Outcome measures: An observation and a reflection

Sports science and strength & conditioning practice is built on a foundation of identifying a problem, testing the problem, applying an intervention and then re-testing to ensure progression. Athletes will buy into fitness testing, injury prevention and subsequent high performance behaviours if they are given the impression that their coach and medical team know what they are doing and things are done with a purpose (Kristiansen and Larsson, 2017). This begs the question whether coaches can justify and clinically reason their battery of performance tests.

When applying a performance measure, understanding of the underlying kinematics is essential to understand the validity of the test to the desired outcome. The OptoJump^tm is a valid tool in assessing a reactive strength via  drop jump (Healy et al., 2016) however what components of the jump is the coach wishing to address? The validity of the tool is the not the same as the validity of the test. For example, reactive strength index (RSI) can be influenced by a reduced contact time (stretch shortening cycle via the musculotendinous unit) or via total jump height (power output throughout the lower limb and nervous system) or a combination of both (Healy et al., 2017). Understanding these mechanisms may influence the instructional bias of technique given by the coach in order to test what is desired.

With complexities over a test like an RSI to something seemingly obvious like a jump, testing for broader components of fitness and multiple movement patterns is much more difficult.

The Yo-Yo intermittent recovery test (IRT) is reported to be a valid measure of fitness and correlates to match performance in football (Krustrup et al., 2003). However, this is an example of a fitness capacity test and in fact correlates to fitness capacity in a match scenario. In field based team sports, there are a large number of variables and complex interactions that all contribute towards “performance” as an outcome (Currell and Jeukendrup, 2008). Krustrup’s conclusion was based on correlated Yo-Yo IRT results to high speed running in a game (>15km.h^-1) with a strong correlation (r=0.58). Overlooking the methodological accuracy of this (pre-GPS, using VHS locomotive assessment retrospectively), the correlation is between two differing metrics. Where the high speed running was recorded over 90 minutes of varying intensities and periods of effort (12 players across 18 different games), the Yo-Yo IRT covered 1.7km in a mean time of 14.7mins with incremental increases in pace dictated externally. For a test to be considered a valid indicator of performance, it should meet the same metabolic demands as the sporting activity (Currell and Jeukendrup, 2008). The Krustrup paper does not make this comparison, instead analysing physiological markers from rest to exhaustion during the Yo-Yo IRT, not exhaustion markers in comparison to game data.

Perhaps semantics, but in fact there should be differential terminology to distinguish “fitness performance” from “athletic” or “sporting performance.” It should be considered that sporting performance is influenced by a large number of uncontrollable and non-modifiable factors that would make any comparison of validity and reliability to outcome measures unfair. Essentially, recreating a competitive environment is near impossible. This raises the question whether we are exercising just to improve test scores or, closing the loop and relating exercises to performance? Does raising the envelope of one, consequently improve the other? Something that we should not only be asking ourselves, but a question we could come to expect from coaches and athletes a like.

Does the research answer this?

It has been suggested that stronger athletes produce faster sprint time, quicker change of direction speeds and higher vertical jump scores when compared to weaker athletes of the same sport (Thomas et al., 2016). Squat jump (r = -0.70 to -0.71) and counter movement jump (r = -0.60 to -0.71) demonstrate strong correlations to change of direction speed (Thomas et al., 2016). Peak force during isometric mid thigh pull was significantly correlated to 5m sprint time (p <0.05) however this correlation was only moderate (r = -0.49). But again, does this correlation transfer into performance if the testing protocol doesn’t accurately mirror sporting performance?

Sprint times over 40m have been shown to decrease following an acute bout of heavy loaded squats, hypothesised to be due to post activation potentiation (Mcbride et al., 2005). Higher squat strength scores also correlate with sprint times over 0-30m (r= 0.94, p=0.001) and jump height (r = 0.78, p=0.02) (Wisløff et al., 2004). Importantly, we know sprint performance tests have demonstrated construct validity to the physiological requirements of a competitive field based game (soccer) (Rampinini et al., 2007), which is ultimately what we are aiming to do; relating performance testing to physiological and metabolic markers from a given sport.

