Either Autonomy Support or Enhanced Expectancies Delivered Via Virtual-Reality Benefits Frontal-Plane Single-Leg Squatting Kinematics

Abstract

Our purpose in this study was to determine the effects of a virtual reality intervention delivering specific motivational motor learning manipulations of either autonomy support (AS) or enhanced expectancies (EE) on frontal plane single-leg squatting kinematics. We allocated 45 participants (21 male, 24 female) demonstrating knee, hip, and trunk frontal plane mechanics associated with elevated anterior cruciate ligament injury risk to one of three groups (control, AS, or EE). Participants mimicked an avatar performing five sets of eight repetitions of exemplary single-leg squats. AS participants were given the added option of choosing the color of their avatar. EE participants received real-time biofeedback in the form of green highlights on the avatar that remained on as long as the participant maintained pre-determined ‘safe’ frontal plane mechanics. We measured peak frontal plane knee, hip, and trunk angles before (baseline) and immediately following (post) the intervention. The control group demonstrated greater increases in knee abduction angle (Δ = +2.3°) than did the AS (Δ = +0.1°) and EE groups (Δ = −0.4°) (p = .003; η²_p = .28). All groups demonstrated increased peak hip adduction (p = .01, η_p² = .18) (control Δ = +1.5°; AS Δ = +3.2°; EE Δ = +0.7°). Hip adduction worsened in all groups. AS and EE motivation strategies appeared to mitigate maladaptive frontal plane knee mechanics.

Keywords

anterior cruciate ligament motor learning risk reduction OPTIMAL

Introduction

Despite efforts over the previous two decades to decrease the incidence and impact of anterior cruciate ligament (ACL) injuries in athletes, challenges remain. Recurrent injuries to the contralateral or ipsilateral limb are reported to be as high as 33% among adolescent athletes (Webster et al., 2021; Wiggins et al., 2016). Even when a second ACL injury is avoided, 35% of athletes with a primary ACL injury do not return to play at the same level (Walden et al., 2016). More alarming, poor function one year after primary ACL reconstruction increases the risk of early onset osteoarthritis by over 200% (Odds Ratio = 3.66) (Patterson et al., 2020). Because of the heavy economic, psychological, and physical burdens associated with primary and secondary ACL injury, a need remains for advanced injury mitigation strategies that can be broadly implemented.

Multiple reasons have been postulated for the poor long term outcomes associated with ACL reconstruction, and among them are traditional injury prevention programs that focus on enhancing peripheral neuromuscular control but fail to replicate the sensorimotor demands of real-time sport participation (DiCesare et al., 2019c). Specifically, there is a need to consider those sensorimotor-related processes that are critical for both maintaining safe knee position and ensuring the retention and transfer of learned injury-resistant biomechanics from the lab and clinic to a sport setting (Diekfuss et al., 2020a; Diekfuss et al., 2021; Diekfuss et al., 2020b; Diekfuss et al., 2019a; Grooms, 2022). The emergence of real-time biofeedback technologies provides an opportunity to capitalize on sensorimotor capacity for adaptation and, thus, fully integrate perceptual and motor factors that can support injury-resistant movement behaviors. Real-time biofeedback is capable of modifying peripheral movement patterns via visual, vestibular, and proprioceptive inputs, which may simultaneously enhance functioning for the successful transfer of acquired movement strategies to the sporting environment (e.g., ‘neural efficiency’ to support injury resistant knee motor control) (Diekfuss et al., 2020b; Grooms, 2022).

Motor learning techniques show promise for cognitively engaging learners during movement-focused interventions. Specifically, the Optimizing Performance through Intrinsic Motivation and Attention for Learning, or OPTIMAL, theory (Wulf & Lewthwaite, 2016) is a key theoretical motor learning framework that has been extended and modified to optimize current injury Prevention, Rehabilitation, Exercise, and Play-related training strategies (OPTIMAL PREP) (Diekfuss et al., 2020a; Diekfuss et al., 2021; Diekfuss et al., 2020c). The OPTIMAL theory purports an external focus of attention, which promotes greater automaticity than an internal focus by directing an individual’s attention towards one’s movement effects rather than the movement itself (Wulf et al., 2001), and this has been used successfully to support the acquisition, retention, and transfer of injury-resistant lower extremity biomechanics (Raisbeck & Yamada, 2019; Widenhoefer et al., 2019; Yamada & Raisbeck, 2020). Further, the theoretical concept of external focus likely explains the robust effectiveness of real-time visual biofeedback stimuli that are designed to reduce ACL injury risk landing mechanics by providing an ‘external’ visual display of one’s knee abduction angle and guiding individuals toward safer movement strategies (Benjaminse et al., 2017; Diekfuss et al., 2019a; Diekfuss et al., 2019b; Marshall et al., 2020; Raisbeck & Yamada, 2019; Widenhoefer et al., 2019; Yamada & Raisbeck, 2020). Abundant evidence has established the efficacy of an external focus of attention. Although this one attentional pillar of OPTIMAL PREP has received an abundance of attention in the sports medicine literature, the motivational strategies of enhanced expectancies and autonomy support have received little research consideration, despite their potential to further amplify injury intervention effectiveness via central mechanisms (Diekfuss et al., 2020a; Diekfuss et al., 2021; Diekfuss et al., 2020c). Enhanced expectancies, or EE, references the learner’s expectation for future success, and it is hypothesized to increase learner motivation. Although EE delivery can take many forms, it is common to provide feedback exclusively following successful trials and to ignore unsuccessful trials. Such a strategy is known to improve accuracy tasks (e.g., beanbag tossing, ball throwing, golf putting) (Saemi et al., 2012; Wulf et al., 2014). Although there have been real-time biofeedback studies aimed toward reducing ACL injury risk (Bonnette et al., 2019; Marshall et al., 2020), none have exclusively delivered EE (Armitano et al., 2018). Prior evidence argues that giving learners positive feedback only after good trials and ignoring the bad trials promotes effective learning (Saemi et al., 2012; Wulf et al., 2014). The development of real-time biofeedback systems represents a prime opportunity to deliver automated positive feedback (EE), in line with previous research, without burdening clinicians to provide verbal feedback. Lastly, autonomy support, or AS, refers to one’s preference to perceive ‘control’ within their own learning environment. Providing learners with autonomy to facilitate their perception of self-control over a specific aspect of their practice conditions may increase motivation and foster greater motor learning than control conditions (Chiviacowsky et al., 2012; Wulf et al., 2014). For instance, providing individuals a choice of when to receive feedback (a task-relevant manipulation) leads to greater performance and learning of a sequential timing task (Chiviacowsky & Wulf, 2005) and even seemingly trivial (i.e., task-irrelevant) manipulations, such as selecting which art painting may be displayed on the wall or having the choice of selecting which color dart to throw (Ikudome et al., 2019) or color golf ball to putt (Lewthwaite et al., 2015), can improve performance (Lewthwaite et al., 2015).

