Multicomponent Musculoskeletal Movement Assessment Tools: A Systematic Review and Critical Appraisal of Their Development and Applicability to Professional Practice

Abstract

Multicomponent movement assessment tools have become commonplace to measure movement quality, proposing to indicate injury risk and performance capabilities. Despite popular use, there has been no attempt to compare the components of each tool reported in the literature, the processes in which they were developed, or the underpinning rationale for their included content. As such, the objective of this systematic review was to provide a comprehensive summary of current movement assessment tools and appraise the evidence supporting their development. A systematic literature search was performed using PRISMA guidelines to identify multicomponent movement assessment tools. Commonalities between tools and the evidence provided to support the content of each tool was identified. Each tool underwent critical appraisal to identify the rigor in which it was developed, and its applicability to professional practice. Eleven tools were identified, of which 5 provided evidence to support their content as assessments of movement quality. One assessment tool (Soccer Injury Movement Screen [SIMS]) received an overall score of above 65% on critical appraisal, with a further 2 tools (Movement Competency Screen [MCS] and modified 4 movement screen [M4-MS]) scoring above 60%. Only the MCS provided clear justification for its developmental process. The remaining 8 tools scored between 40 and 60%. On appraisal, the MCS, M4-MS, and SIMS seem to provide the most practical value for assessing movement quality as they provide the strongest reports of developmental rigor and an identifiable evidence base. In addition, considering the evidence provided, these tools may have the strongest potential for identifying performance capabilities and guiding exercise prescription in athletic and sport-specific populations.

Exploring the Application of Pattern Recognition and Machine Learning for Identifying Movement Phenotypes During Deep Squat and Hurdle Step Movements

“…While many believe that movement competency is linked to injury risk and/or performance (where movement competency is defined using conventional a priori definition such as torso is parallel with the tibia when performing the deep squat), there remains little evidence supporting such connections (Gross and Battié, 2006;Mottram and Comerford, 2008;Schneiders et al, 2011;Okada et al, 2011;Parchmann and McBride, 2011). Perhaps our a priori criteria for subjectively evaluating movement competency are incorrect or incomplete (Bennett et al, 2017), or our clinical eye is simply not appropriately tuned to detect important and meaningful changes. As an alternative to this top-down approach, use of emerging tools in machine learning might help us to identify naturally-occurring movement phenotypes, where continued research can then explore phenotypes that are associated with positive or negative health outcomes with respect to specific task objectives.…”

“…The Functional Movement Screen TM (FMS) (Functional Movement Systems, Chatham, VA, United States) remains a popular tool for movement screening (Bennett et al, 2017). Sinden et al (2017) identified the FMS TM as one of the most commonly used approaches for movement screening among Kinesioligists.…”

Utility of FMS to understand injury incidence in sports: current perspectives

“…Movement screens are developed using different methods including known pathomechanics 4 and injury mechanisms, 5 , 6 sport and fundamental movement requirements, 7 and expert opinion. 8 Three broad categories of movement screens have been reported in the literature: jump testing, balance with reaching tests, and movement quality against a standard criterion.…”

“… 10 , 11 Balance with reaching tests, such as the Star Excursion and Y Balance Test (YBT), 12 have been used to associate poor performance with increased rate of injury in high school 13 and collegiate athletes. 14 Movement quality compared against a standard criterion has been used in multiple component assessment tools, 7 including tasks such as squatting, balance, lunge, push-up, and arm movement. These include the foundation matrix, 15 batteries of different movements, 16 and the movement competency screen.…”

“…These include the foundation matrix, 15 batteries of different movements, 16 and the movement competency screen. 7 Perhaps the most well-known of these type of movement screens, 7 the Functional Movement Screen™ (FMS), is the focus of this manuscript. The intent of this manuscript was not to thoroughly review all of the literature on the FMS; several excellent meta-analyses cover measurement aspects of the FMS in detail.…”