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The Development and Application of an Injury Prediction Model for Noncontact, Soft-Tissue Injuries in Elite Collision Sport Athletes

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Limited information exists on the training dose-response relationship in elite collision sport athletes. In addition, no study has developed an injury prediction model for collision sport athletes. The purpose of this study was to develop an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes. Ninety-one professional rugby league players participated in this 4-year prospective study. This study was conducted in 2 phases. Firstly, training load and injury data were prospectively recorded over 2 competitive seasons in elite collision sport athletes. Training load and injury data were modeled using a logistic regression model with a binomial distribution (injury vs. no injury) and logit link function. Secondly, training load and injury data were prospectively recorded over a further 2 competitive seasons in the same cohort of elite collision sport athletes. An injury prediction model based on planned and actual training loads was developed and implemented to determine if noncontact, soft-tissue injuries could be predicted and therefore prevented in elite collision sport athletes. Players were 50-80% likely to sustain a preseason injury within the training load range of 3,000-5,000 units. These training load 'thresholds' were considerably reduced (1,700-3,000 units) in the late-competition phase of the season. A total of 159 noncontact, soft-tissue injuries were sustained over the latter 2 seasons. The percentage of true positive predictions was 62.3% (n = 121), whereas the total number of false positive and false negative predictions was 20 and 18, respectively. Players that exceeded the training load threshold were 70 times more likely to test positive for noncontact, soft-tissue injury, whereas players that did not exceed the training load threshold were injured 1/10 as often. These findings provide information on the training dose-response relationship and a scientific method of monitoring and regulating training load in elite collision sport athletes.
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THE DEVELOPMENT AND APPLICATION OF AN INJURY
PREDICTION MODEL FOR NONCONTACT,SOFT-TISSUE
INJURIES IN ELITE COLLISION SPORT ATHLETES
TIM J. GABBETT
1,2
1
Brisbane Broncos Rugby League Club, Brisbane, Australia; and
2
School of Human Movement Studies,
The University of Queensland, Brisbane, Australia
ABSTRACT
Gabbett, TJ. The development and application of an injury
prediction model for noncontact, soft-tissue injuries in elite
collision sport athletes. J Strength Cond Res 24(10): 2593–2603,
2010—Limited information exists on the training dose–response
relationship in elite collision sport athletes. In addition, no study
has developed an injury prediction model for collision sport
athletes. The purpose of this study was to develop an injury
prediction model for noncontact, soft-tissue injuries in elite
collision sport athletes. Ninety-one professional rugby league
players participated in this 4-year prospective study. This study
was conducted in 2 phases. Firstly, training load and injury data
were prospectively recorded over 2 competitive seasons in elite
collision sport athletes. Training load and injury data were
modeled using a logistic regression model with a binomial
distribution (injury vs. no injury) and logit link function. Secondly,
training load and injury data were prospectively recorded over
a further 2 competitive seasons in the same cohort of elite
collision sport athletes. An injury prediction model based on
planned and actual training loads was developed and
implemented to determine if noncontact, soft-tissue injuries
could be predicted and therefore prevented in elite collision
sport athletes. Players were 50–80% likely to sustain a pre-
season injury within the training load range of 3,000–5,000
units. These training load ÔthresholdsÕwere considerably
reduced (1,700–3,000 units) in the late-competition phase of
the season. A total of 159 noncontact, soft-tissue injuries were
sustained over the latter 2 seasons. The percentage of true
positive predictions was 62.3% (n= 121), whereas the total
number of false positive and false negative predictions was 20
and 18, respectively. Players that exceeded the training load
threshold were 70 times more likely to test positive for
noncontact, soft-tissue injury, whereas players that did not
exceed the training load threshold were injured 1/10 as often.
These findings provide information on the training dose–
response relationship and a scientific method of monitoring and
regulating training load in elite collision sport athletes.
KEY WORDS injury prevention, rugby league, training monitoring,
training load, applied sport science
INTRODUCTION
The training–performance relationship is of partic-
ular importance to coaches to determine the
optimum amount of training required to attain
specific performance levels (2,5,11). Bannister et al.
(4) proposed a statistical model to describe an athlete’s
response to a given training stimulus. According to this
model, the performance of an athlete in response to training
can be estimated from the difference between a negative
function (fatigue) and a positive function (fitness). Studies
have described the training–performance relationship as
analogous with the dose–response relationship reported in
pharmacological studies, with the primary goal of providing
a training stimulus that maximizes performance potential and
minimizes the negative consequences of training (i.e., injury,
illness, fatigue, overtraining) (29).
Several studies have investigated the influence of training
volume, intensity, and frequency on athletic performance,
with performance generally improving with increases in
training load (11,12,28,31,32,37,39). Studies of the training–
performance relationship in individual sports (e.g., swimming
and running) have found a positive relationship between both
greater training volume and performance (12,39) and higher
training intensity and performance (28,32,37). Foster et al.
(11) studied 56 runners, cyclists, and speed skaters during 12
weeks of training and reported that a 10-fold increase in
training load was associated with a ;10% improvement in
performance. Moreover, Stewart and Hopkins (39)
reported a significant relationship between greater training
volume and performance (r= 0.50–0.80) and higher training
intensity and performance (r= 0.60–0.70) in competitive
swimmers. However, it has also been shown that negative
adaptations to exercise training are dose related, with the
Address correspondence to Dr. Tim J. Gabbett, timg@broncos.com.au.
24(10)/2593–2603
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Ó2010 National Strength and Conditioning Association
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highest incidence of illness and injury occurring when
training loads are highest (10,17).
In contrast to individual sports, collision sports (e.g., ice
hockey, rugby, and lacrosse) are characterized by large
numbers of physical collisions and tackles, short repeated
sprints, rapid acceleration, deceleration, and changes of
direction, and an ability to produce high levels of muscular
force extremely rapidly (19). As a result, collision sport
athletes are required to have well-developed speed, strength,
muscular power, agility, and maximal aerobic power
(
_
VO
2
max). Previous studies of collision sport athletes have
reported a significant relationship (r= 0.86) between training
loads and training-injury rates (17), suggesting that the harder
these athletes train, the more injuries they will sustain.
Furthermore, reductions in training loads have been shown
to reduce training-injury rates and result in greater improve-
ments in
_
VO
2
max (18). However, it has also been shown that
collision sport athletes that perform ,18 weeks of preseason
training before sustaining an initial injury are at an increased
risk of sustaining a subsequent injury, whereas players with
a low off-season
_
VO
2
max are at an increased risk of sustaining
a contact injury (20). Clearly, training for collision sports
reflects a balance between the minimum training load
required to elicit an improvement in fitness and the
maximum training load tolerable before sustaining marked
increases in injury rates.
The majority (;60%) of collision sport injuries have been
shown to occur in physical collisions and tackles (14,15,23,24)
and are therefore thought to be mostly unavoidable.
However, a considerable proportion of injuries sustained
by collision sport athletes are noncontact, soft-tissue issues
that occur as a result of excessive training loads, inadequate
recovery, and overtraining (15,17–19,21). These injuries,
which are largely preventable, have the potential to impact
team selections and as a result may influence team perfor-
mance. Therefore, an obvious challenge for coaches is to
identify the athletes that have received an adequate training
stimulus to compete optimally and those that may be
susceptible to overreaching or overtraining. However, no
study has identified the optimum training load to minimize
noncontact, soft-tissue injuries in collision sport athletes.
Although models of the training–performance relationship
have been constructed for athletes from individual sports
(2–4,10,11,30,31,39), studies of the training–performance
relationship of collision sport athletes are limited (9,26)
and have been confined to the identification of hormonal and
psychological markers of overtraining and fatigue. In
addition, studies examining the influence of training load
on injury in collision sports have been limited to subelite
athletes (21). In the elite team-sport environment, it is critical
to have the maximum number of players free from injury and
available for selection in as many games as possible through-
out the season (34,36). Injuries that result in lost playing time
may alter the team structure, leading to reduced cohesion
between players, and subsequent reductions in playing
performance (16). However, to date, limited information
exists on the training dose–response relationship in elite
collision sport athletes. In addition, no study has developed
an injury prediction model for collision sport athletes. With
this in mind, the purpose of this study was to develop an
injury prediction model for noncontact, soft-tissue injuries in
elite collision sport athletes.
