Key words
cardiac autonomic function - training load - performance - wearable technology - athlete monitoring - time series analysis
Introduction
Athletes today have access to previously unimaginable amounts and types of data and
metrics to inform their training, competition, and recovery practices. In this
rapidly evolving landscape of athlete analytics, heart rate variability (HRV)
remains one of the most broadly utilized tools, particularly for endurance athletes.
Further, the applications and the nuanced interpretation of HRV data continue to
evolve and inform athlete practices. While the opportunities for enhancing athletic
practices are abundant, so are the potential pitfalls. This review provides a brief
overview of HRV in the context of endurance athletes, addresses technical
considerations, factors affecting daily HRV, the implementation of HRV-guided
training, and alternative and future uses of HRV for athletes.
Definition and physiological basis of HRV
By definition, HRV analysis is done by measuring the beat-by-beat fluctuations of
the cardiac rhythm, and assessing the patterns of observed variability over a
given time period. This is done through measurement of either R-R intervals (as
seen on ECG) or interbeat-intervals of pulse rate. Rather than smoothing the
data to obtain the average heart rate (HR), HRV analysis assesses the
variability within the sequence of heartbeats. Beat-to-beat variability is a
physiological phenomenon that humans have been aware of for hundreds of years,
which has been described and explored widely since the early 1970s [1]. The physiological basis of HRV was initially
understood as a reflection of an individual’s autonomic nervous system
(ANS), specifically the sympathetic nervous system (SNS) versus parasympathetic
nervous system (PNS), with greater HRV being attributed to increased PNS
activity [2]. Crudely stated, SNS and PNS activity
embody, respectively, the “flight or fight” and “rest
and digest” responses.
A more nuanced understanding of HRV has emerged, as a phenomenon reflective of
complex, non-linear integration of multiple physiological processes including
both the intrinsic cardiac nervous system and external regulators such as
respiration and blood pressure [3]
[4]. Modulation of HR is known to be affected in a
nonlinear manner by the baroreceptor reflex, which responds to pressure
modulations with changes in neural output, classically described as a negative
feedback loop, with increased blood pressure leading to rapidly decreasing HR
[5]
[6].
Respiratory sinus arrhythmia (RSA) is a well-characterized physiological
phenomenon, wherein the phases of ventilation and HR interact, with HR slowing
during expiration and accelerating with inspiration [7]. Slower respiration with larger tidal volumes increases RSA, and
the use of prescribed or “paced” breathing yields different
values than measurements taken without paced breathing [8] and is considered a common source of discrepant values in the
assessment of HRV [9]. During exercise, coupling
(or entrainment) of repetitive movement patterns, such as step rate or cycling
cadence, with respiratory rate may occur, complicating the interpretation of HRV
due to the effect of respiratory modulating effects [10].
While HRV was previously used extensively in a clinical setting to predict
morbidity and mortality [11], a 2003 review paper
noted the inconsistent findings and relative dearth of what is known about HRV
in athletes [12]. Since that time, HRV has been
explored in depth as a means of monitoring training stress and adaptation [13]
[14], detecting
overtraining [15]
[16], as a biofeedback tool [17], and to
assess recovery from concussion [18], among other
applications. Overall, HRV has been established as a reliable tool that can be
useful in monitoring athletes and informing their training practices.
HRV response to exercise
During exercise, cardiac PNS activity decreases (reaching a nadir at
50–60% of VO2max) and SNS activity increases, driving
further increases in HR [19]. In contrast with the
original model proposing that complete PNS withdrawal occurs during exercise, a
more nuanced understanding suggests a process of reciprocal antagonism between
branches, with diminished PNS influences retained [20]
[21]. While SNS activity
predominates at higher intensities, there is evidence to support the measurable
effects of PNS activity even at high intensities of exercise [22].
