Visual Processing and Interference Performance Influences on Knee Angular Impulse in ACLR Individuals: A Cognitive-Biomechanical Analysis of Drop-Jumps

Visual Processing and Interference Performance Influences on Knee Angular Impulse in ACLR Individuals: A Cognitive-Biomechanical Analysis of Drop-Jumps | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Visual Processing and Interference Performance Influences on Knee Angular Impulse in ACLR Individuals: A Cognitive-Biomechanical Analysis of Drop-Jumps Keven Santamaria-Guzman, Hillary Holmes, Jerad Kosek, Brandon Peoples, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8971155/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Visual processing speed and cognitive interference control play crucial roles in athletic movements and anterior cruciate ligament (ACL) injury risk. The relationship between these specific cognitive functions and biomechanical performance following ACL reconstruction (ACLR) remains poorly understood. Given this, we aim to investigate cognitive performance differences between ACLR individuals and matched controls during drop-jump tasks and examine knee angular impulse patterns and their relationship to cognitive function. Methods: Thirty-two females (16 anterior cruciate ligament reconstruction, 16 controls; age 20±1 years) completed cognitive assessments including the Stroop Color and Word Test, Trail Making Test, Digit Span Memory Test, and visual/auditory reaction time tests. Participants performed drop-jumps under four conditions: standard, choice, visual-cued, and audio-cued. Knee angular impulse was calculated for eccentric, concentric, and net phases during landing. Binomial logistic regression identified cognitive predictors distinguishing groups, followed by factorial analyses of variance to assess knee angular impulse differences. Spearman's rank correlation coefficients examined relationships between cognitive performance measures and knee angular impulse phases. Results: Three cognitive predictors distinguished groups: cognitive interference score, visual simple reaction time, and visual complex reaction time (χ²(3)=55.090, p < 0.001). The ACLR group demonstrated faster (shorter) visual reaction times, but impaired interference control compared to controls. ACLR participants showed significantly lower eccentric knee angular impulse compared to controls (p = 0.003, Cohen's d=-0.37), while audio-cued conditions produced higher eccentric knee angular impulse than standard and choice conditions. Despite distinct cognitive profiles, minimal correlations emerged between group-distinguishing cognitive variables and eccentric knee angular impulse, suggesting parallel rather than integrated adaptations. Discussion: ACLR individuals exhibit distinct cognitive-biomechanical profiles characterized by enhanced reactive capabilities alongside reduced interference control and persistent protective movement strategies. Results support incorporating cognitive assessment and training into ACL rehabilitation protocols. Neuro-motor control Anterior Cruciate Ligament Drop-Jump Knee Angular Impulse Biomechanics Cognition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The role of cognitive processes is increasingly recognized for its importance in athletic movements, particularly in tasks requiring rapid decision-making and direction changes. These cognitive processes are essential in motor planning and execution [ 1 , 2 ]. Critical factors such as reaction time, processing speed, and adaptability to visual stimuli have been linked to injury risk, with slower cognitive responses associated with a higher likelihood of injury [ 3 , 4 ]. Notably, cognitive abilities have emerged as significant for understanding injuries such as Anterior Cruciate Ligament (ACL) tears. Research has shown that athletes who suffer non-contact ACL injuries often exhibit longer reaction times, slower visual processing speeds, and lower memory scores [ 5 – 7 ]. ACL injury treatment options include surgical reconstruction and rehabilitation, with costs ranging from $ 20,000 to $ 50,000 per case. With 100,000-200,000 ACL ruptures annually in the US, the total yearly cost reaches $ 2–10 billion [ 8 ]. Understanding the role of cognitive function in shaping motor behaviors is crucial for developing effective strategies and potentially reducing the incidence of ACL injury. For Individuals with ACL injuries, impaired cognitive abilities can adversely affect motor planning, coordination, and reaction times, which in turn influence how the body responds to knee moments during dynamic activities [ 7 , 9 ]. For instance, decreased cognitive performance can lead to slower responses to sudden changes in movement or direction, increasing the risk of improper knee joint alignment and heightened mechanical stress. This relationship underscores that lower cognitive performance can compromise the effectiveness of neuromuscular control, making individuals more susceptible to excessive knee moments and potentially exacerbating the risk of injury or re-injury [ 10 , 11 ]. However, limited research explores how cognitive processes interact with biomechanics in those who have undergone ACL reconstruction (ACLR). Post-injury, the body often modifies movement patterns due to changes in sensorimotor control, which may also affect cognitive processing as the brain integrates new sensory feedback [ 12 ]. In the same way, drop-jump tasks are particularly relevant for ACL injury research as they replicate the high-impact landing mechanics commonly associated with non-contact ACL injuries during deceleration and change-of-direction movements in sport [ 13 – 15 ]. These tasks allow systematic examination of neuromuscular control strategies under varying cognitive demands while maintaining ecological validity for sport-related injury mechanisms [ 16 ]. This study aims to investigate cognitive performance and biomechanical characteristics in individuals with ACLR compared to controls during multiple drop-jump tasks. Additionally, it seeks to explore the relationship between cognitive function and knee angular impulse (KAI) differences between groups. We hypothesize that: (1) specific cognitive functions will significantly distinguish individuals who have undergone ACLR from controls, (2) significant differences in KAI will exist between ACLR and control groups, and (3) these cognitive and biomechanical differences will be related, reflecting integrated neuromuscular adaptations following ACL reconstruction. This study contributes to the existing literature by elucidating the relationship between cognitive function and biomechanical performance post-ACLR, with potential implications for rehabilitation strategies and performance protocols. Materials and methods Participants Thirty-two females participated in this study (16 ACLR, 16 Control). The ACLR group (age = 20.31 ± 1.70 years, height = 1.69 ± 0.06 m, mass = 67.54 ± 9.10 kg) and Control group (CTRL) (age = 20.38 ± 1.09 years, height = 1.67 ± 0.07 m, mass = 62.98 ± 8.79 kg) were well-matched with no significant differences in age (t(30) = -0.124, p = 0.902), height (t(30) = 0.596, p = 0.556), or mass (t(30) = 1.443, p = 0.159). All participants were comfortable jumping from a 1ft box. Of ACLR participants, 12 had a single tear, while 4 had multiple contralateral tears (2 double, 2 triple). All underwent reconstruction using various grafts (8 patellar, 5 hamstring, 1 gracilis, 1 artelon synthetic, 1 unknown). Participants had their last ACL tear 32.81 ± 15.77 months ago, completed 6.88 ± 3.18 months of rehabilitation, and had a minimum of 6 months post-return to play clearance. Exclusion criteria were based on the PAR-Q assessments [ 17 ]. Participants were involved in various sports, including Basketball (4), Cross Country (2), Crossfit (1), Gymnastics (1), Lacrosse (2), Running (1), Soccer (8), Softball (2), Tennis and Badminton (2), Track and Field (5) and Volleyball (4). Informed consent was obtained, and the study was approved by the University's Institutional Review Board. Procedures Surveys Participants completed an online survey assessing current and past sports participation, injury history, ACL reconstruction details, osteoarthritis status, and concussion history. Data included sport types, participation level, training intensity, number of ACL tears, surgeries, graft types, injury mechanisms, therapy details, and other injuries like sprains and strains. Cognitive testing All cognitive tests were administered consistently by the same researcher in a non-distracting