BMJ Open · For peer review only Seasonal change in bone, muscle and fat in professional rugby...

For peer review only

Seasonal change in bone, muscle and fat in professional rugby league players and relationship to injury: a cohort

study

Journal: BMJ Open

Manuscript ID: bmjopen-2012-001400

Article Type: Research

Date Submitted by the Author: 07-Jun-2012

Complete List of Authors: Georgeson, Erin; Griffith University, Centre for Musculoskeletal Research Weeks, Benjamin; Griffith University, Centre for Musculoskeletal Research McLellan, Chris; Gold Coast Titans, National Rugby League

Beck, Belinda; Griffith University, Centre for Musculoskeletal Research

<b>Primary Subject Heading</b>:

Sports and exercise medicine

Secondary Subject Heading: Radiology and imaging

Keywords: Calcium & bone < DIABETES & ENDOCRINOLOGY, Bone diseases < ORTHOPAEDIC & TRAUMA SURGERY, Orthopaedic sports trauma < ORTHOPAEDIC & TRAUMA SURGERY

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

BMJ Open on O

ctober 14, 2020 by guest. Protected by copyright.

http://bmjopen.bm

j.com/

BM

J Open: first published as 10.1136/bm

jopen-2012-001400 on 6 Novem

ber 2012. Dow

nloaded from

http://bmjopen.bmj.com/


1

Title: Seasonal change in bone, muscle and fat in professional rugby league players and

relationship to injury: a cohort study

Authors:

Erin C. Georgeson, BPhty(Hons) BExSc1

Benjamin K. Weeks, PhD1

Chris McLellan, PhD2

Belinda R. Beck, PhD1

1School of Physiotherapy and Exercise Science, Centre for Musculoskeletal Research,

Griffith Health Institute, Gold Coast, Australia; 2Gold Coast Titans, Gold Coast, Australia

Key Words: football, body composition, bone mass, sports injury

Corresponding author:

A/Prof Belinda R. Beck

School of Physiotherapy and Exercise Science

Gold Coast campus

Griffith University, Qld 4222

Australia

Ph: +61 7 5552 8793

Fax: +61 7 5552 8674

Email: [email protected]

Word Count: 2974

Page 1 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

on October 14, 2020 by guest. P

rotected by copyright.http://bm

jopen.bmj.com

/B

MJ O

pen: first published as 10.1136/bmjopen-2012-001400 on 6 N

ovember 2012. D

ownloaded from



2

Article Focus

• Does rugby league player body composition change across a 12 month period in

response to pre-season training, seasonal game play and off-season rest?

• Is pre-season body composition (bone, muscle and fat) related to injury incidence

throughout the season?

• Are changes in body composition during the playing season related to incidence of

injuries?

Key Messages

• Professional rugby league players lose lean mass across a playing season but regain it

with pre-season training

• Strong relationships were not detected between anthropometric characteristics and

incidence of injury.

Strengths and Limitations

• Very comprehensive anthropometric data was collected from a professional rugby

league team at four time points across a 12 month period (pre, mid, post and pre-

season) to track changes in body composition related to pre-season training, playing

and off seasons

• The most valid and reliable instruments (DXA and pQCT) were employed to

determine anthropometric measures. pQCT data is novel in this cohort.

• Low absolute number of injuries limited the ability to detect strong relationships

between injuries and anthropometric measures.

Page 2 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



3

ABSTRACT

Objectives To examine the anthropometric characteristics of an Australian National Rugby

League team and identify the relationship to type and incidence of injuries sustained during a

professional season. It was hypothesized that body composition would not change discernibly

across a playing season and that injury would be negatively related to pre-season bone and

muscle mass.

Design A repeated measure, prospective, observational, cohort study

Setting Griffith University and Titans Centre of Excellence, Gold Coast, Australia

Participants 37 professional male Australian National Rugby League players (mean age

24.3 ± 3.8) were recruited for pre-season 1 testing, of whom 25 were retested pre-season 2.

Primary and Secondary Outcome Measures Primary outcome measures included:

biometrics; body composition (bone, muscle and fat mass; dual-energy x-ray absorptiometry;

DXA; XR800, Norland Medical Systems, Inc); bone geometry and strength (peripheral

quantitative computed tomography; pQCT; XCT 3000, Stratec); calcaneal broadband

ultrasound attenuation (BUA; QUS-2, Quidel); diet and physical activity history. Secondary

outcome measures included player injuries across a single playing season.

Results Lean mass decreased progressively throughout the season (p ≤ 0.05), while whole

body (WB) bone mineral density (BMD) increased until mid-season (p ≤ 0.001) then

decreased thereafter (p ≤ 0.001). Start-of-season WB BMD, fat and lean mass, weight and

tibial mass measured at the 38% site predicted bone injury incidence, but no other

relationship was observed between body composition and injury.

Conclusions Significant anthropometric changes were observed in players across a

professional rugby league season; including an overall loss of muscle and an initial increase,

Page 3 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



4

followed by a decrease in bone mass. Strong relationships between anthropometry and

incidence of injury were not observed. Long term tracking of large rugby league cohorts is

indicated to obtain more injury data in order to examine anthropometric relationships with

greater statistical power.

There is no additional data available.

Page 4 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



5

INTRODUCTION

Rugby league is a physically demanding, high impact, full body contact professional sport,

requiring well developed muscle strength and endurance, speed, agility and aerobic power.[1]

The high frequency and force of physical collisions encountered during a game[2-4] leads to

a higher incidence of musculoskeletal injuries[5] than is typically observed in other team

sports.[2, 3, 6] Injury incidence has been reported from 44.9 to 462.7 injuries per 1000

player hours.[2, 7-9] As 15-30% of total seasonal injuries are classified “severe”[2, 8, 10]

players can miss up to 20% of games in a season.[7] A range of factors have been associated

with an increased risk of injury, including low preseason running speed and maximal aerobic

power, lighter body weight and greater number of playing years’ experience.[5]

Furthermore, injury risk is dependent on player position (e.g. forwards vs. backs); forwards

typically sustaining more injuries than backs.[9, 11]

Some anthropometric and physiological characteristics of rugby league players have been

described, based on traditional body composition measures of weight, height, body mass

index (BMI) and skinfold thickness. Few more direct measures of bone and muscle have

been reported, and the relationship of body composition to injury remains unknown.

Analysis of seasonal anthropometric changes to rugby league players and the examination of

relationships of anthropometrics with rate and type of injury may reveal important risk

factors. In particular, the identification of modifiable risk factors would give rise to

opportunities to reduce the risk of injury to players.

Dual-energy x-ray absorptiometry (DXA) provides a reliable estimate of body composition

(bone, muscle and fat).[12] A recent study of English Super League players observed an

increase in body fat from DXA across the playing season, and a decrease in lean mass.[13]

Page 5 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



6

Bone mass was also observed to increase to mid-season, but decrease thereafter. It is well

known that DXA estimates of bone mass are based on two dimensional measures of areal

bone mineral density that cannot fully account for bone size, discriminate between cortical

and trabecular bone envelopes, or measure elements of bone morphology that are critical to

whole bone strength. In light of the known limitations of DXA measures, seasonal changes

in rugby league player bone strength remains uncertain. Peripheral quantitative computed

tomography (pQCT) can discriminate between cortical and trabecular envelopes, and

measures true volumetric BMD and parameters of bone morphology; thereby providing a

superior indication of bone strength than DXA.[14-17] Such measures have not previously

been reported for a rugby league cohort.

The aim of the current study was to examine pre-, mid- and post season body composition of

a professional Australian team using DXA and pQCT, and to identify relationships between

baseline and change in body composition with rate and type of injuries sustained across a

season.

METHODS

Ethical approval

Ethical approval for the study was granted by the Griffith University Human Research Ethics

Committee (PES/28/08/HREC). Written informed consent was obtained from each

participant.

Page 6 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



7

Subjects

All members of the Gold Coast Titans National Rugby League team (n=44) (mean age 24.6 ±

3.4 years) consented to participate in the study.

Study design and conduct

A repeated measure, prospective, observational study was conducted. Data was collected on

four occasions over a 12-month period, that is, pre- (March), mid- (July) and post-2009

southern hemisphere rugby league season (September/October), and pre-2010 rugby league

season (March). All data was collected in the Bone Densitometry Research Laboratory at

Griffith University, Gold Coast.

Behavioral characteristics – Diet and physical activity

Nutrition was assessed via the Cancer Council Victoria’s Dietary Questionnaire for

Epidemiological Studies (DQES), a diet instrument validated for the Australian

population.[18] The DQES contains a series of questions pertaining to the subjects’ normal

dietary intake over the preceding 12 months. Responses were computer-analysed and an

estimate of total daily energy intake and calcium intake were obtained.

