Attenuation of Resting but Not Load-Mediated Protein Synthesis in Prostate Cancer Patients on Androgen Deprivation

Research Team Members: Erik Hanson, Andre Nelson, Daniel West, John Violet, Lannie O’Keefe, Stuart Phillips, Alan Hayes

These findings were recently published in Journal of Clinical Endocrinology and Metabolism, March 2017, 102(3): 1076-1083

Why did you do this study?

Prostate cancer is the most common non-dermatological form of cancer in US men (diagnosed in 1 in 7 men) and is the second leading cause of cancer-related death. In 2017 alone, it is estimated that 161,360 new cases of prostate cancer will be diagnosed with 26,730 deaths.

Androgen deprivation therapy (ADT) is a type of prostate cancer treatment that slows tumor growth but has several side effects, including the loss of muscle mass, strength, and physical function. All of these changes have a negative impact upon health-related quality of life. Exercise interventions have become more common to help reduce treatment-related side effects. Specifically, resistance training during ADT improves muscle strength, reduces fatigue, and enhances physical function. However, it is unclear as to whether or not significant gains in muscle mass are possible during ADT.

Looking at the recent literature, some studies have reported that no muscle hypertrophy occurs with resistance training during ADT. Others have shown that muscle gains are possible but the response is attenuated. Recently, our lab has demonstrated that high intensity resistance training induces gains in lean mass that are comparable to healthy, age-matched controls.

protein-powder-nutritional-supplementProtein supplementation is often used with resistance training to enhance the response. However, very few studies have combined these two muscle building strategies and used them during ADT as a means of preserving lean muscle mass and function and no studies have looked at the muscle protein synthesis response. Determining the acute response initially will provide important information for desiging future resistance exercise and dietary interventions, as it is repeated transient increases in muscle protein synthesis that may lead to increases in lean tissue over time.

Therefore, the purpose of this study was to determine the acute muscle protein response to whey protein supplementation with and without resistance exercise in men being treated for prostate cancer with ADT and healthy controls. We hypothesized that baseline protein synthesis would be suppressed with ADT but that diet- and exercise-induced increased in protein synthesis would be normal.

What did you do and what did you find in this study?

We measured muscle protein synthesis (MPS) from biopsy samples obtained from the thigh muscles at rest and 4h after participants consumed 40g of whey protein (Fed) and 40g of whey protein plus 3 sets of knee extension resistance exercise (Ex-Fed).

Muscle protein synthesis was lower during ADT at rest and while feeding (Fed) increased the rate of synthesis, the response was attenuated. However, the change in muscle protein synthesis from rest to Ex-Fed was similar to healthy controls.

hanson_fig1

* Significantly different from CON at the specific time point, P<0.01
† Significantly different from baseline value, P<0.001
‡ Significantly different from baseline and FED condition, P<0.001

How do these findings impact the public?

Muscle protein synthesis at rest is lower with ADT, which is likely why there is the loss of muscle mass in these patients. However, muscle protein synthesis increases following large doses of whey protein, although it is not as effective during ADT. But the combination of whey protein and vigorous resistance exercise may be an effective means to offset the side effects of prostate cancer treatment.

Future studies are needed to explore why protein synthesis is activated less with feedling alone, as this ‘anabolic resistance’ may have significant consequences for men on ADT. We are currently developing projects that will help to clarify these findings in the future.

Education Through Athletics – Possibilities for Intercollegiate Athletics Reform

This week’s EXSS Impact Post is developed by Professor Erianne Weight.

In a previous EXSS Impact blog post exploring reform approaches in intercollegiate athleticsweight1, we discussed the tension between athletics and the academy.  Many university stakeholders fully support athletics within the academy and view it as an educational endeavor complimentary to the university mission with added brand-building, relationship-forging, and student-drawing benefits.  On the other end of the spectrum, stakeholders have condemned the academy for allowing athlete exploitation, excessive commercialism, and unprincipled behavior that undermines the educational mission of the university.  Regardless of perspective, athletics has traditionally been supported within the university’s organizational structure as an extra-curricular activity peripherally related to the university mission. Perhaps it is time for this to change.

weight2Why did you do this study?

