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Background The ability to monitor training load accurately in professional sports is proving vital for athlete preparedness and injury prevention. While numerous monitoring techniques have been developed to assess the running demands of many team sports, these methods are not well suited to throwing-dominant sports that are infrequently linked to high running volumes. Therefore, other techniques are required to monitor the differing demands of these sports to ensure athletes are adequately prepared for competition. Objective To investigate the different methodologies used to quantitatively monitor training load in throwing-dominant sports. Methods A systematic review of the methods used to monitor training load in throwing-dominant sports was conducted using variations of terms that described different load-monitoring techniques and different sports. Studies included in this review were published prior to June 2015 and were identified through a systematic search of four electronic databases including Academic Search Complete, CINAHL, Medline and SPORTDiscus. Only full-length peer-reviewed articles investigating workload monitoring in throwing-dominant sports were selected for review. Results A total of 8098 studies were initially retrieved from the four databases and 7334 results were removed as they were either duplicates, review articles, non-peer-reviewed articles, conference abstracts or articles written in languages other than English. After screening the titles and abstracts of the remaining papers, 28 full-text papers were reviewed, resulting in the identification of 20 articles meeting the inclusion criteria for monitoring workloads in throwing-dominant sports. Reference lists of selected articles were then scanned to identify other potential articles, which yielded one additional article. Ten articles investigated workload monitoring in cricket, while baseball provided eight results, and handball, softball and water polo each contributed one article. Results demonstrated varying techniques used to monitor workload and purposes for monitoring workload, encompassing the relationship between workload and injury, individual responses to workloads, the effect of workload on subsequent performance and the future directions of workload-monitoring techniques. Conclusion This systematic review highlighted a number of simple and effective workload-monitoring techniques implemented across a variety of throwing-dominant sports. The current literature placed an emphasis on the relationship between workload and injury. However, due to differences in chronological and training age, inconsistent injury definitions and time frames used for monitoring, injury thresholds remain unclear in throwing-dominant sports. Furthermore, although research has examined total workload, the intensity of workload is often neglected. Additional research on the reliability of self-reported workload data is also required to validate existing relationships between workload and injury. Considering the existing disparity within the literature, it is likely that throwing-dominant sports would benefit from the development of an automated monitoring tool to objectively assess throwing-related workloads in conjunction with well-established internal measures of load in athletes.
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1 23
Sports Medicine
ISSN 0112-1642
Sports Med
DOI 10.1007/s40279-016-0529-6
Monitoring Workload in Throwing-
Dominant Sports: A Systematic Review
Georgia M.Black, Tim J.Gabbett,
Michael H.Cole & Geraldine Naughton
1 23
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SYSTEMATIC REVIEW
Monitoring Workload in Throwing-Dominant Sports:
A Systematic Review
Georgia M. Black
1
Tim J. Gabbett
1,2
Michael H. Cole
1
Geraldine Naughton
3
Ó Springer International Publishing Switzerland 2016
Abstract
Background The ability to monitor training load accu-
rately in professional sports is proving vital for athlete
preparedness and injury prevention. While numerous
monitoring techniques have been developed to assess the
running demands of many team sports, these methods are
not well suited to throwing- dominant sports that are
infrequently linked to high running volumes. Therefore,
other techniques are required to monitor the differing
demands of these sports to ensure athletes are adequately
prepared for competition.
Objective To investigate the different methodologies
used to quantitatively monitor training load in throwing-
dominant sports.
Methods A systematic review of the methods used to
monitor training load in throwing-dominant sports was
conducted using variations of terms that described different
load-monitoring techniques and different sports. Studies
included in this review were published prior to June 2015
and were identified through a systematic search of four
electronic databases including Academic Search Complete,
CINAHL, Medline and SPORTDiscus. Only full-length
peer-reviewed articles investigating workload monitoring
in throwin g-dominant sports were selected for review.
Results A total of 8098 studies were initially retrieved
from the four databases and 7334 results were removed as
they were either duplicates, review articles, non-peer-re-
viewed articles, conference abstracts or articles written in
languages other than English. After screening the titles and
abstracts of the remaining papers, 28 full-text papers were
reviewed, resulting in the identification of 20 articles
meeting the inclusion criteria for monitoring workloads in
throwing-dominant sports. Reference lists of selected arti-
cles were then scanned to identify other potential articles,
which yielded one additional article. Ten articles investi-
gated workload monitoring in cricket, while baseball pro-
vided eight results, and handball, softball and water polo
each contributed one article. Results demonstrated varying
techniques used to monitor workload and purposes for
monitoring workload, encompassing the relationship
between workload and injury, individual responses to
workloads, the effect of workload on subsequent perfor-
mance and the future directions of workload-monitoring
techniques.
Conclusion This systematic review highlighted a number
of simple and effective workload-monitoring techniques
implemented across a variety of throwing-dominant sports.
The current literature placed an emphasis on the relat ion-
ship between workload and injury. However, due to dif-
ferences in chronological and training age, inconsistent
injury definitions and time frames used for monitoring,
injury thresholds remain unclear in throwing-dominant
sports. Furthermore, although research has examined total
workload, the intensity of workload is often neglected.
Additional research on the reliability of self-reported
workload data is also required to validate existing rela-
tionships between workload and injury. Considering the
existing disparity within the literature, it is likely that
throwing-dominant sports would benefit from the
& Georgia M. Black
georgia.black@acu.edu.au
1
School of Exercise Science, Australian Catholic University,
1100 Nudgee Road, Brisbane, QLD 4014, Australia
2
School of Human Movement Studies, The University
of Queensland, Brisbane, QLD, Australia
3
School of Exercise Science, Australian Catholic University,
Melbourne, VIC, Australia
123
Sports Med
DOI 10.1007/s40279-016-0529-6
Author's personal copy
development of an automated monitoring tool to objec-
tively assess throwing-related workloads in conjunction
with well-established internal measures of load in athletes.
Key Points
Optimal techniques for monitoring workload in
throwing-dominant sports have been less researched
than in running-based team sports.
Workload monitoring can be used effectively to
detect and identify injury risks and thresholds in
some throwing-dominant sports. However, a number
of key limitations impair current rese arch. These
limitations include: the lack of reliability of self-
reported load data, inability of current techniques to
monitor all of the training completed by athletes and
the majority of published workload-inju ry data being
based around game loads, with training loads often
neglected.
The use of more than one workload-monitoring
technique potentially provides coaches with an
understanding of the factors influencing performance
and contributing to injury, and may also facilitate
further individualisation of the training process.
1 Introduction
Documentation of training and competition workloads is
increasingly important in team sports, with much interest
on the influence of training volume, intensity and fre-
quency on injury [1, 2]. While positive dose–response [3
5] relationships to load have been reported, negative
responses have also been highlighted, with the greatest
incidence of injuries occurring when workloads are highest
[6, 7]. The importance of monitoring workload in athletes
has stemmed from research supporting a positive rela-
tionship between workload and injury. Although it is
hypothesised that restricting workloads may minimise the
likelihood of athlete injury [6], reducing workloads in
competition and training may also be detrimental to an
athlete’s conditioning and performance in team sports [6].
A recent review highlighted that both under- and over-
training can increase the risk of injury. While conflicting
relationships exist between workload and injury, excessive
and rapid increases in workload result in sharp increases in
injury risk [8].
Given an individual’s response to a specific workload
can be highly variable [3], understanding how each athlete
responds to the demands of training and competition is
paramount. Traditionally, elite and sub-elite teams have
relied on video time-motion analyses to monitor player
workloads and to quantify the individual contributions to
each specific game. This particular method of workload
analysis is both labour-intensive and prone to human error.
Also it cannot be performed in real-time and is typically
restricted to a single player within a given time [9].
Although a numb er of new technologies exist within dif-
ferent sports (Prozone
Ò
, Pitch Fx
Ò
) that have the ability to
monitor player workloads during games, these technologies
are not typically used to monitor performance during
training o r practice. To address the many issues associated
with video time-motion analyses, global positioning sys-
tems (GPS) have more recently been u sed to measure
external workloads in team sport athletes [10, 11] in both
training and game situations. GPS technology that samples
at a frequency of 10 Hz has acceptance as a valid and
reliable measure of velocity, distance and acceleration [12].
As such, this technology allows coaches and sports scien-
tists to quantify the activity profiles and demands of
training and competition in a wide variety of sports. With
the addition of inertial measur ement sensors (i.e.
accelerometers, gyroscopes and magnetometers), these
microtechnology units are increasingly used as a reliable
and accurate method of monitoring athlete workloads [13].
The relationship between running volumes and subse-
quent injury risk has been broadly researched in team
sports [1], with research highlighting links between weekly
training loads [14] and 3-weekly sprint distances [15] and
injury risk in Australian Football players. Similarly, rugby
league players who perform a higher volume of very-high-
speed running have been shown to have an increased risk
of subsequent injury [1]. Notably, an understanding of
these relationships has allowed running load thresholds to
be established to decrease injury risk and protect those
athletes who are involved in running-dominant team sports
[1, 15].