The addition of a jump squat exercise into a training program may help improve 1RM squat and 1RM power cleans (Hoffman et al., 2005). So perhaps yes, there is a perpetuating loop between exercise, tests and performance but the link between them all may not be tangible or direct.

But how do we translate all of these statistics and data sets this to a non-scientific population, as a lot of our athletes are? I’ve developed the following analogy to try and help with this.

 

Solar system analogy:

If we consider that “athletic performance” is the main focus of any intervention, much like the sun at the centre of the solar system. This is the bright light that everything revolves around; media, finance, fan base and support and so on. It could be argued that any intervention we have as coaches will never truly replicate “athletic performance” but should be influenced by it. This influence works both ways, positively and negatively. For example, if we maximally test an athlete before a competition, this will likely have a negative impact on “athletic performance”. Conversely, if we were able to collect data that informed a training program to improve athletic performance, despite not actually replicating “athletic performance” it would (hopefully) have a positive impact. For example, a football game is determined by so many uncontrollable variables that can not be replicated in a gym, but we might identify that a player needs to improve their 5m sprint time which in turn, will benefit performance.

Figure 1: An analogy depicting the relationship between “athletic performance” and controlled interventions / measures. The skill of the coach is identifying which outcome measure or intervention is going to have the greatest influence on athletic performance.

Let’s consider our potential interventions to be orbiting the sun (Figure 1). There is an interaction between the planets and the sun via gravity but they do not have a direct overlap, where the planets do not collide with the sun just as an outcome measure does not truly match sporting performance. We know that larger planets have a greater influence, so as coaches, we are trying to affect the level of positive interaction with “athletic performance”, the gravitational interaction. By influencing links between exercise intervention and outcome measures, we can affect the size of these planets. In turn, this will have a greater interaction with the centre of our solar system, “athletic performance” (Figure 2). Much like the universe, there will be many different solar systems just as there are different sporting codes and contexts, so the skill lies in identifying the most influential planets in your solar system.

Figure 2: The impact of enhancing an intervention or measure on sporting performance, in this case there has been a greater focus and development of the blue “planet” which has changed the interaction with the “athletic performance”

 

A clinical reflection:

For long term injuries, I utilise a continuum to guide return to play (train / play / perform), often these stages are guided by outcome measures linked to goals and aims for stages of rehab. Typically these tests are scheduled in advanced and often follow a planned “de-loading” micro-cycle. This helps with continuity and, as much as you can in sport, standardisation of the test.

A recent case study found me questioning my judgement and to a degree, wondering if my intrigue and curiosity about my rehab plan drove me to test out of sync with the schedule, instead of doing the test for the athletes benefit.

Following a good period of return to train, the proposed testing date previously scheduled clashed with a squad training session. Observational assessment suggested the athlete was coping well with the demands of training and it seemed counter-intuitive to pull them out of training to undertake some tests. A few weeks later, a gap in the daily schedule presented an opportunity to re-test. The test scores were down compared to the previous month, most likely because the athlete had trained in the morning and trained the 4 out of the last 5 days in some capacity. In previous tests, the athlete had come off of a de-load week and tested the day after a day off.

The result:

The athlete began to question their ability and availability to train. They were visibly knocked in their confidence given a drop in scores, despite me being able to rationalise why this could be. Having had the opportunity to feed my own interest and try to prove to myself that a rehab program had worked, the outcome was much worse. I threatened the confidence of a long term injury returning to training, potentially adding doubt and hesitation to their game and I did not get the results I was expecting.

On reflection, given their time out through the season so far, I should have stuck to protocol and tested on the scheduled day (one training session was not going to increase their chances of availability).. or, not tested at all. Instead, i shoe-horned some testing into an already busy schedule. What did I expect given the current level of fatigue?!