Throughout our research exploring the educational impact of intercollegiate athletics on the athlete participants, several studies have highlighted the positive impact intercollegiate athletics can have on occupational,[1] psychological,[2] physiological,[3] and long-term quality of life measures.[4] These findings contribute to a growing body of literature which supports embedding the applied study of athletics within the academy similar in form to music, dance, or theatre. Prior to exploring the interest or form of what an athletics-centric curriculum may entail, we gathered baseline data to examine current practices within NCAA Division I, II, and III institutions. The full results of this research are published in the Journal of Intercollegiate Sport.[5]

weight3What did you do and what did you find in this study?

Through survey of a stratified random sample of National Collegiate Athletics Association (NCAA) Division I, II, and III athletic academic advisors (n = 240), this exploratory study examined the prevalence, design, and institutional perceptions of classes offered exclusively for varsity athletes. Results indicate 33.9% of sample schools award credit for athletic participation (e.g. physical education), and 20.1% offer academic courses specifically for athletes (e.g. first semester “onboarding” courses, leadership courses, etc.).  Academic opportunities for athletes were greater in western, public, Division I institutions, with one of the most startling differences between western schools, wherein 65.3% award credit for participation, and southeastern schools, wherein 17.3% award credit for participation.

weight4How do these findings impact the public?

This study provides some evidence of structural and philosophical academic integration of athletics within the academy. These established courses counter the historically taboo nature of the education through athletics proposal.  This study also documents tremendous inequity in inter-institutional practices of facilitating academic courses for athletes.  This variance in institutional procedure can result in significant fluctuations in athlete time, competitive advantages, and opportunities for education through athletics.  Given the extensive policies the NCAA regulates to facilitate an even playing field, this dramatic divide in inter-institutional procedure presents an interesting challenge that warrants further inquiry.

weight5As the political-educational arena grapples with unprecedented scrutiny, faculties and administrators should focus their efforts on facilitating rich holistic educational opportunities and experiences. The athlete-educational experience that has been a concern since them inception of intercollegiate athletics has led many faculties to fear athlete-centric programming for reasons including an exacerbation of social isolation and/or the perceived nonacademic collective hubris and entitlement of athletes. Although there is a degree of isolation within every academic discipline with major-only courses and experiences that do not require justification, the unique nature of the athlete experience may necessitate additional consideration due to the social, commercial, and administrative pressures that could lead to academic clustering and athlete-segregation.

For this reason, a practical approach to athlete-centric educational experiences should be conscious of these realities and address concerns judiciously through credit limitations, cross-disciplinary faculty involvement, and the inclusion of non-athlete elite performers in the programming. Three approaches Weight & Huml (2016) recommend and expand upon in the Journal of Intercollegiate Sport article include:

  1. A 3-credit “onboarding” course specific for athletes to institutionalize many of the first-semester mandatory NCAA trainings in addition to life-skills initiatives
  2. Credit for participation in intercollegiate athletics with an infusion of faculty-led education grounded in experiential learning theory practices (e.g. a faculty-led strength training course with elements of exercise physiology and nutritional principles coupled with the strength training they engage in as a team).
  3. A minor in “elite performance” which could include varsity athletes, club sport athletes, musicians, orators, dancers, thespians, etc. Courses might include performance psychology, leadership and group dynamics, performance nutrition, media training, entrepreneurship, etc. in addition to two 3-credit “field experience opportunities that allow the students to reflect upon their elite experiences, apply literature to their (on-the-court) study, meet with a faculty and field supervisor (coach) to set and track learning goals, and infuse institutionalized scholarship and growth structures into their elite pursuits of excellence.

[1] Chalfin, P., Weight, E.A., Osborne, B., Johnson, S. (2015). The value of intercollegiate athletics participation from the perspective of employers who target athletes. Journal of Issues in Intercollegiate Athletics. 8, 1-27.