HRV monitoring for athletes
The use of HRV monitoring for endurance athletes has been studied as far back as
the 1990s [23]. Endurance athletes generally
exhibit greater resting HRV than healthy controls [24]
[25]. This has been observed across
a wide range of endurance sports, including rowers, cyclists, Nordic skiers,
distance runners [12]
[24]. Gradual increases in average HRV appear to correspond with
improved aerobic fitness. In a study of distance runners, changes in HRV were
correlated with both changes in aerobic capacity and 10-km time [26]. A cross-sectional study of male masters
runners also found significant correlations between resting HRV and 10-km race
time [27]. In recent years, use of HRV has become
more common and widespread, driven by the ease, availability, and portability of
monitoring tools. It is now commonplace for athletes, coaches, and sports
medicine staff to collect HRV data, with short-term measurements taken first
thing in the day frequently recommended [13].
Given the accessibility of tools and technologies, the use of HRV is likely to
become more commonplace. Thus, an understanding of the different measures,
techniques, applications, and the complex physiological interactions that
produce differing patterns of HRV can help guide this practice and improve
interpretation of the data.
Technical considerations
Analysis techniques
The explosion of HRV research in 1980s and early 1990s led to the establishment
of the “Task Force” guidelines for standardization of analysis
procedures and considerations [2]. The numerous
analysis methods have been broadly categorized into time, frequency, and
nonlinear domains [28]. While the Task Force
Standards focused on the more traditional time and frequency domain measures, a
variety of nonlinear methods of HRV analysis have been developed and widely
utilized in more recent years [29]
[30].
The most commonly utilized time domain measures are the standard deviation of the
normal-normal intervals (SDNN) and the root mean square of the successive
differences (RMSSD) [28]. In brief, SDNN
characterizes the average difference from the mean, whereas RMSSD captures the
average difference from the previous interval. Thus, SDNN is a measure of the
overall variability of the time series, whereas RMSSD captures short-term
variability. Given the more rapid effect of the PNS than the SNS on HR, RMSSD is
considered a reflection of PNS activity [9]. Of
all the measures of HRV, RMSSD (or the natural log of RMSSD – lnRMSSD)
is perhaps the most broadly utilized owing to the its low typical error, ease of
calculation, and robustness to differences in respiratory patterns [13].
Frequency domain (or spectral analysis) measures are calculated by transforming
the time domain data, most typically through the fast Fourier method, to a
frequency spectrum of cycles per second (Hz), reflecting the contribution of
various frequencies to the overall variability of the signal [2]. There are typically three peaks observed: Very
low frequency (VLF,≤0.04 Hz), low frequency (LF,
0.04–0.15 Hz), and high frequency HF,
0.15–0.4 Hz). Traditional theory proposed HF power as reflective
of PNS activity and LF power as capturing a combination of SNS and PNS activity.
This theory has been challenged and the notion of LF/HF as a reflection
of sympathovagal balance is likely an oversimplification at best [19]
[31]. Spectral
analysis may be particularly sensitive to the effects of respiration and ectopic
beats, leading some to advocate for the use of ECG and requiring a respiratory
rate greater than 10 breaths per minute in order to reliably use these measures
in athletes [32].
Nonlinear methods of analysis, including detrended fluctuation analysis (DFA)
move beyond traditional means of quantifying variability, attempting to capture
the complexity of the time series [29].
Complexity, in brief, refers to the amount of information, across multiple time
scales, contained within a system or set of information [29]. Complex systems contain multiple interconnected feedback loops,
such that the integrated whole contains properties that cannot be understood
simply through examination of the component parts [28].
Sample entropy (SampEn) measures the relative regularity of the data sequence by
quantifying template matches across a data sequence [33]. More frequent matches indicates more regularity, and therefore a
lower entropy score, whereas a higher entropy score indicates more irregularity
and less predictability in the data sequence [28]
[33]. One benefit of SampEn is that
it has been shown to be relatively reliable with short time series, though a
minimum of 200 data points has been recommended [34].
Detrended fluctuation analysis is a nonlinear technique developed to distinguish
between internal fluctuations and external perturbations to the system, making
it a preferred method for non-static conditions such as exercise [35]. Patterns of variation across multiple
measurement scales of differing window lengths are examined, with local trends
subtracted. The scaling exponent α is related to the length of the time
window, where α1 indicates short-range correlations and α2
specifies long-term correlations. An α value of 0.5 is indicative of no
correlations (random data) and an alpha value of 1.5 is equivalent to highly
correlated data. Values of α ~ 1.0 are consistent with complex
systems data where self-similarity across time scales is observed. An optimal
middle range can make traditional statistical analysis of group data
challenging, as initially high and low values may both move toward the middle
following training [36].