Although multiple techniques for monitoring training
load have been suggested [16, 17], their invasive nature
(e.g. blood sampling [18]), makes their recurring use
problematic with elite athletes [19]. Therefore, the use of
the session-rating of perceived exertion (RPE) method has
emerged to monitor training loads in team sports [2, 6, 15,
16]. Other monitoring techniques include self-reported
measures of mood states [2022] and wellness [23], and
have been reported to be sensitive to subt le changes in
training load. Collectively, these studies [2023] support
the inclusion of wellness questionnaires as a technique to
monitor workload in team sport athletes. Using a subjective
(session-RPE 9 training duration) method to monitor
training load, Australian Football research indicated that
injury risk was significantly higher for players who exerted
G. M. Black et al.
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larger ‘1- and 2-weekly loads’ or large week-to-week
changes ([1250 arbitrary units, AU) in workload (odds
ratio: 2.58) [2]. Multiple studies have monitored training
loads using the session-RPE method in rugby league [1, 6,
24], with conflicting results being reported. However, a
recent review of literature [8] has highlighted the acute:-
chronic workload ratio as a critical variable in workload
monitoring. The findings [8] highlight the importance of
monitoring acute and chron ic training loads and their
ability to identify and understand injury thresholds and
injury risk in team sport athletes.
Despite the wealth of research documenting workload
and its relationship with injury in many running-dominant
team sports, evidence investigating workload-monitoring
techniques in throwing-dominant sports is far less sub-
stantive. Furthermore, considering a large number of sports
include physically demanding activities involving few
locomotor demands (e.g. bowling, pitching, throwing), it is
likely that research which has focussed on characterising
the locomotive, kinematic demands of team sports [13 ]asa
measure of total workload may not provide an accurate
representation of the physical demands of throwing-domi-
nant sports. Given that throwing sports are largely under-
represented in the monitoring and managing of athlete
workloads, it was the purpose of this systematic review to
investigate the literature surrounding the methodologies
most commonly implemented to monitor worklo ad in
throwing-dominant sports. Specific sports explored (base-
ball, cricket, handball, javelin, shot put, softball and water
polo) were chosen in order to investigate player workloads
predominantly involving physically demanding throwing
activities with lower locomotor demands than other team
sports. This review shifts the focus from running-based
team sports to throwing-dominant sports and will provide
coaches, sport sci entists, and strength and conditioni ng
staff with a perspective on the evidence relating to work-
load-monitoring techniques in throwing-dominant sports.
2 Methods
2.1 Literature Search Strategy
This review investigated different methodologies used to
quantify and monitor training load in throwing-dominant
sports. Articles for this review were systematically identi-
fied through the search of electronic academic databases
that included Academic Search Complete, CINAH L,
Medline and SPORTDiscus. These databases were sear-
ched using the combinations of the following key words:
(1) ‘baseball’; ‘cricket’; ‘softball’; ‘handball; ‘water polo’;
‘javelin’; ‘shot put’; (2) ‘work load’; ‘workload’; ‘training
load’; ‘pitch’; ‘bowl’; ‘throw’. Terms were connected with
‘OR’ within each of the two combination groups and these
two search categories were combined using ‘AND’.
2.2 Selection Criteria
The process used for select ing articles is outlined in Fig. 1.
Duplicate articles were eliminated from the initial search
results and the titles and abstracts of remaining articles
were then independently reviewed by three assessors
(GMB, TJG and MHC) for relevance to the review. For the
purpose of the review, articles included were required to
describe methods to monitor workload in throwing-domi-
nant sports. As such, articles that only provided a technical
description of throwing, pitching or bowling movements
were excluded. Publications were also excluded from this
research if they were review articles, not a full-length
paper, non-peer-reviewed or studies that described or
reported general training or game-r elated demands of
sports (i.e. did not separate throwing-related demands from
running-related demands, for example). In situations where
one or more of the three independent reviewers disagreed
regarding the suitability of a paper for inclusion, the merits
of the paper were discussed until a consensus was reached.
The selected articles included papers published prior to
June 2015 that were written in English and included the
search terms in the title or abstract. The full-text of the
manuscripts was assessed for inclusion using the same
criteria, once articles were selected. Reference lists of
selected articles were then scanned to detect any potentially
relevant articles not identified by the original search. Sec-
ondary-sourced articles were then subjected to the same
screening procedures.
2.3 Quality of Research
The quality of reporting in the included research studies
was assessed based on a modified version of currently
established scales used in sport science, healthcare and
rehabilitation [i.e. Cochrane, Coleman, Delphi and Phys-
iotherapy Evidence Databa se (PEDro)] to evaluate research
conducted in athletic-based training environments [25].
The current scale (Table 1) was adapted and modified from
a recent review [26], where study quality was appraised
based on ten items that were each scored on a scale that
ranged from zero (no), to one (maybe) or two (yes). As no
intervention studies were included in this review, the score
attributed to the ‘intervention’ criterion was replaced with a
criterion assessing the overall thoroughness with which the
data collection procedures were reported in each paper.
Considering observational study designs are most com-
monly used in applied sport science, the ‘control group’
criterion was removed from the scale, leaving nine criteria
yielding a maximum of 18 points. For those studies not
Workload Monitoring in Throwing Sports
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involving cohorts being allocated to different groups (e.g.
injured vs. uninjured), the criterion related to the reporting
of subject assignment was omitted from the quality of
assessment and these papers were scored out of 16. To
ensure that the quality assessment was equitable for all of
the include d studies, the scores were summed and expres-
sed as a percentage that ranged from zero to 100 %.
3 Results
A total of 8098 studies were initially retrieved from the
four databases, of which 2291 were duplicates, 94 were
non-English papers, 88 were conference abstracts, 4799
were not full-length articles and 62 were review articles.
Non-peer-reviewed articles that included magazine articles,
newspaper articles and opinion pieces were also excluded.
The titles and abstracts of the remaining 764 unique
research articles were screened, resulting in 736 being
excluded and 28 progressing to full-text review. After full-
text review, a further eight papers were omitted and one
was included in the review after the references of selected
articles were scanned (Fig. 1). Therefore, 21 articles
remained for inclusion in this review.
Ten articles investigated workload monitoring in cricket
(Table 2), reporting on the relationship between workload
and injury (n = 8), fatigue responses (n = 1) and the use
Fig. 1 Flowchart of the selection process for inclusion of articles in the systematic review
Table 1 Study quality scoring system [26]
No. Item Score
1 Inclusion criteria stated 0–2
2 Subjects assigned appropriately (random/equal
baseline)
0–2
3 Intervention described 0–2
4 Dependent variables defined 0–2
5 Assessments practical 0–2
6 Training duration practical (acute vs. long term) 0–2
7 Statistics appropriate (variability, repeated measures) 0–2
8 Results detailed (mean, standard deviation, percent
change, effect size)
0–2
9 Conclusions insightful (clear concise, future directions) 0–2
Total 0–18
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Table 2 Summary of results from studies investigating workload in cricket
Study Sample Load-monitoring techniques Method Findings Quality
score
Dennis
et al. [29]
90 elite adult
cricket fast
bowlers
Balls bowled/session
Balls bowled/week
Balls bowled/month
Workload was quantified by
examining scorecards and
conducting surveillance at
training sessions. Risk ratios
were used to identify the
relationship between bowling
workload and injury
Bowlers who completed, on
average, \123 or [188
deliveries/week had an
increased risk of injury
compared to those who bowled
between 123 and 188 deliveries/
week
100 %
Dennis
et al. [28]
12 elite adult
cricket fast
bowlers
Balls bowled/session
Balls bowled/week
Balls bowled/month
Balls bowled/season
Workload was quantified by
examining scorecards and
conducting surveillance at
training sessions. Risk ratios
were used to identify the
relationship between bowling
workload and injury
Players who bowled C5 sessions
in a week were 4.5 times more
likely to be injured. Injured
bowlers bowled significantly
more deliveries/week. Injured
bowlers had a spike in
deliveries/session in the
8–21 days prior to injury as
compared to the average
number of deliveries/session
(p \ 0.02). Bowlers who
bowled [522 balls in a 30-day
period were at an increased risk
of injury (p \ 0.01)
100 %
Dennis
et al. [27]
44 elite junior
cricket fast
bowlers
Daily diary to assess:
Balls bowled/match innings
Training sessions/week Balls
bowled/training session
Prospective cohort study. Bowlers
completed a daily diary over
one season to record bowling
workloads and self-reported
injuries. Bowling workload
prior to injury was compared to
workload across a whole season
for uninjured bowlers
Injured bowlers had been bowling
significantly more frequently
than uninjured bowlers.
Increased risk of injury was
associated with bowling
C2.5 days/week or C50
deliveries/day
94 %
Hulin et al.
[34]
a
28 elite adult
cricket fast
bowlers
Balls bowled/week (external
workload)
RPE multiplied by training
duration in minutes (internal
workload)
Training stress balance
Workload data was accessed from
Cricket Australia from 2006 to
2012. Data were categorised
into weekly blocks. One-week
data, together with 4-week
average rolling data were
calculated for external and
internal loads. Training stress
balance was calculated by
dividing the acute by the
chronic workload and expressed
as a percentage. The likelihood
of sustaining an injury was
determined for the current week
and subsequent week
A negative training stress balance
was associated with an
increased risk of injury in the
subsequent week for internal
and external workload.
