Fusionetics Movement Efficiency Test As a Tool for Lower Extremity Injury Prediction in Division I Collegiate Athletes

FUSIONETICS MOVEMENT EFFICIENCY TEST AS A TOOL FOR LOWER EXTREMITY INJURY PREDICTION IN DIVISION I COLLEGIATE ATHLETES

Zoe Marie Quirk-Royal

A thesis submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Master of Arts in the Department of Exercise and Sports Science (Athletic Training).

Chapel Hill 2020

Approved by:

Erik Wikstrom

Zachary Kerr

Kimmery Migel

Spencer Cain

ABSTRACT

Zoe Quirk-Royal: Fusionetics Movement Efficiency Test as a Tool for Lower Extremity Injury Prediction in Division I Collegiate Athletes (Under the direction of Erik Wikstrom)

Movement quality screenings are used by sports medicine professionals who work with competitive sports. They can help give insight into an athlete’s biomechanics. Existing tests lack the ability to predict injuries.

The purpose is to determine the predictive value of the Fusionetics test at identifying those at risk of a lower extremity musculoskeletal injury.

Using data collected from division 1 athletes, multivariable binomial risk regression models were used to model injury risk within the season, estimate risk ratios, and 95% confidence intervals.

The analysis determined that every 5-point increase in the Fusionetics ME score there was an associated risk ratio of 0.9276 (p=0.5611). Next, the Fusionetics ME score as a categorical variable, risk ratios were moderate vs poor RR=0.9114 (p=0.7489), good vs poor

RR=0.7273 (p=0.3017), good vs moderate RR=0.789 (p=0.1061). The results were not significant but gave insight to how Fusionetics movement efficiency test can be used to manage injuries.

iii

TABLE OF CONTENTS

LIST OF TABLES ...... vi

LIST OF ABBREVIATIONS ...... vii

CHAPTER 1: INTRODUCTION ...... 1

Research Question ...... 3

Research Hypothesis ...... 3

Outcome ...... 3

Exposures ...... 3

Covariates ...... 3

CHAPTER 2: LITERATURE REVIEW ...... 4

Epidemiology of Lower Extremity Injury ...... 4

Injury Risk Factors ...... 5

Non-Modifiable Injury Risk Factors ...... 5

Anatomical Alignment ...... 5

Foot Morphology ...... 6

Limb Dominance ...... 7

Modifiable Injury Risk Factors ...... 8

Flexibility and Joint Laxity ...... 8

Muscle Strength/Imbalance ...... 10

Postural Stability ...... 11

Biomechanics ...... 11

iv Functional Movement Patterns and Testing ...... 12

CHAPTER 3: METHODS ...... 17

Design ...... 17

Epidemiological Background ...... 18

Data Collection ...... 19

Outcome Measure – injury risk ...... 20

Exposure Measure – Fusionetics Movement Efficiency Test scores ...... 20

Covariates ...... 21

Statistical Analysis ...... 21

CHAPTER 4: METHODS ...... 23

CHAPTER 5: RESULTS ...... 24

Clinical Meaningfulness ...... 28

REFERENCES ...... 32

v LIST OF TABLES

TABLE

1. Movement Instructions for Fusionetics Test ………………………...……….……..27

2. Possible Compensations during Fusionetics Exam ……………………………...... 28

3. Athletes and Injuries Totals ………………………………………………………... 29

4. Total Number of Injuries by Body Part ………………………………………...... 29

5. Summary of Injuries by Type ……………………………………………………….29

6. Fusionetics Score vs. Healthy/Injured ………………………………………………29

7. Descriptive Data ………………………………………………………………...... 30

8. Model 2 (Fusionetics ME category as a categorical variable) ……………………...30

vi LIST OF ABBREVIATIONS

ACL anterior cruciate ligament

AT athletic trainer

BMI body mass index

CAI chronic ankle instability

CI confidence interval

DLS double leg overhead squat

DLS-HL double leg overhead squat with heel lifts

FMS functional movement screen

Fusionetics ME Fusionetics Movement Efficiency

LE lower extremity

LESS landing error scoring system

NCAA national collegiate athletic association

Q-angle quadriceps angle

RR risk ratio

SEBT star excursion balance test

SLS single leg squat

SPSS Statistical Product and Service Solutions software

VDJ vertical drop jump

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CHAPTER 1: INTRODUCTION

Each year, more than 179,000 Division 1 athletes who compete in various sports across the United States sustain over 210,000 injuries. A single injury can cost several thousands of dollars in medical expenses, resulting in significant time loss, and potentially leading to future complications. Lower extremity injuries make up almost two-thirds of all injuries in a collegiate setting. 1 Given the volume and potential seriousness of these events, there is a significant demand to identify players who are at risk of sustaining an injury in an effort to prevent them from occuring. 2,3 There are several risk factors attributed to injury risk, many of which are modifiable and can be influenced by an intervention. Examples of modifiable factors include increased knee valgus movement and tight hip musculature, gastrocnemius tightness, and increased postural sway scores.4–8 Assessing modifiable risk factors and allowing athletes, coaches, and medical staff to deliver targeted interventions can mitigate injury risk. However, there is no single identifiable risk factor that captures every aspect of injury risk. Being such, movement evaluations that attempt to quantify multiple injury risk factors and provide a framework for prevention strategies have been created.

