A qualitative screening tool to identify athletes with ‘high-risk’ movement mechanics during cutting: The cutting movement assessment score (CMAS)

Highlights

• CMAS is a valid and reliable screening tool for evaluating side-step cutting movement quality.

• A very large significant relationship was observed between CMAS and peak KAM.

• CMAS is a cost-effective and easily applicable field-based screening tool to identify athletes who generate high peak KAMs.

• CMAS allows practitioners to identify “high-risk” cutting mechanics in athletes.

• CMAS can be used as a potential technical framework for coaching “safer” cutting.

Abstract

Objective

To assess the validity of the cutting movement assessment score (CMAS) to estimate the magnitude of peak knee abduction moments (KAM) against three-dimensional (3D) motion analysis, while comparing whole-body kinetics and kinematics between subjects of low (bottom 33%) and high CMASs (top 33%).

Design

Cross-sectional study.

Setting

Laboratory.

Participants

Forty-one participants (soccer, rugby, netball, and cricket).

Main outcome measures

Association between peak KAM and CMAS during a 90° cut. Comparison of 3D whole-body kinetics and kinematics between subjects with low (bottom 33%) and high CMASs (top 33%).

Results

A very large significant relationship (ρ = 0.796, p < 0.001) between CMAS and peak KAM was observed. Subjects with higher CMASs displayed higher-risk cutting postures, including greater peak knee abduction angles, internal foot progression angles, and lateral foot plant distances (p ≤ 0.032, effect size = 0.83–1.64). Additionally, greater cutting multiplanar knee joint loads (knee flexion, internal rotation, and abduction moments) were demonstrated by subjects with higher CMASs compared to lower (p ≤ 0.047, effect size = 0.77–2.24).

Conclusion

The CMAS is a valid qualitative screening tool for evaluating cutting movement quality and is therefore a potential method to identify athletes who generate high KAMs and “high-risk” side-step cutting mechanics.

Introduction

Side-step lateral foot plant-and-cut actions are frequently performed movements in numerous sports (Fox, Spittle, Otago, & Saunders, 2014; Wheeler, Askew, & Sayers, 2010) and are also linked to decisive moments in matches, such as evading an opponent to penetrate the defensive line in rugby (tackle-break success in rugby) (Wheeler et al., 2010), or getting into space to receive a pass in netball (Fox et al., 2014). Side-step cutting, however, are also actions associated with non-contact anterior cruciate ligament (ACL) injuries in sports (Johnston et al., 2018; Koga et al., 2010). Although ACL injury-risk factors are multifactorial (Quatman, Quatman-Yates, & Hewett, 2010) and a complex interaction of internal and external factors (i.e. anatomical, hormonal, environmental, shoe-surface interface, anticipation, and fatigue) (Bittencourt et al., 2016; Hewett, 2017; Krosshaug et al., 2007b), a large proportion of ACL injuries are non-contact in nature during high velocity and impact sporting tasks, such as side-stepping (Boden, Dean, Feagin, & Garrett, 2000; Johnston et al., 2018; Krosshaug et al., 2007b). This occurrence can be attributed to the tendency to generate large multiplanar knee joint loading, such as knee abduction moments (KAM) and internal rotation moments (KIRM) (Besier, Lloyd, Cochrane, & Ackland, 2001; Dempsey et al., 2007; Jones, Herrington, & Graham-Smith, 2016a), which increase ACL strain (Bates, Myer, Shearn, & Hewett, 2015; Markolf et al., 1995; Shin, Chaudhari, & Andriacchi, 2011). These potentially hazardous knee joint loads are amplified when poor initial postures and movement is demonstrated (biomechanical and neuromuscular control deficits) during cutting (Fox, 2018; Hewett, 2017; Myer, Brent, Ford, & Hewett, 2011), but importantly these deficits are modifiable (Hewett, 2017; Padua et al., 2018). As such, understanding the mechanics, interventions, and screening tools that can reduce ACL injury-risk factors is of critical importance.