The potential beneficial effects of AS on motor performance and learning have been inconsistent, particularly for paradigms that aim to promote self-control via a task-irrelevant choice or manipulation (McKay et al., 2022). However, an overwhelming majority of studies that failed to replicate AS effects were focused on discrete tasks with obvious goal-oriented outcomes (e.g., whether a dart hit a bullseye), potentially masking an AS influence on more granular mechanistic processes of motor behavior (e.g., the movement mechanics of a dart throw).

Therefore, our purpose in this study was to determine the relative efficacy of two motivational-based, single-leg squatting interventions to improve frontal plane mechanics in individuals exhibiting poor frontal plane knee, hip, and trunk mechanics compared with an attentional-based intervention. Because an external focus of attention (with and without additive visual biofeedback) has established efficacy, we focused on the unique effects of autonomy support (AS) and enhanced expectancies (EE) for developing injury resistant movement. Compared to participants who trained with a standard virtual stimulus, we hypothesized that those who trained with a stimulus incorporating either an EE or an AS feature would display improved frontal plane single-leg squatting kinematics following a brief intervention. Our secondary, exploratory, purpose in this study was to determine potential sex-specific responses to these motor learning manipulations.

Method

Ethical Considerations

We obtained advanced approval for this research protocol from our university’s Institutional Review Board (IRB) before involving any participants in this research. All participants provided written informed consent, approved by the university’s IRB before participating.

Participants

We engaged 45 participants (21 male and 24 female) in a single 90-minute research session (Table 1) (Hogg et al., 2022; Williams et al., 2022). Of these, 41 reported prior engagement in moderate-to-vigorous exercise for 30 minutes, three times a week. Exclusion criteria were: (a) history of concussion, (b) history of vertigo, (c) history of lower extremity surgery, and (d) any lower extremity injuries within the previous six months. Inclusion criteria were to be aged between 18- 30 years and to have a high-risk frontal plane biomechanics, as determined by a pre-screening session. Frontal plane biomechanics were pre-screened via a video-recorded performance of single-leg squats on the left side.

Table 1.

Participant Demographic Characteristics With Mean Values (and 95% Confidence Intervals).

	Control	Autonomy support	ENHANCED expectancies	Anova p value
Age (yr)	21.6 (20.3, 22.9)	21.8 (20.7, 22.9)	22.7 (21.4, 23.9)	0.33
Height (cm)	172.0 (164.3, 179.8)	171.5 (164.2, 178.9)	170.9 (165.2, 176.5)	0.49
Mass (kg)	72.1 (61.8, 82.4)	70.4 (61.3, 79.5)	70.0 (61.1, 78.9)	0.28

To determine eligibility and confirm the presence of high-risk frontal plane biomechanics, we assessed participants’ frontal plane angles of medial knee displacement, contralateral pelvic drop, and lateral trunk lean at the point of maximum knee flexion prior to the intervention and assessed with ImageJ software (Rasband, 2018). Risky frontal plane mechanics were operationally defined as exhibiting at least two of the following: medial knee displacement >10° for males or >13° for females (Cronström, Creaby, Nae, Ageberg, et al., 2016aCronström, Creaby, Nae, & Ageberg, 2016), contralateral pelvic tilt > 5°, and ipsilateral trunk lean > 5°. We assigned participants to one of three groups (control, autonomy support [AS], or enhanced expectancies [EE]) in a quasi-random fashion (to ensure that there were 8 females and 7 males in each group).

Instrumentation

We affixed retro-reflective adhesive markers to the following anatomical landmarks bilaterally: C7 vertebrae, T10 vertebrae, manubrium, sternum, iliac crest, acromion process, greater trochanter, medial and lateral femoral condyles, medial and lateral malleoli, posterior and medial and lateral heel, 5^th metatarsal head, 1^st metatarsal head, and great toe. In addition, we affixed five tracking plates, each containing four retroreflective markers, on the participants’ left and right lateral thighs, left and right shanks, and the sacrum. Participants’ movements were digitized using Vicon Nexus software (Vicon, Oxford Metrics, UK, https://www.vicon.com/software/nexus/). Ankle and knee joint centers were defined as the midway point between the medial and lateral malleoli and medial and lateral femoral condyles, respectively. Hip joint centers were determined using the Bell method (Bell et al., 1989). Data were obtained with an 8-camera Vicon motion capture system sampling at 100 Hz.

The instrumentation we used for kinematic biofeedback delivery has been reported in detail elsewhere (Hogg et al., 2022; Williams et al., 2022). Briefly, kinematic data for biofeedback delivery were collected using the Microsoft Azure Kinect DK (https://azure.microsoft.com/en-us/services/kinect-dk/). Through Kinect DK, we obtained joints’ transient position and corresponding Quaternion rotation (denoted as ${(〈 x, y, z 〉}_{t}^{k}, {〈 θ, \vec{v} 〉}_{t}^{k}$ ), where t and k indicates the time step and joint identity, respectively), which represents the rotation of a rigid body in three-dimensional space using four degrees-of-freedom. Data were preprocessed and the resulting kinematic data were reconstructed over virtual reality goggles using the Long-Term-Support release of Unit3D software (https://unity3d.com/unity/qa/lts-releases).

