Association between double-leg squat and single-leg squat performance and injury incidence among incoming NCAA Division I athletes: A prospective cohort study

Accepted Manuscript Association between double-leg squat and single-leg squat performance and injury incidence among incoming NCAA division I athletes: A prospective cohort study Timothy Eckard, Darin Padua, Timothy Mauntel, Barnett Frank, Laura Stanley, Rebecca Begalle, Shiho Goto, Michael Clark, Kristen Kucera PII:

S1466-853X(18)30199-8

DOI:

10.1016/j.ptsp.2018.10.009

Reference:

YPTSP 962

To appear in:

Physical Therapy in Sport

Received Date: 14 May 2018 Revised Date:

11 October 2018

Accepted Date: 17 October 2018

Please cite this article as: Eckard, T., Padua, D., Mauntel, T., Frank, B., Stanley, L., Begalle, R., Goto, S., Clark, M., Kucera, K., Association between double-leg squat and single-leg squat performance and injury incidence among incoming NCAA division I athletes: A prospective cohort study, Physical Therapy in Sports (2018), doi: https://doi.org/10.1016/j.ptsp.2018.10.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Association between double-leg squat and single-leg squat performance and injury incidence among incoming NCAA Division I athletes: a prospective cohort study.

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Timothy Eckard, PT, DPT1 Darin Padua, PhD, ATC1 Timothy Mauntel, PhD, ATC2 Barnett Frank, PhD, ATC1 Laura Stanley, PT, DPT1 Rebecca Begalle, PhD, ATC3 Shiho Goto, PhD, ATC4 Michael Clark, PT, DPT5 Kristen Kucera, PhD, ATC1 1

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Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC. 2 Department of Orthopedics, Walter Reed National Military Medical Center, Bethesda, MD. 3 Division of Health and Human Services, Daemon College, Amherst, NY. 4 Texas Health Ben Hogan Sports Medicine, Fort Worth, TX. 5 Fusionetics LLC, Milton, GA. This study was approved by the Institutional Review Board of the University of North Carolina at Chapel Hill.

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Declarations of interest: Partial funding for this study was provided by the National Academy of Sports Medicine (NASM). Timothy Eckard is supported by a Promotion of Doctoral Studies (PODS) award from the Foundation for Physical Therapy. These sponsors had no involvement in study design, data collection, data analysis and interpretation, or decision to submit this manuscript for publication.

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Address correspondence to: Timothy Eckard Fetzer Hall, CB #8700 Chapel Hill, NC 275998700. Email: [email protected].

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Association between double-leg squat and single-leg squat performance and injury incidence among incoming NCAA Division I athletes: a prospective cohort study.

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Declarations of interest: Partial funding for this study was provided by the National Academy of Sports Medicine (NASM). [REMOVED FOR BLIND REVIEW] is supported by a Promotion of Doctoral Studies (PODS) award from the Foundation for Physical Therapy. These sponsors had no involvement in study design, data collection, data analysis and interpretation, or decision to submit this manuscript for publication.

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Objectives: Determine the association between performance on the double-leg squat (DLS) and single-leg squat (SLS) and prospective injury incidence in athletes. Design: Prospective cohort study. Setting: National Collegiate Athletic Association (NCAA) Division I university. Participants: 111 incoming NCAA Division I athletes from 10 varsity sports teams Main Outcome Measures: Performance on the DLS and SLS were assessed as “poor” or “nonpoor.” Lower extremity (LE) injury data for the following year were extracted from electronic medical records. Multivariate Poisson regression was used to compare the incidence of LE injuries in athletes with poor versus non-poor performance on the DLS and SLS. Results: The final models for the DLS and SLS were adjusted for sex and LE injury history and yielded an incidence rate ratio (IRR) of 1.33 (95% CI: 0.80 2.22) for the DLS and 1.62 (95% CI: 0.98, 2.66) for the SLS when comparing poor to non-poor movers. Conclusions: Athletes with poor LE movement quality assessed on the DLS or SLS had greater incidence of LE injury than those with non-poor movement quality. This is the first study to demonstrate an association between performance on the double-leg squat and single-leg squat and injury risk in NCAA athletes.

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Key words: Injury prevention, lower extremity, movement quality, single-leg squat, double-leg squat

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INTRODUCTION

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Assessments of lower extremity (LE) movement quality are used to identify athletes with

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movement patterns that increase risk for future LE injury. (Whittaker et al., 2016) Aberrant

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movement patterns, or “errors” during the performance of LE movement quality assessments

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have been linked to impairments in muscle strength, (Bell, Padua, & Clark, 2008) range of

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motion, (Bell et al., 2012) and motor control. (Bell et al., 2012) These impairments contribute to

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injury (Hirth & Padua, 2007) and may be addressed through the prescription of corrective

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exercises in injury prevention programs (IPP). (Hirth & Padua, 2007; Mizner, Kawaguchi, &

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Chmielewski, 2008) IPP utilizing exercises targeting common strength and proprioceptive

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impairments have been demonstrated to reduce the risk of LE injury in athletes. (Hubscher et al.,

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2010), (Soomro et al., 2016)

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Two LE movement quality assessments currently used in clinical and sport-specific settings are

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the double-leg squat (DLS) and the single-leg squat (SLS). These assessments are attractive to

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clinicians because they are easy to administer, have demonstrated reliability (Frank, Stanley, &

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Padua, 2016; Stanley, Frank, & D.A., 2016) and convergent validity with measures of LE muscle

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function, (Bell et al., 2012; Mauntel et al., 2013) and have minimal equipment requirements.