These evaluations have become popular tools to derive information about the efficiency of a person’s gross biomechanical movement pattern instead of focusing on preventing one single factor.

Several different movement screening tests and tools have been developed throughout the years, each claiming to predict specific injuries or individuals at risk. Commonly utilized tests are the Star Excursion Balance Test (SEBT), the Landing Error Scoring System (LESS),

1 and the Functional Movement Screen (FMS). Each of these boast the incorporation of fundamental movement theories and claim to be the best way to identify abnormal movement patterns. However, each test has disadvantages, and no single assessment captures all known injury risk factors.

While the SEBT shows associations between symmetry of scores and ankle injury with 82% accuracy, the primary mode of identification of risk is an interlimb asymmetry rather than a set standard of norms. 9,10 If a person were to injure both limbs, they no longer have a valid comparison limb. The LESS identifies those at greater risk for ACL injury based lower extremity jump landing biomechanics that produce increased anterior tibial shear. 6,11

Unfortunately, the LESS primarily identifies risk for acute knee injury and does not account for the impact of biomechanical changes at other joints or chronic injury. The FMS claims to account for the limitations of the SEBT and LESS, but still fails to produce high sensitivity scores and ease of use without extensive training.12 Fusionetics Movement Efficiency

(Fusionetics ME) test is a new, evidence based assessment that claims to capture risk factors missed due to the disadvantages of its predecessors. It includes tests that identify biomechanical deficiencies proven to be good predictors of injury.13

The purpose of this research study is to determine whether a pre-season Fusionetics

ME test can identify those at risk for a chronic or acute, non-contact, time-loss lower extremity (LE) injury in Division I collegiate athletes. Fusionetics ME test claims to be able to correctly identify those at risk for injury based on biomechanical abnormalities identified during the 3 basic lower extremity movement patterns.

2 Research Question

Are field athletes with poor vs moderate vs good lower extremity Fusionetics ME test scores at a greater risk for lower extremity injury in the following athletic season?

Research Hypothesis

NCAA field sport athletes with a poor-moderate (0.00-74.99) Fusionetics ME score will have a higher risk of non-contact lower extremity injury, for time loss, in the following competitive year.

Outcome

The outcome measure for this study is injury risk collected from the electronic medical records system, Blue Ocean, measured using odds ratios.

Exposures

Previously administered Fusionetics ME test scores are recorded for each player based their performance on the three specific lower extremity tasks.

Covariates

Height, weight, and BMI were treated as continuous variables. Sport and sex were treated as categorial variables.

CHAPTER 2: LITERATURE REVIEW

Epidemiology of Lower Extremity Injury

Participation in sports throughout life has many health benefits but does not come without injury risk. Division 1 college athletes sustain an average of 210,674 injuries in a given year,3 of which, nearly two thirds occur in the lower extremity. 14,15 On average,

460,000 NCAA athletes will participate in sports every year.16 With the average cost of injury at approximately $1,500 per player per year,17 schools are spending approximately

$690 million on medical expenses annually to care for injured athletes. However, this is just an average. If an athlete incurs a larger injury, such as an anterior cruciate ligament

(ACL) tear requiring reconstruction which can cost between $5,000-$17,000 per patient,18 the annual cost of medical care can easily rise. This number does not factor in the costs associated with rehabilitation, time losses, and possible ongoing care for future complications. 19,20 Acute injuries, like ACL tears, occur at a rate of 44.6 per 10,000 athlete-exposures, while overuse injuries occur at an estimated rate of 18.5 per 10,000 athlete-exposures. 21 This trend does not only apply to high risk contact sports like football and hockey, but rather is a common trend across all sport types. 21 Furthermore, among popular Division 1 college sports, acute injuries occur almost equally in both games and practices.1 Soccer has the highest injury rate of the field sports with an estimated game-related injury occurrence rates between 16.4 and 18.75 for every 1000 exposures.3 Nearly 50% of practice injuries occur from non-contact scenarios 1,22 and overuse injuries account for approximately 30% of all lower extremity injuries.23

4 Injury Risk Factors

Researchers have determined what extrinsic and intrinsic risk factors can contribute to a lower extremity injury.15 Extrinsic factors, or factors occurring outside the body, include level of competition, skill level, shoe type, and field surface. 15 Intrinsic risk factors include age, biological sex, menstrual cycle, history of previous injury, aerobic fitness, body size, limb dominance, limb girth, flexibility, joint laxity, muscle tightness, range of motion, muscle strength and imbalance, postural stability, anatomical alignment, and foot morphology. 24 Sports injuries are thought to occur from a complex interaction between many of these factors. 24 Furthermore, injury risk factors can be classified as modifiable or non-modifiable.

Non-Modifiable Injury Risk Factors

Non-modifiable risk factors are those factors inherent to the body that cannot be altered or modified. Changes in non-modifiable risks significantly alter movements or actions. 24,25 These risk factors cannot be taught or trained they are unique to each individual. Thorough examination of risk factors, both modifiable and non-modifiable can increase the understanding of the role they play in injury risk. Determining injury risk can be a crucial component of preventing injuries.

Anatomical Alignment

The body is constructed so that each joint has a degree of stability and mobility.

