Volleyball is a popular sport in the United States, with more than 27,000 women competing at the collegiate level during 2017.1 Collegiate volleyball players are at risk for injury, with most (> 70%) time-loss injuries occurring in the lower quadrant region (eg, trunk/back and lower extremities).2,3 The rate of musculoskeletal injury (all regions of the body) for female collegiate volleyball players is 4.1 per 1,000 athletic exposures during practices and 4.58 per 1,000 athletic exposures during games.3
The ability to identify volleyball athletes at risk for a sport-related injury prior to the start of the season would provide coaches and/or sports medicine professionals the opportunity to develop and/or apply a targeted injury prevention program. A recent trend in sports science research is to evaluate the ability of functional performance tests to discriminate injury risk in athletic populations.4–18 One test that has shown promise as a screening tool is the Lower Quarter Y Balance Test (YBT-LQ).4–8
The creation of the YBT-LQ was inspired by the results from a prospective cohort study that evaluated the ability of the Star Excursion Balance Test (SEBT) to identify high school basketball players at risk for an injury.4,9 Plisky et al.4 reported that asymmetrical anterior reach scores, with a difference between limbs greater than 4 cm, were associated with a 2.5-fold increased risk of a time-loss lower extremity injury during the basketball season. Female basketball players who had a lower composite reach score (< 94% of leg length) had a 6.5-fold increased risk of lower extremity injury.4 Subsequent studies using the YBT-LQ to discriminate injury risk have been inconsistent. Some studies that have reported no association between pre-season scores and time-loss injury have had a common characteristic—heterogeneous populations.10–12 Contrary to those reports, Smith et al.7 reported a significant association between preseason anterior reach scores and injury in a heterogeneous population of Division I collegiate athletes; however, the authors included all injuries regardless of time-loss status in their statistical analysis. Other studies that have reported an association between scores and injury have used homogeneous populations.5,6
There is a need for additional studies to determine the effectiveness of the YBT-LQ to discriminate injury risk in homogeneous populations. One population of athletes whose YBT-LQ scores have not been evaluated independent of other athletes is collegiate volleyball players. The primary purpose of this study was to evaluate the ability of the YBT-LQ to discriminate injury risk in female collegiate volleyball players. It was hypothesized that female collegiate volleyball players who presented with either an asymmetrical reach score, a lower composite score, or a lower individual reach score at the start of the preseason would have a significantly greater risk of sustaining a non-contact time-loss lower quadrant injury.
Female volleyball players were recruited from National Collegiate Athletic Association (NCAA) Division II, NCAA Division III, and National Association of Intercollegiate Athletics (NAIA) universities/colleges from the Portland, Oregon region and from Azusa, California. Prior to participant recruitment, the lead investigators at each site reviewed the YBT-LQ protocol.9 The investigators recruited athlete participation by e-mailing each team's head coach and athletic trainer. If the coach and team athletic trainer agreed to allow their athletes to participate, the investigators recruited team members, via e-mail, to a testing session at each investigator's laboratory. A total of 134 female collegiate volleyball players were tested during the 2015 to 2017 seasons: (2015: 1 Division III team [n = 17]; 2016: 1 Division III team [n = 15]; 2017: 2 Division II teams [n = 32], 3 Division III teams [n = 45], 2 NAIA teams [n = 25]). An athlete was excluded from study participation if she was younger than 18 years or unable to participate in testing due to an injury. Informed consent was obtained from each participant prior to testing. The institutional review boards of George Fox University (Newberg, OR) and Azusa Pacific University (Azusa, CA) approved this study.
Prior to performing the YBT-LQ, each athlete completed a baseline questionnaire (age, year in school) and performed a 5-minute dynamic warm-up. The dynamic warm-up consisted of the following lower extremity movements: forward lunging, backward lunging, heel walking, tip toe walking, marching, and toy soldiers.
