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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
... Foster et al., developed the session rating perceived exertion (sRPE) composed of the relationship between training intensity and training volume. Recently (Williams et al., 2017) reported that sRPE is the most sensible method to record workloads in the FFT program. These data are needed because no studies reported the relationship between subjective and objective assessments such as HRV and sRPE with well-being involving the FFT program in athletes. ...
... Based on the literature, we hypothesized that HRV function would lower after the training workout. There are negative associations between subjective assessment represented by sRPE and objective assessment represented by HRV function with wellbeing, based on a previous (Williams et al., 2017) study. ...
... The HRV function recovery was completed only 72 hours after the training workout was done. Furthermore, the loss of acute HRV recovery in FFT workout is associated with overuse injury (Williams et al., 2017). FFT programs use high intensity during all its application, and it is a potential contributor to decrease the HRV function. ...
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We monitored fatigue and stress using heart rate variability and session rating perceived exertion in trained athletes performing a single bout of functional-fitness training workout. Also, we verified the association between heart rate variability and session rating perceived exertion with well-being. In the first week of tapering, eleven national athletes (age: 25.7 ± 3.3y; body mass index: 27.7 ± 2.8 kg·m-2; training history: > 4y) participated in this study. Heart rate variability was analyzed basal, before and after the experimental protocol. Session rating perceived exertion was analyzed after the experimental protocol, and after the assessments, the association between them and well-being was performed. Repeated measures of ANOVA were performed to compare condition x time, and Pearson correlation was used to analyze the associations. Heart rate variability decreased its values after the training workout (ηp2=11.5, p<0.001), and session rating perceived exertion was high (25.8 ± 6.9 a.u.). We did not find associations between heart rate variability or session rating perceived exertion and well-being (r between-0.34 and 0.35, p>0.05). This study did not support the idea of a significant relationship between objective/subjective, physiological assessments and well-being in one bout of training workout. Functional-fitness coaches and athletes should know the limited evidence about objective/subjective assessments and well-being.
... Prior work has established that reductions in HRV during rapid increases in workload are associated with an increased risk of developing overuse injuries. 116 These results imply that larger changes in workload may be well tolerated when HRV trends remain "normal" or "high." 117 Therefore, HRV monitoring may be used by practitioners to adjust and individualize training load prescriptions to minimize the risk of overuse injury. ...
... RTP outcomes can be improved by viewing injury risk as a regularly changing, non-linear, and dynamic system. 116 By honing-in on the more influential determinants, practitioners may prevent more athletes from tipping into the "injury trough" and having a poor result when returning from quarantine or COVID-19. We have provided a sampling of devices commonly used by athletes to help the sports medicine team to choosing and translating the data acquired from these devices (Supplementary Tables 1 and 2) to meet their clinical specifications (Table 5). ...
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The coronavirus disease 2019 (COVID-19) pandemic has enabled the adoption of digital health platforms for self-monitoring and diagnosis. Notably, the pandemic has had profound effects on athletes and their ability to train and compete. Sporting organizations worldwide have reported a significant increase in injuries manifesting from changes in training regimens and match schedules resulting from extended quarantines. While current literature focuses on the use of wearable technology to monitor athlete workloads to guide training, there is a lack of literature suggesting how such technology can mediate the return to sport processes of athletes infected with COVID-19. This paper bridges this gap by providing recommendations to guide team physicians and athletic trainers on the utility of wearable technology for improving the well-being of athletes who may be asymptomatic, symptomatic, or tested negative but have had to quarantine due to a close exposure. We start by describing the physiologic changes that occur in athletes infected with COVID-19 with extended deconditioning from a musculoskeletal, psychological, cardiopulmonary, and thermoregulatory standpoint and review the evidence on how these athletes may safely return to play. We highlight opportunities for wearable technology to aid in the return-to-play process by offering a list of key parameters pertinent to the athlete affected by COVID-19. This paper provides the athletic community with a greater understanding of how wearable technology can be implemented in the rehabilitation process of these athletes and spurs opportunities for further innovations in wearables, digital health, and sports medicine to reduce injury burden in athletes of all ages.