The anatomical alignment of a joint refers to the orientation of the articular surfaces, ligaments, and musculature that produce movement and resist forces placed upon the body. 26 Alignment of the lower extremity may present as a potential risk factor for injury.

For example, research has identified an association between a narrower femoral

5 intercondylar notch and non-contact ACL ruptures across both males and females. 27 It has also been reported that players who sustained lower extremity injuries had an increase in weight bearing rearfoot valgus and athletes with a leg length discrepancy were at a higher risk for injury. 28 in another study, athletes that presented with severe knee valgus in combination with a Q angle of less than 10° were at an increased risk for an overuse lower extremity injury such as stress fracture.29

Researchers have found that some potential risk factors vary by sex. In men, there is an increased injury risk for individuals with a greater Q angle (greater than 15 °). 28

However, there is no evidence indicating the same relationship exists in women. 7

Research investigating the risk of increased tibial varum found that it is not a risk factor for men, but is a for women regarding incidence of ankle sprains. 30

Foot Morphology

Foot morphology can largely influence biomechanics due to the kinetic chain theory. 31 The alignment and position of the forefoot, midfoot, and rearfoot is an important factor due to its relationship between the ground reaction force vector and the axis of rotation in the ankle, knee, and hips. The height of a players arch (low, normal, or high) results in a different magnitude of ground reaction forces during the stance phase of movement. 32 The impact of arch height on the kinetic chain may contribute to injury at the foot or another joint in the lower extremity through malalignment such as knee valgum or varum. 33 Examination of foot alignment in a shod condition revealed that those with pes planus (flat arches) and pes cavus (high arches) seemed to be at an increased risk for stress fractures. 4 Investigators were unable to determine a relationship between any other injuries. 4

6 Similarly, there is a relationship between arch height and injury location.

Individuals with lower arches were more likely to injure the medial soft tissue structures throughout their arches and knees, while a high arch foot posture created lateral boney injuries in the foot, specifically stress fractures at the fifth metatarsal. 34 A significant relationship has been identified between individuals with knee pain and either a pronated or supinated foot. 35 This same study did not find a significant relationship between foot type (supinated, pronated, or neutral) and ankle sprains. 35 While there is good evidence available regarding foot type and potential injuries, the difference in classification standards of foot types remain concerning. Inconsistency of classification definitions make it difficult to generalize the results other populations.

Limb Dominance

Limb dominance is defined as the preferred leg when a player is kicking a ball. 15

While many studies have worked to establish an association between limb dominance and risk for injury, the evidence is mixed. In male soccer players, there is no association between limb dominance and injuries to muscles, however, the dominant leg is much more susceptible to ligamentous injuries such as an ankle sprain. 36 In fact, researchers identified that an athlete is more likely to injure their dominant ankle if they are left foot dominant compared to their right foot dominant teammates. 37 However, players at an increased risk of a knee ligament sprain did not show a pattern for whether their dominant or non-dominant leg was injured. 38 Hamstrings and triceps surae muscle strains do not occur in either a dominant or non-dominant leg preferentially, but the quadriceps has a higher likelihood of injury in the dominant leg. 39 In contrast, three studies found no correlations whatsoever between limb dominance and ankle injury

7 rates. 30,40,41 There remains to be any consensus on the impact of limb dominance on lower extremity injury.

Non-modifiable risk factors cannot be changed by rehabilitation or prevention efforts, but they often contribute to injuries when combined with modifiable factors.

Being able to identify and provide appropriate modifications, at minimum, help reduce injury risk for individuals with high exposure rates.

Modifiable Injury Risk Factors

Modifiable risk factors are those innate to the body that can be altered to influence the outcome of movements or actions. 39 Athletic trainers and other allied health professionals can help provide these interventions. Examining modifiable risk factors and understanding the role they play in injury risk is crucial to the ultimate goal of being able to identify risk and prevent injuries.

Flexibility and Joint Laxity

The flexibility of a joint is determined by articular surface geometry, muscle/tendon/ligament extensibility, and joint capsule laxity. 15 The relationship between flexibility and injury risk remains unclear. Global joint laxity is cited as having a negative impact on injury risk rates and is commonly measured using the Beighton scale. 15 The Beighton scale is a simple 9-point scoring system to determine joint hypermobility. 42 A higher score indicates an increase in hypermobility.42 Female soccer players with scores of four or more on the scale are at a fivefold increased risk for a lower extremity injury than those with lower scores. 43 Although generalized joint laxity may only be a direct factor for traumatic leg injuries, hypermobility may still contribute to overall injury risk.7

8 Local joint mobility has been associated with specific injuries. Athletes that demonstrated smaller ratios of dorsi- to plantar flexion range of motion had a higher incidence of inversion ankle sprains. The injured population also demonstrated a higher ratio of eversion to inversion range of motion when comparing the injured group to the non-injured. 37 The only agreement among investigators involves specifically the ankle joint and that excessive joint laxity, assessed by the talar tilt and anterior drawer tests, impacts the risk of ankle injuries.30,36,38,44,45 Laxity in ankle joints could make someone prone to ankle sprains by allowing the joint to move into vulnerable positions during sport participation. These studies included male and female soccer players, football players, and lacrosse players. Male soccer and lacrosse athletes were more susceptible to ankle sprains if they had increased ankle laxity quantified by a talar tilt score, but not female soccer or lacrosse players. 30