After completing the dynamic warm-up, an investigator from each testing site provided test performance instructions and each athlete performed six warm-up trials.9 The YBT-LQ is an instrumented device with three polyvinyl chloride pipes extending from the weight-bearing platform in the shape of a “Y” (Figure 1).9 Each athlete was instructed to first assume a standing position barefoot on the weight-bearing platform with her right lower extremity and with her toes positioned behind the red indicator line. Next, the athlete would “reach” in one of three directions (anterior, posteromedial, and posterolateral) using her non–weight-bearing lower extremity to slide the reach indicator (ie, moveable platforms associated with each arm of the Y). Three anterior reach trials were performed on the right (ie, right limb stance with left limb non–weight-bearing) and then three trials on the left.5,9 Following the completion of the anterior reach trials, three trials per lower extremity (right followed by left) were performed in the posteromedial direction and finally the posterolateral direction.5,9 Any failed reach trial was repeated until correctly performed. Common reach trial errors include the athlete: pushing or flicking the reach indicator forward (ie, not sliding it under control), pushing the reach indicator forward by applying pressure outside of the red target area, losing balance, and/or stepping on to the non–weight-bearing limb.5,9 The investigator measured distance reached for each successful trial (ie, no errors). The YBT-LQ was administered by two of the investigators (JB, ES) and a research assistant. The YBT-LQ has excellent intra-rater (0.85 to 0.91) and inter-rater reliability (0.99 to 1.00).9
Lower Quarter Y-Balance Test (posterolateral reach).
After completing the YBT-LQ, the investigator measured the athlete's limb length with the athlete positioned supine. Limb length was measured, using a cloth measuring tape, from the anterior superior iliac spine to the distal aspect of the ipsilateral medial malleolus.4,9 Reach distance was normalized to limb length (see statistical analysis section).
Each team's athletic trainer recorded the following information for each injured athlete: injury location (categorized by region: low back, hip, thigh, knee, leg, ankle, or foot), injury diagnosis, and injury mechanism (eg, non-contact or contact). The operational definition of an injury for this study was any initial non-contact musculoskeletal injury to the lower quadrant (ie, low back or lower extremities) that occurred during practice or a game that required the athlete to be removed from that day's event or prevented the ability of the athlete to participate in the subsequent event (ie, time-loss).18,19 The definition of an initial injury is the first non-contact time-loss injury experienced by an athlete during the season. Only initial time-loss injuries to the low back or lower extremities resulting from a non-contact mechanism were included in the statistical analysis.
A sample size of 67 athletes (based on an a priori calculation; power of 0.80, alpha level of 0.05, relative risk of 2.0) was needed to determine statistically significant associations between YBT-LQ scores and lower quadrant injury.18 Mean ± standard deviation scores were calculated for demographic data (age, years in school) and for each reach distance. Reach distance was normalized as a percentage of limb length ([reach distance / limb length] × 100).4,9 Asymmetry of reach measures between limbs was calculated for each direction of the Y (eg, right anterior reach – left anterior reach). A composite score reach distance for each limb was calculated using the following formula: ([mean anterior + mean posteromedial + mean posterolateral] / [limb length ×3]) × 100.4,9
Analysis of variance was performed to compare baseline demographic measures per level of competition. A post-hoc Bonferroni test was performed after analysis of variance to identify significant differences between groups. Receiver operator characteristic (ROC) curves were constructed for each reach distance, each composite score, and side-to-side differences (Figures A–K, available in the online version of this article). Relative risk was calculated based on group dichotomization using previously reported cut-off scores or mean reach scores from this study (see results section for explanation). Only initial injuries (ie, the first non-contact time-loss injury experienced by an athlete) were included in the relative risk analysis. Statistical analyses were performed using SPSS software (version 24.0; SPSS, Inc., Chicago, IL) with an a priori alpha level set at 0.05.
Receiver operator characteristic (ROC) curve for right anterior reach.
Receiver operator characteristic (ROC) curve for right posteromedial reach.
Receiver operator characteristic (ROC) curve for right posterolateral reach.
