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Heart Rate Variability is a Moderating Factor in the Workload-Injury Relationship of Competitive CrossFit™ Athletes


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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™ athletes 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 intervals (Ln rMSSD week) were parsed into tertiles (low, moder-ate/normal, and high) based on within-individual z-scores. The interaction between Ln rMSSD week and ACWR on overuse injury risk in the subsequent week was assessed using a generalized linear mixed-effects model and magnitude-based inferences. The risk of overuse problems was substantially increased when a 'low' Ln rMSSD week 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 rMSSD week 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.
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©Journal of Sports Science and Medicine (2017) 16, 443-449
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
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 RR 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.
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
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.
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).
athletes (n=3)
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 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 RR 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’ (0.81.3)
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.
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).
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-
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’
(0.81.3) 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.
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
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.
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,
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
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-
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.
Lecturer, Department for Health, Univer-
sity of Bath, Bath, UK
Research interests
Injury surveillance, injury prevention,
training load monitoring, applied statis-
Strength and Conditioning Coach / Sports
Scientist at Bristol City Football Club
Research interests
Athlete monitoring strategies, training
load monitoring, injury surveillance
Matthew WATSON
Undergraduate student, Department for
Health, University of Bath, Bath, UK
Research interests
Athlete monitoring, performance training
and injury prevention
Undergraduate student, Department for
Health, University of Bath, Bath, UK
Research interests
Training load monitoring, nutrition for
human performance and recovery.
ACTLab, University of Passau, Germany.
Research interests
Development and implementation of
machine learning
techniques for health
and wellbeing applications, combining
multiple data sources in large popula-
Sean Williams, PhD, FHEA
Department for Health, University of Bath, 1 west 5.104, Bath,
BA2 7AY, United Kingdom
... Recently, tibana et al. [13], Williams et al. [14], and Crawford et al. [15] used session-RPE for quantification and monitoring of ItL in HIFt. the authors confirmed that the session-RPE method was efficient in differentiating ItL at different stages of training. tibana et al. [8] recommend that the use of this tool is of immense importance in ItL control, since it can indicate the effects of the training, thus helping the coach to make adjustments when necessary, with the objective to individualize the training loads and prevent injuries. ...
... tibana et al. [8] recommend that the use of this tool is of immense importance in ItL control, since it can indicate the effects of the training, thus helping the coach to make adjustments when necessary, with the objective to individualize the training loads and prevent injuries. However, despite the exponential growth of the modality, as well as the number of practitioners, to the best of our knowledge, only 5 studies have analysed the behaviour of ItL [4,8,[13][14][15]. therefore, there is a shortage of research in HIFt practitioners that have monitored ItL in order to analyse the stress/recovery ratio [1] measured via session-RPE, acute/chronic workload ratio (ACWR), and verification of specific aspects of physical fitness after a 6-week training period. ...
... Participants the sampling process was intentional and nonprobabilistic. However, we emphasize that previous studies [4,8,13,14] were carried out with similar or smaller sample sizes. the samples were recruited at an HIFt centre. ...
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Purpose: The present study aimed to analyse the behaviour of training loads and evaluate specific aspects of physical fitness during a period of 6 weeks in high-intensity functional training (HIFt) practitioners. Methods: The study included 12 practitioners (4 men and 8 women; age: 31.08 ± 4.80 years) of HIFt. the session rating of perceived exertion was routinely collected after each training session for 6 weeks. the sum and average of the weekly loads of training, strain, monotony, and acute/chronic workload ratio were recorded for analysis. In addition, the athletes underwent sprint, countermovement jump, and handgrip strength tests before and after the 6 weeks of HIFt. Results: A constant dynamic of the weekly internal training loads and the mean internal training loads was observed, with difference in the results from weeks 1 to 3 (F = 3.283; p = 0.02). In addition, the practitioners obtained superior results in countermovement jump (t = 3.573; p = 0.005) and lower limb muscle power (t = 3.536; p = 0.005) after the 6 weeks. Conclusions: The internal training load varied significantly only from weeks 1 to 3 over the 6 weeks. In addition, we observed that the 6-week HIFt was able to generate functional adaptations only in countermovement jump and lower limb muscle power.