Another factor that influences flexibility is muscular tightness. Similar to other factors, there is no consensus between biological sexes .8 Male athletes with increased muscular tightness had a 23% increase in injury risk, most notable between iliopsoas tightness and overuse knee injuries. 8 Similarly, increased gastrocnemius tightness and decreased range of motion are potential risk factors for Achilles tendonitis in active men.4

Ankle injuries are also more common in athletes that do not warm-up and stretch potentially tight musculature prior to practice than those that do. 46 A warm-up can have a significant influence on muscle tightness and total body readiness. Over a single soccer season there was a 72% decrease in lower extremity musculoskeletal injuries when athletes completed a specific dynamic warm-up prior to games and practices. 47 Muscle

9 tightness appears to have more influence on local injury risk than other flexibility factors and therefore cannot be generalized to the entire lower extremity.

Range of motion at a specific joint level may present as a risk factor. For example, while there is no relationship between limited ankle dorsiflexion, hamstring flexibility, and injury on a same side, between limb differences in these measurements increased injury risk. Regardless of which side is involved, there is a direct relationship between having excessive knee extension (knee hyperextension) and increased injury risk on the ipsilateral side.7 There are some studies that also suggest that there is no relationship between range of motion and injury risk at all, making it difficult to establish a general consensus among investigators. 48,49

Muscle Strength/Imbalance

Balance between agonist and antagonist muscles throughout range of motion is crucial for appropriate contraction to occur. Discrepancies in the strength of agonist and antagonist muscles can lead to an unbalanced co-contraction. It is unclear if changes or asymmetries in muscular strength can lead to an injury. However, asymmetries can be determined between limbs or agonist and antagonist musculature. Both methods of comparison suggest that injury risk increases when the relationship is unbalanced. 7,37

Athletes who sustained an ankle injury demonstrate greater muscle strength imbalances than those who had not sustained an injury. 37 In the thigh, quadriceps strength deficits between legs had a significant impact on injury risk in male soccer athletes who later sustained a non-contact knee injury. 36 However, there appears to be no relationship between acute knee injury and hamstring to quadriceps ratio, however, the quad-to-hamstring ratio may increase an athlete’s risk of lower leg overuse injury. 7,36

10 Based on sport demands, there is often some imbalance between anterior and posterior musculature. This becomes a risk factor, in both the ankle and thigh, when a large enough ratio imbalance exists based on the researchers results. 7,36,37

Postural Stability

Postural stability is the ability of an athlete to control their center of gravity within their base of support.50 Postural instability can be considered an injury risk factor because of its association with altered neuromuscular control and increased joint forces.

Athletes with an increased postural sway score during unilateral balance tests, showed a large increase in risk for ankle sprain or re-injury compared to those who were considered to have normal balance. 51,52 Diminished balance could be associated with lower extremity injury as a whole and not just ankle injury. 7 There is agreement among researchers that postural stability is an important factor for assessing lower extremity injury risk. 7,51–53

Overall, there is a lack of consensus regarding exactly what combination or individual factors increase injury risk. Injury risk is likely mitigated by a combination of several factors contributing to overall movement patterns.33 Based on the kinetic chain theory, the human body functions through a series of linked segments, meaning athletes may develop impairments at multiple segments. 31 Therefore, biomechanical evaluations are necessary for a global, multi-segment assessment to highlight several different risk factors while in motion.

Biomechanics

Biomechanics is the study of forces that act upon the body and their effect during movement. When an athlete demonstrates good biomechanics, it can lead to improved technique, increased training efficiencies, and improved performance.54 Several studies

11 have examined the biomechanics of functional movements to determine what deviations from the norm may impact injury risk. 29,55–59 For example, patients that demonstrate increased knee motion, especially into valgus in the sagittal plane, during squats and jump landings are at an increased risk for ACL ligament injuries. 6,60,61 Muscular tightness in the hip joint can lead to increased injury risk down the kinetic chain into the knee and ankle due to alterations in movement mechanics in dynamic movement.8 Even further down the kinetic chain, Achilles tendon injuries have a direct relationship in individuals with tight triceps surae complexes and altered mechanics due to decreased ankle dorsiflexion.4 Movement screens assess modifiable risk factors in combination with task performance in an effort to determine whether a person demonstrates dysfunctional movement that could lead to injury.

Functional Movement Patterns and Testing

Functional movement patterns are movements that incorporate real world, multiplanar movements, multiple body segments, and groups of muscles that work together to create a specific outcome. Assessing quality across several tasks and patterns allows for a comprehensive assessment of muscles and joints to identify dysfunctions limiting normal movement. Tasks well suited for functional movement pattern assessments include a single leg squat, overhead squat, vertical jump, and jump-rebound, because they are relevant to a variety of sport populations and incorporate the whole body. Performance of each task is compared to predetermined standards 6,11,13,62 and deviations from these standards, often referred to as compensations, are thought to be markers of muscle and/or joint dysfunction. For example, excessive knee motion in the sagittal plane during a vertical drop jump (VDJ) is a predictor of ACL injury risk in

12 participants with greater knee motion. 55 However, while there is some association between medial knee displacement and non-contact ACL injury, this study found poor combined sensitivity and specificity of the test. 56 As a result, the VDJ was not recommended for use as a screening tool. 56

The Land Error Scoring System (LESS) was developed to help identify ACL injury risk and other serious lower extremity injuries. The LESS test combines a jump pattern movement, similar to the VDJ, with the forward momentum commonly demonstrated in non-contact ACL injuries. 6 During the LESS test, patients are instructed to jump off a 30-cm high box and land in an area half their height away from the box.