... Regarding the CMJ and LnRMSSD, i) decreases, due to accumulated fatigue resulting from high intensity and short rest intervals (Williams et al., 2017); ii) increases, resulting from adaptive processes and/or indicating adequate recovery (Düking et al., 2020) or; iii) stability, which would relate to high monotony (Miloski et al., 2012), were expected. In this context, the absence of noticeable variations for LnRMSSD, at least from the statistical point of view and the average of the investigated group, could be explained by the characteristic of the loads imposed or by the moment of collection, right after a recuperative training day, which would indicate that the period of 48 hours may be enough to regenerate these indexes (Timón et al., 2019). ...
... However, it is noteworthy that the group values may have hidden changes at the individual level, since 3.4% of the athletes' showed changes that exceeded the MDC thresholds. Regarding the correlations found, CMJ and HRV correlated negatively and moderately with some internal load derivatives, suggesting that changes in the loads imposed may have interfered in lower limbs power and in autonomic control (Williams et al., 2017). ...
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Approach: CrossFit ® is a training program recognized for its rapid growth in popularity in competitive and noncompetitive models and designed to develop different fitness domains simultaneously. In this training model, chronic adaptations are expected to differ from other models due to the high-volume workloads, performed in a self-regulated characteristic. Purpose: Therefore, this study aimed to describe the internal training load imposed on CrossFit ® athletes over a three month period and relate it to physical performance indicators. A second goal was to investigate the effect of this training on aerobic fitness indicators. Methods: To this end, competitive athletes were evaluated daily, weekly, and pre and post training. The instruments consisted of daily perceptual measures regarding pain sensations, recovery, sleep quality, heart rate variability, and tests of lower limb power and aerobic power. The data normality was verified by the Shapiro-Wilk test and compared by the ANOVA test for repeated measures, and the correlations between training load indicators and physical performance were tested by Pearson's coefficient. The alpha value was set at 5%. Results and Conclusion: The investigated training program was not enough to induce detectable overreaching or recovery/compensation, at least by the variables evaluated and in a group-based analysis. Also, no relevant changes in aerobic power were found. However, negative correlations between CMJ and HRV with some training load parameters suggest that changes in training loads along the weeks provided neuromuscular and autonomic variations in the expected directions. In summary, the training load imposed in the preparation of elite CrossFit ® athletes was relatively stable, despite the constant variation of stimuli and settings. Our findings may help to explain the patterns of the sport, which involves high volume and frequency of training maintained for long periods, which does not match (at least theoretically) with high intensity efforts.
... Besides being linked to these mental aspects that may be related to perceived fitness, resting HRV has shown to be associated with physical components of fitness as well. On a between-subject level, resting HRV is positively associated with cardiovascular fitness (Souza et al., 2021;Tomes et al., 2020), and negatively associated with overuse injuries (Gisselman et al., 2016;Lima-Borges et al., 2018;Williams et al., 2017) and pain perception (Forte et al., 2022). Finally, resting HRV has also been linked to viral infections on a withinsubject level (Conroy et al., 2022). ...
... Wearable-measured resting HRV during sleep was a statistically significant positive predictor of perceived physical fitness on the subsequent morning. Although no prior studies utilizing a within-subject design to assess these relationships were identified, these results are in line with prior research that showed that between-subject differences in resting HRV are positively associated with cardiovascular fitness (Souza et al., 2021;Tomes et al., 2020) and negatively associated with overuse injuries (Gisselman et al., 2016;Lima-Borges et al., 2018;Williams et al., 2017) and pain perception (Forte et al., 2022). However, resting HRV explained only a small portion of the variance in perceived physical fitness (3.1% after controlling for TST and RHR). ...
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The emergence of wearable sensor technology may provide opportunities for automated measurement of psychophysiological markers of mental and physical fitness, which can be used for personalized feedback. This study explores to what extent within-subject changes in resting heart rate variability (HRV) during sleep predict the perceived mental and physical fitness of military personnel on the subsequent morning. Participants wore a Garmin wrist-worn wearable and filled in a short morning questionnaire on their perceived mental and physical fitness during a period of up to 46 days. A custom-built smartphone app was used to directly retrieve heart rate and accelerometer data from the wearable, on which open-source algorithms for sleep detection and artefact filtering were applied. A sample of 571 complete observations in 63 participants were analyzed using linear mixed models. Resting HRV during sleep was a small predictor of perceived physical fitness (marginal R² = .031), but not of mental fitness. The items on perceived mental and physical fitness were strongly correlated (r = .77). Based on the current findings, resting HRV during sleep appears to be more related to the physical component of perceived fitness than its mental component. Recommendations for future studies include improvements in the measurement of sleep and resting HRV, as well as further investigation of the potential impact of resting HRV as a buffer on stress-related outcomes.