METHODS
Experimental Approach to the Problem
This study involved (a) the collection of training load and
noncontact, soft-tissue injury data (over 2 competitive
seasons); (b) the modeling of training load with injury, to
determine the relationship between these 2 variables (i.e.,
with a given training load, what is the risk of noncontact, soft-
tissue injury?); and (c) the development and application of an
injury prediction model encompassing planned and actual
training loads (over a subsequent 2 seasons). The study was
conducted in 2 phases: Firstly, training load and injury data
were prospectively recorded over 2 competitive seasons in
elite collision sport athletes. Training load and injury data
were modeled using a logistic regression model with
a binomial distribution (injury vs. no injury) and logit link
function. This model was identical to that previously used to
investigate the training–injury relationship in subelite colli-
sion sport athletes (21). The development of this model
provided statistical information on the likelihood of soft-
tissue injury with a given training load, throughout the dif-
ferent phases of the season (i.e., preseason, early competition,
late competition). Secondly, training load and injury data
were prospectively recorded over a further 2 competitive
seasons in the same cohort of elite collision sport athletes.
Based on the results of the logistic regression model, an injury
prediction model encompassing planned and actual training
loads was developed and implemented to determine if non-
contact, soft-tissue injuries could be predicted and therefore
prevented in elite collision sport athletes. The proportion of
true positive (i.e., predicted injury and player sustained injury)
and negative (i.e., no injury predicted and the player did not
sustain injury) results, and false positive (i.e., predicted injury
but the player did not sustain any injury) and negative (i.e., no
injury predicted but the player sustained an injury) results
were also calculated to describe errors made in the statistical
decision process and to allow the calculation of sensitivity
(i.e., the proportion of injured players who were predicted to
be injured) and specificity (i.e., the proportion of uninjured
players who were predicted to remain injury-free) likelihood
ratios (1).
Subjects
Ninety-one professional rugby league players (mean 6SD
age, height, and body mass; 23.7 63.8 years, 183.2 64.9 cm,
and 94.4 69.2 kg, respectively) participated in this 4-year
prospective study (2006–2009). Of the 91 players, 53 (58.2%)
played 1 season, 22 players (24.2%) played 2 seasons,
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7 players (7.7%) played 3 seasons, whereas 9 players (9.9%)
played all 4 seasons. The number of players participating in
each season was 36, 38, 35, and 45 respectively, giving a total
of 154 player seasons. All players were highly motivated
players from the same professional rugby league club and
were competing in the elite National Rugby League
competition. Players had completed a 4-week active recovery
off-season period and were free from injury at the
commencement of the study. All players received a clear
explanation of the study, including the risks and benefits of
participation, and written consent was obtained. The
Institutional Review Board for Human Investigation ap-
proved all experimental procedures.
Data Collection
This study was conducted in 2 phases: Firstly, training load
and injury data were prospectively recorded over 2 compet-
itive seasons (2006 and 2007). Training load and injury data
were modeled to determine the relationship between training
load and the likelihood of injury (21). Secondly, training load
and injury data were prospectively recorded over a further 2
competitive seasons (2008 and 2009). During this period, an
injury prediction model based on planned and actual training
loads was developed and implemented to determine if
noncontact, soft-tissue injuries could be predicted and
therefore prevented in elite collision sport athletes.
Training Sessions
A periodized, game-specific training program was imple-
mented, with training loads progressively increased in the
general preparatory phase of the season (i.e., November to
February) and reduced during the competitive phase of the
season (i.e., March to October). The training program
progressed from high volume–low intensity activities during
the preseason conditioning period to low volume–high
intensity activities during the in-season conditioning period.
Each player participated in up to 5 organized field-training
sessions, and 4 strength sessions per week in the preseason
period, and 2–4 field-training sessions, and 1–2 strength
sessions per week in the competitive phase of the season.
Training load and injury data were recorded for every session.
To ensure training specificity, players were allocated into 1
of 3 training groups (i.e., hit-up forwards, adjustables, and
outside backs) according to the skills and physiological
demands of their individual positions. The content of sessions
was based largely on perceived strengths and weaknesses
within the club. The training sessions consisted of specific
skills, speed, muscular power, agility, and endurance training
common to rugby league. Skills sessions were designed to
develop passing and catching skills, defensive line speed and
technique, support play, and ball control. Although some
differences existed in the intensity of activities performed
throughout the season, the types of activities performed in the
preseason training phase (e.g., basic skills, light and full
contact tackling drills and longer interval running) were
similar to the early-competition and late-competition training
phases (e.g., light contact tackling drills, advanced skills, and
shorter repeated-sprint training). The duration of training
sessions was recorded, with sessions typically lasting between
60 and 120 minutes. An outline of the training plan for the
season is shown in Table 1.
Quantification of Training Loads
The intensity of individual training sessions was estimated
using a modified rating of perceived exertion (RPE) scale (13).
Training load was calculated by multiplying the training
session intensity by the duration of the training session and
was reported in arbitrary units. Intensity estimates were
obtained 30 minutes after completing the training session.
When compared to heart rate and blood-lactate concentra-
tion, the RPE scale has been shown to provide a valid
estimate of exercise intensity (8,10,27). In addition, before
commencing the study, we investigated the relationship
between heart rate and RPE, and blood-lactate concentration
and RPE on a subset of subjects during typical rugby league
training activities. The correlations between training heart
rate and training RPE, and training blood-lactate
TABLE 1. Outline of yearly training plan for elite collision sport athletes.*
Training phase Objective
Preseason Develop speed, agility, and muscular power
Develop aerobic endurance, anaerobic capacity, and repeated-effort ability
Develop game-specific individual and team skills
Early-competition Continue to develop game-specific skills
Maintain aerobic endurance, anaerobic capacity, repeated-effort ability,
speed, agility, and muscular power
Late-competition Maintain game-specific skills
Maintain aerobic endurance, anaerobic capacity, repeated-effort ability,
speed, agility, and muscular power
*Preseason = 16 weeks; early-competition = 15 weeks; late-competition = 15 weeks.
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concentration and training RPE, were 0.89 and 0.86,
respectively (21). A subset of players (n= 11) also completed
2 identical off-season training sessions, performed 1 week
apart, before the commencement of the study, to determine
test–retest reliability. The intraclass correlation coefficients
for test–retest reliability and typical error of measurement for
the RPE scale were 0.99 and 4.0%, respectively. Collectively,
these results demonstrate that the RPE scale offers an
acceptable method of quantifying training intensity for
collision sport athletes.
Definition of Injury
For the purpose of this study, an injury was defined as any
noncontact, soft-tissue injury sustained by a player during
a training session or match that
prevented the player from com-
pleting the entire training ses-
sion or match (17). To allow
comparisons among seasons,
injuries were also classified
according to matches missed
as a result of the injury (22). All
contact injuries were excluded
from the analysis.
Statistical Analyses
Differences in training loads
among the preseason, early-com-
petition, and late-competition
training phases and between the
2006–2007 and 2008–2009 sea-
sons were analyzed using a 2-way
(season 3training phase) analy-
sis of variance with repeated
measures. Injury exposure was
calculated by multiplying the
number of players by
the session duration. Injury in-
cidence was calculated by di-
viding the total number of injuries
by the overall injury exposure.
Influence of Training Load on
Likelihood of Injury. The training
load and injury data collected in
the first 2 seasons were mod-
eled to provide the likelihood of
soft-tissue injury with a given
training load. Individual train-
ing load and injury data were
modeled using a logistic re-
gression model with a binomial
distribution (injury vs. no in-
jury) and logit link function.
Data were analyzed in SAS
using the PROC GENMOD
procedure. This procedure was used based on its ability to
handle logistic regression. PROC GENMOD also has the
ability to handle unbalanced models (where different
numbers of repeated results were available for each player).
The summary statistic used for assessing the adequacy of the
fitted logistic model (goodness of fit) was the scaled deviance
of the model and significance of independent variables. A
scaled deviance of 1 refers to a perfect fit. Based on the
distribution of the training load data and the scaled deviance,
it was also determined that the model was best fitted with the
log of the training load per week. This model was also chosen
as an identical model had been validated and used previously
to investigate the training–injury relationship in subelite
collision sport athletes (21).
Figure 1. Weekly planned and actual training loads for the preseason, early-competition, and late-competition
training phases in elite collision sport athletes.
Figure 2. Preseason, early-competition, and late-competition training loads for the 2006–2007 and 2008–2009
seasons in elite collision sport athletes.
