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©Journal of Sports Science and Medicine (2017) 16, 443-449
http://www.jssm.org
Received: 05 July 2017 / Accepted: 10 August 2017 / Published (online): 01 December 2017
`
Heart Rate Variability is a Moderating Factor in the Workload-Injury
Relationship of Competitive CrossFitTM Athletes
Sean Williams 1
, Thomas Booton 1, Matthew Watson 1, Daniel Rowland 1 and Marco Altini 2
1 Department for Health, University of Bath, Bath, United Kingdom; 2ACTLab, University of Passau, Germany
Abstract
Heart rate variability (HRV) is a popular tool for monitoring
training adaptation and readiness in athletes, but it also has the
potential to indicate early signs of somatic tissue overload prior
to the onset of pain or fully developed injury. Therefore, the aim
of this study was to investigate the interaction between HRV,
workloads, and risk of overuse problems in competitive Cross-
Fit™ athletes. Daily resting HRV and workloads (duration ×
session-RPE) were recorded in six competitive CrossFit™ ath-
letes across a 16 week period. The Oslo Sports Trauma Research
Center Overuse Injury Questionnaire was distributed weekly by
e-mail. Acute-to-chronic workload ratios (ACWR) and the
rolling 7-day average of the natural logarithm of the square root
of the mean sum of the squared differences between R–R inter-
vals (Ln rMSSDweek) were parsed into tertiles (low, moder-
ate/normal, and high) based on within-individual z-scores. The
interaction between Ln rMSSDweek and ACWR on overuse
injury risk in the subsequent week was assessed using a general-
ized linear mixed-effects model and magnitude-based infer-
ences. The risk of overuse problems was substantially increased
when a ‘low’ Ln rMSSDweek was seen in combination with a
‘high’ ACWR (relative risk [RR]: 2.61, 90% CI: 1.38 – 4.93). In
contrast, high ACWRs were well-tolerated when Ln rMSSDweek
remained ‘normal’ or was ‘high’. Monitoring HRV trends
alongside workloads may provide useful information on an
athlete’s emerging global pattern to loading. HRV monitoring
may therefore be used by practitioners to adjust and individual-
ise training load prescriptions, in order to minimise overuse
injury risk.
Key words: Cardiac parasympathetic function, monitoring,
training load.
Introduction
Overuse injuries occur due to repetitive submaximal load-
ing of the musculoskeletal system when rest is not ade-
quate to allow for structural adaptation to take place
(DiFiori et al., 2014). The prevalence and negative impact
of overuse injuries in competitive sports (Clarsen et al.,
2013) highlights the need for monitoring systems that can
accurately reflect athletes’ evolving adaptations to train-
ing stimuli (Gisselman et al., 2016). Heart rate variability
(HRV) is a popular tool for monitoring wellness and
training adaptation in athletes (Bellenger et al., 2016).
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 (Malik, 1996).
The emergence of smartphone applications and technolo-
gies has dramatically increased the accessibility of HRV
measurement, such that it can now be recorded accurately
using only a smartphone device (Plews et al., 2017).