The Bone-specific Physical Activity Questionnaire (BPAQ) is a validated tool for quantifying

historical physical activity participation relevant to the musculoskeletal system.[19]

Participants were asked to record: 1) all regular physical activities performed throughout their

life and the approximate number of years of participation; and 2) all activities performed on a

Page 7 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



8

regular basis over the previous 12 months, including frequency of participation. The BPAQ

was analysed using a custom-designed program (available at

http://www.fithdysign.com/BPAQ/) developed on LabVIEW software (National Instruments,

Texas, USA) to produce current, past and total bone-specific physical activity history

scores.[19]

Biometrics

Subject height was measured to the nearest 0.01 m using a wall-mounted stadiometer (HART

Sport & Leisure, Brisbane, Australia). Weight was measured to the nearest 0.01 kg using a

robust digital scale (CH-150K, AND Mercury, Brisbane, Australia). Upper extremity skeletal

dominance was determined as the preferred writing hand. Lower extremity skeletal

dominance was determined to be the non-kicking leg according to procedures established and

validated in our laboratory.[20]

Anthropometry

Dual-energy X-ray Absorptiometry

Dual-energy x-ray absorptiometry (DXA) was used to determine whole body (WB), lumbar

spine (LS), non-dominant femoral neck (FN) and forearm (FA) bone mineral content (BMC;

g), bone area (cm2), and bone mineral density (BMD; g/cm

2) (XR800 Norland Cooper

Surgical, USA, Illuminatus software Version 4.2.4). Additionally, WB scans were used to

determine lean and fat mass. Short-term BMD measurement precision in our lab is 0.9%,

1.1%, 0.4%, 0.8% and 0.6% for WB, FN, LS, distal radius-ulna and proximal radius-ulna

respectively.

Page 8 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



9

Peripheral Quantitative Computed Tomography

Peripheral Quantitative Computed Tomography (pQCT; XCT 3000; Stratec Pforzheim,

Germany) was used to examine non-dominant tibiae total, trabecular and cortical tibial

volumetric densities (mg/cm3), strength-strain index (SSI; mm

3), principal moments of inertia

(Imin and Imax; mm4) and muscle area (mm

2) at the 4%, 38% and 66% sites. Short term

measurement precision in our lab is 1.5% and 0.6% for total tibial density at the 4% and 38%

sites respectively.

Quantitative Ultrasound

Quantitative Ultrasound (QUS-2; Quidel, Mountain View, Ca) was used to measure

broadband ultrasound attenuation (BUA; dB/MHz) of the non-dominant calcaneus. Short-

term measurement precision with repositioning was 2.5%.

Performance Measures

Single Leg Stance

Static balance ability was tested using the standard single leg stance (SLS) test.[21]

Participants stood with feet pelvis-width apart, forearms crossed over their chest, and fingers

at shoulders. With eyes closed, one foot was lifted off the ground to the level of the opposite

ankle, but not touching. Timing commenced from foot lift off and ceased when: 1) arms

moved from their starting position; 2) feet touched; 3) elevated foot touched the ground or

moved towards/away from the planted foot; 4) grounded foot adjusted position to maintain

balance; or 5) eyes opened. Each participant was allowed to practice and have up to three

Page 9 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



10

attempts on each leg. The subject’s best time was recorded in seconds and the test repeated

for the opposite leg. A single investigator performed all SLS tests.

Vertical Jump

Leg muscle power was assessed by the vertical jump test.[22] Participants began by standing

beside the Yardstick vertical jump device (Swift Performance Equipment, NSW, Australia),

with both feet grounded and positioned shoulder-width apart. The participant was asked to

reach as high as possible with their preferred arm and the height of reach was recorded.

Participants were then instructed to jump as high as possible in counter-movement fashion

without arm swing, and tap the device at the peak of their jump. The best of three attempts

was recorded in cm. A single investigator measured and recorded all vertical jump trials.

Injury Data Collection

All injuries, new or recurrent, sustained during the study period were recorded by two team

physiotherapists in attendance at all games and training sessions. An injury was recorded if a

rugby league activity/game resulted in any pain, discomfort, illness or disability, and required

the player to seek medical intervention from team medical staff.[23] All injuries were

recorded whether the player missed a subsequent training session/game or not. Injury details

including anatomical location and tissue involvement, cause and severity (determined by the

number of training days and games missed) were recorded.

Game exposure hours were calculated by multiplying the number of players, by the number

of games and the duration of the match (i.e. 13 players x 29 games x 1.33 hours/game).[23]

Page 10 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



11

Injury incidence was recorded per 1000 game or training hours.[2, 23, 24] Match injury

incidence was calculated by dividing the number of recorded match injuries by the game

exposure hours and multiplying by 1000.

Statistical Analysis

Statistical analysis was performed using SPSS version 17.0 for Windows (IBM, Chicago, IL,

USA). Descriptive statistics (mean ± SD) were generated for subject and injury

characteristics and independent T-tests of the original 2009 cohort (n=37) were used to

compare the anthropometric characteristics of injured versus non-injured players. Correlation

analyses were performed to identify relationships between injury incidence, biometrics,

anthropometrics, active test performance, and lifestyle factors of the 2009 cohort (n=37).

Subsequent multiple regression analyses were used to determine the ability of biometrics and

behavioural characteristics to predict variance in anthropometrics and injury incidence.

Levene’s test was used to examine homogeneity of variance. To examine change in

anthropometric characteristics across the season, one-way repeated measures ANOVA was

used, both with and without covariates of calcium, weight, age and past BPAQ score (n=37).

Results were considered statistically significant at p ≤ 0.05.

RESULTS

A total of 44 different subjects were tested. At baseline 2009, 37 players were tested and 32

were tested in 2010. Player relocation resulted in 12 players leaving the cohort at the end of

the 2009 season and seven players joining the study prior to 2010 testing. Table 1 describes

the baseline characteristics of the cohort. Notably players exhibited bone mass that was, on

Page 11 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



12

average, over one standard deviation higher than age- and sex-matched norms for the WB (Z-

score range = + 0.63 to +1.63), spine (Z-score range = + 0.99 to +2.39) and hip (Z-score

range = + 0.63 to +3.03).

Table 1 Baseline Characteristics of Players at 2009 Pre-Season (n = 37)

Age (years) 24.3 ± 3.8

Weight (kg) 95.3 ± 10.4

Height (m) 1.81 ± 0.06

BMI (kg/m2) 29.0 ± 2.3

BPAQ Past 72.4 ± 26.9

BPAQ Current 7.5 ± 1.0

BPAQ Total 40.0 ± 13.5

WB BMD (g/cm2) 1.251 ± 0.088

WB BMD Z-score 1.13 ± 0.50

Total Daily Energy (kJ) 11052 ± 3678

Daily Ca2+

(mg) 1224 ± 454 BMI: body mass index; BPAQ: bone-specific physical activity; Ca

2+: calcium;

WB BMD: whole body bone mineral density.

Anthropometric Measures

Bone, muscle and fat measures at all four testing sessions are presented in Table 2.

Controlling for age, weight, dietary energy intake, calcium consumption and past physical

activity, lean mass decreased at each measurement time point throughout the 2009 season,

but returned to pre-2009 season values by 2010 pre-season (p ≤ 0.05). Fat mass did not

change over the 12-month period (p ≥ 0.05). The lean and fat percent response followed a

similar pattern (Figures 1A and B).

Page 12 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



13

Table 2 DXA and pQCT parameters at all measurement time points

Stage of Season

Measure Pre 2009 Mid 2009 Post 2009 Pre 2010

WB BMD (g/cm2)

(n = 19)

1.235±0.087a,e,f 1.296±0.093b,c 1.256±0.100

1.245±0.087

LS BMD (g/cm

2)

(n = 20)

1.447±0.110

1.460±0.105

1.459±0.116

1.469±0.114

FN BMD (g/cm

2)

(n = 19)

1.319±0.130

1.333±0.132

1.335±0.133

1.324±0.134

Dist. R&U BMD (g/cm2)

(n = 18)

0.580±0.051a

0.573±0.050b

0.580±0.050

0.578±0.049

Prox. Rad BMD (g/cm

2)

(n = 18)

1.065±0.063e

1.071±0.062b

1.078±0.062d

1.068±0.056

WB Lean Mass (kg)

(n = 19)

81.45±7.76a, e

80.72±7.73

79.89±6.72

d

81.22±6.73

WB Fat Mass (kg)

(n = 19)

10.70±3.08

11.05±4.00

11.03±4.87

11.35±4.46

BMI (kg/m

2)

(n = 20)

28.65±1.90

28.62±2.03

28.49±2.08

28.67±1.94

BUA (dB/MHz)

(n = 20)

118.3±15.3

117.9±16.8

117.4±16.3

117.6±15.3

Tibial Mass at 4% site (g/cm)

(n = 20)

5.55±0.85 5.55±0.90

5.52±0.77 5.59±0.88


(n = 19)

5.37±0.53a,e,f

5.41±0.53

5.42±0.57

5.41±0.50

Leg Bone:Muscle Area, 66%

Site(%) (n=20)

5.33±0.52a,f 5.51±0.56 5.49±0.61 5.50±0.62

Leg Fat Area, 66% site (mm2)

(n=20)

1005±590a

1303±549 1195±455 1135±592

BMD: bone mineral density; BMI: body mass index; BUA: broadband ultrasound attenuation; Dist. R&U BMD:

distal radius and ulna BMD; FN BMD: femoral neck BMD; LS BMD: lumbar spine BMD; NS: not significant;

Prox. Rad BMD: proximal radius BMD; WB: whole body; a: sig. difference between pre-2009 and mid-2009;

b: sig. difference between mid-2009 and post-2009; c: sig. difference between mid-2009 and pre-2010; d: sig.

difference between post-2009 and pre-2010; e: sig. difference between pre-2009 and post-2009; f: sig.

difference between pre-2009 and pre-2010.

Only WB BMD changed significantly across the 2009 season (p ≤ 0.001), increasing until

mid-season (p ≤ 0.001) and decreasing thereafter (post-season 2009 and pre-season 2010 p ≤

0.001), but remaining higher than 2009 pre-season values (Figure 2A). No significant

changes were observed for LS or FN BMD over the 12-month period (Figures 2B & C);

however changes were evident for forearm BMD. Distal forearm BMD decreased until mid-

season (p ≤ 0.05; Figure 2D), with a subsequent significant increase from mid- to post-season

Page 13 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



14

(p ≤ 0.05), at which time BMD of the proximal radius and ulna also significantly increased (p

≤ 0.05; Figure 2E). Proximal radius BMD decreased from 2009 post-season to 2010 pre-

season (p ≤ 0.05).