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(Bell et al., 2012) Recently, novel scoring criteria for the DLS and SLS were developed by a

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subset of the authors of this study with the intent of providing clinicians with granular data that

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can be used to inform decisions regarding IPP. These novel criteria can be summed into an

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overall composite score to help identify globally “poor” movers. In a study of between-day intra-

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rater reliability of the DLS and SLS, composite test scores of the DLS (ICC(2,1) = 0.655, SEM

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=1.56) and SLS (ICC(2,1) = 0.52, SEM=2.6) were found to be moderate and prevalence and bias

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adjusted Kappa (PABAK) values for each individual item demonstrated that two DLS items

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(foot flattens and arms forward) and one SLS item (foot flattens) were in the moderate range (κ =

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0.41-0.6), with all others in the good(κ = 0.61-0.8), or excellent(κ = 0.81-1.00), ranges. (Landis

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& Koch, 1977; Stanley et al., 2016) In a study examining interrater reliability of the DLS and

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SLS, composite score reliability was found to be good to excellent DLS (ICC(2,1) = 0.76, SEM

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=1.3) and SLS (ICC(2,1) = 0.78, SEM=0.93) PABAK values were moderate for one variable on

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the DLS (knee valgus) and one variable on the SLS (trunk flexion/rotation/side-bend), with all

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others in the good or excellent ranges. (Frank et al., 2016; Landis & Koch, 1977). The results of

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these reliability studies are promising, but to date no study has examined the association of

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performance on these novel versions of the DLS and SLS with prospective LE injury risk.

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Therefore, the primary purpose of this study was to investigate the association between

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performance on the DLS and SLS and prospective injury incidence in incoming NCAA Division

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I athletes. Our hypothesis was poor performance on the DLS or SLS would be associated with a

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greater incidence of LE injury during the study period.

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METHODS

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Study design. This study was an ancillary study of a parent prospective cohort study

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investigating risk factors for musculoskeletal injury in varsity athletes at one NCAA Division I

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university. All research activities were conducted according to the Declaration of Helsinki and

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reported following the Strengthening the Reporting of Observational Studies in Epidemiology

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(STROBE) Statement guidelines.

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Setting and participants. Participants for this study were a convenience sample of 115 varsity

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athletes enrolled in the parent study described above (Figure 1). All participants were in their

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first year competing for the university, as either a first-year NCAA student-athlete or a transfer

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student-athlete in their first year at the university. Athletes from 10 sports were recruited during

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academic years 2013/14, 2014/15, and 2015/16. Sports included women’s and men’s soccer,

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women’s and men’s cross-country, women’s and men’s lacrosse, women’s and men’s track and

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field, women’s field hockey, and men’s football. Athletes were eligible for participation if they

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were injury free or if they were experiencing any injury or symptoms that did not contraindicate

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performance of the DLS or SLS as described below. Athletes who were not cleared by their team

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sports medicine staff to perform the DLS and/or SLS as described below were excluded from

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this study. All study procedures were approved by the university Institutional Review Board and

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all participants provided written informed consent.

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Study procedures: In their first semester of enrollment at the university, participants filled out a

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standardized questionnaire with demographic, sport, and LE injury history information. A LE

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injury history was defined as having experienced “a previous LE musculoskeletal injury resulting

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in a grade III sprain or strain or a fracture requiring surgical repair, or any of these injuries that

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resulted in 6 or more months of missed or altered physical activity.” After completing the

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questionnaire, participants’ height and weight were assessed. Participants then completed several

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assessments at 8 stations in randomized order. Stations included the DLS, the SLS, a jump-

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landing task, a single-leg triple hop, LE range of motion (ROM), upper extremity ROM, a push-

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up task and LE manual muscle strength testing. As the purpose of this study was to investigate

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the association of performance on the DLS and SLS with LE injury incidence, only data from

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these assessments were included in this study. Assessments were administered and scored by the

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same 5 trained assessors during all 3 years of enrollment. All assessors were certified athletic

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trainers or licensed physical therapists with at least 2 years of experience measuring strength,

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ROM, and movement quality. Following 1 calendar year from the date of initial data collection,

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LE injury data were extracted from each participant’s electronic medical record (EMR)

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maintained by university sports medicine staff. A standardized data collection form was used to

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record information pertaining to each LE injury.

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Assessing Movement Quality

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Double-leg squat test. During testing athletes wore their normal training shoes. Participants

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stood with their feet hip-width apart, toes pointing straight ahead, and arms raised above their

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head. The athlete was instructed to “squat down as if sitting in a chair” to the point of maximal

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comfortable knee flexion. If an athlete did not achieve at least 60 degrees of knee flexion on

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visual inspection, they were encouraged to go deeper on subsequent repetitions if possible.