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Injury Prediction Model. Based on the results of the logistic
regression model, an injury prediction model encompassing
planned and actual training loads was developed and
implemented to determine if noncontact, soft-tissue injuries
could be predicted and therefore prevented. Differences in
planned and actual training loads throughout the season were
analyzed using Cohen’s effect size (ES) statistic. Effect sizes of
0.2, 0.5, and 0.8 were considered small, moderate, and large,
respectively (7). A moderate difference (i.e., ES of 0.5)
between the planned and actual training load was required
for the ÔthresholdÕtraining load to be exceeded. Effect sizes
were calculated by multiplying the between-subject SD for
training load by 0.5 (7). Given
that a relationship was identi-
fied between training load and
injury (from the first 2 seasons),
it was possible to plan training
loads to minimize the effect of
injuries. When actual training
loads for individual players
were higher (based on a mod-
erate ES) than planned training
loads, then judgments were
made on the individual man-
agement of the player (e.g.,
players continued to train with-
out modifications, or training
loads for those individual play-
ers were modified, or reduced
to minimize the risk of injury).
Injury prevalence was calcu-
lated as the proportion of play-
ers injured when actual training
loads exceeded planned train-
ing loads.
Sensitivity and Specificity of the
Injury Prediction Model. Data
were crossvalidated to determine
TABLE 2. Site and type of injuries resulting in missed matches for the 2006–2007 and 2008–2009 seasons.*
2006–2007 Season 2008–2009 Season
Number Injury rate % Number Injury rate %
Site
Thorax/abdomen 6 0.5 (0.1–0.8) 20.0 2 0.2 (0.0–0.4) 7.7
Anterior thigh 6 0.5 (0.1–0.8) 20.0
Posterior thigh 12 0.9 (0.4–1.4) 40.0 10 0.8 (0.3–1.2) 38.5
Groin 2 0.2 (0.0–0.4) 7.7
Knee 6 0.5 (0.1–0.8) 20.0 9 0.7 (0.2–1.1) 34.6
Calf 3 0.2 (0.0–0.5) 11.5
Type
Muscular strains 24 1.8 (1.1–2.6) 80.0 17 1.3 (0.7–1.9) 65.4
Overuse 4 0.3 (0.0–0.6) 13.3 6 0.5 (0.1–0.8) 23.1
Joint sprains 2 0.2 (0.0–0.4) 6.7 3 0.2 (0.0–0.5) 11.5
Total 30 2.3 (1.5–3.1) 100.0 26 2.0 (1.2–2.8) 100.0
*The number and incidence of injuries represents any noncontact, soft-tissue injury that resulted in a missed match.
Injury rates are expressed per 1,000 exposure hours (and 95% confidence intervals).
Figure 3. Incidence of new and recurrent injuries for the 2006–2007 and 2008–2009 seasons in elite collision
sport athletes. The incidence of injuries represents any noncontact, soft-tissue injury that resulted in a missed
match.
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the accuracy of the injury pre-
diction model. The proportion of
true positive (i.e., predicted injury
and player sustained injury) and
negative (i.e., no injury predicted
and the player did not sustain an
injury) results, and false positive
(i.e., predicted injury but the
player did not sustain any injury)
and negative (i.e., no injury pre-
dicted but the player sustained an
injury) results were also calcu-
lated to describe errors made in
the statistical decision process
and to allow the calculation of
sensitivity (i.e., the proportion of
injured players who were pre-
dicted to be injured) and speci-
ficity (i.e., the proportion of
uninjured players who were pre-
dicted to remain injury-free) like-
lihood ratios (1). Sensitivity and
specificity values were calculated
using the following equations:
Sensitivity ¼True Positive=ðTrue Positive
þFalse NegativeÞ;
Specificity ¼True Negative=ðFalse Positive
þTrue NegativeÞ:ð1Þ
Positive and negative likelihood ratios were calculated
using the following equations:
Likelihood ratio positive ¼Sensitivity=ð1SpecificityÞ;
Likelihood ratio negative ¼ð1
SensitivityÞ=Specificity:ð1Þ
.
Although there were 91 players in the sample, injury
predictions based on the training loads performed by
individual players were made on a weekly basis, so that
within the total cohort, there was a total number of true
positive and negative predictions, and a total number of false
positive and negative predictions. Data are expressed as
means and 95% confidence intervals (CIs), and the level of
significance was set at p#0.05.
RESULTS
Training Loads
The planned and actual training loads for the preseason, early-
competition, and late-competition training phases are shown
in Figure 1. The preseason training loads were greater than
the early-competition and late-competition training loads.
Comparison of Training Loads between Seasons
No significant differences (p.0.05, ES = 0.03–0.30) were
detected in training loads for the 2006–2007 and 2008–2009
preseason, early-competition, or late-competition training
phases (Figure 2).
Figure 4. Relationships between training load, training phase, and likelihood of injury in elite collision sport athletes.
TABLE 3. Actual training loads of injured players, and the corresponding prevalence of noncontact, soft-tissue injury.*
Preseason Early competition Late competition
Training load (units) 4,341 (4,082–4,600) 3,395 (3,297–3,493) 2,945 (2,797–3,094)
Injury prevalence (%) 72 (63–81)% 57 (47–67)% 75 (66–84)%
*Injury prevalence = proportion of players injured when actual training loads exceeded planned training loads. Data are expressed as
means (and 95% confidence intervals).
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Incidence of Missed Match Injuries over the 4 Seasons
Site and Type of Missed Match Injuries. Players participated in
13,103 exposure hours over both the 2006–2007 and
2008–2009 seasons (for a total of 26,206 exposure hours).
Of the 80 contracted players in the 2008–2009 seasons, 49
(61.3%) sustained at least 1 noncontact, soft-tissue injury.
Of the players who sustained an injury, 35 (71.4%) sustained 2
or more injuries. The majority of missed match injuries
sustained over the 4 seasons were to the posterior thigh (0.8
[95% CI, 0.5–1.2] per 1,000 hours, 39.3%). Muscular strains
were the most common type of injury (1.6 [95% CI, 1.1–2.0]
per 1,000 hours, 73.2%) (Table 2).
Comparison of Missed Match Injuries between Seasons
The total number of missed match injuries in the 2006–2007
and 2008–2009 seasons was 30 and 25, respectively. The
incidence of soft-tissue injury resulting in a missed match was
2.3 (95% CI, 1.5–3.1) per 1,000 hours and 1.9 (95% CI, 1.2–2.7)
per 1,000 hours for the 2006–2007 and 2008–2009 seasons,
respectively. Although not significant (p.0.05), the incidence
of new injuries was greater in the 2006–2007 seasons (2.0 [95%
CI, 1.2–2.8] per 1,000 hours vs. 1.1 [95% CI, 0.6–1.7] per 1,000
hours), whereas the incidence of recurrent injuries was greater
in the 2008–2009 seasons (0.3 [95% CI, 0.0–0.6] per 1,000
hours vs. 0.8 [95% CI, 0.3–1.2] per 1,000 hours) (Figure 3).
TABLE 4. Site and type of predicted injuries during the 2008–2009 season.*
Site Number Injury rate % Type Number Injury rate %
Thorax 2 0.2 (0.0–0.4) 1.2 Muscular strains 110 8.4 (6.8–10.0) 68.3
Lumbar 8 0.6 (0.2–1.0) 5.0 Overuse 48 3.7 (2.6–4.7) 29.8
Anterior thigh 13 1.0 (0.5–1.5) 8.1 Joint sprains 1 0.1 (0.0–0.2) 0.6
Posterior thigh 57 4.4 (3.2–5.5) 35.4
Groin 22 1.7 (1.0–2.4) 13.7
Knee 25 1.9 (1.2–2.7) 15.5
Calf 21 1.6 (0.9–2.3) 13.0
Foot 10 0.8 (0.3–1.2) 6.2
Other 1 0.1 (0.0–0.2) 0.6
Total 159 12.1 (10.2–14.0) 100.0 Total 159 12.1 (10.2–14.0) 100.0
*The number and incidence of injuries represent any noncontact, soft-tissue injury that prevented the player completing the entire
training session or match.
Injury rates are expressed per 1,000 exposure hours (and 95% confidence intervals).
TABLE 5. Accuracy of model for predicting noncontact, soft-tissue injuries.*
Actual status
Injured Not injured
Predicted status Predicted injury True positive False positive
N= 121 N=20
Predicted no injury False negative True negative
N=18 N= 1589
Sensitivity Specificity
87.1 (80.5–91.7)% 98.8 (98.1–99.2)%
Likelihood ratio positive 70.0 (45.1–108.8)
Likelihood ratio negative 0.1 (0.1–0.2)
*True positive = predicted injury and player sustained injury; false positive = predicted injury but player did not sustain injury; false
negative = no injury predicted but player sustained injury; true negative = no injury predicted and player did not sustain injury; sensitivity =
proportion of injured players who were predicted to be injured; specificity = proportion of uninjured players who were predicted to remain
injury-free; Likelihood ratio positive = sensitivity/(12specificity); likelihood ratio negative = (12sensitivity)/specificity.