Monitoring tools
The gold standard of HRV monitoring is through the use of ECG, though chest strap
heart rate monitors have been used extensively and have shown high reliability
[37]. Pulse rate variability, as measured
through photoplethysmography (PPG) has been used increasingly owing to the ease
of use [38]. The accuracy of PPG may depend on the
tool and condition in which it is used. Watches with embedded PPG have been
shown to be relatively accurate at rest [39]. On
the other hand, accuracy during exercise depends on the model of watch and type
of exercise, with notable increases in error during exercise that is either
vigorous or involves arm movement [40]. Similarly,
PPG technology embedded in a finger clip pulse oximeter are reliable but prone
to artifact when used during movement [41].
Resting measurement of HRV through a finger placed on the camera of a smartphone
is another common use of PPG, with high accuracy reported when done according to
suggested practices [38]. The ubiquity of
smartphones and the ease of use of this technique makes it appealing to a broad
population.
Stationarity and body position
Stationarity of data refers to the stability of the state of the individual, such
that the mean and standard deviation of the data set remain consistent over the
recording period [28]. Resting or steady state
exercise data satisfy the definition of stationarity, but data collected in the
transition from rest to exercise violates the requirement of stationarity [35]. Stationarity is an important consideration for
most HRV measures [28]. A notable exception, DFA,
corrects for non-stationarity [35], though the
efficacy of this correction has been challenged [42].
Given the sensitivity of HRV measures to physiological state, the need for
consistency of data collection procedures is clear. Research on body position
during data collection illustrates this. Numerous studies have compared resting
HRV in different body positions including supine, seated, and standing [15]
[43]
[44]. The highest HRV values are typically seen in
the supine condition, with decreases in the seated condition and further
diminishment in the standing condition. Seated measures have been suggested to
be more strongly associated with changes in fitness level than supine measures
[45]. Thus, when tracking HRV within an
individual, or comparing with population norms, consistency of body position
during measurement is a critical consideration.
Timing and duration of recording window
Measurements can be affected by such factors as time of day of the recording and
duration of data collection. While 24-hour HRV monitoring has been utilized
extensively in clinical and research settings [2],
such long-term monitoring may be impractical for athletes. Further, it includes
periods of varying activity, and therefore will fluctuate extensively over such
an extended window of time [46]. Resting-only
measurements of 30 minutes [47] or as
short as 5 minutes have been recommended broadly [2] or for athletes in particular [13]. Ultra-short term measurements of one minute
have been shown to have acceptable agreement with 5-minute recordings, while 30-
and 10-second readings significantly decreased the level of agreement [48].
Time of day may affect HRV measurements. Long-term measurements show the highest
levels of HRV during sleep [49]. The lowest levels
of HRV are observed during the late morning and early afternoon hours [50]. Thus, use of either overnight readings [51] or at a standardized time of day such as first
thing upon waking in the morning [13] are
recommended to reduce the confounding effects associated with time of day.
Strong correlations have been observed between morning and nocturnal HRV
measures in young athletes [52].
Adjustment for average heart rate
Many measures of HRV are reported in time-specific units, often without adjusting
for mean HR. In such cases, a mathematical association between HRV and HR
exists, with a slower average HR associated with higher HRV values and vice
versa [8]
[53]. If no
adjustment is made for mean HR, the same absolute fluctuation is indicative of a
different magnitude of variability, relative to the mean. Unadjusted HRV,
accordingly, includes information not only on the variability of the data but
also on the mean. Both time and frequency domain – but not nonlinear
measures – are affected by this collinearity [33]
[35].