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(Figure 2) Each athlete performed 1 practice set of 5 repetitions to become familiar with the task.

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No other instructions were provided to the participants other than what constituted a successful

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repetition. The DLS test was scored by a single observer (e.g., research staff member in this

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study) in real time. The researcher observed each athlete from 3 views, anterior, lateral, and

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posterior. The athlete was instructed to perform 3 sets of 5 consecutive repetitions while the

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researcher observed from each view, resulting in 15 total repetitions. No rest was provided

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between sets. Errors observed on the majority of repetitions from any view in which they could

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be seen were considered present and scored as a 1, with all others scored as 0. The number of

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errors present was then summed to yield the total score. For example, the item “foot turns out”

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was considered present if the athlete exhibited this error on 3 of 5 repetitions from an anterior

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view or 3 of 5 repetitions from a posterior view. Movement errors (as defined in Table 1) were

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recorded on a standardized data collection sheet. Possible scores on the DLS ranged from 0 to

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15. Five items on the DLS pertained to errors that can occur only once during a squat (e.g.

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forward trunk lean or weight shift), and 5 pertained to errors that can occur independently in

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each LE and therefore had the potential to occur twice, as 2 separate errors (e.g., knee valgus,

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heel raise).

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Single-leg squat test. During testing athletes wore their normal training shoes. Participants stood

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on one foot pointed straight forward, with the non-weight bearing LE flexed at the knee to 90°

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and at the hip to 45°. The athlete’s hands were placed on the iliac crests with head and eyes

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facing straight ahead. The athlete was instructed to squat to maximum comfortable knee flexion,

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returning to the original starting position between each repetition (Figure 2). Each athlete

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performed 1 practice set to become familiar with the task. No other instructions were provided to

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the participants other than what constituted a successful repetition. Athletes performed 3 sets of 5

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consecutive repetitions (15 total repetitions per limb). Movement quality was visually assessed

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by a single rater who recorded movement errors on a standardized data collection sheet. Athletes

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alternated performance of the SLS on right and left LE to avoid fatigue and confirmed they did

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not feel fatigued prior to initiating each set. The SLS was only assessed from anterior and lateral

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views. Errors observed on the majority of repetitions from any view in which they could be seen

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were considered present and scored as a 1, with all others scored as 0. A list of errors may be

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found in Table 1. The SLS score ranges from 0 to 9 for each LE. Right and left scores were

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summed together to calculate a total SLS score for each participant.

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Defining poor movers. There are no established cutpoints for DLS and SLS to define poor

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movers on this new . Therefore, DLS and SLS scores were grouped into tertiles, quartiles, and

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quintiles, and the injury incidence rate was examined. We looked for potentially meaningful

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increases in the injury incidence rate between the quantiles, using the 95% confidence intervals

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as guides. Graphs of LE injury incidence rates by test score quartiles demonstrated J-shaped

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patterns for the DLS and SLS, meaning the participants in this study with the highest and lowest

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total scores had a greater LE injury incidence than those with moderate scores. In light of these

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exploratory analyses, the choice was made to dichotomize scores to those in the fourth quartile

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versus those in the lower 3.

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Outcomes, covariates, and days at risk.

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Incident LE injuries. LE injuries sustained by each participant during the study period were

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recorded by clinical sports medicine staff. Musculoskeletal conditions, such as “pain,”

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“inflammation,” “tightness,” “contusion,” and “general maintenance” were excluded. Our

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definition of LE included the acetabulum and hip joint in addition to the thigh, leg, foot and all

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associated joints. Time loss and non-time loss LE injuries were included, as well as acute and

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chronic injuries.

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Days at Risk. Each athlete’s participation status, “full,” “limited,” or “out”, was recorded in

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each encounter in the EMR. Exposure in the form of days at risk were calculated as the number

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of calendar days an athlete’s participation status was listed as “out” subtracted from 365 days

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and reported as the number of person-days at risk. (Lynall et al., 2015) For example, if an

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athlete missed 6 days due to injury, that athlete would have a total of 359 person-days at risk. An

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athlete’s participation status was assumed to be unchanged until the date of an encounter with a

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different participation status. We elected to use calendar days to calculate each athlete’s days at

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risk because Division I NCAA athletes routinely perform conditioning, skills practice, and other

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potentially injurious sport-related activities outside of team-sanctioned events. (Lynall, Mauntel,

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Padua, & Mihalik, 2015)

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Potential Covariates. Four potential covariates were examined for inclusion in the final models:

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LE injury history, sport cutting load, sex, and body mass index (BMI). Previous research has

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demonstrated that LE injury history is a significant risk factor for future LE injury. (Kucera,

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Marshall, Kirkendall, Marchak, & Garrett, 2005) Athletes playing different types of sports are

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potentially exposed to differences in LE training loads, player contact, and competition

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schedules. Thus, sport is a potential confounder in the relationship between performance on the

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DLS and SLS and subsequent LE injury incidence. (Gianola et al., 2017) We examined this

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potential confounder through the creation of a variable that dichotomized the cutting load

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inherent to the practice and competition of each sport into high/moderate and low/no cutting

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load. This binary variable was used in place of sport as a more meaningful and parsimonious

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way to account for between-sport differences in the frequency and intensity of potentially-

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injurious cutting motions and LE movement quality training experienced by study participants.