Although there were 91 players in the sample, injury predictions based on the training loads performed by individual players were
made on a weekly basis, so that within the total cohort, there was a total number of true positive and negative predictions, and a total
number of false positive and negative predictions. Sensitivity and specificity data and positive and negative likelihood ratios are
expressed as rates (and 95% confidence intervals).
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Influence of Training Load on Likelihood of Injury
Based on the results of the logistic regression model, players
were 50–80% likely to sustain a preseason injury within the
training load range of 3,000–5,000 units. These training load
ÔthresholdsÕwere considerably reduced (1,700–3,000 units) in
the late-competition phase of the season (Figure 4). The
actual preseason, early-competition, and late-competition
training loads performed by injured players were 4,341 (95%
CI, 4,082–4,600), 3,395 (95% CI, 3,297–3,493), and 2,945
(95% CI, 2,797–3,094) units, respectively. When actual
training loads for individual players were higher than
planned training loads, noncontact, soft-tissue injury oc-
curred in 72 (95% CI, 63–81), 57 (95% CI, 47–67), and 75
(95% CI, 66–84)% of cases, for the preseason, early-
competition, and late-competition training phases, respec-
tively (Table 3).
Injury Prediction Model
Site and Type of Predicted Injuries. A total of 159 noncontact,
soft-tissue injuries were sustained over the latter 2 seasons
(2008–2009). The majority of injuries predicted in the
2008–2009 seasons were to the posterior thigh (4.4 [95%
CI, 3.2–5.5] per 1,000 hours, 35.4%). Muscular strains were the
most common type of injury (8.4 [95% CI, 6.8–10.0] per 1,000
hours, 68.3%) (Table 4).
Sensitivity and Specificity of the Injury Prediction Model
The percentage of true positive predictions was 62.3% (n=
121), whereas the total number of false positive and false
negative predictions was 20 and 18, respectively. The sensi-
tivity and specificity of the injury prediction model were 87.1
(95% CI, 80.5–91.7)% and 98.8 (95% CI, 98.1–99.2)%,
respectively (Table 5). The calculated positive and negative
likelihood ratios were 70.0 (95% CI, 45.1–108.8) and 0.1 (0.1–0.2),
respectively.
DISCUSSION
The present study is the first to describe an injury prediction
model for noncontact, soft-tissue injuries in elite collision
sport athletes. The use of the injury prediction model allowed
medical staff to make informed decisions on training
management on a scientific basis, whereas modeling of
training load and injury data identified the training load
ranges that resulted in an ‘‘acceptable’’ injury risk and also the
training load ranges that resulted in an ‘‘unacceptable’’ injury
risk. These findings provide a scientific method of monitoring
and regulating training load that removes the ÔguessworkÕ
from training.
Previous studies of collision sport athletes have reported
a significant relationship (r= 0.86) between training loads
and training-injury rates (17), suggesting that the harder these
athletes train, the more injuries they will sustain. Further-
more, reductions in training loads have been shown to reduce
training-injury rates and result in greater improvements in
_
VO
2
max (18). However, it has also been shown that collision
sport athletes that perform ,18 weeks of preseason training
before sustaining an initial injury are at increased risk of
sustaining a subsequent injury, whereas players with a low
off-season
_
VO
2
max are at increased risk of sustaining a contact
injury (20). Clearly, training for collision sports reflects
a balance between the minimum training load required to
elicit an improvement in fitness and the maximum training
load tolerable before sustaining marked increases in injury
rates. In the elite team sport environment, it is critical to have
the maximum number of players free from injury and
available for selection in as many games as possible
throughout the season (34,36). The developed injury
prediction model provides a practical framework to monitor
training loads and prevent noncontact, soft-tissue injuries in
elite collision sport athletes.
Modeling of training load and injury data of players
identified safe and unsafe training loads for different phases of
the season. In the preseason training period, players were
50–80% likely to sustain an injury within the training load
range of 3,000–5,000 units. However during the late-
competition phase, significantly less training could be
tolerated before increasing the likelihood of injury; players
were 50–80% likely to sustain an injury within the training
range of 1,700–3,000 units. In all 3 training phases (preseason,
early-competition, and late-competition), small increases in
training load resulted in large increases in injury likelihood.
Indeed, when actual training loads for individual players were
higher than planned training loads, noncontact, soft-tissue
injury occurred in 72, 57, and 75% of cases, for the preseason,
early-competition, and late-competition training phases,
respectively. Collectively, these results give practical in-
formation on the training–injury dose–response relationship
in elite collision sport athletes.
Players that exceeded the training load threshold were
70 times more likely to test positive for noncontact, soft-tissue
injury, whereas players that did not exceed the training load
threshold were injured 1/10 as often. These results demon-
strate that the developed model was adequately sensitive to
detect both players who were susceptible to noncontact, soft-
tissue injuries and players who were not susceptible to injury.
It should be emphasized that in 62.3% of cases, a player was
highlighted as having the potential for injury and no
intervention was undertaken (and the player was sub-
sequently injured). These results compare favorably with
the proportion of players (11.3%) who sustained a soft-tissue
injury despite not being predicted as being at risk for soft-
tissue injury. Although there are numerous examples of highly
skilled individuals using ÔintuitionÕto identify potential
problems within their area of expertise (25), the present
findings suggests that the information generated from
training monitoring could have been used more efficiently.
Despite the additional scientific information generated by the
injury prediction model, strength and conditioning coaches
were more comfortable using their intuitive ÔexpertiseÕto
manage the training loads of players. Unfortunately, the
model had far greater accuracy predicting injuries (62.3%)
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Injury Prediction in Collision Sport Athletes
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
than it did influencing the decision making of strength and
conditioning staff, with ,14% of players provided alternative
training or additional recovery when an injury was predicted.
It is likely that a combination of intuitive ÔexpertiseÕand the
injury prediction model may have provided greater accuracy
in predicting training-related, soft-tissue injuries (38,40).
Given the success in predicting injuries and that the total
proportion of incorrect predictions was small (23.9%), these
results suggest that the injury prediction model provides
greater sensitivity than the sole judgment of strength and
conditioning staff.
No significant differences existed between the initial 2
seasons (where no injury prediction model was used) and
latter 2 seasons (where the injury prediction model was
developed and implemented) for the incidence of missed
match injuries. These findings may be attributed to the similar
training loads employed in the initial and latter seasons.
However, it is also possible that the failure to reduce
noncontact, soft-tissue injuries with the introduction of the
injury prediction model was because of the low overall
incidence of missed match soft-tissue injuries over this period.
Indeed, the soft-tissue injury rates reported in this study are
considerably lower than other running-based team sports
(e.g., Australian football) (35). It is therefore likely that
a training monitoring model designed to capture training
load-related soft-tissue injuries may have better application to
team sports with a greater emphasis on running, and a lower
emphasis on physical collisions. Indeed, team sports such as
Australian football, basketball, and soccer, which typically
employ high running loads to condition players, would likely
benefit more from the developed injury prediction model
than collision sports that have a greater emphasis on
conditioning through physical contact (e.g., American
football). Nonetheless, the present findings demonstrate
the applicability of an injury prediction model to detect
noncontact, soft-tissue injuries in elite collision sports that
incorporate high volumes and intensities of both physical
contact and running into their conditioning programs.
It should be noted that the injury prediction model has
a number of potential limitations. Firstly, the developed
model is unable to predict collision injuries. However,
although collision injuries are multifactorial, it is likely that
players with better developed physical qualities may be less
susceptible to collision injuries, whereas overtrained players
may be at a greater risk of injury. Indeed, several rugby league
studies have reported a significant relationship (r= 0.86)
between training loads and training-injury rates (17),
suggesting that the harder these athletes train, the more
injuries they will sustain. Furthermore, although limited
evidence exists in elite collision sport athletes, previous
studies on subelite collision sport athletes have shown that
performing ,18 weeks of preseason training before sustain-
ing an initial injury increases the risk of sustaining a sub-
sequent injury, whereas players with a low off-season
_
VO
2
max are at an increased risk of sustaining a contact
injury (20). Secondly, the accuracy of the model for
predicting training load-related soft-tissue injuries is de-
pendent on the quality of data entered. In the present study,
training loads were estimated from the session-RPE, a sub-
jective measurement of training load. Future injury prediction
models could include global positioning system data, data on
the number and intensity of collisions, markers of fatigue
(e.g., self-reports, neuromuscular and hormonal data), and
match work rates to further refine the present training
monitoring system. Finally, although the model was success-
ful in predicting injuries over 2 competitive seasons, its
continued success is dependent on knowledge of planned
and actual training loads. Planned training loads for
conditioning, strength, and skill sessions are required for
the injury prediction model to operate effectively.
