The Role of Periodic Health Examination in Determining Lower Extremity Muscle Injury Risk in Elite Football (Soccer): A Protocol for an Investigation Into Multivariable Prognostic Model Development, Validation and Prognostic Factor Exploration
Conditions: Muscle Injury, Sprains and Strains
Sponsor: Manchester United Football Club

 
1 
The Role of Periodic Health Examination in Determining Lower Extremity Indirect 
Muscle Injury Risk in Elite Football (Soccer) Players:  A Protocol for an Investigation 
into Multivariable Prognostic Model Development, Validation and Prognostic Factor 
Exploration  
Protocol v1.0 – 12th December 2018 
 
Tom Hughes, MSc,1 2    
Professor Richard Riley, PhD,3  
Dr. Jamie. C. Sergeant, PhD, 2 4 * 
Professor Michael Callaghan, PhD, 1,2,5 * 
 
1Manchester United Football Club, AON Training Complex, Birch Road, Off Isherwood Road, 
Carrington, Manchester. UK. M31 4BH. 
2 Arthritis Research UK Centre for Epidemiology, Centre for Musculoskeletal Research, Manchester 
Academic Health Science Centre, University of Manchester, Manchester, UK. 
3 Centre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele 
University, Staffordshire, UK.  
4 Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, 
Manchester, UK. 
5 Department of Health Professions, Manchester Metropolitan University, Brooks Building, Bonsall 
Street, Manchester. UK.  
*JCS and MJC contributed equally and are joint last authors. 
 

 
2 
 
Corresponding author: Tom Hughes 
Email: tom.hughes.physio@manutd.co.uk 
Twitter:@dt_hughes 
ORCID ID: orcid.org/0000-0003-2266-6615 
Correspondence address: Manchester United Football Club, AON Training Complex, Birch Road, Off 
Isherwood Road, Carrington, Manchester. UK. M31 4BH. 
Tel: 0161 868 8754 
 
Keywords:  
Athlete; injury prevention; muscle strain, prediction; prognosis; screening; sport; sprains and strains 
 
 
 
 
  
 
 
 
 
 
 

 
3 
ABSTRACT 
BACKGROUND 
Indirect muscle injuries (IMIs) are a considerable burden to elite football (soccer) teams and 
prevention of these injuries offers many benefits. Preseason medical, musculoskeletal and 
performance screening (termed periodic health examination (PHE)) can be used to help 
determine players at risk of injuries such as IMIs, where identification of PHE-derived 
prognostic factors (PF) may inform IMI prevention strategies. Furthermore, using several PFs 
in combination within a multivariable prognostic model may allow individualised IMI risk 
estimation and specific targeting of prevention strategies, based upon an individual’s PF 
profile. No such models have been developed in elite football and the current IMI prognostic 
factor evidence is limited. This study aims to: 1) to develop and internally validate a 
prognostic model for individualised IMI risk prediction within a season in elite footballers, 
using the extent of the prognostic evidence and clinical reasoning; 2) explore potential PHE-
derived PFs associated with IMI outcomes in elite footballers, using available PHE data from 
a professional team. 
 
METHODS 
A retrospective review has been completed of PHE and injury data, routinely collected over 5 
years (from 1st July 2013 to 19th May 2018) from a population of elite male players at an 
English Premier League football club.  Of 60 candidate PFs, 15 were excluded. Ten factors 
will be included in model development that were identified from a systematic review, 
missing data assessment, measurement reliability evaluation and clinical reasoning.  A full 
multivariable logistic regression model will be fitted, to ensure adjustment before backward 
elimination. The performance and internal validation of the model will be assessed. The 

 
4 
remaining 35 candidate PFs are eligible for further exploration, using univariable logistic 
regression to obtain unadjusted risk estimates.  Exploratory PFs will be grouped according to 
type and incorporated into multivariable logistic regression models to determine risk 
estimates. 
 
DISCUSSION 
This study will offer insights into clinical usefulness of a model to predict IMI risk in elite 
football and highlight the practicalities of model development in this setting.  Exploration 
may identify other relevant PFs for future confirmatory studies, model updating, or influence 
future injury prevention research. 
ABSTRACT WORD COUNT= 342 
 
 
 
 
 
 
 
 
 
 

 
5 
BACKGROUND 
Indirect muscle injuries (IMI) are the most common injury type in elite football (soccer), 
predominantly affecting lower extremity muscle groups (1, 2). Such injuries occur in the 
absence of direct impact related trauma (during sprinting for example)(3, 4) and are 
subclassified into functional disorders without macroscopic structural tissue muscle damage, 
or structural injuries with clear evidence of muscle disruption (3, 4).  
 
IMIs are problematic for elite teams in terms of both incidence and severity(5), accounting 
for 30.3% to 47.9% of all injuries that result in time lost to both training and competition (1, 
6-9), with the mean and median absence duration reported as 14.4(1) and 15 days 
respectively (8). Player availability is crucial to team prosperity, with vast commercial and 
financial rewards on offer to successful teams and players (10, 11). Conversely, player 
absences through injury negatively affect team performance (12, 13), increase demand on 
medical services and carry a significant financial burden.  As an illustration, for each first 
team player missing through injury, the daily cost to a participating team in the UEFA 
Champions League, is approximately €17000 to €20000 (14, 15). 
 