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Supporting this decision is previous research demonstrating practice and competition injury rates

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are generally higher in sports we defined as having a high/moderate cutting load: soccer, (Roos

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et al., 2016) lacrosse, (Dick, Romani, Agel, Case, & Marshall, 2007; Kerr et al., 2017) football,

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(Kerr et al., 2016) and field hockey (Dick, Hootman, et al., 2007), compared to the primarily

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sagittal-plane oriented sports we defined as having a low cutting load (cross-country (Kerr et al.,

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2015) and track and field). Sex was examined as a potential covariate because squat patterns

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have been shown to vary between the sexes (Mauntel, Post, Padua, & Bell, 2015; Zeller,

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McCrory, Kibler, & Uhl, 2003) and incidence rates of several LE injuries, including ACL

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sprains, (Arendt, Agel, & Dick, 1999) stress fractures (Jacobs, Cameron, & Bojescul, 2014) and

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muscle strains (Eckard, Kerr, Padua, Djoko, & Dompier, 2017) have been found to differ

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significantly between the sexes. Finally, BMI was examined as a potential covariate because it

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has been previously identified as a LE injury risk factor, (Murphy, Connolly, & Beynnon, 2003)

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particularly for bone stress injuries (BSI). (Jacobs et al., 2014) There is support in the literature

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for utilization of the ratio of 10 events for every 1 potential explanatory variable in multivariate

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regression analyses using categorical outcomes (Peduzzi et al., 1996; Vittinghoff, 2007). 110

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events (lower extremity injuries) occurred in our sample, suggesting that our analyses had

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sufficient statistical power to assess the five explanatory variables (the primary variable of

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interest and four potential covariates) examined.

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Statistical analyses. Descriptive statistics were calculated for demographic, movement quality,

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and injury data. Crude injury incidence rates per 1,000 person-days (IR = number of incident

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injuries / number of person-days at risk) and incidence rate ratios (IRR = exposed IR / unexposed

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IR) were calculated for the main exposures of interest (poor mover on the DLS or SLS versus

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non-poor mover) as well as for each of the 4 potential covariates: LE injury history

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(dichotomized to yes/no), sport cutting load (dichotomized to moderate/high and not/low), BMI

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(continuous variable), and sex (dichotomized to male/female). Sex was also assessed as a

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potential effect modifier using a likelihood ratio test. An initial multivariate Poisson regression

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model with the natural log of calendar days included as an offset variable was used to compare

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the incidence of LE injuries in poor versus non-poor movers on the DLS and SLS with LE injury

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history, sport cutting load, BMI, and sex included as potential covariates. A backward

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elimination, estimation-based strategy was utilized to select covariates included in the final

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model. Confounding was assessed using the log confounding rate ratio (lnCoIRR = |ln (crude

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IRR/adjusted IRR)|*100). A ≥ 10% change in the lnCoIRR when a covariate was removed from

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the adjusted model indicated confounding and the covariate was retained. (Rothman, Greenland

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& Lash, 2008) The final multivariate models included confounders as well as potential

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confounders deemed to be important in previous literature. Regression modelling was performed

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in SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA).

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Participants missing data for any variable included in a model were excluded from multivariate

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analysis. A receiver operating characteristic (ROC) curve was used to assess the sensitivity,

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specificity, and area under the curve (AUC) of the DLS and SLS using our chosen cut-points (≥5

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errors on the DLS, and ≥10 errors on the SLS), with a true positive defined as occurrence ≥1

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injury and true negative as the occurrence of 0 injuries during the study period. ROC curve

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analysis was completed using SPSS Statistics software (version 24; IBM Corp., Armonk, NY,

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USA)

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RESULTS

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Descriptive data

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Overall, 44.4% of the 115 incoming athlete participants were female (n=51) and 55.7% were

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male (n=64) (Table 2). The sports with the largest numbers of participants were football (29.6%,

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n=34), women’s soccer (17.4%, n=20) and men’s lacrosse (17.4%, n=20). Fifty participants

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(43.9%) reported a history of LE injury. A total of 110 LE injuries were incurred during the

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study period, ranging from 0 to 5 injuries per participant. The majority of injuries (65.5%, n=72)

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were acute and 34.5% (n=38) of injuries were overuse/chronic. The most frequently injured

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regions of the LE were the ankle/foot (30.0%, n=33), thigh (23.6%, n=26) and knee (18.2%,

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n=20). The most frequent injury types were strain (34.5%, n=38) and sprain (29.1%, n=32)

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(Table 3). The 3 most common diagnoses were ankle sprain (12.7%, n=14), hamstring strain

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(8.2%, n=9), and adductor strain (4.5%, n=5). Most LE injuries were non-time loss (NTL)

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(54.5%, n=60) while 45.5% (n=50) were time loss (TL) injuries. The mean days at risk among

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all participants was 336.23 ±66.57 person-days with a range of 19 to 365 person-days.