The ability to identify athletes potentially at risk of injury is a critical step in effective ACL injury-risk reduction (Fox, Bonacci, McLean, & Saunders, 2017; Hewett, 2017). Although it is inconclusive whether screening tools can predict non-contact ACL injury (Bahr, 2016; Fox, Bonacci, McLean, Spittle, & Saunders, 2015), evaluating movement quality and identifying biomechanical and neuromuscular control deficits (high-risk movement patterns) can provide important information regarding an athlete's “injury-risk profile” (Herrington, Munro, & Jones, 2018; McCunn & Meyer, 2016; Mok & Leow, 2016). These abnormal deficits include knee abduction angles (KAA) (Jones et al., 2015, 2016b; Kristianslund, Faul, Bahr, Myklebust, & Krosshaug, 2014; McLean, Huang, & van den Bogert, 2005; Sigward, Cesar, & Havens, 2015), lateral trunk flexion (Dempsey et al., 2007; Frank et al., 2013; Jamison, Pan, & Chaudhari, 2012; Jones et al., 2015), extended knee postures (Dai et al., 2014; Koga et al., 2010; Weir, Alderson, Smailes, Elliott, & Donnelly, 2019), and hip internal rotation (Havens & Sigward, 2015; McLean et al., 2005; Sigward et al., 2015; Sigward & Powers, 2007). This information from movement screening can subsequently be used to inform the future prescription of training and conditioning so specific deficits can be targeted through appropriate training interventions to decrease the relative risk of injury (Herrington et al., 2018; Hewett, 2016; Mok & Leow, 2016). Therefore, the inclusion of valid and reliable screening tools that assess movement quality are an important component of sports medicine and strength and conditioning testing batteries to provide an “injury-risk profile” for an athlete (Herrington et al., 2018; Jones, Donelon, & Dos' Santos, 2017).

Three-dimensional (3D) motion analysis is considered the gold standard for evaluating movement kinetics and kinematics (Fox et al., 2015; Hewett, 2017); however, this method can be susceptible to errors, with a diverse range of data collection and analysis procedures available to practitioners which can impact outcome values, reliability, or subsequent evaluations of an athlete's biomechanical profile (Camomilla et al., 2017; Kristianslund, Krosshaug, & van den Bogert, 2012). Given these methodological considerations and issues, and the fact the 3D motion analysis is expensive, time-consuming, requires expert and well trained assessors, and is usually restricted to testing one subject in laboratory setting, time- and cost-effective qualitative field-based screening tools have been developed, such as the landing error scoring system (LESS) (Padua et al., 2009, 2011), tuck jump assessment (TJA) (Herrington, Myer, & Munro, 2013; Myer, Ford, & Hewett, 2008), and qualitative analysis of single leg loading (QASLS) (Almangoush, Herrington, & Jones, 2014; Herrington et al., 2013), to assess lower-limb and whole-body postures associated with increased potential risk of injury (high-risk movement patterns). However, the LESS is the only screening tool of that has been validated against 3D motion analysis (Onate, Cortes, Welch, & Van Lunen, 2010; Padua et al., 2009).

A fundamental shortcoming of the LESS, TJA, and QASLS are these assessments generally assess landing mechanics during a vertical-orientated task. Although screening landing mechanics is indeed applicable to jump-landing sports (netball, basketball, volleyball) where the primary action associated with non-contact ACL injury is landing manoeuvres (Hewett, Torg, & Boden, 2009; Krosshaug et al., 2007b; Stuelcken, Mellifont, Gorman, & Sayers, 2016), these aforementioned assessments may lack specificity to the unilateral, multiplanar plant-and-cut manoeuvres observed when changing direction (Fox et al., 2015; Jones et al., 2017b; Mok & Leow, 2016). This is particularly important when aiming to screen athletes who participate in sports such as soccer (Walden et al., 2015), handball (Olsen, Myklebust, Engebretsen, & Bahr, 2004), American football (Johnston et al., 2018), badminton (Kimura et al., 2010), and rugby (Montgomery et al., 2018), where directional changes are a primary action associated with-non contact ACL injuries. Furthermore, there are mixed findings whether examination of landing mechanics can identify athletes with poor cutting mechanics (Alenezi, Herrington, Jones, & Jones, 2014; Chinnasee, Weir, Sasimontonkul, Alderson, & Donnelly, 2018; Kristianslund & Krosshaug, 2013; O'Connor, Monteiro, & Hoelker, 2009), with evidence suggesting an athlete's mechanics and “injury-risk profile” are task dependent (Chinnasee et al., 2018; Jones, Herrington, Munro, & Graham-Smith, 2014; Kristianslund & Krosshaug, 2013; Munro, Herrington, & Comfort, 2017). As such, screening side-step cutting technique, which is specific to the actions associated with non-contact ACL injuries in cutting sports (i.e. rugby, handball, soccer, American football), could be a more effective strategy for identifying poor cutting movement quality in athletes, which can help inform future injury-risk mitigation training.