[2] Weight, E.A., Navarro, K., Huffman, L., Smith-Ryan, A. (2014). Quantifying the psychological benefits of intercollegiate athletics participation: Implications for higher education policy and practice. Journal of Issues in Intercollegiate Athletics. 7, 390-409.

[3] Weight, E.A., Navarro, K., Smith-Ryan, A., Huffman, L. (2016). Holistic Education through Athletics: Health literacy of intercollegiate athletes and traditional students. The Journal of Higher Education Athletics and Innovation. 1, 38-60.

[4] Weight, E.A., Bonfiglio, A.*, DeFreese, J.D., Kerr, Z., Osborne, B. In Review. Occupational Measures of Former NCAA Athletes and Traditional Students. The Journal of Intercollegiate Sport.

[5] Weight, E.A., Huml, M.* (2016). Facilitating education through athletics: An examination of academic courses designed for NCAA athletes. Journal of Intercollegiate Sport, 9(2), 154-174.

Fat-Free Mass Index in NCAA Division I and II Collegiate American Football Players

Research Team Members: Eric Trexler, Abbie Smith-Ryan, Malia Blue, Richard Schumacher, Jerry Mayhew, J. Bryan Mann, Pat Ivey, Katie Hirsch, Meredith Mock

Links to Study: https://www.ncbi.nlm.nih.gov/pubmed/27930454

uncfbWhy did you do this study?

It is well known that an athlete’s body composition can influence their athletic success. Previous studies have shown that fat-free mass (FFM) is related to strength, power, speed, and sport performance. However, fat-free mass may not be the most valid indicator of an athlete’s sport-related capabilities; most sports require locomotion or propulsion of the athlete’s body, which is affected by both the capacity to produce force and the overall size of the body. In addition, taller people naturally have more FFM due to their height. Fat-free mass index (FFMI) scales an individual’s FFM to their height, which removes the bias of height and may be a more valid characterization of muscularity that translates more directly to sport-related tasks.

In sports such as American football, training and nutrition practices are often geared towards increasing FFM. Researchers have previously suggested that 25 kg∙m-2 is the natural FFMI limit for resistance-trained males; this is important because identifying upper limits would enhance the ability to set realistic body composition goals for athletes. However, the research identifying this limit used a sample of lean individuals who were not competitive athletes. As such, it is possible that this “limit” has been underestimated. Collegiate football players are an ideal population for evaluating high FFMI values, based on the sport’s emphasis on strength, power, and body size. Furthermore, evaluating a large sample of collegiate football players allowed us to determine if FFMI differs between position groups or levels of competition. This information would be tremendously valuable to nutrition and strength & conditioning professionals who assist football players in identifying and reaching body composition goals that are suitable for their playing position.

GEHC-iDXA-for-Bone-Health_OverviewWhat did you do and what did you find in this study?

For this study, we performed dual-energy x-ray absorptiometry (DEXA) scans on three separate college football teams, including two division I teams and one division II team. We compared FFMI values between position groups and levels of play, and results indicated that FFMI was significantly higher in division I players compared to division II. Further, FFMI was drastically different between position groups, with the highest values observed in offensive and defensive linemen, and the lowest values observed in offensive and defensive backs. We provided FFMI ranges for each specific position based on the data from division I athletes, which should assist players in setting position-specific goals for body composition. Most importantly, we found that 62 athletes had FFMI values above 25 kg∙m-2 (26.4% of the sample). This percentage was even higher when specifically looking at division I athletes (31.3%). The 97.5th percentile was 28.1 kg∙m-2, and the highest observed value was 31.7 kg∙m-2.

How do these findings impact the public?

Our results indicate that drug-tested, resistance-trained males can achieve FFMI values well beyond 25 kg∙m-2. We also determined that FFMI effectively discriminates between playing levels and playing positions. Coaches and athletes can use this information to set more realistic body composition goals, and college and professional football teams may use this position-specific data to assist with their recruiting and personnel decisions.

Evaluating the “Threshold theory”: Can head impact indicators help?