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Movement quality assessments

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Descriptive statistics. The mean DLS score was 4.19 ± 2.43 (range 0 to 10). The most

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frequently observed errors were forward lean (57.9%, n=66), foot turns out (56.1 %, n=64), and

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weight shift (54.4%, n=62) (Table 4). Poor movement was defined as the highest quartile, which

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equated to ≥5 errors on the DLS (n=28, 24.6%). The mean SLS score was 7.16 ± 3.31 (range 1 to

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13). The most frequently observed errors were trunk/hip shift (82.5%, n=94), knee valgus

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(70.2%, n=80), and hip drop/hike (60.5%, n=69) (Table 4). Poor movement on the SLS was

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defined as the highest quartile, which equated to ≥10 errors on the right and left LE combined

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(n=24, 21.1%). Sixteen subjects were scored as poor on both the DLS and SLS.

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Multivariate analyses. A total of 111 participants were included in multivariate analyses. The

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unadjusted LE injury incidence rate was 3.26 (95% CI: 2.15, 4.94) per 1,000 person-days in the

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DLS poor mover group and 2.67 per 1,000 person-days (95% CI: 2.04, 3.48) in the non-poor

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mover group (Table 5). The crude IRR for poor versus non-poor mover on the DLS was 1.22

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(95% CI: 0.76, 2.00). Sex was not found to be an effect modifier of the DLS poor mover-LE

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injury association (likelihood ratio test, p=0.83). In multivariate analyses, the log confounding

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rate ratio did not demonstrate a ≥10% change when any covariate was removed from the model

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(sex 8.50%, LE injury history < 0.10%, cutting load 1.29%, BMI <0.10%). The final DLS model

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was adjusted for sex and LE injury history and yielded an IRR of 1.33 (95% CI: 0.80, 2.22) for

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being a poor versus non-poor mover on the DLS.

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The unadjusted LE incidence rate was 3.98 per 1,000 person-days (95% CI: 2.64, 6.01) in the

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SLS poor mover group and 2.51 per 1,000 person-days (95% CI: 1.93, 3.27) in the SLS non-poor

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mover group (Table 5). The crude IRR for poor versus non-poor movers on the SLS was 1.58

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(95% CI: 0.97, 2.58). Sex was not found to be an effect modifier of the SLS poor mover-LE

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injury association (likelihood ratio test, p=0.45). In multivariate analyses, the log confounding

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rate ratio did not demonstrate a ≥ 10% change when any covariate was removed from the model

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(sex 4.84%, injury history history 2.68%, cutting load 0.19%, BMI 0.41%). The final SLS model

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was adjusted for sex and LE injury history and yielded an IRR of 1.62 (95% CI: 0.98, 2.66) for

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being a poor versus non-poor mover on the SLS.

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Sensitivity, Specificity, and Area Under the Curve. Using the cutpoint of ≥5 errors on the

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DLS, the sensitivity was 0.22, the specificity was 0.86, and the AUC was 0.54 (95% CI 0.36,

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0.72). Using the cutpoint of ≥10 errors on the SLS, the sensitivity was 0.17, the specificity was

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0.71, and the AUC was 0.55 (95% CI 0.37, 0.72).

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DISCUSSION

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Our study demonstrated that incoming NCAA Division I athletes exhibiting poor LE movement

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quality assessed on the recently developed scoring criteria for the DLS or SLS had a greater

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incidence of LE injury compared to those who exhibited non-poor movement quality. The

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association found was in the direction of our hypothesis, though neither assessment demonstrated

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statistical significance in its relationship to the prospective injury rate. The moderate associations

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found in this study provide impetus for further investigations of the relationship between

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performance on these movement quality assessments and future injury risk.

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Despite being associated with future injury risk, our study demonstrated poor test characteristics

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for the DLS and SLS. Unfortunately, this is a common occurrence among sports injury risk

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screening assessments, (Bahr, 2016) as strong prognostic test characteristics are not a corollary

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of an association between a test result and future injury risk. (Pepe, Janes, Longton, Leisenring,

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& Newcomb, 2004) The AUC of 0.54 (95% CI: 0.36, 0.72) for the DLS and 0.55 (95% CI: 0.37,

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0.72) for the SLS indicate that the cutpoints used for these 2 tests had combined sensitivity and

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specificity levels that failed to provide meaningful predictions of LE injury. (Metz, 1978) These

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findings are similar to recent studies examining the test characteristics of other LE movement

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quality assessments including the FMS (AUC <0.50 [95% CI not reported]), (Wiese, Boone,

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Mattacola, McKeon, & Uhl, 2014) FMS 9+ (AUC 0.48 ([95% CI: 0.43, 0.54]), (Bakken et al.,

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2017) and vertical drop jump test (AUC 0.60 [95% CI not reported]). (Krosshaug et al., 2016)

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Taken together, these findings suggest that it is not advisable to use existing LE movement

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quality assessments, including the DLS and SLS, in isolation to make accurate predictions of

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injury risk at the level of an individual athlete. (Bahr, 2016)

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Despite poor test characteristics, LE movement quality assessments such as the DLS and SLS are

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valuable to sports medicine staff in two important ways. First, they provide sports medicine staff

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with vital information that can be used in combination with knowledge of other known risk

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factors, such as training load, (Soligard et al., 2016) sex, (Murphy et al., 2003) and previous

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injury history, (Kucera et al., 2005) to assess the injury risk of individual athletes. Because

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different LE movement quality assessments evaluate different structures and systems, (Mauntel

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et al., Analysis of unpublished data.; Munro, Herrington, & Comfort, 2017) they are particularly

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valuable when used in combination with each other. Second, knowledge of the most frequently

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occurring movement errors on a team gleaned from the results of LE movement quality

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assessments helps sports medicine staff create and modify team-level injury prevention

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programs.