environment. Participants completed the Stroop Color and Word Test, the Trail Making Test A & B (TMT), the Digit Span Memory Test (DS), and computerized simple (SRT) and complex reaction time (CRT) tests for both visual and auditory stimuli. Performance on the Stroop Color and Word Test was quantified by the number of correct verbal responses provided within a 45-second time frame in both the color-naming and incongruent conditions. The cognitive interference score (CIS) was calculated using the formula 1 (Fig. 1 ) [ 18 – 20 ]. TMT performance was measured by the time (in seconds) taken to complete the 25-item test, with a difference score (Formula 2 in Fig. 1 ) calculated as the time difference between Trails A and Trails B [ 21 , 22 ]. For the DS, participants were presented with a series of numbers and instructed to repeat them in both forward and backward order. Scoring was based on the sum of the longest sequence correctly recalled in each direction, as outlined in Formula 3 (Fig. 1 ) [ 23 ]. The SRT and CRT tests were conducted on a laptop. Participants responded to an upward-facing triangle for visual SRT (V-SRT) by pressing the up-arrow key as quickly as possible. In the visual CRT (V-CRT), participants responded to either an upward or downward-facing triangle by pressing the corresponding arrow key as accurately and quickly as possible. For the auditory SRT (A-SRT), participants responded to a high-pitched horn by pressing the up-arrow key as quickly as possible. In the auditory CRT (A-CRT), participants responded to either a high- or low-pitched horn by pressing the up- or down-arrow key as appropriate. In the CRT tests, 75% of trials presented frequent stimuli (upward-facing triangle or high-pitched tone), while 25% presented rare stimuli (downward-facing triangle or low-pitched tone) to create an expectancy manipulation that increased task complexity and cognitive load. The CRT score incorporates both speed and accuracy using an inverse-time weighted approach (Formula 5, Fig. 1 ), where higher scores indicate faster reaction times with maintained accuracy. This composite scoring method accounts for speed-accuracy tradeoffs, ensuring that rapid but error-prone responses do not artificially inflate performance scores. Each participant completed all cognitive tests in a single session prior to biomechanical testing. The test battery was administered in fixed order: (1) CIS, (2) TMT A and B, (3) DS, (4) SRT tests (visual then auditory), and (5) CRT tests (visual then auditory). Each SRT condition consisted of 20 trials, and the score was calculate suing the average (Formula 4, Fig. 1 ), while each CRT condition consisted of 40 trials to ensure adequate presentation of both frequent (30 trials) and rare (10 trials) stimuli. Rest periods of 30–60 seconds were provided between tests to minimize fatigue. Cognitive testing was completed on the same day as biomechanical testing, with a minimum 15-minute break between sessions. FIGURE 1 SHOULD APPEAR APPROXIMATELY HERE Participant preparation Kinetic data were collected at 1000 Hz with two force plates (AMTI, Watertown, MA), and kinematic data at 100 Hz using a 17-camera motion capture system (Vicon Motion Systems Inc., Oxford, UK). Participants wore 45 markers following Vicon's Plug-in Gait Full Body Functional Set. Jump cues were triggered by integrating live marker data into MATLAB via Vicon DataStream SDK. Marker data were read in MATLAB at 100Hz, matching the camera frequency. Two markers on the 30cm jumping platform enabled a MATLAB script to determine its position relative to the participant and capture volume. Protocol A standardized warm-up protocol included 3 jogging laps (approximately 12 m each) at a self-selected pace, followed by 10 bodyweight squats, double-leg hops, single-leg hops, and 3 countermovement jumps, with adequate rest between activities. Subjects were then instructed and allowed to practice the drop-jump tasks up to 3 times. For this drop-jump task, subjects jumped forward off a box with both feet, landing simultaneously on bilateral force platforms positioned half their height from the box. Drop jumps required subjects to "jump as high as possible" upon landing, whereas drop lands required a comfortable landing. Trials were repeated if instructions were not followed correctly [ 24 ]. Minimal instructions were provided to minimize performance variability due to verbal cues [ 25 ], and only up to 4 researchers were present to limit crowd influence [ 26 ]. Four conditions were tested: (1) standard (baseline), (2) choice (volitional decision-making), (3) visual, and (4) audio (external cues). The visual and audio conditions were designed to reduce motor planning time by providing probabilistic cues when the subject's pelvis marker crossed the box's edge. Visual cues appeared as an upward-facing triangle on a chest-height screen, while audio cues utilized a horn sound. Both cue types signaled required jump completion, with cue absence indicating no jump necessary (just box landing task). This methodology aimed to elucidate how reduced planning time through external cueing affects movement execution parameters, particularly those relevant to ACL injury risk factors. Three drop-jumps trials were randomized and not blocked by condition, maintaining cognitive demands. A rest period of 30–60 seconds was provided between trials, with fatigue monitored using the Borg Rate of Perceived Exertion scale. FIGURE 2 SHOULD APPEAR APPROXIMATELY HERE Data reduction Force data were filtered using low-pass Butterworth filters with cut-off frequencies of 50Hz. Ground contact time was determined using a 5 N force threshold for foot-strike and foot-off. The center of mass (CoM) was determined using Vicon's Plug-in Gait model. Vertical GRF impulse was calculated by integrating the net vertical ground reaction force with respect to time using the trapezoidal rule throughout each phase [ 27 ]. The first landing phase was analyzed for knee angular components. This phase began at initial ground contact (5 N force threshold) and continued through foot-off [ 28 – 30 ]. Knee angular impulse represents the cumulative effect of joint moment over time. In this research, knee angular impulse in the eccentric phase (KAI ECC ) was calculated from initial ground contact through the lowest point of the CoM, representing the total rotational effect produced as the knee flexes. Knee angular impulse in concentric phase (KAI CON ) was then calculated from the lowest point of the CoM until foot-off, representing the total rotational effect produced as the knee extends during the take-off portion. The knee angular impulse net (KAI NET ) was calculated as the algebraic sum of KAI ECC and KAI CON , representing the total knee moment generated throughout the entire movement. All KAI calculations used the trapezoidal rule for integration and were normalized to body mass. FIGURE 3 SHOULD APPEAR APPROXIMATELY HERE Statistical analysis All statistical analyses were conducted using JASP 0.95.4 (JASP Team, Amsterdam, Netherlands). To identify cognitive variables that distinguish ACLR from control participants, an exploratory binomial logistic regression was performed with group membership as the dependent variable (Control = 1, ACLR = 0). The initial model incorporated all seven cognitive performance measures (CIS, TMT, DS, A-SRT, A-CRT, V-SRT, and V-CRT). A backward elimination procedure was then applied to identify the most parsimonious model. Given that the resulting model had a modest events-per-variable ratio (EPV = 5.3, below the recommended threshold of 10 for logistic regression), this analysis was treated as exploratory and hypothesis-generating. Model performance was evaluated using multiple fit indices, including chi-square tests and Nagelkerke R². To confirm the robustness of the logistic regression findings, the Mann-Whitney U test was conducted for all cognitive variables to examine individual differences across cognitive tests, with effect sizes calculated using the rank biserial correlations. Subsequently, separate 2 (Group: ACLR, Control) × 4 (Condition: Standard, Choice, Audio, Visual) factorial analyses of variance (ANOVAs) were conducted for each KAI phase (ECC, CON, NET) to assess biomechanical differences between groups and across task conditions. Levene's test was used to assess homogeneity of variance across groups and conditions. Visual inspection of Q-Q plots confirmed that the residuals for all three ANOVA models were approximately normal. Levene's tests confirmed homogeneity of variance across groups and conditions