Receiver operator characteristic (ROC) curve for right composite.
Receiver operator characteristic (ROC) curve for anterior asymmetry.
Receiver operator characteristic (ROC) curve for posteromedial asymmetry.
Receiver operator characteristic (ROC) curve for posterolateral asymmetry.
Receiver operator characteristic (ROC) curve for left anterior reach.
Receiver operator characteristic (ROC) curve for left posteromedial reach.
Receiver operator characteristic (ROC) curve for left posterolateral reach.
Receiver operator characteristic (ROC) curve for left composite.
A total of 134 female volleyball players (mean age: 19.3 ± 1.1 years) volunteered to participate in the study. Most athletes competed at the Division III level (n = 77; 57.5% of the population), followed by Division II athletes (n = 32; 23.9% of the population), and NAIA athletes (n = 25; 18.6% of the population). NAIA athletes (n = 25) were significantly older than Division III athletes and had been in college significantly longer than both the Division II and Division III athletes (Table 1).
Baseline Demographic Measures (Mean ± SD) per Level of Competition
There were a total of 19 initial non-contact time-loss injuries to the lower quadrant region for a cumulative incidence of 0.14 (cumulative incidence = injuries [initial injuries only] / population at risk). Most injuries were diagnosed as strains: lumbar (3), hip flexor (1), quadriceps (4), peroneus longus (1), gastrocnemius (1), or Achilles (1). The remaining eight injuries were: meniscus sprain (1), patella tendinopathy (2), lateral ankle sprain (3), plantar fasciitis (1), and great toe sprain (1) (Table 2).
Injury Diagnoses and Time-Loss (Days): Average
ROC curves were calculated to identify a cut-off score to dichotomize athletes into at risk and reference groups. None of the ROC curves were significant; therefore, cut-off scores, based on previously reported asymmetries and composite scores, were used for risk analysis.4,6,7 Cut-off scores based on side-to-side asymmetry were evaluated for each direction: anterior, posteromedial, and posterolateral. Athletes with an asymmetry of greater than 4 cm were categorized as “at risk” and athletes with an asymmetry of 4 cm or less were categorized as the reference group.4,6,7 Athletes with a composite score of less than 94% were categorized as the at risk group, whereas those with a composite score of 94% or greater composed the reference group.4 In addition, normalized mean reach scores from this population were also analyzed. The cut-off scores for normalized mean reach distances were as follows: right lower extremity anterior (65.99 or less / 66.00 or more [reference]), posteromedial (105.99 or less / 106.00 or more [reference]), posterolateral (101.99 or less / 102.00 or more), composite (98.99 or less / 99.00 or more [reference]); left lower extremity anterior (65.99 or less / 66.00 or more [reference]), posteromedial (106.99 or less / 107.00 or more [reference]), posterolateral (101.99 or less / 102.00 or more [reference]), and composite (99.99 or less / 100.00 or more [reference]).
Relative risk for lower quadrant injury per YBT-LQ variables are presented in Table 3. There were no significant associations between preseason scores (per reach asymmetry, composite scores as a percentage of limb length, or normalized mean reach distances) and injury.
Relative Risk for Non-contact Time-Loss Lower Quadrant Injury per YBT-LQ Variables
The purpose of this study was to determine if the YBT-LQ could be used as a screening tool to identify female collegiate volleyball players at risk for a non-contact time-loss lower quadrant injury. There was no association found between preseason YBT-LQ scores and injury during this study; therefore, this test should not be used to discriminate injury risk in female collegiate volleyball players. This prospective cohort study adds to a growing body of research that has failed to validate the YBT-LQ as a screening tool to discriminate injury risk in athletic populations.