Compared with a training stress
balance between 50 and 99 %,
the relative risk of injury
associated with a training stress
balance greater than 200 % was
4.5 times and 3.3 times for
internal and external workload,
respectively
100 %
McNamara
et al. [35]
26 elite youth
cricketers.
Classified as
fast (n = 9) or
non-fast
(n = 17)
bowlers
Movement analysed using GPS
units (MinimaxX, Catapult
Innovations, Melbourne, VIC,
Australia)
CMJ relative power and flight
time
Perceptual well-being
Cortisol and testosterone
concentration
Workloads and markers of
neuromuscular, endocrine and
perceptual fatigue were
compared in male fast and non-
fast bowlers in response to a
7-week physical preparation
period and a 10-day intensive
period of competition. GPS
units were worn during all
training and competitive
sessions. CMJs were completed
pre-training and pre-match,
perceptual fatigue scores were
completed daily, salivary
analyses were completed
weekly during the preparation
phase and daily during
competition
Fast bowlers covered greater
total, low and high speed
distances during competition.
Cortisol concentrations were
higher in the preparation and
competition phases, and
testosterone concentrations
were lower in the competition
phase for fast bowlers.
Perceptual well-being was
poorer during competition for
fast bowlers compared to non-
fast bowlers. No differences
were reported in neuromuscular
function between groups
100 %
Workload Monitoring in Throwing Sports
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Table 2 continued
Study Sample Load-monitoring techniques Method Findings Quality
score
McNamara
et al.
[47]
a
12 highly-skilled
fast bowlers
Comparison of MinimaxX S4 unit
(Catapult Innovations,
Melbourne, Australia) and
manually recorded balls
bowled. True-positive, true-
negative, false-positive, false-
negative
Bowlers performed a series of
bowling, throwing and fielding
activities during training and
competition. Sensitivities and
specificities of the bowling-
detection algorithm were
determined by comparing the
device outputs with manually-
recorded bowling counts
No significant differences were
reported between direct
measures of bowling and the
true positive and negative
events recorded by the
MinimaxX unit. Sensitivities
during training (99.0 %) and
competition (99.5 %) were
acceptable. Specificities during
training were also high
(98.1 %), but lower during
competition (74.0 %)
100 %
Orchard
et al.
[31]
a
198 elite adult
cricket fast
bowlers
Overs bowled/match Prospective cohort study
following bowlers to compare
overs bowled in a match and
injury risk subsequent to the
match
Players who bowled [50 overs
had an increased injury risk in
the next 21 days of 3.37 injuries
per 1000 overs bowled. Bowling
[30 overs in the second innings
increased injury risk per over
bowled in the next 28 days (RR
2.42)
100 %
Orchard
et al.
[32]
a
235 elite adult
cricket fast
bowlers
Overs bowled/match Prospective cohort study using
bowling workload data
extracted from Cricket Australia
databases. Bowling workloads
monitored during time periods
from 5 to 26 days were
examined to highlight an
increased injury rate during the
month subsequent to the
workload
Players who bowled [50 match
overs in a 5-day period had an
increase in injury rate over the
next month compared to those
who bowled \50 overs (RR
1.54)
100 %
Orchard
et al.
[33]
a
235 elite adult
cricket fast
bowlers
Acute match overs C50
Career overs C1200
Overs in previous season C400
Overs in previous 3 months C150
Career overs C3000
Prospective cohort study
investigating the relationship
between injury risk and
workload status. All game
workload data were extracted
from official scorecards
High acute match workload and
high previous season workload
were risk factors for developing
tendon injuries. High medium-
term workload (3-month
workload C150 overs) was
protective. Low (\1200 overs)
and also very high (C3000)
career workloads were
protective for tendon injuries
compared with medium–high
career workloads (1200–3000
overs)
100 %
Saw et al.
[30]
a
28 elite adult
cricketers
Throws/day
Throws/week
Prospective cohort study
monitoring daily throwing
workload over one cricket
season. All throws completed
during the 1st and 2nd XI
training and matches were
video recorded or manually
recorded by direct observation
and were used to determine
workload. Risk ratios were
calculated to describe the
association between throwing
workload and injury
Injured players threw 40 more
throws/week and 12.5
throws/day. Players were at an
increased risk of injury if they
completed 40 throws per day
88 %
RPE rating of perceived exertion, CMJ countermovement jump, GPS global positioning system, RR relative risk
a
Denotes papers that were scored out of 16
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of microtechnology to detect fast-bowling events (n = 1).
Eight addressed workload in baseball (Table 3) investi-
gating the relationship between workload and injury
(n = 6), the impact of pitch count on performance (n = 1)
and fastball velocity trends (n = 1). The final three articles
(Table 4) investigated workload in water polo (n = 1),
handball (n = 1) and softball (n = 1).
Furthermore, five of the 21 articles selected sought to
characterise the use of different methods to monitor
workload, while 16 articles assessed workload to establish
its relationship to injury. Fifteen articles assessed workload
using objective measures, and three articles used self-re-
ported methods to monitor workload. Three articles
included a combination of objective and subjective meth-
ods to monitor workload.
Assessment of the reporting quality of the selected
articles provided a mean quality rating of 92.6 ± 7.7 %
(see Tables 2, 3, 4). Eight of the 21 studies assigned sub-
jects appropriately into comparative groups by similar
baseline measures, comparing injured and non-injured
groups. Each of these studies stated the inclusion criteria
and dependent variables examined in their respective
research. The least reported criterion was ‘subject assign-
ment’ and the best reported criteria were ‘inclusion criteria
stated’ and ‘dependent variables defined’.
4 Discussion
The aim of this systemat ic review was to investigate the
methods used to monitor workload in throwing-dominant
sports. From the studies included in this review, it was
apparent that workload-monitoring techniques are not
advanced in throwing-dominant sports, although simple
monitoring methods have been shown to have the capacity
to identify injury risks and injury thresholds in instances
within these sports. A clear need exists for more reliable
and less labour-intensive workload-monitoring techniques
to provide further understanding of the physical and tech-
nical demands experie nced by athletes in throwing-domi-
nant sports.
A large number of studies included in this review
monitored workload and its relationship with injury in
cricket (67 %) [2733] or pain [3640] and injury in
baseball (86 %) [ 41]. Research has highlighted that cricket
fast bowlers and baseball pitchers are the positional groups
most prone to injury in their respective sports. While the
location of injury and pain differed between sports, this can
be attributed to differences in pitching and fast bowling
technique. Fast bowlers are more likely to sustain injuries
to the lower back and lower limb [31], due to the fast
bowling action involving a run-up. Baseball pitchers are
more susceptible to elbow and shoulder pain [38, 39] and
injury [ 40] due to the accumulation of microtrauma from
the repetitive pitching motion [48]. Notwithstanding the
different injury types between cricket fast bowlers and
baseball pitchers, the results of this review demonstrated
similar relationships between high workloads and the
likelihood of injury or pain in these sports.
4.1 Reporting Quality
On the basis of this review, it is evident that research that
has focussed on quantifying workload in throwing-domi-
nant sports has typically adhered to a high standard of
reporting. The study quality was most commonly affected
by items three (rigor of data collection), five (assessments
practical) and six (training duration practical) in Table 1.
Improving the descriptions around accuracy, reliability and
relevance of specific workload-monitoring techniques in
throwing-dominant sports may improve the quality of
future research.
4.2 Workload Monitoring in Cricket
4.2.1 Workload and Injury in Cricket
Of the 21 studies included in this review, eight (38 %)
monitored workload in cricket fast bowlers to establish
the relationship between workload and injury risk. The
majority (75 %) of studies examining workload in cricket
were limited to subjective monitoring techniques [2733].
Dennis et al. [27] monitored workload using log books,
completed by participants, detailing the number of
bowling deliveries completed each day over a 6-month
period. Altho ugh this study [27] provided evidence to
support optimal rest days between bowling in junior
cricketers, the small sample size provided insufficient
power to detect small differences in bowling workload
between the injured and uninjured bowlers. Furthermore,
this study [27] was based on self-reported load data and
the reliability of the log books completed by the b owlers
was not reported.
More objective reports of the number of deliver ies
bowled per week [28] and the number of sessions bowled
per week [29] have also been used to monitor the rela-
tionship between load and injury risk in cricket fast bow-
lers. During one study [28] bowling workloads were
evaluated by filming each participant’s training session.
Match workloads were recorded from scorecards and par-
ticipants were asked to keep a personal record of deliveries
completed at any session where filming was not possible
[28]. While video-based methods have been shown to be an
accurate method to monitor bowling workloads in profes-
sional matches, it can be an expensive and time-consuming
technique to implement, especially at lower levels of
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Table 3 Summary of results from studies investigating workload in baseball
Study Sample Load-
monitoring
techniques
Method Findings Quality
score
Bradbury
and
Forman
[42]
a
1058 elite baseball pitchers Pitches/game Pitching workload data was
obtained from a baseball
statistics website. Data from
pitchers who started games
after \15 days rest were
analysed. Multiple
regression analyses were
used to assess the immediate
and cumulative effect of
pitches thrown and the days
of rest on performance
Pitches thrown were
negatively correlated with
future performance.