No significant changes were observed in tibial mass at the 4% tibial site (p = 0.364). Tibial

mass increased at the 14% site over the 12 month period (p ≤ 0.05) and at the 38% site at

each time-point relative to pre-season 2009 (p ≤ 0.05; Figure 2F).

Exposure and Injuries

A total of 29 games were played during the 2009 National Rugby League season, inclusive of

three pre-season and two finals matches for a total team game exposure of 501 hours (13

players x 29 games x 1.33 hours).[25] A total of 51 injuries were reported across the 2009

season, equating to an incidence of roughly 101 injuries per 1000 player hours. There were

43 new injuries and 8 re-injuries. Twelve injuries occurred when players were running, 21

while being tackled, 4 when tackling another player, 1 when kicking, 8 from a collision, 1

from a fall, and 4 were of unknown cause. The players’ dominant sides were affected 45% of

the time. Injury types are presented in Table 3. Injuries were classified into one of four

categories: bone (fracture or bone bruise), muscle (tear or strain), joint disruption (connective

tissue/ligament/tendon injury or dislocation) or impact injury (haematoma/contusion,

concussion or rib/sternal injury) and plotted in Figure 4.

Page 14 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



15

Table 3 Injuries sustained throughout the 2009 season (n = 37)

Type of Injury

Upper Limb

Glenohumeral Joint dislocation

Glenoid Labral lesion

Rotator Cuff impingement

Acromioclavicular Joint sprain

Scapho-lunate Ligament sprain

Carpometocarpal Joint sprain

Metacarpal fracture

Lower Limb

Calcaneal Fat Pad bruise

Lateral Ankle sprain

Gastrocnemius contusion

Hamstring strain

Achilles Tendinopathy

Adductor muscle tear

Gastrocnemius tear

Femoral Condyle bruise

Patella bruise

Quadriceps strain

Patella tendinosis

Fibula bruise

Anterior Cruciate Ligament tear

Head and Torso

Rib Cartilage sprain

Concussion

Sternal bruise

Back contusion

Intercostal muscle strain

3

3

3

3

3

3

1

4

3

4

2

3

2

2

1

1

2

3

1

3

4

4

4

4

2

1: Bone-related injury; 2: Muscle-related injury; 3: Joint-related injury;

4: Impact-related injury

Page 15 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



16

There were no differences in baseline BMI, BUA, WB BMD and BMC, WB lean and fat

mass, and tibial mass at the 38% site (p > 0.05) between injured (n=20) and non-injured

(n=16) players. No factor predicted injury incidence or muscle, connective tissue or impact

injuries. By contrast, baseline WB BMD, fat mass, lean mass, weight and tibial mass at the

38% site accounted for around 53% of the variance in bone-related injuries (R2 = 0.527; p =

0.003).

DISCUSSION

Our objective was to examine the anthropometric characteristics of Australian National

Rugby League players, pre-, mid- and post season, to determine if relationships exist between

those characteristics, and the incidence and type of injuries sustained during a professional

season. We found body composition changed throughout the season, with players tending to

lose lean mass and gain fat mass as the season progressed. We also observed WB BMD

increased until mid-season and decreased thereafter. Body composition elements were

largely unrelated to injury incidence and type throughout the season. Only start-of-season

WB BMD, fat and lean mass, weight and tibial mass (38% site) contributed to the prediction

of bone injury incidence.

Soft tissue changes

Our observation that players lost lean mass across the season paralleled those of a recent

report of body composition changes of English Super League players, however we did not

observe the increase in body fat of that study,[13] or of a skinfold study of adult amateur

Australian Rugby League players.[26]

Page 16 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



17

We suggest that the lean mass changes reflect the reduction in players’ strength and

resistance training regime from 2-4 sessions per week in the pre-season, to only 1-2 sessions

per week during the season, when a greater emphasis was placed on match preparation and

physical recovery. Markedly increased playing exposure and intensity, including

representative games, had the effect of reducing between-game recovery time such that

fatigue, microtrauma and injuries[5, 11, 13] reduce the capacity for fitness and strength

training. The implications of the observed lean tissue changes are unclear. It is possible that

reductions in lean mass may reduce muscle strength and endurance, speed, agility and power

across the course of the season;[1] however the degree of such an effect would be difficult to

quantify.

Bone changes

It was not unexpected to find that the average bone mass of the elite rugby league players at

each DXA measured site was considerably higher than the “normal” population. The

majority of players participating in the current study began playing football prior to, or during

their adolescent years, a time when bone is highly responsive to mechanical loading.[27]

While it is difficult to differentiate the influence of self-selection (more physically robust

individuals tolerating the rigours of rugby league playing at the elite level better than more

diminutive individuals) from the osteogenic effect of rugby league playing, the observed

seasonal variation in bone mass is informative. The WB BMD gain to mid-season, is likely

attributable to the highly favourable pre-season loading stimulus of high intensity fitness and

resistance training, coupled with a typically tardy bone remodelling response. The

subsequent loss in the latter stages of the season may reflect a relative “detraining” effect on

an “over-adapted” skeleton as training time and intensity is dramatically reduced to optimise

Page 17 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



18

game and injury recovery. An increase in WB BMD from preseason 2009 to preseason 2010

is indicative of continued maturational growth in the relatively young cohort. This

observation is especially meaningful in light of the observed bone changes across a playing

season suggesting that even the robust skeletons of very highly trained young adult male

athletes are sensitive to subtle changes in mechanical loading.

The current study is the first to examine the baseline and seasonal changes in elite rugby

league player bone morphology and volumetric density from pQCT. While the significant

increase in proximal tibial mass (38% site) across the season has not previously been

observed, we found no relationship between any pQCT parameter and player injury. The

lack of observed relationship is potentially related to the small sample size and relatively

small number of injuries. Further pQCT data collection is indicated.

Our results revealed that the average BMI of all players, irrespective of position, was higher

than the recommended BMI guidelines (18.5-25 kg/m2),[28] thereby classifying the cohort as

overweight or obese. In this case the metric clearly misrepresents the endomorphic

mesomorph physiques of rugby league players.[28] Indeed, our DXA findings indicate that

rugby league player body composition is largely comprised of lean mass (average 80.6 kg =

84.9 %) with only a small proportion of fat (average 12.2 kg = 8.7 %). Our findings confirm

that BMI should not be used to track changes in body composition in mesomorphic

athletes.[13]

Page 18 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



19

Injuries

Joint disruption injuries were the most common injury sustained (Figure 4) which is in partial

agreement with previous reports,[2, 5, 29] but not all [9-11, 30]. Start-of-season

anthropometric factors had limited ability to predict injury incidence or type throughout the

season. Furthermore, there were no correlations between any anthropometric factors and

muscle, connective tissue or impact injuries; however, WB BMD, fat and lean mass, weight

and tibial mass at the 38% site were predictive of bone-related injuries.

Limitations

While the total participant numbers in our study are comparable to that of other reports,[13,

31] reduced player attendance at 2009 post-season testing somewhat weakened statistical

power. The relatively low injury numbers also limited our ability to detect significant

associations with risk factors.

Conclusion

We observed an overall loss in lean mass of players throughout a professional rugby league

season. This was accompanied by an increase in WB BMD until mid-season, which

progressively decreased thereafter. We did not identify any strong relationships between body

composition and injuries, with the exception of a relationship between baseline WB BMD, fat

and lean mass, weight and tibial mass (38% site) and bone injury incidence. Longer term

tracking of rugby league player body composition and injuries is warranted.

Page 19 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



20

What this study adds

• Rugby league player muscle mass decreases across a professional season, and WB BMD

increases until mid-season, decreasing thereafter.

• Start of season WB BMD, fat and lean mass, weight and tibial mass (38% site) may be

associated with incidence of rugby league player bone injury.

Acknowledgements The authors would like to thank the Gold Coast Titans staff and players

who participated in the study.

Funding None

Competing Interests None

Page 20 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



21

REFERENCES

1. Meir R. Evaluating players fitness in professional rugby league : reducing

subjectivity. Strength Cond Coach 1993;1:11-7.

2. Gibbs N. Injuries in professional rugby league: a three-year prospective study of the

South Sydney Professional Rugby League Football Club. Am J Sports Med 1993;21:696-700.

3. Seward H, Orchard J, Hazard H, et al. Football injuries in Australia at the elite level.

Med J Aust 1993;159:298-301.

4. Gabbett TJ, Domrow N. Relationships between training load, injury, and fitness in

sub-elite collision sport athletes. J Sports Sci 2007;25:1507-19.

5. Gabbett TJ, Domrow N. Risk factors for injury in subelite rugby league players. Am J

Sports Med 2005;33:428-34.

6. Gabbett T, Kelly J, Ralph S, et al. Physiological and anthropometric characteristics of

junior elite and sub-elite rugby league players, with special reference to starters and non-

starters. J Sci Med Sport 2009;12:215-22.

7. Gissane C, Jennings D, Kerr K, et al. A pooled data analysis of injury incidence in

rugby league football. Sports Med 2002;32:211-6.

8. Phillips LH, Standen PJ, Batt ME. Effects of seasonal change in rugby league on the

incidence of injury. Br J Sports Med 1998;32:144-8.

9. Stephenson S, Gissane C, Jennings D. Injury in rugby league: a four year prospective

survey. Br J Sports Med 1996;30:331-4.