Periodic Health Examination (PHE) consists of medical examination, musculoskeletal 
assessment, functional movement evaluation and performance tests during preseason and in-
season periods and is used by 94% of elite teams (16). PHE is considered important because 
its intended purposes are to: 1) allow regular health monitoring for underlying but 
asymptomatic pathology (17); 2) establish baseline measures for setting rehabilitation or 
training targets (18); 3) identify individuals who are susceptible to common or severe injury 
types (such as IMIs)(19). For the latter function, PHE cannot detect causes of injury, but can 

 
6 
highlight factors that may be associated with an injury outcome (prognostic factors) and 
therefore help explain differences in injury risk across individuals within the team (18). 
Several prognostic factors could also be used in combination within a multivariable 
prognostic model to predict an individual’s absolute injury risk (20, 21). Importantly, both 
prognostic models and prognostic factors (PFs) can be used inform management approaches 
designed to modify an individual’s absolute risk (21). Despite the potential benefits of 
prognostic models for shaping injury prevention strategies aimed at clinically important 
injuries such as IMIs, none have been developed in elite football (22). In addition, there are 
significant methodological limitations in the evidence base on PHE-derived PFs (22).  
 
Therefore, this study will consist of two primary objectives: 1) to develop and internally 
validate a prognostic model for individualised IMI risk prediction during a season in elite 
footballers, using a small number of PHE-derived candidate PFs selected from a previous 
systematic review (22) and clinical reasoning; 2) to explore potential PFs associated with IMI 
outcomes during a season  in this elite cohort using available PHE data from a professional 
team. 
 
METHODS 
Study Design  
This study will be of retrospective cohort design, using a population of male elite football 
players aged 16-40 years old at an English Premier League club. The first objective will be 
conducted in accordance with existing guidelines for model development and internal 
validation (23, 24),  and reported in accordance with the Transparent Reporting of a 

 
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Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement 
(25, 26). The second objective will be conducted in accordance with existing guidelines (27) 
and reported in accordance with the REporting recommendations for MARKer prognostic 
studies (28, 29). 
 
Data sources  
Both studies will use routinely collected data that was obtained over five seasons (from 1st 
July 2013 to 19th May 2018). Data collected from the musculoskeletal and performance test 
components of the club’s PHE will be used to identify candidate PFs. Injury outcome data 
will also be used to establish the available number of IMI outcomes. 
 
Preseason PHE data collection 
Each new season commenced from July 1st. Available players completed a mandatory PHE 
on one of three days during the first week of the season. Typically, the musculoskeletal and 
performance components of the PHE included: 1) anthropometric measurements; 2) medical 
history (i.e. previous injury history); 3) musculoskeletal examination tests; 4) functional 
movement and balance tests; 5) strength and power tests. The PHE test order was self-
selected by each player and a standardised warm up was not implemented, although players 
could undertake their own warm up procedures if they wished. Each component of the PHE 
test battery was standardised according to a written protocol and examined by 
physiotherapists, sports scientists or club doctors. The same examiners performed the same 
test every season, to avoid inter-tester variability. Throughout the five-year data collection 
period, no examiner attrition occurred.  

 
8 
Participant follow up and injury data collection 
Players were followed up to the last day of each competitive domestic season (defined as the 
date of the last first team game of the season) irrespective of whether they had completed the 
PHE procedure or not. Players completed their routine training and match programmes 
throughout. For every player in the squad, any injuries that occurred during the season were 
assessed and electronically documented within 24 hours by a club doctor or physiotherapist 
in accordance with the Consensus Statement on Injury Definitions and Data Collection 
Procedures in Studies of Football Injuries (30). Musculoskeletal assessments were dependent 
on the clinical presentation, although typically consisted of observation, effusion, range of 
movement, muscle length and resisted muscle tests, palpation and special diagnostic manual 
tests. Radiological imaging was used to assist diagnosis as required. Ultrasound scans were 
performed by the club doctor using a Toshiba Aplio 500 or 1900 machine (Toshiba 
Corporation, Tokyo, Japan). Magnetic Resonance Imaging (MRI) was performed as 
appropriate, using a Canon Vantage Titan 3T Scanner (Canon Medical Systems, Otowara, 
Japan) according to sequences determined by the club doctor. Images were evaluated by a 
club doctor and an independent musculoskeletal radiologist.  
 
The medical professionals were not blinded to PHE data at the time of diagnosis. These data 
were not routinely used to inform diagnoses, but instead used to identify functional 
rehabilitation targets and for benchmarking. Following injury, players completed a 
rehabilitation programme as directed by club medical staff to enable them to return to 
training and match participation. 
 
 

 
9 
Participants and eligibility criteria 
Eligible participants were identified from a review of the PHE database entries during the 
dates stated above. During any season, participants were eligible for inclusion into the 
analysis if they: 1) were aged between 16-40 years; 2) had an outfield position (i.e. not a 
goalkeeper); 3) participated in PHE testing for the relevant season. Participants were 
excluded from the analysis for any season if they were a triallist player or not contracted to 
the club at the time of PHE.  
 
Ethics and Data Use 
Because all data were captured from the mandatory PHE completed through the participants’ 
employment, informed consent was not required. The anonymity and rights of all players 
were protected. The football club granted permission to use these data and the use of the data 
for this study was approved by the Research Ethics service at the University of Manchester. 
This study has been registered on ClinicalTrials.gov, with registered number 
as……………………….. 
 
Data extraction 
All PHE records from eligible participants were extracted and placed into a separate 
database. Using the club’s electronic medical records system, a further database was 
generated of all recorded injuries for each season and a manual review of each eligible 
participant’s medical record was undertaken to ensure accuracy. Each injury was categorised 
according to: 1) contact or non-contact mechanism of injury; 2) injured side; 3) affected body 
area; 4) injury type i.e. IMI/ligament/tendon/cartilage/contusion or 

 
10 
laceration/bone/concussion/other musculoskeletal injury; 5) muscle group and diagnostic 
classification if recorded as an IMI. This process allowed an in-house audit of injury 
incidence and absolute risk evaluation for each injury type for the squad overall and for those 
who underwent PHE. All IMIs were then extracted and merged with the PHE database of 
included participants, for each season in which they remained eligible.  
 