Although the injury prediction model used in this study had
sufficient predictive accuracy to warrant systematic use in an
elite team sport program, a fine balance exists between
training, detraining, and overtraining. Furthermore, although
a relationship was observed between training load and
likelihood of injury, training programs must also be
physiologically efficient and psychologically appropriate to
allow players to cope with the demands of competition (6).
Indeed, exposing the brain to hard physical work and fatigue
on a regular basis appears to improve the body’s ability to
cope with fatigue; physically intense training not only
improves physical fitness but equally importantly also
increases the mental durability of players (33). Physically
(and mentally) unfit players are more likely to pace
themselves as a self-preservation and protection strategy
(33). If players have not been exposed to hard physical work
on a regular basis, the brain instructs the body to stop
exercise earlier to prevent exhaustion (33). With this in mind,
it may be argued that it is worthwhile prescribing high
training loads (note, not excessive) to players to determine
which players are most susceptible to injury under physically
stressful situations (these players most likely will not tolerate
the intensity and fatigue of competition), and which players
are not susceptible to injury under physically stressful
situations (these players are more likely to tolerate the
intensity and fatigue of competition).
In conclusion, this study developed an injury prediction
model for noncontact, soft-tissue injuries in elite collision
sport athletes. Players that exceeded the training load
threshold were 70 times more likely to test positive for
noncontact, soft-tissue injury, whereas players that did not
exceed the training load threshold were injured 1/10 as often.
Modeling of training load and injury data identified the
training load ranges that resulted in an ‘‘acceptable’’ injury
risk and also the training load ranges that resulted in an
‘‘unacceptable’’ injury risk. From a practical perspective,
these findings provide information on the training dose–
response relationship, and a scientific method of moni-
toring and regulating training load in elite collision sport
athletes.
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PRACTICAL APPLICATIONS
There are several practical applications of this study that are
relevant to the strength and conditioning coach. Monitoring
and regulating training loads is critical to ensure that players
receive a progressively overloaded periodized training pro-
gram and are given adequate recovery between high-volume
and high-intensity sessions. It is important for strength and
conditioning coaches to work closely with sport scientists to
determine the appropriate training and recovery periods to
maximize improvements in performance without unduly
increasing injury incidence.
Players that exceeded the training load threshold were
70 times more likely to test positive for noncontact, soft-tissue
injury, whereas players that did not exceed the training load
threshold were injured 1/10 as often. In 62.3% of cases,
a player was highlighted as having the potential for injury, and
no intervention was undertaken (and the player was
subsequently injured). Given the success in predicting injuries,
and that only a small proportion (23.9%) of incorrect
predictions were made, these results suggest that the injury
prediction model provides greater sensitivity than the sole
judgment of strength and conditioning staff. Indeed, a large
proportion of injuries may have been prevented if strength
and conditioning staff heeded the warnings provided through
the scientific analysis of the training loads.
Finally, although the injury prediction model provided
a successful framework to manage noncontact, soft-tissue
injuries it should be recognized that the model was based on
planned and actual training loads. An assumption with this
model is that players possess adequate physical qualities to
perform rigorous physical training and that the planned
training loads were adequate to develop and maintain
physical fitness. To some extent, this framework constrains
the amount of physical adaptation permitted through
training, by limiting the amount of physical work that can
be performed. Allowing players to exert themselves above
and beyond the planned training loads may result in soft-
tissue injury but could also produce greater physical
adaptations and mental durability in players.
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... The 30 studies collectively included 9,142 participants, with 6,549 males (median: 96; range 25-2322), and 860 females (median: 105; range 26-187). Ten (33%) studies included soccer athletes [34,39,44,45,47,48,[50][51][52]62], five (17%) athletes from undefined sports [33,42,43,49,55], five (17%) basketball athletes [41,44,54,56,58], two (7%) Australian Rules Football athletes [36,53], two (7%) volleyball athletes [44,58], and 12 (40%) studies included athletes from baseball, floorball, football, gymnastics, handball, korfball, ice hockey, rugby, or swimming [35,37,38,41,45,46,49,[57][58][59][60][61]. Fourteen (47%) studies included professional or elite athletes [33, 34, 36, 38, 39, 45, 46, 52-54, 56-59, 62], eight (27%) college athletes [35,44,47,49,50,55,57,60,61], five (17%) youth athletes [37,40,41,48,51], two (7%) recreational adult athletes [43,54], and two (7%) high school athletes [42,55]. ...
... Ten studies (33%) dichotomized continuous predictors prior to developing the model [36, 42-45, 47-49, 60, 61], and an additional three studies (10%) were unclear about whether continuous predictors were dichotomized prior to developing the model [33,34,50]. Twelve studies (40%) selected predictors through univariable analyses (i.e., based on their unadjusted association with the outcome) before model development [36,37,43,48,49,54,56,57,59,61], seven studies (23%) were unclear on how predictors were selected before or during model development [38,40,45,46,50,58], one study (3%) used recursive cross-validation methods (sic) [52], four (13%) included all predictors [44,47,51,53], three (10%) used backwards selection [33,35,39], three (10%) used least absolute shrinkage and selection operator (LASSO) [41,42,62], and two (7%) included both univariable (before model development) and backward step-wise selection (during model development) [55,60]. Three machine learning studies (10%) [45,52,53] over-sampled to reportedly control for class imbalance, and two (7%) [45,62] under-sampled to control for class imbalance. ...
... Twenty-four (80%) studies reported measures of musculoskeletal injury prediction model performance ( [37,39,43,50], one (2%) study reported clinical decisions curves and net benefit [39], seven (23%) studies reported overall accuracy [34,35,38,[51][52][53]59], and one (2%) study reported root mean square error approximation [40]. Content courtesy of Springer Nature, terms of use apply. ...
Article
Full-text available
Background An increasing number of musculoskeletal injury prediction models are being developed and implemented in sports medicine. Prediction model quality needs to be evaluated so clinicians can be informed of their potential usefulness. Objective To evaluate the methodological conduct and completeness of reporting of musculoskeletal injury prediction models in sport. Methods A systematic review was performed from inception to June 2021. Studies were included if they: (1) predicted sport injury; (2) used regression, machine learning, or deep learning models; (3) were written in English; (4) were peer reviewed. Results Thirty studies (204 models) were included; 60% of studies utilized only regression methods, 13% only machine learning, and 27% both regression and machine learning approaches. All studies developed a prediction model and no studies externally validated a prediction model. Two percent of models (7% of studies) were low risk of bias and 98% of models (93% of studies) were high or unclear risk of bias. Three studies (10%) performed an a priori sample size calculation; 14 (47%) performed internal validation. Nineteen studies (63%) reported discrimination and two (7%) reported calibration. Four studies (13%) reported model equations for statistical predictions and no machine learning studies reported code or hyperparameters. Conclusion Existing sport musculoskeletal injury prediction models were poorly developed and have a high risk of bias. No models could be recommended for use in practice. The majority of models were developed with small sample sizes, had inadequate assessment of model performance, and were poorly reported. To create clinically useful sports musculoskeletal injury prediction models, considerable improvements in methodology and reporting are urgently required.
... Differently, performing tests periodically (monitoring approach) showed a moderate-high injury prediction accuracy [3]. Monitoring training workloads along the season permits having an overview of the players' fitness status and consequently of their injury risk [7,[13][14][15][16][17][18]. As a matter of fact, it was demonstrated that the greater the training exposure is, the greater the injury risk is [13,19,20]. ...
... Monitoring training workloads along the season permits having an overview of the players' fitness status and consequently of their injury risk [7,[13][14][15][16][17][18]. As a matter of fact, it was demonstrated that the greater the training exposure is, the greater the injury risk is [13,19,20]. However, there may also be currently unknown factors, e.g., sleep, nutrition, and blood markers that could have a role in injury prediction [3]. ...
... ACWR values higher than 1 indicate that a player performs a higher workload in the past week compared to the past month. In particular, values higher than 1.5 result in a high risk of injury (over-training status), while values between 1 and 1.5 indicate the optimal training zone [13,19,20]. Differently, values lower than 1 indicate under-training status [13,19,20]. ...