Immediately upon landing, the patient performs a maximal vertical height.63 Researchers identified increased knee valgus, decreased sagittal excursion of the knee, hip, and trunk, and leg rotation in uninjured individuals as dysfunctional movements that could serve as potential risk factors.6,63 These biomechanical compensations are similar to those described by other clinicians. 29,55 Patients who displayed poor jump-landing biomechanics via the LESS presented with movements resulting in a greater anterior tibial shear force using motion capture, potentially predisposing them to a noncontact

ACL injury. 6 While the LESS is a valid and reliable test, 6 it requires motion capture equipment and a clinic based setting in order to perform it correctly. It can be expensive and time consuming. 64

The SEBT looks more broadly to predict lower extremity injuries. The SEBT relies on neuromuscular control and balance to stress lower extremity coordination, flexibility, and strength. 53 Deficits between left and right reach distances can be a result of unilateral dysfunction. The test was able to identify that individuals with chronic ankle

13 instability often show reach distance deficits compared to the healthy uninvolved side. 10

Those with CAI may be at a greater injury risk for ankle sprains and muscle strains based on the known relationship. 30,36,38,44,45 A significant limitation of this test is the inability to identify a specific joint as the cause of any interlimb discrepancy. Without being able to target specific areas affecting the kinetic chain, it is difficult to prescribe targeted prevention exercises. 20 Additionally, the assessor must be extensively trained by another experienced rater to achieve satisfactory reliability. 9

The FMS consists of seven movement patterns that are meant to highlight compensations and limitations in normal movement. These movements include an active straight leg raise, shoulder mobility, trunk stability push-up, rotary stability quadruped, deep squat, hurdle step, and in-line lunge.62 Each movement is scored on a numeric scale based on specific performance criteria determined by the creators of the test. 62,61 The

FMS system provides the test administrator with a series of exercises that can be prescribed based on the outcome of each test item. Researchers have reported that there is an 11-fold and 4-fold increase, respectively, in injury risk for men and women with low scores and that the identified limitations are purported to lead to poor biomechanics and the possibility of future injury 59,65 While there have been several studies that praise the ability of the FMS to identify areas of dysfunction, there are also limitations to the test.

Results of one study suggest that in order to achieve high inter-rater reliability, an athletic trainer must be trained or experienced clinically in FMS for at least 6 months.66 Someone without prior knowledge, even with the test’s extensive instructions, may not score the screen correctly. 57 A meta-analysis conducted in 2018 suggests that FMS has a sensitivity score of .24 (95% CI 0.15 to 0.36),12,55 meaning that the FMS will only

14 identify 24% of athletes who will sustain an injury, leaving approximately three quarters of athletes without appropriate preventative strategies employed. Additionally, the evaluation does not include dynamic activities and therefore does not capture the ability to withstand loads and speeds placed on an athlete during practice and competition. The low demand activities that comprise the FMS create an incomplete picture of an athlete’s full capacity, which is essential to injury prediction. 67 Finally, most studies supporting the FMS’ utility are performed in small homogenous populations and therefore may be limited in external validity.12 The FMS is more progressive. However, the time- consuming test carries many challenges as a screening test for potential injury.

Similar to FMS, the Fusionetics ME analyzes movement quality based on seven different tasks, three of which focus on the lower extremity. The tasks are a double leg squat, double leg squat with heel lift, single leg squat, push-up, shoulder movement, trunk/lumbar spine movement, and cervical spine movements. The test can be performed as a whole or separate into the upper or lower body only. Fusionetics ME is scored by assessing each individuals’ compensations against criterion movement and generating an overall score out of 100. Because Fusionetics ME examines global movement patterns based on normal standards rather than comparing dysfunction bilaterally, the test is more sensitive than other screening tools. 12,13,56 Fusionetics ME has “excellent” intra-rater test- retest reliability as an exam for functional movement quality,13 however there is limited evidence regarding its utility as test to identify injury risk. To date, a single study has demonstrated that NCAA athletes with “poor” movement quality while performing a double leg and single leg squat have a greater incidence of lower extremity injury but has

15 yet to assess the test as a whole.58 Fusionetics ME is still a very new test and requires more research to discover the extent of its clinical applicability.

In conclusion, there is a high non-contact injury prevalence in sport both in acute and chronic time frames. Many researchers have worked to identify modifiable factors which may lead to these injuries. Functional screening tools have been produced to identify dysfunctional movements which are thought to be indicative of injury risk.

Fusionetics ME test is a potential solution to the limitations of its predecessors and has the potential to improve the identification of those requiring injury prevention based on scores for each athlete. While it is impossible to prevent all injuries, it is critical that an accurate and reliable screening tool be reviewed in order to prevent injuries from occurring and help save the athlete’s body, time, and money.

CHAPTER 3: METHODS

Design

Our study uses an ambispective design in which prospectively collected data will be retrospectively examined. We will examine the association between Fusionetics ME test scores and injury risk within a single season. Fusionetics ME scores for athletes were collected if an athlete participated in at least one season over the following academic years: 2014/2015, 2015/2016, 2016/2017, and 2017/2018. All research and participation will be reviewed and approved by the International Review Board. All data collection was previously approved for use in the study.