for ECC (F = 0.563, p = 0.786), CON (F = 1.058, p = 0.391), and NET (F = 0.334, p = 0.938). These findings support the appropriateness of parametric ANOVA despite the violation of multivariate normality observed in the correlation analysis. When significant main effects were detected, post-hoc pairwise comparisons with Bonferroni correction were performed to control for Type I error inflation. Effect sizes were calculated using partial eta-squared (η²) for ANOVA main effects and interactions, and Cohen's d with 95% confidence intervals for pairwise comparisons. To examine the relationship between cognitive performance and biomechanical measures, we first assessed multivariate normality using the Shapiro-Wilk test. Given that the assumption of multivariate normality was violated (W = 0.921, p = 0.001), Spearman's rank correlation coefficients (rho) were computed between all seven cognitive variables and each of the three KAI phases. The strength of correlations was interpreted using Cohen's guidelines (small: r ≥ 0.10, medium: r ≥ 0.30, large: r ≥ 0.50). Statistical significance was set at p < 0.05 for all analyses. Results Cognitive performance distinguishes ACLR from control groups To identify cognitive variables that distinguish ACLR individuals from matched controls, we conducted an exploratory binomial logistic regression with group membership as the outcome variable. The initial model incorporated all the cognitive performance measures. Following backward elimination, three cognitive measures emerged as significant predictors of group status: CIS, V-SRT, and V-CRT. These variables significantly enhanced the model's predictive power relative to the null model (χ²(3) = 55.090, p < 0.001), demonstrating their collective ability to reliably differentiate between the ACLR and Control groups. The final model accounted for 25.8% of the variance in group status (Nagelkerke R² = 0.258). All three retained cognitive variables exhibited significant predictive capacity for group status. The V-SRT Score (β = 1.205, Odds Ratio = 3.338, z = 5.145, p < 0.001) revealed that for each unit increase, the odds of belonging to the Control group (versus the ACLR group) increased by a factor of 3.338, indicating that slower visual simple reaction times were associated with control group membership. Conversely, the V-CRT Score (β = -4.520, Odds Ratio = 0.011, z = -5.025, p < 0.001) indicated that each unit increase was associated with a 98.9% decrease in the odds of being in the Control group (1–0.011), suggesting that higher V-CRT scores (indicating faster complex reaction times) were associated with ACLR group membership. Lastly, the CIS (β = -0.038, Odds Ratio = 0.963, z = -2.164, p = 0.030) showed that for every unit increase in the interference score, the odds of belonging to the Control group decreased by 3.7% (1–0.963), indicating poorer interference control in the ACLR group. FIGURE 4 SHOULD APPEAR APPROXIMATELY HERE Given the exploratory nature of this analysis and the modest events-per-variable ratio (EPV = 5.3, below the recommended threshold of 10), being inconclusive [ 31 ]. We confirmed these findings using Mann-Whitney U test and a rank biserial r to quantify the magnitude of group differences (Table 1 ). ACLR participants demonstrated significantly faster V-SRT than controls, with a moderate-to-large effect size. ACLR participants also showed faster V-CRT than controls, indicating a moderate effect. However, ACLR participants exhibited a non-significant poorer interference control compared to controls. These findings indicate that ACLR individuals possess a distinct cognitive profile characterized by enhanced visual reactive capabilities compared to matched controls. Table 1 Descriptive Statistics and Group Comparisons for Cognitive Performance Variables. Cognitive Variable ACLR Control Mann-Whitney U p-value Rank Biserial r CIS 14.38 ± 5.64 12.61 ± 10.04 8064 .829 0.016 TMT 21.81 ± 17.13 21.05 ± 17.49 8448 .666 -0.031 DS 16.63 ± 2.68 17.19 ± 3.79 7776 .480 0.051 A-SRT 2.62 ± 0.59 2.67 ± 0.35 7936 .666 0.031 A-CRT 1.67 ± 0.23 1.69 ± 0.13 8896 .235 -0.086 V-SRT 4.04 ± 0.66 4.48 ± 0.74 4992 < .001*** 0.391 V-CRT 2.22 ± 0.20 2.14 ± 0.16 10624 < .001*** -0.297 Notes: *** p < 0.001. CIS = Cognitive Interference Score; TMT = Trail Making Test difference score (B-A); DS = Digit Span total score; A-SRT = Auditory Simple Reaction Time; A-CRT = Auditory Complex Reaction Time; V-SRT = Visual Simple Reaction Time; V-CRT = Visual Complex Reaction Time. Higher scores indicate better performance for all variables except TMT (where lower scores indicate better executive function performance) and simple reaction times. Rank biserial correlation represents the effect size for Mann-Whitney U tests. Table 1 SHOULD APPEAR APPROXIMATELY HERE Biomechanical Differences Between Groups We conducted a 2 (Group: ACLR, Control) × 4 (Condition: Standard, Choice, Visual, Audio) factorial analysis of variance (ANOVA) for each KAI phase. This analysis examined whether group and condition factors independently or interactively influenced knee biomechanics during drop-jump landings. Eccentric Knee Angular Impulse The ANOVA conducted for KAI ECC revealed a significant main effect of Group (F(1, 248) = 8.71, p = 0.003, η² = .034) and Condition (F(3, 248) = 5.01, p = 0.002, η² = .057). The interaction between Group and Condition was not significant (F(3, 248) = 0.42, p = 0.738, η² = .005), indicating that both groups responded similarly to the different task conditions. Levene's test confirmed homogeneity of variance across groups and conditions (F(7, 248) = 0.56, p = 0.786). Post-hoc pairwise comparisons with Bonferroni correction indicated that the ACLR group (M = 0.324, SD = 0.078) exhibited significantly lower KAI ECC compared to the Control group (M = 0.350, SD = 0.072). This suggests that ACLR participants generated less KAI ECC during the landing phase across all conditions, representing a small-to-moderate effect size difference (Mean Difference = -0.027, p = 0.003, Cohen's d = -0.37). Regarding condition effects, post-hoc comparisons revealed that the Auditory-cued condition (M = 0.359, SD = 0.073) produced significantly higher KAI ECC than both the Standard condition (M = 0.318, SD = 0.083; p = 0.009, Cohen's d = 0.57) and the Choice condition (M = 0.322, SD = 0.074; p = 0.025, Cohen's d = 0.51). No significant differences emerged between other condition pairs (all p > 0.20). Concentric and Net Knee Angular Impulse The analysis of KAI CON revealed non-significant main effects for Group (F(1, 248) = 0.58, p = 0.447, η² = .002) and Condition (F(3, 248) = 0.03, p = 0.993, η² < .001). The interaction between Group and Condition was also non-significant (F(3, 248) = 0.38, p = 0.768, η² = .005). Levene's test indicated homogeneity of variance (F(7, 248) = 1.06, p = 0.391). For KAI NET , the ANOVA revealed non-significant main effects for Group (F(1, 248) = 1.44, p = 0.231, η² = .006) and Condition (F(3, 248) = 1.75, p = 0.158, η² = .021). The interaction between Group and Condition was not significant (F(3, 248) = 0.01, p = 0.998, η² < .001). FIGURE 5 SHOULD APPEAR APPROXIMATELY HERE Figure 5 . Differential Sensitivity of KAI Phases to Condition Effects in ACLR and Control Participants. Data presented as mean ± SEM. *p < 0.05, **p < 0.01. ACLR (open circles), n = 16; Control (filled circles), n = 16. (A) Eccentric KAI shows significant group and condition effects. (B) Concentric KAI shows no significant effects. (C) Net KAI shows no significant effects. KAIECC = Eccentric Knee Angular Impulse; KAICON = Concentric Knee Angular Impulse; KAINET = Net Knee Angular Impulse. These results demonstrate that KAI ECC exhibited sensitivity to both Group and Condition effects, with ACLR participants consistently generating lower impulse across all task conditions. External auditory cues resulted in increased eccentric knee control during landing compared to self-initiated movements. In contrast, KAI CON and KAI NET remained relatively consistent across groups and conditions, suggesting that biomechanical adaptations in these ACLR sample individuals are phase-specific rather than global. Relationship Between Cognitive Performance and Biomechanical Measures To examine whether cognitive performance is related to biomechanical outcomes, we computed Spearman's rank correlation coefficients (W = 0.921, p = 0.001) between the seven cognitive variables and each KAI phase across all participants (Table 2 ). Table 2 Spearman Rank Correlations Between Cognitive Variables and Knee Angular Impulse Phases. KAI ECC