... The final value of the hrv index is calculated according to the following formula: hrv = 10 * hrv maxdev_hrv (6) where hrv is the intermediate value of HRV, and maxdev_hrv is the absolute maximum deviation of hrv . Formula (6) gives us a range of values for hrv from −10 to 10. Values from −7 to 8 coincide with low risk of injury, whereas other values indicate a greater possibility of injuries [28]. In the figures, performance indicators were compared by session type (practice, match) and by training period (preparation, pre-competition, competition). ...
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The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment.
... A tendency of the fitness segment in the world over the last decade 35 , characterized as another variant of sports performance (training the body as required in competition), as well as recreational and general training (performing movements that mimic those in daily living) 9 . The method has increasingly more adherents around the world, despite the scarce scientific evidence concerning its efficacy or harm 8 , and its practitioners may consume indiscriminately ergogenic substances, mainly with nutritional inadequacy concerning micronutrients and macronutrients 36 , such as ED, to improve recovery, as occurs for dietary supplements 37 , offered as the only response to better performance in various sports modalities and practices, since insufficient rest periods can promote high levels of exercise-induced stress 38,39 . ...
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The aim of this study was to analyze the acute effects of energy drink (ED) ingestion on CrossFit® performance in a randomized, double-blind, cross-over study, with 8 CrossFit®-trained (26.5 ± 2.7 years; 70.2 ± 13.0 kg; 1.7 ± 0.09 m; 23.0 ± 3.2 kg/m2; ∑ skinfold thickness: 34.1 ± 6.9 mm; body fat: 13.3 ± 3.0 %), that were randomly allocated to 2 groups and underwent 2 trials separated by a 7-day washout period. Participants ingested either a dose of 300 mL of ED or Placebo (soda), 30 minutes before the start of tests of muscular strength (MS), 10 and 12 maximum repetitions (MRs) in barbell bench press (BBP) and barbell squat (BS), respectively and localized muscular endurance (LME) using Workout of the Day (WOD) selected. The rating of perceived exertion (RPE) and the rating of perceived pain (RPP) were evaluated immediately after the tests. The total volume of repetitions (TVR) was evaluated to each test. The TVR was significantly higher after consuming the ED (p = 0.012) and of the Placebo (p = 0.027). There was a reduction in the rate of RPE after the consumption of both drinks (p = 0.023 and p = 0.024). The consumption of ED significantly reduced the rate of RPP (p = 0.017). Acute ED ingestion improved CrossFit® performance by increased the TVR and the pain tolerance.
... For day-to-day monitoring of individual recovery (i.e., sympathovagal balance) HRV was measured as the root mean squared of successive differences (RMSSD). Due to the lack of normality, the RMSSD was transformed using the natural logarithm (LnRMSSD), which was then multiplied by two so that LnRMSSD (HRVdaily) could be viewed on a scale of approximately one to ten for ease of interpretation and to reflect the application display [22]. ...
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Heart rate variability (HRV) may be useful for prescribing high-intensity functional training (HIFT) exercise programs. This study aimed to compare effects of HRV-guided and predetermined HIFT on cardiovascular function, body composition, and performance. Methods: Recreationally-active adults (n = 55) were randomly assigned to predetermined HIFT (n = 29, age = 24.1 ± 4.1 years) or HRV-guided HIFT (n = 26, age = 23.7 ± 4.5) groups. Both groups completed 11 weeks of daily HRV recordings, 6 weeks of HIFT (5 d·week-1), and pre- and post-test body composition and fitness assessments. Meaningful changes in resting HRV were used to modulate (i.e., reduce) HRV-guided participants' exercise intensity. Linear mixed models were used with Bonferroni post hoc adjustment for analysis. Results: All participants significantly improved resting heart rate, lean mass, fat mass, strength, and work capacity. However, no significant between-groups differences were observed for cardiovascular function, body composition, or fitness changes. The HRV-guided group spent significantly fewer training days at high intensity (mean difference = -13.56 ± 0.83 days; p < 0.001). Conclusion: HRV-guided HIFT produced similar improvements in cardiovascular function, body composition, and fitness as predetermined HIFT, despite fewer days at high intensity. HRV shows promise for prescribing individualized exercise intensity during HIFT.