The ANS plays a dynamic role in the regulation of
pain, inflammation and tissue repair (Ackermann et al.,
2016). Thus, some authors have postulated that monitor-
ing HRV, as an indirect measurement of ANS homeosta-
sis, has the potential to indicate early signs of somatic
tissue overload prior to the onset of pain or fully devel-
oped injury (Gisselman et al., 2016). It is hypothesised
that, relative to each athlete’s baseline HRV measure-
ments, imbalances in the parasympathetic and sympathet-
ic nervous systems may indicate an athlete is in a state of
ongoing repair and recovery versus an athlete who is
adapting positively to training load (Gisselman et al.,
2016). HRV measurements may therefore be used to
improve our understanding of the mediators and modera-
tors in the workload-injury relationship (Windt et al.,
2017). CrossFit™ is a strength and conditioning pro-
gramme promoted as both an exercise methodology for
the general population, and as the ‘sport of fitness’ for
competitive athletes. Whilst concerns have been raised by
some regarding the potential for disproportionate muscu-
loskeletal injury risk in extreme conditioning programmes
such as CrossFit™ (Bergeron et al., 2011), initial injury
epidemiology studies have reported the injury incidence
rate in CrossFit™ training to be relatively low (2.1 – 3.1
per 1000 training hours), and comparable to other forms
of recreational fitness activities (Hak et al., 2013;
Montalvo et al., 2017; Moran et al., 2017; Weisenthal et
al., 2014). However, the methods used for injury registra-
tion in these studies are likely to have substantially under-
estimated the true burden of overuse injuries (defined as
those without a specific, identifiable event responsible for
their occurrence) due to a reliance on time-loss injury
definitions (Clarsen et al., 2013). Overuse injuries are
thought to be the predominant injury type in sports that
involve high volumes of repetitive movement patterns,
and/or high training loads (Clarsen et al., 2013); both of
these factors are likely to be prevalent in CrossFit™ train-
ing, especially for competitive athletes who report signifi-
cantly greater training hours than non-competitors
(Montalvo et al., 2017).
At present, there is a paucity of research pertaining
to competitive CrossFit™ athletes, despite the rapidly
growing popularity of the sport. The number of athletes
available for research studies from this elite population is
inevitably small, which may increase the likelihood of
making type II errors (whereby a practically important
effect remains undetected through null hypothesis signifi-
Research article
HRV and injury in CrossFitTM
444
cance testing). However, the use of magnitude-based
inferences to determine the practical importance of out-
comes can mitigate this issue, as sample sizes approxi-
mately one-third those of null hypothesis significance
testing are required (Batterham and Hopkins, 2006).
Accordingly, the aim of this study was to investigate the
interaction between HRV, workloads, and risk of overuse
problems in competitive CrossFit™ athletes.
Methods
Subjects
Six (three males, three females) competitive CrossFit™
athletes from one CrossFit™ training facility participated
in this study and provided written consent prior to data
collection. ‘Competitive’ was defined as training for the
purpose of competing in organised CrossFit™ competi-
tions. The descriptive characteristics of the six competi-
tive CrossFit™ athletes at baseline are shown in Table 1.
Two athletes (one male, one female) finished in the top 40
of the ‘CrossFit™ Open’ in Europe (out of 38,238 and
20,908 male and female competitors, respectively), and
qualified for the ‘CrossFit™ Regionals’ competition. Of
the remaining athletes, three finished the ‘CrossFit™
Open’ in the >95th percentile in Europe, whilst the re-
maining athlete was in the 85th percentile. The study was
conducted in accordance with the principles of the Decla-
ration of Helsinki (World Medical Association, 2013) and
a local university research ethics committee provided
ethical approval.
Table 1. Descriptive characteristics (mean ± SD) of competi-
tive CrossFit™ athletes at baseline. Data are means (±SD).
Male
athletes (n=3)
Female
athletes (n=3)
Age (years)
26 (4)
27 (2
Height (m)
1.83 (.06)
1.67 (.10)
Mass (kg)
88 (2)
67 (9)
VO2 Max (ml/min/kg)
50 (1)
48 (3)
Training volume (h/week)
8.6 (2.2)
7.1 (1.8)
Data collection and processing
Data were collected across a 16 week period (November
2016 – March 2017), culminating in the athletes’ partici-
pation in the ‘CrossFit™ Open’; a worldwide, five-week
online competition in which the top 40 athletes in each
region qualify for the ‘CrossFit™ Regionals’ competition.
Preliminary tests were administered to establish partic-
ipants’ maximal oxygen uptakes on a friction braked
cycle ergometer (Monark Peak 894E, Varberg, Swe-
den), as previously described (Toone and Betts, 2010),
alongside other descriptive characteristics (age, height,
and mass).