Research Team Members: Jason Mihalik, PhD, ATC, Robert Lynall, PhD, ATC (HMSC PhD Student now at University of Georgia), Erin Wasserman, PhD (EXSS Gfeller Center Postdoc now at Datalys Center), Kevin Guskiewicz, PhD, ATC, Steve Marshall, PhD

Why did you do this study?

concussion-blog-featured-imageAs many as 1.6 – 3.8 million sport and recreation traumatic brain injuries (TBI) occur in the US on an annual basis. The direct and indirect costs for managing all forms of TBI exceed %56B annually. Proper detection and management of sport related concussion continues to challenge clinicians working with athletes. A number of options are available to clinicians, but mostly rely on subjective and clinical expertise. One example is the Sport Concussion Assessment Tool Version 3 (includes symptom inventories, mental status tests, and balance assessments). These acute injury screening tools are typically administered only after the clinician has sufficient evidence to suspect a concussion diagnosis. In the absence of obvious concussion signs (e.g., loss of consciousness, staggered gait, etc.), clinicians must rely solely on subjective symptoms reported by athletes. Research has documented a large portion of athletes either underreport concussion symptoms or fail to report them entirely. Thus, the medical field has looked to emerging technologies to fill this shortfall and provide heightened objectivity to the dilemma.

Technological advances have resulted in the emergence of commercially available head impact measurement devices. These devices typically serve two broad functions: 1) collect data for research-based inquiry, and 2) signal to clinical staff the occurrence of high-level impacts in near real-time during sports participation. Head impact indicators—the latter function—seek to identify athletes who have sustained pronounced head impacts so that they can be evaluated for symptomology. These products are usually worn directly on the head or affixed to a helmet, and are designed to indicate to medical personnel, players, coaches, and parents when a head impact magnitude has exceeded a pre-programmed threshold. There are no fewer than 20 different products that have permeated the marketplace in the last decade, and many use differing thresholds (some unknown to the user!).

JasonSlide35

Head impact indicators are believed to identify athletes who otherwise would elect not to report symptoms to the clinical staff. If an ‘alert’ is triggered, some of the manufacturers recommend the athlete be removed from activity and evaluated for a head injury, regardless of whether or not the athlete is exhibiting signs or reporting symptoms consistent with concussion. Our own work here at UNC suggests that a single impact injury threshold is not obvious, which question the clinical utility of these head impact indicators.

What did you do and what did you find in this study?

The purpose of this study was to investigate the clinical utility of head impact magnitude thresholds employed by various commercially available head impact indicators to positively predict concussion among American football players. We hypothesized these tools, by themselves, would be limited in helping clinicians make informed decisions regarding head injury during athletics due to the inherent variability of biomechanical values observed in concussed individual and the low incidence of concussion even at very high measured impact levels.

Over the last 10 years, we have collected hundreds of thousands of head impact biomechanics from hundreds of football players. A multidisciplinary clinical team independently made concussion diagnoses during this same time period (n=24). We dichotomized each impact using diagnosis (‘yes’ they were injured, ‘no’ they were not), and across a range of plausible impact indicator thresholds (10g increments beginning with a resultant linear head acceleration of 50g and ending with 120g). We then performed computations to determine the sensitivity, specificity, negative predictive value and positive predictive value, which are common measures used to assess the clinical utility of any diagnostic or screening assessment.

JasonSlide38

How do these findings impact the public?

In particular, any head impact indicator must demonstrate that it has predictive value; that is, it is an efficient use of time and resources and that it yields a practical frequency of identified concussions to be clinically useful. All thresholds we studied had low positive predictive value (<0.4%). Even when conservatively adjusting the frequency of diagnosed concussions by a factor of 5 to account for unreported/undiagnosed injuries, the positive predictive value of head impact indicators at any threshold was no greater than 1.94%. Simply put, fewer than 4 out of 1000 trigger alerts would result in a diagnosed concussion. Or, looking at it from the vantage of clinician time resources, 996 sideline evaluations would be done in vain and at the possible detriment of distracting the sideline medical personnel from observing and intervening in other emergencies and injuries during that time.