KAI CON KAI NET r p r p r p CIS 0.016 0.803 -0.107 0.089 -0.052 0.404 TMT -0.028 0.655 -0.021 0.733 -0.036 0.562 DS 0.144 0.021* 0.027 0.668 0.125 0.046* A-SRT -0.231 < .001*** -0.073 0.242 -0.170 0.007** A-CRT 0.096 0.126 0.285 < .001*** 0.219 < .001*** V-SRT 0.098 0.118 0.169 0.007** 0.152 0.015* V-CRT -0.106 0.092 0.123 0.05* 0.009 0.887 Notes: * p < 0.05, ** p < 0.01, *** p < 0.001 Table 2 SHOULD APPEAR APPROXIMATELY HERE Critically, the three cognitive variables that distinguished groups in the logistic regression (V-SRT, V-CRT, CIS) showed minimal and non-significant correlations with KAI ECC . This pattern suggests that the cognitive measures distinguishing ACLR from control groups and eccentric knee biomechanics represent largely independent constructs. Furthermore, the absence of significant Group × Condition interactions across all KAI phases (all p > 0.74) indicates that ACLR and control participants responded similarly to varying task demands despite their distinct cognitive profiles. These findings suggest that while ACLR individuals exhibit both cognitive and biomechanical differences compared to controls, these differences appear to reflect parallel adaptations rather than integrated or causally linked processes. The near-zero correlations between the group-distinguishing cognitive variables and the group-differentiating biomechanical measure (KAI ECC ) provide strong evidence for this dissociation. This implies that cognitive and motor control adaptations following ACL reconstruction may occur through separate, albeit concurrent, mechanisms. Discussion This study investigated the impact of cognitive performance on motor control during multiple drop-jump tasks in individuals with ACLR and healthy controls. Our findings revealed several key points that contribute to our understanding of both cognitive and biomechanical aspects of ACL injury: Cognitive performance effectively differentiated between participants with and without ACLR. Specifically, better scores in visual reaction time tests (both simple and complex) and poor cognitive interference are related to the ACLR group, highlighting a differentiated cognitive component present in participants with ACLR. Individuals with ACLR surgery demonstrate lower KAI ECC during the landing phase in the drop jump. This difference suggests that these individuals may employ unique protective strategies for their knees, particularly during the high-impact moment of landing. Cognitive demands significantly affect knee mechanics during jumping. Audio-cued jumps led to increased eccentric knee control during landing compared to planned jumps, suggesting an adaptive neuromechanical strategy where increased attentional demands result in extended processing time during the eccentric phase. A critical and theoretically significant finding of this study is that cognitive and biomechanical differences in ACLR individuals appear to represent parallel rather than integrated adaptations. The three cognitive variables that distinguished the groups (V-SRT, V-CRT, CIS) showed minimal, non-significant correlations with KAI ECC , the only biomechanical measure that showed group differences. Furthermore, the absence of significant interactions across all KAI phases (all p > 0.74) indicates that ACLR and control participants responded similarly to varying task demands despite their distinct cognitive profiles. This dissociation may suggest that ACL reconstruction may trigger adaptations in multiple systems, cognitive processing, and motor control, through separate, albeit concurrent, mechanisms rather than through a single integrated pathway. From a rehabilitation perspective, this finding implies that cognitive and biomechanical interventions may need to target these systems independently rather than assuming that improvements in one domain will automatically transfer to the other. The differentiated cognitive profile observed in ACLR participants reveals a complex adaptation pattern in neurocognitive function. While the ACLR group demonstrated superior performance in both simple and complex visual reaction time tasks, they showed impaired cognitive interference control. This pattern of enhanced reactive capabilities alongside reduced interference control suggests potential compensatory mechanisms in the central nervous system following ACL injury. Previous studies have typically reported global cognitive deficits in ACLR populations [ 5 , 32 , 3 , 33 ], making our finding of enhanced reaction times particularly noteworthy. This enhancement might reflect neural reorganization following injury, potentially as an adaptation to maintain rapid response capabilities despite altered proprioceptive feedback. However, the increased susceptibility to cognitive interference could indicate a trade-off in attentional resources, where improved reactive speed comes at the cost of reduced ability to filter irrelevant information, similar to findings in other injury adaptation contexts [ 12 , 34 ]. On the other hand, examining the biomechanical aspects of our findings, the observed reduction in KAI ECC during landing in ACLR individuals provides insight into long-term movement adaptations following reconstruction. This decreased eccentric loading suggests a persistent protective strategy, even in individuals who have completed rehabilitation and returned to sport. Similar protective mechanisms have been documented in previous studies [ 35 – 37 ], but our findings specifically identify the eccentric phase as the target of this adaptation. The selective nature of this modification (occurring only during the eccentric phase without significant changes in concentric or net impulse) suggests a sophisticated neural control strategy rather than global movement inhibition. This specificity might represent an unconscious optimization between protecting the reconstructed ligament and maintaining functional performance [ 5 , 7 , 38 , 32 , 24 , 12 , 33 ]. Further analysis of the sensorimotor aspects revealed that audio-cued jumps elicited increased eccentric knee control compared to planned jumps, providing important insights about sensorimotor integration in dynamic tasks. This enhancement of eccentric control under audio cueing may reflect not only the influence of sensory modality but also the temporal and rhythmic properties of auditory cues. Recent evidence suggests that the temporal structure of auditory cues, particularly rhythmic properties, plays a critical role in regulating movement timing and neuromuscular coordination during landing tasks [ 39 ]. The additional processing time required for auditory stimuli, combined with their temporal structure, might facilitate more complete motor planning and enhance eccentric control mechanisms. This finding aligns with recent work showing that slower processing can sometimes lead to more controlled movement execution [ 24 ]. Given these findings, the role of cognitive function emerges as a critical consideration in ACL injury risk and rehabilitation. The distinctive cognitive profile observed in ACLR participants (characterized by superior reaction times but impaired interference control) may represent more than just a post-injury adaptation. This pattern could potentially identify individuals at higher risk for non-contact ACL injuries, particularly in situations requiring sustained attention amid distractions, which is common in sport environments. Recent studies have shown that neurocognitive deficits precede and may predict ACL injury risk [ 5 , 6 , 32 , 3 , 4 ], with specific impairments in visuospatial attention and processing speed increasing injury odds by up to 3-fold [ 7 , 40 ]. The relationship between cognitive processing and movement control aligns with emerging evidence that decreased neurocognitive performance correlates with higher-risk biomechanical patterns during dynamic tasks [ 5 , 9 , 4 , 33 ]. Furthermore, studies have demonstrated that athletes with lower cognitive performance scores show decreased dynamic postural control and increased landing forces, particularly during dual-task conditions [ 41 – 43 ]. The parallel nature of cognitive and biomechanical adaptations has important clinical implications for ACL rehabilitation. Current rehabilitation protocols typically focus predominantly on restoring physical function, with cognitive factors receiving less systematic attention. Our findings suggest that rehabilitation programs should incorporate both cognitive training (e.g., improving interference control, enhancing rapid decision-making under pressure) and biomechanical