Heart rate variability: Photoplethysmography
(PPG) was used to acquire HRV measurements via a
commercially available smartphone application known as
“HRV4training” (see http://www.hrv4training.com). This
method has been shown to have acceptable agreement
with heart rate chest strap and electrocardiography meth-
ods (Plews et al., 2017). Athletes were instructed to take a
one-minute HRV measurement each morning upon wak-
ing whilst in a supine position (Esco and Flatt, 2014). The
square root of the mean sum of the squared differences
between R–R intervals (rMSSD) was the HRV measure
used for analysis, as this has been demonstrated to have
greater reliability than spectral indices (Al Haddad et al.,
2011). The rMSSD data were then log-transformed (Ln)
to reduce non-uniformity of error (Plews et al., 2012), and
multiplied by two to be placed on an approximate 1-10
scale. The 7-day rolling average of this variable (Ln
rMSSDweek) was then calculated and used in further anal-
yses, as this has been shown to provide better methodo-
logical validity compared with values taken on a single
day (Plews et al., 2013). There is currently no evidence to
suggest that gender influences HRV trends (Plews et al.,
2012), and so male and female data were analysed togeth-
er to maximise sample size in this study.
Training load: After taking their daily HRV meas-
urement each morning, athletes were then required to
record the intensity (using the modified Borg CR-10
rating-of-perceived-exertion [RPE] scale; Foster et al.,
2001) and duration (minutes) of their previous day’s train-
ing session within the “HRV4training” application. Ses-
sion RPE (sRPE) was derived by multiplying the RPE and
session duration to provide a workload value in arbitrary
units. This approach has been shown to be a valid method
for estimating exercise intensity across multiple training
modalities (Herman et al., 2006), and is temporally robust
up to 24 h post-exercise (Christen et al., 2016). The varied
modalities that are inherent to CrossFit™ training (i.e.,
weightlifting, gymnastics and aerobic exercises) made the
sRPE method the most sensible approach for recording
workloads in this setting. From this workload data, the
acute-to-chronic workload ratio (ACWR) was calculated
by dividing athletes’ acute (seven day) workload by their
chronic (28 day) workload (Gabbett, 2016), using the
exponentially-weighted moving average approach
(Murray et al., 2016; Williams et al., 2016). The total
number of days that athletes’ daily ACWR values were
outside of the previously described ‘safe zone’ (0.8 – 1.3)
for injury risk reduction (Gabbett, 2016) across the study
period was also calculated.
Overuse injury: The Oslo Sports Trauma Research
Center (OSTRC) Overuse Injury Questionnaire (Clarsen
et al., 2013) was distributed to all athletes via email every
Sunday throughout the study period. The questionnaire
consisted of four questions for each anatomical area of
interest (Clarsen et al., 2013); these included the knee,
wrist, elbow, lower back, and shoulder, based upon exist-
ing injury epidemiology data within CrossFit™ popula-
tions (Hak et al., 2013; Montalvo et al., 2017; Moran et
al., 2017; Weisenthal et al., 2014). The responses to each
of the four questions were allocated a numerical value
from 0 to 25, and these were summed in order to calculate
a severity score from 0 to 100 for each overuse problem
(Clarsen et al., 2013). The prevalence of overuse prob-
lems was calculated for each week of the study by divid-
ing the number of athletes that reported any problem (i.e.,
anything but the minimum value in any of the four ques-
tions) by the number of questionnaire respondents. The
average weekly prevalence of overuse problems was
subsequently calculated. This process was repeated for
substantial overuse problems (defined as those leading to
Williams et al.
445
moderate or severe reductions in training volume, or
moderate or severe reduction in sports performance, or
complete inability to participate in sport).