Motor Learning Assessment Procedures

Motor learning is broadly defined by relatively permanent gains in the capability for skilled performance via processes associated with practice or experience (Schmidt & Lee, 2013). To evaluate motor learning in the present study, participants completed three single-leg squats on the left limb with their hands on their hips at baseline and ∼10 minutes following the intervention (post-test). Baseline and post-test trials were repeated if participants touched down with the right limb, or if they did not attain ∼45° of knee flexion with their squat. No other instructions were given. Testing was conducted at baseline and approximately ten minutes following completion of the intervention (post-test). Of note, the virtual reality headset (see Intervention Procedures below) was not worn during baseline or post-testing given its potential to alter biomechanics (e.g., postural sway) (Brock et al., 2023; Imaizumi et al., 2020).

Intervention Procedures

We employed a one-day intervention consisting of single leg squats while participants viewed a digital avatar displayed in real-time through a virtual reality headset (Hogg et al., 2022; Williams et al., 2022). The same investigator delivered all interventions in a standardized manner but could not be made unaware of the participant’s allocation to an experimental group and so was not tasked with data reduction or analysis. For the intervention, each participant completed five sets of eight single-leg squats on the left leg with self-selected rest periods between each set. Single-leg squatting is an appropriate task during which to deliver real-time biofeedback. We chose the single leg squat task as this slow, continuous movement allows for real-time biofeedback to be successfully delivered and provides participants time to immediately correct their movement prior to their next repetition (Bonnette et al., 2019). During the single-leg squats participants wore a VR headset (HTC Vive Pro, New Taipei City, Taiwan) that displayed a posterior view of a squatting avatar demonstrating exemplar frontal plane knee, hip, and trunk mechanics. All participants were told that the avatar exemplified proper squatting mechanics and were simply instructed to mimic the avatar as closely as possible. Using minimal instructions, we aimed to isolate the effects of the motor learning manipulations.

The control group received no other feedback or autonomy. The AS group was allowed to choose the color of their avatar for each set but did not receive feedback (Figure 1). The EE group received real-time biofeedback in the form of green highlights placed on the avatar’s left knee, right buttock, and middle of the back. The highlights remained on as long as the participant did not exceed predetermined knee valgus, pelvic drop, or trunk lean thresholds. Participants in the EE group were instructed to move in such a way as to retain the green lights on the avatar but were given no explicit feedback on how to accomplish this (Figure 2) (Williams et al., 2022).

Figure 1.

Representation of the Avatar with Examples of Color Choice Options Seen by Participants in the Autonomy Support Group.

Figure 2.

Representation of the Avatar with Positive Feedback (Green Highlights) as seen by Participants in the Enhanced Expectancies Group.

Data Processing

We collected data from single-leg squatting trials serially in a single data capture period. Trials were separated at the point of maximum knee extension. Following interpolation of gaps up to 30 milliseconds, kinematic data were filtered with a 10 Hz, low-pass, 4^th order, zero-lag Butterworth filter. We calculated joint rotations with the following rotation sequence: (a) flexion-extension about the x-axis, (b) adduction-abduction about the y-axis, and (c) internal-external rotation about the z-axis. Peak joint angles occurring between the point of maximum knee extension and maximum knee flexion were extracted. All flexions, adductions, and internal rotations were defined as positive angles. All extensions, abductions, and external rotations were defined as negative angles. Trials were averaged within participants for analysis. Data processing was conducted in Visual3D (C-Motion, Rockville, MD, USA).

Statistical Analysis

We assessed data normality with histograms and used Levene’s test to determine homogeneity of the repeated measures factor. We used a one-way analysis of variance (ANOVA) for each dependent variable to determine the need to covary for baseline differences. We conducted a 3x2 (Group by Session) MANOVA to determine omnibus differences. Our primary analysis consisted of three 3x2 repeated measures ANOVAs (between factor: Group [control, AS, EE]; within factor: Time [baseline, post-test]), for measures of peak knee abduction, peak hip adduction, and peak trunk ipsilateral lean. Given the preliminary pilot and proof of concept nature of this study and consistent with similar prior work (Hogg et al., 2022; Williams et al., 2022), we used effect sizes (η_p² and Cohen’s d) to interpret the magnitude of main effects and interactions. We interpreted η_p² effect sizes as small (.01), moderate (.06), and large (.14) and Cohen’s d effect sizes as small (0.2), moderate (0.5) and large (0.8) as according to Cohen (Cohen, 1988). We further investigated main effects of η_p² > .06 with Tukey post-hoc testing and computed delta scores (Δ) to aid in interpretation. To determine potential sex-specific effects for future studies, we also conducted sex-stratified analyses and inspected between-sex change score differences. This analysis was conducted in JASP (0.14.1) (JASP, 2024).

Results

We were unable to use data from six participants due to technology-related problems (4 in the control group, 1 in AS, and 1 in EE), resulting in our use of a total of 39 participants for all final reported analyses. Data distributions were normal and Levene’s test indicated homogeneity of variances between pre- and post-testing for all variables.

Joint angles for each group and session, stratified by sex, are presented in Table 2. There were no baseline differences between groups for any of the three dependent variables (p range = .58 - .73). The MANOVA interaction effect between Group and Session was non-significant (Wilks’ λ = .70, F_(6,60) = 1.95, p = .09). There was a significant MANOVA main effect for Session (baseline to post-test) (Wilks’ λ = .72, F_(3,30) = 3.85, p = .02). The main effect for Group was not significant (Wilks’ λ = .93, F_(6,60) = .37, p = .90). For peak knee abduction angle, there was a Group by Time interaction effect (F_(2,36) = 6.95, η_p² = .28; p = .003, a posteriori power = .65) (Figure 3). Tukey’s post-hoc testing did not reveal any significant pairwise differences (t range = -.73 – 1.26, p range = .43 - .84), but delta scores indicated that the control group displayed greater pre-post changes in knee abduction angle (Δ = +2.3°, Cohen’s d = 0.74) compared to the two intervention groups (AS Δ = +0.1°, Cohen’s d = 0.05; EE Δ = −0.4°, Cohen’s d = 0.09). For peak hip adduction angle, there was a main effect for Time (F_(1,34) = 7.40, η_p² = .18; p = .01, a posteriori power = .86) (Figure 4), with greater hip adduction angle at post-testing compared to baseline (control Δ = +1.5°, Cohen’s d = 0.25; AS Δ = +3.2°, Cohen’s d = 0.45; EE Δ = +0.7°, Cohen’s d = 0.13). For peak ipsilateral trunk lean, there were no interactions or main effects (F range = .08 - .79, η_p² range = .002 - .04; p range = .46 −.80, a posteriori power range = .05 - .12) (Figure 5).

