Beyond the Sets and Reps: An Introduction.

Therein lies a correlation between the brain and body that is a direct result of an interdisciplinary approach to understanding sports science. Beyond the programming is an interaction of disciplines closely examining the chaotic nature of our being. With The Rebel Movement blog, our mission is to investigate and further the science and philosophy of motor performance. Through our collaborative approach of skill acquisition and movement analysis, we will provide thought-provoking content applicable to athletes, coaches, researchers, and practitioners.  Your feedback is vital to the blog’s mission, so please feel free to provide your thoughts, comments, and considerations. We look forward to building a stronger foundation for the sports science community.

– Harjiv and Jason

Genes with Jeb: Advancements in Exercise with Omics Technology

Screen Shot 2020-05-25 at 3.09.49 PM

Jeb Struder is currently a doctoral student at the University of Connecticut studying  the cellular and molecular responses of skeletal muscle to exercise and stressful environments. He also serves as the Director of Research for the Korey Stringer Institute


Twitter:  @j_struder



Transcriptomic Profiling of Skeletal Muscle Adaptations to Exercise and Inactivity


Sports-Related Concussion: Pt. 1 (Injury Incidence, Physiological Mechanisms, and Head Impact Biomechanics)

Over the last decade or so, we have seen an exponential rise in research and media attention pertaining to sports-related concussions (SRCs).  And for good reason, as it is estimated that upwards of 4 million SRCs occur each year in the United States alone.  I’ve previously written about the newfound association between SRC and lower extremity injuries (, but in this blog series I want to take a much deeper dive into SRCs themselves, particularly on the latest research and clinical practices.  Fair warning: SRCs are a difficult injury to study and manage due to the heterogeneous nature of symptomology and recovery.  Simply put, there are still a lot of unknowns, but I hope this blog post gives you an idea of our current knowledge base and where we are heading in the future.  Feel free to reach out via Twitter (@JasonAvedesian) or email ( in you want to talk more about SRCs!

SRC Injury Incidence

A good starting point is to discuss the incidence of SRCs.  Contact-centric sports such as football, rugby, ice hockey, and soccer make up the majority of SRCs across all levels of competition.  Recent reports suggest that SRCs account for 9.6% and 4.0% of total injuries in youth and high school football athletes, respectively.10  Overall, SRCs comprise approximately 6.2% of total injuries sustained in NCAA athletes,24 and certain sports such as basketball and lacrosse have seen the rates of SRCs nearly double compared to the previous 15 years.7  Athletes appear to be at the greatest risk for SRC during competitions, as recently it has been reported that male and female collegiate soccer athletes were at a 5.54 and 9.05 times greater risk for an in-game SRC versus one sustained in practice.24  Across 20 high school sports, investigators recently reported an incidence rate of 10.37 SRCs per 10,000 athletic exposures in competition versus a 2.04 SRCs per 10,000 athletic exposures during practice.18  Furthermore, over three-quarters of collegiate athletes report an SRC during the in-season sport phase, with the majority (61.4%) occurring in competition.7  It is speculated that a more aggressive playing style and a higher frequency of head impacts during games, compared to practice, may lead to this increased risk for SRC.22

Concussion rates for NCAA athletes from 2009-2014 (Zuckerman, 2015)

Physiological Mechanisms of SRC

An SRC is typically viewed as a functional injury rather than an injury with both functional and structural damages.  Once an athlete sustains an SRC, a cascade of neurometabolic events occur in an attempt to restore ionic balance within the injured brain.12  A release of glutamine and aspartate may lead to cell permeability alterations that damage and ultimately kill the cell.13  The aforementioned amino acids lead to potassium ions exiting the cell, while a sodium and calcium influx occurs, thereby changing cellular pH levels and causing the blood vessels to constrict.11  An “energy crisis” occurs as the brain requires increased glucose metabolism to restore membrane potential, all while being in a state of reduced cerebral blood.11  This mismatch in energy supply and demand is thought to express itself through acute psychological and motor behavior changes commonly seen in concussed athletes.11

Neurometabolic cascade of concussive injury (Giza, 2014)

