Longitudinal Dynamic Web of Determinants – The Future of Injury “Prediction” and “Prevention”

 The deeper I get into research and sports science, the more I think about this one question:

Can we predict and prevent injury?

I know your answer (NO!), but simply viewing injury in sport as a black and white construct is missing the point entirely. Like any challenging athletic endeavor, the true answer likely lies in the grey areas (and there is quite lot when it comes to injury).

(Quick note: For this post, I’m going to draw on my experience as an ACL injury researcher. But really, the concepts I discuss can apply to all other sports injuries.)

I’ve analyzed a ton of injury prediction and prevention literature as an ACL researcher. Here’s what I can conclude…the rates of ACL injury remain incredibly stable over the years.

Figure 1. ACL injury rates over a 20-year period in adolescents (6-18 years old)1
 Figure 2. ACL / MCL injury rates over a 6-year period in NFL athletes2

So, what gives? Why has all the time and effort spent researching and understanding injuries led us no further in being able to, at minimum, reduce their occurrence? To find some answers, we first need to understand the questions we’ve been asking.

Are We Asking the Right Question About Injury Prediction and Prevention?

A scenario you’ve likely come across as a sports scientist: Pre-season camp is starting, and you want to identify which athletes will most likely get injured. What do you do? Typically, we turn to a screening test. For ACL injury, this can come in the form of drop-vertical jumps or jump-cutting assessments. From there, we can analyze variables such as frontal plane knee motion or muscular activity and use this data to drive an intervention in athletes who performed poorly on it. In some athletes, frontal plane knee motion predicts ACL injury.3 In others, it does not.4,5

I do believe these tests can be useful. For one, they at least get us in the ballpark of variables we should be aware of when designing intervention strategies. Additionally, screening tests are usually cost feasible, quick to administer, and can provide a ton of data in a relatively short amount of time.

While well intentioned, the issue in isolated pre-season assessments is that they fail to take into account a very important component: athletes are like hurricanes,6 constantly changing and evolving based upon their interactions with their environment. When you’re conducting a pre-season screening test, you’re only getting one frame of data on an athlete system that will likely never behave in that way again. Further compounding the issue is the use of statistical approaches such as regression to examine how isolated or multiple factors from this single time frame can “predict” athlete behavior many months in advance. This leads us to a linear, isolated risk factor model for injury prediction and prevention (Figure 3).

Figure 3. Isolated risk factor model using pre-season screenings

So instead of asking, “which screening test should I be doing”?, the better question becomes, “how can I predict a hurricane?”

Modeling Injury through a Longitudinal Dynamic Web of Determinants

Hurricane forecasts collect millions of data points each day on variables including wind speed and direction, humidity, temperature, and moisture to inform meteorologists whether a hurricane is upcoming.7 While sports scientists will never come close to this level of data depth in the high-performance setting, there are still principles we can take from hurricane forecasts and apply to injury prediction and prevention.

1) More frequent athlete monitoring

Screening athletes once or twice per year is likely to create a very false sense of the true signal driving high injury risk behavior. Take for example the drop-vertical jump test for ACL injury risk screening. The frontal plane knee motion an athlete demonstrates at pre-season could be drastically different at early-season, mid-season, and / or during post-season. The ultimate goal should be to monitor athletes longitudinally throughout the year to determine trends in athlete behavior. Let’s say our athlete completes drop-vertical jump tests at different frequencies throughout the year. Our frontal plane knee motion may look something like the below figures.

Figure 4. Example frontal plane knee angle tracked with different frequencies during the season

While this example is extremely simplistic, it drives home an important point. Infrequent monitoring of athlete behavior leaves us guessing in terms of risk classification. Longitudinal information can assist the sports scientist (alongside the medical and strength staff) in determining the most appropriate intervention(s) at the most appropriate time(s) to bring athletes back into a lower risk category and keep them on the field.