The YBT-LQ, and its precursor the SEBT, had shown early promise as a screening tool. As previously mentioned, Plisky et al.4 reported an association between injury and preseason SEBT reach scores in high school basketball players. Two studies, each evaluating homogeneous samples (male collegiate football players = 59 and male professional and amateur soccer players = 74), with the YBT-LQ reported significant associations between reach scores and injury.5,6 However, these two aforementioned studies reported different cut-off scores than those originally reported by Plisky et al.4 For example, Butler et al.5 reported a composite score of less than 89.6% was associated with injury, whereas Gonell et al.6 reported a greater than 4 cm asymmetrical reach in the posteromedial direction as a significant risk factor. The inconsistency in scores associated with injury poses a challenge for clinicians when evaluating the potential significance of preseason scores. In addition, these initial reports have not yet been validated by a follow-up study.
Additional studies that have evaluated the ability of the YBT-LQ to discriminate injury risk have been equivocal.5–8,10–12 Potential reasons for the equivocal findings include several methodological considerations. The first major methodological consideration that can affect the significance of an association between YBT-LQ scores and injury relates to the operational definition of an injury. For example, Smith et al.7 reported an asymmetrical anterior reach score of greater than 4 cm was associated with injury in a heterogeneous population of NCAA Division I collegiate athletes; however, this study included both time-loss and non–time-loss injuries in the analysis of risk. Most researchers only track time-loss injuries when calculating risk.5,6,12,16–18 A limitation associated with including non–time-loss injuries in the analysis of risk is that there is a degree of subjectivity to an “injury” that does not limit sport participation. Wright et al.12 also evaluated the risk of injury in Division I athletes based on YBT-LQ scores and used the same operational definition of an injury as reported in Smith et al.7 However, Wright et al.12 reported no relationship between scores and injury.
The inclusion of time-loss injuries due to a contact mechanism may also influence the significance of an association. The potential benefit of a screening tool to identify athletes at risk for injury is that if the test identifies an at risk athlete, a coach or a sports medicine clinician could prescribe an injury prevention program. However, training programs may not be able to reduce the risk of injury associated with contact mechanisms. Two studies included injuries resulting from a contact mechanism. Hartley et al.8 found an association between YBT-LQ scores and ankle sprain injury in a heterogeneous population of collegiate athletes (n = 551), whereas Lai et al.10 found no relationship between scores and injury (n = 294 Division I athletes). There are other limitations associated with Hartley et al.8 that may have influenced the significance of the association. The authors tracked injury occurrence over a 2-year period; however, YBT-LQ testing only occurred at the start of the study. It is possible that one's YBT-LQ performance could change year-to-year based on one's training habits and/or if one experienced an injury. Hartley et al.8 also deviated from the standard YBT-LQ test performance (as defined by Plisky et al.9) by having athletes maintain their hands on their hip during test performance.
A second major methodological consideration relates to the heterogeneity of a study's population. As previously mentioned, two studies7,8 reported significant relationships between YBT-LQ scores and injury; however, the limitations associated with these studies (eg, inclusion of non–time-loss injuries, inclusion of injuries caused by contact, frequency of YBT-LQ testing, and test performance modifications) challenge the strength of these associations. Contrary to these reports, several studies using heterogeneous populations10–12,20 have reported no association between preseason scores and sports injury.
Although the YBT-LQ has not demonstrated a consistent ability to discriminate injury risk, the SEBT has demonstrated significant associations between preseason test performance and injury.4,21–23 Athletes demonstrate different postural-control strategies when performing the SEBT compared to the YBT-LQ, which is reflected in their anterior reach performance.24,25 It is possible that the SEBT could be a better screening tool than the YBT-LQ; however, this would require future research to determine its effectiveness in a female collegiate volleyball population.
The strengths and weaknesses of this study should be highlighted. The strengths of this study include the study's sample size, the prospective collection of YBT-LQ measures, the operational definition of an injury, the collection of injury data by certified athletic trainers, and the weekly monitoring of injury data by the principal investigator. There are five potential weakness associated with this study. First, although the sample size was large enough to evaluate the ability of the YBT-LQ to discriminate injury risk for all potential non-contact time-loss injuries of the lower quadrant region, the sample size was likely not large enough to assess injury risk per specific diagnoses. For example, Hartley et al.8 recruited more than 500 athletes to analyze ankle sprain risk based on YBT-LQ scores. To determine the ability of a test to discriminate injury risk for a specific condition, the study would need to recruit a larger sample size.
