ArticlePDF Available

Accelerometer and GPS-Derived Running Loads and Injury Risk in Elite Australian Footballers

Authors:
  • West Coast Eagles
  • West Coast Eagles Football Club

Abstract and Figures

Colby, MJ, Dawson, B, Heasman, J, Rogalski, B, and Gabbett, TJ. Accelerometer and GPS-derived running loads and injury risk in elite Australian footballers. J Strength Cond Res 28(8): 2244-2252, 2014-The purpose of this study was to investigate the relationship between overall physical workload (global positioning systems [GPS]/accelerometer) measures and injury risk in elite Australian football players (n = 46) during a season. Workload data and (intrinsic) injury incidence were monitored across preseason and in-season (18 matches) phases. Multiple regression was used to compare cumulative (1-, 2-, 3-, and 4-weekly loads) and absolute change (from previous-to-current week) in workloads between injured and uninjured players for all GPS/accelerometer-derived variables: total distance, V1 distance (total distance above individual's aerobic threshold speed), sprint distance, force load, velocity load, and relative velocity change. Odds ratios (ORs) were calculated to determine the relative injury risk. Cumulative loads showed the strongest relationship with greater intrinsic injury risk. During preseason, 3-weekly distance (OR = 5.489, p = 0.008) and 3-weekly sprint distance (OR = 3.667, p = 0.074) were most indicative of greater injury risk. During in-season, 3-weekly force load (OR = 2.530, p = 0.031) and 4-weekly relative velocity change (OR = 2.244, p = 0.035) were associated with greater injury risk. No differences in injury risk between years of Australian Football League system experience and GPS/accelerometer data were seen. From an injury risk (prevention) perspective, these findings support consideration of several GPS/accelerometer running load variables in Australian football players. In particular, cumulative weekly loads should be closely monitored, with 3-weekly loads most indicative of a greater injury risk across both seasonal phases.
Content may be subject to copyright.
ACCELEROMETER AND GPS-DERIVED RUNNING LOADS
AND INJURY RISK IN ELITE AUSTRALIAN FOOTBALLERS
MARCUS J. COLBY,
1,2
BRIAN DAWSON,
1,2
JARRYD HEASMAN,
2
BRENT ROGALSKI,
2
AND TIM J. GABBETT
3,4
1
School of Sport Science, Exercise and Health, The University of Western Australia, Perth, Australia;
2
West Coast Eagles
Football Club, Perth, Australia;
3
School of Exercise Science, Australian Catholic University, Brisbane, Australia; and
4
School of
Human Movement Studies, The University of Queensland, Brisbane, Australia
ABSTRACT
Colby, MJ, Dawson, B, Heasman, J, Rogalski, B, and Gabbett, TJ.
Accelerometer and GPS-derived running loads and injury risk in
elite Australian footballers. J Strength Cond Res 28(8): 2244–
2252, 2014—The purpose of this study was to investigate the
relationship between overall physical workload (global positioning
systems [GPS]/accelerometer) measures and injury risk in elite
Australian football players (n= 46) during a season. Workload
data and (intrinsic) injury incidence were monitored across pre-
season and in-season (18 matches) phases. Multiple regression
was used to compare cumulative (1-, 2-, 3-, and 4-weekly loads)
and absolute change (from previous-to-current week) in work-
loads between injured and uninjured players for all GPS/
accelerometer-derived variables: total distance, V1 distance (total
distance above individual’s aerobic threshold speed), sprint dis-
tance, force load, velocity load, and relative velocity change.
Odds ratios (ORs) were calculated to determine the relative injury
risk. Cumulative loads showed the strongest relationship with
greater intrinsic injury risk. During preseason, 3-weekly distance
(OR = 5.489, p= 0.008) and 3-weekly sprint distance (OR =
3.667, p= 0.074) were most indicative of greater injury risk.
During in-season, 3-weekly force load (OR = 2.530, p=
0.031) and 4-weekly relative velocity change (OR = 2.244, p=
0.035) were associated with greater injury risk. No differences in
injury risk between years of Australian Football League system
experience and GPS/accelerometer data were seen. From an
injury risk (prevention) perspective, these findings support con-
sideration of several GPS/accelerometer running load variables
in Australian football players. In particular, cumulative weekly
loads should be closely monitored, with 3-weekly loads most
indicative of a greater injury risk across both seasonal phases.
KEY WORDS odd ratios, injury prevention, load monitoring,
team sports
INTRODUCTION
The objectives and game structure of Australian
football are similar to those of soccer, being
described as a running game combining athleti-
cism with speed and requiring skillful foot and
hand passing (9). In addition to high movement demands,
acts of bumping, tackling, and “wrestling” opposition players
when contesting a mark or ground ball adds a challenging
physical aspect to the game. At the elite (national competi-
tion) level, where movement demands and intensities are
greater than in state leagues or junior competitions (1,3),
injury risk is high, with both intrinsic (internal; overuse,
overexertion) and extrinsic (external; collision, contact) in-
juries being commonly reported (14,16). An upward trend in
injury prevalence in the past decade in Australian football
(14) has prompted great interest in the multifactorial aspects
of injury prevention. Training “overload,” where training
stress is not balanced by adequate recovery, is often attrib-
uted as an important (although largely preventable) cause of
injury (particularly soft tissue) (7,8,15,16). Therefore, moni-
toring training and game workloads and other variables such
as player wellness scores to (potentially) reduce injury risk is
of great importance to professional sporting teams (8,16).
In recent years, the use of global positioning systems (GPS)
and accelerometers in team sports has rapidly increased. In
particular, teams in the Australian Football League (AFL) have
extensively applied these new technologies to both game and
training environments (2,10,19). For example, GPS data have
provided in-depth information on activity profiles of athletes,
including objective measures such as total distance, distances
traveled within velocity bands, and average movement speed
(2,10,19). However, the full potential of this athlete monitoring
system (compared with other methodology) is yet to be fully
explored, especially from an injury prevention perspective. Fur-
thermore, relatively few studies have explored the relationship
between physical workload and injuries (6,7,15,16).
Rogalski et al. (16) used session ratings of perceived exertion
(RPE) to analyze training and game loads in AFL players
from 1 club across a whole season. Larger 1-weekly (odds
ratios: ORs = 3.38) and 2-weekly (OR = 4.74) cumulative
loads, and week-to-week absolute change in load from the
previous to the current week (OR = 2.58), were associated
Address correspondence to Marcus Colby, marcus_colby@hotmail.com.
28(8)/2244–2252
Journal of Strength and Conditioning Research
Ó2014 National Strength and Conditioning Association
2244
Journal of Strength and Conditioning Research
the
TM
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
with a greater injury risk throughout the in-season (competi-
tion) phase. Furthermore, players with 2–3 and 4–6 years of
AFL system experience had a significantly lower injury risk
compared with .7 year players (OR = 0.22, OR = 0.28).
These results emphasize the importance of carefully assessing
workloads, both in a cumulative weekly manner and also
through week-to-week load changes. Additionally, with
RPE scores, the sensation of fatigue may be elevated during
submaximal tasks (13). Therefore, adding workload quantifi-
cation through GPS and accelerometer measures to RPE
scores can provide a more complete range of workload assess-
ment variables and may prove to be more useful.
Piggott et al. (15) used GPS variables (total distance and
distance above 12 km$h
21
) as measures of training load in
elite Australian football players, reporting that correspond-
ing spikes (.10% change) in weekly training load explained
;40% of illness and injury in the subsequent 7 days. How-
ever, this study was limited to a small sample (n= 16) over
a 15-week preseason phase and analyzed only individual
workload gradients from one week to the next.
In rugby league, Gabbett and Ullah (7) recently used GPS
variables to quantify workload in 36 elite players across 117
training sessions. Interestingly, when only .9 m of sprinting
per session was performed, a 2.7 times greater relative risk of
injury was observed than when no or lower amounts of
sprinting were completed. Although the sprinting demands
of Australian football are likely to be greater than reported
for rugby league (3,19), these results do demonstrate a rela-
tionship between the amount of sprinting performed and
lower-body soft tissue injury risk. Therefore, GPS/acceler-
ometer variables measuring high-speed running and force
actions may be important predictors of injury risk.
Greater player workloads have commonly been recorded in
AFL games when compared with subelite and junior matches
(1,3,17). Therefore, it is important to carefully manage a young
newly recruited player because their less mature bodies may be
unable to cope with the initial training and game demands of
the AFL environment. Junior (under 18) players have been
showntobe7.7and5.8kglighterinbodymassandleanmass,
respectively, than AFL players (17). Additionally, greater
bone mineral content and density was noted in the AFL play-
ers used for comparison (17); such bone remodeling and struc-
tural adaptation are likely because of greater workloads
experienced at the elite level. This finding underscores the need
to identify appropriate workloads for different player groups, as
the amount of experience in the AFL system may play a key
role in coping with training and game loads.
The application of GPS/accelerometer data for load
monitoring and injury prediction and prevention is yet to
be fully explored. This study aimed to examine the relation-
ship between physical workload (GPS/accelerometer)
measures and injury risk in elite AFL players across a season.
It was hypothesized that very high absolute workload values,
plus very large increments from 1 week to the next, would
significantly increase injury risk.
METHODS
Experimental Approach to the Problem
Each day a player was involved in a training session or game,
and their previous 1-, 2-, 3-, and 4-weekly individual loads
were calculated. Based on the work of Rogalski et al. (16),
relationships between workloads and injury were then inves-
tigated in 2 different ways. First, the likelihood that accumu-
lated load (over 1–4 weeks) could contribute to an injury at
a later date was considered by examining any link between 1-,
2-, 3-, and 4-weekly cumulative loads and subsequent injury.