Previous results had reached a satisfactory level to return to train and I was now chasing the final few percentages available. To give them confidence? Probably not, as they were training and enjoying the return to train. So perhaps it was just to give myself confidence. An interesting lesson learnt, mostly about myself.

 

Yours in sport,

Sam

“Has the athlete trained enough to return to play safely?” Acute:Chronic workloads and rehabilitation – a guest blog by Jo Clubb

We are delighted to have the excellent Jo Clubb agree to write a blog for us. Admittedly, this blog is a little more high-brow than our usual ramblings, so thanks to Jo for adding some class to our library. Jo has recently broken into the American sports scene, working as a sports scientist with the Buffalo Sabres NHL, bringing with her expertise from her years in football (..soccer) in the UK (previously with Chelsea & more recently with Brighton & Hove Albion FC). What makes this blog extra special to us is that Jo already has an excellent blog page of her own that is read and commended worldwide (Sports Discovery – here). Jo demonstrates how & why sports science plays a massive part in return from injury in professional sport…

Introduction:

Training Stress Balance and the Acute:Chronic Workload Ratio are real buzz words in Sports Science at the moment. They also have important implications for the Physiotherapy and Conditioning communities in terms of rehabilitation and Return To Play.

This concept is derived from Banister’s modelling of human performance back in the 1970s (and then later added to by Busso in the 1990s) that put forward an impulse-response model to predict training load induced changes in performance. If we consider a single block of training, this stimulus will have a temporary negative influence represented as ‘fatigue’ but over a longer time frame will have a positive influence, represented as ‘fitness’. Performance will consequently be a product of the Fitness Fatigue relationship (see Figure 1). Within this theoretical model of Training Theory, it is suggested that with regular training stimuli we can manipulate these processes of fitness and fatigue via training load, recovery and overcompensation, to have a positive influence on performance (see Figure 2).

Figure 1: Used with permission from Professor Aaron Coutts

 

Figure 2: Used with permission from Professor Aaron Coutts

The Acute:Chronic Workload

Whilst this concept of training stress balance has been cited since these early, groundbreaking days, it has recently been developed into the acute:chronic workload ratio by Tim Gabbett and colleagues, which they suggest is the best practice predictor of training-related injuries (Gabbett, 2015).

It has previously been represented as a % for Training Stress Balance, but the focus now seems to be on utilising it in a ratio form, for example:

= Acute workload / Chronic workload

= 3000 (Au) / 4000 (Au) = 0.75

In this example acute workload is represented as the total load over the previous one week and chronic workload is the average weekly load for the previous four weeks, both utilising an arbitary unit (Au) such as session RPE.

So a ratio below 1, as per the above example, suggests the athlete is more likely to be in a state of “freshness”; their load over the past week has been less than their average weekly load over the past four weeks.

On the other hand a ratio above 1 represents that the workload over the past week has been greater than the average weekly load over the past four weeks, so they may be more likely to be in a state of “fatigue” and potentially less prepared for that workload. Recent research has suggested a ratio greater than 1.5 represents a “spike” in workload that is related to a significantly higher risk of injury (Blanch and Gabbett, 2015 here).

Training and Game Loads and Injury Risk

Tim Gabbett and his colleagues have collected consistent data within the training environment, statistically modelled the relationships between workload and injury risk, applied their model to help reduce injury risk in the training environment and published this data – for me this is the gold standard process of Sports Science and a method we should strive to replicate within each of our own environments. The relationship between workloads and injury risk has included just some of the following research:

• Running loads and soft tissue injury in rugby league (Gabbett and Ullah, 2012)
• Training and game loads and injury risk in Australian football (Rogalski et al, 2013; Colby et al, 2014)
• Pitching workloads and injury risk in youth baseball (Fleisig et al, 2011)
• Spikes in acute workload and injury risk in elite cricket fast bowlers (Hulin et al, 2014)
• Acute:chronic workload ratio and injury risk in elite rugby league players (Hulin et al, 2015)