So, is that it? We just need to track a variable long enough over time to be able to predict and prevent injury? I wish it were that simple! This leads me to my second important point…

2) Viewing injury prediction and prevention as a web of dynamic interactions

In my opinion, a great benefit of isolated risk factor models is that they can provide the sport scientist with multiple perspectives into what ultimately contributes to injury. While excessive frontal plane knee motion is a risk factor for ACL injury,8 other researchers have found that worse neurocognitive performance puts athletes at greater risk.9 Some contribute ACL injury to familial history,10 or perhaps its due to hip strength.11

Taking into account multiple isolated risk factors can help the sports scientist develop what’s known as a dynamic web of determinants.12 Think of this as a web of variables all related to a particular injury, interacting with each other in ways that may or may not be linear. The excessive frontal plane knee motion our athlete demonstrates in weeks 6–8? What if that was from decreased reaction time abilities,13 brought on by a loss of sleep14 due to high stress?15

Athlete behaviors are extremely complex and can elicit a wide range of responses due to small or large changes in how factors interact with each other over time. Taking this into consideration, the sports scientist must consider the updated injury model.

Figure 5. Dynamic web of determinants for ACL injury risk17

While the actual statistical procedures are outside the scope of this post (see the papers from Meeuwisse, 2007,16 Bittencourt, 2016,17 and Stern, 20206 for more specifics), the sports scientist should understand that isolated factor analysis simply cannot account for all of the important grey area information that contributes to the injury event. Therefore, it’s important that the sports scientist examine the most relevant features of a particular injury and attempt to quantify their contributions.

So, how can we put this new model into practice?

Practical Considerations for Injury Prediction and Prevention

In order to make use of the dynamic web of determinants model, sports scientists must be aware of the following factors:

Explain the why

The worst thing a sports scientist can do is roll out a bunch of new assessments at once without explaining their importance to the team and coaching staff. Some athletes may be hesitant at first, as they might think the data will be used against them in terms of playing time and future opportunities. It’s extremely important to continuously discuss with your team how you will leverage this data to keep the athletes on the field at peak performance. This is where our “soft skills” essentially make or break our sports science initiatives. In order for this model to work, everyone within the team (athletes, coaches, sports science, S&C, medical) needs to be on board.

Time and feasibility of assessments

Assessments should be quick to administer and not be a burden to athlete schedules. Bonus points if your assessments can be done in group settings or by position groups.

You will have to tailor your model to meet the needs and demands of your athletes, but ideal settings would allow for weekly assessments. Certain variables, such as stress and sleep quality, can be collected daily via athlete management systems or Excel / Google Sheets. Make use of the wearable technology available, as these can give you accurate information comparable to gold-standard devices (but certainly look for wearables that have been validated against laboratory-based equipment).

Athlete compliance

Athletes are competitive, so assessments should tap into this trait to not only get high quality data, but also high athlete compliance. Make use of weekly leaderboards to display the athletes that scored the highest on each assessment. Do not punish athletes for low compliance. Rather re-iterate the importance of compliance in terms of using the data for their benefit.

Bandwidth for data collection and analysis

At the end of the day, the data we collect needs to be actionable. While it’s important to collect as much information as possible, it’s equally important that we not spend our entire day collecting and analyzing data. In my experiences, coaches and athletes take notice when you are “in the trenches”. Ideally, we can assign a single value to our macro-level metrics of interest and monitor trends over time. Important macro-level variables to consider: workload, recovery, sleep, stress / anxiety, movement biomechanics, strength / power output, neurocognitive performance, nutrition / hydration status, and prior injury history.


Injuries in sports represent the most complex problem for sports scientists. While it is likely impossible that a true injury prevention and prediction solution will ever be possible, there are certainly ways in which we can improve our monitoring techniques to give us the best possible insights into injury. Taking a longitudinal approach and exploring a range of related variables will certainly be helpful for sports scientists.


1.         Beck NA, Lawrence JTR, Nordin JD, DeFor TA, Tompkins M. ACL tears in school-aged children and adolescents over 20 years. Pediatrics 2017;139:.