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Future studies should continue to develop these valuable screening tools by investigating

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associations between performance on the DLS and SLS and LE injury incidence in larger and

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more highly-controlled studies, such as with more homogenous cohorts (e.g. collegiate female

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soccer players) and more precisely calculated athlete exposures, thus progressing from Stage 3

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(developing potential solutions) to Stage 4 (testing under idealized conditions) of the Translating

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Research into Injury Prevention Practice (TRIPP) framework. (Finch, 2006)

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Strengths and limitations.

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A strength of this study was the inclusion of sex and LE injury history in the final multivariate

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models. Despite indications these variables were not confounders in this analysis, they were

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retained in the final models because they have been well-established as risk factors for LE injury,

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and it is thus considered good practice to adjust for them. (Bahr & Holme, 2003) A second

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strength of this study was the use of injury rates to calculate incidence, as rates provide a more

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accurate estimate of the association between an exposure and an outcome than injury

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proportions. (Knowles, Marshall, & Guskiewicz, 2006) Finally, the diversity of our population in

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terms of sex and sport improves generalizability to other groups of incoming NCAA Division I

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athletes.

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There are also some limitations to this study. First, all participants were incoming NCAA

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Division I athletes, limiting generalizability to other populations of athletes. Second, our

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definition of exposure included all calendar days on which an athlete was not listed as “out” in

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the EMR, which may have resulted in an underestimate of the true IR. However, this definition

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of exposure has been used in studies examining similar populations, (Lynall et al., 2015) and is

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conservative, as it errs towards underestimating the true IR.

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CONCLUSION

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Our results demonstrate an association between poor performance on newly developed versions

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of the DLS and SLS and greater prospective injury rates in incoming NCAA Division I

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collegiate athletes. We suggest that clinicians working with collegiate athletes consider adopting

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the scoring criteria used in this study to administer the DLS or SLS, as well as further

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investigations into the association between performance on these versions of the DLS and SLS

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and prospective injury risk in athletes.

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Pepe, M. S., Janes, H., Longton, G., Leisenring, W., & Newcomb, P. (2004). Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol, 159(9), 882-890. Roos, K. G., Wasserman, E. B., Dalton, S. L., Gray, A., Djoko, A., Dompier, T. P., & Kerr, Z. Y. (2016). Epidemiology of 3825 injuries sustained in six seasons of National Collegiate Athletic Association men's and women's soccer (2009/2010-2014/2015). Br J Sports Med. doi:10.1136/bjsports-2015-095718 Rothman, K.J., Greenland, S., & Lash, T.L. (2008). Modern Epidemiology (3rd ed.). Philadelphia, PA: Wolters Kluwer Health/Lippincott Williams & Wilkins. Soligard, T., Schwellnus, M., Alonso, J. M., Bahr, R., Clarsen, B., Dijkstra, H. P., . . . Engebretsen, L. (2016). How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury. Br J Sports Med, 50(17), 1030-1041. doi:10.1136/bjsports-2016-096581 Soomro, N., Sanders, R., Hackett, D., Hubka, T., Ebrahimi, S., Freeston, J., & Cobley, S. (2016). The efficacy of injury prevention programs in adolescent team sports: a meta-analysis. Am J Sports Med, 44(9), 2415-2424. doi:10.1177/0363546515618372 Stanley, L. E., Frank, B., & D.A., P. (2016). Between-day reliability of lower extremity movement quality during double and single leg squatting tasks. J Athl Train, 51(6 (Supplement)). Vittinghoff, E., & McCulloch, C. E. (2007). Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol, 165(6), 710-718. doi:10.1093/aje/kwk052. Whittaker, J. L., Booysen, N., de la Motte, S., Dennett, L., Lewis, C. L., Wilson, D., . . . Stokes, M. (2016). Predicting sport and occupational lower extremity injury risk through movement quality screening: a systematic review. Br J Sports Med. doi:10.1136/bjsports2016-096760 Wiese, B., Boone, J., Mattacola, C., McKeon, P., & Uhl, T. (2014). Determination of the functional movement screen to predict musculoskeletal injury in intercollegiate athletics. . Athl Train Sports Healthcare, 6(4), 161-169. Zeller, B. L., McCrory, J. L., Kibler, W. B., & Uhl, T. L. (2003). Differences in kinematics and electromyographic activity between men and women during the single-legged squat. Am J Sports Med, 31(3), 449-456. doi:10.1177/03635465030310032101

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Total number of freshman athletes on participating teams during years of study n=211

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• 106 freshmen declined to participate (50.2%) • 105 freshmen elected to participate • 10 transfers elected to participate

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FIGURE 1. Flow diagram of data collection.