Estimates indicate each
pitch thrown in the
preceding game increased
ERA by 0.007 in the
following game. Increased
number of rest days was not
associated with performance
94 %
Bushnell
et al.
[38]
23 elite baseball pitchers Pitch velocity
using a radar
gun
Prospective cohort study.
Pitch velocity was recorded;
the ball speed was recorded
for the fastest pitch thrown
for a strike during the game
(maximum pitch velocity).
Pitchers followed over three
seasons and the association
between maximum pitch
velocity and elbow injury
were analysed
Nine players had elbow
injuries during the study.
The injured players had a
higher average pitch
velocity (89.22 ± 5.36 vs.
85.22 ± 3.24 mph). There
was a statistically significant
relationship between pitch
velocity and elbow injury.
The three pitchers with the
highest maximum pitch
velocity had injuries
requiring elbow surgery
83 %
Crotin
et al.
[41]
a
12 elite baseball pitchers Pitch velocity
Pitch type
Baseball pitchers monitored
over an eight-game period.
Ball velocity was recorded
for each pitch using a radar
gun. Pitch types were
manually recorded. Pitcher
data were grouped and the
mean fast ball velocity was
computed for each game.
Regression analyses were
performed to compare
pitching velocity and the
game number
The FBV increased linearly
over the eight-game period.
The mean FBVs increased
0.56 mph over the eight
games
81 %
Karakolis
et al.
[37]
a
3760 elite baseball pitcher seasons
b
Games pitched/
season
Total innings
pitched/
season
Pitches thrown/
season
Average
number of
innings
pitched/
appearance
Average
number of
pitches
thrown/
appearance
Pitcher statistics were
obtained from a baseball
statistics website. Work
metrics were analysed to
determine if there was a
correlation between
cumulative work and injury
in the following season
Based on the regression
analyses performed, none of
the cumulative work metrics
investigated were significant
predictors of injury in the
following season
100 %
G. M. Black et al.
123
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competition where resources are often limited. Based on
this, Dennis et al. [29] employed research assistants to
attend, observe and monitor bowling workload during all
training sessions completed by participants. While these
studies provided innovative results for fast bowli ng
workload and injury, total throwing workload was not
assessed. Therefore, the findings of these studies [27 , 28]
may be limited to the sub-group of fast bowlers in a cricket
team and, hence, may not provide an accurate indication of
the workload-injury risk faced by other players in the team.
Table 3 continued
Study Sample Load-
monitoring
techniques
Method Findings Quality
score
Karakolis
et al.
[36]
a
761 elite baseball pitcher seasons
b
Number of
innings
pitched in a
season
Difference in
total innings
pitched
between
consecutive
seasons
Pitching workload data were
obtained from a baseball
statistics website.
Regression analyses were
performed to determine
whether the number of
innings pitched during a
single season or the
difference in innings pitched
over consecutive seasons
were correlated with future
injury (measure of time
spent on disabled list)
No significant correlations
were found between innings
pitched and future injury.
No significant differences
were found when pitchers
were split into groups based
on consecutive innings
pitched difference cut-offs
88 %
Lyman
et al.
[41]
a
298 youth baseball pitchers Pitches/game
Pitches in a
season
Innings pitched
Games pitched
Pitch types
Prospective cohort study in
which coaches completed a
pitch count book following
each game. Participants
were contacted by phone
after each game to identify
arm complaints
Risk factors for elbow pain
included throwing \300 or
[600 pitches during a
season. Risk factors for
shoulder pain included
throwing [75 pitches per
game, and throwing \300
pitches in a season
81 %
Lyman
et al.
[40]
a
172 youth baseball pitchers Pitches/game
Cumulative
season pitches
Pitch type
Prospective cohort study
using a pitch count log of
pitches thrown per pitcher
during the season. Phone
interviews were completed
post-game to identify arm
complaints
There was a significant
association between the rate
of elbow and shoulder pain
and the number of pitches
thrown in a game and during
the season. The curveball
was associated with a 52 %
increased risk of shoulder
pain and the slider was
associated with an 86 %
increased risk of elbow pain
81 %
Olsen
et al.
[39]
150 adolescent baseball pitchers.
Further grouped into pitchers
who had shoulder or elbow
surgery (n = 95) and pitchers
who had never had a significant
pitching injury (n = 45)
Months/year
competitive
pitching
Pitch velocity
Number of:
pitching
appearances/
year innings/
appearance
pitches/
appearance
pitches/year
warm-up pitches
Pitchers responded to a survey
and results were compared
between pitchers who had
shoulder or elbow surgery
and pitchers who had never
had a significant pitching
injury. Multivariable
logistic regression models
were developed to identify
the risk factors for injury
The injured group pitched
more months/year, games/
year, innings/game, pitches/
game, pitches/year and
warm-up pitches. High pitch
velocity was also associated
with increased risk of injury
83 %
ERA estimated run average, FBV fast ball velocity
a
Denotes papers that were scored out of 16
b
Baseball pitcher seasons—number of individual seasons pitched and analysed
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In an attempt to monitor throwing workload in all
cricketers, the relationship between total throwing work-
load and injury risk in elite cricketers supported by the
objective data available from video-f ootage of matches and
training was investigated [30]. Despite the novel approach
of the research, this study [ 30 ] did not account for bowling
workload or any throwing workload completed during
players’ participation in sub-elite competition or practice
matches. Consistent with previous research [28], the use of
video recording to monitor workload is not ideal due to the
labour-intensive nature of the technique.
Although relationships between fast bowling wor kloads
and injury are well established [2729, 31], currently
interest lies in the investigation of the delayed effect of
high bowling workloads on injury. Comparisons have been
made between the number of overs bowled by players
during a match with the player’s injury risk subsequent to
the match [31]. Bowling injury and workload data were
extracted from a pre-existing database and findings sug-
gested that elite bowlers who bowled more than 30–50
overs had an increased injury risk in the next 21–28 days
[31]. Similarly, when pre-existing load data was consid-
ered, bowlers who bowled more than 50 match overs in a
5-day period had a greater incidence of injury over the next
month than players bowling less than 50 overs (relative
risk = 1.54) [32]. While high acute match workloads and
high previous season workloads have also been identified
as risk factors for developing tendon injuries in cricket,
workloads that induce a protective and beneficial response
have been further investigated [33]. Coll ectively, these
studies provide evidence that workloads can be both ben-
eficial and detrimental to elite fast bowlers. However, the
results of these studies should be interpreted with a degree
of caution as the training workloads of other competitions
not involving Australian teams and the workloads of other
training sessions (e.g. strength and conditioning sessions)
were not included.
While balls and overs completed are the most common
methods used to monitor workload in cricket, Hulin et al.
[34] was the first to combine wor kload using both external
(balls bowled) and internal (session-RPE 9 training dura-
tion) monitoring techniques. This study [34] compared
acute (1-week data) and chronic (4-week average rolling
data) workloads and associated injury risk in elite fast
bowlers. An acute:chronic workload ratio was also asses-
sed by divid ing the acute by the chronic workload. An
acute:chronic workload ratio [1.5 for both internal and
external workload was associated with an increased risk of
injury in the subsequent week [34]. Furthermore, an
acute:chronic workload ratio greater than 2.0 (i.e. acute
workload was double that of the chronic workload) had a
relative risk of injury of 4.5 and 3.3 compared with those
Table 4 Summary of results from studies investigating workload in handball, water polo and softball
Study Sample Load
monitoring
techniques
Method Findings Quality
score
Bresciani
et al.
[44]
a
14 elite
handball
players
Session RPE
multiplied by
training
duration
Haematological
analyses
POMS
Questionnaire
and
REST-Q Sport
Players were monitored over a 40-week
season. Session-RPE was collected
following each session and match. Blood
samples were collected and the POMS
completed on five occasions throughout
the season
Blood C-reactive protein and oxidised
glutathione concentrations increased
during high-load periods. Reduced/
oxidised glutathione ratio decreased
during periods of high load. No changes
were observed in total mood based on the
POMS test. Following high training load,
injury, being in shape and physical
recovery (REST-Q) correlated with
workload
94 %
Lupo
et al.
[46]
a
13 elite
youth
water
polo
players
Heart rate
Session RPE
multiplied by
training
duration
Players monitored during eight sessions.
The Edwards summated heart-rate-zone
method was used and session-RPE rating
(CR-10 scale) was obtained following
each sessions. Correlations between the
two measures were completed
Strong and significant (p \ 0.001)
correlations between the Edwards heart-
rate-zone and session-RPE methods were
reported
94 %
Shanley
et al.