retraining (e.g., enhancing landing mechanics, increasing eccentric knee loading capacity) as complementary rather than redundant components. The dissociation between cognitive and biomechanical measures implies that addressing movement patterns alone may not resolve cognitive adaptations, and vice versa. Specifically, interventions targeting visual processing speed and interference control, perhaps through sport-specific reactive drills, dual-task training, or neurocognitive exercises, may be warranted alongside traditional strength and movement retraining. Additionally, the enhanced reactive capabilities observed in ACLR individuals, while potentially compensatory, could be leveraged as a strength in return-to-sport programming if appropriately channeled [ 6 , 32 , 11 ]. While this study provides novel insights through its comprehensive assessment of both cognitive and biomechanical parameters across multiple jump conditions, certain limitations must be considered when interpreting these results. First, the logistic regression analysis, while revealing meaningful cognitive distinctions between groups, had a modest events-per-variable ratio (EPV = 5.3, below the recommended 10), being inconclusive [ 31 ] and should therefore be interpreted as exploratory and hypothesis-generating rather than definitive. The confirmation of these findings through independent statistical tests (Mann-Whitney U tests and effect size calculations) provides additional confidence, but replication in larger samples is needed. However, considering our sample size, future studies would include larger samples to confirm null findings, particularly for KAICON and KAINET, where no group differences emerged. Second, our sample exhibited heterogeneity in injury characteristics, including variation in the number of ACL tears (12 single tears, 4 multiple tears), graft types (patellar, hamstring, gracilis, synthetic), time since surgery (range: 17–64 months, M = 32.81 ± 15.77 months), and sport backgrounds (10 different sports). While this heterogeneity enhances external validity by representing the diverse ACLR population, it may have increased within-group variability and reduced statistical power to detect effects. Future research with larger, more homogeneous samples could clarify whether specific injury or surgical characteristics moderate the cognitive-biomechanical relationships observed here. Third, the cross-sectional design precludes determination of whether the observed cognitive and biomechanical differences preceded injury, resulted from injury and reconstruction, or reflect ongoing compensatory adaptations. Longitudinal research tracking individuals from pre-injury through return-to-sport could clarify the temporal relationships and causal mechanisms underlying these observations. Additionally, while we carefully controlled for time since return-to-sport (minimum 6 months), participants were not formally matched by sport type or competitive level, which may have contributed to heterogeneity in both cognitive and biomechanical performance. Fourth, although our findings suggest parallel rather than integrated cognitive-biomechanical adaptations, we cannot rule out the possibility that more complex, non-linear relationships exist that were not captured by correlation analyses. Advanced analytical approaches such as machine learning or dynamical systems analysis might reveal subtle interactions between cognitive and motor systems that are not apparent in traditional statistical frameworks. Based on these findings and limitations, several key research directions warrant investigation. Longitudinal studies are urgently needed to examine whether the observed cognitive profile represents a pre-existing risk factor for ACL injury or develops as a consequence of injury. This aligns with recent calls for prospective studies investigating cognitive function as a predictor of injury risk [ 33 ]. Additionally, investigation of targeted interventions incorporating both cognitive and motor training could help optimize injury prevention strategies, particularly given evidence that dual-task training can improve both cognitive performance and movement control [ 44 , 45 ]. Moreover, examination of these cognitive-motor interactions under more complex, sport-specific conditions would enhance ecological validity and clinical applicability, potentially leading to more effective screening tools for injury risk. Future research should also explore the development of cognitive training protocols specifically designed to enhance interference control while maintaining quick reaction times, as this combination appears particularly relevant to injury risk and prevention. Conclusions This study provides novel evidence that individuals with ACLR exhibit distinct cognitive and biomechanical adaptations that appear to develop through parallel rather than integrated mechanisms. Three cognitive variables, V-SRT, V-CRT and CIS successfully distinguished ACLR from control groups, with ACLR individuals demonstrating enhanced visual reactive capabilities alongside impaired interference control. Biomechanically, ACLR individuals showed persistent reductions in eccentric knee angular impulse during landing, suggesting protective movement strategies that remain even after return to sport. Critically, minimal correlations between group-distinguishing cognitive variables and eccentric knee biomechanics, combined with the absence of interactions, indicate that these cognitive and biomechanical differences represent independent adaptations rather than causally linked processes. These findings have important implications for ACL rehabilitation and injury prevention. The parallel nature of cognitive and biomechanical adaptations suggests that rehabilitation protocols should incorporate targeted interventions for both systems independently. Cognitive training focused on improving interference control and maintaining rapid decision-making, combined with biomechanical retraining to optimize eccentric loading strategies, may be more effective than assuming improvements in one domain will transfer to the other. Future research should examine whether targeted dual-domain interventions, addressing cognitive and biomechanical systems as separate but complementary targets, can reduce re-injury risk and improve return-to-sport outcomes in ACLR populations. Longitudinal studies are particularly needed to determine whether the observed cognitive profile represents a pre-existing injury risk factor or a post-injury adaptation. Declarations Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by XXX, XXX, XXX, XXX, XXX, and XXX. The first draft of the manuscript was written by XXX, XXX, XXX, XXX, XXX, XXX, XXX, and XXX, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Ethics approval This study was conducted in accordance with the principles outlined in the Declaration of Helsinki. Approval was granted by the XXXXXXXX Institutional Review Board (approval number 19-242 EP 1808). Data availability statement The dataset used and analyzed during the current study is available from the corresponding author on reasonable request. References Mejane, J., Faubert, J., Romeas, T., & Labbe, D. R. (2019). The combined impact of a perceptual–cognitive task and neuromuscular fatigue on knee biomechanics during landing. The Knee, 26 (1), 52-60. Scharfen, H.-E., & Memmert, D. (2019). The relationship between cognitive functions and sport-specific motor skills in elite youth soccer players. Frontiers in psychology, 10 , 817. Swanik, C. B. (2015). 