I can talk about this all day (and probably will in a number of other blogs); however the focus of this specific blog is on the application in the rehabilitation environment so I will leave it at that for now. If you do want to read more of this topic, I highly recommend reading the following OPEN ACCESS paper:

The training-injury prevention paradox: should athletes be training smarter and harder? (Gabbett, 2016) Br J Sports Med doi:10.1136/bjsports-2015-095788

 

Rehabilitation

There is plenty of application to this approach in the training environment however; it is just as important in the rehabilitation setting as highlighted in the following paper:

Has the athlete trained enough to return to play safely? The acute:chronic workload ratio permits clinicians to quantify a player’s risk of subsequent injury (Blanch and Gabbett, 2015).

Rehabilitation is without doubt a very complex continuum in which medical staff assist the athlete through early stage rehabilitation to the multifaceted return to train, play and performance decisions, which I have tried to tackle previously (here)  and specifically for hamstring injuries (here). Previous to the paper by Peter Blanch and Tim Gabbett much of the literature on Return to Play failed to acknowledge the consideration of the progression of load in the RTP decision.

Often the evaluation of health status that directly influences the Return To Play decision may incorporate instantaneous physical testing results such as isokinetics or force plate assessment, as well as functional on pitch activity profile targets such as peak speeds, distances, high intensity running and velocity changes. Whilst there is no doubt these have their place, there also needs to be consideration for the loading achieved throughout the rehabilitation continuum in preparation for the acute and chronic loading demands of training and matchplay.

Blanch and Gabbett present a real world example from rugby league (Figure 3) in which a player suffered a hamstring injury after an acute:chronic workload ratio of 1.6 in training week 15. After two low-minimal weeks of high speed running due to the injury, the acute:chronic workload three weeks later spiked to 1.9 (presumably as high speed running was reincorporated into the rehabilitation phase in week 18) and then suffered a reinjury. This example also reminds us to consider which measure(s) of load is most relevant to each sport, injury and individual. High speed running is no doubt important to a hamstring injury but may be of less importance with other sports and injuries. The acute:chronic workload ratio can be applied to any of the variables you collect and may represent a different picture across different metrics.

Figure 3: From Blanch and Gabbett (2015), p2.

Rod Whiteley recently gave an excellent presentation at the Aspire Monitoring Training Loads conference entitled “The conditioning-medical paradox: should service teams be working together or as enemies on the training load battlefield?” He applied Tim Gabbett’s work to rehabilitation workloads and related it to the “chronic rehabber”; s/he who never gets to build a consistently high base of chronic workload to prepare themselves for returning to the training environment, so suffers a spike in acute:chronic workload and then a reinjury (Figure 4). He called upon us to “fundamentally rethink how we’re reintroducing the athletes” as well as breaking down the traditional silo structure between medical staff and conditioning staff.

Figure 4: Presented by Rod Whiteley, Aspire Monitoring Training Load Conference February 2016

Now we obviously cannot keep athletes away from the training environment forever and nor would we want to. However, it seems avoiding spikes in acute:chronic workloads with returning athletes may help the transition into return to training and competition, and to reduce reinjury risk. This may be achieved via further progressing the load achieved prior to RTP and/or reducing the load from reintegration by using modified training (or a mixture of both). In reality it may not be as simple as that – a major challenge for the Science and Medicine team is to manage expectations of both the athlete and the coaches. I’m sure if the athlete is looking good and undergoing a substantial training load there will be pressure to incorporate them into training.

I believe this paper highlights the need firstly to consider and plan (where possible) the progression of load throughout rehabilitation, end stage and continued into training and games. Whilst the athlete may be physically prepared for the demands of a one off training session, we must also pay attention to the demands in terms of acute and chronic load. It also highlights the need to consider the consequences of each decision relating to loading of the athlete; whether that is the decision to offload the athlete for a day (which may of course be truly necessary based on the clinical presentation) or the decision of how much load to put the athlete through day to day. In another example from the Blanch and Gabbett paper the authors put forward a representation of an injured player’s Return to Play and demonstrate how the variations in load in that week directly influence the likelihood of injury – i.e. 90% acute load would return an 11% likelihood of injury, compared to 120% which would be related to 15% risk.