Participants

Participants were athletes participating in five field sports (men’s soccer, women’s soccer, women’s field hockey, men’s lacrosse, women’s lacrosse) at a single

Division I university. Participants are from diverse backgrounds and were selected based on sport participation not skill or performance. In order to be included in the study,

Fusionetics ME test must have been performed on the athlete during the respective preseason baseline testing session and injury information must have been submitted via the online EMR system. Participants must have data available for each of the three lower extremity tasks included in the Fusionetics test as described below. Exclusion criteria for participants include: 1) athletes that do not participate in the full season following testing,

2) an inability to perform the entire lower extremity Fusionetics ME test and 3) athletes

17 that were prescribed the associated corrective exercises based on their Fusionetics ME scores prior to participation in their respective season. No subject incentive was provided.

Height and weight data were collected from each respective team website or EMR system over the years accessed. Body Mass Index (BMI) was then calculated and recorded. Data for each athlete were then compiled into a single spread sheet with their overall Fusionetics ME scores, individual Fusionetics score for each task, lower extremity injury history, and height/weight for each season between 2014/2015 and

2017/2018 in which the athlete participated.

Epidemiological Background

For the purpose of our study, we used the following epidemiological operational definitions. The population associated with this research is determined to be the group of athletes, healthy or injured, who would be counted as cases if they have the disease/criteria being studied. This study is examining those with poor-moderate scores as the “disease” that qualifies the athlete as being part of the population. Prevalence is defined as the portion of the population that sustain the specific outcome being examined.68 In this analysis, prevalence is the number of all participants that sustain an acute or chronic lower extremity injury. Incidence is the portion of the population that sustain the outcome being examined but also meet the time frame or variable criteria. 68

For example, an individual that has a good Fusionetics ME test score and sustains an injury is a prevalent but not incident case. A risk ratio is determined by calculating the number of incident cases divided by the portion of the population that are determined to be at risk (i.e. poor-moderate Fusionetics ME test score). An odds ratio must also be computed for the population that sustains the outcome and were not determined to be at

18 risk. Division of athletes that sustain the injury by the number that were “at risk” is computed to provide a risk ratio. An odds ratio is a number that can be translated to how much more likely a person who is “at risk” will sustain the outcome/injury being examined and determines the association between an exposure and an outcome.68,69 This represents the odds of an outcome occurring given a particular exposure compared to the odds of the same event occurring without the exposure. 70 This study will analyze risk ratios because we are more concerned with increases in injury risk associated with a poor-moderate score compared to the relationship of exposure to competition or practice.

Data Collection

Data were collected by the entire research team and compiled into a single document. Each individual on the research team was instructed on what data were required to create a full player profile and appropriate groupings, based on the presence or absence of a new injury in the following season. A participant was assigned to the injury group if they sustained an injury for time loss that was either chronic or a non- contact acute injury. A non-contact acute injury is defined as an injury that occurred during weightlifting, practice, or game play that resulted without direct involvement of another player or equipment used in the sport (i.e. lacrosse stick, field hockey stick, ball contact, etc.). An injury qualified as time loss if a player was required to miss an entire day of participation, meaning the player was not allowed to participate in any organized sport except rehabilitation for at least 1 day. Fusionetics ME test data and Blue Ocean

(online electronic medical records system) information must have been entered by a

Certified Athletic Trainer to ensure accuracy of information. Certified Athletic Trainers

19 are highly skilled professionals whose ability to evaluate athletes has been confirmed across several studies. 71,72

Outcome Measure – injury risk

Injury data were collected from Blue Ocean. Participants who appeared in the injury search and met all necessary inclusion/exclusion criteria had their data recorded into a compiled document. All data recorded included basic injury description (i.e., body part and affected side), mechanism of injury, event at which the injury occurred, amount of time lost from sport participation, and dates during which the participant was impacted by the injury. In order to be included in the injured group, a participant only had to sustain one injury that meets all the criteria. If a participant sustained more than one injury with time loss over the same season, only the initial injury that occurred after that season’s recorded Fusionetics test score was recorded.

Exposure Measure – Fusionetics Movement Efficiency Test scores

The Fusionetics ME test scores consisted of three lower extremity assessments.

This included five repeated tests of a double leg overhead squat (DLS), double leg overhead squat with heel lifts (DLS-HL), and single leg squat (SLS). Specific instructions regarding each test can be found in TABLE 1. Movement errors were recorded as either

“yes” or “no” based on whether the athlete performed the error, or not. Possible movement compensations can be seen in TABLE 2. Each of these compensations have been suggested to represent “… poor movement quality”. 73 Based on the errors recorded, the Fusionetics Scoring System calculates a score and a performance level during the three administered tasks using a proprietary algorithm. Each participant is given an overall score from 0 to100 accounts for the number of errors, the type of errors, and the

20 body region where each of the errors occurred. Overall scores are interpreted as poor (0-

49.99), moderate (50-74.99), or good movement efficiency (75-100).13 Fusionetics ME data will be examined in its original score form as continuous data and in its categorical form as ordinal data.