2.         Injury Data Since 2015. NFL.com.

3.         Hewett TE, Myer GD, Ford KR, Heidt RS, Colosimo AJ, McLean SG, van den Bogert AJ, Paterno MV, Succop P. Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes: a prospective study. Am J Sports Med 2005;33:492–501.

4.         Krosshaug T, Steffen K, Kristianslund E, Nilstad A, Mok KM, Myklebust G, Andersen TE, Holme I, Engebretsen L, Bahr R. The Vertical Drop Jump Is a Poor Screening Test for ACL Injuries in Female Elite Soccer and Handball Players: A Prospective Cohort Study of 710 Athletes. Am J Sports Med 2016;44:874–883.

5.         Nilstad A, Petushek E, Mok KM, Bahr R, Krosshaug T. Kiss goodbye to the ‘kissing knees’: no association between frontal plane inward knee motion and risk of future non-contact ACL injury in elite female athletes. Sports Biomech 2021;1–15.

6.         Stern BD, Hegedus EJ, Lai YC. Injury prediction as a non-linear system. Phys Ther Sport 2020;41:43–48.

7.         Magnusson L, Bidlot JR, Bonavita M, Brown AR, Browne PA, Chiara GD, Dahoui M, Lang STK, McNally T, Mogensen KS, Pappenberger F, Prates F, Rabier F, Richardson DS, Vitart F, Malardel S. ECMWF Activities for Improved Hurricane Forecasts. Bulletin of the American Meteorological Society 2019;100:445–458.

8.         Della Villa F, Buckthorpe M, Grassi A, Nabiuzzi A, Tosarelli F, Zaffagnini S, Della Villa S. Systematic video analysis of ACL injuries in professional male football (soccer): injury mechanisms, situational patterns and biomechanics study on 134 consecutive cases. Br J Sports Med 2020;54:1423–1432.

9.         Swanik, Covassin T, Stearne DJ, Schatz P. The relationship between neurocognitive function and noncontact anterior cruciate ligament injuries. Am J Sports Med 2007;35:943–948.

10.       Myer GD, Heidt RS, Waits C, Finck S, Stanfield D, Posthumus M, Hewett TE. Sex comparison of familial predisposition to anterior cruciate ligament injury. Knee Surg Sports Traumatol Arthrosc 2014;22:387–391.

11.       Khayambashi K, Ghoddosi N, Straub RK, Powers CM. Hip Muscle Strength Predicts Noncontact Anterior Cruciate Ligament Injury in Male and Female Athletes: A Prospective Study. Am J Sports Med 2016;44:355–361.

12.       Philippe P, Mansi O. Nonlinearity in the epidemiology of complex health and disease processes. Theor Med Bioeth 1998;19:591–607.

13.       Herman, Barth JT. Drop-jump landing varies with baseline neurocognition: implications for anterior cruciate ligament injury risk and prevention. Am J Sports Med 2016;44:2347–2353.

14.       Benjaminse A, Webster KE, Kimp A, Meijer M, Gokeler A. Revised Approach to the Role of Fatigue in Anterior Cruciate Ligament Injury Prevention: A Systematic Review with Meta-Analyses. Sports Med 2019;49:565–586.

15.       Almojali AI, Almalki SA, Alothman AS, Masuadi EM, Alaqeel MK. The prevalence and association of stress with sleep quality among medical students. Journal of Epidemiology and Global Health 2017;7:169–174.

16.       Meeuwisse WH, Tyreman H, Hagel B, Emery C. A dynamic model of etiology in sport injury: the recursive nature of risk and causation. Clin J Sport Med 2007;17:215–219.

17.       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;50:1309–1314.

Jason’s Articles of the Month (March 2021)

Another monthly addition of my articles of the month! These articles cover sports-related concussion, repetitive head impacts, ACL injury risk, and the relationship between cognition and neuromotor performance.

Summary: Large prospective study of nearly 5,000 athletes that determined musculoskeletal injury rate was 87% greater in athletes who reported a prior sports-related concussion (SRC) within the previous 12 months. Interesting, this relationship was only present in non-contact, acute musculoskeletal injuries after SRC.