... In the same sense, there is an increased risk of injury when athletes presented low rMSSD (low HRV) and high training load (ACWR -acute / chronic workload ratio), also highlighting that injuries are more frequent in those athletes with high LF/HF and who, nevertheless, train intensively (high ACWR), influencing the process of capacity/demand 10 . ...
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RESUMO O objetivo desta revisão busca compreender o uso da variabilidade da frequência cardíaca (VFC) para identificar sua relação com a ocorrência de lesões esportivas que não envolvem contato, além de indicar padrões da VFC após concussões para orientar o retorno seguro ao esporte. Foi realizada uma revisão sistemática nas bases de dados Pubmed, EMBASE e PEDRo, incluindo artigos até dezembro de 2020, utilizando os seguintes termos: ((((athletes OR players) AND (Heart Hate Variability OR HRV)) AND (sport OR sports OR exercises OR physical activity)) AND (injuries OR injury)). Os princípios de elegibilidade de PICOS foram: P (population): atletas, I (intervention): o uso da VFC, C (control): atletas não lesionados, O (outcomes): índices de VFC e suas relações com lesões esportivas, e S (study): estudos em seres humanos. De 62 artigos identificados na busca, 12 foram incluídos na revisão, sendo 6 mostrando que a diminuição da VFC e o desequilíbrio simpatovagal estão relacionados à fadiga, overtraining e overreaching; e 6 artigos relacionados com a avaliação da VFC pós-concussão, onde identificaram alteração de modulação autonômica nos atletas concussionados que vão além da ausência dos sintomas. Em conclusão, a VFC pode ser uma ferramenta utilizada no âmbito esportivo para identificar maior risco de lesões esportivas sem contato, identificando situações de fadiga, overtraining e overreaching, como também auxiliar no processo de retorno ao esporte pós-concussão cerebral pela avaliação do balanço autonômico.
... Many studies reported that measures five, 10 or 30min after the sports activity is not enough to stabilize the HRV to basal levels [9]. It seems that at least 90min is requested to verify the positive/negative stress responses in high-intensity exercises, such as CrossFit, soccer, handball or weightlifting [10][11][12][13]. ...
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Heart rate variability (HRV) became one of the most used physiological variables to quantify stress and recovery in sports. HRV can be assessed by different forms, for instance, the root mean square of successive R-R interval differences (RMSSD) is commonly used to predict the parasympathetic activity. Reduced RMSSD indicates high sympathetic activity that means more stress and possibly more indisposition for training.
... This application can be used on both Android OS and iOS. There are some studies using HRV4Training [87,88]. The appeal point of HRV4Training is that the external sensor is not needed and HRV can be measured using the camera and the flash of the smartphone [89]. ...
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Many accidents are caused by sudden changes in the physical conditions of professional drivers. Therefore, it is quite important that the driver monitoring system must not restrict or interfere with the driver's action. Applications that can measure a driver's heartbeat without restricting the driver's action are currently under development. In this review, examples of heartbeat-monitoring systems are discussed. In particular, methods for measuring the heartbeat through sensing devices of a wearable-type, such as wristwatch-type, ring-type, and shirt-type devices, as well as through devices of a nonwearable type, such as steering-type, seat-type, and other types of devices, are discussed. The emergence of wearable devices such as the Apple Watch is considered a turning point in the application of driver-monitoring systems. The problems associated with current smartwatch-and smartphone-based systems are discussed, as are the barriers to their practical use in vehicles. We conclude that, for the time being, detection methods using in-vehicle devices and in-vehicle cameras are expected to remain dominant, while devices that can detect health conditions and abnormalities simply by driving as usual are expected to emerge as future applications.