Article
Full-text available
Purpose By analyzing external workloads with machine learning models (ML), it is now possible to predict injuries, but with a moderate accuracy. The increment of the prediction ability is nowadays mandatory to reduce the high number of false positives. The aim of this study was to investigate if players’ blood sample profiles could increase the predictive ability of the models trained only on external training workloads. Method Eighteen elite soccer players competing in Italian league (Serie B) during the seasons 2017/2018 and 2018/2019 took part in this study. Players’ blood samples parameters (i.e., Hematocrit, Hemoglobin, number of red blood cells, ferritin, and sideremia) were recorded through the two soccer seasons to group them into two main groups using a non-supervised ML algorithm (k-means). Additionally to external workloads data recorded every training or match day using a GPS device (K-GPS 10 Hz, K-Sport International, Italy), this grouping was used as a predictor for injury risk. The goodness of ML models trained were tested to assess the influence of blood sample profile to injury prediction. Results Hematocrit, Hemoglobin, number of red blood cells, testosterone, and ferritin were the most important features that allowed to profile players and to analyze the response to external workloads for each type of player profile. Players’ blood samples’ characteristics permitted to personalize the decision-making rules of the ML models based on external workloads reaching an accuracy of 63%. This approach increased the injury prediction ability of about 15% compared to models that take into consideration only training workloads’ features. The influence of each external workload varied in accordance with the players’ blood sample characteristics and the physiological demands of a specific period of the season. Conclusion Field experts should hence not only monitor the external workloads to assess the status of the players, but additional information derived from individuals’ characteristics permits to have a more complete overview of the players well-being. In this way, coaches could better personalize the training program maximizing the training effect and minimizing the injury risk.
... In the last decade, several papers proposed models to assess athletes' injury risk. The first injury risk model was developed by Gabbett and colleagues in 2010 [6], who modeled the risk of soft-tissue injury using a monodimensional approach. This risk was estimated through the evaluation of the training load (i.e., the cumulative amount of stress perceived by a player during a single training session) sustained by the athletes during a competitive season. ...
... An athlete is commonly defined as injured when they are absent in physical activities for at least the day after the day of the onset (time-loss definition) [6,15,20,21,23,73]. Injuries could be classified into two main areas, i.e., contact and non-contact (soft-tissue) injuries. ...
... Van Eetvelde et al. [74], in their review about injury forecasting by machine learning model, asserted that the results detected in the previous papers are promising in the sense that these models might help coaches, physical trainers, and medical practitioners in the decisionmaking process for injury prevention and prediction. The most common supervised machine learning models used for injury forecasting are decision trees [15,20,23,24], binary logistic regression [6,11,15,24], random forests [15,22,24], and supporting vector machines [22,24]. Carrey et al. [22] also tested generalised-estimating equations, which are an extension of generalised linear models that account for correlations between repeated observations taken from the same subjects. ...
Article
Full-text available
In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models.
... Finalmente, El fútbol femenino ha ganado popularidad en todos los niveles y ha aumentado el interés de la Unión de la Asociación Europea de Fútbol (UEFA). Por lo tanto, la evaluación de una prueba de velocidad repetida es interesante, ya que puede discriminar el nivel de rendimiento en jugadoras de fútbol (Gabbett, 2010). ...
... ).Una explicación sencilla pero justificada científicamente, es que se ha encontrado que los períodos de intensificación de la carga de entrenamiento, como la pretemporada, los períodos de mayor competencia y los jugadores lesionados que regresan al entrenamiento completo, aumentan el riesgo de lesiones. Por ejemplo, los atletas que regresan para la pretemporada tienen un riesgo significativamente mayor de sufrir lesiones, posiblemente debido a la intensificación de la carga de trabajo de entrenamiento y al efecto de desentrenamiento de la temporada baja(Gabbett et al., 2010;Rogalski et al., 2013). Esto puede resultar en que el atleta no pueda tolerar la carga de entrenamiento externa e interna expuesta. ...
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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.
... Although the thesis does not provide explicit load benchmarks such as those (i.e., ACWR) initially theorised by Gabbett et al (2010), the various chapters offer insight into the likely responses of, and approaches to managing players of varied maturity status. For example, chapter 5 offers guidance on the most appropriate method to monitor maturation in adolescent footballers' ...
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The period surrounding the adolescent growth spurt is a turbulent but crucial stage of development for young footballers in their pursuit of becoming full-time athletes. At a time of almost constant talent (re)selection which coincides with major physical and physiological changes players experience large fluctuations in performance and a heightened injury incidence. Adding to the complexity of this period, the timing and tempo of biological maturation varies between individuals causing a diversity in physical and physiological capabilities, influencing the dose-response to training. Although differences in biological maturation and the links with injury are acknowledged in literature, little evidence exists to quantify the magnitude and extent to which these impacts perceptions of load and subsequent performance. This thesis aims to quantify the maturity-specific responses to load using ecologically valid approaches to aid the enhancement of provision offered to young academy players. To provide a context and informed backdrop for the rest of the thesis, it was deemed important to first identify the current practices of, and perceived barriers to monitoring training load and biological maturation in academies. A cross-sectional survey design was used to ascertain perceptions of staff from male (EPPP) and female (RTC) academies during the 2017/18 soccer season. In total, 49 respondents completed the survey who advocated injury prevention as highest importance for conducting training load and maturation monitoring across academy groups, with overall athletic development, load management, coach and player feedback considered important. However, there were clear differences in monitoring strategies that academies of different categories adopted, which were often associated with resources or staffing. Survey responses suggest that despite routine monitoring of biological maturation and training load being commonplace within adolescent soccer the communication and dissemination of this information is often lacking, which may ultimately impede the impact of the monitoring practices for the players. Resource and environmental constraints create natural diversity around the strategies adopted, but academies are recommended to adopt sustainable and consistent approaches to monitor key variables to inform the coaching, selection, and development process. The survey chapter identified that most clubs employ one of the various ‘non-invasive’, somatic equations to estimate biological maturation. However, the methodological differences associated with calculations often mean they provide variable estimations, even when using the same anthropometrical data. Therefore, it was deemed important to this thesis to observe the agreement of maturity estimations and compare concordance between methods when looking to estimate maturity status. Thus, anthropometric data from 57 participants was collected from a single assessment point during the 2017-18 season, with an additional 55 participants providing three repeated measurements during the 2018-19 season, resulting in 222 somatic estimations observed. Results indicated that all methods of maturity-offset (MO) produced an identical estimate of age of peak height velocity (13.3 years) with mean prediction of adult height (PAH%) providing a mean estimate of 93.6%, which also aligns closely. However, when looking to identify circa-PHV individuals there is greater concordance when using conservative thresholds (44-67%) than when using more stringent bandwidth thresholds (31-60%), with both being considered moderate concordance at best. Therefore, although overall findings indicate that there is very high to near perfect agreement between all approaches when predicting APHV, concordance of categorisation between these methods is less useful. Therefore, this chapter indicates that PAH% and MO methods are not interchangeable, and practitioners should utilise one approach routinely for all maturity-specific interventions. Academy squads are comprised of players within chronological parameters but often present significant variations in physical characteristics including body mass (~50%), stature (~17%), percentages of predicted adult height (10-15%) and fat free mass (~21%). These maturational changes likely influence performance and dose-responses to load, but limited studies using standardised activity profiles have directly observed this influence. Therefore, this thesis aimed to quantify the neuromuscular performance (CMJ, RSI absolute and relative stiffness) and psycho-physiological (d-RPE) responses to a simulated soccer-specific activity profile (Y-SAFT60) and analyse whether this dose-response was moderated by maturation in EPPP academy players. Data illustrated an interaction between perceived psycho-physiological load (RPE-T) and maturation, with absolute stiffness, relative stiffness and playerload (PL) showing slope significance across various stages of maturation (~86-96% PAH). These interactions suggest that psycho-physiological dose responses are influenced by maturation and should be considered for training prescription purposes, which is likely a result of the musculotendinous changes that occur around peak height velocity (PHV). Therefore, practitioners are urged to consider the maturational load-response variation to reduce injury incidence from inappropriate levels of physical and cognitive stress, which are likely compounded chronically with multiple weekly sessions. Typically, players experience between 3-4 acute bouts of specific training on a weekly basis, proposing that the maturity-specific load-responses observed above may be exacerbated over the course of a season. 55 male soccer players from a Category 2 EPPP academy were monitored during the 2018-19 season. Self-reported perceptions of psycho-physiological (d-RPE) intensity were collected approximately 15-minutes after each training session for a period of 40-weeks using the CR100® centi-Max scale. Analysis indicated that a 5% increase in PAH%, resulted in a reduction of ~7AU per session, with a ~14AU difference for a 10% difference in PAH%. Therefore, players less biologically mature are consistently working harder just to compete with more biologically advanced teammates of a similar chronological age. Again, these changes are mostly attributed to musculotendinous changes because of maturation and therefore a higher relative mechanical load experienced by less mature individuals. When accrued, these small inter-individual differences lead to a substantial variation in training load (~40-50%) over the 40-week season. This has the potential to undermine the whole developmental pathway, as the assumption that players of a similar chronological age are experiencing similar load-responses is precarious. Failure to act, by adopting more maturity sensitive ways of working for example, will result in a ‘survival of the fittest’ environment, rather than the systematic, considered, and individualised approach to optimal loading proposed in policy documents and literature. Bio-banding is a method to group individuals based on biological maturation rather than chronological age. Supplementing the chronological programme with bio-banded activities may offer practitioners a practical method to better control load exposure and ultimately mechanical load related injury risk. Therefore, the final thesis study explored effects of standardised chronological and bio-banded training sessions on neuromuscular performance and psycho-physiological perceptions of intensity in 55 male soccer players from a single academy. Players participated in bio-banded and chronologically categorised bouts (x5) of 5-minute 6v6 (including GK) SSG on a playing area 45 x 36 m (135m2 per player). Prior to and following this, players performed a standardised sub-maximal run using the audio controlled 30-15IFT wearing foot-mounted inertial devices. Findings indicate that the introduction of bio-banded training sessions minimises the decrement in neuromuscular and locomotor markers and psycho-physiological ratings of intensity for players across the maturation spectrum. From a load management point of view, the relatively smaller pre-post changes observed in bio-banded SSGs offer promising early indications that biologically categorising training may help to stabilise the stress-response for players across maturity groups and facilitate a load management option for practitioners. Based on this, practitioners should actively seek opportunities to integrate biologically classified training activity alongside chronologically categorised sessions within their training schedules. In doing so they may alleviate the consistent stress placed on less mature players as part of standard chronologically categorised sessions without compromising the development of those more mature and able to tolerate greater workloads.