Table 2.

Participants’ Joint Angles at Baseline and at Post-testing Stratified by Group and Sex as Represented With Mean Values (and 95% Confidence Intervals).

Variable	Group	Sex	Baseline	Post-testing	Change (Δ)
Knee abduction (+)	Control	Female	1.7° (−1.6, 4.9)	5.2° (1.4, 9.1)	3.5°
		Male	1.4° (−1.3, 4.1)	2.7° (−1.1, 6.4)	1.3°
	Autonomy support	Female	1.2° (−1.2, 3.6)	0.8° (−1.4, 3.0)	−0.4°
		Male	0.8° (−0.6, 2.2)	1.4° (−1.0, 3.9)	0.6°
	Enhanced expectancies	Female	2.4° (−1.0, 5.9)	2.1° (−2.1, 6.4)	−0.3°
		Male	1.7° (−1.2, 4.6)	1.2° (−3.6, 6.1)	−0.5°
Hip adduction (+)	Control	Female	17.5° (8.5, 26.4)	18.5° (7.8, 29.2)	1.0°
		Male	12.4° (7.6, 17.3)	15.7° (10.9, 20.6)	3.3°
	Autonomy support	Female	16.7° (10.3, 23.1)	19.2° (14.3, 24.1)	2.5°
		Male	12.7° (6.4, 18.9)	16.5° (8.3, 24.8)	3.8°
	Enhanced expectancies	Female	16.1° (10.0, 22.2)	16.4° (11.0, 21.7)	0.3°
		Male	15.3° (10.7, 20.0)	18.2° (14.1, 22.4)	2.9°
Trunk lean (+)	Control	Female	−3.8° (−7.4, −0.2)	−2.3° (−7.5, 3.0)	1.5°
		Male	0.7° (−7.0, 8.5)	−0.3° (−3.8, 3.2)	−1.0°
	Autonomy support	Female	−2.7° (−4.9, −0.5)	−1.0° (−4.5, 2.4)	1.7°
		Male	−2.0° (−5.7, 1.6)	−0.8° (−5.1, 3.5)	1.2°
	Enhanced expectancies	Female	−1.4° (−5.4, 2.7)	−2.1° (−4.6, 0.3)	−0.7°
		Male	−0.8° (−6.5, 5.0)	−0.4° (−3.1, 2.3)	0.4°

Note. *Positive direction denotes worsening kinematics.

Figure 3.

Pre-Post Changes in Knee Abduction Angles for Each Group.

Figure 4.

Pre-Post Changes in Hip Adduction Angles for each Group.

Figure 5.

Pre-Post Changes in Ipsilateral Trunk Lean Angle for Each Group.

Discussion

Our hypothesis that participants in the OPTIMAL PREP intervention groups would display improved frontal plane kinematics compared to a control group was partially supported. At post-testing, we observed a marked difference in knee abduction angle between the intervention groups and the control group, but this difference resulted from a worsening of knee angle movement patterns for the control group that was not evident in the intervention groups. At the hip, all groups displayed high-risk adduction at post-testing, while trunk lean kinematics did not change following the intervention.

Following our brief single-leg squatting intervention, the control group displayed 2.3° greater peak knee abduction angle, while the AS and EE groups displayed changes of 0.1° and −0.4°, respectively. The control group, without the motivational aids of AS or EE, may have succumbed to poorer knee movement patterns at post-testing. Conversely, participants in the two intervention groups were able to maintain neutral knee positions at post-testing, even though participants in all groups increased their hip adductions at post-testing. Although not our intent, our intervention of five sets of eight single-leg squat repetitions may have introduced muscle fatigue that was either attenuated for knee angle in AS and EE groups or not in the control group and attenuated in no groups for hip movementsat post-testing. We are speculating that fatigue was the causative factor for these results, because we did not capture muscle strength or activation data. Squatting and jumping fatigue protocols typically require >100 squat and jumps to attain fatigue (Collins et al., 2016), while our protocol consisted of only 40 squats on each leg with self-selected rest. The hip and knee are often thought to be coupled joints, with hip adduction explaining up to 30% of knee abduction (Hogg et al., 2019; Imwalle et al., 2009), which has been documented as a primary predictor of ACL injury (Hewett et al., 2005), In the present study, all groups demonstrated worsening hip adduction from baseline to post-testing. Nevertheless, the interventions implemented here appeared to override the influences of greater hip adduction, since participants in the control group showed these influences while those in treatment groups did not. Our observed effects align with contemporary views of the three pillars of OPTIMAL PREP. Our control group was directed with an external focus of attention, which is theorized to drive primary motor cortex (M1) efficiency (reduced neural activity) via increased sensorimotor integration activity concomitant with intracortical inhibition and corticospinal excitability of M1 (Diekfuss et al., 2021; Kuhn et al., 2017). Conversely, the ‘reward-boosting’ motivational-oriented pillars of autonomy support and enhanced expectancies in our treatment groups are hypothesized to modulate cortical excitability and also trigger a dopamine response throughout various neural pathways (e.g., fronto-striatal circuity) that positively facilitates synaptic transmission between neurons and cells important for sensorimotor control (Diekfuss et al., 2021; Lo & Wang, 2006; Wulf & Lewthwaite, 2016). Thus, giving learners control over their learning environment (AS) or enhancing their expectations for success (EE) theoretically leads to greater motivation (Wulf & Lewthwaite, 2016) and improved dopaminergic transmission that supports stronger within- and between-network brain functional connectivity (Schultz, 2013).