Summary: Individuals with a history of ACLR and matched controls completed neurocognitive testing, a lower extremity proprioception assessment, measures of dynamic lower extremity control, and neuroimaging. Increased visual cognition was associated with better proprioception and decreased time to stability during the jump-landing. Visual cognition was also associated with increased activation in brain regions related to sensory processing and motor control.

Summary: Knee biomechanics have been heavily studied as it relates to noncontact knee injuries in athletes. In this review, knee kinematics and kinetics were not associated with injury. This may be due to biomechanical assessments often ignoring any sort of cognitive constraint (e.g, temporal, space, obstacles) that is commonly seen in a sporting environment.

Summary: This was the first investigation to examine the relationship between repetitive head impacts and cervical spinal cord white matter integrity. White matter tracts associated with balance and postural control were most negatively affected following one season of football. Subsequent studies following concussive events may provide greater insight into the neural underpinnings of greater risk for lower extremity injury post-SRC.

Summary: A seminal paper defining various constructs of sports injury occurrence, data analysis, and injury risk factors and prevention. Recommended reading for anyone involved in sports injury research and clinical practice.

Jason’s Articles of the Month (February 2021)

At the beginning of each month, I am going to start posting 5-10 articles I’ve reviewed and believe to be important for clinicians, coaches, parents, etc.

I’ll likely focus these articles on sports-related concussion, ACL injury, adolescent athletes, and biomechanics. Enjoy!

Summary: While there have been previous lower extremity injury surveillance datasets conducted in a variety of athletes post-concussion, this article was the first to demonstrate a specific relationship between concussion and ACL injury. Those with a concussion history in the previous 3 years were 1.6x more likely to sustain and ACL injury compared to controls. About half of the total cases examined in this study were due to sport.

Summary: Injury prediction is the holy grail of sports science. This article provides a nice overview of why current injury prediction methods are flawed (namely due to cross-sectional nature of screening) and provides opportunities to improve our models.

Summary: This article reviews biomechanical and physiological adaptations that occur after ACL injury and offers integrated strategies to restore motor control post-ACLR. Commentary is provided through perspectives including neuroscience, biomechanics, motor control/learning, and psychology.

Summary: One of the first articles to demonstrate the influence of neurocognition on musculoskeletal injury. Collegiate athletes who sustained a noncontact ACL injury performed worse on assessments of reaction time, working memory, and processing speed compared to matched controls.

Summary: This paper offers possible neuromuscular explanations for increased risk of musculoskeletal injury after concussion. Neuromuscular control post-concussion may be better understood by utilizing dynamic tasks during clinical rehabilitation, including gait and/or sport-specific scenarios.

Rethinking ACL Rehabilitation and Prevention

Hi all! Below is a preliminary list of ACL literature connecting motor learning to prevention and rehabilitation. By no means is this an exhaustive live. It’ll be updated. PDF’s are available! Just email one of us.

ACL injury prevention, more effective with a different way of motor learning?


Optimization of the anterior cruciate ligament injury prevention paradigm: novel feedback techniques to enhance motor learning and reduce injury risk.


Principles of Motor Learning to Support Neuroplasticity After ACL Injury: Implications for Optimizing Performance and Reducing Risk of Second ACL Injury


Novel methods of instruction in ACL injury prevention programs, a systematic review

Click to access Novel-methods-of-instruction-in-ACL-injury-prevention-programs-a-systematic-review.pdf

Mechanisms Underlying ACL Injury-Prevention
Training: The Brain-Behavior Relationship


The effects of attentional focus on jump performance and knee joint kinematics in patients after ACL reconstruction

Click to access GokelerPhysTherSport2015.pdf

Immersive virtual reality improves movement patterns in patients after ACL reconstruction: implications for enhanced criteria- based return-to-sport rehabilitation

Click to access 544a22e90cf2f6388084f5a5.pdf

Using principles of motor learning to enhance ACL injury prevention programs


Training for Prevention of ACL Injury: Incorporation of Progressive Landing Skill Challenges Into a Program