A second limitation relates to the sample recruited for this study. This study did not include any athletes from the NCAA Division I level. The Division I level is the highest level of collegiate competition in the United States. Although unlikely, it is possible that there is a risk profile unique to these athletes that could be discriminated by the YBT-LQ.
A third limitation is that this study only investigated the ability of the YBT-LQ to discriminate injury risk. Although this test did not discriminate injury risk, athletic trainers and other health care professionals should screen athletes for prior injury history. Some injuries (eg, hamstring strains or anterior cruciate ligament injuries) increase the risk of that athlete sustaining a subsequent injury to the same region of the body.26–30
A fourth limitation of this study is that the authors relied on previously reported reliability measures for the YBT-LQ.
A final limitation of this study is the potential for a type II error. In this study, no association between pre-season scores and injury was observed; however, a type II error would be the failure to recognize that an actual relationship between scores and injury existed. This type of error occurs when a study is underpowered. Post-hoc power in this study was low. However, post-hoc power “is just a reexpression of the P value.”31 In a prospective cohort study, the distribution of injuries between the at risk group and the reference group is important. For example, this study recruited 134 participants, with 14% of the population experiencing an injury. The distribution of injuries between the at risk and reference groups was similar. For example, in the normalized right anterior reach score category, there were 10 injuries (10 of 69 participants = 15%) in the at risk group versus 9 injuries (9 of 65 participants = 14%) in the reference group. Quadrupling the sample size (from 69 and 65 to 276 and 260) for the aforementioned example and assuming the same percentage of injuries per category (15 and 14) would only increase the post-hoc power from 0.036 to 0.051. On the other hand, Butler et al.5 found a significant association between YBT-LQ composite score and injury. Their study only had 59 participants and six injuries (10.1% of the population had an injury). However, the six injuries all occurred in at risk athletes (28.6% of the at risk group were injured). As a result, their post-hoc power was 0.897. Although it is possible that a study using a larger sample size might find significant associations that were not observed in this study, researchers should consider evaluating athletes with different tests.
Implications for Clinical Practice
This study adds to a growing body of research that has found no association between preseason YBT-LQ scores and non-contact time-loss injury in athletic populations. Athletic trainers, and other sports medicine professionals, who administer preseason screening clinics should consider other tests. The YBT-LQ may have value as a tool to measure dynamic balance in injured athletes. Future research is warranted to determine the best application of this test during rehabilitation.
Preseason YBT-LQ scores were not associated with non-contact time-loss injury in a population of female collegiate athletes. This study adds to a growing list of studies that find no association between preseason scores and subsequent injury.
- O'Rourke P. ScholarshipStats.com. College Volleyball and Scholarship Opportunities. http://scholarshipstats.com/volleyball.htm. Accessed September 1, 2018.