The Influence of Movement Profile on The Female Athlete’s Biomechanical Resilience & Training Load Response to Exercise

Research Team Members: Barnett Frank, PhD, ATC, Claudio Battaglini, PhD, Troy Blackburn, PhD, ATC, Anthony Hackney, PhD, DSc, Darin Padua, PhD, ATC

Why did you do this study?

Lack of physical activity is directly responsible for 9% of global premature mortality. Remarkably, exercise is consistently identified as a fundamental health behavior to effectively reduce one’s risk of disease. However, exercise participation carries a concerning high risk of musculoskeletal injury. Musculoskeletal injury amounts to a socioeconomic burden >6% of the U.S. gross domestic product. Paradoxically, injury is the primary barrier to exercise participation. Thus there is a need to prevent exercise-related musculoskeletal injury to promote the health and quality of life enhancing benefits of exercise.

Faulty movement patterns (i.e. knee collapsing and stiff hips and knees when landing – Figure 1) and elevated biochemical markers of musculoskeletal tissue stress are predictive of future injury during physical activity participation. Exercise interventions aimed at correcting faults in motion during physical activity reduce risk of injury. However, the underlying physiological mechanisms by which movement patterns modify risk for injury are unknown.

figure-1-bsf-exss-impact-blog-post

Abnormal movement patterns are theorized to impart a greater cumulative physical stress and systemic demand on the body during exercise, resulting in musculoskeletal system tissue failure and ultimately injury. Currently, it is unknown if there is a combined effect of an individual’s movement profile and exercise exposure on tissue and systemic stress measures associated with injury.

The purpose of this research was to investigate the influence of an individual’s movement profile on their physiological and biomechanical response to high training loads experienced during exercise and sport participation. Specifically, we investigated if a high injury risk / “stiff” or a low injury risk / “soft” movement profile affects the body’s systemic stress (cortisol), muscle loading (creatine kinase), cartilage degradation, and biomechanical response to high training load exposure.

What did you do and what did you find in this study?

figure-2-bsf-exss-impact-blog-post

43 college-aged female athletes were enrolled in this study and were assigned to a low-risk / “soft” (n=22) or a high-risk / “stiff” (n=21) movement profile group using a clinical movement injury risk assessment – The Landing Error Scoring System (Figure 1.) Jump-landing 3D biomechanics and blood samples were collected prior to and following a high training load exercise bout (Figure 2 & 3). Changes in biomechanics, circulating biomarkers of joint cartilage (cartilage oligomeric matrix protein) and skeletal muscle loading (creatine kinase), and of systemic stress (cortisol), were compared between movement profiles to better understand the influence of movement profile on the body’s response to the demands of exercise exposure (Figures 2 & 3).

figure-3-bsf-exss-impact-blog-post

We observed the high-risk / “stiff” landing group to experience greater degradation of movement strategies that effectively and efficiently dissipate landing forces experienced during high-intensity exercise. Specifically, we observed the high-risk / “stiff” group to land with a high-load landing posture and greater landing forces compared to the low-risk / low-load group when exposed to exercise. Furthermore, we observed movement profile to influence systemic stress hormone levels. Individuals with a high-risk / “stiff” movement profile exhibited an elevated stress level in contrast to their low-risk / “soft” landing profile counterparts. Additionally, it seems the low-risk / “soft” movement profile is linked to greater utilization of dynamic muscle tissue to efficiently dissipate the high loading stresses experienced during exercise and physical activity.

Interestingly, we did not observe a direct influence of movement profile on cartilage loading during exercise. However, we observed greater variability of cartilage loading responses in the high-risk / “stiff” landing group (standard deviation = ±43.9%) with over 1.5 times the range of responses compared to the low-risk / low-load group (standard deviation = ±29.4%). Implicating individuals with a low-risk / “soft” movement profile have a more uniform cartilage loading response compared to their high-risk / “stiff” landing counterparts.

How do these findings impact the public?