... Thus, the acute effect of this training method is able to increase blood lactate concentration (Maté-Muñoz et al., 2017), oxygen consumption (Bellar et al., 2015), heart rate (Claudino et al., 2018) and rate of perceived exertion (Tibana et al., 2018). Furthermore, it has a hypotensive effect (Tibana et al., 2017) and reduce heart rate variability (HRV) (Williams et al., 2017). ...
... Therefore, HRV is adequate to control training load. Williams et al. (2017) studied the relationship between reduced HRV and increased injury after 16 weeks of CrossFit® training and found that athletes who showed a RMSSD and parasympathetic control reduction were more likely to have some type of musculoskeletal overuse injury. Shaun and Vecchio (2018) when comparing autonomic responses between two different protocols, observed that HIIT based on full-body exercises (burpee; mountain climber; jumping jacks; squat, thruster) resulted in high parasympathetic inhibition immediately after the training session, with subsequent recovery within 24 hours. ...
Full-text available
CrossFit® is a training program characterized by high intensity stimulus with constantly varied and multifunctional movements that induces a significant range of physiological, hemodynamic and biochemical responses. Heart rate variability (HRV) can be used to measure how individuals react to physiological stress and fatigue. Thus, the aim of this study was to verify HRV and blood pressure acute responses during and after three sessions of Crossfit®. Nine subjects with more than one year of experience performed three different sessions of CrossFit® to verify the response of systolic blood pressure (SBP), diastolic blood pressure (DBP) and HRV. Significant reductions in HRV were observed through parasympathetic indexes (High Frequency(HF), p<0.001) and an increase in the activity of sympathetic indexes (Low Frequency (LF), p < 0.01; LF/HF, p<0.001) after all Crossfit® workouts. SBP decreased (p<0.05) and there were no significant differences between workouts of the day in both HRV and SBP. Different CrossFit® sessions induced similar activity of the autonomic nervous system with reduced HRV and post-exercise hypotension. Keywords. high intensity interval training; Fran; Megan; Diane; autonomic response; post-exercise hypotensive effect.
... Resting-state photoplethysmography (PPG)-measured HRV data was obtained via an Android App utilized in prior research trials (42)(43)(44) and validated with the Polar H7 device and electrocardiography (ECG) (45) at CVI and CVII. HRV recordings of 1 min were used to assess cardiac-vagal HRV parameters: the root mean square of successive differences (rMSSD), high-frequency (HF) HRV, and the percentage of successive RR intervals that differ by more than 50 ms (pNN50). ...
... Long-term HRV recordings still represent the typical reference standard for predicting health outcomes whereas short-term values are proxies of longterm values with unknown predictive validity; therefore, ultrashort HRV measurements could be considered as "proxies of proxies" (63). Despite the fact that our HRV recording method, instrumentation, and procedure has been validated with both the Polar H7 device and electrocardiography (ECG) (42)(43)(44)(45) evaluating HRV utilizing classic 5-min ECG recording windows during pre and post clinical visitations could have aided in determining whether the observed changes in pain intensity were associated with changes in HRV (64). Based on clinical theories such as the vagal-tank theory, longer vagus nerve innervation treatment methods for those with FM may be needed to detect a significant perturbance of low HRV levels and help understand how the ≪vagal tank≫ sustains self-regulatory efforts to build a higher resting cardiac vagal control over time, yet this is speculative (65). ...