Statistical procedures
The Ln rMSSDweek and ACWR data were converted to
within-individual z-scores, which were subsequently
parsed into tertiles (low, moderate/normal, high) for anal-
ysis (Buchheit, 2014). The low, normal and high tertiles
for Ln rMSSDweek corresponded to z-scores of <-0.31, -
0.31 to 0.41, and >0.41, respectively. For ACWR data,
the corresponding z-scores were <-0.36, -0.36 to 0.41, and
>0.41. All estimations were made using the lme4 package
(Bates et al., 2015) with R (version 3.3.1, R Foundation
for Statistical Computing, Vienna, Austria). A generalized
linear mixed-effects model (GLMM) was used to model
the association between ACWR, HRV, and risk of over-
use problems in the subsequent week (modelled as a bina-
ry dependent variable). ACWR and HRV measures were
modelled as categorical fixed effect predictor variables,
whilst ‘athlete ID’ was included as a random effect. A
multiplicative term was included in the model to assess
the interaction between ACWR and HRV. The odds ratios
obtained from the GLMM model were converted to rela-
tive risks (RR) in order to interpret their magnitude
(Hopkins et al., 2007). The smallest important increase in
injury risk was a relative risk of 1.11, and the smallest
important decrease in risk was 0.90 (Hopkins, 2010). An
effect was deemed ‘unclear’ if the chance that the true
value was beneficial was >25%, with odds of benefit
relative to odds of harm (odds ratio) of <66. Otherwise,
the effect was deemed clear, and was qualified with a
probabilistic term using the following scale: <0.5%, most
unlikely; 0.5-5%, very unlikely; 5-25%, unlikely; 25-
75%, possible; 75-95%, likely; 95-99.5%, very likely;
>99.5%, most likely (Hopkins, 2007). Data are presented
as means ± 90% confidence intervals (CI) unless stated
otherwise as standard deviation (SD).
Results
Response rate
The average response rate to the 16 weekly overuse injury
questionnaires was 82% (range: 63-100%), with 4/6 ath-
letes responding to at least 80%. Overall average compli-
ance to the daily HRV and workload monitoring was 94%
(range: 85-100%), with 4/6 athletes having a compliance
rate of at least 94%..
Overuse injuries
Four of the six athletes reported some form of overuse
problem over the course of the study period (Figure 1),
with one athlete reporting a substantial overuse problem.
The average prevalence of overuse injury problems in any
anatomical location was 9% (90% CI: 6-14%). The aver-
age prevalence of substantial overuse problems was 3%
(90% CI: 0-7%). Overuse problems affected the following
anatomical areas: Knee (two cases); wrist (two cases);
lower back (two cases); elbow (one case). The substantial
overuse problem was to the elbow. The average severity
score for reported overuse problems was 33 (90% CI: 27-
40).
Heart rate variability and workloads
Daily training loads and Ln rMSSDwee k patterns for each
of the six athletes across the study period are shown in
Figure 2. Average weekly training loads (± SD) were
2591 ± 890 AU. Individual athletes’ daily ACWR values
were outside of the previously described ‘safe zone’
(0.8 – 1.3) for injury risk reduction on a total of 228 days
(32%) across the study period.
Heart rate variability, acute:chronic workloads, and
overuse injury risk
A significant interaction effect was observed between Ln
rMSSDweek and ACWR z-score tertiles (P = 0.009). The
probability of reporting an overuse problem in the subse-
quent week was very likely higher (RR: 2.61, 90% CI:
1.38 – 4.93) when a ‘low’ Ln rMSSDweek z-score was
combined with a ‘high’ ACWR z-score, in comparison to
being in the ‘moderate/normal’ tertiles for both measures
(Figure 3). All other comparisons were unclear.
Discussion
The purpose of the current study was to explore the poten-
tial moderating role of HRV upon the workload-injury
relationship within competitive CrossFit™ athletes. A
clear interaction effect was identified, such that the risk of
overuse problems was substantially increased when a
‘low’ Ln rMSSDweek was seen in combination with a
‘high’ ACWR. In contrast, high ACWRs were well-
tolerated when Ln rMSSDweek remained ‘normal’ or was
‘high’. In addition, the OSTRC overuse injury question-
naire and PPG smartphone technology were shown to be
effective methods for data collection in this population.