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviews received at journal 30 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers invited by journal 04 Mar, 2026 Editor assigned by journal 27 Feb, 2026 Submission checks completed at journal 27 Feb, 2026 First submitted to journal 25 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8971155","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602287595,"identity":"61c0b19a-8e9d-4fbd-9669-c8811cd8a19b","order_by":0,"name":"Keven Santamaria-Guzman","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"","firstName":"Keven","middleName":"","lastName":"Santamaria-Guzman","suffix":""},{"id":602287596,"identity":"2815912f-4426-44d1-85ff-f165eae84e22","order_by":1,"name":"Hillary Holmes","email":"","orcid":"","institution":"High Point University","correspondingAuthor":false,"prefix":"","firstName":"Hillary","middleName":"","lastName":"Holmes","suffix":""},{"id":602287597,"identity":"31d5b00a-1523-4d25-8c9f-cba8311b4932","order_by":2,"name":"Jerad Kosek","email":"","orcid":"","institution":"University of Evansville","correspondingAuthor":false,"prefix":"","firstName":"Jerad","middleName":"","lastName":"Kosek","suffix":""},{"id":602287598,"identity":"a3da409b-0253-4eeb-927d-505086cd3f1e","order_by":3,"name":"Brandon Peoples","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"","firstName":"Brandon","middleName":"","lastName":"Peoples","suffix":""},{"id":602287599,"identity":"17a1d2ce-fdd4-4eab-9c13-a9e3a48a30ec","order_by":4,"name":"Kenneth Harrison","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"","lastName":"Harrison","suffix":""},{"id":602287600,"identity":"d4235c98-319f-426a-8e49-fc5c9235126f","order_by":5,"name":"Silvia Campos-Vargas","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"","firstName":"Silvia","middleName":"","lastName":"Campos-Vargas","suffix":""},{"id":602287601,"identity":"b205cdca-1266-4ec9-acc4-6e67a896c263","order_by":6,"name":"Wendi Weimar","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"","firstName":"Wendi","middleName":"","lastName":"Weimar","suffix":""},{"id":602287602,"identity":"543e0f64-d925-425a-a3b8-bb2f6f8f974f","order_by":7,"name":"Kristina Neely","email":"","orcid":"","institution":"Auburn University","correspondingAuthor":false,"prefix":"","firstName":"Kristina","middleName":"","lastName":"Neely","suffix":""},{"id":602287603,"identity":"ea5043a8-d296-4ba7-98eb-48673ea39868","order_by":8,"name":"Francisco Siles-Canales","email":"","orcid":"","institution":"University of Costa Rica","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Siles-Canales","suffix":""},{"id":602287604,"identity":"0ea17eb4-df81-46f7-9731-fd827704990f","order_by":9,"name":"Jaimie Roper","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACxgYwZcPAIIEiQFhLGglaoOAwCVqYZx8+9uDjnvPyBrd7zD4XMNjIbjhAyGF9aemGM57dNtxw54zx7BkMacaEtfTwmEnzHLjNuOFGjjEzD8PhROK0/Dlwzh6q5T+RWhgOHEiEajlAjBa2dMOeA8nJM+8cK2bmMUg2nklIi2EP87EHPw7Y2fbdbt7MzFNhJ9tHUEsDAxsS14CAchCQZ0DRMgpGwSgYBaMACwAAupFC4EESeMgAAAAASUVORK5CYII=","orcid":"","institution":"Auburn University","correspondingAuthor":true,"prefix":"","firstName":"Jaimie","middleName":"","lastName":"Roper","suffix":""}],"badges":[],"createdAt":"2026-02-25 20:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8971155/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8971155/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104405257,"identity":"9db7e617-9150-4028-b596-8109d3a3a81d","added_by":"auto","created_at":"2026-03-11 12:22:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25523,"visible":true,"origin":"","legend":"\u003cp\u003eFormulas for Calculating Individual Cognitive Test Scores\u003c/p\u003e\n\u003cp\u003eNotes: Formula 1: Cognitive Interference Score (CIS) derived from the Stroop Color and Word Test, calculated from the number of correct verbal responses within 45 seconds in color-naming and incongruent (color word) conditions. Formula 2: Trail Making Test (TMT) difference score, calculated as the time difference (in seconds) between Trails B and Trails A performance on the 25-item test. Formula 3: Digit Span (DS) total score, calculated as the sum of the longest sequence correctly recalled in both forward (\u003csup\u003eF\u003c/sup\u003e) and backward (\u003csup\u003eB\u003c/sup\u003e) order. Formula 4: Simple Reaction Time (SRT) average score for both visual (responding to upward-facing triangle with up-arrow key) and auditory (responding to high-pitched horn with up-arrow key) modalities. Formula 5: Complex Reaction Time (CRT) score incorporating both speed and accuracy using an inverse-time weighted approach for visual (responding to upward/downward-facing triangles) and auditory (responding to high/low-pitched horns) modalities, with 75% frequent and 25% rare stimuli presentations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8971155/v1/0ed8e42b065da1c2bd66d249.png"},{"id":104337832,"identity":"da13c25d-b69c-4d02-abf2-a9d621572fbf","added_by":"auto","created_at":"2026-03-10 16:16:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90810,"visible":true,"origin":"","legend":"\u003cp\u003eThe four drop-jump conditions: (1) standard, (2) choice, (3) visual, and (4) auditory.\u003c/p\u003e\n\u003cp\u003eParticipants jumped forward off a box with both feet, landing simultaneously on bilateral force platforms positioned half their height in four conditions. (1) Standard; participants to jump as high as possible upon landing without additional constraints once they receive the instruction to jump; (2) Choice; participants decided whether to complete a maximal jump or perform a comfortable landing; (3) Visual; probabilistic visual cue condition where an upward-facing triangle appeared on a chest-height screen when the pelvis marker crossed the box edge, signaling required jump completion, with cue absence indicating a landing-only task; (4) Audio; probabilistic auditory cue condition where a horn sound was presented when the pelvis marker crossed the box edge, signaling required jump completion, with cue absence indicating a landing-only task.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8971155/v1/70c89100c4501dc38931d793.png"},{"id":104337828,"identity":"c861ba3d-9594-4ff1-a544-4aab2beae241","added_by":"auto","created_at":"2026-03-10 16:16:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":120535,"visible":true,"origin":"","legend":"\u003cp\u003eDrop-jump phase identification for knee angular impulse calculation using ground reaction force and center of mass data.\u003c/p\u003e\n\u003cp\u003eNotes: The figure illustrates the key temporal landmarks used to calculate the phases of knee angular impulse. Initial ground contact (marked by 5 N force threshold) defines the start of the eccentric phase (navy blue area). The lowest point of the center of mass (CoM) marks the transition between eccentric and concentric phases. Foot-off (marked by force dropping below 5 N threshold) defines the end of the concentric phase (orange area). The red cicle clearly distinguishes the CoM lowest point from the initial ground contact marker. KAI\u003csup\u003eECC\u003c/sup\u003e = Eccentric Knee Angular Impulse (ground contact to lowest CoM); KAI\u003csup\u003eCON\u003c/sup\u003e = Concentric Knee Angular Impulse (lowest CoM to foot-off); KAI\u003csup\u003eNET\u003c/sup\u003e = Net Knee Angular Impulse (algebraic sum of KAI\u003csup\u003eECC\u003c/sup\u003e and KAI\u003csup\u003eCON\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8971155/v1/a4cb2deef3dfa2e732f17f5b.png"},{"id":104337831,"identity":"cd8b3e59-46f2-4ea9-88df-f846a67a7f5d","added_by":"auto","created_at":"2026-03-10 16:16:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43664,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic Regression Predicting Group Membership (Control vs. ACLR).\u003c/p\u003e\n\u003cp\u003eNotes: Blue dots represent Control participants (n=16), orange dots represent ACLR participants (n=16), with the black line showing the logistic prediction curve and gray shading indicating 95% confidence intervals. Arrows indicate the direction of increased likelihood for each group membership. V-SRT = Visual Simple Reaction Time; V-CRT = Visual Complex Reaction Time; CIS = Cognitive Interference Score.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8971155/v1/13d6a9fcf9a1bbe8daff8902.png"},{"id":104405980,"identity":"331465bb-ff7e-4cbf-896f-6b1f67155071","added_by":"auto","created_at":"2026-03-11 12:24:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":67643,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Sensitivity of KAI Phases to Condition Effects in ACLR and Control Participants.\u003c/p\u003e\n\u003cp\u003eNotes: Orange circles/SD bars represent ACLR participants (n=16), blue circles/SD bars represent Control participants (n=16). *p \u0026lt; 0.05. Panel (a) Eccentric Knee Angular Impulse (KAI\u003csup\u003eECC\u003c/sup\u003e) shows significant main effects for Group (F=8.71, p = 0.003, η²=.034; ACLR \u0026lt; Control, Cohen's d=-0.37) and Condition (F=5.01, p = 0.002, η²=.057; Audio \u0026gt; Standard, p = 0.009, d=0.57; Audio \u0026gt; Choice, p = 0.025, d=0.51), with no significant interaction (p = 0.738). Panel (b) Concentric Knee Angular Impulse (KAI\u003csup\u003eCON\u003c/sup\u003e) shows no significant effects for Group (p = 0.447), Condition (p = 0.993), or interaction (p = 0.768). Panel (c) Net Knee Angular Impulse (KAI\u003csup\u003eNET\u003c/sup\u003e) shows no significant effects for Group (p = 0.231), Condition (p = 0.158), or interaction (p = 0.998). Results indicate ACLR individuals exhibited persistent reductions in eccentric knee loading across all conditions, while auditory cueing increased eccentric control in both groups. N.m.s = Newton-meters per second normalized to body mass.