SRCs general reflect a pathophysiological disturbance rather than an injury readily seen on standard neuroimaging measures.  As such, traditional medical imaging techniques (e.g. CT scans) may not demonstrate the sensitivity to detect micro alterations following a concussive event.  However, recent medical advances have allowed researchers to gain further insight into the subtle, yet lingering physiological alterations that occur following an SRC.  These techniques include functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), fluid biomarkers, and brain metabolites.  During verbal and visual memory tasks, symptomatic and asymptomatic male athletes demonstrated significantly less activation of the dorsolateral prefrontal cortex,5,6 a brain region associated with working memory performance.  More recently, previously concussed athletes have demonstrated increased activation of various brain regions (right/left dorsolateral prefrontal cortex and cerebellum) during spatial processing tasks versus controls, potentially indicative of cortical compensation to match performance of those without a concussive history.23  Even in the absence of a diagnosed SRC, researchers have seen abnormal white matter characteristics within the brain that is linked to worse memory performance following a single season of high school football.8  Utilizing DTI and magnetic resonance, female athletes who sustained an SRC displayed abnormalities within the primary motor cortex, white matter tract, and corticospinal tract compared to non-concussed athletes.4  Most concerning, imaging was performed in symptom-free athlete on average 18.9 months post-SRC, suggesting chronic impairments in decision-making and motor execution.4  Various TMS studies have also shown that post-concussive athletes demonstrate an elevated cortical silent period, a mechanisms of motor cortex inhibition.  Prolonged cortical silent period has been shown both acutely (i.e. 8 weeks)20 and chronically (i.e. 19 months)9 following a concussive event.

Overall, it does appear that post-concussive athletes demonstrate physiological alterations beyond traditional clinical resolution (i.e. symptom free, return to baseline on cognitive / balance assessments).  While the aforementioned techniques offer promise for SRC diagnosis and management, much remains in terms of clinical validation and establishing appropriate monitoring protocols.  For example, there is a severe lack of longitudinal study within the imaging literature that currently limits our understanding of when(if?) physiological recovery occurs following an SRC.17

Head Impact Biomechanics and SRC Risk

To ascertain biomechanical mechanisms of SRC, researchers have conducted head impact studies in a variety of sporting populations.  The proposed rationale for analyzing variables such as linear and rotational head acceleration during impact events is that “thresholds” for a concussive event can be determined, along with the analysis of total “subconcussive” impacts that an athlete may sustain over the course of a practice, game, and/or season.  We know with great certainty that many SRCs go unreported, therefore, measuring head accelerations in real-time may provide us with an objective tool to immediately pull an athlete from the field and conduct a clinical evaluation if an SRC is suspected.  There are a few different impact sensor systems, including (1) multi-sensor units placed in the helmet; (2) a single sensor attached to the skin (forehead or neck); (3) sensor-equipped mouthguards.  While these sound like a great devices to add to the SRC assessment toolbox, there are some significant limitations that I will address at the end of this section.

Unsurprisingly, football and soccer athletes sustain thousands of head impacts (defined as greater than 10g) each season.15,22  Most head impacts in collegiate athletes range between 20–30g,14 although significantly greater head accelerations, at a more frequent rate, are sustained in competition versus practice.22  While impacts located on the front, side, and top of the head are associated with higher risk for SRC,2 the frequency and location in which an athlete receives a head impact may be affected by their visual and sensory performance.16

There does appear to be a negative cumulative effect of head impacts in the absence of a diagnosed SRC.  In non-concussed high school football athletes, those with demonstrated cognitive performance declines sustained a significantly greater amount of median head impacts versus counterparts without change in cognitive performance (1103 versus 438 head impacts).1  Researchers have also demonstrated a dose-response relationship between cumulative head impacts and cognitive impairments following the completion of an athletic career.  In a cohort of former high school and collegiate football players, 1,800–2,400 cumulative head impacts were found to be significant thresholds for risk of developing depression, with an additional 2,800 impacts associated with a 2x risk for later life neurological consequences such as white matter brain changes.21

Head impact characteristics of high school individuals: (1) diagnosed with concussion and demonstrated functional impairment (COI+/FOI+); (2) not diagnosed with concussion and no functional impairment (COI-/FOI-); (3) not diagnosed with concussion and demonstrated functional impairment (COI-/FOI+) (Breedlove, 2012)

There are some strong limitations with the current head impact technology.  First, helmet impact sensor systems assume that the helmet and skull move as a single body, therefore, improper helmet fit may overestimate true acceleratory values.  Presently, there is limited data on the accuracy of single sensor systems; it’s unclear if these sensors can differentiate between received head impacts versus those due to purposeful neck motion.  The biggest issue surrounding impact sensor technology is that researchers have been unable to determine a distinct head impact threshold leading to an SRC, as athletes may be concussed following a wide range of recorded head accelerations during sport.3  For example, Mihalik et al. (2017) found that “diagnosed concussion impacts ranged from 40.3g to 173.22g in linear acceleration and 163.35 to 15393.07 rad/s2 in rotational acceleration” while “noninjury impacts ranged from 10.00g to 350.00g in linear acceleration and 0.15 to 30,601.02 rad/s2.19  Simply put, there are many influential risk factors that these impact devices cannot account for, including head impact / SRC history, gender, and anthropometric measures.