The results of the current study go some way to
supporting the hypothesis proposed by Gisselman et al.
(2016); that in the pathogenesis of overuse injuries, an
abnormal inflammatory response occurs within somatic
tissue (potentially before pain is perceived), which can
disrupt the normal remodelling process, and that this may
modulate ANS activity at the level of HRV. Indeed, in the
present study a reduction in HRV (low Ln rMSSDweek)
concurrent with increases in workloads (high ACWR) was
associated with a very likely higher (RR: 2.61) probability
of reporting an overuse injury in the subsequent week.
This finding suggests that the modulation of HRV did
reflect an abnormal somatic tissue response to the accu-
mulating load. As such, HRV monitoring has the potential
to aid the accurate detection and prevention of overuse
injuries in athletic populations.
Obtaining high chronic training loads (i.e., ‘fit-
ness’), without rapid spikes in workloads (i.e., an ACWR
greater than ~1.3) is currently considered the ‘best prac-
tice’ approach for optimising performance whilst mini-
mising injury risk in elite sport (Gabbett, 2016). However,
some athletes with a collection of characteristics that
‘dim’ workload related injury risks (e.g., high aerobic
fitness, optimal sleep habits) may benefit from
higher training loads (e.g., an ACWR beyond 1.3) for
performance purposes (Windt et al., 2017). The present
HRV and injury in CrossFitTM
446
Figure 1. Overuse severity scores for each athlete across the 16 week study period. Triangles: wrist severity score, Circles: Lower
back severity score, Square: Elbow severity score, Diamond: Knee severity score.
study suggests that HRV is a useful, non-invasive marker
of the athletes’ physiological response to accumulating
training load, which may be used by practitioners to allow
for more nuanced training load prescriptions. Specifically,
those athletes who experience a reduction in their HRV
(as determined by Ln rMSSDweek) during periods of inten-
sified training may benefit from recovery interventions,
whilst those with normal or increasing HRV trends may
benefit from further increases in load.
HRV is known to be influenced by a wide range of
factors, including physiological/pathological, neuropsy-
chological, non-modifiable, lifestyle and environmental
factors (Fatisson et al., 2016). Similarly, the aetiology of
injury is complex, dynamic, multifactorial and context
dependent (Windt and Gabbett, 2017), and likely deter-
mined by interacting factors within a ‘web of determi-
nants’ (Bittencourt et al., 2016). For instance, a spike in
workload may produce increased levels of neuromuscular
fatigue, but the strength of that relationship may be mod-
erated by lifestyle factors such as work-stress and sleep
quality, and/or physiological factors such as aerobic fit-
ness (Windt et al., 2017). Thus, monitoring trends in
HRV, alongside workloads, may provide useful infor-
mation on an athlete’s emerging global pattern to loading
(i.e., injury or adaptation; Bittencourt et al., 2016), and
together can be used to optimally balance the ‘risk and
reward’ of training (Gabbett et al., 2016).
The present study also supports the use of PPG
smartphone technology for recording HRV, with high
compliance rates observed for the daily HRV recording
procedures (94%). Due to the relative noise of HRV re-
cordings, daily recordings are required to produce rolling
averages that accurately reflect an athlete’s current physi-
ological state (Plews et al., 2012; Plews et al., 2013).
Given the superior practicality and acceptable validity of
HRV recorded via PPG (Plews et al., 2017), this method
represents a viable solution for practitioners aiming to
assess HRV on athletes in the field.
The most commonly reported sites for overuse
problems in this study (knee, lower back, and wrist) were
consistent with existing injury epidemiology studies in
CrossFit™ (Hak et al., 2013; Montalvo et al., 2017;
Moran et al., 2017; Weisenthal et al., 2014). The high
response rate and identification of problems that did not
impact athletes’ ability to train (i.e., non-substantial over-
use problems) suggests that the OSTRC overuse injury
questionnaire represents a promising method for capturing
a complete and nuanced picture of overuse problems in
Williams et al.