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468 469

Included in multivariate analyses n=111

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Completed data collection n=111

• 1 participant did not respond to question on previous LE injury • 1 participant did not complete DLS/SLS (reason unknown) or have BMI measurement • 2 other participants did not have BMI measurements

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Freshmen/transfers enrolled in parent study n=115

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FIGURE 2. Beginning and ending positions of the DLS (top) and SLS (bottom).

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3.0

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Injury Incidence Rate (per 1,000 person-days)

4.0

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0.0 1

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DLS and SLS Quartile Score

DLS Incidence Rate

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FIGURE 3. Total injury incidence rate by quartile score on DLS and SLS.

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SLS Incidence Rate

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ACCEPTED MANUSCRIPT TABLE 1. Double-leg Squat (DLS) and Single-leg Squat (SLS) Test Item Operational Definitions Double-leg Squat Item Operational Definition Foot Turns Out Any lateral deeviation from the starting position Foot Flattens Fifth metatarsal is elevated and/or toes lift Knee Valgus The middle of the patella moves medial to the first toe Knee Varus The middle of the patella moves lateral to the fifth toe Forward Lean Inability to maintain a torso parallel to the tibia Low Back Arch Increased lumbar extension from starting position Low Back Round Increased lumbar flexion from starting position (occurring before 90 degrees of hip extension) Arms Forward Inability to maintain a straight line as an extension of the torso Heel Lift Off Inability to keep heels in contact with the floor Weight Shift Weight shifts to being asymmetrical Single-leg Squat (SLS) Item Operational Definition Foot Flattens Fifth Metatarsal is elevated and/or toes lift Knee Valgus The middle of the patella moves medial to the first toe Knee Varus The middle of the patella moves lateral to the fifth toe Trunk/Hip Shift Inability to maintain torso paralle to the tibia and/or change from starting/neutral position Balance Loss Two or more touches with the non-involved foto and/or any hopping to retain balance Knee Flexion < 60 Failure to reach at least 60 degrees of knee flexion at deepest point of squat Low Back Round Trunk Flexion/Rotation/ Lumbar flexion, trunk rotation or side bending away from starting/neutral position Side-Bend Hip Drop/Hike Drop or hike of either hip away from horziontal with the floor

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Table 2. Participant Demographic Information Year of entry into cohort n % 2013 62 53.9 2014 45 39.1 2015 8 7.0 Year n % Freshmen 105 91.3 Sophomore Transfers 7 6.1 Junior Transfers 1 0.9 Senior Transfers 1 0.9 Graduate Transfers 1 0.9 Sex n % Female 51 44.4 Male 64 55.7 Sport n % Men's Cross Country 3 5.2 Men's Football 34 29.6 Men's Lacrosse 20 17.4 Men's Soccer 2 1.7 Men's Track and Field 2 1.7 Women's Cross Country 11 9.6 Women's Field Hockey 8 7.0 Women's Lacrosse 7 6.1 Women's Soccer 20 17.4 Women's Track and Field 5 4.4 a Sport cutting intensity n % High/moderate 91 79.1 Low/none 24 201 Previous LE injury n % Yes 50 43.9 No 64 56.1 b Missing 1 < 0.1

a

mean 22.3 27.6 25.3

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Body Mass Index (BMI)c Female Male Overall

SD 3.6 4.8 5.1

High/moderate includes women's and men's soccer, women's and men's lacrosse, football, and field hockey; low/none includes women's and men's track and field and women's and men's crosscountry b Subject missing data excluded from multivariate analyses using previous LE injury c Missing data from 3 participants, excluded from multivariate analyses using BMI

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TABLE 4. Item Frequencies on Double-leg Squat (DLS) and Single-leg Squat (SLS) Double-leg Squat (DLS)

Item Foot Flattens Knee Valgus Knee Varus Trunk/Hip Shift Balance Loss Knee Flexion < 60 Low Back Round Trunk Flexion/Rotation/ SideBend Hip Drop/Hike

Unilateral 19 28 7 15 32 18 17

a

Bilateral 40 23 11 19 --------6 --Single-leg Squat (SLS) Bilateral 42 52 2 79 36 4 1

4

62

14

55

Items that cannot occur independently on each LE were scored yes/no

b

Yesa --------66 36 13 43 --62

No. Participants Displaying Error 64 26 29 36 66 36 13 43 9 62

No. Participants Displaying Error 61 80 9 94 68 22 18

---------------

66 69

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Unilateral 24 3 18 17 --------3 ---

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Item Foot Turns Out Foot Flattens Knee Valgus Knee Varus Forward Lean Low Back Arch Low Back Round Arms Forward Heel Lift Off Weight Shift

---

-----

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TABLE 5. Crude and Adjusted Estimates for Primary Explanatory Variables and Covariates Double-leg Squat (DLS) Unadjusted Estimates

Injury Historya b,c

Cutting load

Body Mass Index (BMI) b

Follow-up time (days)