This study is the first to identify movement profile as a moderator of systemic responses to exercise. Collectively our findings suggest that an individual with a movement profile associated with a lower risk of injury may be more mechanically and systemically resilient to exercise exposure. Decreased system resilience in individuals with high-risk / “stiff” movement profiles may explain their elevated risk of sustaining a debilitating musculoskeletal injury during physical activity. Correcting a physically active individual’s faulty movement patterns may enhance their response to exercise while also decreasing their risk of musculoskeletal injury.

East Africian Distance Runners: Female Athlete Triad and Relative Energy Deficiency in Sport (RED-S) Conditions

Research Team Members: Martin Mooses, Anthony C. Hackney, Diresibachew H Wondimu, Robert Ojiambo and Amy R. Lane

hackney4

International research team (R to L): Amy Lane, Marin Mooses, Silva Suvi, Robert Ojiambo, Diresibachew Wondimu, and Anthony Hackney

Why did you do this study?

The Female Athlete Triad (TRIAD) and more recently, Relative Energy Deficiency in Sport (RED-S) health conditions in men (male hypogonadism) have been linked a state called “low energy availability” (LEA). LEA occurs when an individual’s energy intake (food) minus their exercise energy expenditure is below a level that will ensure adequate energy for exercise as well as all physiological processes within the body (<30 kcal/kg body weight/day).

hackney1

Maximal oxygen uptake test of elite athlete

LEA results in hormonal, metabolic and bone disorders which compromise overall health, but are specially related to reproductive system dysfunctions. These disorders also can have detrimental effects on exercise performance, injury rate, as well as impact aspects of the athletes’ health later in life (e.g., increased risk of osteoporosis).

Athletes who participate in endurance sports are at increased risk for LEA due to the extremely high volumes of exercise training they perform. The research conducted in this area so far has been done predominantly in Caucasian (US and European) populations. Very little is known, however, about prevalence in East African endurance athletes, who are some of the best runners in the world based upon the numerous world records and Olympic medals.

Earlier research by our group suggests that the low body mass and BMI of these African runners have benefitted their performance; i.e., through a more advantageous running economy, but those same anthropometric factors (body mass, BMI) could also put them at risk for the TRIAD or RED-S conditions. Therefore, the focus of our study is to identify the prevalence and risk factors for the TRIAD and RED-S in East African elite distance runners (both females and males). The intent is to collect data in field settings where the athletes live and train.

What did you do and what did you find in this study?

hackney2

Field tests for blood lactate responses (lactate threshold test)

The study has been operating for the last 9 months. This past fall and winter, members of our international team from Ethiopia, Kenya, Estonia as well as UNC-CH have been on-site in Kenya working collecting data on male and female athletes at a running training camp at Eldoret, Kenya (2200 meters elevation, southwestern Kenya). UNC-CH EXSS professor Anthony Hackney and his doctoral student Amy Lane have been on sight collecting body composition, training, blood samples, nutritional and psychological data from the research subjects (pictures 1,2,3). The logistics of how to collect some of this data in rural areas of Kenya have been challenging at times.

The work is moving towards a commencement of the final data collection in fall 2017. Participants will include 30 female and 30 male elite East African endurance runners, with 30 female and 30 male adult non-athletes matched for age and ethnicity as controls. This study is funded on a 2 year grant through the International Athletics Foundation (project # 417) and is  truly collaborative international project. The main member of the research team are shown in picture 4.

How do these findings impact the public?

hackney3

UNC doctoral student interviewing one of the research subjects relative to there training and dietary history

The findings from this study will help build knowledge about the impact that LEA has on elite East African endurance athletes. Through the identification of prevalence rates, this work can contribute to development of future interventions to minimize the prevalence, prevent occurrence and improve recovery from TRIAD and/or RED-S. Additionally, bringing this research to Kenya may increase education about energy availability to a broader audience.

Enhancing Public Dissemination and Understanding of Injury Risk in Sport

This week’s EXSS Impact Post is by Dr. Zack Kerr, Assistant Professor in Exercise and Sport Science.  Dr. Kerr is an epidemiologist and explores different reporting methods of injury epidemiology data to improve public dissemination and understanding.

Why did you do this study?