Unfortunately, there is a paucity of field-based cutting screening tools available for practitioners. McLean et al. (McLean et al., 2005) initially evaluated two-dimensional (2D) estimates of frontal plane knee motion during cutting against the gold standard of 3D, and found 2D estimates correlated well with side-step (r² = 0.58) and side-jump (r² = 0.64) 3D valgus angles, but poorer associations were observed with 180° turn knee valgus angle (r² = 0.04); thus, highlighting the difficulty in assessing 2D valgus motion in the frontal plane using a single camera during sharp CODs. Weir et al. (Weir et al., 2019) has recently demonstrated that 2D measures of dynamic knee valgus angle, knee flexion angle at foot-strike and ROM, trunk flexion ROM, when inserted in regression equations, can be used to predict 3D peak knee flexor, KAM and KIRMs during unanticipated side-steps. Despite these promising relationships, such 2D side-step screening methods are not widely adopted by practitioners and clinicians. This lack of adoption could be attributed to the 2D method requiring additional time and software to measure joint kinematics, thus potentially limiting its applicability in field settings.

In light of the issues associated with 2D analysis, Jones et al. (Jones et al., 2017) have recently developed the cutting movement assessment score (CMAS), which is a qualitative screening tool that assesses cutting movement quality and specific lower-limb and trunk characteristics that are associated with (Fox, 2018; Kristianslund et al., 2014; Weir et al., 2019) peak KAMs (Supplement 1), such as penultimate foot contact (PFC) braking strategy, and trunk, hip, knee, and foot positioning and motions. In this preliminary study, a strong relationship between CMAS and peak KAM (ρ = 0.633; p < 0.001) was demonstrated, while moderate to excellent intra-and inter-rater agreements for all CMAS variables (Intra-rater: k = 0.60–1.00, 75–100% agreements; inter-rater: k = 0.71–1.00, 87.5–100% agreements) were observed, although lower inter-rater agreements for trunk positioning were observed (k = 0.40, 62.5% agreement). In light of these findings, the CMAS may have the potential to identify athletes displaying “high-risk” cutting mechanics but more importantly, could be used as a technical framework for coaching safer cutting mechanics. It should be noted, however, that the preliminary study contained a small sample size (n = 8 subjects, 36 trials) and must be expanded with a greater sample size to confirm its validity and reliability. Furthermore, the authors recommended an additional camera to be placed at 45° relative to the COD and using a higher video capture rate (≥100 Hz) to permit more accurate and reliable assessments for frontal and transverse plane technique deficits (i.e. trunk positioning, knee valgus).

The aim of this study, therefore, was to assess the validity of the CMAS tool to estimate the potential peak KAMs against the gold standard of 3D motion analysis, expanding on the work of Jones et al. (Jones et al., 2017) by examining a larger sample size and using an additional camera recording at a higher sampling rate. A further aim to was to determine whether “higher-risk” movement mechanics were displayed by subjects with higher CMASs compared to subjects with lower CMASs. Firstly, it was hypothesised that excellent inter- and intra-rater reliability would be demonstrated for CMAS items. Secondly, in line with Jones et al. (Jones et al., 2017), it was hypothesised that a strong relationship would be demonstrated between CMAS and peak KAM, and the CMAS would be able to discriminate between “low” and “high” CMASs in terms of “high-risk” whole-body kinetics and kinematics.