Second, whether a large increment in load between successive
weeks contributed to an injury was also explored, by analyzing
the week-to-week change between the current and previous
week’s total loads. Particular emphasis was placed on the intrin-
sic (rather than extrinsic and total) injuries recorded, as these
are more directly related to soft-tissue injuries, especially from
a training-load perspective (6).
Subjects
Data were collected from elite Australian footballers (n=46)
from 1 AFL club. Their mean age, stature, and body mass
were 25.1 63.4 years, 188.0 66.8 cm, and 87.0 68.2 kg,
respectively. Players competed in matches within the AFL
or Western Australian Football League (WAFL) competition
during the 2012 season. Within the squad, 12 players had
1–2 years, 19 had 3–6 years, and 15 had .7 years of AFL
system experience. All data were obtained from the club’s
database, but without any identifying player information.
Ethical approval was obtained from the Human Research
Ethics Committee of The University of Western Australia.
Procedures
Workload was quantified through GPS/accelerometer units,
with data collected from any session (training or game) in which
a player undertook a running load. The GPS units (SPI Pro X;
GPSports, Canberra, Australia), which incorporate a tri-axial
accelerometer, were placed on the back of players (between the
scapulae) in either a pocket sewn into the player’s jumper or in
a fitted GPSports harness. These GPS units were sampled at an
interpolated rate of 15 Hz (true sampling at 5 Hz), and the
accelerometers at 100 Hz. After each session, the data were
downloaded into a specialized analysis program (TEAM
AMS—release 1.9 2012).
On occasions (n= 334 of 3,601; 9%) where a player had
not worn a GPS/accelerometer unit during a running ses-
sion, not participated in certain drills, or the data were
deemed unreliable because of an intermittent signal (,6
“locked on” satellites), data were predicted, as follows:
Main training session data: predicted by calculating indi-
vidual player (positional) averages for drills completed.
Rehabilitation session data: predicted using rehabilita-
tion drill averages for drills completed.
Game data: predicted using individual season game
averages (from 18 matches) while taking into consider-
ation the time spent on ground.
Journal of Strength and Conditioning Research
the
TM
|
www.nsca.com
VOLUME 28 | NUMBER 8 | AUGUST 2014 | 2245
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
Several authors have found that the accuracy of GPS
technology for measuring movement demands of athletes to
be very good (4,11,12). Recently, sprint performance and
accelerometer variables have also been reported as reliable
measures (10,18); however, caution should still be taken when
interpreting sprint distance results (11). The variables below
were selected for this study because of their relevance to run-
ning loads (and potential injury) and their ,10% coefficient of
variation, as reported by Hiscock et al. (10) and Waldron et al.
(18). Some variables are taken directly from GPSports (Team
AMS) software, whereas others are derived from a separate
data analysis package (Athletic Data Innovations [ADI]).
GPSports (TEAM AMS):
Distance = total distance covered (m): this includes
walking, jogging, fast running, and sprinting.
Sprint distance = total distance covered (m) above 75% of
the individual player’s maximum speed, as determined
from preseason 20-m sprint (electronic timing gates) tests
(where available) or GPS game data.
Athletic Data Innovations GPS-derived data:
V1 distance = total distance covered (m) above the indi-
vidual player’s aerobic (blood lactate ;2mmol$L
21
)
threshold speed, as determined from a preseason incremen-
tal speed (1% gradient) treadmill running test to exhaustion.
The term “aerobic threshold speed” is used by the GPS
software, which analyzed the data. Commonly, individual
player V1 speeds were between 12.5 and 14.5 km$h
21
.
Velocity load = a measurement of running power and
momentum. The more continuous and higher the
velocity equates to a higher velocity load.
Relative velocity change (RVC) load = a calculated
function (algorithm) of accelerations, decelerations,
and changes of direction, which are summed together
to produce an overall “acceleration load” value.
TABLE 1. Session type averages for season phases.*
Session type
Preseason In-season Whole-season
Mean load per session
Rehab
Distance (m) 7,139 (6,897–7,383) 6,575 (6,270–6,907) 6,935 (6,734–7,136)
V1 distance (m) z3,455 (3,341–3,563) 3,045 (2,903–3,183) 3,306 (3,218–3,394)
Sprint distance (m) 148 (122–178) 97 (73–122) 130 (110–149)
Force load (AU) 518 (483–572) 454 (428–480) 495 (464–526)
Velocity load (AU) z827 (799–855) 724 (692–755) 789 (768–811)
RVC load (AU) 3.54 (3.31–3.80) 3.46 (3.12–3.82) 3.51 (3.30–3.72)
Main training
Distance (m) z10,302 (10,154–10,472) 7,205 (7,096–7,325) 9,184 (9,061–9,308)
V1 distance (m) z2,808 (2,741–2,875) 1,522 (1,478–1,569) 2,344 (2,290–2,397)
Sprint distance (m) z160 (150–171) 90 (85–96) 135 (128–142)
Force load (AU) z787 (773–801) 579 (569–589) 712 (701–722)
Velocity load (AU) z943 (926–961) 606 (595–617) 821 (808–835)
RVC load (AU) z11.5 (11.28–11.72) 8.5 (8.28–8.72) 10.42 (10.24–10.59)
AFL game
Distance (m) 9,420 (8,838–10,000) §13,399 (13,150–13,644) 12,554 (12,281–12,827)
V1 distance (m) 3,059 (2,863–3,275) §4,091 (3,984–4,201) 3,872 (3,769–3,975)
Sprint distance (m) 200 (178–222) §268 (254–283) 253 (241–266)
Force load (AU) 766 (715–815) §1,133 (1,108–1,155) 1,055 (1,030–1,080)
Velocity load (AU) 1,003 (935–1,069) §1,406 (1,377–1,433) 1,320 (1,290–1,351)
RVC load (AU) 11.91 (11.17–12.58) §16.87 (16.46–17.27) 15.82 (15.41–16.22)
WAFL game
Distance (m) 10,573 (8,975–11,886) §12,348 (12,027–12,661) 12,183 (11,853–12,513)
V1 distance (m) 3,562 (3,018–4,014) §4,267 (4,121–4,401) 4,201 (4,062–4,341)
Sprint distance (m) 220 (171–265) 259 (242–277) 256 (239–272)
Force load (AU) 822 (706–924) §966 (1,237–1,310) 953 (922–984)
Velocity load (AU) 1,080 (917–1,219) §1,274 (1,237–1,310) 1,256 (1,219–1,294)
RVC load (AU) 10.94 (9.28–12.56) §13.74 (13.29–14.16) 13.48 (13.04–13.92)
*AFL = Australian Football League; WAFL = Western Australian Football League; AU = arbitrary units; V1 = aerobic threshold
speed; RVC = relative velocity change.
Data are expressed as mean (95% confidence intervals).
zPreseason (p,0.001) significantly greater load than in-season.
§In-season (p,0.001) significantly greater load than preseason.
Injury Risk in Elite Australian Footballers
2246
Journal of Strength and Conditioning Research
the
TM
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
(Both these variables are measured in arbitrary units
[AU]).
Athletic Data Innovations accelerometer-derived data:
Force load = a cumulative measurement that sums the
forces produced from both foot strikes and collisions.
Higher speed running will correspond with higher
force load values, reflecting a measure of the “number”
and “intensity” of foot strikes (i.e., total g-force from
foot strikes). In addition, physical contact through col-
lisions and jumping forces will also contribute to force
load values.
(This variable is measured in AU).
Data were collected in both the preseason and in-season
(competition) phases. Movement demands and running
loads were typically higher (z5 “on-legs” sessions per week)
during the preseason (late November to March), as players
trained to improve their fitness capacities. The in-season
phase, where fitness maintenance and match availability
took priority, generally consisted of 2 “on-legs” training ses-
sions per week, plus games. This phase (March–September)
was limited to 18 matches (rather than 22) for data collec-
tion because of changes in the training schedule that
restricted data availability in the lead up to finals.
Injury information was classified by the club’s senior phys-
iotherapist, collated, and then updated in the club’s database.
Injuries were classified as being either: low severity (the
player was given modified training and did not miss a game),
and/or moderate severity (the player missed 1–2 weeks of
training and missed 1–2 games), and/or high severity (the
player missed .2 weeks of training and missed .2 games)
(16). Injuries were also categorized by injury type (descrip-
tion) and body site (injury location). The mechanism in
which a player acquired an injury was also classified, as being
intrinsic (internal; overuse, overexertion) or extrinsic (exter-
nal; collision, contact) in nature (6,14,16), with only intrinsic
injuries being considered with respect to injury risk.
Statistical Analyses
The analysis was performed in a similar manner to the
previous work of Gabbett (6) and Rogalski et al. (16). Injury
incidence was calculated by dividing the total number of
injuries by the “on-legs” exposure time and reported as rates
per 1,000 training and game hours. Injury data were analyzed
per 1,000 (combined) training and game hours, and x
2
anal-
ysis compared the frequency of injuries between preseason
and in-season periods. A multiple regression model was used
TABLE 2. Workload data for different years of AFL system experience for season phases.*
Preseason In-season Whole-season
Distance (m)
1–2 y 350,674 (313,731–387,616) 344,088 (299,321–388,855) 694,762 (629,839–759,685)
3–6 y 375,136 (339,277–410,995) 373,924 (354,243–393,605) 749,060 (705,808–792,312)
.7 y 356,431 (316,662–396,200) z320,417 (262,034–378,800) 676,848 (597,150–756,547)
V1 distance (m)
1–2 y §k99,883 (90,090–109,676) 99,574 (81,572–117,577) 199,458 (180,025–218,890)
3–6 y §120,903 (111,984–129,822) 106,281 (96,846–115,716) 227,184 (211,123–243,245)
.7 y §113,757 (100,480–127,034) 92,534 (78,612–106,457) 206,292 (182,857–229,727)
Sprint distance (m)
1–2 y k4,322 (2,756–5,888) 5,753 (3,770–7,735) 10,075 (6,645–13,506)
3–6 y 7,480 (6,048–8,930) 7,170 (6,330–8,010) 14,660 (12,649–16,671)
.7 y 5,848 (4,900–6,796) ¶4,076 (2,819–5,332) 9,924 (8,393–11,454)
Force load (AU)
1–2 y 26,890 (23,474–30,307) 26,787 (23,090–30,483) 53,677 (47,792–59,563)
3–6 y 28,043 (25,370–30,716) 29,814 (27,067–32,560) 57,857 (53,445–62,269)
.7 y 27,613 (23,322–31,904) 26,798 (20,973–32,622) 54,411 (45,668–63,154)
Velocity load (AU)
1–2 y 31,608 (27,192–36,025) 31,446 (27,078–35,814) 63,055 (56,000–70,109)
3–6 y 36,475 (33,386–39,565) 36,117 (34,011–38,224) 72,593 (68,545–76,641)
.7 y 35,898 (31,536–40,260) 32,281 (26,404–38,159) 68,180 (59,331–77,029)
RVC (AU)
1–2 y 365 (324–407) 385 (321–450) 751 (663–839)
3–6 y 386 (321–452) 440 (396–384) 827 (733–920)
.7 y 345 (290–399) ¶347 (251–443) 692 (567–817)
*AU = arbitrary units; RVC = relative velocity change.