Covariates

In statistical analysis, height and weight were calculated into BMI and treated as a continuous variable. Sport and biological sex were treated as categorical variables.

Statistical Analysis

All data were analyzed within the Statistical Product and Service Solutions software (SPSS, IBM Corp. Armonk, NY). The unit of analysis was athlete-season, i.e. one athlete’s participation in one season. If an athlete participates in more than one season within the study period, each season of play will be treated as independent data.

For example, the data from an athlete who participates for 3 seasons will be treated as three independent observances.

We first determined descriptives of our measures of interest. Next, multivariable binomial risk regression models were used to model the injury risk within that season and estimate risk ratios (RR) and 95% confidence intervals (CI). All binomial risk regression models used Poisson residuals and robust variance estimation to stabilize model fit. 74,75

Two models were selected. The first model treated Fusionetics ME scores as a continuous variable. We are deeming a clinically meaningful difference in Fusionetics score to be 5. As a result, we will transform this variable so that every one-point difference represents a 5-point increase or decrease in Fusionetics score. For example, a

Fusionetics score of 50 will be represented by 10. Therefore, the resulting RR represents

21 the change in injury risk associated with a 5-point increase in Fusionetics score. Another model treated Fusionetics ME scores as a categorical variable, based upon previous classifications of poor, moderate, and good. 13 As a result, we will calculate RR that indicate change in risk associated with good vs. poor, moderate vs. poor, and good vs. moderate. All RR with 95% CI excluding 1.00 were considered statistically significant.

CHAPTER 4: RESULTS

Data were collected for a total of 494 division 1 field sport athletes (m= 175, f=

319) from the men’s/women’s lacrosse teams, men’s/women’s soccer teams, and the women’s field hockey teams for the years included in this study. Two participants were disqualified from the final results because it was explicitly mentioned in their injury history notes that they were prescribed the corrective exercises recommended by

Fusionetics prior to participation in the season, an exclusion criterion. Data from the men’s lacrosse team only included freshman/1 st year members because they are only

Fusionetics tested during their first season of participation. Descriptive details regarding the data can be found in tables 3-7.

The first risk regression model, which examined the Fusionetics ME score as a continuous variable, was not significant. For every 5-point increase in the Fusionetics ME score there was an associated risk ratio of 0.9276 (CI= 0.7198-1.935; p=0.5611; SE=

0.1294). The second risk regression model treated the Fusionetics ME score as a categorical variable based on the predetermined movement quality groups defined by

Fusionetics. This model, like the first, was not significant (Table 8).

CHAPTER 5: DISCUSSION

The purpose of this study was to evaluate the predictive value of the Fusionetics

ME test for lower extremity injuries. Our findings indicate that there is no significant relationship between a person’s Fusionetics ME score and their future risk of a lower extremity injury throughout the subsequent season which is contrary to our hypothesis.

Despite there being no significant results of this study, there were some trends observed that may be useful for future research. For example, across all sports, injured and non- injured populations, the mean Fusionetics score sits right around the cusp of moderate and good (Table 7). While there was still some reduction in risk associated with being categorized as good instead of moderate, there is a greater trend observed between good and poor (Table 8). This suggests that the categorization, rather than the actual

Fusionetics ME score, when appropriate power is available, may hold more predictive value.

Despite the lack of statistical significance, the risk ratios described by this investigation have values that suggest a clinical significance. Wilkerson et al76 Indeed,

Wilkerson et al recommend using the credible lower limit to describe the smallest true effect size. The credible lower limit suggests that even though a CI exceeds 1.00, the true effect size often actually lies closer to the lower limit. Given that our effect sizes fall within the magnitude suggested to represent a clinical significance, further research should be pursued with a larger sample size to determine the true effect of Fusionetics at predicting lower extremity musculoskeletal injuries.

24 This investigation included a broad number of sports so that the results could be applied to a large population of collegiate athletes. Unfortunately, our choice to support generalization may have negatively impacted the statistical results. Several factors that differentiate field sports from each other may have had an impact on the research team’s ability to draw conclusions across the entire study sample. All sports were similar in that the bulk of their athletes were categorized as having moderate movement quality (Soccer:

113; Lacrosse: 120; Field Hockey: 78). However, soccer had the highest percentage of injuries (42.36%) across the included sports. Further, muscle strains and ankle sprains were the most often identified injuries. Soccer players sustained nearly 63% of the total number of strains identified in this investigation. This is likely due to the fact that soccer, among the included sports, requires the greatest use of the lower extremity (i.e. locomotion and ball manipulation). On the other hand, field hockey had the lowest amount of time loss injuries across the three sports (table 3). This is likely due to the fact that field hockey players at this institution perform sport specific training throughout their preseason to help prevent lower extremity injuries. These examples highlight sports specific demands and team training regiments that may cause and/or compensate for movement quality’s influence on injury risk. As such, future research should aim to explore the influence of Fusionetics ME scores on lower extremity injuries with larger sport specific samples.

Similar to other functional movement screening assessments, the Fusionetics ME test may not be able to statistically predict general lower extremity injury risk. Indeed, different lower extremity injuries have different risk factors and the proprietary risk algorithm used by Fusionetics does not weight all of these risk factors equally. As a

25 result, Fusionetics ME scores are likely better suited for one type of injury over others

(e.g. ACL injury risk vs. muscle strains). Similarly, the LESS test has been regarded as one of the premier testing options for ACL injury risk 6 but has not been evaluated for its ability to predict other lower extremity injuries.