Outcome Measures 
The primary outcome measure for both studies will be the occurrence of an initial (index) 
lower extremity IMI sustained by a participant during a season. Only time-loss injuries will 
be included; that is, any index lower extremity IMI that occurred during match play or 
training that resulted in the player being unable to take full part in future match play or 
training (30). An IMI was confirmed during the injury assessment procedure outlined above, 
and graded by the club doctor or physiotherapist according to the Munich Consensus 
Statement for the Classification of Muscle Injuries in Sport (4). This diagnostic classification 
system is the primary method of muscle injury classification used by the club and has been 
validated previously (31).    
 
If an index lower extremity IMI occurred, the participant’s outcome for the season will be 
determined and that participant will no longer be considered at risk beyond the time of IMI 
occurrence. In these circumstances, participants will be included for further analysis at the 
start of the consecutive season, providing they remain eligible. If participants sustained any 
upper limb IMI or non-IMI injury type, these will be ignored and the participant will still be 
considered at risk of a lower extremity index IMI.  
 

 
11 
Each participant-season will be treated as independent. Eligible participants who were loaned 
out or transferred to another club throughout that season, but had not sustained an index IMI 
prior to the loan or transfer will still be considered in the risk set. Participants who sustained 
an index IMI while on loan will be included for analysis, as outlined above. Any participants 
who were permanently transferred during a season (anticipated to be very few) will be 
recorded as not having an IMI event during the relevant season and they will exit the cohort 
at this point. A sensitivity analysis may be conducted to evaluate the effect of player loans or 
transfers on the results.  
 
If there are a sufficient number of index IMI outcomes, secondary analyses may be 
performed, which may include outcomes of hamstring muscle IMIs, functional IMIs 
(classified as type 1A to 2B), or structural IMIs (classified as type 3 or 4) according to the 
Munich Consensus Statement criteria.(4) 
 
Sample Size 
As this study will retrospectively utilise the available dataset, a formal sample size 
calculation has not been conducted, as the total sample size and IMI outcomes are already 
fixed. To maximise power, we have elected to use all data from the 5-season period. This 
approach agrees with methodological recommendations that data splitting should be avoided, 
and all available data should be used for model validation (32). 
 
The number of candidate PFs for inclusion in model development will be restricted to be a 
minimum of 10 events per variable, which is recommended to reduce overfitting and 

 
12 
optimism during development of a logistic regression model (33). Note that ‘variable’ here 
means a parameter included (or considered for inclusion) in the model that corresponds to 
one of the PFs. Although data cleaning for the final season is still in progress, we anticipate 
that the minimum number of index IMIs will be 100, so will restrict to 10 candidate PFs 
(variables) for inclusion in the model.  
 
Assuming the model will have a modest R Nagelkerke-squared of 25%, then with an 
anticipated outcome proportion of 0.381, our 10 events per variable restriction corresponds to 
targeting an expected shrinkage of 0.85, and thus a relatively small amount of overfitting 
(15%) (34). 
 
Candidate prognostic factors 
A complete list of 60 candidate PFs extracted from the PHE dataset is presented in Table 1.  
 
Table 1: Candidate prognostic factors extracted from PHE data  
Type of Prognostic Factor 
Candidate Prognostic factor 
Measurement method 
Data type 
Anthropometrics 
Age 
Date of birth 
Continuous 
Height 
Standing height measure 
Continuous 
Weight 
Digital scales 
Continuous 
Body fat 
Skin callipers 
Continuous 
BMI 
Composite height (cm) 
and weight (kg) 
Continuous 
Medical history 
Frequency of previous muscle injuries within 3 years prior to PHE 
Medical records 
Discrete (treated as 
continuous) 
Most recent previous muscle injury within 3 years prior to baseline PHE 
Medical records 
Categorical 
Frequency of previous foot or ankle injuries within 3 years prior to PHE 
Medical records 
Discrete (continuous) 
Most recent previous foot or ankle injury within 3 years prior to PHE 
Medical records 
Categorical 