- Powell JW, Dompier TP. Analysis of injury rates and treatment patterns for time-loss and non-time-loss injuries among collegiate student-athletes. J Athl Train. 2004;39(1):56–70.15085213
- Agel J, Palmieri-Smith RM, Dick R, Wojtys EM, Marshall SW. Descriptive epidemiology of collegiate women's volleyball injuries: National Collegiate Athletic Association Injury Surveillance System, 1988–1989 through 2003–2004. J Athl Train. 2007;42(2):295–302.17710179
- Plisky PJ, Rauh MJ, Kaminski TW, Underwood FB. Star Excursion Balance Test as a predictor of lower extremity injury in high school basketball players. J Orthop Sports Phys Ther. 2006;36(12):911–919. doi:10.2519/jospt.2006.2244 [CrossRef]17193868
- Butler RJ, Lehr ME, Fink ML, Kiesel KB, Plisky PJ. Dynamic balance performance and noncontact lower extremity injury in college football players: an initial study. Sports Health. 2013;5(5):417–422. doi:10.1177/1941738113498703 [CrossRef]
- Gonell AC, Romero JA, Soler LM. Relationship between the Y balance test scores and soft tissue injury incidence in a soccer team. Int J Sports Phys Ther. 2015;10(7):955–966.26673848
- Smith CA, Chimera NJ, Warren M. Association of Y balance test reach asymmetry and injury in Division I athletes. Med Sci Sports Exerc. 2015;47(1):136–141. doi:10.1249/MSS.0000000000000380 [CrossRef]
- Hartley EM, Hoch MC, Boling MC. Y-balance test performance and BMI are associated with ankle sprain injury in collegiate male athletes. J Sci Med Sport. 2018;21(7):676–680. doi:10.1016/j.jsams.2017.10.014 [CrossRef]
- Plisky PJ, Gorman PP, Butler RJ, Kiesel KB, Underwood FB, Elkins B. The reliability of an instrumented device for measuring components of the Star Excursion Balance test. N Am J Sports Phys Ther. 2009;4(2):92–99.21509114
- Lai WC, Wang D, Chen JB, Vail J, Rugg CM, Hame SL. Lower Quarter Y-Balance test scores and lower extremity injury in NCAA Division I athletes. Orthop J Sports Med. 2017;5(8):2325967117723666. doi:10.1177/2325967117723666 [CrossRef]28840153
- Brumitt J, Sikkema J, Mair S, Zita CJ, Wilson V, Peterson J. Pre-season Y Balance test scores are not association with sports injury in a heterogeneous population of Division III collegiate athletes. Sports (Basel). 2019;7:4. doi:10.3390/sports7010004 [CrossRef]
- Wright AA, Dischiavi SL, Smoliga JM, Taylor JB, Hegedus EJ. Association of Lower Quarter Y-Balance Test with lower extremity injury in NCAA Division 1 athletes: an independent validation study. Physiotherapy. 2017;103(2):231–236. doi:10.1016/j.physio.2016.06.002 [CrossRef]
- Moran RW, Schneiders AG, Mason J, Sullivan SJ. Do Functional Movement Screen (FMS) composite scores predict subsequent injury? A systematic review with meta-analysis. Br J Sports Med. 2017;51(23):1661–1669. doi:10.1136/bjsports-2016-096938 [CrossRef]28360142
- Landis SE, Baker RT, Seegmiller JG. Non-contact anterior cruciate ligament and lower extremity injury risk prediction using functional movement screen and knee abduction moment: an epidemiological observation of female intercollegiate athletes. Int J Sports Phys Ther. 2018;13(6):973–984. doi:10.26603/ijspt20180973 [CrossRef]30534463
- Lisman P, Nadelen M, Hildebrand E, Leppert K, de la Motte S. Functional movement screen and Y-Balance test scores across levels of American football players. Biol Sport. 2018;35(3):253–260. doi:10.5114/biolsport.2018.77825 [CrossRef]30449943
- Brumitt J, Wilson V, Ellis N, Petersen J, Zita CJ, Reyes J. Preseason lower extremity functional test scores are not associated with lower quadrant injury—a validation study with normative data on 395 Division III athletes. Int J Sports Phys Ther. 2018;13(3):410–421. doi:10.26603/ijspt20180410 [CrossRef]30038827