Over the past decade, I have immersed myself in the world of sports injury surveillance. The findings are of great importance because they are the basis for many data-driven decisions regarding the rules and safety in sports organizations such as the National Collegiate Athletic Association (NCAA) and the National Federation of State High School Associations (NFHS).

However, at times, I found it difficult to discuss findings to stakeholders of sports organizations, such as parents, coaches, and administrators, because we in academia like to rely on the injury rate to measure injury incidence. The injury rate is defined as:

injury-rate

Injury rates are preferred because they account for all injury events in the numerator, regardless of whether or not these were sustained by the same athletes. They also account for variation in the amount of exposure time via the denominator; thus, an athlete that plays more across a season provides more exposure time. But for many parents, what matters most to them is knowing the risk; in other words, what is the probability that their child will get injured within a specific timeframe (e.g. one season).

As a result, using data from the NCAA Injury Surveillance Program, I worked with a team of epidemiologists and athletic trainers to examine a variety of methods of measuring injury incidence. This team included:

  • Karen G. Roos, California State University – Long Beach
  • Aristarque Djoko, Datalys Center for Sports Injury Research and Prevention
  • Sara L. Dalton, Datalys Center for Sports Injury Research and Prevention
  • Steve P. Broglio, University of Michigan
  • Stephen W. Marshall, University of North Carolina at Chapel Hill
  • Thomas P. Dompier, Datalys Center for Sports Injury Research and Prevention

Given the interest, we opted to examine these measures with concussion.

What did you do and what did you find in this study?

We used concussion data from the NCAA Injury Surveillance Program during the 2011/12-2014/15 academic years. The NCAA Injury Surveillance Program has been in existence since the early 1980s and have been assisting the NCAA in assessing sport-related safety in their sponsored programs. Participation in the NCAA Surveillance Program varied by sport and academic year. Data were collected by team athletic trainers that worked with these sport programs during the season.

We computed four measures of concussion incidence in a 13 different sports:

Men’s sports Women’s sports
–   Baseball

–   Basketball

–   Football

–   Ice Hockey

–   Lacrosse

–   Soccer

–   Wrestling

–   Basketball

–   Field hockey

–   Ice hockey

–   Lacrosse

–   Softball

–   Volleyball

The four measures are described in the table below.

Concussion rate

At what rate are concussions sustained during at-risk exposures?

Example: Across 10,000 NCAA football athlete-exposures, we expect to see 6-7 concussions

One-season risk of concussion

What is the probability of an athlete obtaining a concussion in one season?

Example: In one season, we expect 1 in 20 NCAA football players to have a concussion?

Average # concussion per team per season

How many concussions does a team sustain in one season?

Example: In one season, we expect 5-6 concussions within a NCAA football team

% teams with a concussion

How many teams have a concussion occur within a season?

Example: In one season, we expect 80.6% of all NCAA football teams to have at least one concussion

The computations for the measures included alongside rates are included below:

risk

avg-per-season

teams

What we found is that despite some variation in the rank-order of included sports, full contact sports such as wrestling, football, and ice hockey consistently generated the highest incidence of concussion.

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Despite the similar rankings across sports, we believe that squad size may serve as a confounder, particularly in football. Furthermore, such measures can be biased when comparing incidence across teams (or sports) that vary greatly by the number of athletic sessions per season. Thus, it is important for readers to understand the strengths and limitations of measures of injury incidence utilized by various researchers.

How do these findings impact the public?

Although injury rates are the most preferred method of gauging injury incidence in academia, they may not be intuitive to non-scientists, including members of sports organizations concerned about the incidence of injury among their players. To help parents, coaches, and athletes, and to drive the development of data-driven, evidence-based policy and rule changes, we need to ensure that we are providing our findings in an easily understood manner.

This research presents a collection of “alternative facts” that still utilize the data collected by athletic trainers in a valid manner, but may be easier to interpret and disseminate to stakeholders. Better yet, these measures are applicable to other injuries and settings. This more diverse “toolbox” of measures, in combination with traditional athlete-based rates, may help sports organizations better identify specific athletes at risk for injury.