Data are expressed as mean (95% confidence intervals).
§Preseason load significantly greater than in-season (p#0.05).
k1–2 y significantly lower load than 3–6 y (p#0.05).
.7 years significantly lower load than 3–6 y (p#0.05).
Journal of Strength and Conditioning Research
the
TM
|
www.nsca.com
VOLUME 28 | NUMBER 8 | AUGUST 2014 | 2247
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
TABLE 3. Classification of preseason and in-season injuries.*
Preseason (1,405.8 h) In-season (1,700.4 h) Overall (3,106.2 h) Preseason vs. in-season
NInjury incidence % NInjury incidence % NInjury incidence % x
2
p
110 78.2 (63.6–92.9) 37.0 187 110 (94.2–125.7) 62.96 297 95.6 (84.7–106.5) 100 8.102 0.004
Site
Thigh 44 31.3 (22.1–40.5) 40.0 57 33.5 (24.8–42.2) 30.5 101 32.5 (26.2–38.9) 34.0 0.12 0.732
Hip/groin 11 7.8 (3.2–12.4) 10.0 7 4.1 (1.1–7.2) 3.7 18 5.8 (3.1–8.5) 6.1 1.83 0.177
Knee 6 4.3 (0.9–7.7) 5.5 13 7.7 (3.5–11.8) 7.0 19 6.1 (3.4–8.9) 6.4 1.43 0.231
Pelvis/low back 9 6.4 (2.2–10.6 8.2 15 8.8 (4.4–13.3) 8.0 24 7.7 (4.6–10.8) 8.1 0.58 0.445
Head/neck 6 4.3 (0.9–7.7) 5.5 20 11.8 (6.6–16.9) 10.7 26 8.4 (5.2–11.6) 8.8 5.16 0.023
Ankle/foot 12 8.5 (3.7–13.4) 10.9 32 18.8 (12.3–25.3) 17.1 44 14.2 (10.0–18.4) 14.8 5.74 0.017
Lower leg 9 6.4 (2.2–10.6) 8.2 22 12.9 (7.5–18.3) 11.8 31 10.0 (6.5–13.5) 10.4 3.29 0.070
Shoulder/arm/elbow 6 4.3 (0.9–7.7) 5.5 10 5.9 (2.2–9.5) 5.3 16 5.2 (2.6–7.7) 5.4 0.39 0.533
Abdomen
Chest/ribs/upper back 1 0.7 (20.7–2.1) 0.9 2 1.2 (20.5–2.8) 1.1 3 1 (20.1–2.1) 1.0 0.17 0.678
Forearm/wrist/hand 6 4.3 (0.9–7.7) 5.5 9 5.3 (1.8–8.8) 4.8 15 4.8 (2.4–7.3) 5.1 0.17 0.682
Injury type
Muscle strain 55 39.1 (28.8–49.5) 50.0 54 31.8 (23.3–40.2) 28.9 109 35.1 (28.5–41.7) 36.7 1.19 0.275
Haematoma/contusion 21 14.9 (8.5–21.3) 19.1 63 37 (27.9–46.2) 33.7 84 27 (21.3–32.8) 28.3 13.91 0.000
Joint injury 22 15.6 (9.1–22.2) 20.0 40 23.5 (16.2–30.8) 21.4 53 17.1 (12.5–21.7) 17.8 2.39 0.122
Fracture/dislocation 4 2.8 (0.1–5.6) 3.6 9 5.3 (1.8–8.8) 4.8 13 4.2 (1.9–6.5) 4.4 1.10 0.294
Concussion 4 2.8 (0.1–5.6) 3.6 9 5.3 (1.8–8.8) 4.8 13 4.2 (1.9–6.5) 4.4 1.10 0.294
Laceration 1 0.7 (20.7–2.1) 0.9 6 3.5 (0.7–6.4) 3.2 7 2.3 (0.6–3.9) 2.4 2.71 0.100
Other 3 2.1 (20.3–4.5) 2.7 6 3.5 (0.7–6.4) 3.2 9 2.9 (1.0–4.8) 3.0 0.52 0.472
Mechanism
Intrinsic 62 44.1 (33.1–55.1) 56.4 72 42.3 (32.6–52.1) 38.5 134 43.1 (35.8–50.4) 45.1 0.06 0.814
Extrinsic 48 34.1 (24.5–43.8) 43.6 115 67.6 (55.3–80.0) 61.5 163 52.5 (44.4–60.5) 54.9 16.45 0.000
Severity
Low (1) 84 59.8 (47.0–72.5) 76.4 154 90.6 (76.3–104.9) 82.4 238 76.6 (66.9–86.4) 80.1 9.54 0.002
Moderate (2) 14 10 (4.7–15.2) 12.7 24 14.1 (8.5–19.8) 12.8 38 12.2 (8.3–16.1) 12.8 1.09 0.297
High (3) 12 8.5 (3.7–13.4) 10.9 9 5.3 (1.8–8.8) 4.8 21 6.8 (3.9–9.7) 7.1 1.20 0.274
Activity performed
Game 24 17.1 (10.2–23.9) 21.8 165 97 (82.2–111.8) 88.2 189 60.8 (52.2–69.5) 63.6 80.87 0.000
Training 86 61.2 (48.2–74.1) 78.2 22 12.9 (7.5–18.3) 11.8 108 34.8 (28.2–41.3) 36.4 51.50 0.000
*Mean injury incidence reported per 1,000 on-legs training and game hours (95% confidence intervals).
Injury Risk in Elite Australian Footballers
2248
Journal of Strength and Conditioning Research
the
TM
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
to compare cumulative and absolute change in workloads
between injured and uninjured players for all GPS/accelerom-
eter variables. For each variable, the data cases were split into 3
even groups, with the first (low load) group used as the refer-
ence group for analysis. Odds ratios were calculated to deter-
minetheinjuryriskatagiven cumulative workload or for
absolute change in workload from the previous to current
week.WhenanORwasgreaterthan1,anincreasedriskof
injury was reported (i.e., OR = 1.50 is indicative of a 50%
increased risk) and vice versa. For an OR to be significant,
95% confidence intervals (CIs) did not contain the null OR
of 1.00. Multiple regression was also used to analyze player
groupings of 1–3 years, 4–6
years, and .7 years of AFL sys-
tem experience, to explore any
differences in workloads due to
this factor. The .7 years group
wasusedasthereferencegroup
for analysis. Data were analyzed
using IBM SPSS Statistics 20.0
and reported as means and
95% CI. Significance was
accepted at p#0.05. Based on
a total of 297 injuries (intrinsic +
extrinsic) from 3,601 player-
sessions (i.e., 46 players partici-
pating in 79 training and game
sessions across the whole sea-
son), the calculated statistical
power to establish the relation-
ship between running loads and
injury risk was $90%.
RESULTS
Training Loads
Average training loads for pre-
season were significantly (p,
0.001) greater than in-season for
all GPS/accelerometer variables
(distance, V1 distance, sprint dis-
tance, force load, velocity load,
and RVC load). Similarly, the
average V1 distance and velocity
load during rehabilitation training
sessions were significantly (p,
0.001) greater for preseason than
in-season. Game running loads
TABLE 4. Injury incidence and AFL system experience.*z
Playing experience (y)
Preseason In-season Whole-season
Injury incidence (per 1,000 h)
1–2 (n= 12) 71.5 (40.3–102.7) 101.3 (70.7–131.9) 86.4 (65.3–107.5)
3–6 (n= 19) 86.8 (55.8–117.6) 107.6 (83.8–131.5) 97.2 (78.3–116.1)
.7(n= 15) 87.3 (46.6–127.9) 113.6 (73.6–153.5) 100.4 (73.2–127.6)
*AFL = Australian Football League.
Data are expressed as mean (95% confidence intervals).
zNo significant differences were found in injury incidence for AFL system experience.