10. Gabbett TJ. Severity and cost of injuries in amateur rugby league: a case study. J

Sports Sci 2001;19:341-7.

11. Gabbett TJ. Incidence, site, and nature of injuries in amateur rugby league over three

consecutive seasons. Br J Sports Med 2000;34:98-103.

Page 21 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



22

12. Van der Ploeg GE, Withers RT, Laforgia J. Percent body fat via DEXA: comparison

with a four-compartment model. J App Phys 2003;94:499-506.

13. Harley JA, Hind K, O'Hara JP. Three-compartment body composition changes in elite

rugby league players during a super league season, measured by dual-energy X-ray

absorptiometry. J Strength Cond Res 2011;25:1024-9.

14. Haapasalo H, Kontulainen S, Sievänen H, et al. Exercise-induced bone gain is due to

enlargement in bone size without a change in volumetric bone density: a peripheral

quantitative computed tomography study of the upper arms of male tennis players. Bone

2000;27:351-7.

15. Sawada K, Morishige K, Ohmichi M, et al. Peripheral quantitative computed

tomography (pQCT) is useful for monitoring bone mineral density of the patients who

receive hormone replacement therapy. Maturitas 2007;56:343-9.

16. Schoenau E, Neu CM, Rauch F, et al. The development of bone strength at the

proximal radius during childhood and adolescence. J Clin Endocrinol Metab 2001;86:613-8.

17. Tsurusaki K, Ito M, Hayashi K. Differential effects of menopause and metabolic

disease on trabecular and cortical bone assessed by peripheral quantitative computed

tomography (pQCT). Br J Radiol 2000;73:14-22.

18. Giles GC, Ireland PD. Dietary Questionnaire for Epidemiological Studies (Version 2).

Melbourne: The Cancer Council Victoria; 1996.

19. Weeks BK, Beck BR. The BPAQ: a bone-specific physical activity assessment

instrument. Osteoporos Int 2008;19:1567-77.

20. Beck BR, Weeks BK. Which limb to scan? Revisiting the relationship between

functional and skeletal limb dominance. J Bone Miner Res 2010;25.

21. Guskiewicz KM, Perrin DH. Research and clinical applications of assessing balance. J

Sport Rehab 1996;5:45-63.

Page 22 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



23

22. Cochrane DJ, Stannard SR. Acute whole body vibration training increases vertical

jump and flexibility performance in elite female field hockey players. Br J Sports Med

2005;39:860-5.

23. King DA, Gabbett TJ, Gissane C, et al. Epidemiological studies of injuries in rugby

league: suggestions for definitions, data collection and reporting methods. J Sci Med Sport

2009;12:12-9.

24. Gabbett TJ. Incidence of injury in amateur rugby league sevens. Br J Sports Med

2002;36:23-6.

25. King T, Jenkins D, Gabbett T. A time-motion analysis of professional rugby league

match-play. J Sports Sci 2009;27:213-9.

26. Gabbett TJ. Changes in physiological and anthropometric characteristics of rugby

league players during a competitive season. J Strength Cond Res 2005;19:400-8.

27. Ginty F, Rennie KL, Mills L, et al. Positive, site-specific associations between bone

mineral status, fitness, and time spent at high-impact activities in 16- to 18-year-old boys.

Bone 2005;36:101-10.

28. Lundy B, O'Connor H, Pelly F, et al. Anthropometric characteristics and competition

dietary intakes of professional rugby league players. Int J Sport Nutr Exerc Metab

2006;16:199-213.

29. Gabbett TJ. Influence of the limited interchange rule on injury rates in sub-elite rugby

league players. J Sci Med Sport 2005;8:111-5.

30. Gabbett. Incidence of injury in semi-professional rugby league players. Br J Sports

Med 2003;37:36-43.

31. Elloumi M, Ben Ounis O, Courteix D, et al. Long-term rugby practice enhances bone

mass and metabolism in relation with physical fitness and playing position. J Bone Miner

Metab 2009;27:713-20.

Page 23 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



24

FIGURE LEGENDS

Figure 1 Change in percent lean (A) and percent fat (B) of players measured at all testing

time points (n=19). ( * Significantly different from baseline; ∆ Significant change between time points).

Figure 2 Seasonal change in whole body (A; n=19), lumbar spine (B; n=20), femoral neck

(C; n=19), distal radius (D; n=18) and proximal radius (E; n=18) bone mineral density

(BMD) and tibial bone mass (F) at 4% (n=20) and 38% (n=19) sites of professional rugby

league players. (* Significantly different from baseline; ∆ Significant change between time points).

Figure 3 Frequency distribution by injury category of professional rugby league players

across a playing season (n=37)

Page 24 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



201x74mm (300 x 300 DPI)

Page 25 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



206x217mm (300 x 300 DPI)

Page 26 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



153x127mm (300 x 300 DPI)

Page 27 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cohort studies

Section/Topic Item

# Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract 1

(b) Provide in the abstract an informative and balanced summary of what was done and what was found 3

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 5

Objectives 3 State specific objectives, including any prespecified hypotheses 6

Methods

Study design 4 Present key elements of study design early in the paper 7

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection

7

Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up 7

(b) For matched studies, give matching criteria and number of exposed and unexposed N/A

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if

applicable

7-10

Data sources/

measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group

7-11

Bias 9 Describe any efforts to address potential sources of bias N/A

Study size 10 Explain how the study size was arrived at 7

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and

why

11

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 11

(b) Describe any methods used to examine subgroups and interactions 11

(c) Explain how missing data were addressed N/A

(d) If applicable, explain how loss to follow-up was addressed N/A

(e) Describe any sensitivity analyses N/A

Results

Page 28 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

on October 14, 2020 by guest. Protected by copyright. http://bmjopen.bmj.com/ BMJ Open: first published as 10.1136/bmjopen-2012-001400 on 6 November 2012. Downloaded from



Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed

eligible, included in the study, completing follow-up, and analysed

11,12,13 (Table 2)

(b) Give reasons for non-participation at each stage 11

(c) Consider use of a flow diagram N/A

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential

confounders

11-12

(b) Indicate number of participants with missing data for each variable of interest Table 2 page 12

(c) Summarise follow-up time (eg, average and total amount) 7

Outcome data 15* Report numbers of outcome events or summary measures over time 11-16

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and why they were included

12-16

(b) Report category boundaries when continuous variables were categorized N/A

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period N/A

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses N/A

Discussion

Key results 18 Summarise key results with reference to study objectives 16

Limitations

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from

similar studies, and other relevant evidence

19

Generalisability 21 Discuss the generalisability (external validity) of the study results 19

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based

20

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

Page 29 of 29


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960




Seasonal change in bone, muscle and fat in professional rugby league players and relationship to injury: a cohort

study

Journal: BMJ Open

Manuscript ID: bmjopen-2012-001400.R1

Article Type: Research

Date Submitted by the Author: 24-Jul-2012

Complete List of Authors: Georgeson, Erin; Griffith University, Centre for Musculoskeletal Research Weeks, Benjamin; Griffith University, Centre for Musculoskeletal Research McLellan, Chris; Bond University, Faculty of Health Sciences and Medicine

Beck, Belinda; Griffith University, Centre for Musculoskeletal Research

<b>Primary Subject Heading</b>:

Sports and exercise medicine

Secondary Subject Heading: Radiology and imaging

Keywords: Orthopaedic sports trauma < ORTHOPAEDIC & TRAUMA SURGERY, Musculoskeletal disorders < ORTHOPAEDIC & TRAUMA SURGERY, SPORTS MEDICINE


BMJ Open on O

ctober 14, 2020 by guest. Protected by copyright.

http://bmjopen.bm

j.com/

BM

J Open: first published as 10.1136/bm

jopen-2012-001400 on 6 Novem

ber 2012. Dow

nloaded from



1

1

Title: Seasonal change in bone, muscle and fat in professional rugby league players and 2

relationship to injury: a cohort study 3

4

Authors: 5

Erin C. Georgeson, BPhty(Hons) BExSc1 6

Benjamin K. Weeks, PhD1

7

Chris McLellan, PhD2

8

Belinda R. Beck, PhD1 9

1School of Physiotherapy and Exercise Science, Centre for Musculoskeletal Research, 10

Griffith Health Institute, Gold Coast, Australia; 2Faculty of Health Sciences and Medicine, 11

Bond University, Gold Coast, Australia 12

13

Key Words: football, body composition, bone mass, sports injury 14

15

Corresponding author: 16

A/Prof Belinda R. Beck 17

School of Physiotherapy and Exercise Science 18

Gold Coast campus 19

Griffith University, Qld 4222 20

Australia 21

Ph: +61 7 5552 8793 22

Fax: +61 7 5552 8674 23

Email: [email protected] 24

25

Word Count: 26

3798 - Intro to Conclusion including tables 27

3251 - Intro to Conclusion excluding tables 28

29

Page 1 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



2

Article Focus 1

• Does rugby league player body composition change across a 12 month period in 2

response to pre-season training, seasonal game play and off-season rest? 3

• Is pre-season body composition (bone, muscle and fat) related to injury incidence 4

throughout the season? 5

• Are changes in body composition during the playing season related to incidence of 6

injuries? 7

Key Messages 8

• Professional rugby league players lose lean mass across a playing season but regain it 9

with pre-season training 10

• Strong relationships were not detected between anthropometric characteristics and 11

incidence of injury. 12

Strengths and Limitations 13

• Comprehensive anthropometric data was collected from a professional rugby league 14

team at four time points across a 12 month period (pre, mid, post and pre-season) to 15

track changes in body composition related to pre-season training, playing and off 16

seasons. 17

• The most valid and reliable instruments (DXA and pQCT) were employed to 18

determine anthropometric measures. pQCT data is novel in this cohort. 19

• Low absolute number of injuries limited the ability to detect strong relationships 20

between injuries and anthropometric measures. 21

22

Page 2 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



3

ABSTRACT 1

Objectives To examine the anthropometric characteristics of an Australian National Rugby 2