... In the absence of other measurement tools with which autonomic regulation or stress response may be quantified (due to impracticality of application in tactical contexts outside of a laboratory), HRV shows promise as a viable field measurement for determining maladaptive stress responses [23,24]. Other high-intensity settings, largely in elite athletics, have demonstrated the utility of HRV in calibrating training loads [25,26] with measures potentially used to augment injury risk predictions [27,28]. Additionally, correlations between HRV and cardiorespiratory fitness [29] and psychological stress [22,30] have been described. ...
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Police work exposes officers to high levels of stress. Special emergency response team (SERT) service exposes personnel to additional demands. Specifically, the circadian cycles of SERT operators are subject to disruption, resulting in decreased capacity to compensate in response to changing demands. Adaptive regulation loss can be measured through heart rate variability (HRV) analysis. While HRV Trends with health and performance indicators, few studies have assessed the effect of overnight shift work on HRV in specialist police. Therefore, this study aimed to determine the effects overnight shift work on HRV in specialist police. HRV was analysed in 11 SERT officers and a significant (p = 0.037) difference was found in pRR50 levels across the training day (percentage of R-R intervals varying by >50 ms) between those who were off-duty and those who were on duty the night prior. HRV may be a valuable metric for quantifying load holistically and can be incorporated into health and fitness monitoring and personnel allocation decision making.
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DOCTORADO EN EDUCACIÓN (RD09/11) ESCUELA INTERNACIONAL DE DOCTORADO Efectos fisiológicos del entrenamiento basado en la variabilidad de la frecuencia cardíaca en corredores de fondo-Physiological effect of training based on heart rate variability in endurance runners
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The aim of this study was to determine if an abnormal HRV status would have negative effects on simulated individual time trial (ITT) performance in recreational cyclists. Recreational male (n=23, 42.8±8.3 years, 78.0±11.0 kg) and female (n=2, 37.0±6.8 years, 68.0±4.4 kg) cyclists completed simulated indoor 40-minute ITTs (40TT) over ten weeks. Participants were asked to complete simulated 40TTs under two HRV conditions: HRV normal values and HRV abnormal values. Participants recorded daily morning HRV readings to determine HRV status. Each participant performed all 40TTs on their personal indoor bike trainer and bike without external race simulation (e.g., Zwift). All cycling performance data were recorded on personal bike computers and submitted via a Qualtrics survey. A total of 138 ITTs (Normal = 75; Abnormal = 63) were assessed for relationships between HRV status and performance outcomes using a linear mixed-effects model with Cohen's D for effect sizes (ES). A significant main effect of HRV status was found for peak power (F = 6.61; Normal: 372 ± 121.5 watts; Abnormal: 349 ± 105.9 watts; 380; p = 0.01; ES = 0.20) and peak speed (F = 6.12; Normal = 10.8 ± 1.2 m/s; Abnormal: 10.4 ± 1.2 m/s; p = 0.02; ES = 0.33). No significant main effect or effect sizes exceeding 0.20 were observed for all other performance variables. Daily HRV monitoring provides valuable insight that an individual's peak power and speed may be compromised during cycling performance despite no changes in physiological or psychological indicators of effort. Coaches and cyclists can use morning HRV to inform race strategy ensuring desired performance outcomes, especially for those who rely on high power outputs.
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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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Spikes in training and competition workloads, especially in undertrained athletes, increase injury risk. However, just as attributing athletic injuries to single risk factors is an oversimplification of the injury process, interpreting this workload-injury relationship should not be done in isolation. Instead, we must further unpack how (ie, through which mechanisms) workload spikes might result in injury, and what characteristics make athletes more robust or more susceptible to injury at any given workload. In other words, which factors mediate the workload-injury relationship, and which moderate the relationship.