TABLE 5. Preseason training and game load risk factors for intrinsic injury in elite
Australian footballers.*
Load calculation
OR 95% CI
Significant pExp (B) Lower Upper
Cumulative load (sum)
3-weekly velocity load
,6,737 AU (reference) 1.00
6,737–8,046 AU 0.239 0.062 0.92 0.037
.8,046 AU 0.368 0.068 1.994 0.246
3-weekly sprint distance
,864 m (reference) 1.00
864–1,453 m 0.229 0.054 0.966 0.045
.1,453 m 3.667 0.884 15.214 0.074
3-weekly distance
,73,721 m (reference) 1.00
73,721–86,662 m 5.489 1.572 19.164 0.008
.86,662 m 1.115 0.125 9.944 0.922
Absolute change (6)
Sprint distance
,(2) 49 m (reference) 1.00
(2) 49–155 m 0.356 0.099 1.278 0.113
.155 m 3.284 0.915 11.784 0.068
Force load
,(2) 13 AU (reference) 1.00
(2) 13–556 AU 2.772 0.757 10.157 0.124
.556 AU 0.096 0.009 1.037 0.054
RVC load
,0.1 AU (reference) 1.00
0.1–9.4 AU 0.04 0.004 0.393 0.006
.9.4 AU 1.25 0.265 5.881 0.778
*OR = odds ratio; CI = confidence interval; AU = arbitrary units; RVC = relative velocity
change.
p#0.05.
Journal of Strength and Conditioning Research
the
TM
|
www.nsca.com
VOLUME 28 | NUMBER 8 | AUGUST 2014 | 2249
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
for all variables were significantly
(p,0.001) greater for in-season
than for preseason practice
matches for both AFL and
WAFL competitions (Table 1).
Regarding AFL system experi-
ence, players with .7yearshad
significantly (p#0.05) lower
total distance, sprint distance,
and RVC load values during in-
season than 1–-3 and 4–6 year
players (Table 2).
Injury Incidence
Injury incidence increased (x
2
= 8.102, df = 1, p= 0.004) from
preseason (78 per 1,000 hours)
to in-season (110 per 1,000
hours) (Table 3). The thigh
(33 per 1,000 hours, 30.5%)
and ankle/foot (19 per 1,000
hours, 17.1%) were the most
common sites of injury during
the in-season, with the most
common type of injury being
hematomas/contusions
(37 per 1,000 hours, 33.7%) and
muscle strains (32 per 1,000
hours, 28.9%). Extrinsic injuries
were significantly (x
2
= 16.45,
df = 1, p= 0.000) greater dur-
ing in-season (68 per 1,000
hours) than preseason (34 per
1,000 hours). Injury incidence
during in-season was lowest
for 1–2 year players (101 per
1,000 hours) and highest for
.7 year players (114 per
1,000 hours); however, no sig-
nificant differences were found
between any of these player
groupings (Table 4).
Likelihood of Intrinsic Injury
With Different Training Loads
For both seasonal phases, accu-
mulated workloads (primarily
3-week) were found to have
the greatest association with
intrinsic injury risk.
Preseason
In preseason, 3-weekly total
distances between 73,721 and
86,662 m were found to be
associated with a greater injury
Figure 1. Injury probability in-season as 3-weekly force load (cumulative) increases.
TABLE 6. In-season training and game load risk factors for intrinsic injury in elite
Australian footballers.*
Load calculation
OR 95% CI
Significant pExp (B) Lower Upper
Cumulative load (sum)
1-weekly velocity load (AU)
,1,927 (reference) 1.00
1,927–2,387 0.685 0.311 1.509 0.347
.2,387 1.901 0.745 4.853 0.179
3-weekly force load (AU)
,4,561 (reference) 1.00
4,561–5,397 1.518 0.716 3.218 0.277
.5,397 2.53 1.091 5.871 0.031
4-weekly RVC load (AU)
,84 (reference) 1.00
84–102 1.417 0.688 2.919 0.345
.102 2.244 1.057 4.765 0.035
2-weekly V1 distance (m)
,10,321 (reference) 1.00
10,321–12,867 0.407 0.2 0.829 0.013
.12,867 0.276 0.11 0.689 0.006
2-weekly distance (m)
,39,618 (reference) 1.00
39,618–45,257 0.426 0.204 0.891 0.024
.45,257 0.423 0.173 1.033 0.059
Absolute change (6)
Distance (m)
,2549 (reference) 1.00
2549 to 6,955 0.492 0.248 0.977 0.043
.6,955 0.477 0.207 1.096 0.081
*OR = odds ratio; CI = confidence interval; AU = arbitrary unit; V1 = aerobic threshold
speed; RVC = relative velocity change.
p#0.05.
Injury Risk in Elite Australian Footballers
2250
Journal of Strength and Conditioning Research
the
TM
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
risk when compared with ,73,721 m (OR = 5.489, 95%
CI = 1.57–19.16, p= 0.008). Conversely, a 3-weekly velocity
load of between 6,737 and 8,046 AU recorded a lower injury
risk when compared with ,6,737 AU (OR = 0.239, 95%
CI = 0.06–0.92, p= 0.037). Although a 3-weekly sprint dis-
tance between 864 and 1,453 m was shown to have a lower
injury risk when compared with ,864 m (OR = 0.229, 95%
CI = 0.05–0.97, p= 0.045), a 3-weekly sprint distance .1,453
m was also shown to have a greater injury risk when com-
pared with ,864 m (OR = 3.667, 95% CI = 0.88–15.21, p=
0.074). Finally, a previous to current weekly change in RVC
load between 0.1 and 9.4 AU recorded a lower injury risk
when compared with ,0.1 units (OR = 0.040, 95% CI =
0.004–0.393, p= 0.006) (Table 5).
In-season
A 3-weekly force load of .5,397 AU recorded a greater injury
risk when compared with ,4,651 AU (OR = 2.53, 95% CI =
1.09–5.87, p= 0.031). Figure 1 demonstrates the increase in
injury probability as 3-weekly force load increases. A 4-weekly
RVC load .102 AU had a higher injury risk when compared
with ,84 AU (OR = 2.244, 95% CI = 1.06–4.77, p= 0.035).
Conversely, a 2-weekly V1 distance of .12,867 m was asso-
ciated with a lower injury risk when compared with ,10,321
m(OR=0.276,95%CI=0.110.69,p= 0.006). Similarly,
2-weekly total distances between 39,618 and 45,257 m
recorded a lower injury risk when compared with ,39,618
m (OR = 0.426, 95% CI = 0.20–0.89, p= 0.024). Additionally,
a previous to current weekly change in distance within 2549
to 6,955 m was shown to have a lower injury risk when
compared with less than 2549 m (OR = 0.492, 95% CI =
0.25–0.98, p= 0.043) (Table 6).
DISCUSSION
This study is the first to investigate the relationship between
training and game loads (derived from GPS/accelerometer
data) and injury risk in elite Australian football. For all
measured variables, training load was greater during the
preseason phase, where fitness improvements and skill devel-
opment take priority, in comparison to the in-season, where
games are played weekly and where the injury incidence was
significantly higher. The careful management of training
running loads, to balance those exerted during games, is an
on-going challenge for strength and conditioning staff.
In contrast to previous reports (15,16), this study identified
a number of GPS/accelerometer running load variables that
were significant intrinsic injury predictors during the preseason
phase. Here, a 3-weekly distance between 73 and 86 km
was associated with a 5.5 times greater intrinsic injury
risk when compared with distances of ,73 km. Similarly,
3-weekly sprint distances of .1,453 m recorded a trend
(p= 0.074) for a greater (3.7 times) injury risk, compared with
,864 m. However, in contrast, a 3-weekly sprint distance of
864–1,453 m was associated with a significantly lower injury
risk. These contrasting results support previous literature (7),
in not only highlighting the fine balance between restricting
training loads for injury prevention purposes but also prescrib-
ing sufficient loads to adequately prepare players for game
demands. It is important to acknowledge that although exces-
sive training loads may increase intrinsic injury risk, insufficient
loads may achieve the same outcome, with a certain level of
load (in-between an underload and overload) likely to be pro-
tective for injury. Finally, in contrast to previous literature
regarding preseason injury risk in Australian footballers (16),
this study found no relationship between absolute change in
load from the previous to current week and subsequent injury.
The reasons for these divergent findings are not clear and
were unexpected in this study.
During the in-season phase, exerting a 3-weekly force load
of .5,397 AU was associated with a 2.5 times greater injury
risk when compared with ,4,561 AU. Similarly, exerting
a 4-weekly RVC load of .102 AU was associated with
a 2.2 times greater injury risk when compared with
,84 AU. In contrast (and as was found with the preseason
results), a 2-weekly distance of 39–45 km was associated
with a lower (0.5 times) injury risk than ,39 km. Similarly,
a 2-weekly V1 distance of .12,867 m was associated with
a 0.7 times lower injury risk than ,10,321 m. These findings
suggest a protective effect of certain (moderate) load levels.
Gabbett and Ullah (7) have recently reported that the rela-
tive risk of soft-tissue injury is lower in elite rugby league
players who cover more distance at lower intensities, con-
sistent with the contention that moderate running loads and
intensities may offer some protection against intrinsic injury.
Musculoskeletal immaturity of players with less AFL
system experience has been hypothesized to be associated
with an increased injury risk when they are exposed to elite
training and game loads (17). However, in our study, no
significant relationships were found between GPS/
accelerometer-derived running loads and injury risk for 1–2
year players when compared with other years of AFL sys-
tem experience (3–6 years, .7 years). Perhaps, because of
the strict load modification strategy of 1–2 year players used
within the club studied here, overall injury incidence was
slightly lower (NS: 86/1,000 “on-legs” hours) in this group.
Preseason V1 and sprint distance running loads were also
lower for this group than for 3–6 year players, but not dif-
ferent for the .7 year players, who, in turn, had lower total
distance and sprint distance loads in-season than the 3–6
year players. Injury risk was slightly greater (NS: 114/1,000
“on-legs” hours) for the .7 year grouping during the in-
season, but whether the running load data reflect a deliberate
management practice by the club or reflects less training
load because of slightly more injuries occurring is unclear.