SRC specificity and sensitivity of recorded head impacts (Mihalik, 2017)

As you can see, there is a lot to unpack with this complex injury.  Hopefully this gives you a better understanding of SRC from an incidence and physiological / biomechanical perspective. This wraps up Part 1 in our SRC deep dive. Stay tuned for more coming soon!





  1. Breedlove EL, Robinson M, Talavage TM, et al. Biomechanical correlates of symptomatic and asymptomatic neurophysiological impairment in high school football. Journal of Biomechanics. 2012;45(7):1265-1272.
  2. Broglio SP, Schnebel B, Sosnoff JJ, et al. Biomechanical properties of concussions in high school football. Med Sci Sports Exerc. 2010;42(11):2064-2071.
  3. Brolinson PG, Manoogian S, McNeely D, Goforth M, Greenwald R, Duma S. Analysis of linear head accelerations from collegiate football impacts: Current Sports Medicine Reports. 2006;5(1):23-28.
  4. Chamard E, Lassonde M, Henry L, et al. Neurometabolic and microstructural alterations following a sports-related concussion in female athletes. Brain Injury. 2013;27(9):1038-1046.
  5. Chen J, Johnston KM, Collie A, McCrory P, Ptito A. A validation of the post-concussion symptom scale in the assessment of complex concussion using cognitive testing and functional MRI. J Neurol Neurosurg Psychiatry. 2007;78(11):1231-1238..
  6. Chen J-K, Johnston KM, Frey S, Petrides M, Worsley K, Ptito A. Functional abnormalities in symptomatic concussed athletes: an fMRI study. Neuroimage. 2004;22(1):68-82.
  7. Covassin T, Moran R, Elbin RJ. Sex differences in reported concussion injury rates and time loss from participation: An update of the National Collegiate Athletic Association Injury Surveillance Program from 2004–2005 through 2008–2009. Journal of Athletic Training. 2016;51(3):189-194.
  8. Davenport EM, Whitlow CT, Urban JE, et al. Abnormal white matter integrity related to head impact exposure in a season of high school varsity football. Journal of Neurotrauma. 2014;31(19):1617-1624.
  9. De Beaumont L, Mongeon D, Tremblay S, et al. Persistent motor system abnormalities in formerly concussed athletes. Journal of Athletic Training. 2011;46(3):234-240..
  10. Dompier TP, Kerr ZY, Marshall SW, et al. Incidence of concussion during practice and games in youth, high school, and collegiate American football players. JAMA Pediatr. 2015;169(7):659.
  11. Giza CC, Hovda DA. The new neurometabolic cascade of concussion: Neurosurgery. 2014;75:S24-S33.
  12. Giza CC, Kutcher JS. An introduction to sports concussions: Lifelong Learning in Neurology. 2014;20:1545-1551.
  13. Grady MF. Concussion in the Adolescent Athlete. Current Problems in Pediatric and Adolescent Health Care. 2010;40(7):154-169.
  14. Guskiewicz KM, Mihalik JP. Biomechanics of sport concussion: Quest for the elusive injury threshold. 2011;39(1):9.
  15. Gysland SM, Mihalik JP, Register-Mihalik JK, Trulock SC, Shields EW, Guskiewicz KM. The relationship between subconcussive impacts and concussion history on clinical measures of neurologic function in collegiate football players. Ann Biomed Eng. 2012;40(1):14-22.
  16. Harpham JA, Mihalik JP, Littleton AC, Frank BS, Guskiewicz KM. The effect of visual and sensory performance on head impact biomechanics in college football players. Ann Biomed Eng. 2014;42(1):1-10.
  17. Kamins J, Bigler E, Covassin T, et al. What is the physiological time to recovery after concussion? A systematic review. Br J Sports Med. 2017;51(12):935-940.
  18. Kerr ZY, Chandran A, Nedimyer AK, Arakkal A, Pierpoint LA, Zuckerman SL. Concussion incidence and trends in 20 high school sports. Pediatrics. 2019;144(5):e20192180.
  19. Mihalik JP, Lynall RC, Wasserman EB, Guskiewicz KM, Marshall SW. Evaluating the “threshold theory”: Can head impact indicators help? Med Sci Sports Exerc. 2017;49(2):247-253..
  20. Miller NR, Yasen AL, Maynard LF, Chou L-S, Howell DR, Christie AD. Acute and longitudinal changes in motor cortex function following mild traumatic brain injury. Brain Injury. 2014;28(10):1270-1276.
  21. Montenigro PH, Alosco ML, Martin BM, et al. Cumulative head impact exposure predicts later-life depression, apathy, executive dysfunction, and cognitive impairment in former high school and college football players. Journal of Neurotrauma. 2017;34(2):328-340.
  22. Reynolds BB, Patrie J, Henry EJ, et al. Effects of sex and event type on head impact in collegiate soccer. Orthopaedic Journal of Sports Medicine. 2017;5(4):232596711770170.
  23. Slobounov SM, Zhang K, Pennell D, Ray W, Johnson B, Sebastianelli W. Functional abnormalities in normally appearing athletes following mild traumatic brain injury: a functional MRI study. Exp Brain Res. 2010;202(2):341-354..
  24. Zuckerman SL, Kerr ZY, Yengo-Kahn A, Wasserman E, Covassin T, Solomon GS. Epidemiology of sports-related concussion in NCAA athletes from 2009-2010 to 2013-2014: Incidence, recurrence, and mechanisms. Am J Sports Med. 2015;43(11):2654-2662.