Beyond an external focus of attention, autonomy support and enhanced expectancies are the two motivational tenets of the OPTIMAL theory (Wulf & Lewthwaite, 2016). Using these motor learning techniques to improve performance outcomes has been robustly supported in the literature (Bahmani et al., 2018), but support for their use in improving specific biomechanical, injury-related risk factors has been limited. Specifically, employing real-time visual biofeedback mitigated high-risk biomechanics (Bonnette et al., 2019), and delivering simple EE and AS instructions improved postural balance measures (Chua et al., 2020). To our knowledge, there have been no data addressing the role of these motivational strategies on biomechanics following a potentially fatiguing manipulation. As the third, and most heavily researched, tenet of the OPTIMAL theory, an external focus of attention has repeatedly resulted in both better performance and more efficient muscle contractions (Lohse et al., 2010; Marchant et al., 2009). Thus, it is reasonable to theorize that autonomy support and enhanced expectancies may also result in greater cognitive engagement and muscle efficiency, explaining our observation that our two experimental groups maintained frontal plane knee position despite increased hip adduction.

Because lower extremity biomechanics are known to differ between sexes (Cronström et al., 2016b), we also inspected sex-stratified results. Interestingly, worsening of the control group’s knee abduction appears to have been more pronounced among our female than in our male participants (Table 2). The addition of either an AS or EE feature to the intervention may have eliminated normally identified between-sex differences, suggesting that motor learning motivational strategies may benefit females more than males. An alternative explanation is that because females rely more on frontal plane restraints than males (Cronström et al., 2016b), the improvement in participants’ knee abduction angles could be partially attributed to the targeted movement plane. Future investigators should seek to determine the role that sex-specific biomechanical tendencies and planes of movement may play in the efficacy of motor learning intervention strategies.

Clinical Implications

The present findings have important implications for clinical practice. The advantage of motor learning principles is their ease of use, often requiring only simple adjustments in instructional wording or allowing patient autonomy within the confines of an already existing rehabilitation or prevention protocol (Diekfuss et al., 2020a). Delivering autonomy support could mean allowing a patient to choose the order of exercises or when to receive performance feedback. However, we emphasize that our autonomy support manipulation—asking individuals to choose the color of one’s avatar—would be considered ‘task-irrelevant’ and warrants additional research before clinical implementation. The beneficial effects of task-irrelevant choices on motor performance and learning are not universally supported, as they have had notably less or null effects when delivered to learners possessing high intrinsic motivation (Ikudome et al., 2019; McKay et al., 2022). Nevertheless, our study is unique in that our assessments were not goal-oriented outcomes, but, instead, evaluated more mechanistic biomechanical process for which the ‘goal’ was more nebulous to individuals. A replication of the present findings and a consideration of other means to promote autonomy for improving complex movement patterns is warranted (e.g., task-relevant choices).

As for EE, this intervention is broadly inclusive of any method that results in the learner expecting their own success. This often takes the form of supplying feedback only after successful repetitions, or comparing the learner’s current performance to their own prior performances or to normative performances of lesser quality to immediately bolster self-confidence. Using these strategies in a clinic or sports setting has the added benefit of creating a learner-centered environment, and as such, learning strategies can be personalized to individual preferences.

Limitations and Directions for Further Research

As a preliminary investigation, there are several limitations to our study. First, we did not quantify participants’ cognitive engagement (e.g., motivation) or physical fatigue (e.g., electromyography; rating of perceived exertion), limiting our ability to interpret these findings. Future investigators should add measures to better determine mechanistic drivers underlying the positive motor learning strategies employed herein. For instance, recent investigators have implemented manipulation checks to evaluate the influence of OPTIMAL-based interventions to elicit participant self-efficacy, confidence, motivation, etc. (Simmonds et al., 2023; Simpson et al., 2020; Wulf et al., 2014). We expect our present intervention to produce similar positive cognitive outcomes, but future researchers should employ appropriate manipulation checks to confirm this hypothesis (e.g., via self-report questionnaires or semi-structured interviews). Also, we did not measure physical fatigue and cannot speak directly speak to the possibility that our motor learning manipulations delayed fatigue. Future work should assess learner motivation and include more task-relevant AS options. Similarly, we did not assess transfer learning of the single-leg squatting task to a task more representative of injury-risk scenarios (e.g., simulated sport in virtual reality), and this too might be achieved in subsequent research. Lastly, we assessed AS and EE in separate motor learning interventions, each combined with an external attention focus, to determine which conferred the greatest biomechanical benefit. Prior evidence indicates benefits to employing additive OPTIMAL PREP pillars (Wulf et al., 2014), and future research might include multiple motor learning principles in a single manipulation, perhaps including a “true” control group in which participants are exposed to no motor learning pillars, not even an external attention focus.

Conclusions

The preliminary data from this study indicate that the motor learning strategies of autonomy support and enhanced expectancies can support the immediate retention of injury-resistant frontal plane knee kinematics. Specifically, AS and EE motivational strategies applied during exercise performance may be useful to mitigate maladaptive frontal plane knee mechanics. While not determined in this project, practitioners should consider the potential dose-response effects that can better optimize adaptive responses from autonomy support and the enhanced expectancies enhancement that can be best retained and transferred to other, more sport-specific, tasks.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Tennessee Higher Education Commission’s Center of Excellence in Applied Computational Science and Engineering, R041302265. Jennifer A. Hogg and Jed A. Diekfuss receive textbook royalties from Kendall Hunt Publishing. Gregory D. Myer consults with commercial entities to support commercialization strategies and applications to the US Food and Drug Administration but has no direct financial interest in commercialization of the products. Dr. Myer’s institution receives current and ongoing grant funding from National Institutes of Health/NIAMS (U01AR067997, R01AR070474, R01AR055563, R01AR076153, R01AR077248, R61AT012421), Department of Defense (W81XWH22C0062), and Arthritis Foundation (OACTN). Dr. Myer has received industry-sponsored research funding to his institutions related to injury prevention and sport performance and has current ongoing funding from Arthrex Inc. to evaluate ACL surgical treatment optimization strategies. Dr. Myer receives author royalties from Human Kinetics and Wolters Kluwer. Dr. Myer is an inventor of biofeedback technologies (patent #US11350854B2, Augmented and Virtual Reality for Sport Performance and Injury Prevention Application, approval date: 06/07/2022, software copyrighted), designed to enhance rehabilitation and prevent injuries. Dr. Myer receives inventor-related royalties from biofeedback technologies (Include Health: LIC1907082014-0706).