Click to access Training_for_Prevention_of_ACL_Injury__.10.pdf

Feedback Techniques to Target Functional Deficits Following Anterior Cruciate Ligament Reconstruction: Implications for Motor Control and Reduction of Second Injury Risk




Neuroplasticity Following Anterior Cruciate Ligament Injury: A Framework for Visual-Motor Training Approaches in Rehabilitation


Review of the Afferent Neural System of the Knee and Its Contribution to Motor Learning


Altered electrocortical brain activity after ACL reconstruction during force control


Neuroplasticity Associated With Anterior Cruciate Ligament Reconstruction


Does brain functional connectivity contribute to musculoskeletal injury? A preliminary prospective analysis of a neural biomarker of ACL injury risk

Click to access diekfuss_brain_connectivity_injuries.pdf

A novel approach to enhance ACL injury prevention programs


Is neuroplasticity in the central nervous system the missing link to our understanding of chronic musculoskeletal disorders?


Sports-Related Concussion in the Adolescent Athlete

In this blog post, I’m going to discuss sports-related concussion in adolescent athletes.  I’ll also discuss the research I conducted at UNLV, in which I examined lower body injury risk in previously concussed youth athletes.

Sports-related concussions (SRCs) are a major epidemiological concern among the adolescent athletic population.  The majority of SRCs in the United States are sustained by adolescents athletes (< 18 years old), as it is estimated that 1.1–1.9 million cases occur annually.3  Similarly to collegiate and professional counterparts, sports such as football, lacrosse, ice hockey, and soccer account for the highest rates of SRCs in youth athletics.1,11,16  Additionally, it appears that the risk of SRC in youths is increasing at comparable rates to older sport competitors.  Over an 11 year study period consisting of 158,430 high school athletes, Lincoln et al. (2011) reported a 15.5% increase in reported SRCs, a trend similar to collegiate male football participants.19

It has been suggested that adolescent athletes require a more conservative approach to SRC management and return-to-sport.6  The majority of collegiate and professional competitors receive clinical clearance to resume sport participation 5–7 days post-SRC,13,14  however, it appears that youth athletes take longer for symptoms to resolve,7,17 as well as a return to pre-concussive performance on NP tests5 and postural control tasks9,15 compared to older individuals.  While reported SRC symptoms (headache, dizziness, and difficulty concentrating) were similar across age groups, 19.5% and 16.3% of high school and adolescent football athletes required at least 30 days to resume sport, respectively, compared with 7% of collegiate competitors.10

It appears that task difficulty may influence SRC recovery trajectories in the adolescent athlete.  While the majority of adolescent athletes return-to-sport within four weeks post-SRC,7 locomotor deficits may still be present when paired with a secondary cognitive task.  In a study comparing adolescent (mean age = 15 years old) and young adult (mean age = 20 years old) recovery trajectories following a concussive injury, Howell et al. (2014) found that adolescents were less accurate on a Stroop task and displayed greater ML COM displacement during a dual-task walking condition compared to adolescent controls at two months post-SRC.9  These cognitive and motor deficits were not determined in the concussed young adult group when matched to their control group.9  Interestingly, Howell et al. (2018) revealed that post-concussive adolescent athletes who reported a future sports-related injury (SRC or musculoskeletal) demonstrated an approximately 8% increase in dual-task cost walking speed over a one year time period.8  This recent finding suggests that while clinical clearance may be granted within a four week time period for the majority of adolescents, subtle locomotor deficits may linger beyond sport resumption and contribute to future injury risk.  Presently, researchers have not be able to adequately predict indicators of prolonged recovery,20 potentially attributed to large inter-individual variances in cognitive growth and maturation among adolescents.  It has been suggested that prolonged SRC recovery in the adolescent athlete may be due to various factors including continued cognitive development,10 inadequate neck strength,4 and the time to which one seeks medical care from a concussion specialist.2  In their examination of factors related to delayed recovery from SRC, Bock et al. (2015) reported that 62.3% of concussed adolescents did not seek medical care until at least one week post-injury.2  Those who were evaluated by a concussion specialist within a week of injury reported significantly shorter RTP time (median = 16 days) versus those who waited beyond one week (median = 36 days).2