 
13 
Frequency of previous hip or groin injuries within 3 years prior to PHE 
Medical records 
Discrete (continuous) 
Most recent previous hip or groin injury within 3 years prior to PHE 
Medical records 
Categorical 
Frequency of previous knee injuries within 3 years prior to PHE 
Medical records 
Discrete (continuous) 
Most recent previous knee injury within 3 years prior to PHE 
Medical records 
Categorical 
Frequency of previous shoulder injuries within 3 years prior to PHE 
Medical records 
Discrete (continuous) 
Most recent previous shoulder injury within 3 years prior to PHE 
Medical records 
Categorical 
Frequency of previous lumbar spine injuries within 3 years prior to PHE 
Medical records 
Discrete (continuous) 
Most recent previous lumbar spine injury within 3 years prior to PHE 
Medical records 
Categorical 
Frequency of previous iliopsoas IMIs within 3 years prior to PHE 
Medical records 
Discrete (continuous) 
Most recent previous iliopsoas IMIs within 3 years prior to baseline PHE 
Medical records 
Categorical 
Frequency of previous adductor IMIs within 3 years prior to PHE 
Medical records 
Discrete (continuous) 
Most recent previous adductor IMIs within 3 years prior to PHE 
Medical records 
Categorical 
Frequency of previous hamstring IMIs within 3 years prior to PHE 
Medical records 
Discrete (continuous) 
Most recent previous hamstring IMIs within 3 years prior to PHE 
Medical records 
Categorical 
Frequency of previous quadriceps IMIs within 3 years prior to PHE 
Medical records 
Discrete (continuous) 
Most recent previous quadriceps IMIs within 3 years prior to PHE 
Medical records 
Categorical 
Frequency of previous calf IMIs within 3 years prior to PHE 
Medical records 
Discrete (continuous) 
Most recent previous calf IMIs within 3 years prior to PHE 
Medical records 
Categorical 
Musculoskeletal tests 
PROM R hip joint internal rotation 
Digital inclinometer 
Continuous 
PROM L hip joint internal rotation 
Digital inclinometer 
Continuous 
PROM R hip joint external rotation 
Digital inclinometer 
Continuous 
PROM L hip joint external rotation 
Digital inclinometer 
Continuous 
R Hip flexor muscle length 
Thomas test using 
digital inclinometer 
Continuous 
L Hip flexor muscle length 
Thomas test using 
digital inclinometer 
Continuous 
R Hamstring muscle length /neural mobility 
SLR using digital 
inclinometer 
Continuous 
L Hamstring muscle length /neural mobility 
SLR using digital 
inclinometer 
Continuous 
R Quadriceps muscle length 
Ely’s test using digital 
inclinometer 
Continuous 
L Quadriceps muscle length 
Ely’s test using digital 
inclinometer 
Continuous 
R Calf muscle length 
WBL using digital 
inclinometer 
Continuous 
L Calf muscle length 
WBL using digital 
inclinometer 
Continuous 
Toe touch in standing 
Measurement of 
distance from fingertips 
to floor 
Continuous 
Sacroiliac joint kinematic function 
Gillets test 
Categorical 

 
14 
Functional 
movement/balance tests 
R relative tibial angles 
SLS measurement with 
Dorsavi ViPerform IMU 
Continuous 
L Relative tibial angles 
SLS measurement with 
Dorsavi ViPerform IMU 
Continuous 
Y Balance Test – R anterior translation 
Y Balance Test 
Continuous 
Y Balance Test – L anterior translation 
Y Balance Test 
Continuous 
Y Balance Test – R posteromedial translation 
Y Balance Test 
Continuous 
Y Balance Test – L posteromedial translation 
Y Balance Test 
Continuous 
Y Balance Test – R posterolateral translation 
Y Balance Test 
Continuous 
Y Balance Test – L posterolateral translation 
Y Balance Test 
Continuous 
Strength/power tests 
R upper body peak power 
Double horizontal press 
using a Keiser Chest 
Press Air 350 machine 
Continuous 
L upper body peak power 
Double horizontal press 
using a Keiser Chest 
Press Air 350 machine 
Continuous 
R maximal loaded leg extension power 
Double leg press test using 
a Keiser Air 300 machine 
Continuous 
L maximal loaded leg extension power 
Double leg press test using 
a Keiser Air 300 machine 
Continuous 
R maximal loaded leg extension velocity 
Double leg press test using 
a Keiser Air 300 machine 
Continuous 
L maximal loaded leg extension velocity 
Double leg press test using 
a Keiser Air 300 machine 
Continuous 
R maximal loaded leg extension force 
Double leg press test using 
a Keiser Air 300 machine 
Continuous 
L maximal loaded leg extension force 
Double leg press test using 
a Keiser Air 300 machine 
Continuous 
CMJ height 
CMJ using force plates 
Continuous 
CMJ force per kilogram of body mass 
CMJ using force plates 
Continuous 
CMJ power 
CMJ using force plates 
Continuous 
Key: PHE=periodic health examination; R=right limb; L= left limb; WBL=weight bearing lunge; CMJ=countermovement jump; PROM=passive range of movement; SLR= 
straight leg raise; BMI= body mass index; Kg=kilos; f=force; Kg=kilos m = mass; SLS = single leg squat; IMU= inertial measurement units. 
 
 
Model development and internal validation  
We have chosen to conduct the model development before the PF exploration because of the 
restrictions on the number of PFs permitted to limit potential overfitting of the model.  
Because only 10 PFs will be used in model building, we have defined these candidate PFs a 
priori, (Table 2). Three candidate PFs have known importance based on the results of our 
previous systematic review so were selected for inclusion (22). All other PFs listed in Table 1 

 
15 
were eligible unless there were >15% missing observations or if reliability (where applicable) 
was classed as fair to poor (ICC < 0.70) (35). In these cases, the relevant candidate PFs were 
excluded (Table 3). This was to ensure that only the highest quality data will be used in the 
analysis, with PFs that would generally be available and routinely measured.  
 
Co-linearity amongst factors within a logistic regression model can cause inaccuracies in 
standard error and confidence interval estimates (36) so a scatterplot matrix was used to 
informally assess between-factor correlations for eligible PFs. If PFs were highly correlated, 
one of the PFs were dropped or new composite PFs were generated and replaced the original 
factors (highlighted in Tables 2-4). Typically, this occurred where measurements examined 
both right and left limbs separately; composite factor variables were therefore created for 
both between-limb measurement differences and the mean of the measurements for both 
limbs.  
 
Of the remaining eligible PFs, 7 further candidate factors were selected for inclusion, through 
use of clinical reasoning to identify those with a biologically plausible association with IMI 
development. The final set of 10 PFs is shown in Table 2. 
 