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Importance Vagus nerve innervation via electrical stimulation and meditative-based diaphragmatic breathing may be promising treatment avenues for fibromyalgia. Objective Explore and compare the treatment effectiveness of active and sham transcutaneous vagus nerve stimulation (tVNS) and meditative-based diaphragmatic breathing (MDB) for fibromyalgia. Design Participants enrolled from March 2019–October 2020 and randomly assigned to active tVNS ( n = 28), sham tVNS ( n = 29), active MDB ( n = 29), or sham MDB ( n = 30). Treatments were self-delivered at home for 15 min/morning and 15 min/evening for 14 days. Follow-up was at 2 weeks. Setting Outpatient pain clinic in Oslo, Norway. Participants 116 adults aged 18–65 years with severe fibromyalgia were consecutively enrolled and randomized. 86 participants (74%) had an 80% treatment adherence and 107 (92%) completed the study at 2 weeks; 1 participant dropped out due to adverse effects from active tVNS. Interventions Active tVNS is placed on the cymba conchae of the left ear; sham tVNS is placed on the left earlobe. Active MDB trains users in nondirective meditation with deep breathing; sham MDB trains users in open-awareness meditation with paced breathing. Main outcomes and measures Primary outcome was change from baseline in ultra short-term photoplethysmography-measured cardiac-vagal heart rate variability at 2 weeks. Prior to trial launch, we hypothesized that (1) those randomized to active MDB or active tVNS would display greater increases in heart rate variability compared to those randomized to sham MDB or sham tVNS after 2-weeks; (2) a change in heart rate variability would be correlated with a change in self-reported average pain intensity; and (3) active treatments would outperform sham treatments on all pain-related secondary outcome measures. Results No significant across-group changes in heart rate variability were found. Furthermore, no significant correlations were found between changes in heart rate variability and average pain intensity during treatment. Significant across group differences were found for overall FM severity yet were not found for average pain intensity. Conclusions and relevance These findings suggest that changes in cardiac-vagal heart rate variability when recorded with ultra short-term photoplethysmography in those with fibromyalgia may not be associated with treatment-specific changes in pain intensity. Further research should be conducted to evaluate potential changes in long-term cardiac-vagal heart rate variability in response to noninvasive vagus nerve innervation in those with fibromyalgia. Clinical trial registration , Identifier: NCT03180554.
... This has been demonstrated in various studies conducted on athletes, in which HRV has been related to elements of training such as intensity, duration, or recovery (Seiler et al., 2007) as well as overtraining (Hedelin et al., 2000), training load (Javaloyes et al., 2019), and psychological (Ortigosa- Márquez, 2017) or performance profiles (Buchheit et al., 2012). Therefore, HRV analysis is a useful method for measuring the heart's ability to adapt to endogenous and exogenous loads (Parrado et al., 2010) and can be used for individual assessment of training load responses and recovery adaptation in young footballers (Williams et al., 2017). ...
... HRV has traditionally been captured in medical settings using electrocardiography but there is a growing market of wearable devices and smart-phone applications capable of collecting HRV data (Hernando et al., 2018). For example, the HRV4Training app has been shown to be a valid and reliable method for capturing HRV (Altini & Amft, 2018;Plews et al., 2017;Williams et al., 2017). ...
Objective: Anorexia nervosa (AN) is associated with significant individual mental and physical suffering and public health burden and fewer than half of patients recover fully with current treatments. Comorbid exercise dependence (ExD) is common in AN and associated with significantly worse symptom severity and treatment outcomes. Research points to cognitive inflexibility as a prominent executive function inefficiency and transdiagnostic etiologic and maintaining mechanism linking AN and ExD. This study will evaluate the initial efficacy of adjunctive Cognitive Remediation Therapy (CRT), which has been shown to produce cognitive improvements in adults with AN, in targeting cognitive inflexibility in individuals with comorbid AN and ExD. As an exploratory aim, this study also addresses the current lack of quick and cost-effective assessments of cognitive flexibility by establishing the utility of two proposed biomarkers, heart rate variability and salivary oxytocin. Method: We will conduct a single-group, within-subjects trial of an established CRT protocol delivered remotely as an adjunct to inpatient or intensive outpatient treatment as usual (TAU) to adult patients (n = 42) with comorbid AN and ExD. Assessments, including self-report, neuropsychological, and biomarker measurements, will occur at three time points. Results: We expect CRT to increase cognitive flexibility transdiagnostically and consequently, along with TAU, positively impact AN and ExD compulsivity and symptom severity, including weight gain. Discussion: Findings will inform the development of more effective integrative interventions for AN and ExD targeting shared mechanisms and facilitate the routine assessment of cognitive flexibility as a transdiagnostic risk and maintaining factor across psychopathologies in clinical and research settings. Public significance: Patients with anorexia nervosa often engage in excessive exercise, leading to harmful outcomes, including increased suicidal behavior. This study examines the preliminary efficacy of an intervention that fosters flexible and holistic thinking in patients with problematic eating and exercise to, along with routine treatment, decrease harmful exercise symptoms. This study also examines new biological markers of the inflexible thinking style thought to be characteristic of anorexia nervosa and exercise dependence.