Sex and age differences
Population norms and applications in HRV have been described in pediatric and
adolescent [54]
[55],
geriatric [56], and in various clinical
populations [57]
[58]. A large meta-analysis of healthy individuals including all ages
showed higher HR and lower SDNN but greater HF power in women than men [59]. Females were shown to have 7% higher
HF power in the supine condition than males, but experienced a greater magnitude
of reductions in HRV with standing [44]. Men peak
in LF power in the morning hours, while women do so in the afternoon [49]. An analysis of data collected through a
commercial app found no sex differences on average, but among females HRV was
3.2% higher during the luteal than follicular phase of the menstrual
cycle [60]. The same study found a moderate
negative correlation between age and RMSSD. Other studies have found that, with
aging, adults decrease in both overall HRV and in magnitude of variation over
the course of a 24-hour period [49]
[61].
Factors affecting resting HRV
Factors affecting resting HRV
The most widely used application of HRV for athletes, and increasingly among other
health-conscious individuals, is the daily HRV measurement. It is typically taken
first thing in the morning in order to minimize confounding effects [13], yet some devices alternatively measure nocturnal
HRV during sleep, which has the advantage of less potential for influence of
extrinsic factors [51]. Previously available only to
researchers and the technically savvy, daily HRV can now be monitored at home using
a variety of devices, apps, or software. Some of these options provide raw data
while others utilize HRV as part of black box algorithms to provide the user with a
recovery score or strain score [62]. Popular brands
that may be familiar to the general public include Whoop, Apple Watch, Fitbit, and
Oura Ring. Many durations and types of measures are utilized by such devices, which
can influence the outcome measurements. For the practical purposes of daily
monitoring, consistency of measurement for the specific individual is the most
important consideration.
Daily monitoring can provide insight into autonomic response to training.
Undoubtedly, there are individual differences in HRV response to training, and
longitudinal tracking of individual athletes allows greater insight into how these
measures are best utilized for the individual [13]
[47]. Population differences such as
training status, cardiorespiratory fitness, age, sex, and other factors may all
affect not only daily HRV readings but also how they respond to different training
and lifestyle stimuli. However, some common observations of daily HRV responses can
provide a framework for understanding that may help athletes interpret their
responses.
Overall training load
Chronic training may lead to increased HRV in athletes, especially where training
includes a strong aerobic component as is common practice among endurance
athletes [12]
[24].
However, acute reductions in daily HRV have been observed in response to heavy
training [63]
[64]
[65], and days with decreased
morning HRV may indicate a reduced performance ability [66]. Athletes with overtraining syndrome have been shown to exhibit
reduced HRV compared to healthy, trained athletes [15]. Plotted against training load, a bell-shaped curve for HF power
was observed in marathon runners, indicating high parasympathetic activity at
moderate levels of training, and lower levels with both abnormally high and low
training loads [67]. Similar suppression of HF
power has been observed when tracking elite rowers over the course of a year of
training and competition [68]. A study of
elite-level French Nordic skiers over four years found suppression of HRV
parameters in those tested in a fatigued state, as determined by a validated
questionnaire [69]. Those in the fatigued state
also exhibited greater intra-subject variance, indicating that HRV response may
vary between individuals in response to fatigue [69]. In a population of middle- and long-distance runners and
triathletes, an intensive 2-week training camp induced significant reductions in
HRV, which rebounded within 3–4 days of rest [47]. This is consistent with an “over-reached” state,
in which a heavy training load temporarily alters autonomic function but is
reversible within a few days of reduced training.
Exercise intensity
The intensity of an exercise session appears to have an influence on its impact
on HRV measures, either immediately or in some cases the day after exercise. For
example, HRV was suppressed in highly trained endurance athletes after exercise
above but not below the first ventilatory threshold (VT1), regardless of
duration [70]. In the same study, highly trained
(national class) athletes returned to baseline more quickly that trained
(non-elite) athletes. Similarly, comparing 3,500 m and 7,000 m
runs at low (50% of VO2max), moderate (63% of
VO2max), and vigorous (74% of VO2max;
3,500 m only) intensity, HRV was higher after the low intensity
sessions, regardless of duration [71].