ORCID iDs

Jennifer A. Hogg

Gary B. Wilkerson

Author Biographies

Jennifer A. Hogg is an Associate Professor for the Graduate Athletic Training Program within the Department of Health and Human Performance at the University of Tennessee Chattanooga.

Gary B. Wilkerson is a Professor for the Graduate Athletic Training Program within the Department of Health and Human Performance at the University of Tennessee Chattanooga.

Shellie N. Acocello is an Associate Professor and Program Director for the Graduate Athletic Training Program within the Department of Health and Human Performance at the University of Tennessee Chattanooga.

Bryan R. Schlink is a biomedical engineer for Battelle Memorial Institute in Columbus, Ohio.

Yu Liang is an Associate Professor in the Department of Computer Science and Engineering at the University of Tennessee Chattanooga.

Dalei Wu is an Associate Professor in the Department of Computer Science and Engineering at the University of Tennessee Chattanooga.

Gregory D. Myer is a Professor within the Department of Orthopaedics at Emory University School of Medicine and Director of the Emory Sports Performance and Research Center in Flowery Branch, Georgia.

Jed A. Diekfuss is an Assistant Professor within the Department of Orthopaedics at Emory University School of Medicine and Director of Neuroimaging for Emory Sports Performance and Research Center in Flowery Branch, Georgia.

References

Armitano

C. N.

Haegele

J. A.

Russell

D. M.

(2018). The use of augmented information for reducing anterior cruciate ligament injury risk during jump landings: A systematic review. J Athl Train, 53(9), 844–859. https://doi.org/10.4085/1062-6050-320-17

Bahmani

Diekfuss

J. A.

Rostami

Ataee

Ghadiri

(2018). Visual illusions affect motor performance, but not learning in highly skilled shooters. Journal of Motor Learning and Development, 6(2), 220–233. https://doi.org/10.1123/jmld.2017-0011

Bell

A. L.

Brand

R. A.

Pedersen

(1989). Prediction OF HIP joint centre location from external landmarks. Human Movement Science, 8(1), 3–16. https://doi.org/10.1016/0167-9457(89)90020-1

Benjaminse

Otten

Gokeler

Diercks

R. L.

Lemmink

K. A. P. M.

(2017). Motor learning strategies in basketball players and its implications for ACL injury prevention: A randomized controlled trial, Knee Surgery, Sports Traumatology, Arthroscopy, 25, 2365–2376. https://doi.org/10.1007/s00167-015-3727-0

Bonnette

DiCesare

C. A.

Kiefer

A. W.

Riley

M. A.

Barber Foss

K. D.

Thomas

Kitchen

Diekfuss

J. A.

Myer

G. D.

(2019). Injury risk factors integrated into self-guided real-time biofeedback improves high-risk biomechanics. Journal of Sport Rehabilitation, 28(8), 831–839. Division of Sports Medicine, SPORT Center, Cincinnati Children's Hospital Medical Center. https://doi.org/10.1123/jsr.2017-0391

Brock

Vine

S. J.

Ross

J. M.

Trevarthen

Harris

D. J.

(2023). Movement kinematic and postural control differences when performing a visuomotor skill in real and virtual environments. Experimental Brain Research, 241(7), 1797–1810. https://doi.org/10.1007/s00221-023-06639-0

Chiviacowsky

Wulf

(2005). Self-controlled feedback is effective if it is based on the learner's performance. Research Quarterly for Exercise & Sport, 76(1), 42–48. https://doi.org/10.1080/02701367.2005.10599260

Chiviacowsky

Wulf

Lewthwaite

(2012). Self-controlled learning: The importance of protecting perceptions of competence. Frontiers in Psychology, 3, 458. https://doi.org/10.3389/fpsyg.2012.00458

Chua

L. K.

Wulf

Lewthwaite

(2020). Choose your words wisely: Optimizing impacts on standardized performance testing. Gait & Posture, 79, 210–216. https://doi.org/10.1016/j.gaitpost.2020.05.001

10.

Cohen

(1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates.

11.

Collins

J. D.

Almonroeder

T. G.

Ebersole

K. T.

Connor

M. O.

(2016). The effects of fatigue and anticipation on the mechanics of the knee during cutting in female athletes. In JCLB. Elsevier B.V. https://doi.org/10.1016/j.clinbiomech.2016.04.004

12.

Cronström

Creaby

M. W.

Nae

Ageberg

(2016a). Gender differences in knee abduction during weight-bearing activities: A systematic review and meta-analysis. Gait Posture, 49, 315–328. https://doi.org/10.1016/j.gaitpost.2016.07.107

13.

Cronström

Creaby

M. W.

Nae

Ageberg

Cronstrom

Creaby

M. W.

Nae

Ageberg

Cronström

Creaby

M. W.

Nae

Ageberg

(2016b). Modifiable factors associated with knee abduction during weight-bearing activities: A systematic review and meta-analysis. Sports Medicine, 46(11), 1647–1662. https://doi.org/10.1007/s40279-016-0519-8

14.

DiCesare

C. A.

Kiefer

A. W.

Bonnette

S. H.

Myer

G. D.

(2019c). Realistic soccer-specific virtual environment exposes high-risk lower extremity biomechanics. Journal of Sport Rehabilitation, 4, 1–23.

15.