[45]
12 youth
amateur
softball
players
Pitch count/
game
Pitches/season
Total games
pitched
Prospective cohort study in which each
coach collected pitch counts for
individual players following each game
No significant differences between injured
and non-injured groups
83 %
RPE rating of perceived exertion, POMS profile of mood states, REST-Q recovery-stress questionnaire, CR category ratio
a
Denotes papers that were scored out of 16
G. M. Black et al.
123
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shown to have an acute:chronic workload ratio betwee n 0.5
and 0.99 for internal and external workloads, respectively
[34]. In summary, while the majority of research has
investigated the influence of workload on injury, further
studies considering other methods to monitor load in
cricket are warranted.
4.2.2 Other Workload-Monitoring Techniques Used
in Cricket
McNamara et al. [35] conducted the only study using
methods other than balls bowled to monitor load in crick-
eters. The researchers investigated key fatigue and work-
load variables of elite youth fast bowlers and non-fast
bowlers during a 7-week physical preparation period and a
10-day intensified competition period [35]. Using GPS, the
researchers established that fast bowlers performed greater
external workload during competition than other playing
positions, covering greater total, low- and high-speed dis-
tances [35]. Higher cortisol and lower testos terone con-
centrations were also reported in the preparation and
competition phases for fast bowlers [35]. Additionally,
perceptual well-being was poorer during the competition
phase for fast bowlers compared to non-fast bowlers [35].
This study [34] shows that monitoring techniques other
than balls bowled can provide information on the individ-
ual responses to workloads and also distinguish between
positional groups.
4.3 Workload Monitoring in Baseball
4.3.1 Workload, Injury and Pain in Baseball
Of the eight studies examining workload in baseball, four
used injury [36, 37, 40, 41] and two used pain [38, 39]as
their outcome measure. Consistent with the findings in
cricket players [28], Lyman et al. [39] reported a positive
relationship between pitching load and arm pain. Coaches
were required to complete a pitch count book for each
pitcher during games, and pitchers were contacted for a
postgame interview via telephone to collect details on each
game and any pitching-related pain complaints [39]. In a
subsequent study [38], similar relationships were found
between pitch counts, pitch types and arm pain. Using
similar workload-monitoring methodology to Lyman et al.
[39], a significant association was reported between the
number of pitches thrown in a game and during the season
and the rate of elbow and shoulder pain [39]. One study
compared youth pitchers who had required surgery for a
pitching-related injury with uninjured pitchers [41]. A
telephone survey was conducted containing questions on
injury history, playing history and potential risk factors less
than 1 year following the pitching-related injury [41]. The
group who required surgery pitched more months per year,
games per year, innings per game, pitches per game, pitches
per year and warm-up pitches than the uninjured group.
Despite demonstrating an increased risk of injury or pain in
response to a high pitching load, there are a number of
potential limitations of these studies that should be consid-
ered. First, they were reliant on self-reported recall of
pitching practice, which may have led to biases in the data.
For example, it is possible that the injured group of players
may have reported higher workloads as they may have been
primed to believe that higher workloads lead to greater
injury or pain risk. Second, the specific methodologies of
these studies made it impossible to examine the effect of
pitching intensity on pain and injury risk. Third, these
studies [37, 38, 41] lacked any description of any procedures
implemented to determine the validity of the surveys used to
collect the aggregated self-repor ted data for their research.
Twenty-three baseb all pitchers were monitored during a
spring game period to investigate the association between
maximum pitch velocity (defined as the fastest ball thrown
for a strike during one game) and subsequent elbow inju-
ries in professional baseball pitchers [40]. Pitch velocity
was recorded using a standardised radar gun and workload
information for the following three seasons were deter-
mined using a baseball statistical website [40]. Although
the injured group (n = 9) had a higher mean pitch velocity
(89.2 ± 5.4 vs. 85.2 ± 3.2 mph), the small sample size
may have contributed to the lack of significant between-
group differences [40]. Nevertheless, the three pitchers
with the highest maximum pitch velocity sustained the
injuries requiring elbow surgery [40].
A recent investigation focussed on the relationship
between cum ulative workload metrics and injury risk [37].
Cumulative metrics included: games pitched in a season,
total innings pitched during a season, pitches thrown in one
season, average number of innings pitched per appea rance
and average number of pitches thrown per appearance. All
pitcher statistics were obtained from a baseball statistical
website and results demonstrated that none of the cumu-
lative work metrics investigated were significant predictors
of injury in the following season [37]. This study [37] had
limitations that warrant consideration when interpreting the
results. First, an injury was defined as a pitcher missing 15
games or more, therefore potentially under-reporting injury
rates. Second, none of the metrics (e.g. games pitched,
pitches thrown) analysed in this research accounted for
pitching intensity. Additionally, between-game cumulative
work that would have contributed to total workload
throughout the season was not reported.
Further research using pitcher data from a baseball
statistical website [36], extended upon previous findings to
examine the relationship between the number of innings
pitched and future injury in elite baseball pitchers
Workload Monitoring in Throwing Sports
123
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\25 years of age. The number of innings pitched during a
single season and the difference between the number of
innings pitched over consecutive seasons were compared to
predict future injury. Despite this study suggesting limita-
tions to the number of innings that a younger elite pitcher
can pitch may not be an effective means of protecting
players, the number of pitches thrown during a game or
innings were not accounted for. While innovative and
beneficial in providing basic workload-injury data, the
extensive research in baseball has not yet included the
between-game cumulative work. Considering the signifi-
cant impact of extra throwing practice and off-field practice
on cumulative load, it is important to accurately examine
these variables. Further research is warranted to investigate
techniques to monitor both game and training workloads in
baseball.
4.3.2 Workload and Performance in Baseball
Bradbury and Forman [42] quantified the relationship
between the number of pitches thrown and pitcher perfor-
mance, and found that the number of pitches thrown was
negatively associated with future performance. Results
indicated that each pitch thrown in the preceding game
increased the estimated run average by 0.007 runs in the
following game [42], suggesting that higher pitching loads
can hinde r future performance. Velocity trends as a mea-
sure of workload in elite baseball pitchers have been
monitored [43]. The researchers found the fast ball velocity
increased linearly over an eight-game period [43]. How-
ever, it is likely that the sole use of pitch velocity as a
workload-monitoring tool would be insufficient as it fails
to take into account any other load completed by individual
players. In addition, this study was only conducted over an
eight-game period; consequently different trends may
emerge towards the latter end of the season as cumulative
fatigue develops.
4.4 Workload Monitoring in Handball, Softball
and Water Polo
Handball, softball and water polo accounted for 14 % (3/
21) of the articles selected within this review. Bresciani
et al. [44] monitored biological and psychological mea-
sures through an entire handball season. Training load was
calculated across the season using four monitoring tools
that included the session-RPE (training intensity 9 train-
ing duration), blood analyses [e.g. blood C-reactive protein
concentration; oxidised glutathione (GSSG) concentration;
reduced/oxidised glutathione ratio (GSH/GSSG)], a stress
questionnaire ‘[Profile of Mood States (POMS) Question-
naire] and the Recovery-Stress Questionnaire (REST-Q
Sport). Handball players developed small increas es in
inflammatory and oxidative states during periods of high
training load [44 ]. Positive correlations were reported
between biological and psychological markers and training
load [44]. This study effectively implemented a number of
techniques to monitor workload across a season of
handball.
A prospective monitoring of 12 softball pitchers over a
competitive season assessed the relat ionship between pitch
count and upper extremity injury [45]. Team coaches col-
lected pitch counts during each game for the pitcher;
however, no attempt was made to account for pitches or
throws completed outside of game situations. An injury
was defined as any shoul der or elbow muscle, joint, tendon,
ligament, bone or nerve complaint reported by a player
during the season [45]. Although trends were evident, the
small sample size and low incidence of injury limited the
ability to perform statistical analyses on the relationship
between pitch count and injury. Furthermore, game load is
not representative of total weekly load, therefore further
research is required to assess the effect of throws and pit-
ches completed during training situations on total weekly
workload and injury.
Only one study has investigated workloads in water polo
players [46]. In this study, the validity of the session-RPE
method was evaluated and compared with the Edwards
heart-rate-zone method in 13 players. Strong correlations
(r = 0.88; p \ 0.001) were reported between the Edwards
heart-rate-zone and session-RPE methods [46]. This was
one of only two studies selected for review that validated a
workload-monitoring tool in their respective sport.
4.5 Future Directions of Workload Monitoring
Workload monitoring is often subjective and as such is
reliant on players’ capacity to accurately recall and report
their individual training and competitive workload. How-
ever, there are potential inaccuracies associated with ath-
letes self-reporting duri ng training and game situations.
This has led to the development and validation of specific
microtechnology algorithms for the automated detection of
bowling counts and events in cricket fast bowlers. Through
the use of an accelerometer, gyroscope and magnetometers
(Catapult Innovations, Melbourne, Australia), researchers
cross-validated the direct bowling counts and microtech-
nology outputs using notational analysis, using a bowling
detection algorithm embedded in the software [47]. No
significant differences were reported between direct mea-
sures of bowling with true positive and negative events
recorded by the algorithm [47]. Sensitivity of the unit
during training (99.0 %) and competition (99.5 %) were
both acceptable [47]. Although further development is
required, the use of microtechnology to automatically
detect and monitor load is the next logical step in the
G. M. Black et al.
123
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advancement of monitoring techniques used in throwing-
dominant sports.