Comparing 30 minutes of exercise at 45%, 60%, and
75% of VO2max, nocturnal HR was elevated with the higher
intensity exercise, but there was no effect on HRV [72]. In a study comparing interval training at 75 and 95% of
VO2max, the higher intensity interval session led to a
suppression of HRV at 1-hour post-exercise, but it returned to baseline within
the first 24 hours [73]. A high-intensity
interval training session (5-km run in 1-min increments at peak velocity
attained during a VO2max test, with 1-min passive recovery) produced
a moderate to large effect-size suppression of HRV at 30 min and
1 hour after exercise but small effect size differences from baseline
between 4 and 24 hours after exercise [74]. On the other hand, a similar study of ten intervals of 1-min
duration at 85% of HR reserve suppressed HRV only immediately after
exercise, with a return to baseline by the 1-hour mark, continuing through
8 hours after exercise [75].
In a study comparing 30-minute supine cycling bouts at three intensities (2, 3,
and 4 mmol of lactate), HRV suppression was present in all three
conditions 5 minutes after exercise, in the 3- and 4-mmol conditions
15 minutes after exercise but had returned to baseline in all three
conditions by 30 minutes after exercise [76]. Similarly, participants undergoing a 300-kcal exercise bout at
either 50% or 80% of VO2 reserve returned to baseline
HRV levels within 30 minutes after exercise, though levels remained
suppressed longer (up to 25 minutes) after the higher intensity
condition [77].
Most studies of a single exercise bout have found minimal disturbance to HRV from
the single session, though most of the sessions were not particularly
challenging in intensity. In contrast, when comparing constant velocity exercise
at VT1 with the same overall workload on a bike, but including
9×1 min of maximal intensity intervals, HRV was suppressed for
both conditions in the hour immediately afterwards but with greater suppression
in the high intensity group [78]. Further, there
may be some effect of body position on the persistence of HRV suppression, as
decreased HRV was observed in the standing but not supine conditions at 24 and
48 hours after exercise in both conditions [78].
The fitness level of the participant may impact the level of intensity that will
lead to prolonged perturbation of HRV. After a maximal intensity 75-km ski race,
for example, HRV was suppressed one but not two days after the race, and the
time to return to normal was inversely correlated with individual’s
VO2max [79]. However, HRV measured
after a marathon and an hour at 70% HRmax in recreational runners found
no relationship between VO2max and nocturnal perturbation of HRV
[80].
Exercise duration
Exercise duration is an important component of overall training load, and
increases in overall exercise volume have been shown to decrease HRV [63]
[64]
[65]. However, the amount of training volume
necessary to produce HRV suppression may be population-dependent and driven by
relative difference rather than absolute volume. For example, in highly trained
endurance athletes, HRV measures were unaffected by a 60- and 120-minute runs,
provided they were performed below VT1 [70]. Even
in the immediate post-exercise period, HRV returns to baseline levels fairly
quickly after endurance exercise, provided it is of low to moderate intensity,
such as 50 and 63% of VO2max [71] or below VT1 [70]. On the other
hand, in a population of moderately active non-athletes, a 90-minute moderate
(60% of VO2max) session reduced nocturnal HRV compared to
control, whereas 30- and 60-minute sessions at the same intensity did not [72]. In ultra-endurance events, decreased HRV
(measured as HF power) and decreased baroreceptor sensitivity have been observed
in both elite and recreational athletes [81].
Exercise modality
Numerous studies have been done examining HRV in response to different types of
training interventions, including continuous aerobic endurance, high-intensity
interval, resistance, coordinative, and multimodal exercise [82]. However, few studies have compared the effects
of different modalities of endurance exercise on athletes. A study of healthy
young men performing three modalities of maximal exercise tests (cycling,
walking, and running) found that HRV recovered more quickly after the cycling
test, which also elicited a lower maximal HR [83].
A few studies have assessed differences in HRV during exercise in different
modalities, though the challenges of matching intensity across different
activities make it difficult to draw conclusions [19]. Research has focused primarily on land-based sports without
restrictions on breathing, which may alter the interpretation of HRV data [84].
Lifestyle factors
The sensitivity of HRV to lifestyle factors such as life stress, diet, alcohol
consumption, smoking, sleep, and body composition has been well-established in
literature examining HRV as a means of assessing cardiometabolic risk [85]
[86]. It is not
unreasonable to expect that endurance athletes may also be subject to the
influence of such factors on resting HRV.