We have previously shown a higher in-season injury risk in
this playing group using load data based on RPE values (16),
and future studies should aim to analyze multiple seasons of
data to further investigate any effect of experience in the
AFL system (and in other sports) on running load-injury
relationships. The players’ injury history used here was also
Journal of Strength and Conditioning Research
the
TM
|
www.nsca.com
VOLUME 28 | NUMBER 8 | AUGUST 2014 | 2251
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
not considered, and this is recognized as an important factor
in subsequent injury incidence (5,14). Thus far, there is some
evidence to suggest that the careful management of players,
both on entering the elite system and when in the latter
stages of their career, may potentially assist in reducing
injury risk (5,6,14,16).
In this study, a total of 9% of GPS data were predicted,
primarily because of unit malfunction caused by poor/
intermittent satellite signal reception. Future advances in
technology, including enabling data collection in roofed
stadiums, plus greater player compliance in wearing these
units during training sessions and games may assist further
studies in this area. In addition, as all conditioning work-
loads (i.e., cross-training and weight training) cannot be
quantified through the use of GPS/accelerometers, com-
bined research incorporating these objective measures
with RPE-values and other data such as perceived muscle
soreness, fatigue, mood, and sleep ratings (2,8,16) may pro-
vide additional insight into the training load–injury risk
relationship of elite Australian football players. Having
in-season fitness test data available may also provide useful
information about the influence of higher (or lower) aero-
bic capacity and repeated sprint ability on subsequent
injury incidence.
Across both seasonal phases, GPS/accelerometer-derived
running load variables were shown to significantly relate to
injury risk in elite Australian footballers. Overall, 3-weekly
cumulative loads were found to have the strongest relation-
ships with intrinsic injury incidence across both the pre-
season (3-weekly distance and 3-weekly sprint distance) and
in-season (2-weekly distance and 3-weekly force load)
phases. To reduce injury risk, the specific GPS/accelerom-
eter variables identified in this study should be considered
when monitoring and modifying player’s weekly workload
on an individual basis.
PRACTICAL APPLICATIONS
With microtechnology (incorporating GPS and accelerom-
eter measures) now appropriately validated for recording
movement demands in athletes, derived running loads
(particularly 2, 3, and 4-weekly cumulative loads) should
be regularly monitored, as they may significantly relate to
player injury risk. The specific loads identified in this study
provide initial guidelines for the volumes that should be
considered in Australian football for representing increases
in injury risk. In a practical sense, load thresholds might
then be determined for individual players, above which
injury risk substantially increases. Medical and conditioning
staff may then be able to make more objective and
informed decisions on when player training or game loads
should be modified or reduced, to limit their injury risk.
However, applying these data to other AFL teams with
different players and other team-sports should be per-
formed carefully, as movement demands are specific to
both player and sport.
ACKNOWLEDGMENTS
No external financial support was received for this study.
REFERENCES
1. Brewer, C, Dawson, B, Heasman, J, Stewart, G, and Cormack, S.
Movement pattern comparisons in elite (AFL) and sub-elite
(WAFL) Australian football games using GPS. J Sci Med Sport 13:
618–623, 2010.
2. Buchheit, M, Racinais, S, Bilsborough, JC, Bourdon, PC, Voss, SC,
and Hocking, J. Monitoring fitness, fatigue and running performance
during a pre-season training camp in elite football players. J Sci Med
Sport 16: 550–555, 2013.
3. Burgess, D, Naughton, G, and Norton, K. Quantifying the gap
between under 18 and senior AFL football: 2003 and 2009. Int J
Sports Physiol Perform 7: 53–58, 2012.
4. Coutts, AJ and Duffield, R. Validity and reliability of GPS devices for
measuring movement demands of team sports. J Sci Med Sport 13:
133–135, 2010.
5. Gabbe, BJ, Bennell, KL, Finch, CF, Wajswelner, H, and Orchard, JW.
Predictors of hamstring injury at the elite level of Australian football.
Scand J Med Sci Sports 16: 7–13, 2006.
6. Gabbett, TJ. The development and application of an injury
prediction model for non-contact, soft-tissue injuries in elite
collision sport athletes. J Strength Cond Res 24: 2593–2603, 2010.
7. Gabbett, TJ and Ullah, S. Relationship between running loads and
soft-tissue injury in elite team sport athletes. J Strength Cond Res 26:
953–960, 2012.
8. Gastin, PB, Meyer, D, and Robinson, D. Perceptions of wellness to
monitor adaptive responses to training and competition in elite
Australian football. J Strength Cond Res 27: 2518–2526, 2013.
9. Gray, AJ and Jenkins, DG. Match analysis and the physiological
demands of Australian football. Sports Med 40: 347–360, 2010.
10. Hiscock, D, Dawson, B, Heasman, J, and Peeling, P. Game
movements and player performance in the Australian Football
League. Int J Perform Anal Sport 12: 531–545, 2012.
11. Jennings, D, Cormack, S, Coutts, AJ, Boyd, LJ, and Aughey, RJ.
Variability of GPS units for measuring distance in team sport
movements. Int J Sports Physiol Perform 5: 565–569, 2010.
12. Johnston, R, Watsford, M, Pine, M, Spurrs, R, Murphy, A, and
Pruyn, E. The validity and reliability of 5-Hz global positioning
systems units to measure team sport movement demands. J Strength
Cond Res 26: 758–765, 2012.
13. Knicker, AJ, Renshaw, I, Oldham, ARH, and Cairns, SP. Interactive
processes link the multiple symptoms of fatigue in sport
competition. Sports Med 41: 307–328, 2011.
14. Orchard, J and Seward, H. 20th Annual injury report: Season 2011.
Available at: http://www.afl.com.au/injury%20report/tabid/
13706/default.aspx. Accessed August 27, 2012.
15. Piggott, B, Newton, M, and McGuigan, M. The relationship
between training load and injury and illness over a pre-season at an
AFL Club. J Aust Strength Cond 17: 4–17, 2009.
16. Rogalski, B, Dawson, B, Heasman, J, and Gabbett, T. Training and
game loads and injury risk in elite Australian footballers. J Sci Med
Sports 16: 499–503, 2013.
17. Veale, JP, Pearce, AJ, Buttifant, D, and Carlson, JS. Anthropometric
profiling of elite junior and senior Australian football players. Int J
Sports Physiol Perform 5: 509–520, 2010.
18. Waldron, M, Worsfold, P, Twist, C, and Lamb, K. Concurrent
validity and test-retest reliability of a global positioning system
(GPS) and timing gates to assess sprint performance variables.
J Sports Sci 29: 1613–1619, 2011.
19. Wisbey, B, Montgomery, PG, Pyne, DB, and Rattray, B. Quantifying
movement demands of AFL football using GPS tracking. J Sci Med
Sport 13: 531–536, 2010.
Injury Risk in Elite Australian Footballers
2252
Journal of Strength and Conditioning Research
the
TM
Copyright © National Strength and Conditioning Association Unauthorized reproduction of this article is prohibited.
... This approach employs a wide variety of tools to measure TL [21]. Approaches to measuring TL can be organized into external and internal loads, and in sports are typically measured via technology such as global positioning system (GPS) units or subjective wellness questionnaires [20][21][22]. Measures of external loads include variables such as distance runs, the volume of weight lifted, or the number of accelerations, while internal loads include variables such as heart rate or ratings of perceived exertion (RPE) [20,21]. Rapid changes in external or internal loads may be indicative of future injury risk or performance change. ...
... Additional research on AFL players found that three-weekly cumulative distances between 73.72 and 86.66 km increased the risk of injury (OR = 5.49; 95% CI = 1.57-19.16) [22]. In this study's population, week-to-week changes of over 30% were recorded, particularly from Week 1 to Week 2 where distances covered almost doubled, as well as large three-weekly cumulative distances (Weeks 2-4 showed approximately 60 km in distance covered). ...
Article
Full-text available
Law enforcement academies, designed to prepare recruits for their prospective career, represent periods of high physical and mental stress, potentially contributing to recruits’ injuries. Managing stress via monitoring training loads may mitigate injuries while ensuring adequate preparation. However, it is vital to first understand an academy’s typical training load. The aim of this study was to profile the typical training load of law enforcement recruits over the course of 22 weeks. Data were prospectively collected using global positioning system (GPS) units placed on recruits during a portion of the academy training, while a desktop analysis was retrospectively applied to six other classes. A Bland–Altman plot was conducted to assess the agreement between the two methods. A linear mixed model was conducted to analyse the difference in distances covered per week, while other variables were presented graphically. Adequate agreement between the desktop analysis and GPS units was observed. Significant differences (p-value < 0.01) in distance covered (9.64 to 11.65 km) exist between weeks during early academy stages, which coincide with increases (~6 h) in physical training. Significant decreases in distances were experienced during the last five weeks of academy training. Most acute:chronic workload ratios stayed between the proposed 0.8 to 1.3 optimal range. Results from this study indicate that large increases in training occur early in the academy, potentially influencing injuries. Utilizing a desktop analysis is a pragmatic and reliable approach for instructors to measure load.
... En esta revisión, se encontró un número total de 28 publicaciones que realizaron un análisis externo de la carga de trabajo a través de índices basados en acelerometría aplicado al entrenamiento y la competición. La mayoría de los estudios analizaron la carga de trabajo semanal general (entrenamiento y competición) y no proporcionaron horas de entrenamiento y competición distintas, por lo que la normalización no es posible, lo que dificulta su comparación (Boyd et al., 2013;Colby, Dawson, Heasman, Rogalski & Gabbett, 2014;Gabbett et al., 2010;Gabbett, 2013;Graham, Cormack, Parfitt & Eston, 2017;Peterson & Quiggle, 2017;Svilar, Castellano, & Jukic, 2018a). ...