Finally, an absence of research surrounding workload
monitoring in individual throwing-dominant sports has
become apparent. Although it is possible that some case
studies may have been excluded during the selection phase
of this review, to our knowledge there has been no research
to investigate workload-monitoring techniques in individ-
ual throwing-dominant sports. Considering this gap in the
literature, future research should focus on effective
methodologies for monitoring throwing load in individual
sports.
5 Conclusion
This review provides a comprehensive profile of workload-
monitoring techniques used in throwing-dominant sports.
While the monitoring of throwing loads is likely to be
implemented in high performance sporting environments,
as this study only included peer-reviewed literature, it is
possible that some innovative throwing monitoring
approaches may have been excluded. However, from the
studies identified, the most commonly used workload-
monitoring techniques lacked reliability and validity and
were not capable of monitoring all aspects of training
completed by the athletes. Currently, without greater con-
sistency in design and more reports of reliability and
validity, confidence in these instruments to improve our
understanding of the relationships between total workload,
performance and injury remains limited. While the results
highlight a large variety of workload-monitoring tech-
niques examined in throwing-dominant sports, there is
currently no gold standard workload measure. The use of
objective microtechnology should be further explored to
establish its reliability and validity for monitoring throwing
load in all throwing-dominant sports. The use of an auto-
mated load-monitoring system has the ability to provide
coaches and researchers with a tool to further understand
and report accurate and cumulative individual workloads
for athletes involved in these sports. In conclusion, we have
found inco nsistencies in the reporting of terminology,
monitoring methods, units of measure, periods of measure
and populations being studied within throwing-dom inant
sports.
Compliance with Ethical Standards
Funding No sources of funding were used to assist in the prepa-
ration of this article.
Conflicts of interest Georgia Black, Tim Gabbett, Michael Cole
and Geraldine Naughton declare they have no conflicts of interest
relevant to the context of this review.
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... The rise in popularity of Twenty20 cricket over the last decade has led to a number of player management issues for professional cricket high-performance staff: players who have fewer days of rest, and increases in total cricket exposure over time, leading to increases in total cricket exposure (English, 2011;Warren et al., 2019). Over the course of a match, throwing frequency in fielders can be high, especially those in the infield, leading to high amounts of physical stress on the intrinsic structures of the shoulder and elbow (Bartlett, 2003;Black et al., 2016). Increases in training and competition frequency over the past decade may be one the reasons for an increase in overuse injuries in professional cricket, with total injuries in elite male cricket increasing from 54.6 new injuries per 100 players per year in 2006/07 to a high of 74.2 per 100 players in 2011/12 (Orchard et al., 2016;. ...
... A second frequently used throwing load monitoring technique is observational analysis which often uses throw/bowling diary from a staff member or online databank (Black et al., 2016). Generally, staff members from the team or external statisticians will observe and measure throwing load during training and/or during competition. ...
... This form of load assessment, has much fewer time requirements, as less manipulation of the data is needed. Best practice of observational load monitoring is supported with video analysis to make sure no false-positives are present, however, with time and resource constraints this collaboration is not always implemented (Black et al., 2016;Jayal et al., 2018;Saw et al., 2011). In cricket, observation monitoring of throwing load is difficult in training as at any point more than 25 players may be present. ...
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Objectives: To determine the relationship between elite cricket player’s self-reported and independently observed throwing volume. Examine whether sex, playing position, or time to upload self-reported data post training influences the accuracy of self-reported throwing loads. Describe the type and number of throws performed during elite cricket training, and identify characteristics such as type, distance and accuracy of throws. Design: Cross-sectional study. Methods: A total of eight female and 18 male professional cricket players participated in the study. Overarm throws from 12 training sessions during the 2020-21 cricket year were observed. Player self-reported throwing volume data were retrieved post training, with the time difference between session completion and self-reported data upload recorded. Observations on throwing type (warm-up, drill throw), distance (± 30 meters) and accuracy (hit or miss target) of throws was noted. Correlation and agreement was assessed using a Spearman’s Rank Correlation Coefficient and a Bland-Altman plot of agreement. Two, Independent-Samples Kruskal-Wallis tests were used to investigate if playing position and sex had an influence on absolute magnitude of error of reporting. Results: A moderate positive correlation was found between self-reported and observed throwing loads (rho = 0.65), however only 22% of players reported values within a clinically acceptable error of 10%. Players reported a mean absolute magnitude of error of 11.2 (9.8) throws and a mean magnitude of error of 24.8% (SD 16.0%). Sex did not influence reporting accuracy (p = 0.414). Playing position had a statistically significant (p = 0.031), though not clinically meaningful, relationship. Females uploaded self-reported data the day of training, whereas most males reported the day following. Reporting the day of training, or the day following training did not appear to result in poorer self-reported throwing load accuracy. Conclusions: The findings of this study question the validity of player self-reported throwing load as most players recorded in excess of 10% error. Sport support staff and players should consider whether the current accuracy of self-reported throwing load justifies its collection and use in the high-performance environment.
... 12,17,44,45 With this in mind, monitoring chronic TL (CTL) and progressions in TL (eg, through the use of the ACWR) would provide a physiotherapist, an athletic trainer, and a strength and conditioning coach with valuable information that could be used to adapt (increase or decrease) TLs and potentially decrease the risk of injury within the multidisciplinary team. 2,53 The association of TL with injuries has previously been studied in tennis. 36,42 Myers et al 42 found that injured junior advanced tennis players (with a mean age of 14 years and competing in regional or national tournaments) had 1.5 times greater tennis TL in the past week compared to the previous 4 weeks. ...
Article
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Background: During the COVID-19 pandemic, training routines of most athletes around the world were abruptly interrupted, potentially increasing the risk of injury. The purpose of this study was to compare three measures of training load (TL): tennis-specific TL, physical TL, and total TL management before, during and after lockdown in three professional male tennis players. Case Presentation: Three professional male tennis players were monitored throughout the 2020 season. Outcome and Follow-up: The median total weekly TL (CR 10 Borg RPE • session length in minutes) was 5783 AU. Before, during and after lockdown total weekly TL were 7195, 3753 and 5950, respectively. During lockdown tennis TL was reduced to 0 while physical TL (including preventive loads) increased by 73%. All players suffered an injury/illness during tennis training resumption following lockdown, two of them musculoskeletal related and the other due to COVID-19. There was no association between total ACWR and injury. However, one case had a spike (>1.5) in tennis-specific ACWR two weeks before injury, despite maintaining total ACWR between 0.8-1.5. Discussion: Tennis and physical differential TL monitoring should be carried out separately in order to ensure tennis-specific player readiness. If only total load is monitored during lockdown or rehabilitation from injury, subsequent increases in tennis load upon return to play could potentially increase the risk of injury. The three participants showed a similar pattern of total TL throughout the season with pre-lockdown loads being the highest.
... Further improvements in specific event detection could be reached with the inclusion of machine learning classifiers in the methods, but estimating simple jumps was the main focus for this work [19,20]. The capacity to measure overall training intensity and specific activities in the water can improve the overall monitoring strategies for the team support staff [21]. The inclusion of these measurable parameters are essential for the coaches to understand how the training prescribed is being executed in the water [22]. ...
Article
Background Training volume can be estimated for players in team sports using inertial measurement units. Specific variables such as jumps and jump intensity have shown correlation with fatigue markers in players at the goalkeeper position. Therefore, the goal of this study was to explore the feasibility of analyzing training volume and intensity for water polo goalkeepers using IMU variables. Methods Three senior national water polo team female goalkeepers were recruited for this study. Data from seven training sessions and seven international matches were recorded. Training volume was calculated and specific events were counted (jumps and kicks). Activity intensity score was calculated as a proportion of time spent in high-intensity activities. A paired t test was performed to compare training and match conditions. Results The mean duration of training sessions was significantly shorter than matches (74.4 ± 14.4 min vs 91.4 ± 5.4 min, p < 0.01). The mean activity intensity score was also significantly lower in training sessions vs. matches (47.9 ± 3.5% vs 58.6 ± 3.8%, p = 0.04). Acceleration rate of change per minute was not different between training and matches (p = 0.10). Conclusion This study showed the feasibility of using IMU to provide meaningful training and match metrics in a small sample of female water polo goalkeepers. This type of analysis can be used to describe both volume and intensity of training in future studies.
... Uno de los roles principales de los científicos del deporte para contribuir a la mejora del rendimiento, es cuantificar la carga de trabajo en deportes individuales y colectivos (Kellmann, 2010) mediante diversos factores que afectan el rendimiento. Algunos de estos factores son entrenables y/o modificables (fisiología, psicología y biomecánica), algunos enseñables (tácticas) y otros fuera del control del atleta y el entrenador como la genética y edad (Black et al., 2016;Buceta, 1998). ...