Partial sleep deprivation in the form of a reduction of hours of sleep to 3 or
4.5 hours has been shown to decrease HRV [87]
[88]. Alcohol use may also impact
HRV, with a dose-response relationship. Only small effects were observed from a
low dose (0.3 g/kg) [89], and
significantly more suppression of HRV was seen with two drinks when compared
with one [90]. In addition to the more obvious
deleterious effects of smoking on endurance athletes, smoking has been shown to
acutely reduce HRV and negatively impact leg cycling performance [91]. The use of other drugs and medications
consumed either acutely or chronically, particularly those acting upon the
autonomic nervous system, may also have a marked impact on HRV [92]. Given that the “stress
response” is typically characterized by a reduction in parasympathetic
activity, it is not surprising that some types of life stress have been shown to
reduce HRV [93]. These may include perceived
emotional stress [94], anxiety [95], depression [96],
and transient stress before bed [97].
Self-perception may modify HRV, as the changes in HRV due to a mental stress
task were smaller in students with higher self-esteem [98].
Training prescription based on daily HRV
Training prescription based on daily HRV
HRV-guided training
The use of HRV-guided training prescription has been investigated in the
literature and is being used in the real world to make training decisions [99]
[100]
[101]
[102]
[103]. Most studies of this approach have compared a
training schedule with a set schedule of workouts with one that varies the
inclusion of higher intensity work depending on the daily HRV score. Typically,
an individual baseline resting HRV is established over a period ranging from 3
days to 4 weeks [99]
[100]. Protocols of HRV-guided training vary, but typically moderate
and higher intensity workouts are performed only when the HRV score falls within
the normal range [99]
[104]. Lower intensity workouts and/or rest days are
prescribed until HRV returns to normal.
Defining the physiologically normal range is done on an individual basis, using
the smallest worthwhile change [100]
[105] established over a number of days. A number of
applications, such as HRV4Training and EliteHRV [106], ithlete[107], and Welltory [108] have been validated for measurement of daily
HRV and can be used by athletes to establish baseline levels and assess daily
recovery status. Alternative approaches to the smallest worthwhile change have
been studied. For example, vigorous training was prescribed if HRV either
increased or stayed the same, while a decreased training load was prescribed
when the HRV decreased [101]
[103]. The interpretation of increases in HRV above
the normal range remains a subject of debate, and one where individual
variability of response may come into play [13].
A systematic review on HRV-guided training for runners found greater improvements
in performance in the HRV-based groups than in those who followed a
predetermined training plan [99]. In most cases,
the HRV-guided training led to reduced volume of moderate and high intensity
training. Another review including healthy cyclists, runners, and skiers,
ranging from untrained to elite, found HRV-guided training to be as effective as
but not superior to pre-determined training [104].
A meta-analysis of studies on HRV-guided training for endurance athletes found
significantly greater improvements in submaximal exercise measures, whereas
performance and maximal exercise measures did not significantly differ between
groups [109]. However, there were proportionally
more positive responders and fewer negative responders to HRV-guided training
groups.
An 8-week intervention in distance runners found greater improvements in 3-km
time trial in the HRV-guided training group compared to the traditional training
group [100]. In a recreationally active
population, the use of two different HRV-guided training approaches compared to
a pre-planned program resulted in similar gains in VO2peak, though
women in the study gained the same benefits with fewer vigorous intensity
sessions [101]. Another study from the same group
also favored the HRV-guided training group in maximal running velocity and
VO2peak [103].
In endurance trained males, there were greater improvements in peak treadmill
velocity, countermovement jump, and resting HRV in the time and frequency
domains when compared to a pre-determined block training paradigm, with no
difference in 3-km running performance [110]. In a
study of professional runners, HRV-guided training improved peak treadmill
velocity and peak respiratory exchange ratio [111]. Unlike previous studies, where HRV-guided groups achieve comparable
performance through fewer sessions above VT1, the HRV-guided group in this study
spent more time between VT1 and VT2, raising the possibility that HRV-guided
training may be beneficial for some runners who are able to handle more
high-intensity sessions.