... Finalmente, con respecto a la empresa que desarrolla cada dispositivo, se encuentran otras variables en el área de la ciencia del deporte, como Dynamic Stress Load (Gaudino et al., 2015), Body Load (Cunniffe et al., 2009), Total Load (Bowen et al., 2017), Force Load (Colby et al., 2014), Impulse Load (Gentles et al., 2018), PLRT (Pino-Ortega, Rojas-Valverde, et al., 2019) or PLRE (Dalen et al., 2016) para cuantificar la carga de trabajo acumulada durante las sesiones de entrenamiento o partidos oficiales en deportes de equipo. Estos índices se basan en los datos brutos de acelerometría en los 3 ejes de movimiento aplicando diferentes algoritmos y valores escalados. ...
Thesis
Full-text available
La acelerometría es un método de cuantificación de la carga externa que está teniendo una aplicación exponencial gracias a su integración en dispositivos electrónicos para el análisis del rendimiento en deportes colectivos. La carga externa ha sido comúnmente cuantificada a través del desplazamiento (distancia y velocidad), no teniendo en cuenta su efecto a nivel neuromuscular. Por ello, el objetivo principal de la presente Tesis Doctoral es el análisis de la carga externa que soportan múltiples ubicaciones anatómicas de forma simultánea en los desplazamientos deportivos, específicamente en baloncesto. Para ello, se realiza una revisión sistemática detectando que diferentes aspectos técnicos requieren una evaluación previa al registro así como que los acelerómetros miden la aceleración del segmento al que están unidos. Para subsanar ambos aspectos, se realiza un análisis de la precisión y fiabilidad del sensor, se identifican los índices de carga y frecuencia de muestreo adecuados, así como se diseña y valida un protocolo de registro multi-ubicación y una batería de evaluaciones que representa los desplazamientos más comunes en los deportes de invasión. Finalmente, se realiza la evaluación multi-ubicación de la carga externa en test de laboratorio y test de campo para evaluar el efecto de la velocidad, sexo y tipo de desplazamiento, así como establecer perfiles de rendimiento individual. A partir de estos resultados, los entrenadores podrán identificar la carga externa específica de cada estructura musculoesquelética para diseñar programas individualizados de acondicionamiento físico, prevención de lesiones y recuperación adaptados a los grupos musculares con mayor carga externa. Accelerometry is a method for quantifying external load that is having an exponential application thanks to its integration in electronic performance and tracking systems in team sports. External load has been commonly quantified through displacement (distance and speed), not considering its effect at the neuromuscular level. Therefore, the main objective of this Doctoral Thesis is the analysis of the external load supported by multiple anatomical locations simultaneously in sports movements, specifically in basketball. To do this, a systematic review is carried out, detecting those different technical aspects that require an evaluation prior to registration, as well as that the accelerometers measure the acceleration of the segment to which they are attached. To correct both aspects, an analysis of the precision and reliability of the sensor was performed, the appropriate load index and sampling frequency were identified, as well as a multi-location registration protocol and a battery of evaluations that represent the most common displacements in invasion sports were designed and validated. Finally, the multi-location evaluation of the external load was performed in laboratory and field tests to evaluate the effect of speed, sex and type of movement, as well as to establish individual performance profiles. From these results, trainers will be able to identify the specific external load of each musculoskeletal structure to design individualized programs for physical conditioning, injury prevention and recovery adapted to the muscle groups with the highest external load.
... p = 0.074) for Australian footballers, becomes challenging to interpret as the strength of evidence is defined in relative terms. 31 Therefore, it wasn't possible to define specific indicators for identifying peaks/troughs in player external training loads using GPS-based metrics from the literature. However, it was evident that longitudinal durations considered in cumulative external training load information (e.g. ...
Article
Full-text available
Standards are pivotal for generating the evidence required to manage players in professional sport environments like rugby union. Resultantly, using a three-step qualitative approach, this study aimed to formulate a consensus as a subjective standard for evidence generation pertaining to player management. The consensus statement intended to identify evidence on peaks/troughs in player external training loads using Global Positioning System (GPS)-based information in the High-Performance Unit (HPU) of a Gallagher Premiership rugby union club. Initially, a systematic review adhering to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) framework was conducted to unravel the factors considered (literature-based cues) when identifying peaks/troughs in player external training loads using GPS information. Next, thematic analysis conducted on the data obtained from 7 semi-structured interviews with HPU staff highlighted that they consider 6 factors with 38 elements (practitioner-based cues) during player external training load management. Thereafter, guided by the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument and by utilising selected elements representing 4/6 factors (healthy player, GPS information, longitudinal durations and practitioner judgements on information), a consensus among practitioners for identifying peaks/troughs in player external training loads was developed with the participation of five HPU members using the nominal group technique (NGT). Practitioners reached an agreement with regard to 12 indicators to subjectively identify peaks/troughs in player external training loads within the considered environment.
... 15,24,29,41,44,48 Studies in rugby, basketball, and Australian football have shown that the greatest incidence of illness and injury is observed when current training loads are greatest. 2,19,20,33 Several studies have found that a large percentage of injuries are associated with rapid changes or spikes in weekly loads 31,34,55 and that competition congestion leads to increased risk of injury. 10,27 Previous studies have also found a relationship between relative change in training load, measured by the acute/chronic workload ratio (ACWR), and injury risk. ...
Article
Full-text available
Background Training and game loads are potential risk factors of injury in junior elite ice hockey, but the association of training and game loads to injuries is unknown. Purpose To investigate the association of chronic training and game loads to injury risk in junior male elite ice hockey players. Study Design Cohort study; Level of evidence, 2. Methods In this prospective cohort study, we monitored all health problems among 159 male junior ice hockey players (mean age, 16 years; range, 15-19 years) at sports-specific high schools during the 2018-2019 school year. Players reported their health problems every week using the Oslo Sports Trauma Research Center Overuse Questionnaire on Health Problems (OSTRC-H2). The number of training sessions and games was reported for 33 weeks. We calculated the previous 2-week difference in training/game loads as well as the cumulative training/game loads of the previous 2, 3, 4, and 6 weeks and explored potential associations between training/game loads and injury risk using mixed-effects logistic regression. Results The players reported 133 acute injuries, 75 overuse injuries, and 162 illnesses in total, and an average of 8.8 (SD ±3.9) training sessions and 0.9 (SD ± 1.1) games per week. We found no association between the difference of the two previous weeks or the previous 2- 3- and 4-week cumulative, training or game load and acute injuries, nor the difference of the two previous weeks, or the previous 4- and 6-week cumulative, training or game load and overuse injuries (OR, ∼1.0; P > .05 in all models). Conclusion In the current study of junior elite ice hockey players, there was no evidence of an association between cumulative exposure to training/game loads and injury risk.
... et al., 2016) have been related to increased risk of injury, and recent studies declare that higher ACWR combined with low cumulative chronic workloads (Stares et al., 2018;Bowen et al., 2020), and rapid increases in workload (week-to-week changes) (Hulin et al., 2014;Bowen et al., 2020) may lead to a higher risk of injury. Colby et al. reported that there is a positive linear relationship between cumulative loads and injury risk (Colby et al., 2014), which is in agreement with our results. In our study, the mean of CW deviated from 0.53 to 0.43 in high load level to low load level, with a higher proportion of injuries in weeks of high load compared to weeks of low load, although there were no significant differences. ...
Article
Full-text available
Injuries in professional soccer are a significant concern for teams, and they are caused amongst others by high training load. This cohort study describes the relationship between workload parameters and the occurrence of non-contact injuries, during weeks with high and low workload in professional soccer players throughout the season. Twenty-one professional soccer players aged 28.3 ± 3.9 yrs. who competed in the Iranian Persian Gulf Pro League participated in this 48-week study. The external load was monitored using global positioning system (GPS, GPSPORTS Systems Pty Ltd) and the type of injury was documented daily by the team's medical staff. Odds ratio (OR) and relative risk (RR) were calculated for non-contact injuries for high- and low-load weeks according to acute (AW), chronic (CW), acute to chronic workload ratio (ACWR), and AW variation (Δ-Acute) values. By using Poisson distribution, the interval between previous and new injuries were estimated. Overall, 12 non-contact injuries occurred during high load and 9 during low load weeks. Based on the variables ACWR and Δ-AW, there was a significantly increased risk of sustaining non-contact injuries (p < 0.05) during high-load weeks for ACWR (OR: 4.67), and Δ-AW (OR: 4.07). Finally, the expected time between injuries was significantly shorter in high load weeks for ACWR [1.25 vs. 3.33, rate ratio time (RRT)] and Δ-AW (1.33 vs. 3.45, RRT) respectively, compared to low load weeks. The risk of sustaining injuries was significantly larger during high workload weeks for ACWR, and Δ-AW compared with low workload weeks. The observed high OR in high load weeks indicate that there is a significant relationship between workload and occurrence of non-contact injuries. The predicted time to new injuries is shorter in high load weeks compared to low load weeks. Therefore, the frequency of injuries is higher during high load weeks for ACWR and Δ-AW. ACWR and Δ-AW appear to be good indicators for estimating the injury risk, and the time interval between injuries.
... Workload in sport can influence performance and injury risk in individual athletes (Colby et al., 2014). Workload is defined as the ratio between an athlete's short-term training load and the mean of their long-term training load (Blanch & Gabbett, 2016). ...
Article
Full-text available
In recent years, the use of advanced wearable technologies in tennis has improved the ability to monitor workload and performance indicators. Using the device Armbeep Tennis, attached to the wrist of an entry level female tennis player, we recorded 97% of the tennis training and all official matches over two annual competitive seasons. The aim of the study was to determine the variation of different workload indicators during the preparation and competition phases in one annual season and to compare the workload indicators between the two seasons. We found no significant differences in the results of the training, tournament, and performance indicators between the two seasons. Our tennis player trained more on average in the second year (Y1 = 90.9 min, Y2 = 97.5 min) and the percentage of active time was also higher (Y1 = 30.6%, Y2 = 32.4%). A higher number of shots per week (Y1 = 3109.1, Y2 = 2869.4) was observed in the first year, while the number of shots per hour was higher in the second year (Y1 =420.6, Y2 = 430.1). The pace of the rally was higher in the first year (Y1 = 24.6, Y2 = 23.4). The differences between the other workload indicators were not significant in the two years. This single case study provides good insight into the overall progression of training and competition over two annual seasons and can serve as a basis for determining workload indicators for novice tennis players or those just embarking on this path.