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8971155/v1/b53d49692cc703c613b0521d.png"},{"id":104409599,"identity":"201e9a04-6325-4286-9910-322bdd92ffe2","added_by":"auto","created_at":"2026-03-11 12:46:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1117324,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8971155/v1/ad13657c-cf30-4628-a1aa-5aff8a1d0c16.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eVisual Processing and Interference Performance Influences on Knee Angular Impulse in ACLR Individuals: A Cognitive-Biomechanical Analysis of Drop-Jumps\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe role of cognitive processes is increasingly recognized for its importance in athletic movements, particularly in tasks requiring rapid decision-making and direction changes. These cognitive processes are essential in motor planning and execution [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Critical factors such as reaction time, processing speed, and adaptability to visual stimuli have been linked to injury risk, with slower cognitive responses associated with a higher likelihood of injury [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, cognitive abilities have emerged as significant for understanding injuries such as Anterior Cruciate Ligament (ACL) tears. Research has shown that athletes who suffer non-contact ACL injuries often exhibit longer reaction times, slower visual processing speeds, and lower memory scores [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. ACL injury treatment options include surgical reconstruction and rehabilitation, with costs ranging from \u003cspan\u003e$\u003c/span\u003e20,000 to \u003cspan\u003e$\u003c/span\u003e50,000 per case. With 100,000-200,000 ACL ruptures annually in the US, the total yearly cost reaches \u003cspan\u003e$\u003c/span\u003e2\u0026ndash;10\u0026nbsp;billion [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Understanding the role of cognitive function in shaping motor behaviors is crucial for developing effective strategies and potentially reducing the incidence of ACL injury.\u003c/p\u003e \u003cp\u003eFor Individuals with ACL injuries, impaired cognitive abilities can adversely affect motor planning, coordination, and reaction times, which in turn influence how the body responds to knee moments during dynamic activities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For instance, decreased cognitive performance can lead to slower responses to sudden changes in movement or direction, increasing the risk of improper knee joint alignment and heightened mechanical stress.\u003c/p\u003e \u003cp\u003eThis relationship underscores that lower cognitive performance can compromise the effectiveness of neuromuscular control, making individuals more susceptible to excessive knee moments and potentially exacerbating the risk of injury or re-injury [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, limited research explores how cognitive processes interact with biomechanics in those who have undergone ACL reconstruction (ACLR). Post-injury, the body often modifies movement patterns due to changes in sensorimotor control, which may also affect cognitive processing as the brain integrates new sensory feedback [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the same way, drop-jump tasks are particularly relevant for ACL injury research as they replicate the high-impact landing mechanics commonly associated with non-contact ACL injuries during deceleration and change-of-direction movements in sport [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These tasks allow systematic examination of neuromuscular control strategies under varying cognitive demands while maintaining ecological validity for sport-related injury mechanisms [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to investigate cognitive performance and biomechanical characteristics in individuals with ACLR compared to controls during multiple drop-jump tasks. Additionally, it seeks to explore the relationship between cognitive function and knee angular impulse (KAI) differences between groups. We hypothesize that: (1) specific cognitive functions will significantly distinguish individuals who have undergone ACLR from controls, (2) significant differences in KAI will exist between ACLR and control groups, and (3) these cognitive and biomechanical differences will be related, reflecting integrated neuromuscular adaptations following ACL reconstruction. This study contributes to the existing literature by elucidating the relationship between cognitive function and biomechanical performance post-ACLR, with potential implications for rehabilitation strategies and performance protocols.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThirty-two females participated in this study (16 ACLR, 16 Control). The ACLR group (age\u0026thinsp;=\u0026thinsp;20.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70 years, height\u0026thinsp;=\u0026thinsp;1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06 m, mass\u0026thinsp;=\u0026thinsp;67.54\u0026thinsp;\u0026plusmn;\u0026thinsp;9.10 kg) and Control group (CTRL) (age\u0026thinsp;=\u0026thinsp;20.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09 years, height\u0026thinsp;=\u0026thinsp;1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07 m, mass\u0026thinsp;=\u0026thinsp;62.98\u0026thinsp;\u0026plusmn;\u0026thinsp;8.79 kg) were well-matched with no significant differences in age (t(30) = -0.124, p\u0026thinsp;=\u0026thinsp;0.902), height (t(30)\u0026thinsp;=\u0026thinsp;0.596, p\u0026thinsp;=\u0026thinsp;0.556), or mass (t(30)\u0026thinsp;=\u0026thinsp;1.443, p\u0026thinsp;=\u0026thinsp;0.159). All participants were comfortable jumping from a 1ft box. Of ACLR participants, 12 had a single tear, while 4 had multiple contralateral tears (2 double, 2 triple). All underwent reconstruction using various grafts (8 patellar, 5 hamstring, 1 gracilis, 1 artelon synthetic, 1 unknown). Participants had their last ACL tear 32.81\u0026thinsp;\u0026plusmn;\u0026thinsp;15.77 months ago, completed 6.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18 months of rehabilitation, and had a minimum of 6 months post-return to play clearance. Exclusion criteria were based on the PAR-Q assessments [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Participants were involved in various sports, including Basketball (4), Cross Country (2), Crossfit (1), Gymnastics (1), Lacrosse (2), Running (1), Soccer (8), Softball (2), Tennis and Badminton (2), Track and Field (5) and Volleyball (4). Informed consent was obtained, and the study was approved by the University's Institutional Review Board.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSurveys\u003c/h2\u003e \u003cp\u003eParticipants completed an online survey assessing current and past sports participation, injury history, ACL reconstruction details, osteoarthritis status, and concussion history. Data included sport types, participation level, training intensity, number of ACL tears, surgeries, graft types, injury mechanisms, therapy details, and other injuries like sprains and strains.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCognitive testing\u003c/h3\u003e\n\u003cp\u003eAll cognitive tests were administered consistently by the same researcher in a non-distracting environment. Participants completed the Stroop Color and Word Test, the Trail Making Test A \u0026amp; B (TMT), the Digit Span Memory Test (DS), and computerized simple (SRT) and complex reaction time (CRT) tests for both visual and auditory stimuli.