Learning Through Observation

Recently, I sat in a lecture on observational learning, which was supplemented by a another lecture on self-regulated learning. Surprisingly, there hasn’t been a handful of research regarding observational learning as it pertains to motor skill learning and performance. I wanted to dig in a little deeper and share with you a quick overview.

It all started with Giacomo Rizzolatti (Discovery of mirror neurons) when his work with canonical neurons and the neural representation of motor movements in monkeys led to the discovery of mirror neurons. The basic idea was that canonical neurons are transforming affordances of objects. Meaning, it’s enough to observe a door knob for you know how to grasp it (in this case, it was a peanut). But it was soon realized that in some cases, just showing, wasn’t enough. In order for the monkey’s neurons to fire, the object had to be grasped. This led to the idea of congruence. The monkey needed to be able to also grasp the object in order for its mirror neurons to fire when a person grasped the same object.

Mirror neurons are neurons with motor processes that fire not only during action execution, but also when observing someone else performing the same or similar action. Likewise, observation of movements also activates the same areas that are used to preform those movements. In a review by Iacoboni (2009), it was concluded that;


  • Mirror neurons fire during observation, but not during observation of pantomime. Meaning, they are goal oriented.
  • Mirror neurons fire when say graspable objects are occluded by a screen.
  • Mirror neurons discharge in response to the intention or goal rather than action itself.
  • Mirror neurons are Multimodal; they can discharge to sound.
  • Mirror neurons are related to motor experience with a given action.

As a function of experience, Orgs et al., (2008) found that event related desynchronization in alpha and beta frequency of experts was modulated by an individual’s expertise with a certain movement style. So yes, the more you see it, the better. Further, the phenomena of observational learning has played in imperative role in motor learning albeit the research evidence is not immense. Coaches and clinicians are constantly using demonstration as a tool to enhance the rate of learning or optimizing current performance.

Observational Learning

Horn et al., (2007) found that novices in a model group, where they were observed a video model, learned a maximum velocity back-handed reverse baseball pitch to a greater extent than those who practiced based on just verbal instruction/guidance. What was really interesting was that the model group individuals showed immediate change in their intra-limb coordination, mimicking closely to the model’s relative motion pattern. In fact, ball speed was also improved. As a result, in early acquisition (i.e., rehabilitation) a model may represent an efficient and stable behavioral change that can enhance the rate of learning. In other words, a model can also be used as a constraint that allows salient information to be perceived. Watching an expert is typically the approach coaches adhere to when teaching a motor skill. Sometimes, its even the coach itself, whom they think is the expert. Clark & Ste-Marie (2007) wanted to see what would happen if the self was used as a model intervention. They used children who were learning how to swim. One group either saw a videotape of their own performance (self observation) where as the other group saw an edited video of their own best performance (self modeling). The self modelling group performed better. They concluded that implementing self-modelling interventions is a useful strategy to optimize learning.

Practicing in Groups

 Dyad training is considered effective and efficient. In fact, most teams practice in dyads. In slight contrast, rehab settings most often times don’t. Shea and colleagues (1999) compared three groups (individual, dyad alternate, and dyad control) using a balance task. In the dyad alternate group the order looked something like this; partner 1 went while partner 2 observed and on the next trial, partner 2 went and partner 1 observed. In the dyad control group, partner 1 went, performed all trials while partner 2 observed, and then they switched. They found that the dyad alternate group did best in retention whereas the dyad control group came in second, still doing better than the individual group. It was concluded that dyad training is beneficial due to observational learning, increased motivation (support/competition) and sharing or receiving feedback. Thus, practice should include observation and dialog between learners. Applying this type of framework is something coaches should strive to do. Lastly, Granados & Wulf (2007) found that observation and dialogue are also beneficial to motor learning in dyad practice, though these results should be extended to larger groups.