League team and identify the relationship to type and incidence of injuries sustained during a 3

professional season. It was hypothesized that body composition would not change discernibly 4

across a season and that injury would be negatively related to pre-season bone and muscle 5

mass. 6

Design A repeated measure, prospective, observational, cohort study 7

Setting Griffith University, Gold Coast, Australia 8

Participants 37 professional male Australian National Rugby League players, 24.3(3.8) 9

years of age were recruited for pre-season 1 testing, of whom 25 were retested pre-season 2. 10

Primary and Secondary Outcome Measures Primary outcome measures included: 11

biometrics; body composition (bone, muscle and fat mass; DXA; XR800, Norland Medical 12

Systems, Inc); bone geometry and strength (pQCT; XCT 3000, Stratec); calcaneal broadband 13

ultrasound attenuation (BUA; QUS-2, Quidel); diet and physical activity history. Secondary 14

outcome measures included player injuries across a single playing season. 15

Results Lean mass decreased progressively throughout the season (pre = 81.45(7.76) kg; 16

post = 79.89(6.72) kg; P ≤ 0.05), while whole body (WB) bone mineral density (BMD) 17

increased until mid-season (pre = 1.235(0.087)g/cm2; mid =1.296(0.093) g/cm

2; P ≤ 0.001) 18

then decreased thereafter (post = 1.256(0.100); P ≤ 0.001). Start-of-season WB BMD, fat 19

and lean mass, weight and tibial mass measured at the 38% site predicted bone injury 20

incidence, but no other relationship was observed between body composition and injury. 21

Conclusions Significant anthropometric changes were observed in players across a 22

professional rugby league season; including an overall loss of muscle and an initial increase, 23

Page 3 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



4

followed by a decrease in bone mass. Strong relationships between anthropometry and 1

incidence of injury were not observed. Long term tracking of large rugby league cohorts is 2

indicated to obtain more injury data in order to examine anthropometric relationships with 3

greater statistical power. 4

5

Data Sharing: There is no additional data available. 6

Contributorship: EG: primary researcher; contributed to study design, data collection and 7

analysis; prepared the manuscript draft; assisted with editing and approved the final text. 8

BW: researcher; contributed to data collection and analysis; reviewed the manuscript draft; 9

edited and approved the final text. CM: Liaison with the Australian National Rugby League 10

club; assisted in recruiting and scheduling player testing times; contributed to data collection. 11

BB: researcher; responsible for initial study concept, design and liaising with the football 12

club; contributed to data collection and analysis; contributed to manuscript structure and 13

drafting; edited and approved the final text. 14

Funding: This research received no specific funding. 15

Competing Interests: There are no competing interests. 16

17

Page 4 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



5

INTRODUCTION 1

Rugby league is a physically demanding, high impact, full body contact professional sport It 2

requires well developed muscle strength and endurance, speed, agility and aerobic power.[1] 3

The high frequency and force of physical collisions encountered during a game[2-4] leads to 4

a higher incidence of musculoskeletal injuries[5] than is typically observed in other team 5

sports.[2, 3, 6] Injury incidence has been reported from 44.9 to 462.7 injuries per 1000 6

player hours.[2, 7-9] Such large variations in reported injury incidence may be attributed to 7

inter-study differences in definitions, data collection and reporting methods used.[10] 8

Furthermore, skill level, playing intensity and seasonal conditions have been suggested to 9

influence injury incidence.[2, 8, 9] As 15-30% of total seasonal injuries are classified 10

“severe” (i.e. causing a player to miss five or more games)[2, 8, 11] players can miss up to 11

20% of games in a season.[7] A range of factors have been associated with an increased risk 12

of injury, including low preseason running speed and maximal aerobic power, lighter body 13

weight and greater number of playing years’ experience.[5] Furthermore, injury risk is 14

dependent on player position (e.g. forwards vs. backs); forwards typically sustaining more 15

injuries than backs.[9, 12] 16

17

Some anthropometric and physiological characteristics of rugby league players have been 18

described, based on traditional body composition measures of weight, height, body mass 19

index (BMI) and skinfold thickness.[13, 14] Few more direct measures of bone and muscle 20

have been reported, and the relationship of body composition to injury remains unknown.[15] 21

Analysis of seasonal anthropometric changes to rugby league players and the examination of 22

relationships of anthropometrics with rate and type of injury may reveal important risk 23

factors. In particular, the identification of modifiable risk factors would give rise to 24

opportunities to reduce the risk of injury to players. 25

Page 5 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



6

1

Dual-energy x-ray absorptiometry (DXA) provides a reliable estimate of body composition 2

(bone, muscle and fat).[16] A recent study of English Super League players observed an 3

increase in body fat from DXA across the playing season, and a decrease in lean mass.[15] 4

Bone mass was also observed to increase to mid-season, but decrease thereafter. It is well 5

known that DXA estimates of bone mass are based on two dimensional measures of areal 6

bone mineral density that cannot fully account for bone size, discriminate between cortical 7

and trabecular bone envelopes, or measure elements of bone morphology that are critical to 8

whole bone strength. In light of the known limitations of DXA measures, seasonal changes 9

in rugby league player bone strength remains uncertain. Peripheral quantitative computed 10

tomography (pQCT) can discriminate between cortical and trabecular envelopes, and 11

measures true volumetric BMD and parameters of bone morphology; thereby providing a 12

superior indication of bone strength than DXA.[17-20] Such measures have not previously 13

been reported for a rugby league cohort. 14

15

The aim of the current study was to examine pre-, mid- and post season body composition of 16

a professional Australian team using DXA and pQCT, and to identify relationships between 17

baseline and change in body composition with rate and type of injuries sustained across a 18

season. 19

20

METHODS 21

Ethical approval 22

Ethical approval for the study was granted by the Griffith University Human Research Ethics 23

Committee (PES/28/08/HREC). Written informed consent was obtained from each 24

participant. 25

Page 6 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



7

Subjects 1

All members of an Australian National Rugby League team (n=44),age 24.6(3.4) years, 2

playing an average of 15.7(7.2) games (58% of the season) consented to participate in the 3

study. 4

5

Study design and conduct 6

A repeated measure, prospective, observational study was conducted. Data was collected on 7

four occasions over a 12-month period, that is, pre- (March), mid- (July) and post-2009 8

southern hemisphere rugby league season (September/October), and pre-2010 rugby league 9

season (March). All data was collected in the Bone Densitometry Research Laboratory at 10

Griffith University, Gold Coast. 11

12

Behavioral characteristics – Diet and physical activity 13

Nutrition was assessed via the Cancer Council Victoria’s Dietary Questionnaire for 14

Epidemiological Studies (DQES), a diet instrument validated for the Australian 15

population.[21] The DQES contains a series of questions pertaining to the subjects’ normal 16

dietary intake over the preceding 12 months. Responses were computer-analysed and an 17

estimate of total daily energy intake and calcium intake were obtained. 18

19

The Bone-specific Physical Activity Questionnaire (BPAQ) is a validated tool for quantifying 20

historical physical activity participation relevant to the musculoskeletal system.[22] 21

Participants were asked to record: 1) all regular physical activities performed throughout their 22

Page 7 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



8

life and the approximate number of years of participation; and 2) all activities performed on a 1

regular basis over the previous 12 months, including frequency of participation. The BPAQ 2

was analysed using a custom-designed program (available at 3

http://www.fithdysign.com/BPAQ/) developed on LabVIEW software (National Instruments, 4

Texas, USA) to produce current, past and total bone-specific physical activity history 5

scores.[22] All participants (n=44) completed diet and BPAQ questionnaires. 6

7

Biometrics 8

Subject height was measured to the nearest 0.01 m using a wall-mounted stadiometer (HART 9

Sport & Leisure, Brisbane, Australia). Weight was measured to the nearest 0.01 kg using a 10

robust digital scale (CH-150K, AND Mercury, Brisbane, Australia). Upper extremity skeletal 11

dominance was determined as the preferred writing hand. Lower extremity skeletal 12

dominance was determined to be the non-kicking leg according to procedures established and 13

validated in our laboratory.[23] 14

15

Anthropometry 16

Dual-energy X-ray Absorptiometry 17

Dual-energy x-ray absorptiometry (DXA) was used to determine whole body (WB), lumbar 18

spine (LS), non-dominant femoral neck (FN) and forearm (FA) bone mineral content (BMC; 19

g), bone area (cm2), and bone mineral density (BMD; g/cm

2) (XR800 Norland Cooper 20

Surgical, USA, Illuminatus software Version 4.2.4). Additionally, WB scans were used to 21

determine lean and fat mass. Short-term DXA measurement precision in our lab is 0.9%, 22

Page 8 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



9

1.1%, 0.4%, 0.8%, 0.6%, 0.8% and 2.3% for WB, FN, LS, distal radius-ulna, proximal 1

radius-ulna, lean and fat mass, respectively. 2

3

Peripheral Quantitative Computed Tomography 4

Peripheral Quantitative Computed Tomography (pQCT; XCT 3000; Stratec Pforzheim, 5