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Introducción: El desarrollo de dispositivos portátiles de espectroscopia de infrarrojo cercano no invasivo (NIRS) ha permitido que las mediciones de oxígeno muscular se realicen fuera de un entorno de laboratorio para investigar cambios musculares locales en pruebas campo para guiar el entrenamiento. En general, durante el ejercicio los NIRS portátiles utiliza la saturación de oxígeno muscular (SmO2) como parámetro principal para el estudio de la hemodinámica porque proporciona información sobre el rendimiento y el metabolismo muscular durante el ejercicio. Un uso novedoso de NIRS portátil, es la medición de la oxigenación muscular en reposo a través del método de oclusión arterial (AOM). AOM consiste en realizar breves oclusiones arteriales para conocer el consumo de oxígeno muscular en reposo (mVO2). En la actualidad, AOM es una técnica para obtener información de la capacidad oxidativa del músculo en reposo, lo cual significa que el atleta no realiza ningún esfuerzo físico. Sin embargo, existe poca literatura científica de cómo está implicado el mVO2 en el proceso de entrenamiento. Por otro lado, el monitoreo de la acumulación de fatiga pre y post competencia es importante dentro de la planificación del entrenamiento. Uno de los roles de los científicos del deporte es conocer el perfil de fatiga y recuperación con el fin de optimizar los procesos de entrenamiento para buscar un mejor rendimiento deportivo. Pero existen limitaciones, debido a que el estudio de la fatiga es un fenómeno multifactorial que envuelve diferentes mecanismos fisiológicos. En cuanto a la relación que pueda tener NIRS portátil y la medición de SmO2 con la fatiga dentro de un contexto deportivo se desconoce, debido a que es una variable que no se ha puesto en práctica en el deporte, pero con un gran potencial. En el contexto de la salud, existen numerosas investigaciones que han asociado la SmO2 a enfermedades cardiovasculares, respiratorias y metabólicas como el sobrepeso y obesidad, que son patologías que afectan la entrega de oxígeno durante la actividad física. Uno de los factores claves para prescribir el ejercicio físico es conocer las zonas de metabólica, es decir la intensidad de ejercicio donde existen cambios metabólicos y que se aplica según el objetivo de la sesión de entrenamiento en personas que realizan actividad física para la salud. Por último, existen algunos vacíos científicos de la aplicación de NIRS portátil en contextos de fatiga, rendimiento y salud. Por lo tanto, con esta tesis podemos brindar nuevos aportes científicos del metabolismo muscular a través de la medición de la SmO2 en reposo y durante el ejercicio, necesario para conocer estados de condición física de un deportista, fatiga, recuperación y la prescripción de ejercicio de ejercicio físico. Objetivos: La tesis presenta como objetivo general: Utilizar la saturación de oxígeno muscular y estudiar su implicación en la fatiga, rendimiento y salud. Para realizar el objetivo general se llevó a cabo los siguientes objetivos específicos: 1. Examinar la relación de la saturación de oxígeno muscular en reposo con marcadores de fatiga en futbolistas femeninos. 2. Interpretar el rol de la saturación de oxígeno muscular como un marcador de rendimiento deportivo durante una prueba de alta intensidad (sprint-repetidos) en futbolistas femeninos. 3. Evaluar los cambios de oxigenación muscular en reposo después de un periodo de entrenamiento y correlacionarlos con la composición corporal y la potencia de salto en futbolistas. 4. Comparar y correlacionar los parámetros fisiológicos en función de la saturación de oxígeno muscular por zonas metabólicas durante una prueba de esfuerzo en personas con sobrepeso/obesidad y normo-peso. Métodos: Los cuatro objetivos de esta tesis fueron investigados con cuatro estudios científicos. Los participantes fueron futbolistas femeninos y masculinos que competían en segunda y tercera división respectivamente, y mujeres con sobrepeso/obesidad y normo-peso. En todas las pruebas se utilizó un NIRS portátil marca MOXY colocado en el músculo gastrocnemio y músculo vasto lateral. El primer estudio consistió en medir marcadores de fatiga neuromuscular, escalas psicológicas y marcadores sanguíneos utilizados para medir fatiga a nivel biológico. En conjunto se midió la prueba de oxígeno muscular en reposo (mVO2 y SmO2) mediante la técnica AOM. Todas las mediciones se realizaron pre, post y post 24 h tras un partido de futbol femenino. El segundo estudio consistió en que los futbolistas femeninos realizaran una prueba de sprint repetidos, donde se evaluó la frecuencia cardiaca, velocidad y SmO2 en conjunto. El tercer estudio consistió en observar cambios de SmO2 en reposo después de un periodo de pretemporada en jugadores de futbol y relacionarlo con la composición corporal y la potencia de salto. El cuarto estudio consistió en realizar una prueba de esfuerzo incremental con detección de zonas metabólicas: fatmax, umbrales de entrenamiento VT1 y VT2 y potencia aeróbica máxima para compararlo y relacionarlo con la SmO2. Resultados y Discusión: En base a los objetivos de la tesis: Primero, en las jugadoras de futbol se encontró un aumento de mVO2 y SmO2 en reposo a las 24 h post partido oficial [(mVO2: 0.75 ± 1.8 vs 2.1± 2.7 μM-Hbdiff); (SmO2: 50 ± 9 vs 63 ± 12 %)]. Principalmente, este aumento es resultado de la correlación de la vasodilatación mediada por el flujo sanguíneo y el trasporte de oxígeno muscular que es un mecanismo implicado en los procesos de recuperación de la homeostasis del músculo esquelético y la restauración del equilibrio metabólico. El aumento del consumo de oxígeno se relacionó con la disminución de la potencia de salto (r= −0.63 p <0.05) y el aumento del lactato deshidrogenada (LDH) (r = 0.78 p <0.05) como marcadores de fatiga. Seguidamente en el segundo estudio, encontramos que la disminución del rendimiento durante una prueba de sprint repetidos, comienza con el aumento gradual de la SmO2, debido al cambio de la presión intramuscular y la respuesta hiperémica que conlleva, mostrando una disminución en la respuesta inter-individual [desaturación desde el cuarto sprint (Δ= 32%) y re-saturación después del sexto sprint (Δ= 89%)]. Además, la extracción de oxígeno por parte del músculo tiene una asociación no-lineal con la alta velocidad (r = 0.89 p <0.05) y con la fatiga mostrada el % decremento del sprint (r = 0.93 p <0.05). En el estudio 3 se encontró que la dinámica de SmO2 en reposo es sensible a cambios después de un periodo de pretemporada (SmO2-Pendiente de recuperación: 15 ± 10 vs. 5 ± 5). Asimismo, se mostró que la SmO2 en reposo está relacionado paralelamente con el porcentaje de grasa del cuerpo (r= 0,64 p <0.05) y una relación inversa con la potencia de salto a una sola pierna (r = -0,82 p<0.01). Esto significa que a través del entrenamiento se mejoró el metabolismo y hemodinámica muscular con un tránsito más rápido del oxígeno muscular, y se asoció a las mejoras del peso corporal, somatotipo, CMJ y SLCMJ. En el cuarto estudio, basado en los parámetros fisiológicos de una prueba de esfuerzo para prescribir ejercicio: se encontró una relación entre la SmO2 y el VO2max durante la zona fatmax y VT1 (r=0,72; p=0,04) (r=0,77; p=0,02) en mujeres con normo-peso. Sin embargo, en el grupo sobrepeso obesidad no se encontró ninguna correlación ni cambios de SmO2 entre cada zona metabólica. Conclusión: La investigación de esta tesis ha demostrado avances en la medición de la SmO2. El uso de mVO2 y SmO2 en reposo es una variable de carga de trabajo que se puede utilizar para el estudio de la fatiga después de un partido de futbol femenino. Asimismo, la SmO2 en reposo puede ser interesante tomarlo en cuenta como un parámetro de rendimiento en futbolistas. Siguiendo el contexto, en el rendimiento durante una prueba de sprint repetidos, la SmO2 debe interpretarse basado en la respuesta individual del porcentaje de extracción de oxígeno muscular (∇%SmO2). El aporte de ∇%SmO2 es un factor de rendimiento limitado por la capacidad de velocidad y soporte de la fatiga de los futbolistas femeninos. Respecto a los aspectos de salud y prescripción del ejercicio, proponemos utilizar la SmO2 como un parámetro fisiológico para controlar y guiar el entrenamiento en zonas fatmax y VT1, pero solo en mujeres normo-peso. En patologías metabólicas como el sobrepeso y obesidad se necesitan más estudios. Como conclusión general, esta tesis muestra nuevas aplicaciones prácticas de cómo utilizar la SmO2 y su implicación en la fatiga, en contraste la adaptación al entrenamiento, pruebas de rendimiento y prescripción de la actividad física para la salud.
... Strategies for monitoring shoulderspecific load in overhead athletes lack reliability and validity, prompting calls for new technologies to be developed. 12 Shoulder-specific loads vary with the number of throws, playing position, type of training, and intensity of training. High overall shoulder-specific load exposure of more than 16 hours per week 55 (eg, high-speed throws in baseball) and large weekly increases in training and/or match load (greater than 60% increase compared with the average of the previous 4 weeks) are associated with an increased shoulder injury rate. ...