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The objective of the study was to examine injury epidemiology and risk factors for injury in CrossFit athletes. A survey was administered to athletes at four owner-operated facilities in South Florida. Respondents reported number, location of injury, and training exposure from the preceding six months and answered questions regarding potential risk factors for injury. Fifty out of 191 athletes sustained 62 injuries during CrossFit participation in the preceding six months. The most frequently injured locations were the shoulder, knee, and lower back. Injury incidence was 2.3/1000 athlete training hours. Competitors were more likely to be injured (40% v 19%, p = 0.002) and had greater weekly athlete training hours (7.3 ± 7.0 v 4.9 ± 2.9, p <0.001) than non-competitors. Athletes who reported injury also reported significantly higher values for the following risk factors: years of participation (2.7 ± 1.8 v 1.8 ± 1.5, p = 0.001), weekly athlete training hours (7.3 ± 3.8 v 4.9 ± 2.1, p = 0.020), weekly athlete-exposures (6.4 ± 3.8 v 4.7 ± 2.1, p = 0.003), height (1.72 ± 0.09 m v 1.68 ± 0.01 m, p = 0.011), and body mass (78.24 ± 16.86 kg v 72.91 ± 14.77 kg, p = 0.037). Injury rates during CrossFit and location of injuries were similar to those previously reported. Injury incidence was similar to related sports, including gymnastics and powerlifting. While being a competitor was related to injury, increased exposure and length of participation in CrossFit likely underlied this association. Specifically, increased exposure to training in the form of greater weekly athlete training hours and weekly participations may contribute to injury. Increased height and body mass were also related to injury which is likely reflective of increased load utilized during training. Further research is warranted to determine if biomechanical factors associated with greater height and ability to lift greater loads are modifiable factors that can be adapted to reduce the increase risk of injury during CrossFit.
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Objective: To determine if any differences exist between the rolling averages and exponentially weighted moving averages (EWMA) models of acute:chronic workload ratio (ACWR) calculation and subsequent injury risk. Methods: A cohort of 59 elite Australian football players from 1 club participated in this 2-year study. Global positioning system (GPS) technology was used to quantify external workloads of players, and non-contact 'time-loss' injuries were recorded. The ACWR were calculated for a range of variables using 2 models: (1) rolling averages, and (2) EWMA. Logistic regression models were used to assess both the likelihood of sustaining an injury and the difference in injury likelihood between models. Results: There were significant differences in the ACWR values between models for moderate (ACWR 1.0-1.49; p=0.021), high (ACWR 1.50-1.99; p=0.012) and very high (ACWR >2.0; p=0.001) ACWR ranges. Although both models demonstrated significant (p<0.05) associations between a very high ACWR (ie, >2.0) and an increase in injury risk for total distance ((relative risk, RR)=6.52-21.28) and high-speed distance (RR=5.87-13.43), the EWMA model was more sensitive for detecting this increased risk. The variance (R(2)) in injury explained by each ACWR model was significantly (p<0.05) greater using the EWMA model. Conclusions: These findings demonstrate that large spikes in workload are associated with an increased injury risk using both models, although the EWMA model is more sensitive to detect increases in injury risk with higher ACWR.
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We read with great interest the recent letter, “Time to bin the term ‘overuse’ injury: is ‘training load error’ a more accurate term?”1 and in particular its associated PostScript correspondence, “Are rolling averages a good way to assess training load for injury prevention?”2 We are currently investigating the association between training loads and injury risk,3 and so we have also been considering the best way to model this relationship. We share Dr Menaspa's concerns regarding the use of rolling averages for the calculation of ‘acute’ and ‘chronic’ loads. Namely, that they fail to account for the decaying nature of fitness and …
Full-text available
Injury prediction is one of the most challenging issues in sports and a key component for injury prevention. Sports injuries aetiology investigations have assumed a reductionist view in which a phenomenon has been simplified into units and analysed as the sum of its basic parts and causality has been seen in a linear and unidirectional way. This reductionist approach relies on correlation and regression analyses and, despite the vast effort to predict sports injuries, it has been limited in its ability to successfully identify predictive factors. The majority of human health conditions are complex. In this sense, the multifactorial complex nature of sports injuries arises not from the linear interaction between isolated and predictive factors, but from the complex interaction among a web of determinants. Thus, the aim of this conceptual paper was to propose a complex system model for sports injuries and to demonstrate how the implementation of complex system thinking may allow us to better address the complex nature of the sports injuries aetiology. According to this model, we should identify features that are hallmarks of complex systems, such as the pattern of relationships (interactions) among determinants, the regularities (profiles) that simultaneously characterise and constrain the phenomenon and the emerging pattern that arises from the complex web of determinants. In sports practice, this emerging pattern may be related to injury occurrence or adaptation. This novel view of preventive intervention relies on the identification of regularities or risk profile, moving from risk factors to risk pattern recognition.