- Brumitt J, Engilis A, Isaak D, Briggs A, Mattocks A. Preseason jump and hop measures in male collegiate basketball players: an epidemiologic report. Int J Sports Phys Ther. 2016;11(6):954–961.27904797
- Brumitt J, Heiderscheit BC, Manske RC, Niemuth PE, Rauh MJ. Lower extremity functional tests and risk of injury in Division III collegiate athletes. Int J Sports Phys Ther. 2013;8(3):216–227.23772338
- Rauh MJ, Koepsell TD, Rivara FP, Margherita AJ, Rice SG. Epidemiology of musculoskeletal injuries among high school cross-country runners. Am J Epidemiol. 2006;163(2):151–159. doi:10.1093/aje/kwj022 [CrossRef]
- Walbright PD, Walbright N, Ojha H, Davenport T. Validity of functional screening tests to predict lost-time lower quarter injury in a cohort of female collegiate athletes. Int J Sports Phys Ther. 2017;12(6):948–959. doi:10.26603/ijspt20170948 [CrossRef]29158956
- Attenborough AS, Sinclair PJ, Sharp T, et al. The identification of risk factors for ankle sprains sustained during netball participation. Phys Ther Sport. 2017;23:31–36. doi:10.1016/j.ptsp.2016.06.009 [CrossRef]
- Stiffler MR, Bell DR, Sanfilippo JL, Hetzel SJ, Pickett KA, Heiderscheit BC. Star Excursion Balance Test anterior asymmetry is associated with injury status in division I collegiate athletes. J Orthop Sports Phys Ther. 2017;47(5):339–346. doi:10.2519/jospt.2017.6974 [CrossRef]28355980
- Gribble PA, Terada M, Beard MQ, et al. Prediction of lateral ankle sprains in football players based on clinical tests and body mass index. Am J Sports Med. 2016;44(2):460–467. doi:10.1177/0363546515614585 [CrossRef]
- Coughlan GF, Fullam K, Delahunt E, Gissane C, Caulfield BM, Sci M. A comparison between performance on selected directions of the Star Excursion Balance test and the Y balance test. J Athl Train. 2012;47(4):366–371. doi:10.4085/1062-6050-47.4.03 [CrossRef]22889651
- Fullam K, Caulfield B, Coughlan GF, Delahunt E. Kinematic analysis of selected reach directions of the Star Excursion Balance Test compared with the Y-Balance Test. J Sport Rehabil. 2014;23(1):27–35. doi:10.1123/JSR.2012-0114 [CrossRef]
- Paterno MV, Rauh MJ, Schmitt LC, Ford KR, Hewett TE. Incidence of contralateral and ipsilateral anterior cruciate ligament (ACL) injury after primary ACL reconstruction and return to sport. Clin J Sport Med. 2012;22(2):116–121. doi:10.1097/JSM.0b013e318246ef9e [CrossRef]22343967
- Paterno MV, Rauh MJ, Schmitt LC, Ford KR, Hewett TE. Incidence of second ACL injuries 2 years after primary ACL reconstruction and return to sport. Am J Sports Med. 2014;42(7):1567–1573. doi:10.1177/0363546514530088 [CrossRef]24753238
- Brumitt J, Mattocks A, Engilis A, Isaak D, Loew J. Prior history of anterior cruciate ligament reconstruction is associated with future anterior cruciate ligament injury in female collegiate athletes. J Orthop Sports Phys Ther. 2019;49:CSM35.
- Verrall GM, Slavotinek JP, Barnes PG, Fon GT, Esterman A. Assessment of physical examination and magnetic resonance imaging findings of hamstring injury as predictors for recurrent injury. J Orthop Sports Phys Ther. 2006;36(4):215–224. doi:10.2519/jospt.2006.36.4.215 [CrossRef]16676871
- Arnason A, Sigurdsson SB, Gudmundsson A, Holme I, Engebretsen L, Bahr R. Risk factors for injuries in football. Am J Sports Med. 2004;32(1)(suppl):5S–16S. doi:10.1177/0363546503258912 [CrossRef]14754854
- Lenth RV. Post hoc Power: Tables and Commentary. The University of Iowa Department of Statistics and Actuarial Science. Technical Report No. 378. https://stat.uiowa.edu/sites/stat.uiowa.edu/files/techrep/tr378.pdf. July 2007. Accessed May 20, 2019.