Diekfuss

J. A.

Bonnette

Hogg

J. A.

Riehm

Grooms

D. R.

Singh

Anand

Slutsky-Ganesh

A. B.

Wilkerson

G. B.

Myer

G. D.

(2020a). Practical training strategies to apply neuro-mechanistic motor learning principles to facilitate adaptations towards injury-resistant movement in youth. Journal of Science in Sport and Exercise, 3(1), 3–16. https://doi.org/10.1007/s42978-020-00083-0

16.

Diekfuss

J. A.

Grooms

D. R.

Bonnette

DiCesare

C. A.

Thomas

MacPherson

R. P.

Ellis

J. D.

Kiefer

A. W.

Riley

M. A.

Schneider

D. K.

Gadd

Kitchen

Barber Foss

K. D.

Dudley

J. A.

Yuan

Myer

G. D.

(2020b). Real-time biofeedback integrated into neuromuscular training reduces high-risk knee biomechanics and increases functional brain connectivity: A preliminary longitudinal investigation. Psychophysiology, 57, 135455. https://doi.org/10.1111/psyp.13545

17.

Diekfuss

J. A.

Grooms

D. R.

Hogg

J. A.

Singh

Slutsky-Ganesh

A. B.

Bonnette

Riehm

Anand

Nissen

K. S.

Wilkerson

G. B.

Myer

G. D.

(2021). Targeted application of motor learning theory to leverage youth neuroplasticity for enhanced injury-resistance and exercise performance: Optimal PREP. Journal of Science in Sport and Exercise, 3(1), 17–36. https://doi.org/10.1007/s42978-020-00085-y

18.

Diekfuss

J. A.

Grooms

D. R.

Nissen

K. S.

Schneider

D. K.

Foss

K. D. B.

Thomas

Bonnette

Dudley

J. A.

Yuan

Reddington

D. L.

Ellis

J. D.

Leach

Gordon

Lindsey

Rushford

Shafer

Myer

G. D.

(2019a). Alterations in knee sensorimotor brain functional connectivity contributes to ACL injury in male high-school football players: A prospective neuroimaging analysis. In Brazilian journal of physical therapy: Associação Brasileira de Pesquisa e Pós-graduação em Fisioterapia.

19.

Diekfuss

J. A.

Grooms

D. R.

Yuan

Dudley

Barber Foss

K. D.

Thomas

Ellis

J. D.

Schneider

D. K.

Leach

Bonnette

Myer

G. D.

(2019b). Does brain functional connectivity contribute to musculoskeletal injury? A preliminary prospective analysis of a neural biomarker of ACL injury risk. In Journal of Science and Medicine in Sport, 22(2), 169–174. https://doi.org/10.1016/j.jsams.2018.07.004

20.

Diekfuss

J. A.

Hogg

J. A.

Grooms

D. R.

Slutsky-Ganesh

A. B.

Singh

Bonnette

Anand

Wilkerson

G. B.

Myer

G. D.

(2020c). Can we capitalize on central nervous system Plasticity in young athletes to inoculate against injury? Journal of Science in Sport and Exercise, 2(4), 305–318. https://doi.org/10.1007/s42978-020-00080-3

21.

Grooms

(2022). Preliminary report on the train the brain project: Neuroplasticity of augmented neuromuscluar training and improved injury risk biomechanics - Part II. Journal of Athletic Training, 57(9-10), 911–920.

22.

Hewett

T. E.

Myer

G. D. D.

Ford

K. R.

Heidt

R. S.

Jr. Colosimo

A. J.

McLean

S. G.

van den Bogert

A. J.

Paterno

M. V.

Succop

(2005). Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes: A prospective study. The American Journal of Sports Medicine, 33(4), 492–501. https://doi.org/10.1177/0363546504269591

23.

Hogg

J. A.

Riehm

C. D.

Diekfuss

J. A.

Simon

J. E.

Acocello

S. N.

Liang

Myer

G. D.

Wilkerson

G. B.

(2022). An exemplar frontal plane visual kinematic stimulus elicits sex-specific learned behavior: An exploratory report. The Journal of Strength & Conditioning Research. 36(3), 857–861. https://doi.org/10.1519/jsc.0000000000004203

24.

Hogg

J. A.

Schmitz

R. J.

Shultz

S. J.

(2019). The influence of hip structure on functional valgus collapse during a single-leg Forward landing in females. Journal of Applied Biomechanics, 35(6), 370–376. https://doi.org/10.1123/jab.2019-0069

25.

Ikudome

Kou

Ogasa

Mori

Nakamoto

(2019). The effect of choice on motor learning for learners with different levels of intrinsic motivation. Journal of Sport & Exercise Psychology, 41(3), 159–166. https://doi.org/10.1123/jsep.2018-0011

26.

Imaizumi

L. F. I.

Polastri

P. F.

Penedo

Vieira

L. H. P.

Simieli

Navega

F. R. F.

Monteiro

C. B. d. M.

Rodrigues

S. T.

Barbieri

F. A.

(2020). Virtual reality head-mounted goggles increase the body sway of young adults during standing posture. Neuroscience Letters, 737, 135333. https://doi.org/10.1016/j.neulet.2020.135333

27.

Imwalle

L. E.

Myer

G. D.

Ford

K. R.

Hewett

T. E.

(2009). Relationship between hip and knee kinematics in athletic women during cutting maneuvers: A possible link to noncontact anterior cruciate ligament injury and prevention. J Strength Cond Res, 23(8), 2223–2230. https://doi.org/10.1519/JSC.0b013e3181bc1a02

28.

JASP . (2024). JASP. In

29.

Kuhn

Y. A.

Keller

Ruffieux

Taube

(2017). Adopting an external focus of attention alters intracortical inhibition within the primary motor cortex. Acta Physiologica, 220, 289–299

30.

Lewthwaite

Chiviacowsky

Drews

Wulf

(2015). Choose to move: The motivational impact of autonomy support on motor learning. Psychonomic bulletin & review, 22(5), 1383–1388. https://doi.org/10.3758/s13423-015-0814-7

31.

C.-C.

Wang

X.-J.