Recent research suggests that concussed adolescent athletes are at a greater risk for lower body injury.  In a study of 18,216 male and female high school athletes, investigators determined that lower body injury risk resulting in time-loss from sport (defined as greater than the day of injury) increased by 34% for every previous SRC.12  However, a prior SRC did not result in greater risk of a non-time loss injury, although the distinction between the lower body injury classification following an SRC in high school athletes is presently unclear.12  The mechanisms responsible for an elevated lower body injury risk post-SRC in the adolescent athlete are presently unclear, however, Reed, Taha, Monette, and Keightley (2016) found that concussed teenage hockey players performed significantly worse on isometric handgrip and squat jump tests during the symptomatic and asymptomatic time periods compared to controls.18

While neuromuscular alterations may exist beyond clinical clearance to resume sport, my doctoral research at UNLV sought to examine biomechanical patterns during drop-landing tasks in adolescent athletes with and without an SRC history.  The video below is a from the UNLV 3-Minute Thesis competition (I placed second overall) and the link is from a recent interview with the UNLV Graduate College.

3MT – https://www.youtube.com/watch?v=d0gnBNnhV3E

Interview – https://www.unlv.edu/news/article/concussions-ripples-felt-throughout-body

Essentially, I found biomechanical alterations at both the ankle and knee joints that would suggest post-concussive adolescents are at greater risk for lower body injury during landing tasks.  We’re in the peer-review process for this particular study, so be on the lookout for that (hopefully) soon.  I’m still attempting to determine the why post-concussive athletes are at greater risk for lower body injury well beyond symptom resolution and a (seemingly) return to baseline cognitive performance; my next research studies will be examining neuropsychological correlates to lower body injury risk in collegiate athletes who have a prior SRC history.  Hopefully this will give us a better understanding of the association between SRC and lower body injury.  Stay tuned…