Whilst injuries are undoubtedly complex, multifaceted and influenced by many factors, and statistical modelling of the risk has its own limitations, it seems the evidence is strong enough to suggest that the interaction of acute and chronic load through rehabilitation and RTP is another piece of the puzzle that is worthwhile considering.

Jo Clubb (@JoClubbSportSci)

 

References

Banister EW & Calvert TW. (1975) A systems model of training for athletic performance. Aust J Sports Med; 7: 57-61.

Blanch P & Gabbett TJ. (2015) Has the athlete trained enough to return to play safely? The acute:chronic workload ratio permits clinicians to quantify a player’s risk of subsequent injury. Br J Sports Med;0:1–5. doi:10.1136/bjsports-2015-095445

Busso T, Hakkinen K, Pakarinen A, et al. (1990) A systems model of training responses and its relationship to hormonal responses in elite weight-lifters. Eur J Appl Physiol; 61: 48-54.

Colby MJ, Dawson B, Heasman J, et al. (2014) Accelerometer and GPS-dervied running loads and injury risk in elite Australian footballers. J Strength Cond Res; 28: 2244-52.

Fleisig GS, Andrews JR, Cutter GR, et al. (2011) Risk of serious injury for young baseball pitchers: a 10-year prospective study. Am J Sports Med; 39: 253-7.

Gabbett, TJ. (2016) The training-injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med doi:10.1136/bjsports-2015-095788

Gabbett TJ & Ullah S. (2012) Relationship between running loads and soft-tissue injury in elite team sport athletes. J Strength Cond Res; 26:953-60.

Hulin BT, Gabbett TJ, Blanch P, et al. (2014) Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med; 48: 708-12.

Hulin BT, Gabbett TJ, Lawson DW, et al. (2015) The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players. Br J Sports Med; Published Online First: 28 Oct 2015. doi:10.1136/bjsports-2015-094817doi:10.1136/bjsports-2015-094817

Rogalski B, Dawson B, Heasman J, et al. (2013) Training and game loads and injury risk in elite Australian footballers. J Sci Med Sport; 16: 499-503.

 

Concussion Assessment – a guest blog by Kate Moores

Following our last blog on concussion, I started talking to Kate Moores via twitter (@KLM390) who had some very intersting experiences and ways of managing concussion. So, I am very pleased to introduce Kate as a guest blogger on the topic of Concussion assessment & management – we have decided to split Kates blog into 2 more manageable parts rather than one super-blog (My contribution may have been to add the occassional picture to the blog).

The previous blog discussed generalized pitchside assessment of a concussion, irrelevant of age. However Kate has drawn on her knowledge and experience with young rugby players to highlight in particular, the ongoing assessment of young athletes as well as adults and how it differs. Kate raises some very good points throughout but the point that really made me reflect was the consideration over “return to learn.” Looking back at concussions I’ve managed in academy football, I didn’t properly respect the impact that a day at school may have had on symptom severity or neurocognitive recovery. I was mostly interested in “have you been resting from activity?” I think this blog is an excellent resource for medical professionals, but also for teachers, coaches and parents to consider the impact of this hidden injury.

Part 1 (of Blog 2)

Conor McGoldricks first day at school

Children are not just little adults… a phrase commonly heard within healthcare. It’s particularly true when it comes to concussion. Children’s brains are structurally immature due to their rapid development of synapses and decreased levels of myelination, which can leave them more susceptible to the long term consequences of concussion in relation to their education and sporting activities. With adults the focus is usually on return to play, with similar protocols being used in managing youth concussions, albeit in a more protracted time frame.