Article
Synopsis: There is an absence of high-quality evidence to support rehabilitation and return-to-sport decisions following shoulder injuries in athletes. The Athlete Shoulder Consensus Group was convened to lead a consensus process that aimed to produce best-practice guidance for clinicians, athletes, and coaches for managing shoulder injuries in sport. We developed the consensus via a 2-round Delphi process (involving more than 40 content and methods experts) and an in-person meeting. This consensus statement provides guidance with respect to load and risk management, supporting athlete shoulder rehabilitation, and decision making during the return-to-sport process. This statement is designed to offer clinicians the flexibility to apply principle-based approaches to managing the return-to-sport process within a variety of sporting backgrounds. The principles and consensus of experts working across multiple sports may provide a template for developing additional sport-specific guidance in the future. J Orthop Sports Phys Ther 2022;52(1):11-28. doi:10.2519/jospt.2022.10952.
... 12 Lack of shoulder range of motion (ROM) has been shown to correlate strongly with shoulder injuries in swimming and overhead throwing sports. [13][14][15][16] Water polo players show greater ER, decreased IR, and increased total range of motion in their dominant shoulders compared to their contralateral side. 8,17 However, Elliott 18 found no statistical correlations between shoulder pain and ROM in a group of 13 male national team water polo players. ...
Article
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Background: Very limited investigations have been conducted exploring risk factors for injury in water polo players. A gap remains in the literature regarding identification of variables that should be considered as part of player screening evaluations. Purpose: To estimate whether previous injury, changes in strength, range of motion (ROM) or upward scapular rotation (UR) are related to shoulder injuries in water polo players. Study Design Descriptive: cohort study Methods: Thirty-nine international-level players participated (19 males). Shoulder internal (IR) and external rotation (ER) peak torque was measured using an isokinetic device (CONtrex MJ). Shoulder ROM was measured passively using standard goniometry. Scapular UR was measured using a laser digital inclinometer. At baseline players were divided into groups: those with and without previous shoulder injuries. Independent t-tests and Mann-Whitney U tests were used to compare the study variables between groups. After nine months, a second analysis compared the same athletes, who were then grouped by those who had or had not sustained new injuries. Effect sizes were calculated with a Hedge's g. Chi squared analysis compared proportion of injured players with and without previous injury. Results: Eighteen participants (46%) had previous injuries at baseline. Players with a previous injury showed higher peak torques for IR (0.62±0.15 vs 0.54±0.13N/kg, p=0.04, g=0.60); larger loss of IR ROM (9.9±9.1 vs 4.1±7.5°, p=0.04, g=0.68), but no statistical difference in UR (p=0.70). After nine months, there were no statistical strength differences between groups. Loss of IR ROM was significantly higher in the injured group (9.8±9.8 vs 4.0±6.7°, p=0.04, g=0.68), as well as UR (13.0±3.0 vs 10.4±3.3°, p=0.01, g=0.81). History of previous injury was significantly related to developing a new injury (OR 6.5, p=0.02). Logistic regression found previous injury and UR most important contributors to injury risk. Conclusions: Previous injury, changes in IR ROM and UR are related to new shoulder injuries in water polo, but further variables such as rest, training load, or psychosocial factors may explain the incidence of new injuries.
... Of these variations, the overhead throw appears to be most important for injury research for two reasons: 1) they seem to be the most common variant, although no previous research has established this, and 2) most of the high-intensity throws are likely overhead throws. To the best of our knowledge, there have been no attempts to develop a method for automatically identifying handball throws Black et al. (2016). Therefore, the primary aim of the present study is to develop and validate a method to identify overhead throws in handball players based on measurements from a wearable inertial measurement unit (IMU) placed on the forearm. ...
Article
Shoulder injuries are a common problem in handball. One likely cause of such injuries is excessive throwing. However, it is difficult to measure the number of player throws in large cohort studies using existing methods accurately. Therefore, the purpose of this study is to develop and validate a method for identifying overhead throws using a low-cost inertial measurement unit (IMU) worn on the wrist. In a two-stage approach, we developed a threshold-based automatic identification method for overhead throws in a laboratory study using the IMU. Subsequently, we validated the suggested thresholds in a field setting by comparing throws identified by the threshold-method to throws identified by video recordings of handball practices. The best set of threshold values resulted in a per-player median sensitivity of 100% (range: 84–100%) and a median positive predictive value (PPV) of 96% (range: 86–100%) in the development study. In the validation study, the per-player median sensitivity dropped to 78% sensitivity (range: 52–91%), while the per-player median PPV dropped to 79% (range: 47–90%). The proposed method is a promising method for automatically identifying handball throws in a cheap and feasible way.
Article
Objectives:To determine the concurrent validity of player self-reported and independently observed throwing volume. Examine whether sex, playing position, or time to upload self-reported data post training influences the accuracy of self-reported throwing loads. Design: Cross-sectional cohort study. Methods: A total of 8 female and 18 male elite cricket players participated in the study. Overarm throws from 12 training sessions during the 2020–21 cricket year were observed. Player self-reported throwing volume data were retrieved post training, with the time difference between session completion and self-reported data upload recorded. Results: A moderate positive correlation was found between self-reported and observed throwing loads (rho = 0.65), however only 22 % of players reported values within a 10 % level of error. Players reported a mean (SD) absolute inaccuracy of 11.17 (9.77) throws, and a mean (SD) relative inaccuracy of 24.76 (16.04) percent. Sex did not influence reporting accuracy (p = 0.41). Females tended to upload self-reported data the day of training, whereas men report the day following. Players who uploaded their data greater than one day after training were the most inaccurate with a mean relative inaccuracy of 36 %. Conclusions: While there is a clear relationship between observed and self-reported throwing volumes, the findings of this study question the validity of using player self-reported throwing load as a marker of true throwing loads with most players recording in excess of 10 % error. High performance staff and players should consider whether the current accuracy of self-reported throwing load justifies the additional reporting burden on the players during training.
Article
Shoulder injuries account for two-thirds of all injuries in overhead athletes. Maximizing pitching performance while mitigating injury risk requires an objective understanding of the complex biomechanics—both motion and loads—between the lower and upper extremity. Recent advances in both marker-based and marker-less motion capture systems have advanced our knowledge of shoulder biomechanics. Marker-based 3D motion analysis systems remain the gold-standard in sports performance analysis, but recent technical improvements in marker-less and IMU-based systems have provided new insights into pitching biomechanics both on and off the field. These research tools have identified that excessive shoulder external rotation and inefficient transfer of energy from the lower body through the kinetic chain during pitches leads to increased injury risk, particularly in youth and high school pitchers. These motion capture tools should be used to objectively measure cumulative pitching workload and identify the factors that can predispose pitchers to injury.
Article
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Background: There is dogma that higher training load causes higher injury rates. However, there is also evidence that training has a protective effect against injury. For example, team sport athletes who performed more than 18 weeks of training before sustaining their initial injuries were at reduced risk of sustaining a subsequent injury, while high chronic workloads have been shown to decrease the risk of injury. Second, across a wide range of sports, well-developed physical qualities are associated with a reduced risk of injury. Clearly, for athletes to develop the physical capacities required to provide a protective effect against injury, they must be prepared to train hard. Finally, there is also evidence that under-training may increase injury risk. Collectively, these results emphasise that reductions in workloads may not always be the best approach to protect against injury. Main thesis: This paper describes the 'Training-Injury Prevention Paradox' model; a phenomenon whereby athletes accustomed to high training loads have fewer injuries than athletes training at lower workloads. The Model is based on evidence that non-contact injuries are not caused by training per se, but more likely by an inappropriate training programme. Excessive and rapid increases in training loads are likely responsible for a large proportion of non-contact, soft-tissue injuries. If training load is an important determinant of injury, it must be accurately measured up to twice daily and over periods of weeks and months (a season). This paper outlines ways of monitoring training load ('internal' and 'external' loads) and suggests capturing both recent ('acute') training loads and more medium-term ('chronic') training loads to best capture the player's training burden. I describe the critical variable-acute:chronic workload ratio)-as a best practice predictor of training-related injuries. This provides the foundation for interventions to reduce players risk, and thus, time-loss injuries. Summary: The appropriately graded prescription of high training loads should improve players' fitness, which in turn may protect against injury, ultimately leading to (1) greater physical outputs and resilience in competition, and (2) a greater proportion of the squad available for selection each week.
Article
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The Mental Health Model (MHM) of sport performance purports that an inverse relationship exists between psychopathology and sport performance. The model postulates that as an athlete’s mental health either worsens or improves performance should fall or rise accordingly, and there is now considerable support for this view. Studies have shown that between 70 and 85% of successful and unsuccessful athletes can be identified using general psychological measures of personality structure and mood state, a level superior to chance but insufficient for the purpose of selecting athletes. Longitudinal MHM research indicates that the mood state responses of athletes exhibit a dose-response relationship with their training load, a finding that has shown potential for reducing the incidence of the staleness syndrome in athletes who undergo intensive physical training. The MHM also has implications for the general care of athletes as support services have traditionally been limited to preventing or treating physical problems. Despite its simple premise and empirical support, the MHM has often been mischaracterised in the sport psychology literature and recently some authors have questioned its validity. This overview will summarise MHM research, including the more recent work involving the model’s dynamic features in an effort to resolve disputes surrounding the model.
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