... et al., 2016) have been related to increased risk of injury, and recent studies declare that higher ACWR combined with low cumulative chronic workloads (Stares et al., 2018;Bowen et al., 2020), and rapid increases in workload (week-to-week changes) (Hulin et al., 2014;Bowen et al., 2020) may lead to a higher risk of injury. Colby et al. reported that there is a positive linear relationship between cumulative loads and injury risk (Colby et al., 2014), which is in agreement with our results. In our study, the mean of CW deviated from 0.53 to 0.43 in high load level to low load level, with a higher proportion of injuries in weeks of high load compared to weeks of low load, although there were no significant differences. ...
Article
Full-text available
Injuries in professional soccer are a significant concern for teams, and they are caused amongst others by high training load. This cohort study describes the relationship between workload parameters and the occurrence of non-contact injuries, during weeks with high and low workload in professional soccer players throughout the season. Twenty-one professional soccer players aged 28.3 ± 3.9 yrs. who competed in the Iranian Persian Gulf Pro League participated in this 48-week study. The external load was monitored using global positioning system (GPS, GPSPORTS Systems Pty Ltd) and the type of injury was documented daily by the team’s medical sta􀀀. Odds ratio (OR) and relative risk (RR) were calculated for non-contact injuries for high- and low-load weeks according to acute (AW), chronic (CW), acute to chronic workload ratio (ACWR), and AW variation (1-Acute) values. By using Poisson distribution, the interval between previous and new injuries were estimated. Overall, 12 non-contact injuries occurred during high load and 9 during low load weeks. Based on the variables ACWR and 1-AW, there was a significantly increased risk of sustaining non-contact injuries (p < 0.05) during high-load weeks for ACWR (OR: 4.67), and 1-AW (OR: 4.07). Finally, the expected time between injuries was significantly shorter in high load weeks for ACWR [1.25 vs. 3.33, rate ratio time (RRT)] and 1-AW (1.33 vs. 3.45, RRT) respectively, compared to low load weeks. The risk of sustaining injuries was significantly larger during high workload weeks for ACWR, and 1-AW compared with low workload weeks. The observed high OR in high load weeks indicate that there is a significant relationship between workload and occurrence of non-contact injuries. The predicted time to new injuries is shorter in high load weeks compared to low load weeks. Therefore, the frequency of injuries is higher during high load weeks for ACWR and 1-AW. ACWR and 1-AW appear to be good indicators for estimating the injury risk, and the time interval between injuries.
Article
El objetivo del presente trabajo consistió en analizar las relaciones existentes entre los parámetros de carga interna (objetiva y subjetiva) y externa. La muestra estuvo compuesta por un total de 15 partidos disputados en el Eurobasket femenino U-16, donde participaron un total de nueve árbitros (6 masculinos y 3 femeninos). Las variables analizadas de carga interna fueron la frecuencia cardiaca y la percepción subjetiva de esfuerzo. Las variables de carga externa se dividieron en cinemáticas y neuromusculares, medidas a través de dispositivos inerciales. Los resultados explican que existen relaciones entre los valores de carga interna objetiva y carga externa, así como entre las variables de carga externa. En cambio, no existen relaciones entre la carga interna subjetiva y las variables de carga interna y externa objetiva, exceptuando el PowerMetabolic. Estos resultados demuestran que la competición y el nivel de los árbitros influyen en gran medida en las variables subjetivas.
Article
Full-text available
Background: The monitoring of accelerometry derived load has received increased attention in recent years. However, the ability of such measures to quantify training load during sport-related activities is not well established. Thus, the current study aimed to assess the validity and reliability of tri-axial accelerometers to identify step count and quantify external load during several locomotor conditions including walking, jogging, and running. Method: Thirty physically active college students (height = 176.8 ± 6.1 cm, weight = 82.3 ± 12.8 kg) participated. Acceleration data was collected via two tri-axial accelerometers (Device A and B) sampling at 100 Hz, mounted closely together at the xiphoid process. Each participant completed two trials of straight-line walking, jogging, and running on a 20 m course. Device A was used to assess accelerometer validity to identify step count and the test-retest reliability of the instrument to quantify the external load. Device A and Device B were used to assess inter-device reliability. The reliability of accelerometry-derived metrics Impulse Load (IL) and Magnitude g (MAG) were assessed. Results: The instrument demonstrated a positive predictive value (PPV) ranging between 96.98%–99.41% and an agreement ranging between 93.08%–96.29% for step detection during all conditions. Good test-retest reliability was found with a coefficient of variation (CV) <5% for IL and MAG during all locomotor conditions. Good inter-device reliability was also found for all locomotor conditions (IL and MAG CV < 5%). Conclusion: This research indicates that tri-axial accelerometers can be used to identify steps and quantify external load when movement is completed at a range of speeds.
Article
Competitive sport involves physical and psychological stressors, such as training load and stress perceptions, that athletes must adapt to in order to maintain health and performance. Psychological resilience, one’s capacity to equilibrate or adapt affective and behavioral responses to adverse physical or emotional experiences, is an important topic in athlete training and performance. The study purpose was to investigate associations of training load and perceived sport stress with athlete psychological resilience trajectories. Sixty-one collegiate club athletes (30 females and 31 males) completed self-reported surveys over 6 weeks of training. Athletes significantly differed in resilience at the beginning of competitive training. Baseline resilience differences were associated with resilience trajectories. Perceived stress and training load were negatively associated with resilience. Physical and psychological stressors had a small but statistically significant impact on resilience across weeks of competitive training, indicating that both types of stressors should be monitored to maintain athlete resilience.
Article
Full-text available
This study examined the relationship between game movements and team and individual performance in Australian football. Movement data (GPS) was collected from 30 elite players from one club in 17 matches during the 2011 season. Selected movement variables were related to individual (possession number, Champion Data© player rankings and pressure points) and team [quarter points (scored) margin] performance indicators. Playing position (nomadic vs. key position), years of experience, game location (home/away), environmental conditions (wet/dry), time of day (day/night), break between games (6-12 days), quarter number (1-4) and quarter score (+/-) margin were also analysed. Overall, some small-moderate (but inconsistent) positive relationships between individual movement data and performance indicators were observed. Nomadic players had higher movement profiles and performance indicators than key position, whilst players with 7+ years’ experience recorded lower movement profiles than 1-3 and 4-6 years, but were only lower in performance in pressure points. min⁻¹. Dry vs. wet (one exception), home vs. away and day vs. night, saw no differences in movements or performance. A 12 day turnaround saw higher movement profiles and performance indicators than for 6-8 days. For team performance, few moderate, inverse relationships were found between quarter points (scored) margin and movement profiles.
Data
Full-text available
This study examined the relationship between game movements and team and individual performance in Australian football. Movement data (GPS) was collected from 30 elite players from one club in 17 matches during the 2011 season. Selected movement variables were related to individual (possession number, Champion Data © player rankings and pressure points) and team [quarter points (scored) margin] performance indicators. Playing position (nomadic vs. key position), years of experience, game location (home/away), environmental conditions (wet/dry), time of day (day/night), break between games (6-12 days), quarter number (1-4) and quarter score (+/-) margin were also analysed. Overall, some small-moderate (but inconsistent) positive relationships between individual movement data and performance indicators were observed. Nomadic players had higher movement profiles and performance indicators than key position, whilst players with 7+ years' experience recorded lower movement profiles than 1-3 and 4-6 years, but were only lower in performance in pressure points.min -1 . Dry vs. wet (one exception), home vs. away and day vs. night, saw no differences in movements or performance. A 12 day turnaround saw higher movement profiles and performance indicators than for 6-8 days. For team performance, few moderate, inverse relationships were found between quarter points (scored) margin and movement profiles.
Article
Full-text available
Objectives: To examine the relationship between combined training and game loads and injury risk in elite Australian footballers. Design: Prospective cohort study. Methods: Forty-six elite Australian footballers (mean±SD age of 22.2±2.9 y) from one club were involved in a one-season study. Training and game loads (session-RPE multiplied by duration in min) and injuries were recorded each time an athlete exerted an exercise load. Rolling weekly sums and week-to-week changes in load were then modelled against injury data using a logistic regression model. Odds ratios (OR) were reported against a reference group of the lowest training load range. Results: Larger 1 weekly (>1750 AU, OR=2.44-3.38), 2 weekly (>4000 AU, OR=4.74) and previous to current week changes in load (>1250 AU, OR=2.58) significantly related (p<0.05) to a larger injury risk throughout the in-season phase. Players with 2-3 and 4-6 years of experience had a significantly lower injury risk compared to 7+ years players (OR=0.22, OR=0.28) when the previous to current week change in load was more than 1000 AU. No significant relationships were found between all derived load values and injury risk during the pre-season phase. Conclusions: In-season, as the amount of 1-2 weekly load or previous to current week increment in load increases, so does the risk of injury in elite Australian footballers. To reduce the risk of injury, derived training and game load values of weekly loads and previous week-to-week load changes should be individually monitored in elite Australian footballers.