Unadjusted rate/1,000 person-days

95% confidence interval

Unadjusted rate ratio

95% confidence interval

28 87 51 64 50 64 91 24

32 78 56 54 42 67 86 24

9814 29258 17008 22064 16534 22173 30396 8676

3.26 2.67 3.29 2.45 2.54 3.02 2.83 2.77

2.15-4.94 2.04-3.48 2.41-4.50 1.78-3.37 1.76-3.66 2.26-4.03 2.19-3.65 1.71-4.48

1.22 --1.35 --0.84 --1.02 ---

0.76-2.00 --0.86-2.10 --0.53-1.34 --0.59-1.76 ---

1.33 --1.50 --0.79 -------

0.80-2.22 --0.93-2.41 --0.49-1.26 -------

`

---

---

---

0.98

0.93-1.02

---

---

Poor Not poor Female Male Yes No Mod/high None/low ---

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a

No. of injuries

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Sex

No. of participants

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DLSa

---

Injury Historya Cutting loadb,c Body Mass b Index (BMI)

Adjusted rate 95% confidence ratio interval

Adjusted Estimates

No. of injuries

Follow-up time (days)

Unadjusted rate/1,000 person-days

95% confidence interval

Unadjusted rate ratio

95% confidence interval

24 91 51 64 50 64 91 24

32 78 56 54 42 67 86 24

8034 31038 17008 22064 16534 22173 30396 8676

3.98 2.51 3.29 2.45 2.54 3.02 2.83 2.77

2.64-6.01 1.93-3.27 2.41-4.50 1.78-3.37 1.76-3.66 2.26-4.03 2.19-3.65 1.71-4.48

1.58 --1.35 --0.84 --1.02 ---

0.97-2.58 --0.86-2.10 --0.53-1.34 --0.59-1.76 ---

1.62 --1.47 --0.83 -------

0.98-2.66 --0.93-2.32 --0.52-1.33 -------

`

---

---

---

0.98

0.93-1.02

---

---

Poor Not poor Female Male Yes No Mod/high None/low ---

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No. of participants

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Single-Leg Squat (SLS) Unadjusted Estimates

SLSa

Adjusted Estimates

---

Adjusted rate 95% confidence ratio interval

a

Values represent final model

b

Not included in final model High/moderate includes women's and men's soccer, women's and men's lacrosse, football, and field hockey; low/none includes women's and men's track and field and women's and men's cross-country c

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Total 10 26 20 12 33 9 110

% 9.1% 23.6% 18.2% 10.9% 30.0% 8.2% 100.0%

Total 50 60 110

% 45.5% 54.5% 100.0%

Total 32 38 7 5 2 3 1 1 1 20 110

% 29.1% 34.5% 6.4% 4.5% 1.8% 2.7% 0.9% 0.9% 0.9% 18.2% 100.0%

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TABLE 3. Lower Extremity (LE) Injury Characteristics Region Acute % Overuse % Hip 7 6.4% 3 2.0% Thigh 20 18.2% 6 3.9% Knee 14 12.7% 6 3.9% Leg 4 3.6% 8 5.3% Ankle/Foot 24 21.8% 9 5.9% Multiple/unspecified 3 2.7% 6 3.9% Total 72 65.5% 38 34.5% Time Loss Acute Overuse Time loss 37 33.6% 13 8.6% Non-time loss 35 31.8% 25 16.4% Total 72 65.5% 38 34.5% Diagnosis Acute Overuse Sprain 32 29.1% 0 0.0% Strain 30 27.3% 8 7.3% Tendinitis 2 1.8% 5 4.5% Medial Tibial Stress Syndrome 0 0.0% 5 4.5% Stress Fracture 0 0.0% 2 1.8% Stress Reaction 0 0.0% 3 2.7% Fracture 1 0.9% 0 0.0% Meniscal Tear 1 0.9% 0 0.0% Joint Dislocation 1 0.9% 0 0.0% Other 5 4.5% 15 13.6% Total 72 65.5% 38 34.5%

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Highlights: o Novel double- and single-leg squat tasks have been developed to screen athletes. o Scores on these tasks are meaningfully associated with lower extremity injury risk.

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o Continued development of these screening tools is recommended in larger samples.

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Ethics Statement Ethical approval: This study was approved by the Institutional Review Board of [REMOVED FOR BLIND REVIEW].

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Ethics statement: All study procedures were approved by the university Institutional Review Board, informed consent and HIPAA authorization were obtained from each participant prior to data collection, and the rights of all participants were protected

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throughout the study.

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Conflict of interest: Partial funding for this study was provided by [REMOVED FOR BLIND REVIEW] is supported by [REMOVED FOR BLIND REVIEW]. These sponsors had no involvement in study design, data collection, data analysis and interpretation, or decision to submit this manuscript for publication.

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Ethical approval: This study was approved by the Institutional Review Board of [REMOVED FOR BLIND REVIEW].

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Funding: Funding for this study was provided by [REMOVED FOR BLIND REVIEW] is supported by [REMOVED FOR BLIND REVIEW]. These sponsors had no involvement in study design, data collection, data analysis and interpretation, or decision to submit this manuscript for publication.

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Acknowledgements: None.