“The multifactorial complex nature of sports injuries arises not from the linear interaction between isolated and predictive factors, but from the complex interaction among a web of determinants”

Bittencourt et al. (2016)1

Shoulder overload injuries in the career of elite level throwing & overhead athletes, tendon problems in jumping athletes, hamstring injuries in soccer players; high risk events consistent with the sporting demands. The leaps made in lower limb monitoring in recent years should be the catalyst for creatively challenging the way we throw our energy into developing impactful systems for upper limb dominant sports.

The narrative contained within published literature on shoulder injury prediction continues to be predictable. In spite of some well written summaries and some excellent large cohort studies in handball, reductionist models that could be deemed to oversimplify modifiable risk factors (range of motion, rotator cuff muscle weakness, and training load) do not seem to address the complex interplay of factors that warn us whether an athlete will have a higher likelihood of becoming injured or experiencing a drop in performance level. (2)(3) In part this may be because isolated measures are often taken in the form of screening or lab-based testing and do not take into account the individual athlete’s potential for adaptation throughout a competitive season or training block. This echoes the thoughts of Roald Bahr (4) in his editorial titled “Why screening doesn’t work and never will”, and is summarised nicely in terms of athletes as complex systems by Sergio Fonseca; “Athletes (complex systems) evolve and adapt to constantly changing demands of their environment”. (5) So how, why and what should we monitor to keep our best athletes healthy and consistently able to perform?

Interpretation and suggestions

State versus Trait – Screening at the start of the season or profiling at regular time points during a season should form only part of a continuous in-season monitoring process that attempts to provide information on the state of an athlete to warn about upcoming events (injury risk profile / drops in performance – “early warning signals”).

• Trainable Deficits – The interaction between risk factors (web of determinants), not just isolated risk factors should be used to identify & plan appropriate interventions.
• Build real intelligence – Identify those factors that more strongly determine the outcome.
• Use valid, reliable & sensitive measures (6)
• Ensure they are easily interpretable (field based rather than lab based turned round quickly) & impactful (used to influence a change in programming / training) (6)
• Train Smarter & Harder – Training should be sufficient to optimise athletic performance and mitigate against injury risk. (7) Linking workloads (in particular high intensity actions) to changes in athlete state is a change that needs to be embraced at all levels of an organisation.

The challenge

So, what’s the solution? By doing great work with great people, we dive into the complex interplay of factors that influence athlete injury risk & athlete performance levels as they relate to shoulder health & performance. We open up the challenge to a diverse group of high performing individuals and teams in this space to share what they can (sensitivity around share of information is recognised) and close this knowledge gap. We create a web of determinants model for shoulder injury and performance (see example Figure 1) that starts to address some unanswered questions, and harness applied knowledge to produce a change in thinking.

Figure 1 – Web of determinants for shoulder overuse injury (rotator cuff) (adapted from Bittencourt et al. 2016)

Progress

Already through conversations over the last 5 years with researchers and practitioners in high performing teams worldwide, there are a number of interesting factors that are being linked to injury and performance. Below is a snapshot of some more simple isolated measures and some more complex interactions that differ from published research.

• Strength matters – Baseball pitchers who were able to perform unilateral dumbbell press at 50% of their bodyweight for 3RM showed lower likelihood of time loss injury over the preceding 3 seasons. Judo throwing athletes had a higher likelihood of missing training sessions if their hand held dynamometer (HHD ER in prone 90/0) score fell below 20% of their bodyweight.
• A ground up approach – By looking at the interaction between lower body and upper body force production utilising a combination of tests (Isometric mid-thigh pull , Belt Squat, ASH Test ⁸, Force Frame ER:IR) it is possible to create a more useful profile than by testing shoulder force production in isolation. Profiles from tennis and baseball showed athletes who either produced high lower body force tended to overload their shoulders with low upper body force that was unable to cope, or athletes who were unable to generate enough lower body force who were required to compensate by generating force from the shoulder.
• Build resilience – Throwing athletes with low force and low rate of force production were more likely to pick up a time loss injury over a baseball season. 
• Rate of force development (RFD) has been shown to be a reliable measure once athletes are familiarised and testing conditions are set up for RFD.^9,10 In one baseball organisation, RFD @ 100ms in an ASH Test (Y-position) accounted for 0.56 (31%) of the variance in pitch velocity (unpublished data) of 92 players who had pitched a fast ball through the season.
• Secret Squirrel – a number of organisations are monitoring changes in the throwing movement signature (kinematics), using motion capture and other on-field / on-court measures indicative of variability that may act as early warning signals when used in conjunction with measures of force (kinetics) and tissue health.