447
Figure 2. Daily sRPE training load values (grey bars) and Ln rMSSD 7-day rolling average (black line) for each athlete across
the 16 week study period.
Figure 3. Probability of reporting an overuse problem in the
subsequent week when collectively considering acute:chronic
workload ratios and Ln rMSSD 7-day rolling average.
this population. However, further studies with larger sam-
ple sizes are required before any clear conclusions regard-
ing the profile of overuse injuries in competitive Cross-
Fit™ can be made.
A clear limitation of the current study was the rela-
tively small number of subjects, which precluded
the investigation of sex differences and potentially re-
duced the generalisability of these results to other sporting
populations. However, the athletes included in this sample
were of a high competitive standard (thus limiting the
available population), and magnitude-based inferences
were used to appropriately determine the practical im-
portance of the observed effects, which reduces the re-
quired sample size when compared to traditional null-
hypothesis significance testing (Batterham and Hopkins,
2006).
Conclusions
Monitoring HRV trends alongside workloads may pro-
vide useful information on an athlete’s emerging global
pattern to loading. Specifically, overuse injury risk in
competitive CrossFit™ athletes was substantially in-
creased when ‘low’ Ln rMSSDweek values were observed
alongside high ACWRs, but high ACWRs were well-
tolerated when Ln rMSSDweek was ‘normal’ or ‘high’.
Therefore, monitoring HRV responses alongside work-
loads may assist practitioners in their efforts to optimally
HRV and injury in CrossFitTM
448
balance the ‘risk and reward’ of training. Future studies
should explore the utility of ‘HRV-guided training’ in
reducing the burden of injuries (Vesterinen et al., 2016),
whilst larger studies are warranted to investigate the prev-
alence and nature of overuse injuries in CrossFit™ ath-
letes.
Acknowledgements
The authors would like to acknowledge with considerable gratitude all
those who volunteered to take part in this study. All authors contributed
to data collection and manuscript preparation. Marco Altini is the owner
and developer of HRV4Training. No funding to declare. The authors
have no conflict of interest.
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Key points
• Reductions in
HRV concurrent with workload
spikes were associated with an increased risk of
developing overuse problems.
• High workloads were well-
tolerated when HRV
trends remained ‘normal’ or ‘high’.
• HRV monitoring may therefore be used by practi-
tioners to adjust and individualise training load pre-
scriptions, in order to minimise overuse injury risk.
AUTHOR BIOGRAPHY
Sean WILLIAMS
Employment
Lecturer, Department for Health, Univer-
sity of Bath, Bath, UK
Degree
PhD
Research interests
Injury surveillance, injury prevention,
training load monitoring, applied statis-
tics.
E-mail: S.Williams@bath.ac.uk
Thomas BOOTON
Employment
Strength and Conditioning Coach / Sports
Scientist at Bristol City Football Club
Academy
Degree
BSc
Research interests
Athlete monitoring strategies, training
load monitoring, injury surveillance
E-mail: Thomas.Booton8@gmail.com
Matthew WATSON
Employment
Undergraduate student, Department for
Health, University of Bath, Bath, UK
Degree
BSc
Research interests
Athlete monitoring, performance training
and injury prevention
E-mail:
matthewwatson1993@hotmail.co.uk
Daniel ROWLAND
Employment
Undergraduate student, Department for
Health, University of Bath, Bath, UK
Degree
BSc
Research interests
Training load monitoring, nutrition for
human performance and recovery.
E-mail: dan.rowland@bath.edu
Marco ALTINI
Employment
ACTLab, University of Passau, Germany.
Degree
PhD
Research interests
Development and implementation of
machine learning
techniques for health
and wellbeing applications, combining
multiple data sources in large popula-
tions.
E-mail: altini.marco@gmail.com
Sean Williams, PhD, FHEA
Department for Health, University of Bath, 1 west 5.104, Bath,
BA2 7AY, United Kingdom