 
 
 
 

 
16 
Table 2: Restricted set of candidate prognostic factors for model development and validation. 
Selection 
method 
Candidate Prognostic factor 
Composite PF  
Measurement unit 
Measurement 
method 
Data type 
Reliability (if 
applicable) 
Systematic 
review 
Age 
No 
Years & days 
Date of birth 
Continuous 
N/A 
Frequency of previous muscle injuries within 3 
years prior to PHE 
No 
Count 
Medical records 
Discrete 
(treated as 
continuous) 
N/A 
Most recent previous muscle injury within 3 years 
prior to baseline PHE 
No 
< 6 months, 6-12 
months, > 12 months 
Medical records 
Categorical 
N/A 
Clinical 
reasoning/ 
data quality 
CMJ power 
No 
Normalised peak force 
(N/Kg-0.67 ) 
CMJ using force 
plates 
Continuous 
Test-retest ICC 
= 0.92-0.98.(37) 
 
PROM hip joint internal rotation difference* 
Yes 
Degrees 
Digital 
inclinometer 
Continuous 
Intra-rater ICC= 
0.90.(38) 
PROM hip joint external rotation difference* 
Yes 
Degrees 
Digital 
inclinometer 
Continuous 
Intra-rater ICC = 
0.90.(38) 
Hip flexor muscle length difference* 
Yes 
Degrees 
Thomas test using 
digital inclinometer 
Continuous 
Inter-rater ICC = 
0.89.(39) 
Hamstring muscle length /neural mobility 
difference* 
Yes 
Degrees 
SLR using digital 
inclinometer 
Continuous 
Intra-rater ICC 
=0.95-0.98 (40) 
Interrater ICC = 
0.80-0.97.(41) 
Calf muscle length difference* 
No 
Degrees 
WBL using digital 
inclinometer 
Continuous 
Inter-rater = ICC 
0.80- 0.95.(42, 
43) Intra-rater = 
ICC 0.88.(43) 
BMI 
Yes 
Kg/m2 
Composite height 
(cm) and weight 
(kg) 
Continuous 
- 
Key: PF= prognostic factor; PHE=periodic health examination; WBL=weight bearing lunge; CMJ=countermovement jump; PROM=passive range of movement; 
ICC=intraclass correlation coefficient; SLR= straight leg raise; BMI= body mass index; Kg=kilos; N= newtons (note that N/kg-0.67 is a scaling factor to normalise force to body 
mass) m = mass; Note: composite factors are identified in the table with  * denoting  between limb differences. 
 
 
 
 
 
 
 
 
 

 
17 
Table 3: Candidate Prognostic factors excluded from both model development and 
prognostic factor exploration 
Type of prognostic factor 
Candidate Prognostic Factor 
Composite 
variable 
created 
Measurement 
unit 
Measurement method 
Data type 
Reason for 
elimination 
Anthropometric 
Body fat 
No 
Percentage 
Skin callipers 
Continuous 
Missing data > 
15% 
Musculoskeletal test 
Quadriceps muscle length difference* 
Yes 
Degrees 
Ely’s test using digital 
inclinometer 
Continuous 
Intra-rater ICC = 
0.69(44)  
 Inter-rater ICC 
= 0.66. (44) 
Mean quadriceps muscle length** 
Yes 
Degrees 
Ely’s test using digital 
inclinometer 
Continuous 
Intra-rater ICC = 
0.69(44)   
Inter-rater ICC = 
0.66. (44) 
Toe touch in standing 
No 
Centimetres 
Fingertips to floor distance 
Continuous 
Missing data > 
15% 
Sacroiliac joint kinematic function 
No 
Subjective score 
Gillets test 
Categorical 
Missing data > 
15% 
Functional movement/balance 
tests 
Y Balance Test – anterior 
translation difference* 
Yes 
Centimetres 
Y Balance Test 
Continuous 
Missing data 
>15% 
Y Balance Test – Mean anterior 
translation** 
Yes 
Centimetres 
Y Balance Test 
Continuous 
Missing data 
>15% 
Y Balance Test – posteromedial 
translation difference* 
Yes 
Centimetres 
Y Balance Test 
Continuous 
Missing data 
>15% 
Y Balance Test – Mean 
posteromedial translation** 
Yes 
Centimetres 
Y Balance Test 
Continuous 
Missing data 
>15% 
Y Balance Test –posterolateral 
translation difference* 
Yes 
Centimetres 
Y Balance Test 
Continuous 
Missing data 
>15% 
Y Balance Test – Mean 
posterolateral translation** 
Yes 
Centimetres 
Y Balance Test 
Continuous 
Missing data 
>15% 
R relative tibial angles 
No 
Degrees 
SLS measurement with 
Dorsavi Viperform IMU 
Continuous 
Within-session 
ICCs=0.27-
0.75 
 
Between-
session ICCs = 
0.55-0.77(45) 
L relative tibial angles (left leg) 
No 
Degrees 
SLS measurement with 
Dorsavi Viperform IMU 
Continuous 
Within-session 
ICCs= 0.27-
0.75 
 
Between-
session ICCs = 
0.55-0.77(45) 
Strength/power tests 
Upper body peak power difference* 
Yes 
Normalised watts 
per kilo (W/kg-
0.67) 
Double horizontal press 
using a Keiser Chest Press 
Air 350 machine 
Continuous 
Missing data > 
15% 
Mean upper body peak power** 
Yes 
Normalised watts 
per kilo (W/kg-
0.67) 
Double horizontal press 
using a Keiser Chest Press 
Air 350 machine 
Continuous 
Missing data > 
15% 
Key: PHE=periodic health examination; WBL=weight bearing lunge; CMJ=countermovement jump; PROM=passive range of movement; ICC=intraclass correlation 
coefficient; SLR= straight leg raise; SLS = single leg squat; BMI= body mass index; f=force; W= watts; (note that W/kg-0.67 has a scaling factor to normalise power to body 
mass) Kg=kilos; IMU= inertial measurement units; m = mass; Note: composite factors are identified in the table with  * denoting  between limb differences and **denoting  
combined mean values of both limbs. 
 