However a child is physically, cognitively and emotionally different to adults, therefore is it appropriate for these return to play protocols to be used with youth athletes? Youth athletes are still children – still students as well as athletes. It is during these years that children develop & learn knowledge & skills (academic and social), in a similar way these youth athletes need to be learning the tactical knowledge and motor skills they will need for their sport. Shouldn’t “return to learning” be as much the focus in youth athletes as a “return to play” protocol?

“Youth Athletes are still children balancing studies with sports”

Assessment

So, the pitchside decision on management has been made (blog 1) and now the assessment continues in the treatment room

The use of the SCAT3 (here) and Child SCAT3 (age 5-12) (here) have been validated as a baseline test, a sideline assessment and to guide return to play decisions. O’Neil et al 2015 compared the then SCAT2 test against neuropsychological testing. They found that SCAT2 standardised assessment of concussion scores were correlated to poorer neuropsychological testing for memory, attention and impulsivity. However symptom severity scores had poor correlation with those same components. Therefore simply being symptom free may not be a good enough indicator that youth athletes are ready to return to learning or sport.

There has been recent research into the King Devick (K-D) test as another option for the assessment on concussion in children with research being done comparing SCAT scores with K-D testing (Tjarks et al 2013)

One of the benefits of using the KD test is that it has stronger links with the neurocognitive processing which may mean that it has a greater role to play with regard to return to learning as well as return to play. Another benefit is that unlike the SCAT3 tests the KD test does not require a health care professional to administer the test.

We educate people about how robust their body is, but should we be more cautious with brain injuries?

At a club with full time staff and consistent exposure to players, the SCAT3 can be useful to compare to pre-injury tests conducted as part of an injury screening protocol. It also helps if you know that person, for some the memory tests are challenging without a concussion so post injury assessment with the SCAT3 may score badly, but is that the person or the injury? It is also important that this assessment is done in their native language. These reasons throw up some complexities if you are working part time for a club, or covering ad hoc fixtures as part of physio-pool system. Its advisable in this instance to get a chaperone in with the athlete to help your assessment – this may be a partner for an adult player or a parent / teacher for a child. A quick conversation with them to say “please just look out for anything odd in what they say or how they say it.”

Beyond the assessment tool, there is evidence now to suggest we should be asking about pre-injury sleep patterns. Sufrinko et al (2015) (here) look prospectively at 348 athletes in middle school, high school and colligate athletes across three different states in America (aged 14-23). At the start of the season the researchers grouped the athletes as those with “sleep difficulties” (trouble falling asleep, sleeping less than normal” and a control group of “no sleeping difficulties”. Following a concussion, assessment was conducted at day 2, day 5-7 and day 10-14 using the Post Concussion Symptom Scale (PCSS) and found that those with pre-injury sleep difficulties had significantly increased symptom severity and decreased neurocognitive function for longer than the control group.

Looking in the other direction, Kostyun et al (2014) (here) assessed the quality of sleep after a concussion and its subsequent impact on recovery. Looking at 545 adolescent athletes, the results indicated that sleeping less than 7 hours post-concussion significantly correlated with increased PCSS scores, where as sleeping over 9 hours post injury significantly correlated with worse visual memory, visual motor speed and reaction times. A word of caution with this study, the authors assumed that “normal” sleep was between 7-9 hours – but anyone who has adolescent children, or hasn’t blocked the memory of being an adolescent themselves, knows that sleep duration does increase when you are growing. Saying that, the impact of both of these studies suggests that we should be:

1) Asking about normal sleep patterns prior to injury to help us gauge recovery times (disrupted sleepers may take longer than we originally predict) and;

2) We need to keep monitoring sleep quality along with regular re-assessment as sleeping more than normal may indicate ongoing recovery from concussion.

 

In Part two (here), Kate continues to discuss ongoing assessment and the recovery process.

Kate is a band 6 MSK physiotherapist, having graduated in 2011 from Cardiff Univeristy. Beyond her NHS work, Kate has worked for semi-pro Rugby League teams in Wales, the Wales Rugby League age grade teams and is now in her 3rd season as lead physio for the Newport Gwent Dragons u16 squad.

