A Technical Report on the Development of a Real-Time Visual Biofeedback System to Optimize Motor Learning and Movement Deficit Correction.

Author:Bonnette, Scott
 
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Introduction

Due to increased participation in athletics over the last few decades, the associated cost of anterior cruciate ligament (ACL) injuries has grown to exceed 2 billion dollars a year in the United States (Kim et al., 2011; Myer et al., 2004), with female athletes incurring knee injuries four to six times more frequently than males (Arendt and Dick, 1995; Malone et al., 1993; Mandelbaum et al., 2005). Beyond the acute debilitation and prolonged recovery, there are long term complications following the ACL injury. For instance, there is a strong link between ACL injuries and post-traumatic knee osteoarthritis (Lohmander et al., 2007; Myklebust and Bahr, 2005), reduced athletic identity (Brewer and Cornelius, 2010), and depression (Garcia et al., 2016) in young individuals. Because of the associated costs and these debilitating sequelae, the National Public Health Agenda for Osteoarthritis and the National Athletic Trainers' Association advocate the need for the development of preventative programs to stem the tide of rising injury rates and the negative complications associated with ACL injury (Padua et al., 2018; Waynes and Klippel, 2010).

Neuromuscular training programs have become popular tools for ACL injury prevention in female athletes. Recent meta-analyses have revealed that the majority of these programs have at least some success in preventing ACL injuries (i.e., participants' ACL injury risk were reduced) (Myer et al., 2013; Yoo et al., 2010), but in practice they still suffer from several limitations that hold back these interventions from reaching their full potential for injury risk reduction (Gagnier et al., 2013; Myer et al., 2013; Pappas et al., 2015; Sugimoto et al., 2012a; Sugimoto et al., 2012b; Webster and Hewett, 2018). Briefly, these problems include: (a) The questionable or limited ability of training to successfully bring about the desired changes in motor behavior (Sugimoto et al., 2012b), (b) The general susceptibility of training programs to participant noncompliance (Sugimoto et al., 2012a), (c) The limited capability of training--and the difficulty in assessing improvements from training--to transfer to behavior outside of the training program (DiCesare et al., 2019), and (d) The resources associated with the need for trained specialists to supervise training (Hewett et al., 2006). Future efforts to optimize injury prevention strategies should aim to mitigate limiting factors in order to enhance the efficacy of current approaches. The current experiment targets these limitations through the development and deployment of an innovative, real-time, interactive biofeedback system that targets high-risk movement biomechanics associated with ACL injury in females. The authors have chosen to use the term biofeedback instead of feedback because biofeedback is commonly defined as a technique to induce physiological changes by utilizing technological devices to provide information to a person about a targeted physiological variable (Giggins et al., 2013). In the case of the current experiment, kinematic and kinetic information related to ACL injury risk is visually provided to a participant so that they can self-guide themselves to modify such variables in real time by interacting with the biofeedback.

The design and use of real-time biofeedback is motivated by the possibility that it can reduce the previously described barriers to implementing an effective ACL injury prevention program (Giggins et al., 2013). Specifically, it attempts to overcome previous problems by designing real-time biofeedback that: (A) can be provided largely independent of an expert's (e.g., a physical therapist) presence and involvement with individual athletes, (B) is interactive and personalized, which may enhance athlete motivation and compliance (Kiefer et al., 2015), (C) may improve learning and performance by directing athletes' attentional focus to an external source (e.g., (Mechsner et al., 2001; Wulf, 2013; Wulf and Prinz, 2001)), and (D) engages implicit motor learning strategies that may result in faster learning and improved transfer (e.g., (Swinnen et al., 1997; Varoqui et al., 2011)).