 
 
 
 

 
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Prognostic factor exploration 
Candidate PFs that were that were not selected for use in model development but not 
excluded, will be eligible for further exploratory analysis (Table 4). This will allow 
identification of other potentially useful associations which may assist future analyses or 
updating of the model created under the first objective of this investigation.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
19 
Table 4: Candidate prognostic factors - exploratory study 
Type of 
prognostic 
factor 
Candidate Prognostic factor 
Composite 
prognostic factor 
Measurement 
unit 
Measurement method 
Data type 
Reliability (if 
applicable/available) 
Anthropometric 
Height 
No 
Centimetres 
Standing height measure 
Continuous 
- 
Weight 
No 
Kilograms 
Digital scales 
Continuous 
- 
Medical history 
Frequency of previous foot or ankle injuries within 3 years prior 
to PHE. 
No 
Count 
Medical records 
Continuous 
- 
Most recent previous foot or ankle injury within 3 years prior to 
PHE. 
No 
< 6 months, 
6-12 months, 
> 12 months 
Medical records 
Categorical 
- 
Frequency of previous hip or groin injuries within 3 years prior 
to PHE. 
No 
Count 
Medical records 
Continuous 
- 
Most recent previous hip or groin injury within 3 years prior to 
PHE. 
No 
< 6 months, 
6-12 months, 
> 12 months 
Medical records 
Categorical 
- 
Frequency of previous knee injuries within 3 years prior to PHE. 
No 
Count 
Medical records 
Continuous 
- 
Most recent previous knee injury within 3 years prior to PHE. 
No 
< 6 months, 
6-12 months, 
> 12 months 
Medical records 
Categorical 
- 
Frequency of previous shoulder injuries within 3 years prior to 
PHE. 
No 
Count 
Medical records 
Continuous 
- 
Most recent previous shoulder injury within 3 years prior to 
PHE. 
No 
< 6 months, 
6-12 months, 
> 12 months 
Medical records 
Categorical 
- 
Frequency of previous lumbar spine injuries within 3 years prior 
to PHE. 
No 
Count 
Medical records 
Continuous 
- 
Most recent previous lumbar spine injury within 3 years prior to 
PHE. 
No 
< 6 months, 
6-12 months, 
> 12 months 
Medical records 
Categorical 
- 
Frequency of previous iliopsoas IMIs within 3 years prior to 
PHE. 
No 
Count 
Medical records 
Continuous 
- 
Most recent previous iliopsoas IMIs within 3 years prior to 
baseline PHE. 
No 
< 6 months, 
6-12 months, 
> 12 months 
Medical records 
Categorical 
- 
Frequency of previous adductor IMIs within 3 years prior to 
PHE. 
No 
Count 
Medical records 
Continuous 
- 
Most recent previous adductor IMIs within 3 years prior to PHE. 
No 
< 6 months, 
6-12 months, 
> 12 months 
Medical records 
Categorical 
- 
Frequency of previous hamstring IMIs within 3 years prior to 
PHE. 
No 
Count 
Medical records 
Continuous 
- 
Most recent previous hamstring IMIs within 3 years prior to 
PHE. 
No 
< 6 months, 
6-12 months, 
> 12 months 
Medical records 
Categorical 
- 
Frequency of previous quadriceps IMIs within 3 years prior to 
PHE. 
No 
Count 
Medical records 
Continuous 
- 
Most recent previous quadriceps IMIs within 3 years prior to 
PHE. 
No 
< 6 months, 
6-12 months, 
> 12 months 
Medical records 
Categorical 
- 
Frequency of previous calf IMIs within 3 years prior to PHE. 
No 
Count 
Medical records 
Continuous 
- 
Most recent previous calf IMIs within 3 years prior to PHE. 
No 
< 6 months, 
6-12 months, 
> 12 months 
Medical records 
Categorical 
- 
Musculoskeletal 
tests 
Mean PROM hip joint internal rotation** 
Yes 
Degrees 
Digital inclinometer 
Continuous 
Intra-rater ICC = 
0.90.(38) 
Mean PROM hip joint external rotation** 
Yes 
Degrees 
Digital inclinometer 
Continuous 
Intra-rater ICC =  
0.90.(38) 
Mean hip flexor muscle length** 
Yes 
Degrees 
Thomas Test for using 
digital inclinometer 
Continuous 
Inter-rater ICC = 
0.89.(39)  
Mean hamstring muscle length /neural mobility** 
Yes 
Degrees 
SLR using digital 
inclinometer 
Continuous 
Intra-rater ICC = 0.95-
0.98.(41) 
Inter-rater ICC = 0.80-
0.97.(41) 
Mean calf muscle length** 
Yes 
Degrees 
WBL using digital 
inclinometer 
Continuous 
Inter-rater ICC = 0.80- 
0.95.(42, 43) 
Intra-rater ICC = 
0.88.(43) 
Strength/power 
test 
Maximal loaded leg extension power difference* 
Yes 
Normalised 
watts per kilo 
(W/kg-0.67) 
Double leg press test 
using a Keiser Air 300 
machine 
Continuous 
Test-retest ICC = 
0.886(46) 
Mean of maximal loaded leg extension power** 
Yes 
Normalised 
watts per kilo 
(W/kg-0.67) 
Double leg press test 
using a Keiser Air 300 
machine 
Continuous 
Test-retest ICC = 
0.886(46) 
Loaded maximal leg extension velocity difference* 
Yes 
Peak velocity 
(m.s -1) 
Double leg press test 
using a Keiser Air 300 
machine 
Continuous 
Test-retest ICC = 
0.792(46) 
Mean of maximal loaded leg extension velocity** 
Yes 
Peak velocity 
(m.s -1) 
Double leg press test 
using a Keiser Air 300 
machine 
Continuous 
Test-retest ICC = 
0.792(46) 
Loaded maximal leg extension force difference* 
Yes 
Normalised 
peak force 
(N/Kg-0.67 ) 
Double leg press test 
using a Keiser Air 300 
machine 
Continuous 
Test-retest ICC = 
0.914(46) 
Mean of maximal loaded leg extension force** 
Yes 
Normalised 
peak velocity 
(N/Kg-0.67 ) 
Double leg press test 
using a Keiser Air 300 
machine 
Continuous 
Test-retest ICC = 
0.914(46) 
CMJ force per kilogram of body mass 
No 
Force per kg 
(F/kg) 
CMJ using force plates 
Continuous 
- 
CMJ height 
No 
Centimetres 
CMJ using force plates 
Continuous 
Test-retest ICC = 0.80-
0.88.(47) 