 

 

 

 

 

 

Screening: A window or just smoke?

So this our first attempt at a blog and we have decided to make the task that little bit harder by co-writing it! We hope to develop the blog as we progress covering topics (old and new) in the world of Sports Science and Medicine as well as delivering insights into our roles and how we work as part of a Multi-disciplinary team. We considered a few topics to christen our new blog, trying to emphasise the importance of communication and teamwork in a multidisciplinary team. Working in professional football, we have just conducted our end of season screening with all of our squads, from 1^st team down to under-9s.

“So because I’m no good at planks, I’m bound to get injured..? Hmm”

 

The first thing we want to emphasise is that screening and testing is not the be all and end all of injury prevention and performance development. We ourselves are sceptics and regularly question what we are actually looking at and what the results mean. Are we testing what we think we are testing? However, if you can maintain this mind-set, the use of screening is very useful indeed.

 

As an athlete experiences new exercise’s they will, through experience and adequate coaching, improve their understanding of what is required and therefore become better at that exercise. The tests that are used in the physical screening are, by nature, subject to the same learning effect. If the players struggle with an exercise at the start of pre-season, their naturally competitive nature means they will try harder at that test in the next screening. This can make it difficult to distinguish between actual improvements and the athletes increased awareness of what is required for a “good score”. Do they just get better at just that test or are they showing actual improvement??

 

It is basically impossible to cancel out the learning effect but by carefully selecting the other tests used in a testing/screening process we can try to identify common issues e.g. hip hike or knee valgus hidden in an Overhead squat may present itself in a single leg variation of a squat or lunge. It is also important not to coach the athlete through the movements so we can get a true picture of what movements come naturally to them..

 

“Screening is a snapshot of that athlete at a given time on a given day”

 

From a Physiotherapists’ perspective, we can use the screening to gather some very important objective outcome measures. This can provide us with some valuable pre-injury data that can be referred back to later in the season following an injury to a player, while from a Strength and Conditioning view baseline testing acts as the foundation for which subsequent training programs should be built. We only screen and test our healthy players, so any current injuries are not recorded. This way, we know that when the player was fit and competing on the field, they were able to score “X” in “Y“ test, so we should aim for at least those scores again before we consider them able to return to training. There is a debate that the results obtained from screening are a snapshot of that athlete at a given time on a given day, conducted a week earlier or later and we may have completely different scores. We agree. For this reason, it is essential that screening is not relied upon all season. We conduct Pre-Season, end of pre-season, Mid-Season and End-Season assessments to give us a “snap-shot” of the players through a competitive season. It is important to consider two key aspects of screening and testing, those being the;

 

Validity does the test you are using actually measure the thing you are trying to test for?

And Reliability;are the scores/results consistent? Is it repeatable? Any test cannot be considered valid if there is no reliability in the test- if the test is not consistent and has no repeatability then the testing method is invalid.

 

The answer? Screen EVERYDAY…

 

But away from all the formal data and testing environments, we continuously monitor other outcome measures such as heart rate variability, mood questionnaires, GPS data etc. as well as actually just looking and watching the athlete’s train and play. Essentially, this is screening all season, just not in our lab coats with a goniometer, optojump and a piece of paper.

Screening should confirm or deny your clinical assessments. It should not guide your treatment, programming or management. We use a variety of single planar and multi-planar tests, looking at rotational control and anti-rotational control, speed, strength and jump tests but essentially these movements are building blocks to allow our athletes to play sport. Screening allows us to conduct controlled, repeatable tests to give us an indication (not a definition) of movement patterns, strength, speed and control, all invaluable measures that we cannot gain on the field of play.

 

Essentially, if you know your players well enough, you should be able to predict who will have limited hip mobility, or who will produce the strongest isometric hamstring recordings, who requires additional speed or gym work, but you now have objective numbers and scores to work from so for your next screening date, you would hope to have influenced those scores.

 

Yours in Sport,

 

Conor and Sam

 

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