Germany) was used to examine non-dominant tibiae total, trabecular and cortical tibial 6

volumetric densities (mg/cm3), strength-strain index (SSI; mm

3), principal moments of inertia 7

(Imin and Imax; mm4) and muscle area (mm

2) at the 4%, 38% and 66% sites. Short term 8

measurement precision in our lab is 1.5% and 0.6% for total tibial density at the 4% and 38% 9

sites respectively. 10

11

Quantitative Ultrasound 12

Quantitative Ultrasound (QUS-2; Quidel, Mountain View, Ca) was used to measure 13

broadband ultrasound attenuation (BUA; dB/MHz) of the non-dominant calcaneus. Short-14

term measurement precision with repositioning was 2.5%. 15

16

Performance Measures 17

Single Leg Stance 18

Static balance ability was tested using the standard single leg stance (SLS) test.[24] 19

Participants stood with feet pelvis-width apart, forearms crossed over their chest, and fingers 20

at shoulders. With eyes closed, one foot was lifted off the ground to the level of the opposite 21

ankle, but not touching. Timing commenced from foot lift off and ceased when: 1) arms 22

moved from their starting position; 2) feet touched; 3) elevated foot touched the ground or 23

Page 9 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



10

moved towards/away from the planted foot; 4) grounded foot adjusted position to maintain 1

balance; or 5) eyes opened. Each participant was allowed to practice and have up to three 2

attempts on each leg. The subject’s best time was recorded in seconds and the test repeated 3

for the opposite leg. A single investigator performed all SLS tests. 4

5

Vertical Jump 6

Leg muscle power was assessed by the vertical jump test.[25] Participants began by standing 7

beside the Yardstick vertical jump device (Swift Performance Equipment, NSW, Australia), 8

with both feet grounded and positioned shoulder-width apart. The participant was asked to 9

reach as high as possible with their preferred arm and the height of reach was recorded. 10

Participants were then instructed to jump as high as possible in counter-movement fashion 11

without arm swing, and tap the device at the peak of their jump. The best of three attempts 12

was recorded in cm. A single investigator measured and recorded all vertical jump trials. 13

14

Injury Data Collection 15

All injuries, new or recurrent, sustained during the study period were recorded by two team 16

physiotherapists in attendance at all games and training sessions. An injury was recorded if a 17

rugby league activity/game resulted in any pain, discomfort, illness or disability, and required 18

the player to seek medical intervention from team medical staff.[10] All injuries were 19

recorded whether the player missed a subsequent training session/game or not. Injury details 20

including anatomical location and tissue involvement, cause and severity (determined by the 21

number of training days and games missed) were recorded. 22

23

Page 10 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



11

Game exposure hours were calculated by multiplying the number of players, by the number 1

of games and the duration of the match (i.e. 13 players x 29 games x 1.33 hours/game).[10] 2

Injury incidence was recorded per 1000 game or training hours.[2, 10, 26] Match injury 3

incidence was calculated by dividing the number of recorded match injuries by the game 4

exposure hours and multiplying by 1000. 5

6

Statistical Analysis 7

Statistical analysis was performed using SPSS version 17.0 for Windows (IBM, Chicago, IL, 8

USA). Descriptive statistics, mean(sd) were generated for subject and injury characteristics 9

and independent T-tests of the original 2009 cohort (n=37) were used to compare the 10

anthropometric characteristics of injured versus non-injured players. Correlation analyses 11

were performed to identify relationships between injury incidence, biometrics, 12

anthropometrics, active test performance, and lifestyle factors of the 2009 cohort (n=37). 13

Subsequent multiple regression analyses were used to determine the ability of biometrics and 14

behavioural characteristics to predict variance in anthropometrics and injury incidence. Q-Q 15

plots were generated to determine if data were normally distributed and Levene’s test was 16

used to examine homogeneity of variance. To examine change in anthropometric 17

characteristics across the season, repeated measures ANOVA was used, both with and 18

without covariates of calcium, weight, age and past BPAQ score (n=37). Results were 19

considered statistically significant at P ≤ 0.05. 20

21

RESULTS 22

A total of 44 different subjects were tested. At baseline 2009, 37 players were tested and 32 23

were tested in 2010. Player relocation resulted in 12 players leaving the cohort at the end of 24

Page 11 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



12

the 2009 season and seven players joining the study prior to 2010 testing. Table 1 describes 1

the baseline characteristics of the cohort. All data were normally distributed. Notably 2

players exhibited bone mass that was, on average, over one standard deviation higher than 3

age- and sex-matched norms for the WB (Z-score range = +0.63 to +1.63), spine (Z-score 4

range = +0.99 to +2.39) and femoral neck (Z-score range = +0.63 to +3.03). 5

Table 1 Baseline Characteristics of Players at 2009 Pre-Season (n = 37) 6

Age (years) 24.3(3.8)

Weight (kg) 95.3(10.4)

Height (m) 1.81(0.06)

BMI (kg/m2) 29.0(2.3)

BPAQ Past 72.4(26.9)

BPAQ Current 7.5(1.0)

BPAQ Total 40.0(13.5)

Age began playing rugby league (years) 8.0(3.8)

WB BMD (g/cm2) 1.251(0.088)

WB BMD Z-score 1.13(0.50)

Total Daily Energy (kJ) 11052(3678)

Daily Ca2+

(mg) 1224(454) BMI: body mass index; BPAQ: bone-specific physical activity; Ca

2+: calcium; 7

WB BMD: whole body bone mineral density. 8

9

Anthropometric and Performance Measures 10

Bone, muscle and fat measures at all four testing sessions are presented in Table 2. 11

Controlling for age, weight, dietary energy intake, calcium consumption and past physical 12

activity, lean mass decreased at each measurement time point throughout the 2009 season, 13

but returned to pre-2009 season values by 2010 pre-season (mean difference = 0.230, 95%CI 14

= -0.987-1.168-kg; P = 0.86). Neither weight (mean difference = 0.300, 95%CI =

-0.624-15

Page 12 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



13

1.225 kg, P = 0.51), nor fat mass (mean difference = 0.650, 95%CI = -0.441-1.656 kg, P = 1

0.24) changed over the 12-month period. The lean and fat percent response followed a 2

similar pattern (Figures 1A and B). 3

4

Table 2 DXA and pQCT parameters at all measurement time points 5

Stage of Season

Measure Pre 2009 Mid 2009 Post 2009 Pre 2010

WB BMD (g/cm2)

(n = 19)

1.235(0.087)a,e,f 1.296(0.093)

b,c 1.256(0.100)

1.245(0.087)

LS BMD (g/cm2)

(n = 20)

1.447(0.110)

1.460(0.105)

1.459(0.116)

1.469(0.114)

FN BMD (g/cm

2)

(n = 19)

1.319(0.130)

1.333(0.132)

1.335(0.133)

1.324(0.134)

Dist. R&U BMD (g/cm

2)

(n = 18)

0.580(0.051)a

0.573(0.050)

b

0.580(0.050)

0.578(0.049)

Prox. Rad BMD (g/cm2)

(n = 18)

1.065(0.063)e

1.071(0.062)b

1.078(0.062)d

1.068(0.056)

WB Lean Mass (kg)

(n = 19)

81.45(7.76)a, e

80.72(7.73)

79.89(6.72)d

81.22(6.73)

WB Fat Mass (kg)

(n = 19)

10.70(3.08)

11.05(4.00)

11.03(4.87)

11.35(4.46)

BMI (kg/m2)

(n = 20)

28.65(1.90)

28.62(2.03)

28.49(2.08)

28.67(1.94)

BUA (dB/MHz)

(n = 20)

118.3(15.3)

117.9(16.8)

117.4(16.3)

117.6(15.3)


(n = 20)

5.55(0.85) 5.55(0.90)

5.52(0.77) 5.59(0.88)


(n = 19)

5.37(0.53)a,e,f

5.41(0.53)

5.42(0.57)

5.41(0.50)

Leg Bone:Muscle Area, 66%

Site(%) (n=20)

5.33(0.52)a,f

5.51(±0.56) 5.49(0.61) 5.50(0.62)

Leg Fat Area, 66% site (mm2)

(n=20)

1005(590)a

1303(549) 1195(455) 1135(592)

BMD: bone mineral density; BMI: body mass index; BUA: broadband ultrasound attenuation; Dist. R&U BMD: 6

distal radius and ulna BMD; FN BMD: femoral neck BMD; LS BMD: lumbar spine BMD; NS: not significant; 7

Prox. Rad BMD: proximal radius BMD; WB: whole body; a: sig. difference between pre-2009 and mid-2009; 8

b: sig. difference between mid-2009 and post-2009; c: sig. difference between mid-2009 and pre-2010; d: sig. 9

difference between post-2009 and pre-2010; e: sig. difference between pre-2009 and post-2009; f: sig. 10

difference between pre-2009 and pre-2010. 11

12

Only WB BMD changed significantly across the 2009 season (mean difference = 0.021, 13

95%CI = 0.008-0.035 g/cm2, P = 0.01), increasing until mid-season (mean difference = 14

Page 13 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



14

0.061, 95%CI = 0.054-0.069 g/cm2, P = 0.001) and decreasing thereafter (mean difference =

-1

0.050, 95%CI = -0.060-0.025 g/cm

2, P = 0.001), but remaining higher than 2009 pre-season 2

values (mean difference = 0.010, 95%CI = 0.002-0.019 g/cm2, P = 0.02; Figure 2A). No 3

significant changes were observed for LS or FN BMD over the 12-month period (Figures 2B 4

& C); however changes were evident for forearm BMD. Distal forearm BMD decreased until 5

mid-season (mean difference = -0.007, 95%CI = -0.013-0.001 g/cm

2, P = 0.01; Figure 2D), 6

with a subsequent significant increase from mid- to post-season (mean difference = 0.007, 7