 
20 
Key: PF= prognostic factor; PHE=periodic health examination; WBL=weight bearing lunge; CMJ=countermovement jump; PROM=passive range of movement; 
ICC=intraclass correlation coefficient; SLR= straight leg raise; BMI= body mass index ;f=force; Kg=kilos; m = mass; m.s -1= metres/second;  Note: composite factors are 
identified in the table with  * denoting  between limb differences and **denoting  combined mean values of both limbs. 
 
Statistical analysis  
Model development and internal validation  
Multivariable logistic regression will be used for the analysis as this is an appropriate method 
where outcomes are binary (26) and independent variables (PFs) are continuous, categorical 
or a combination (36). Initially we will fit a full multivariable model containing all 10 
candidate PFs to ensure a fully adjusted model prior to potential elimination of unimportant 
candidate factors (23). Backward elimination will then be used to successively remove non-
significant factors with p-values of greater than 0.157. This threshold was set to approximate 
equivalence with Akaike’s Information Criterion (48). Using backward elimination in this 
way may deliver a more parsimonious model which is therefore easier to implement in 
clinical practice than a full model.  Where possible, we will retain continuous candidate PFs 
in their continuous form to avoid statistical power loss (49). 
We will assume that any missing data are missing at random (MAR) so multiple imputation 
(MI) will be used, using 50 imputations for missing values. We have chosen to utilise MI 
because it avoids excluding participants from the analysis, is an effective method of handling 
missing prognostic factor information and can be used to account for uncertainty in missing 
data (50).  
 
The apparent performance of the developed model will be summarised in the development 
datasets (averaged over imputation datasets), via calibration and discrimination. Model 
calibration determines performance in terms of the agreement between predicted outcome 

 
21 
risks and those actually observed (51). Graphical plots are useful to assess calibration (23), so 
will be produced and utilised in the analysis. We will calculate calibration-in-the-large 
(CITL, ideal value of 0), which quantifies the systematic error in model predictions (overall 
agreement). A related measures is E/O (ideal value of 1), which gives the ratio of the mean of 
the predicted risks against the mean of the observed risks (O) (51, 52). A calibration slope 
will also be calculated, where a value of 1 equals perfect calibration (26). Models 
demonstrate perfect calibration within development data, but in new data the slope may be <1 
due to overfitting in the model development dataset (see below for how this will be handled) 
(52).  
 
Discrimination performance is a measure of a model’s ability to separate participants who 
have experienced an outcome compared to those who have not, quantified using the C 
(concordance) statistic (equivalent to the area under the ROC curve) (23). This index measure 
will be calculated for the development model, where 1 demonstrates perfect discrimination, 
while 0.5 indicates that discrimination is no better than by chance alone.  
 
To quantify the degree of optimism due to overfitting, our model will be internally validated 
using bootstrap re-sampling. This will be conducted as previously outlined (26, 53). The 
prognostic factor variable selection procedure and model construction will be repeated for 
200 bootstrap samples. For each sample, the difference in bootstrap apparent performance (of 
the bootstrap model in the bootstrap data) and test performance (of the bootstrap model in the 
original dataset) will be averaged across the 200 samples, to obtain a single estimate of 
optimism for each performance statistic. Then, to calculate optimism-adjusted estimate of 

 
22 
performance for our new model, the estimates of optimism will be subtracted from the 
original apparent estimates of performance.  
The optimism-adjusted calibration slope will provide a uniform shrinkage factor, which will 
be applied to all prognostic factor effects in the developed model to adjust (shrink) for 
overfitting. The intercept of the model will then be re-estimated accordingly. This will then 
form our final model.   
 
Prognostic factor exploration 
All candidate factors eligible for the exploratory study will undergo univariable logistic 
regression analyses to determine unadjusted associations with IMIs. Candidate PFs will be 
grouped according to PF type (Table 4) and incorporated into multivariable logistic 
regression models to determine regression coefficients and odds ratios after adjustment for 
age, BMI, frequency of previous IMIs and most recent IMI within the 3 years prior to PHE, 
(which were included as candidates in the original model). During multivariable analysis of 
the anthropometric candidate factors, BMI will not be included in these models due to 
collinearity with height and weight factors. Exploration of non-linear associations between 
candidate factors and index IMI outcomes will be evaluated using a fractional polynomial 
approach (49). This analysis will allow examination of added prognostic value over and 
above other factors included in the original model. 
 
 

 
23 
DISCUSSION 
Although previous studies in elite football have investigated the association between factors 
obtained during PHE and IMIs using multivariable models, none have developed, validated 
or evaluated the performance a prognostic model for injury prediction purposes (22). While it 
is possible develop a prognostic model from PHE data (18), our investigation will offer 
valuable insights into the practical aspects of this process and the clinical usefulness of a 
model when applied to an individual football club. Our findings may also outline how these 
principles may be used in future at other clubs or sports, or on larger datasets which could be 
derived from several collaborating clubs. 
 