... However, there is still a dearth of information about the impact of HIITCE (i.e., CrossFit ® ) on CAF in individuals who practice this modality exclusively. In addition, to our knowledge, only two articles described the CAF in CrossFit ® practitioners, but none have compared the HIITCE with another sport modality [23,24]. Hence, considering the positive effect of regular, continuous aerobic training (i.e., triathlon) on CAF, as described above, and the scarcity of studies showing the impact of CrossFit ® on CAF and the worldwide interest in this modality, it becomes essential to describe and analyze the CAF (modulation, reactivity, and reactivation) in CrossFit ® athletes and compare these two distinct modalities. ...
Full-text available
Unlabelled: It is well established that endurance exercise has positive effects on cardiac autonomic function (CAF). However, there is still a dearth of information about the effects of regular high-intensity interval training combined with different types of exercises (HIITCE) on CAF. Objective: The aim of this study is to compare CAF at rest, its reactivity, and reactivation following maximal exercise testing in HIITCE and endurance athletes. Methods: An observational study was conducted with 34 male athletes of HIITCE (i.e., CrossFit®) [HG: n = 18; 30.6 ± 4.8 years] and endurance athletes (i.e., triathlon) [TG.: n = 16; 32.8 ± 3.6 years]. We analyzed 5 min of frequency-domain indices (TP, LF, HF, LFn, HFn, and LF/HF ratio) of heart rate variability (HRV) in both supine and orthostatic positions and its reactivity after the active orthostatic test. Post-exercise heart rate recovery (HRR) was assessed at 60, 180, and 300 s. Statistical analysis employed a non-parametric test with a p-value set at 5%. Results: The HG showed reduced HFn and increased LFn modulations at rest (supine). Overall cardiac autonomic modulation (TP) at supine and all indices of HRV at the orthostatic position were similar between groups. Following the orthostatic test, the HG showed low reactivity for all HRV indices compared to TG. After the exercise, HRR does not show a difference between groups at 60 s. However, at 180 and 300 s, an impairment of HRR was observed in HG than in TG. Conclusion: At rest (supine), the HG showed reduced parasympathetic and increased sympathetic modulation, low reactivity after postural change, and impaired HRR compared to TG.
Full-text available
The design of high-intensity functional training (HIFT; e. g., CrossFit ® ) workouts and targeted physiological trait(s) vary on any given training day, week, or cycle. Daily workouts are typically comprised of different modality and exercise combinations that are prescribed across a wide range of intensities and durations. The only consistent aspect appears to be the common instruction to maximize effort and workout density by either completing “as many repetitions as possible” within a time limit (e.g., AMRAP, Tabata) or a list of exercises as quickly as possible. However, because effort can vary within and across workouts, the impact on an athlete's physiology may also vary daily. Programming that fails to account for this variation or consider how targeted physiological systems interrelate may lead to overuse, maladaptation, or injury. Athletes may proactively monitor for negative training responses, but any observed response must be tied to a quantifiable workload before meaningful changes (to programming) are possible. Though traditional methods exist for quantifying the resistance training loads, gymnastic movements, and cardiorespiratory modalities (e.g., cycling running) that might appear in a typical HIFT workout, those methods are not uniform, and their meaning will vary based on a specific exercise's placement within a HIFT workout. To objectively quantify HIFT workloads, the calculation must overcome differences in measurement standards used for each modality, be able to account for a component's placement within the workout and be useful regardless of how a workout is commonly scored (e.g., repetitions completed vs. time-to-completion) so that comparisons between workouts are possible. This review paper discusses necessary considerations for quantifying various HIFT workout components and structures, and then details the advantages and shortcomings of different methods used in practice and the scientific literature. Methods typically used in practice range from being excessively tedious and not conducive for making comparisons within or across workouts, to being overly simplistic, based on faulty assumptions, and inaccurate. Meanwhile, only a few HIFT-related studies have attempted to report relevant workloads and have predominantly relied on converting component and workout performance into a rate (i.e., repetitions per minute or second). Repetition completion rate may be easily and accurately tracked and allows for intra- and inter-workout comparisons. Athletes, coaches, and sports scientists are encouraged to adopt this method and potentially pair it with technology (e.g., linear position transducers) to quantify HIFT workloads. Consistent adoption of such methods would enable more precise programming alterations, and it would allow fair comparisons to be made between existing and future research.
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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.
Full-text available
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 …
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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.
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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.