95%CI = 0.002-0.010 g/cm2, P = 0.001), at which time BMD of the proximal radius and ulna 8

also significantly increased (mean difference = 0.007, 95%CI = 0.002-0.014 g/cm2, P = 0.01; 9

Figure 2E). Proximal radius BMD decreased from 2009 post-season to 2010 pre-season 10

(mean difference = -0.010, 95%CI = -0.016-

-0.004 g/cm

2, P = 0.01). 11

No significant changes were observed in tibial mass at the 4% tibial site (P = 0.364). Tibial 12

mass increased at the 14% site over the 12 month period (mean difference = 0.017, 95%CI = 13

0.002-0.031 g/cm2, P = 0.03) and at the 38% site at each time-point relative to pre-season 14

2009 (P ≤ 0.05; Figure 2F). 15

No significant changes were observed in single leg stance or vertical jump measures across 16

the season. 17

Exposure and Injuries 18

Table 3 demonstrates the type and frequency of training sessions performed throughout the 19

2009 season. Pre-season training consisted of a greater focus on strength, skills and 20

conditioning, while a greater emphasis was placed on football drills and between-match 21

recoveries during the latter stages of the season. A total of 29 weekly games were played 22

during the 2009 National Rugby League season, inclusive of three pre-season and two finals 23

matches for a total team game exposure of 501 hours (13 players x 29 games x 1.33 24

Page 14 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



15

hours).[27] A total of 51 injuries were reported across the 2009 season, equating to an 1

incidence of roughly 101 injuries per 1000 player hours. There were 43 new injuries and 8 2

re-injuries. Twelve injuries occurred when players were running, 21 while being tackled, 4 3

when tackling another player, 1 when kicking, 8 from a collision, 1 from a fall, and 4 were of 4

unknown cause. The players’ dominant sides were affected 45% of the time. Injury types are 5

presented in Table 4. Injuries were classified into one of four categories: bone (fracture or 6

bone bruise), muscle (tear or strain), joint disruption (connective tissue/ligament/tendon 7

injury or dislocation) or impact injury (haematoma/contusion, concussion or rib/sternal 8

injury) and plotted in Figure 3. 9

Table 3. Type and frequency of training sessions performed throughout the 2009 season 10

Type of Training Pre-Season

In-Season

Weeks 1-15

In Season

Weeks 16-26

Number of sessions per week

Team Skills 2-4 0-2 0

Football 0-2 3-4 3-4

Strength 3-4 1-3 1-2

Speed/Power 1-2 1-2 0-1

Conditioning 2 0-2 0-1

Cross Train 1 0-1 0

Recovery 2 2 2-4

11

12

Table 4 Category and frequency of injuries sustained throughout the 2009 season (n = 51) 13

Page 15 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



16

Type of Injury Category Frequency

Upper Limb

Glenohumeral Joint dislocation

Glenoid Labral lesion

Rotator Cuff impingement

Acromioclavicular Joint sprain

Scapho-lunate Ligament sprain

Carpometocarpal Joint sprain

Metacarpal fracture

Lower Limb

Calcaneal Fat Pad bruise

Lateral Ankle sprain

Syndesmosis sprain

Gastrocnemius contusion

Hamstring strain

Achilles Tendinopathy

Adductor muscle tear

Gastrocnemius tear

Femoral Condyle bruise

Patella bruise

Quadriceps strain

Quadriceps contusion

Lateral knee joint sprain

Patella tendinosis

Fibula bruise

Anterior Cruciate Ligament tear

Head and Torso

Rib Cartilage sprain

Concussion

Sternal bruise

Back contusion

Intercostal muscle strain

3

3

3

3

3

3

1

4

3

3

4

2

3

2

2

1

1

2

4

3

3

1

3

4

4

4

4

2

6

1

1

1

1

1

1

1

5

2

1

7

1

4

2

1

1

3

1

1

1

1

1

2

1

1

1

1

1: Bone-related injury; 2: Muscle-related injury; 3: Joint-related injury; 1

4: Impact-related injury 2

Page 16 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



17

1

There were no differences in baseline BMI, BUA, WB BMD and BMC, WB lean and fat 2

mass, and tibial mass at the 38% site (P= > 0.05) between injured (n=20) and non-injured 3

(n=16) players. No factor predicted injury incidence or muscle, connective tissue or impact 4

injuries. By contrast, baseline WB BMD, fat mass, lean mass, weight and tibial mass at the 5

38% site accounted for around 53% of the variance in bone-related injuries (R2 = 0.527; P = 6

0.003). 7

8

DISCUSSION 9

Our objective was to examine the anthropometric characteristics of Australian National 10

Rugby League players, pre-, mid- and post season, to determine if relationships exist between 11

those characteristics, and the incidence and type of injuries sustained during a professional 12

season. We found body composition changed throughout the season, with players tending to 13

lose lean mass and gain fat mass as the season progressed. We also observed WB BMD 14

increased until mid-season and decreased thereafter. Body composition elements were 15

largely unrelated to injury incidence and type throughout the season. Only start-of-season 16

WB BMD, fat and lean mass, weight and tibial mass (38% site) contributed to the prediction 17

of bone injury incidence. 18

Soft tissue changes 19

Our observation that players lost lean mass across the season paralleled those of a recent 20

report of body composition changes of English Super League players. However, we did not 21

observe the increase in body fat of that study,[15] or of a skinfold study of adult amateur 22

Australian Rugby League players.[28] 23

Page 17 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



18

We suggest that the lean mass changes reflect the reduction in players’ strength and 1

resistance training regime from 2-4 sessions per week in the pre-season, to only 0-2 sessions 2

per week during the season, when a greater emphasis was placed on match preparation and 3

physical recovery. Markedly increased playing exposure and intensity, including 4

representative games, had the effect of reducing between-game recovery time such that 5

fatigue, microtrauma and injuries [5, 12, 15] reduce the capacity for fitness and strength 6

training. The implications of the observed lean tissue changes are unclear. It is possible that 7

reductions in lean mass may reduce muscle strength and endurance, speed, agility and power 8

across the course of the season;[1] however the degree of such an effect would be difficult to 9

quantify. 10

11

Bone changes 12

It was not unexpected to find that the average bone mass of the elite rugby league players at 13

each DXA measured site was considerably higher than the “normal” population. The 14

majority of players participating in the current study began playing football prior to, or during 15

their adolescent years, a time when bone is highly responsive to mechanical loading.[29] 16

While it is difficult to differentiate the influence of self-selection (more physically robust 17

individuals tolerating the rigours of rugby league playing at the elite level better than more 18

diminutive individuals) from the osteogenic effect of rugby league playing, the observed 19

seasonal variation in bone mass is informative. The WB BMD gain to mid-season, is likely 20

attributable to the highly favourable pre-season loading stimulus of high intensity fitness and 21

resistance training, coupled with a typically tardy bone remodelling response. The 22

subsequent loss in the latter stages of the season may reflect a relative “detraining” effect on 23

an “over-adapted” skeleton as training time and intensity is dramatically reduced to optimise 24

Page 18 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



19

game and injury recovery. An increase in WB BMD from preseason 2009 to preseason 2010 1

may be indicative of continued maturational growth in a relatively young cohort that is yet to 2

attain peak bone mass. This observation is especially meaningful in light of the observed 3

bone changes across a playing season suggesting that even the robust skeletons of very highly 4

trained young adult male athletes are sensitive to subtle changes in mechanical loading. 5

6

The current study is the first to examine the baseline and seasonal changes in elite rugby 7

league player bone morphology and volumetric density from pQCT. While the significant 8

increase in proximal tibial mass (38% site) across the season has not previously been 9

observed, we found no relationship between any pQCT parameter and player injury. The 10

lack of observed relationship is potentially related to the small sample size and relatively 11

small number of injuries. Further pQCT data collection is indicated. 12

13

Our results revealed that the average BMI of all players was 29(2.3)kg/m2 Irrespective of 14

position, this value was higher than the recommended BMI guidelines (18.5-25 kg/m2),[14] 15

thereby classifying the cohort as overweight or obese. In this case the metric clearly 16

misrepresents the endomorphic mesomorph physiques of rugby league players.[14] Indeed, 17

our DXA findings indicate that rugby league player body composition is largely comprised of 18

lean mass (average 80.6 kg = 84.9 %) with only a small proportion of fat (average 12.2 kg = 19

8.7 %). Our findings confirm that BMI should not be used to track changes in body 20

composition in mesomorphic athletes.[15] 21

22

Injuries 23

Page 19 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



20

Joint disruption injuries were the most common injury sustained (Figure 3) which is in partial 1

agreement with previous reports,[2, 5, 30] but not all [9, 11, 12, 31]. Start-of-season 2

anthropometric factors had limited ability to predict injury incidence or type throughout the 3

season. Furthermore, there were no correlations between any anthropometric factors and 4

muscle, connective tissue or impact injuries; however, WB BMD, fat and lean mass, weight 5

and tibial mass at the 38% site were predictive of bone-related injuries. 6

7

Limitations 8

While the total participant numbers in our study are comparable to that of other reports,[15, 9

