHOOP’s daily “Strain”, which measures “total cardio-
vascular load” on a proprietary scale of 0 to 21 [
27
]. For each week (i.e., Monday to Sunday)
during the 16-week recording period, mean daily Strain was calculated for each athlete
to reflect weekly training load. Mean weekly training load was then calculated for each
athlete over the entire 16-week recording period. Subsequently, individual weekly training
loads were calculated as a percentage of the 16-week mean training load, such that each
training week for each athlete could be presented as a percentage of mean weekly training
load during the 16-week recording period.
Finally, to determine the day-to-day variability in WHOOP3.0-derived HR and HRV
during a typical/baseline training load, and to compare this load to training loads below
and above this typical/baseline training load, percent weekly training load was categorised
to the following loads:
≤
85% (n= 22 data points); 85–95% (n= 45 data points); 95–105%
(n= 49 data points); 105–115% (n= 35 data points); >115% (n= 25 data points), and mean
weekly lnRMSSD
CV
and HR
CV
were calculated for each category. The typical/baseline
training load was considered to be 95–105% of the mean 16-week training load.
3. Results
Figure 1a depicts the various reporting approaches used in lnRMSSD
CV
. The weekly
lnRMSSD
CV
ranged between 4.2
±
1.0% (mean
±
95% confidence intervals) and 7.2
±
2.7%
across the 16-week recording period, while the mean weekly lnRMSSD
CV
was 5.4
±
0.7%. The
mean lnRMSSD
CV
ranged between 5.0
±
0.6% and 6.0
±
0.9% when categorised by percent
training load, and was 5.0 ±0.6% during weekly training loads of 95–105% of mean load.
Figure 1.
Weekly, 16-week mean,
≤
85% training load mean,
85–95% training
load mean,
95–105% training
load mean, 105–115% training load mean and >115% training load mean for
(
a
) lnRMSSD
CV
and (
b
) HR
CV
. Data are mean
±
95% confidence interval. n= 11. HR, heart
rate; HR
CV
, coefficient of variation in heart rate; lnRMSSD, natural logarithm of the root mean square
of successive RR interval differences; lnRMSSD
CV
, coefficient of variation in natural logarithm of the
root mean square of successive RR interval differences; TL, training load.
Sensors 2022,22, 6723 4 of 7
Figure 1b depicts the various reporting approaches in HR
CV
. The weekly HR
CV
ranged
between 5.0
±
1.9% and 9.5
±
4.5% across the 16-week recording period, while the mean
weekly HR
CV
was 7.6
±
1.3%. The mean HR
CV
ranged between 6.7
±
0.5% and 9.1
±
0.9%
when categorised by percent training load, and was 6.7
±
0.5% during weekly training
loads of 95–105% of mean load.
4. Discussion
The primary aim of this study was to determine the typical day-to-day variability
in WHOOP3.0-derived HR and HRV in Olympic water polo athletes during a period of
habitual training. Additionally, the impact of training load on day-to-day variability in
WHOOP3.0-derived HR and HRV was determined, with a specific focus on determining
day-to-day variability during weeks corresponding to typical/baseline training load. The
primary finding was that the typical day-to-day variability in WHOOP3.0-derived HR
and HRV is comparable to that of other recording devices and protocols reported in the
scientific literature [
2
,
13
–
20
], regardless of training load. Consequently, the assessment of
HR and HRV by WHOOP3.0 does not introduce any more variability than that which is
naturally present in these variables.
The present study demonstrated typical day-to-day variability in WHOOP3.0-derived
lnRMSSD of 5.4
±
0.7%, ranging between 4.2
±
1.0% and 7.2
±
2.7% across individual
weeks of the 16-week recording period (Figure 1a). Additionally, the day-to-day variabil-
ity ranged from 5.0
±
0.6% to 6.0
±
0.9% when categorised for weekly training load as
percentages of mean 16-week training load, and was 5.0
±
0.6% during weekly training
loads of 95–105% of mean load (Figure 1a). The typical day-to-day variability in lnRMSSD
ranges between 3 and 13% [
2
,
13
–
19
], and this range is likely explained by subtle differ-
ences in assessment protocols, namely, the timing of assessment (i.e., morning waking
versus nocturnal), posture (i.e., supine versus sitting versus standing), recording device and
training load exposure at the time of assessment (i.e., no training/minimal training versus
typical/baseline training versus overload training). Since the WHOOP3.0-derived HRV
was obtained during overnight sleep periods in the present study, specific regard should
also be given to the typical day-to-day variability in overnight sleep-derived lnRMSSD,
where Costa et al. [
18
] demonstrated 4–6% variability using a Firstbeat Bodyguard electro-
cardiogram device. Given that the level of day-to-day variability in WHOOP3.0-derived
lnRMSSD is comparable to that of other recording devices and assessment protocols, the
present study indicates that the assessment of HRV by WHOOP3.0 does not introduce
variability above that which exists organically. This finding, along with the acceptable level
of validity in WHOOP3.0-derived HRV previously demonstrated [
22
], indicates that sport
and exercise science practitioners may confidently utilise WHOOP3.0 to record HRV in
practical settings.
The practical applicability of WHOOP3.0-derived HRV is further supported by contex-
tualisation of the signal-to-noise ratio in the day-to-day variability in WHOOP3.0-derived
lnRMSSD. Specifically, studies of physiological responses to acute training sessions indi-
cate 10–20% changes in lnRMSSD [
3
,
4
], while a systematic review by Bellenger et al. [
5
]
indicated 7–45% changes in lnRMSSD following chronic training interventions facilitating
improved exercise performance. Together, the results of these studies indicate that utilising
WHOOP3.0-derived HRV in practical settings would not limit the identification of a true
physiological change in HRV, since the signal in HRV (i.e., the physiological change) is
greater than the noise (i.e., the inherent day-to-day variability) that would be introduced
by utilising WHOOP3.0-derived HRV.