It is also important to note that several other factors may be important to consider when statistically predicting injury risk. For example, training load should be considered.

Training load is the amount of perceived demand that a person requires to complete a single training session and is often used to monitor fatigue. Trends of increased training loads are often linked to subsequent injury.77 As a result, future research should examine if there is any relationship between training load reporting, Fusionetics ME scores, and subsequent lower extremity injury risk. In soccer specifically, most strains occurred in athletes who had moderate scores (28/46 total). However, if training load was increased, one could hypothesize that the biomechanical profile of an athlete may break down due to fatigue (i.e. movement quality would be poor) and injury risk could be increased.

The consistency of Fusionetics ME scores over the course of an athletic season should also be considered in future investigations. For the current investigation, we assumed that the baseline assessment score was maintained over the course of the subsequent season. However, fatigue, wear and tear on an athlete’s body may, and/or conditioning within a season may result in movement quality changing over the course of a season. If true, a more complex design would be needed to prospectively quantify movement quality over the course of a season and quantify injury risk on a moving basis adjusted for changing Fusionetics ME scores. Most factors discussed would have a negative impact on movement quality, but it is also possible that movement quality could

26 improve over the course of a season because of strength and conditioning programs and/or the completion of corrective exercises prescribed by sports medicine professionals. Currently, there is no data that shows whether these activities impact movement quality and thus should be the focus of future research.

This investigation is one of the first to test the predictive value of the Fusionetics

ME test. Because of this, there were some limitations identified by the research team.

First, future research should include the asymmetrical difference score that is often calculated as part of the exam. Based on the conclusions made by the SEBT, between limb differences is one of the best identifiers for risk of ankle injury. 20 The Fusionetics

ME assessment also has the capacity to assess between limb differences but these scores were not available for this investigation. Thus, future research should determine the influence of between limb differences on general and specific lower extremity injury risk.

Another factor that would be important to consider for additional analyses would be comparing the results against not only the sports themselves but for factors such as sex or age which are known to have a potential link to injury risk. Finally, this investigation did not include patient related feedback which is crucial to taking an evidence based practice approach. Even though there was no statistical significance. It could be integral to report whether the patients feel as though they have poor biomechanical movement and if the prescribed exercises associated with their test score feel as though they are beneficial. If the patient feels that they test and exercises benefit their performance and injury prevention, then it should be performed regardless with adherence to considering patient values.

27 Clinical Meaningfulness

Despite the results of this study, the Fusionetics system may still be useful to clinicians. The Fusionetics ME test has good intra-rater reliability compared to other movement quality screenings (e.g. FMS) 13,66 and requires little to no equipment compared to other movement quality assessments (e.g. LESS). 64 Additionally,

Fusionetics has good face validity as previous research supports the concept that deviations from the standards set for biomechanics can still impact performance and increase injury risk. 55–59 Thus Fusionetics ME could be used to assess movement quality over time (e.g. following a rehabilitative process). Examining biomechanics and functional movement that still heavily contributes sports performance regardless of its link to predicting time loss injuries.

There is a myriad of risk factors associated with sports participation. Sports medicine teams dedicate a significant amount of time to helping treat and rehabilitate these injuries. While the tests performed in this study did not produce significant results, there are observable trends and important descriptive information gained from the data.

Future research should focus on combining other factors with the Fusionetics ME test and use a more focused approach examining each sport individually. The Fusionetics ME test still has value as an important biomechanics assessment tool to record observed deviations and track progress.

28 Table 1: Movement Instructions for Fusionetics Test

DOUBLE LEG DOUBLE LEG SINGLE LEG OVERHEAD OVERHEAD SQUAT (BOTH SQUAT SQUAT WITH SIDES) HEEL LIFT POSITION - Barefoot - Barefoot - Barefoot INSTRUCTIONS - Feet shoulder width - Heels on plates with - Hands on hips apart feet shoulder width - Leg being - Arms straight above apart tested stacked in their head with - Arms straight above line with hip elbows extended their head with elbows extended EQUIPMENT None 5.1 cm plate None NEEDED

MOVEMENT - Descent and Ascent - Descent and Ascent - Squat should PATTERN should last approx. 2 should last approx. 2 lower to ~45-60 seconds seconds degrees of knee - Squat to level of - Squat to level of flexion before chair height before chair height before returning to squat returning to start returning to start position position position

Table 2: Possible Compensations during Fusionetics Exam

DOUBLE LEG TASKS SINGLE LEG TASKS MOVEMENT Foot Turns Out Foot Flattens COMPENSATIONS Foot Flattens Knee Moves In Heel Lifts Off Ground Knee Moves Out Knee Moves In Uncontrolled Trunk Knee Moves Out Loss of Balance Low Back Arches Low Back Rounds Arms Fall Forward Asymmetrical Weight Shift

29 Table 3. Total Athletes and Injuries

Table 4. Total Number of Injuries by Body Part

Table 5. Summary of Injuries by Type

Table 6. Fusionetics Score vs Healthy/Injured

30 Table 7. Descriptive Data

Uninjured Athletes

Injured Athletes

Table 8. Model 2 (Fusionetics ME category as a categorical variable)

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