Despite the availability of high quality PHE and injury data, the relatively small number of 
outcomes in this dataset is problematic and will permit only a limited selection of candidate 
prognostic factors for use in model development. Utilising more than one prognostic factor 
for every 10 injury outcomes may cause significant issues with model overfitting, where 
spurious observed relationships occur because of regression value distortion (33). This leads 
to overestimation of predictive performance (optimism) which is especially evident in small 
datasets (54). To limit the effects of overfitting, only 10 PFs will be permitted and use of data 
reduction methods have been required to select which appropriate candidate factors are 
included. 
 
PFs for clinical injury outcomes are either intrinsic (person specific) or extrinsic 
(environment specific) (55) and can be modifiable or non-modifiable (56). Only the non-

 
24 
modifiable factors of increasing age and history of previous muscle injury have been shown 
to have modest prognostic value for hamstring muscle injuries in elite footballers (22), so  
will be included in model development. However, their non-modifiable nature means that 
they have limited use in terms of informing injury prevention strategies. To enhance the 
clinical applicability of the model, other potentially relevant and modifiable factors have been 
selected for inclusion.  
 
The methodological shortcomings in the literature mean that only three candidate prognostic 
factors could be selected for model development from our previous systematic review (22). 
Therefore, candidate PF selection for our model has been largely based upon evaluation of 
collinearity, measurement reliability and clinical reasoning. Therefore, it is possible that 
some important factors have not been considered. As such, only modest performance of this 
initial model is expected.  
 
It is acknowledged that the proposed prognostic model will assume that participants are 
independent for each season and utilise the binary outcome of at least one IMI in a season, 
rather than evaluating time to individual IMI events. This means that we will not account for 
within-person correlations from season to season. Although this is not fully representative of 
the real world, because this is a novel area and we are restricted to a relatively small dataset, 
we have elected to perform the analyses in a more simplistic manner in the first instance.  
Further, more complex analyses may be conducted in future.  
 

 
25 
To assess the generalisability of a prognostic model it should be externally validated using 
data from another location (21, 24) such as a dataset from another comparable elite level 
football team. Because there is likely to be considerable between-team heterogeneity in PHE 
processes (16), candidate prognostic factors within our model may not translate externally at 
this time. There are no immediate plans to externally validate this model. However, 
depending on the outcome of the model development and PF exploratory studies, it may be 
possible to conduct a future prospective temporal validation study within the same football 
club, or external validation study in different population. If feasible, such investigations will 
require a separate associated protocol.  
 
The current evidence relating to PFs for injury in football is frequently flawed due to issues 
with the reliability of data measurement, adjustment, dichotomisation and potential 
diagnostic misclassification, so there is a need for further studies that address these issues 
(22). Further hypothesis-free exploratory studies that investigate many factors, (including 
those that are not necessarily biologically plausible) may assist with identification of new 
factors that may help inform management decisions and monitoring purposes (20). 
Furthermore, these types of studies are helpful because new PFs may be used to update a 
developed model to improve performance (57). We have therefore outlined an associated 
phase 1 exploratory study to investigate the association between IMIs and other factors from 
the current dataset using a validated diagnostic outcome classification system, recommended 
statistical approaches and ensuring where possible, analysis of continuous data remains on 
the continuous scale to explore linear and non-linear associations.  
 

 
26 
We anticipate that this investigation will provide a comprehensive evaluation of what is 
currently possible in terms of using PHE data to predict IMIs at an elite football club, by 
adhering to transparent reporting procedures and current best practice for model 
development, validation and exploration of potential PFs. We hope these studies will also 
identify further research priorities for this novel and potentially valuable area of 
sports/football medicine research.  
 
DECLARATIONS 
 
Ethical approval and consent to participate 
Informed consent was not required; all data were captured from mandatory PHE processes 
completed through the participant’s employment. The anonymity and rights of all players 
were protected. The use of these data for the current purpose was approved by the Research 
Ethics Service at the University of Manchester. 
 
 
Availability of data and materials 
An anonymised summary of the dataset that will be generated and analysed during this 
current study may be available from the corresponding author on reasonable request. 
 
 
Competing interests 
The authors declare that they have no known competing interests.  

 
27 
 
Funding  
The lead researcher (TH) is receiving sponsorship from Manchester United Football Club to 
complete a postgraduate PhD study programme. This work was also supported by Arthritis 
Research UK: grant number 20380.  
 
 
Authors’ contributions 
TH was responsible for the conceptualisation of the project, study design, database 
construction, data extraction and cleaning, protocol development and protocol writing. TH 
will also conduct the data analysis, interpretation and write up of the studies. RR has 
provided statistical guidance and assisted with development of the study design, analysis 
plan, as well as editing protocol drafts. RR will also provide guidance for the statistical 
analysis and edit the study manuscripts. JS has provided guidance with the study design, 
development of the analysis and protocol, in addition to editing protocol drafts. JS will also 
assist with conducting and interpreting the analysis, as well as editing the study manuscripts. 
MC has assisted with the study conceptualisation and design, protocol development, clinical 
guidance and editing the protocol drafts. MC will assist with the clinical interpretation of the 
analysis and edit the study manuscripts. All authors read and approved this final manuscript. 
 
 
Acknowledgments 
The authors would like to thank all staff within the Medical and Sports Science Department 
at Manchester United for their continuing help and support with this manuscript and thank all 
players for their participation (without whom this study would not be possible). The authors 

 
28 
also thank Arthritis Research UK for their support: Arthritis Research UK grant number 
20380.  
 
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