Twitter – @JasonAvedesian

Email – jason.avedesian@unlv.edu


  1. Bakhos LL, Lockhart GR, Myers R, Linakis JG. Emergency Department Visits for Concussion in Young Child Athletes. PEDIATRICS. 2010;126(3):e550-e556. doi:10.1542/peds.2009-3101.
  2. Bock S, Grim R, Barron TF, et al. Factors associated with delayed recovery in athletes with concussion treated at a pediatric neurology concussion clinic. Child’s Nervous System. 2015;31(11):2111-2116. doi:10.1007/s00381-015-2846-8.
  3. Bryan MA, Rowhani-Rahbar A, Comstock RD, Rivara F, Seattle Sports Concussion Research Collaborative. Sports- and Recreation-Related Concussions in US Youth. PEDIATRICS. 2016;138(1):e20154635-e20154635. doi:10.1542/peds.2015-4635.
  4. Collins MW, Kontos AP, Reynolds E, Murawski CD, Fu FH. A comprehensive, targeted approach to the clinical care of athletes following sport-related concussion. Knee Surg Sports Traumatol Arthrosc. 2014;22(2):235-246. doi:10.1007/s00167-013-2791-6.
  5. Covassin T, Elbin RJ, Harris W, Parker T, Kontos A. The Role of Age and Sex in Symptoms, Neurocognitive Performance, and Postural Stability in Athletes After Concussion. Am J Sports Med. 2012;40(6):1303-1312. doi:10.1177/0363546512444554.
  6. Foley C, Gregory A, Solomon G. Young age as a modifying factor in sports concussion management: what is the evidence? Curr Sports Med Rep. 2014;13(6):390-394. doi:10.1249/JSR.0000000000000104.
  7. Halstead ME, Walter KD, Moffatt K, Council on Sports Medicine and Fitness. Sport-Related Concussion in Children and Adolescents. Pediatrics. 2018;142(6). doi:10.1542/peds.2018-3074.
  8. Howell DR, Buckley TA, Lynall RC, Meehan WP. Worsening Dual-Task Gait Costs after Concussion and their Association with Subsequent Sport-Related Injury. Journal of Neurotrauma. 2018;35(14):1630-1636. doi:10.1089/neu.2017.5570.
  9. Howell DR, Osternig LR, Koester MC, Chou L-S. The effect of cognitive task complexity on gait stability in adolescents following concussion. Exp Brain Res. 2014;232(6):1773-1782. doi:10.1007/s00221-014-3869-1.
  10. Kerr ZY, Zuckerman SL, Wasserman EB, Covassin T, Djoko A, Dompier TP. Concussion Symptoms and Return to Play Time in Youth, High School, and College American Football Athletes. JAMA Pediatrics. 2016;170(7):647. doi:10.1001/jamapediatrics.2016.0073.
  11. Lincoln AE, Caswell S V., Almquist JL, Dunn RE, Norris JB, Hinton RY. Trends in Concussion Incidence in High School Sports. The American Journal of Sports Medicine. 2011;39(5):958-963. doi:10.1177/0363546510392326.
  12. Lynall RC, Mauntel TC, Pohlig RT, et al. Lower Extremity Musculoskeletal Injury Risk After Concussion Recovery in High School Athletes. Journal of Athletic Training. 2017;52(11):1062-6050-52.11.22. doi:10.4085/1062-6050-52.11.22.
  13. Makdissi M, McCrory P, Ugoni A, Darby D, Brukner P. A Prospective Study of Postconcussive Outcomes after Return to Play in Australian Football. The American Journal of Sports Medicine. 2009;37(5):877-883. doi:10.1177/0363546508328118.
  14. McCrea M, Guskiewicz KM, Marshall SW, et al. Acute Effects and Recovery Time Following Concussion in Collegiate Football Players. The Journal of the American Medical Association. 2003;290(19):2556-2563. doi:10.1001/jama.290.19.2556.
  15. Nelson LD, Guskiewicz KM, Barr WB, et al. Age Differences in Recovery After Sport-Related Concussion: A Comparison of High School and Collegiate Athletes. Journal of athletic training. 2016;51(2):142-152. doi:10.4085/1062-6050-51.4.04.
  16. O’Connor KL, Baker MM, Dalton SL, Dompier TP, Broglio SP, Kerr ZY. Epidemiology of Sport-Related Concussions in High School Athletes: National Athletic Treatment, Injury and Outcomes Network (NATION), 2011–2012 Through 2013–2014. Journal of Athletic Training. 2017;52(3):175-185. doi:10.4085/1062-6050-52.1.15.
  17. Purcell L, Harvey J, Seabrook JA. Patterns of Recovery Following Sport-Related Concussion in Children and Adolescents. Clinical pediatrics. 2016;55(5):452-458. doi:10.1177/0009922815589915.
  18. Reed N, Taha T, Monette G, Keightley M. A Preliminary Exploration of Concussion and Strength Performance in Youth Ice Hockey Players. International Journal of Sports Medicine. 2016;37(09):708-713. doi:10.1055/s-0042-104199.
  19. Westermann RW, Kerr ZY, Wehr P, Amendola A. Increasing Lower Extremity Injury Rates Across the 2009-2010 to 2014-2015 Seasons of National Collegiate Athletic Association Football. The American Journal of Sports Medicine. 2016;44(12):3230-3236. doi:10.1177/0363546516659290.
  20. Zemek RL, Farion KJ, Sampson M, McGahern C. Prognosticators of persistent symptoms following pediatric concussion: A systematic review. JAMA Pediatrics. 2013;167(3):259-265. doi:10.1001/2013.jamapediatrics.216.

The Rebel Movement Podcast (Episode 2) – ACL and the Brain: From Research to Application

We had a great time chatting away last night on ACL injury, prevention, rehabilitation and more! We had a privilege to talk through the lens of our own disciplines (biomechanics and motor learning). Let us know what you like, dislike, and how we can improve. As always, if you’d like to write for our blog, please reach out to either one of us!

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