32] reduced player attendance at 2009 post-season testing somewhat weakened statistical 10

power. The relatively low injury numbers also limited our ability to detect significant 11

associations with risk factors. 12

13

Conclusion 14

We observed an overall loss in lean mass of players throughout a professional rugby league 15

season. This was accompanied by an increase in WB BMD until mid-season, which 16

progressively decreased thereafter. We did not identify any strong relationships between body 17

composition and injuries, with the exception of a relationship between baseline WB BMD, fat 18

and lean mass, weight and tibial mass (38% site) and bone injury incidence. Longer term 19

tracking of rugby league player body composition and injuries is warranted. 20

21

What this study adds 22

Page 20 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



21

• Rugby league player muscle mass decreases across a professional season, and WB BMD 1

increases until mid-season, decreasing thereafter. 2

• Start of season WB BMD, fat and lean mass, weight and tibial mass (38% site) may be 3

associated with incidence of rugby league player bone injury. 4

5

Acknowledgements The authors would like to thank the staff and players of the Australian 6

National Rugby League team who participated in the study. 7

8

Page 21 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



22

1

REFERENCES 2

3

1. Meir R. Evaluating players fitness in professional rugby league : reducing 4

subjectivity. Strength Cond Coach 1993;1:11-7. 5

2. Gibbs N. Injuries in professional rugby league: a three-year prospective study of the 6

South Sydney Professional Rugby League Football Club. Am J Sports Med 1993;21:696-700. 7

3. Seward H, Orchard J, Hazard H, et al. Football injuries in Australia at the elite level. 8

Med J Aust 1993;159:298-301. 9

4. Gabbett TJ, Domrow N. Relationships between training load, injury, and fitness in 10

sub-elite collision sport athletes. J Sports Sci 2007;25:1507-19. 11

5. Gabbett TJ, Domrow N. Risk factors for injury in subelite rugby league players. Am J 12

Sports Med 2005;33:428-34. 13

6. Gabbett T, Kelly J, Ralph S, et al. Physiological and anthropometric characteristics of 14

junior elite and sub-elite rugby league players, with special reference to starters and non-15

starters. J Sci Med Sport 2009;12:215-22. 16

7. Gissane C, Jennings D, Kerr K, et al. A pooled data analysis of injury incidence in 17

rugby league football. Sports Med 2002;32:211-6. 18

8. Phillips LH, Standen PJ, Batt ME. Effects of seasonal change in rugby league on the 19

incidence of injury. Br J Sports Med 1998;32:144-8. 20

9. Stephenson S, Gissane C, Jennings D. Injury in rugby league: a four year prospective 21

survey. Br J Sports Med 1996;30:331-4. 22

10. King DA, Gabbett TJ, Gissane C, et al. Epidemiological studies of injuries in rugby 23

league: suggestions for definitions, data collection and reporting methods. J Sci Med Sport 24

2009;12:12-9. 25

Page 22 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



23

11. Gabbett TJ. Severity and cost of injuries in amateur rugby league: a case study. J 1

Sports Sci 2001;19:341-7. 2

12. Gabbett TJ. Incidence, site, and nature of injuries in amateur rugby league over three 3

consecutive seasons. Br J Sports Med 2000;34:98-103. 4

13. Meir R. Seasonal changes in estimates of body composition in professional rugby 5

league players. Sport Health 1993;11:27-31. 6

14. Lundy B, O'Connor H, Pelly F, et al. Anthropometric characteristics and competition 7

dietary intakes of professional rugby league players. Int J Sport Nutr Exerc Metab 8

2006;16:199-213. 9

15. Harley JA, Hind K, O'Hara JP. Three-compartment body composition changes in elite 10

rugby league players during a super league season, measured by dual-energy X-ray 11

absorptiometry. J Strength Cond Res 2011;25:1024-9. 12

16. Van der Ploeg GE, Withers RT, Laforgia J. Percent body fat via DEXA: comparison 13

with a four-compartment model. J App Phys 2003;94:499-506. 14

17. Haapasalo H, Kontulainen S, Sievänen H, et al. Exercise-induced bone gain is due to 15

enlargement in bone size without a change in volumetric bone density: a peripheral 16

quantitative computed tomography study of the upper arms of male tennis players. Bone 17

2000;27:351-7. 18

18. Sawada K, Morishige K, Ohmichi M, et al. Peripheral quantitative computed 19

tomography (pQCT) is useful for monitoring bone mineral density of the patients who 20

receive hormone replacement therapy. Maturitas 2007;56:343-9. 21

19. Schoenau E, Neu CM, Rauch F, et al. The development of bone strength at the 22

proximal radius during childhood and adolescence. J Clin Endocrinol Metab 2001;86:613-8. 23

Page 23 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



24

20. Tsurusaki K, Ito M, Hayashi K. Differential effects of menopause and metabolic 1

disease on trabecular and cortical bone assessed by peripheral quantitative computed 2

tomography (pQCT). Br J Radiol 2000;73:14-22. 3

21. Giles GC, Ireland PD. Dietary Questionnaire for Epidemiological Studies (Version 2). 4

Melbourne: The Cancer Council Victoria; 1996. 5

22. Weeks BK, Beck BR. The BPAQ: a bone-specific physical activity assessment 6

instrument. Osteoporos Int 2008;19:1567-77. 7

23. Beck BR, Weeks BK. Which limb to scan? Revisiting the relationship between 8

functional and skeletal limb dominance. J Bone Miner Res 2010;25. 9

24. Guskiewicz KM, Perrin DH. Research and clinical applications of assessing balance. J 10

Sport Rehab 1996;5:45-63. 11

25. Cochrane DJ, Stannard SR. Acute whole body vibration training increases vertical 12

jump and flexibility performance in elite female field hockey players. Br J Sports Med 13

2005;39:860-5. 14

26. Gabbett TJ. Incidence of injury in amateur rugby league sevens. Br J Sports Med 15

2002;36:23-6. 16

27. King T, Jenkins D, Gabbett T. A time-motion analysis of professional rugby league 17

match-play. J Sports Sci 2009;27:213-9. 18

28. Gabbett TJ. Changes in physiological and anthropometric characteristics of rugby 19

league players during a competitive season. J Strength Cond Res 2005;19:400-8. 20

29. Ginty F, Rennie KL, Mills L, et al. Positive, site-specific associations between bone 21

mineral status, fitness, and time spent at high-impact activities in 16- to 18-year-old boys. 22

Bone 2005;36:101-10. 23

30. Gabbett TJ. Influence of the limited interchange rule on injury rates in sub-elite rugby 24

league players. J Sci Med Sport 2005;8:111-5. 25

Page 24 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



25

31. Gabbett. Incidence of injury in semi-professional rugby league players. Br J Sports 1

Med 2003;37:36-43. 2

32. Elloumi M, Ben Ounis O, Courteix D, et al. Long-term rugby practice enhances bone 3

mass and metabolism in relation with physical fitness and playing position. J Bone Miner 4

Metab 2009;27:713-20. 5

6

7

8

Page 25 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



26

FIGURE LEGENDS 1

2

Figure 1 Change in percent lean (A) and percent fat (B) of players measured at all testing 3

time points (n=19). ( * Significantly different from baseline; ∆ Significant change between time points). 4

5

Figure 2 Seasonal change in whole body (A; n=19), lumbar spine (B; n=20), femoral neck 6

(C; n=19), distal radius (D; n=18) and proximal radius (E; n=18) bone mineral density 7

(BMD) and tibial bone mass (F) at 4% (n=20) and 38% (n=19) sites of professional rugby 8

league players. (* Significantly different from baseline; ∆ Significant change between time points). 9

10

Figure 3 Frequency distribution by injury category of professional rugby league players 11

across a playing season (n=37) 12

13

Page 26 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



201x74mm (300 x 300 DPI)

Page 27 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



206x217mm (300 x 300 DPI)

Page 28 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



Frequency distribution by injury category of professional rugby league players across a playing season (n=37)

157x122mm (300 x 300 DPI)

Page 29 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



jopen.bmj.com

/B

MJ O


ovember 2012. D

ownloaded from



STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cohort studies

Section/Topic Item

# Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract 1

(b) Provide in the abstract an informative and balanced summary of what was done and what was found 3

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 5

Objectives 3 State specific objectives, including any prespecified hypotheses 6

Methods

Study design 4 Present key elements of study design early in the paper 7

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection

7

Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up 7

(b) For matched studies, give matching criteria and number of exposed and unexposed N/A

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if

applicable

7-10

Data sources/

measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group

7-11

Bias 9 Describe any efforts to address potential sources of bias N/A

Study size 10 Explain how the study size was arrived at 7

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and

why

11

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 11

(b) Describe any methods used to examine subgroups and interactions 11

(c) Explain how missing data were addressed N/A

(d) If applicable, explain how loss to follow-up was addressed N/A

(e) Describe any sensitivity analyses N/A

Results

Page 30 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960




Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility, confirmed

eligible, included in the study, completing follow-up, and analysed

11,12,13 (Table 2)

(b) Give reasons for non-participation at each stage 11

(c) Consider use of a flow diagram N/A

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential

confounders

11-12

(b) Indicate number of participants with missing data for each variable of interest Table 2 page 12

(c) Summarise follow-up time (eg, average and total amount) 7

Outcome data 15* Report numbers of outcome events or summary measures over time 11-16

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and why they were included

12-16

(b) Report category boundaries when continuous variables were categorized N/A

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period N/A

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses N/A

Discussion

Key results 18 Summarise key results with reference to study objectives 16

Limitations

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from

similar studies, and other relevant evidence

19

Generalisability 21 Discuss the generalisability (external validity) of the study results 19

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based

20

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

Page 31 of 31


BMJ Open

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960



BMJ Open · For peer review only Seasonal change in bone, muscle and fat in professional rugby...

Documents

Transcript of BMJ Open · For peer review only Seasonal change in bone, muscle and fat in professional rugby...