 The aforementioned literature goes in line with the soft versus hard assembled mechanisms debate. A hard assembled mechanism is independent of the immediate context, but is revealed across multiple contexts. For example, my rule about a “swish” in basketball remains whether on a basketball court, shooting a paper ball into a garbage can, etc. In contrast, a soft assembled mechanism is constrained within context. As mentioned by Kloos & Van Orden (2009), this can be the kinematics of a limb in a particular action. Like Bernstein (1967) alludes to, repetition of this soft assembly will reveal assemblies that have unique kinematics, albeit not context free. I bring this up because motor learning is typically associated with prescriptive, direct learning approaches that enable such “soft assemblies” to be formed, providing only temporary solutions. Here, it is demonstrated that observational learning is a powerful tool that enables coaches and clinicians to constrain information without using a prescriptive approach. As a result, you no longer have the formation of “soft assemblies,” but more efficient and stable behavioral changes.



  • Coaches should integrate observational learning during practice.
  • A self-modeled approach, where good performances are seen and referenced improve motor performance and learning.
  • When practicing in dyads, allow for dialogue to occur and strategies to be verbalized.
  • Particularly in rehabilitation settings, observational learning is powerful for the efficient and effective behavioral changes that allow for retention and transfer.



 Clark, S. E., & Ste-Marie, D. M. (2007). The impact of self-as-a-model interventions on children’s self-regulation of learning and swimming performance. Journal of sports sciences25(5), 577-586

Granados, C., & Wulf, G. (2007). Enhancing motor learning through dyad practice: contributions of observation and dialogue. Research quarterly for exercise and sport78(3), 197-203.

Horn, R. R., Williams, A. M., Hayes, S. J., Hodges, N. J., & Scott, M. A. (2007). Demonstration as a rate enhancer to changes in coordination during early skill acquisition. Journal of Sports Sciences25(5), 599-614.

Iacoboni, M. (2009). Imitation, empathy, and mirror neurons. Annual review of psychology60, 653-670.

Kloos, H., & Van Orden, G. C. (2009). Soft-assembled mechanisms for the unified theory. Toward a unified theory of development: Connectionism and dynamic systems theory re-considered, 253-267.

 Orgs, G., Dombrowski, J. H., Heil, M., & Jansen‐Osmann, P. (2008). Expertise in dance modulates alpha/beta event‐related desynchronization during action observation. European Journal of Neuroscience27(12), 3380-3384.

 Shea, C. H., Wulf, G., & Whltacre, C. (1999). Enhancing training efficiency and effectiveness through the use of dyad training. Journal of motor behavior31(2), 119-125.



Rethinking Feedback: Creating More Effective and Efficient Training Environments – AVCA 2019

I had a wonderful time at the American Volleyball Coaches Association (AVCA) convention presenting alongside Coach Speraw on the role of feedback in the sport of volleyball. Sad I couldn’t be there the whole time, but glad I was able to make the long drive despite a cancelled flight…with no sleep! In hindsight, there are some things I wished we hit on, and perhaps some things I hoped to make clearer. I humbly apologize for any mistakes made when relaying the information related to the research literature. It was actually my first time talking to coaches (and a lot of them!), not so much researchers or those in the field of psychology/motor learning/biomechanics. I learned so much and so I thank each and every one who attended. I do wish you took at least one tangible concept that you can apply to your practice. There was a great mix of coaches who represented different levels of volleyball in attendance (club, college, and national levels!). It goes to show the power of growth our sport has been able launch in the United States. Seeing such a diverse crowd was a reminder as to why bridging this gap between research and practice is so important. A big thank you to Coach Speraw and USA Volleyball who allowed me to be part of this. As mentioned, him, Dr. Becker, and myself have had some conversations surrounding this topic and wanted to share some of what we are brainstorming. I wanted to make some of the slides available and also wanted to address questions alongside further explanation. You’ll see I’ve put together a conglomerate of many different research ideas and papers onto this one document. My hope is that after you read this, you have more questions.  As always, feel free to reach out to me with any questions, comments, or concerns. One thing for certain is that I also have many questions for coaches too! Let’s learn together. Here is the link:

Rethinking Feedback


With Love,

Harjiv Singh

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 –

Interview –

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 –


  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.