(2006). Cortico–basal ganglia circuit mechanism for a decision threshold in reaction time tasks. Nature Neuroscience, 9(7), 956–963. https://doi.org/10.1038/nn1722

32.

Lohse

K. R.

Sherwood

D. E.

Healy

A. F.

(2010). How changing the focus of attention affects performance, kinematics, and electromyography in dart throwing. Human Movement Science, 29(4), 542–555. https://doi.org/10.1016/j.humov.2010.05.001

33.

Marchant

Greig

Scott

(2009). Attentional focusing instructions influence Force Production and muscular activity during isokinetic elbow flexions. J Strength Cond Res, 23(8), 2358–2366. https://doi.org/10.1519/JSC.0b013e3181b8d1e5

34.

Marshall

A. N.

Hertel

Hart

J. M.

Russell

Saliba

S. A.

(2020). Visual biofeedback and changes in lower extremity kinematics in individuals with medial knee displacement. J Athl Train, 55(3), 255–264. https://doi.org/10.4085/1062-6050-383-18

35.

McKay

Yantha

Hussien

Carter

Ste-Marie

(2022). Meta-analytic findings of the self-controlled motor learning literature: Underpowered, biased, and lacking evidential value. Meta-Psychology, 6, 256. https://doi.org/10.15626/mp.2021.2803

36.

Patterson

Culvenor

A. G.

Barton

C. J.

Guermazi

Stefanik

Morris

H. G.

Whitehead

T. S.

Crossley

K. M.

(2020). Poor functional performance 1 year after ACL reconstruction increases the risk of early osteoarthritis progression. British Journal of Sports Medicine, 54(9), 546–553. https://doi.org/10.1136/bjsports-2019-101503

37.

Raisbeck

L. D.

Yamada

(2019). The effects of instructional cues on performance and mechanics during a gross motor movement. Human Movement Science, 66, 149–156. https://doi.org/10.1016/j.humov.2019.04.001

38.

Rasband

W. S. ImageJ. U.S.

(2018). National Institutes of Health.

39.

Saemi

Porter

J. M.

Ghotbi-Varzaneh

Zarghami

Maleki

(2012). Knowledge of results after relatively good trials enhances self-efficacy and motor learning. Psychology of Sport and Exercise, 13(4), 378–382. https://doi.org/10.1016/j.psychsport.2011.12.008

40.

Schmidt

Lee

(2013). Motor learning and performance 5th edition: From principles to application. Human Kinetics.

41.

Schultz

(2013). Updating dopamine reward signals. Current Opinion in Neurobiology, 23(2), 229–238. https://doi.org/10.1016/j.conb.2012.11.012

42.

Simmonds

P. J.

Wakefield

C. J.

Coyles

Roberts

J. W.

(2023). Enhanced expectancies benefit performance under distraction, but compromise it under stress: Exploring the OPTIMAL theory. Human Movement Science, 89, 103085. https://doi.org/10.1016/j.humov.2023.103085

43.

Simpson

Cronin

Ellison

Carnegie

Marchant

(2020). A test of optimal theory on young adolescents' standing long jump performance and motivation. Human Movement Science, 72, 102651. https://doi.org/10.1016/j.humov.2020.102651

44.

Waldén

Hägglund

Magnusson

Ekstrand

(2016). ACL injuries in men’s professional football:\r\na 15year prospective study on time trends and\r\nreturntoplay rates reveals only 65% of players still\r\nplay at the top level 3 years after ACL rupture. British Journal of Sports Medicine, 50, 744. https://doi.org/10.1136/bjsports-2015-095952

45.

Webster

K. E.

Feller

J. A.

Klemm

H. J.

(2021). Second ACL injury rates in younger athletes who were advised to delay return to sport until 12 Months after ACL reconstruction. Orthopaedic Journal of Sports Medicine, 9, 1–6. https://doi.org/10.1177/2325967120985636

46.

Widenhoefer

T. L.

Miller

T. M.

Weigand

M. S.

Watkins

Almonroeder

T. G.

Lee

Miller

T. M.

Weigand

M. S.

Widenhoefer

T. L.

Miller

T. M.

Weigand

M. S.

Watkins

E. A.

Almonroeder

T. G.

(2019). Training rugby athletes with an external attentional focus promotes more automatic adaptions in landing forces promotes more automatic adaptions in landing forces. Sports Biomechanics, 18(2), 163–173. https://doi.org/10.1080/14763141.2019.1584237

47.

Wiggins

A. J.

Grandhi

R. K.

Schneider

D. K.

Stanfield

Webster

K. E.

Myer

G. D.

(2016). Risk of secondary injury in younger athletes after anterior cruciate ligament reconstruction. The American Journal of Sports Medicine, 44(7), 1861–1876. https://doi.org/10.1177/0363546515621554

48.

Williams

A. M.

Hogg

J. A.

Diekfuss

J. A.

Kendall

S. B.

Jenkins

C. T.

Acocello

S. N.

Liang

Myer

G. D.

Wilkerson

G. B.

(2022). Immersive real-time biofeedback optimized with enhanced expectancies improves motor learning: A Feasibility study. Journal of Sport Rehabilitation, 4, 1–8. https://doi.org/10.1123/jsr.2021-0226

49.

Wulf

Chiviacowsky

Cardozo

P. L.

(2014). Additive benefits of autonomy support and enhanced expectancies for motor learning. Human Movement Science, 37, 12–20. https://doi.org/10.1016/j.humov.2014.06.004

50.

Wulf

Lewthwaite

(2016). Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning. Psychonomic bulletin & review, 23, 1382–1414. https://doi.org/10.3758/s13423-015-0999-9

51.

Wulf

McNevin

Shea

C. H.

(2001). The automaticity of complex motor skill learning as a function of attentional focus. The Quarterly Journal of Experimental Psychology A, 54(4), 1143–1154. https://doi.org/10.1080/713756012

52.

Yamada

Raisbeck

L. D.

(2020). The effects of attentional focus instructions specific to body movements on movement quality and performance. Journal of Sport Rehabilitation, 5, 1–8. https://doi.org/10.1123/jsr.2019-0344