Baseline Demographic Measures (Mean ± SD) per Level of Competition
|Group||Age (y)||Years in School|
|Volleyball players (N = 134)||19.3 ± 1.1||2.2 ± 1.2|
|NCAA Division II (n = 32)||19.3 ± 0.9||2.0 ± 1.0a|
|NCAA Division III (n = 77)||19.1 ± 1.3b||2.1 ± 1.0a,c|
|NAIA (n = 25)||19.8 ± 1.3b||2.7 ± 1.2a,c|
Injury Diagnoses and Time-Loss (Days): Average
|Region||Injury Diagnosis (No. of Cases)||Average No. of Missed Days (Rangea)|
|Lumbar spine||Lumbar strain (3)||5.3 (2 to 11)|
|Hip||Hip flexor strain (1)||5|
|Thigh/knee||Quadriceps strain (4)||2.25 (2 to 4)|
|Meniscal sprain (1)||4|
|Patellar tendinopathy (2)||2|
|Leg/ankle/foot||Gastrocnemius strain (1)||5|
|Lateral ankle sprain (3)||3.3 (1 to 7)|
|Achilles strain (1)||2|
|Peroneus longus muscle strain (1)||9|
|Great toe sprain (1)||4|
|Plantar fasciitis (1)||4|
Relative Risk for Non-contact Time-Loss Lower Quadrant Injury per YBT-LQ Variables
|Category||No.||Lower Quadrant Injury Counts and (%) per Category||Relative Risk (95% CI)||P||Post-hoc Power|
|Female volleyball athletes (N = 134)|
| Anterior reach asymmetry|
| ≤ 4 cm||84||11 (13)||Reference||.641||0.074|
| > 4 cm||50||8 (16)||1.2 (0.5, 2.8)|
| Posteromedial reach asymmetry|
| ≤ 4 cm||83||12 (15)||Reference||.906||0.035|
| > 4 cm||51||7 (14)||0.9 (0.4, 2.3)|
| Posterolateral reach asymmetry|
| ≤ 4 cm||71||9 (13)||Reference||.596||0.072|
| > 4 cm||63||10 (16)||1.3 (0.5, 2.9)|
| Right composite reach|
| ≥ 94%||86||13 (15)||Reference||.677||0.047|
| < 94%||48||6 (13)||0.8 (0.3, 2.0)|
| Left composite reach|
| ≥ 94%||91||14 (15)||Reference||.561||0.061|
| < 94%||43||5 (12)||0.8 (0.3, 2.0)|
|Normalized mean reach distances|
| Right anterior|
| ≤ 65.99||69||10 (15)||1.0 (0.5, 2.4)||.915||0.036|
| ≥ 66.00||65||9 (14)||Reference|
| Right posteromedial|
| ≤ 105.99||68||7 (10)||0.6 (0.2, 1.3)||.191||0.266|
| ≥ 106.00||66||12 (18)||Reference|
| Right posterolateral|
| ≤ 101.99||60||8 (13)||0.9 (0.4, 2.1)||.800||0.050|
| ≥ 102.00||74||11 (15)||Reference|
| Right composite|
| ≤ 98.99||70||12 (17)||1.6 (0.7, 3.7)||.304||0.165|
| ≥ 99.00||64||7 (11)||Reference|
| Left anterior|
| ≤ 65.99||61||8 (13)||0.8 (0.4, 2.0)||.694||0.051|
| ≥ 66.00||73||11 (15)||Reference|
| Left posteromedial|
| ≤ 106.99||64||7 (11)||0.6 (0.3, 1.5)||.304||0.165|
| ≥ 107.00||70||12 (17)||Reference|
| Left posterolateral|
| ≤ 101.99||56||6 (11)||0.6 (0.3, 1.6)||.330||0.154|
| ≥ 102.00||78||13 (17)||Reference|
| Left composite|
| ≤ 99.99||67||9 (13)||0.9 (0.4, 2.1)||.804||0.052|
| ≥ 100.00||67||10 (15)||Reference|