Article
Full-text available
Perceptions of wellness are often used by athletes and coaches to assess adaptive responses to training. The purpose of this research was to describe how players were coping with the demands of elite level Australian football over a competitive season using subjective ratings of physical and psychological wellness and to assess the ecological validity of such a monitoring approach. Twenty seven players completed ratings for nine items (fatigue, general muscle, hamstring, quadriceps, pain/stiffness, power, sleep quality, stress, wellbeing). Players subjectively rated each item as they arrived at the training or competition venue on a 1 - 5 visual analog scale, with 1 representing the positive end of the continuum. A total of 2,583 questionnaires were analysed from completions on 183 days throughout the season (92 ± 24 per player, 103 ± 20 per week; mean ± SD). Descriptive statistics and multi-level modelling were used to understand how player ratings of wellness varied over the season and during the week leading into game day and whether selected player characteristics moderated these relationships. Results indicated that subjective ratings of physical and psychological wellness were sensitive to weekly training manipulations (i.e., improve steadily throughout the week to a game day low, p < 0.001), to periods of unloading during the season (i.e., a week of no competition, p < 0.05) and to individual player characteristics (e.g., muscle strain after a game was poorer in players with high maximum speed, p < 0.01). It is concluded that self-reported player ratings of wellness provide a useful tool for coaches and practitioners to monitor player responses to the rigorous demands of training, competition and life as a professional athlete.
Article
Full-text available
Although the potential link between running loads and soft-tissue injury is appealing, the evidence supporting or refuting this relationship in high-performance team sport athletes is nonexistent, with all published studies using subjective measures (e.g., ratings of perceived exertion) to quantify training loads. The purpose of this study was to investigate the risk of low-intensity (e.g., walking, jogging, total distances) and high-intensity (e.g., high acceleration and velocity efforts, repeated high-intensity exercise bouts) movement activities on lower body soft-tissue injury in elite team sport athletes. Thirty-four elite rugby league players participated in this study. Global positioning system data and the incidence of lower body soft-tissue injuries were monitored in 117 skill training sessions during the preseason and in-season periods. The frailty model (an extension of the Cox proportional regression model for recurrent events) was applied to calculate the relative risk of injury after controlling for all other training data. The risk of injury was 2.7 (95% confidence interval 1.2-6.5) times higher when very high-velocity running (i.e., sprinting) exceeded 9 m per session. Greater distances covered in mild, moderate, and maximum accelerations and low- and very low-intensity movement velocities were associated with a reduced risk of injury. These results demonstrate that greater amounts of very high-velocity running (i.e., sprinting) are associated with an increased risk of lower body soft-tissue injury, whereas distances covered at low and moderate speeds offer a protective effect against soft-tissue injury. From an injury prevention perspective, these findings provide empirical support for restricting the amount of sprinting performed in preparation for elite team sport competition. However, coaches should also consider the consequences of reducing training loads on the development of physical qualities and playing performance.
Article
Full-text available
There has been no previous investigation of the concurrent validity and reliability of the current 5 Hz global positioning system (GPS) to assess sprinting speed or the reliability of integrated GPS-accelerometer technology. In the present study, we wished to determine: (1) the concurrent validity and reliability of a GPS and timing gates to measure sprinting speed or distance, and (2) the reliability of proper accelerations recorded via GPS-accelerometer integration. Nineteen elite youth rugby league players performed two over-ground sprints and were simultaneously assessed using GPS and timing gates. The GPS measurements systematically underestimated both distance and timing gate speed. The GPS measurements were reliable for all variables of distance and speed (coefficient of variation [CV] = 1.62% to 2.3%), particularly peak speed (95% limits of agreement [LOA] = 0.00 ± 0.8 km · h(-1); CV = 0.78%). Timing gates were more reliable (CV = 1% to 1.54%) than equivalent GPS measurements. Accelerometer measurements were least reliable (CV = 4.69% to 5.16%), particularly for the frequency of proper accelerations (95% LOA = 1.00 ± 5.43; CV = 14.12%). Timing gates and GPS were found to reliably assess speed and distance, although the validity of the GPS remains questionable. The error found in accelerometer measurements indicates the limits of this device for detecting changes in performance.
Article
Full-text available
The understanding of the gap between Under 18 y (U18) and senior-level competition and the evolution of this gap in Australian Football lack a strong evidence base. Despite the multimillion dollars invested in recruitment, scientific research on successful transition is limited. No studies have compared individual players' movement rate, game statistics and ball speed in U18 and senior competition of the Australian Football League across time. This project compared differences in player movement and ball speed between matches from senior AFL competitive matches and U18 players in the 2003 and 2009 seasons. TrakPerformance Software and Global Positioning System (GPS) technology were used to analyze the movement of players, ball speed and game statistics. ANOVA compared the two levels of competition over time. Observed interactions for distance traveled per minute of play (P = .009), number of sprints per minute of play (P < .001), time spent at sprint speed in the game (P < .001), time on field (P < .001), and ball speed (P < .001) were found. Subsequent analysis identified increases in movement patterns in senior AFL competition in 2009 compared with the same level of competition in 2003 and U18 players in 2003 and 2009. Senior AFL players in 2009 were moving further, sprinting relatively more frequently, playing less time and playing at game speeds significantly greater than the same senior competition in 2003 as well as compared with both cohorts of U18 players.
Article
Full-text available
Muscle physiologists often describe fatigue simply as a decline of muscle force and infer this causes an athlete to slow down. In contrast, exercise scientists describe fatigue during sport competition more holistically as an exercise-induced impairment of performance. The aim of this review is to reconcile the different views by evaluating the many performance symptoms/measures and mechanisms of fatigue. We describe how fatigue is assessed with muscle, exercise or competition performance measures. Muscle performance (single muscle test measures) declines due to peripheral fatigue (reduced muscle cell force) and/or central fatigue (reduced motor drive from the CNS). Peak muscle force seldom falls by A model depicting mind-body interactions during sport competition shows that the RPE centre-motor cortex-working muscle sequence drives overall performance levels and, hence, fatigue symptoms. The sporting outputs from this sequence can be modulated by interactions with muscle afferent and circulatory feedback, psychological and decision-making inputs. Importantly, compensatory processes exist at many levels to protect against performance decrements. Small changes of putative fatigue factors can also be protective.We show that individual fatigue factors including diminished carbohydrate availability, elevated serotonin, hypoxia, acidosis, hyperkalaemia, hyperthermia, dehydration and reactive oxygen species, each contribute to several fatigue symptoms. Thus, multiple symptoms of fatigue can occur simultaneously and the underlying mechanisms overlap and interact. Based on this understanding, we reinforce the proposal that fatigue is best described globally as an exercise-induced decline of performance as this is inclusive of all viewpoints.
Article
Objectives: To examine the usefulness of selected physiological and perceptual measures to monitor fitness, fatigue and running performance during a pre-season, 2-week training camp in eighteen professional Australian Rules Football players (21.9±2.0 years). Design: Observational. Methods: Training load, perceived ratings of wellness (e.g., fatigue, sleep quality) and salivary cortisol were collected daily. Submaximal exercise heart rate (HRex) and a vagal-related heart rate variability index (LnSD1) were also collected at the start of each training session. Yo-Yo Intermittent Recovery level 2 test (Yo-YoIR2, assessed pre-, mid- and post-camp, temperate conditions) and high-speed running distance during standardized drills (HSR, >14.4 km h(-1), 4 times throughout, outdoor) were used as performance measures. Results: There were significant (P<0.001 for all) day-to-day variations in training load (coefficient of variation, CV: 66%), wellness measures (6-18%), HRex (3.3%), LnSD1 (19.0%), but not cortisol (20.0%, P=0.60). While the overall wellness (+0.06, 90% CL (-0.14; 0.02) AU day(-1)) did not change substantially throughout the camp, HRex decreased (-0.51 (-0.58; -0.45)% day(-1)), and cortisol (+0.31 (0.06; 0.57) nmol L(-1)day(-1)), LnSD1 (+0.1 (0.04; 0.06) ms day(-1)), Yo-YoIR2 performance (+23.7 (20.8; 26.6) m day(-1), P<0.001), and HSR (+4.1 (1.5; 6.6) m day(-1), P<0.001) increased. Day-to-day ΔHRex (r=0.80, 90% CL (0.75; 0.85)), ΔLnSD1 (0.51 (r=0.40; 0.62)) and all wellness measures (0.28 (-0.39; -0.17)<r<0.25 (0.14; 0.36)) were related to Δtraining load. There was however no clear relationship between Δcortisol and Δtraining load. ΔYo-YoIR2 was correlated with ΔHRex (r=0.88 (0.84; 0.92)), ΔLnSD1 (r=0.78 (0.67; 0.89)), Δwellness (r=0.58 (0.41; 0.75), but not Δcortisol. ΔHSR was correlated with ΔHRex (r = -0.27 (-0.48; -0.06)) and Δwellness (r=0.65 (0.49; 0.81)), but neither with ΔLnSD1 nor Δcortisol. Conclusions: Training load, HRex and wellness measures are the best simple measures for monitoring training responses to an intensified training camp; cortisol post-exercise and LnSD1 did not show practical efficacy here.
Article
The purpose of this research was to investigate the validity and the reliability of 5-Hz MinimaxX global positioning system (GPS) units measuring athlete movement demands. A team sport simulation circuit (files collected from each unit = 12) and flying 50-m sprints (files collected from each unit = 34) were undertaken, during which the total distance covered; peak speed; player load; the distance covered; time spent and number of efforts performed walking, jogging, running, high-speed running, and sprinting were examined. Movement demands were also separately categorized into low-intensity activity, high-intensity running, and very high-intensity running. The results revealed that GPS was a valid and reliable measure of total distance covered (p > 0.05, percentage typical error of measurement [%TEM] < 5%) and peak speed (p > 0.05, %TEM 5-10%). Further, GPS was found to be a reliable measure of player load (%TEM 4.9%) and the distance covered, time spent, and number of efforts performed at certain velocity zones (%TEM <5% to >10%). The level of GPS error was found to increase along with the velocity of exercise. The findings demonstrated that GPS is capable of measuring movement demands performed at velocities <20 km·h(-1), whereas more caution is to be exercised when analyzing movement demands collected by using GPS velocities >20 km·h(-1).