Full-text available
Injury aetiology models that have evolved over the previous two decades highlight a number of factors which contribute to the causal mechanisms for athletic injuries. These models highlight the pathway to injury, including (1) internal risk factors (eg, age, neuromuscular control) which predispose athletes to injury, (2) exposure to external risk factors (eg, playing surface, equipment), and finally (3) an inciting event, wherein biomechanical breakdown and injury occurs. The most recent aetiological model proposed in 2007 was the first to detail the dynamic nature of injury risk, whereby participation may or may not result in injury, and participation itself alters injury risk through adaptation. However, although training and competition workloads are strongly associated with injury, existing aetiology models neither include them nor provide an explanation for how workloads alter injury risk. Therefore, we propose an updated injury aetiology model which includes the effects of workloads. Within this model, internal risk factors are differentiated into modifiable and non-modifiable factors, and workloads contribute to injury in three ways: (1) exposure to external risk factors and potential inciting events, (2) fatigue, or negative physiological effects, and (3) fitness, or positive physiological adaptations. Exposure is determined solely by total load, while positive and negative adaptations are controlled both by total workloads, as well as changes in load (eg, the acute:chronic workload ratio). Finally, we describe how this model explains the load—injury relationships for total workloads, acute:chronic workload ratios and the training load—injury paradox.
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Heart rate variability (HRV) corresponds to the adaptation of the heart to any stimulus. In fact, among the pathologies affecting HRV the most, there are the cardiovascular diseases and depressive disorders, which are associated with high medical cost in Western societies. Consequently, HRV is now widely used as an index of health. In order to better understand how this adaptation takes place, it is necessary to examine which factors directly influence HRV, whether they have a physiological or environmental origin. The primary objective of this research is therefore to conduct a literature review in order to get a comprehensive overview of the subject. The system of these factors affecting HRV can be divided into the following five categories: physiological and pathological factors, environmental factors, lifestyle factors, non-modifiable factors and effects. The direct interrelationships between these factors and HRV can be regrouped into an influence diagram. This diagram can therefore serve as a basis to improve daily clinical practice as well as help design even more precise research protocols.
Background: CrossFitTM is a strength and conditioning programme that has gained widespread popularity since its inception approximately 15 years ago. However, at present little is known about the level of injury risk associated with this form of training. Movement competency, assessed using the Functional Movement ScreenTM (FMS), has been identified as a risk factor for injury in numerous athletic populations, but its role in CrossFit participants is currently unclear. The aim of this study was to evaluate the level of injury risk associated with CrossFit training, and examine the influence of a number of potential risk factors (including movement competency). Methods: A cohort of 117 CrossFit participants were followed prospectively for 12 weeks. Participants' characteristics, previous injury history and training experience were recorded at baseline, and an FMS assessment was conducted. Results: The overall injury incidence rate was 2.10 per 1000 training hours (90% Confidence Limits: 1.32 - 3.33). A multivariate Poisson regression model identified males (rate ratio [RR]: 4.44 ×/÷ 3.30, very likely harmful) and those with previous injuries (RR: 2.35 ×/÷ 2.37, likely harmful) as having a higher injury risk. Inferences relating to FMS variables were unclear in the multivariate model, although number of asymmetries was a clear risk factor in a univariate model (RR per two additional asymmetries: 2.62 ×/÷ 1.53, likely harmful). Conclusions: The injury incidence rate associated with CrossFit training was low, and comparable to other forms of recreational fitness activities. Previous injury and gender were identified as risk factors for injury, whilst the role of movement competency in this setting warrants further investigation.