Similarly to WHOOP3.0-derived lnRMSSD, the day-to-day variability in WHOOP3.0-
derived HR (i.e., 16-week mean = 7.6
±
1.3%; range = 5.0
±
1.9% to 9.5
±
4.5% across
individual weeks of the 16-week recording period; range = 6.7
±
0.5% to 9.1
±
0.9% when
categorised for training load; Figure 1b) is comparable to that reported in the scientific
literature (~10–11%) [
19
,
20
]. Consequently, the variability in measures of HR introduced by
Sensors 2022,22, 6723 5 of 7
WHOOP3.0 derivation is acceptable, and thus WHOOP3.0 may also be confidently utilised
to record HR in practical settings.
While the typical day-to-day variability in WHOOP3.0-derived HR and HRV was
quantified in a specific group of Olympic-level water polo players, the authors are confi-
dent that the results are generalisable to wider athletic populations. Specifically, the 3 to
13% range in day-to-day variability demonstrated in alternative HRV assessment proto-
cols was captured over a range of team [
2
,
13
,
15
–
19
] and endurance [
2
,
14
] sports, with no
evidence to suggest that day-to-day variability was impacted by sport.
The present study quantifies the typical day-to-day variability in WHOOP-derived HR
and HRV using the manufacturer’s WHOOP 3.0 unit. Given that the proprietary hardware,
algorithms and analytical methods of wearable technology are constantly evolving, future
research should quantify the typical day-to-day variability of HR and HRV measured by
contemporary WHOOP straps.
While WHOOP3.0-derived HR and HRV have been demonstrated to be statistically
valid [
22
] and reliable (by the present study), it does need to be acknowledged that the
physiological validity of using WHOOP-derived HR and HRV for inferring readiness to
perform exercise remains unknown. Consequently, future research should determine the
sensitivity of WHOOP-derived HR and HRV to acute and chronic changes in training load
and exercise performance, and whether WHOOP-derived HRV may be used to individually
guide training as has been shown using alternative assessment protocols and devices [
9
–
11
].
By means of its automated assessment of HR and HRV (i.e., by photoplethysmography
during overnight sleep), WHOOP allows for frequent and convenient measurement of
HR and HRV, and therefore enhanced application in athletes. Consequently, practitioners
may be inclined to utilise WHOOP-derived HR and HRV in place of an existing recording
device, but should do so with caution. Given the nuances in HRV assessment protocols (i.e.,
timing of assessment, posture, recording device, etc.), differences in absolute values of HRV
and typical day-to-day variability in HRV are likely to exist between assessment protocols,
and thus comparisons of longitudinal day-to-day changes in HRV between assessment
protocols should be interpreted with appropriate caution.
In an attempt to evaluate the impact of training load on the day-to-day variability in
WHOOP3.0-derived HR and HRV, the present study utilised WHOOP’s daily “Strain” metric
as a measure of training load. However, the validity of this metric is presently unknown, and
thus the training load categorisation analysis of day-to-day variability in WHOOP3.0-derived
HR and HRV should be interpreted with appropriate caution. Future research should evaluate
the validity of WHOOP Strain.
5. Conclusions
WHOOP3.0-derived HR and HRV demonstrate typical day-to-day variability of ~7.5%
and ~5.5%, respectively. The contextualisation of this variability via the day-to-day variabil-
ity in alternative HR and HRV assessment protocols and the signal-to-noise ratio indicates
that the level of variability in WHOOP3.0-derived HR and HRV is acceptable for the infer-
ence of readiness to perform exercise in water polo players, and wider athletic populations.
Given its capacity to measure HR and HRV via photoplethysmography during overnight
sleep periods, WHOOP offers a convenient method of HR and HRV assessment, and its
acceptable level of validity [
22
] and reliability allow it to be confidently utilised by sport
and exercise science practitioners to record HR and HRV in practical settings.
Author Contributions:
Conceptualisation, C.R.B.; methodology, C.R.B., D.M., S.L.H., G.D.R., M.M.
and C.S.; formal analysis, C.R.B.; writing—original draft preparation, C.R.B.; writing—review and
editing, C.R.B., D.M., S.L.H., G.D.R., M.M. and C.S.; visualisation, C.R.B.; project administration,
C.R.B. and M.M.; funding acquisition, M.M. All authors have read and agreed to the published
version of the manuscript.
Sensors 2022,22, 6723 6 of 7
Funding:
This study was supported by the Australian Institute of Sport. The supporters had no
input in the design of the study and interpretation of results. The results of the current study do not
constitute endorsement of the product by the Australian Institute of Sport, authors or the journal.
Institutional Review Board Statement:
The study was conducted according to the guidelines of the
Declaration of Helsinki, and approved by the Human Research Ethics Committee of the University of
South Australia (Application ID: 204858; approved on 11 July 2022).
Informed Consent Statement:
Written informed consent was obtained from all subjects involved in
the study.
Data Availability Statement:
The datasets generated from the current study are available from the
corresponding author on reasonable request.
Conflicts of Interest:
Gregory D. Roach, Charli Sargent and Dean Miller are members of a research
group at Central Queensland University (i.e., The Sleep Lab @ Appleton Institute, Wayville, Australia)
that receives support for research (i.e., funding, equipment) from WHOOP Inc. (CB Rank, Boston,
MA, USA) However, WHOOP was not involved in the design, conduct or reporting of this study.
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