The use of real-time biofeedback has been successful in modifying risk factors related to ACL injuries (Ericksen et al., 2016; Ericksen et al., 2015; Ford et al., 2015). The biofeedback however is often inefficiently localized to a single risk factor or training component when it is well documented that multicomponent training programs reduce ACL injury risk most effectively (Lang et al., 2017; Nessler et al., 2017; Padua et al., 2018). For example, Ford et al. (2015) demonstrated great efficacy in improving an ACL injury risk factor using a biofeedback system that responded in real-time to participants' knee abduction and adduction. However, other biomechanical variables, such as trunk control and vertical ground reaction force symmetry, are also risk factors for ACL injury (Hewett and Myer, 2011; Hewett et al., 2005b; Myer et al., 2009). The integration of multiple variables--each contributing complementary training effects--into a biofeedback system may demonstrate additive benefits (Lang et al., 2017; Nessler et al., 2017; Padua et al., 2018). For example, postural control is an important factor related to ACL injury risk (Paterno et al., 2010; Tsai et al., 2019) and it may be improved by targeting multiple postural control outcome variables. Vertical ground reaction force symmetry is one such variable and if biofeedback about it is combined with another variable related to postural control, such as trunk control (e.g., lateral trunk flexion angle), the biofeedback may improve postural control beyond a single-variable training outcome.

In the present study, participants interacted with a real-time visual biofeedback system, tied to multiple ACL-injury risk variables, during the performance of double-leg bodyweight squats. The squat was chosen as the task for several reasons. First, the squat has close proximity to many everyday tasks and is one of the most utilized exercises in strength, conditioning, and performance enhancement programs (Escamilla et al., 2012; Schoenfeld, 2010), is excellent for improving lower body strength and requires the coordinated activation of nearly 200 muscles (Solomonow et al., 1987). Likewise, the squat is a common prescriptive exercise following ACL reconstruction (Dedinsky et al., 2017; Escamilla et al., 2012; Palmitier et al., 1991; Potach et al., 2018; Sanford et al., 2016; Wilk et al., 2012), and the squat has the ability to target and improve certain biomechanical variables that have previously been linked to ACL injury risk through other injury assessments (i.e., drop vertical jump; DVJ). For example, it is possible to calculate and compare performance of the trunk lean, knee abduction, knee adduction, and vGRF symmetry biomechanical variables during both the DVJ and squat exercise (Hewett and Myer, 2011; Hewett et al., 2005b; Myer et al., 2014). The DVJ is a commonly utilized task to quantify an athlete's ACL injury risk (Hewett et al., 2005b; Redler et al., 2016) and if the current biofeedback system is to effectively reduce injury rates, it must successfully transfer biomechanical improvements from the squat to other activities and environments.

The specific aims of this study were to 1) describe the specific aspects of our prototype real-time biofeedback including hardware, software, and integration of real-time biomechanical data and 2) provide preliminary evidence of our prototype system's effectiveness for altering biomechanics associated with reduced risk of ACL injury. Rather than provide the participants with verbal guidance relative to desired technical performance or an explicit assessment of their performance (which would require trained professionals like physical therapists, athletic trainers, or certified strength and conditioning specialists to administer), the real-time, interactive visual biofeedback system was designed to guide participants' movements to achieve correct form.

Motivation for this type of biofeedback is twofold. First, while verbal feedback is one of the most commonly implemented and influential forms of instruction (Benz et al., 2016; Hodges and Franks, 2002; Sigrist et al., 2013; Storberget et al., 2017; Wulf et al., 2010), it is not without its methodological limitations. Specifically, verbal feedback that is complex and addresses several different targets simultaneously is not as effective as when a single, concise verbal cue is used (Landin, 1994; Raisbeck and Diekfuss, 2017; Rink, 2014; Singer, 1988). Verbal feedback is also typically given after the participant has performed the task, which makes it difficult or impossible for participants to effect immediate biomechanical adjusts in real-time (Ericksen et al., 2016; Ericksen et al., 2015). While verbal feedback has been shown to be highly effective in altering and transferring biomechanical behavior related to ACL injury risk (Benjaminse et al., 2018; Welling et al., 2016; Welling et al., 2017), it was not utilized in the current design because of the inherent methodological constraints imposed by verbal feedback.

Secondly, a series of experiments have shown that biofeedback systems designed to map or transform participant movement into real-time visual feedback are effective at training complex and difficult to achieve movements (Mechsner et al., 2001; Varoqui et al., 2011). For example, participants can learn to turn two hand cranks in a very difficult to achieve 4:3 frequency ratio--one hand turns a crank 4 times for every 3 hand turns of the other crank--when their hand movements are mapped onto a simple visual display. The visual biofeedback achieves this by transforming the two hands' 4:3 movement frequency ratio into a 1:1 frequency ratio in the visual biofeedback. By transforming and simplifying the visual feedback such that participants were focusing on...

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