Next Steps

In terms of external validation, it is not always guaranteed that what works elsewhere (published or unpublished research) will work for every athlete. There will be environmental and individual athlete characteristics (psychological etc.) that mean certain factors will represent less influence on outcome within each organisation. An internal validation process using appropriate data science is the next step to look deeper into these interactions, enabling teams to build intelligence around their own athletes, mastering “the N=1 game” and facilitating more meaningful conversations in the future.

References

1.         Bittencourt NFN, Meeuwisse WH, Mendonça LD, Nettel-Aguirre A, Ocarino JM, Fonseca ST. Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition-narrative review and new concept. Br J Sports Med. 2016;50(21):1309-1314. doi:10.1136/bjsports-2015-095850

2.         Tooth C, Gofflot A, Schwartz C, et al. Risk Factors of Overuse Shoulder Injuries in Overhead Athletes: A Systematic Review. Sports Health. 2020;12(5):478-487. doi:10.1177/1941738120931764

3.         Møller M, Nielsen RO, Attermann J, et al. Handball load and shoulder injury rate: a 31-week cohort study of 679 elite youth handball players. Br J Sports Med. 2017;51(4):231-237. doi:10.1136/bjsports-2016-096927

4.         Bahr R. Why screening tests to predict injury do not work-and probably never will…: a critical review. Br J Sports Med. 2016;50(13):776-780. doi:10.1136/bjsports-2016-096256

5.         Fonseca ST, Souza TR, Verhagen E, et al. Sports Injury Forecasting and Complexity: A Synergetic Approach. Sports Med Auckl NZ. 2020;50(10):1757-1770. doi:10.1007/s40279-020-01326-4

6.         Impellizzeri F, Marcora S. Test Validation in Sport Physiology: Lessons Learned From Clinimetrics. Int J Sports Physiol Perform. 2009;4:269-277. doi:10.1123/ijspp.4.2.269

7.         Gabbett TJ. The training-injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016;50(5):273-280. doi:10.1136/bjsports-2015-095788

8.         Ashworth B, Hogben P, Singh N, Tulloch L, Cohen DD. The Athletic Shoulder (ASH) test: reliability of a novel upper body isometric strength test in elite rugby players. BMJ Open Sport Exerc Med. 2018;4(1):e000365. doi:10.1136/bmjsem-2018-000365

9.         Ashworth B, Cohen DD. Force awakens: a new hope for athletic shoulder strength testing. Br J Sports Med. 2019;53(9):524. doi:10.1136/bjsports-2018-099457

10.       Maffiuletti NA, Aagaard P, Blazevich AJ, Folland J, Tillin N, Duchateau J. Rate of force development: physiological and methodological considerations. Eur J Appl Physiol. 2016;116(6):1091-1116. doi:10.1007/s00421-016-3346-6

Podcast references

Tim Gabbett (Ep. 4), Sergio Fonseca (Ep. 55), Matt Jordan (Ep. 21), “European Shoulder Series” – Fredrik Johansson (Ep. 61), Stig Andersson (Ep. 62) and Martin Asker (Ep. 64).

& sharing what I can & not what I can’t with the ever-growing “Athletic Shoulder Network”; Tim Pelot (USOC), Chris McLeod (LTA), Ryan Crotin (LA Angels), Cory Kennedy (Chicago Cubs), Clive Brewer (formerly Toronto Blue Jays), Dan Howells (formerly Houston Astros), Joseph Coyne (UFC Shanghai), Ian Horsley (English Institute of Sport), Gus Morrison (ISEH).