When implementing HRV-guided training, practitioners should be aware of the
divergent changes in HRV seen with modifications in training volume or intensity
[112]. Further, it is worth noting that all of
these studies compared a pre-determined training plan with HRV-guided training,
whereas athletes often work with an exercise professional such as a coach who
may adapt training according to athlete response.
HRV and maladaptive states
In addition to its use in guiding training, HRV has been suggested as a means of
detecting and assessing severity of maladaptive states, including illness [113]
[114],
overtraining [15]
[115], and concussion [18]. Concussion
is an area of great interest in sport science research in recent years, but is
much more prevalent among team sport athletes. Illness and overtraining, on the
other hand, are common among endurance athletes.
Overtraining is thought to be a result of an imbalance between training and other
stresses and recovery, leading to symptoms such as disturbed sleep, fatigue,
mood disturbances, altered hormone levels, prolonged muscle soreness, and a
reduction in exercise capacity [116]. Detection of
overtraining syndrome is challenging, and with no clear diagnostic criteria, it
often becomes a diagnosis of exclusion [117].
Overtrained athletes have been observed to have reduced HRV in both supine and
upright positions [15]. On the other hand, a case
study of a junior cross-country skiers experiencing overtraining symptoms showed
an increase in HF and total power in the supine position [118]. One study showed an increased strength of
relationship between RR interval length and HF power in overtrained endurance
athletes [115]. In a group of elite canoeists
engaged in a 6-day intensive training camp, VO2max and maximal
lactate decreased, but HRV measures did not change [119]. This suggests the possible use of HRV to distinguish between
overtraining and short-term training fatigue.
A recent meta-analysis found a negative correlation between HRV and inflammatory
processes, such as illness [113]. In a critical
care setting, HRV suppression has been used to predict severity of illness and
mortality risk [120]
[121]. Less severe illness may also impact HRV. In elite-level
swimmers, HRV was suppressed during weeks characterized by upper respiratory or
pulmonary infections [114]. However, the
predictive ability of HRV in this study was unclear; during the week prior to
infection, HRV was higher in the supine position but lower in the upright
position.
The potential for HRV monitoring to successfully identify early-stage illness
and/or overtraining remains to be determined. In the face of such health
issues, it is unclear whether HRV could be utilized to successfully adapt and
adjust in order to reduce severity or duration of illness and other medical
issues. However, HRV-guided training and monitoring of health status using HRV
and other biometric markers represent potentially useful tools to assist
athletes and coaches in navigating such challenges.
Alternative HRV monitoring options
Alternative HRV monitoring options
Exercise HRV
Exercise leads to an increase in heart rate to meet increased metabolic demands,
while time and frequency HRV measures decrease dramatically [19]
[122]
[123]
[124]
[125]. Although there are differences between the
methods as well as possibly between modes of exercise, the general pattern is
that HRV decreases in a dose-response relationship with exercise intensity. At
moderate intensities, HRV reaches a relatively stable, low level, and there are
minimal changes with further increases in exercise intensity [19]. As a result, these measures are difficult to
interpret in the context of exercise at the levels of intensity at which
athletes often train. Evaluation of HRV during exercise is not as common as
resting HRV at rest. It is, however, fairly reliable and reproducible, though
reliability may be compromised at very high intensities [126].
Nonlinear measures of exercise HRV have been used to monitor and adjust exercise
intensity during a discrete workout session. Specifically, a DFAα1 value
of 0.75 has been suggested to be equivalent to the aerobic threshold as measured
using ventilatory data [127]. Monitoring
applications such as Heart Rate Variability Logger and FatMaxxer use
DFAα1 calculated in real time to adjust exercise intensity [127]
[128]. Though it
can “detrend” and adjust for changes in exercise intensity,
staying at the same exercise intensity for a minimum of two minutes is
recommended for these applications.
Post-exercise HRV
As noted in the section above on factors affecting daily HRV, recovery of HRV
upon cessation of exercise may differ between exercise sessions. The monitoring
of HR and HRV recovery after exercise may provide valuable insight into the
stress of the session. Recovery back to baseline HRV levels commonly occurs
within the hour after exercise [19]
[70]
[71]
[75]
[77]
[78]
[129]
[130]
[131]
[132]
[133].