... Başlangıçta askeri amaçlar için geliştirilen bu sistem artık sporcu takibi ve yük miktarının belirlenmesi de dahil olmak üzere çok daha geniş bir uygulama alanına sahiptir 7,8,10,11 . MEMS içerisinde bulunan GPS destekli veriler artık her düzeyde bireysel ve takım sporlarında yaygın olarak kullanılmaktadır 1,2,4 . ...
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The global positioning system (GPS) and inertial sensors are a widely used technology in sports performance to evaluate athletes' movement patterns and monitor training loads, both during competition and training. The data obtained from this system can be used to better understand the demands of both competitions and individual training. With the increasing speed of the competitions, technological developments in training science continue with it in order to better prepare the athletes for the conditions microelectromechanical systems, which is used in the world of sports science, is a product of this need. In order for athletes to be prepared for challenging competition conditions, it is very important to be able to observe the stressor factors in the training environment and the competition environment. By using MEMS, sports scientists and trainers can observe extreme training loads in competitions and trainings, and follow their athletes based on more objective data. MEMS allows us to monitor not only excessive training loads but also individual differences in athletes and follow up insufficient training loads. Thanks to the correct observation of the athletes through MEMS, it can achieve optimum performance by protecting them from injuries. MEMS, which has been widely used in recent years to transform the training of athletes into objective data, is of great importance in sports science. The aim of this review is; To research the information in theory about MEMS for sports scientists and coaches and to bring them together with the readers as applicable information.
... lowerbody strength and power, aerobic power, draw and pass) may provide the foundation which drives our performance outcomes. On this basis, coaches aim to systematically develop these qualities in respect of the player's strengths/weaknesses, the match-play demands of their position [33], to aid muscle integrity [34], improve body composition [35], among others. Indeed, it is commonly regarded that rugby league players require a broad range of physical, physiological, technical and perceptual qualities [2]. ...
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Across team sports, it is critically important to appropriately define, evaluate and then aptly describe individual and team performance. This is of particular significance when we consider that performance models govern the direction of player preparation (short term) and development (long term) frameworks. Within the context of rugby league, this has traditionally been undertaken through hierarchical and linear processes. Such approaches have resulted in research and performance analysis techniques which aim to support these operational outcomes. Yet, these methods may deliver limited application on how or why match-play unfolds and therefore might be sub-optimal in providing insights to truly support coaches. In this paper, we propose the conceptualisation of rugby league performance through the lens of ecological dynamics, which may offer a different view to this traditional approach. We propose that this approach eliminates the silos of disciplinary information (e.g. technical, physical and medical) that may currently exist, allowing for a holistic approach to performance, preparation and development. Specifically, we consider that through the implementation of this ecological approach, all performance coaches (technical, physical and medical) may (co-)design learning environments that more collaboratively develop players for rugby league match-play. As a result, we put forward a new rugby league performance model from which preparation and development programs can be anchored toward. We conclude the paper by offering practical examples where these concepts are contextualised within the landscape familiar to practitioners working within rugby league.
... Both contact and noncontact injuries were included in this investigation based on previous findings that higher workloads were strongly correlated with contact injuries. 7,22 However, most studies investigating the relationship between load and injury did so for noncontact injuries. 27 As such, there may be different mechanisms for how load can influence contact and noncontact injuries. ...
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Background There is limited research regarding the impact of workload on injury risk specific to women’s soccer. Wearable global positioning system (GPS) units can track workload metrics such as total distance traveled and player load during games and training sessions. These metrics can be useful in predicting injury risk. Purpose To examine the relationship between injury risk and player workload as collected from wearable GPS units in National Collegiate Athletic Association (NCAA) Division I women’s soccer players. Study Design Case-control study; Level of evidence, 3. Methods Lower extremity injury incidence and GPS workload data (player load, total distance, and high-speed distance) for 65 NCAA Division I women’s soccer players were collected over 3 seasons. Accumulated 1-, 2-, 3-, and 4-week loads and acute-to-chronic workload ratios (ACWR) were classified into discrete ranges by z-scores. ACWR was calculated using rolling averages and exponentially weighted moving averages (EWMA) models. Binary logistic regression models were used to compare the 7:28 rolling average and EWMA ACWRs between injured and noninjured players for all GPS/accelerometer variables. The prior 1-, 2-, 3-, and 4-week accumulated loads for all GPS/accelerometer variables were compared between the injured and uninjured cohorts using 2-sample t tests. Results There were a total of 53 lower extremity injuries that resulted in lost time recorded (5.76/1000 hours “on-legs” exposure time; 34 noncontact and 19 contact injuries). The prior 2-week (7242 vs 6613 m/s ² ; P = .02), 3-week (10,533 vs 9718 m/s ² ; P = .02), and 4-week (13,819 vs 12,892 m/s ² ; P = .04) accumulated player loads and 2-week (62.40 vs 57.25 km; P = .04), 3-week (90.97 vs 84.10 km; P = .03), and 4-week (119.31 vs 111.38 km; P = .05) accumulated total distances were significantly higher for injured players compared with noninjured players during the same time frames. There were no significant differences in player load, total distance, or high-speed distance ACWR between injured and noninjured players for both the rolling averages and EWMA calculations. Conclusion Higher accumulated player load and total distance, but not ACWR, were associated with injury in women’s soccer players.
Article
In studies reporting rugby league injuries, match injuries varied depending upon participation level. To review and update pooled data estimates for rugby league injury epidemiology and add information for participation levels in match and training environments. A systematic review and pooled analysis for published studies reporting rugby league match and training injuries. Searches were performed in the PubMed, CINHAL, ScienceDirect, Scopus, SPORTDiscus, SpringerLink, and Wiley Online databases. Studies were considered if they reported on rugby league match or training injuries between Jan 1990 to June 2021. Two authors (DK, TC) extracted the study characteristics, numerical data and assessed the article quality, by adhering to the protocol for systematic review of observational studies (MOOSE) and the STrengthening and Reporting of OBservational studies in Epidemiology (STROBE) statement. The 46 studies included a combined exposure of 419,037 h and 18,783 injuries incorporating 158,003 match-hr and 15,706 match injuries (99.4 [95%CI: 97.9–101.0] per 1000 match-hr) and 264,033 training-hr and 3077 training injuries (11.8 [95%CI: 11.4–12.2] per 1000 training-hr). Of included studies, 47.9% utilised a medical attention/treatment injury definition. There was a five-fold difference in injuries for the semi-professional participation level (431.6 per 1000 match-hr) compared with professional (RR: 4.92; p
Chapter
The Global Positioning System (GPS) is a satellite-based navigational technology initially devised in the military environment. Since GPS technology enables three-dimensional movement of a subject or a group to be tracked over time in air, aquatic or land-based environments, its utility in the military context appears very clear (Aaltonen and Laarni 2017). The rapid development of GPS technology has made its use possible also in sports. It was first used in a sport activity in 1997 (Schutz and Chambaz 1997), and it is today widely used in several sport activities, such as soccer, Australian football, rugby, cricket, and hockey (Cummins et al. 2013). The GPS technology in team sports allows the measurement of players’ position, velocity, and movement patterns. The measurement of a player’s movements by means of the GPS allows to quantify, in the most objective manner possible, the subject’s physiologic demand, the training intensity and the competition, and the training performance (McLellan et al. 2011). Basically, the GPS enables the measurement of a player’s movement patterns, speed, and distance travelled, as well as the number of accelerations and decelerations. Furthermore, by adding a triaxial accelerometer, data concerning the physical workloads can be recorded (Cummins et al. 2013). The triaxial accelerometer allows the measurement of a composite vector magnitude that is expressed as a G-force. This is possible by recording the sum of accelerations measured in three axes, that is, the X, Y, and Z planes (Waldron et al. 2011). The data recorded by the GPS are used to calculate the player’s movement pattern (the so-called external loads) and their physiological response to competition and training load (the so-called internal load). Beyond these data, both the number and the intensity of physical contacts and collisions can be calculated between athletes and objects, other athletes, and surfaces, through quantification of body load and impact measure. In particular the body load, which is quantified in G-force, represents the collection of all the forces imposed to the player (i.e., acceleration, deceleration, changes of direction, and impacts). GPS devices are currently manufactured with 1-, 5-,10-, and 15-Hz sampling rates (Johnston et al. 2014), where GPS devices having a higher-frequency rate provide greater reliability in the measurement (Jennings et al. 2010; Aughey 2011). However, some studies show no additional benefits when increasing to 15-Hz sampling rate (Johnston et al. 2014; Scott and Scott 2016). In any case, independently from the sampling rate of the device, the validity of the distance measured improves in relationship with the activities duration (Aughey 2011). For example, the standard of error is reduced in average by 67% when comparing sprinting over 40 and 10 m distances (Jennings et al. 2010). On the contrary, the reliability of GPS decreases with the increased velocity of the movement (Jennings et al. 2010). Indeed, the GPS reliability is reduced when the movement speed is higher than 20 km ⋅ h−1 (Gray et al. 2010), probably because rapid changes in velocity happen when the football player moves at higher speeds (Jennings et al. 2010). In general, in literature the majority of the studies concluded that GPS devices show a sufficient level of both validity and retest reliability during the acquisition of movement patterns performed at lower speeds and over increased distance efforts. Such devices show less reliability during short-duration high-speed straight line running and changes of direction. Those situations do represent a limit for the accuracy in the acquisition of the abovementioned parameters in team sports. For this reason, caution is suggested in the interpretation of the sprints and rapid changes in both direction and velocity (Cummins et al. 2013). Moreover, further research on the validity and reliability in quantifying impacts in collision sports is needed.
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Objectives: To investigate the site, nature, cause, and severity of injuries in semi-professional rugby league players. Methods: The incidence of injury was prospectively studied in one hundred and fifty six semi-professional rugby league players over two competitive seasons. All injuries sustained during matches and training sessions were recorded. Injury data were collected from a total of 137 matches and 148 training sessions. Information recorded included the date and time of injury, site, nature, cause, and severity of injury. Results: During the two seasons, 1694 playing injuries and 559 training injuries were sustained. The match injury incidence was 824.7 per 1000 player-position game hours and training injury incidence was 45.3 per 1000 training hours. Over 20% of the total training (17.4 per 1000) and playing (168.0 per 1000) injuries sustained were to the thigh and calf. Muscular injuries (haematomas and strains) were the most common type of injury sustained during training (22.0 per 1000, 48.7%) and matches (271.7 per 1000, 32.9%). Playing injuries were most commonly sustained in tackles (382.2 per 1000, 46.3%), while overexertion was the most common cause of training injuries (15.5 per 1000, 34.4%). The majority of playing injuries were sustained in the first half of matches (1013.6 per 1000, 61.5% v 635.8 per 1000, 38.5%), whereas training injuries occurred more frequently in the latter stages of the training session (50.0 per 1000, 55.3% v 40.5 per 1000, 44.7%). Significantly more training injuries were sustained in the early half of the season, however, playing injuries occurred more frequently in the latter stages of the season. Conclusions: These results suggest that changes in training and playing intensity impact significantly upon injury rates in semi-professional rugby league players. Further studies investigating the influence of training and playing intensity on injuries in rugby league are warranted.
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In any competitive sporting environment, it is crucial to a team's success to have a maximum number of their players free from injury and illness and available for selection in as many games as possible throughout the season. The purpose of this study was to investigate the relationship between the training load and the incidence of injury and illness over an entire pre-season at an Astralian Football League (AFL) club.
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Malcolm Gladwell; род. 3 сентября 1963, Хэмпшир) — канадский журналист, поп-социолог. В 2005 году «Time» назвало Малкольма Гладуэлла одним из 100 самых влиятельных людей. Книги и статьи Малкольма часто касаются неожиданных последствий исследований в социальных науках и находят широкое применение в научной работе, в частности в областях социологии, психологии и социальной психологии. Некоторые из его книг занимали первые строки в списке бестселлеров «The New York Times». В 2007 году Малкольм получил первую премию Американской Социологической ассоциации за выдающиеся достижения по отчетам в социальных вопросах. В 2007 году он также получил почетную степень доктора филологии Университета Ватерлоо. Малькольм Гладуелл описывает эксперименты, которые показывают, что человеку с поврежденными эмоциональными центрами крайне трудно принимать решения. Он рассказывает про одного такого пациента, которому было предложено прийти на прием либо во вторник, либо в пятницу. И пациент два часа решал во вторник ему прийти или в пятницу — в столбик выписывал плюсы и минусы, их сравнивал, группировал по разному, всяко переставлял. И в жизни своих домашних он просто убивал вот этим. Если его спрашивали, ты что хочешь: омлет или салат? — это задача минут на сорок. Обычный человек очень просто поступает. Он видит омлет, что-то чувствует и говорит: Хочу! Все. Выбор сделан легко и быстро.
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The simplest diagnostic test is one where the results of an investigation, such as an x ray examination or biopsy, are used to classify patients into two groups according to the presence or absence of a symptom or sign. For example, the table shows the relation between the results of a test, a liver scan, and the correct diagnosis based on either necropsy, biopsy, or surgical inspection.1 How good is the liver scan at diagnosis of abnormal pathology?View this table:View PopupView InlineRelation between results of liver scan and correct diagnosis1One approach is to calculate the proportions of patients with normal and abnormal liver scans who are correctly “diagnosed” by the scan. The terms positive and negative are used to refer to the presence or absence of the condition of interest, here abnormal pathology. Thus there are 258 true positives and 86 true negatives. The proportions of these two groups that were correctly diagnosed by the scan were 231/258=0.90 and 54/86=0.63 respectively. These two proportions have confusingly similar names.Sensitivity is the proportion of true positives that are correctly identified by the test.Specificity is the proportion of true negatives that are correctly identified by the test.We can thus say that, based on the sample studied, we would expect 90% of patients with abnormal pathology to have abnormal (positive) liver scans, while 63% of those with normal pathology would have normal (negative) liver scans.The sensitivity and specificity are proportions, so confidence intervals can be calculated for them using standard methods for proportions.2Sensitivity and specificity are one approach to quantifying the diagnostic ability of the test. In clinical practice, however, the test result is all that is known, so we want to know how good the test is at predicting abnormality. In other words, what proportion of patients with abnormal test results are truly abnormal? This question is addressed in a subsequent note.References↵Drum DE, Christacapoulos JS.Hepatic scintigraphy in clinical decision making.J Nucl Med1972;13: 908–15.OpenUrlFREE Full Text↵Gardner MJ, Altman DGGardner MJ, Altman DG.Calculating confidence intervals for proportions and their differences. In: Gardner MJ, Altman DG eds.Statistics with confidence.London: BMJ Publishing Group,1989: 28–33.
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In any competitive sporting environment, it is crucial to a team's success to have the maximum number of their players free from injury and illness and available for selection in as many games as possible throughout the season. The training programme of the club, and therefore training load, can have an impact on the incidence of injury and illness amongst the players. The purpose of this study was to investigate the relationship between the training load and the incidence of injury and illness over an entire pre-season at an Australian Football League (AFL) club. Sixteen players were subjects; all full time professional male AFL players (mean + or - standard deviation; age 23.8 + or - 5.1 years; height 188.9 + or - 7.4 m; weight 90.9 + or - 9.2 kg). A longitudinal research design was employed, where training load, injury and illness were monitored over a 15 week pre-season and Pearson Correlation Coefficients were used to examine relationships.