Low-intensity exercise may even increase HRV immediately after exercise if the
intensity is appreciably low [71]
[134]. However, exercise at intensities above VT1
appears to contribute to greater or longer periods of HRV suppression [70]
[78]
[133]
[135]. Nighttime
monitoring may also be of value, as Hynynen et al. [80] showed moderate to strong correlations between nocturnal HRV and
post-exercise suppression of HRV. Post-exercise HRV may also be correlated with
improvements in anaerobic fitness, as post-exercise RMSSD was correlated with
improvements in repeated sprint ability after a nine-week training program [136]. The monitoring of post-exercise HRV as a tool
for HRV-guided training has not been explored to our knowledge, but could serve
as a useful tool for endurance athletes, particularly those engaged in high
training volumes and/or multiple training sessions per day.
Future directions
Given the abundance of data and metrics available to coaches and athletes, there
is increased interest in physiological forecasting, or predicting outcomes or
performances based on an array of variables that may be collected using wearable
technology and other means. These technologies may be able to quantify stress
and recovery outside of the training period to improve quantification of
training load [137]. It seems clear that HRV alone
is not linearly predictive of performance, as it has been observed that HRV may
diminish slightly during the peaking phase [13].
Additionally, changes in endurance performance are not always coupled with
changes in HRV or other metrics used to monitor training such as the
countermovement jump [138]. However, observation
of individual patterns and responses to training, carried out longitudinally,
allow for an understanding of individual physiological response to training and
other stimuli. In one study, correlations between HRV and measures of training
load were not significant between individuals, but were significant within
individuals when using repeated-measures correlations [139]. This and other studies support the notion that exercise
prescription may be improved with longitudinal HRV monitoring. Some recreational
runners may improve more with high volumes of endurance exercise instead of
greater intensity, and these differences are correlated with changes in
nocturnal HRV [140]. Baseline HRV at the start of
a training period may also be related to improvements in training in a
recreational population [141]. These factors
should be considered when attempting to guide training using HRV in combination
with physiological and psychological variables. Exploration of practices such as
HRV-biofeedback training, which have been used successfully used to increase HRV
[8], could also be assessed as a training
tool.
Previous research applying HRV to sport has focused largely on endurance sport.
While that population is the focus of this review, it is worth noting that
endurance athletes often also engage in resistance exercise and anaerobic
activities. Little is currently known regarding the use of HRV for guiding
resistance training or exercise prescription in team sport environments. A study
of recreationally active adults engaged in high-intensity functional training
found similar improvements in body composition, strength, and VO2peak
when training was guided by HRV, despite a 20% reduction in
high-intensity sessions over a 9-week period [142]. Resistance exercise suppresses HRV but may [143] or may not [144] effectively
differentiate differences in fatigue from varying exercise bouts. The timeline
of HRV recovery may differ from other variables, including countermovement jump
and psychological variables. Nuuttila et al. [135]
observed different fatigue recovery patterns for countermovement jump and
post-exercise HRV depending on exercise intensity. Similarly, Vacher et al.
[145] noted linear changes in HRV despite
nonlinear changes in perceived stress and recovery. Thus, HRV data can be viewed
as one piece of a puzzle, contributing to improvements in predicting
performance, enhancing training and competition strategies, and refining the
science of endurance sport.
Conclusion
Many endurance athletes at all levels are using HRV in a
variety of applications. Increased availability and ease of access to tools for
measuring and processing HRV data carry the promise and possibility for further
enhancement of training and competition practices. However, athletes and coaches are
cautioned to use a rigorous approach to controlling for the quality of data being
collected and to be aware of key variables that can affect HRV measurements.
Practices such as daily HRV monitoring and HRV-guided training are well-established,
promising applications that should be considered by endurance athletes. Finally, the
use of HRV should be understood as one piece in the complex network of physiological
regulation and adaptation. Future research integrating a variety of metrics
– including HRV – promise to further our understanding of endurance
performance and training, and to aid individual endurance athletes in developing a
nuanced understanding of their own physiology.