\u003c/p\u003e \u003cp\u003e Performance on the Stroop Color and Word Test was quantified by the number of correct verbal responses provided within a 45-second time frame in both the color-naming and incongruent conditions. The cognitive interference score (CIS) was calculated using the formula 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. TMT performance was measured by the time (in seconds) taken to complete the 25-item test, with a difference score (Formula 2 in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) calculated as the time difference between Trails A and Trails B [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For the DS, participants were presented with a series of numbers and instructed to repeat them in both forward and backward order. Scoring was based on the sum of the longest sequence correctly recalled in each direction, as outlined in Formula 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe SRT and CRT tests were conducted on a laptop. Participants responded to an upward-facing triangle for visual SRT (V-SRT) by pressing the up-arrow key as quickly as possible. In the visual CRT (V-CRT), participants responded to either an upward or downward-facing triangle by pressing the corresponding arrow key as accurately and quickly as possible. For the auditory SRT (A-SRT), participants responded to a high-pitched horn by pressing the up-arrow key as quickly as possible. In the auditory CRT (A-CRT), participants responded to either a high- or low-pitched horn by pressing the up- or down-arrow key as appropriate. In the CRT tests, 75% of trials presented frequent stimuli (upward-facing triangle or high-pitched tone), while 25% presented rare stimuli (downward-facing triangle or low-pitched tone) to create an expectancy manipulation that increased task complexity and cognitive load.\u003c/p\u003e \u003cp\u003eThe CRT score incorporates both speed and accuracy using an inverse-time weighted approach (Formula 5, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), where higher scores indicate faster reaction times with maintained accuracy. This composite scoring method accounts for speed-accuracy tradeoffs, ensuring that rapid but error-prone responses do not artificially inflate performance scores. Each participant completed all cognitive tests in a single session prior to biomechanical testing. The test battery was administered in fixed order: (1) CIS, (2) TMT A and B, (3) DS, (4) SRT tests (visual then auditory), and (5) CRT tests (visual then auditory). Each SRT condition consisted of 20 trials, and the score was calculate suing the average (Formula 4, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), while each CRT condition consisted of 40 trials to ensure adequate presentation of both frequent (30 trials) and rare (10 trials) stimuli. Rest periods of 30\u0026ndash;60 seconds were provided between tests to minimize fatigue. Cognitive testing was completed on the same day as biomechanical testing, with a minimum 15-minute break between sessions.\u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e SHOULD APPEAR APPROXIMATELY HERE\u003c/p\u003e\n\u003ch3\u003eParticipant preparation\u003c/h3\u003e\n\u003cp\u003eKinetic data were collected at 1000 Hz with two force plates (AMTI, Watertown, MA), and kinematic data at 100 Hz using a 17-camera motion capture system (Vicon Motion Systems Inc., Oxford, UK). Participants wore 45 markers following Vicon's Plug-in Gait Full Body Functional Set. Jump cues were triggered by integrating live marker data into MATLAB via Vicon DataStream SDK. Marker data were read in MATLAB at 100Hz, matching the camera frequency. Two markers on the 30cm jumping platform enabled a MATLAB script to determine its position relative to the participant and capture volume.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProtocol\u003c/h2\u003e \u003cp\u003eA standardized warm-up protocol included 3 jogging laps (approximately 12 m each) at a self-selected pace, followed by 10 bodyweight squats, double-leg hops, single-leg hops, and 3 countermovement jumps, with adequate rest between activities. Subjects were then instructed and allowed to practice the drop-jump tasks up to 3 times.\u003c/p\u003e \u003cp\u003eFor this drop-jump task, subjects jumped forward off a box with both feet, landing simultaneously on bilateral force platforms positioned half their height from the box. Drop jumps required subjects to \"jump as high as possible\" upon landing, whereas drop lands required a comfortable landing. Trials were repeated if instructions were not followed correctly [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Minimal instructions were provided to minimize performance variability due to verbal cues [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and only up to 4 researchers were present to limit crowd influence [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFour conditions were tested: (1) standard (baseline), (2) choice (volitional decision-making), (3) visual, and (4) audio (external cues). The visual and audio conditions were designed to reduce motor planning time by providing probabilistic cues when the subject's pelvis marker crossed the box's edge. Visual cues appeared as an upward-facing triangle on a chest-height screen, while audio cues utilized a horn sound. Both cue types signaled required jump completion, with cue absence indicating no jump necessary (just box landing task). This methodology aimed to elucidate how reduced planning time through external cueing affects movement execution parameters, particularly those relevant to ACL injury risk factors. Three drop-jumps trials were randomized and not blocked by condition, maintaining cognitive demands. A rest period of 30\u0026ndash;60 seconds was provided between trials, with fatigue monitored using the Borg Rate of Perceived Exertion scale.\u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e SHOULD APPEAR APPROXIMATELY HERE\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData reduction\u003c/h3\u003e\n\u003cp\u003eForce data were filtered using low-pass Butterworth filters with cut-off frequencies of 50Hz. Ground contact time was determined using a 5 N force threshold for foot-strike and foot-off. The center of mass (CoM) was determined using Vicon's Plug-in Gait model. Vertical GRF impulse was calculated by integrating the net vertical ground reaction force with respect to time using the trapezoidal rule throughout each phase [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The first landing phase was analyzed for knee angular components. This phase began at initial ground contact (5 N force threshold) and continued through foot-off [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Knee angular impulse represents the cumulative effect of joint moment over time. In this research, knee angular impulse in the eccentric phase (KAI\u003csup\u003eECC\u003c/sup\u003e) was calculated from initial ground contact through the lowest point of the CoM, representing the total rotational effect produced as the knee flexes. Knee angular impulse in concentric phase (KAI\u003csup\u003eCON\u003c/sup\u003e) was then calculated from the lowest point of the CoM until foot-off, representing the total rotational effect produced as the knee extends during the take-off portion. The knee angular impulse net (KAI\u003csup\u003eNET\u003c/sup\u003e) was calculated as the algebraic sum of KAI\u003csup\u003eECC\u003c/sup\u003e and KAI\u003csup\u003eCON\u003c/sup\u003e, representing the total knee moment generated throughout the entire movement. All KAI calculations used the trapezoidal rule for integration and were normalized to body mass.\u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e SHOULD APPEAR APPROXIMATELY HERE\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using JASP 0.95.4 (JASP Team, Amsterdam, Netherlands). To identify cognitive variables that distinguish ACLR from control participants, an exploratory binomial logistic regression was performed with group membership as the dependent variable (Control\u0026thinsp;=\u0026thinsp;1, ACLR\u0026thinsp;=\u0026thinsp;0). The initial model incorporated all seven cognitive performance measures (CIS, TMT, DS, A-SRT, A-CRT, V-SRT, and V-CRT). A backward elimination procedure was then applied to identify the most parsimonious model. Given that the resulting model had a modest events-per-variable ratio (EPV\u0026thinsp;=\u0026thinsp;5.3, below the recommended threshold of 10 for logistic regression), this analysis was treated as exploratory and hypothesis-generating. Model performance was evaluated using multiple fit indices, including chi-square tests and Nagelkerke R\u0026sup2;. To confirm the robustness of the logistic regression findings, the Mann-Whitney U test was conducted for all cognitive variables to examine individual differences across cognitive tests, with effect sizes calculated using the rank biserial correlations.\u003c/p\u003e \u003cp\u003eSubsequently, separate 2 (Group: ACLR, Control) \u0026times; 4 (Condition: Standard, Choice, Audio, Visual) factorial analyses of variance (ANOVAs) were conducted for each KAI phase (ECC, CON, NET) to assess biomechanical differences between groups and across task conditions. Levene's test was used to assess homogeneity of variance across groups and conditions. Visual inspection of Q-Q plots confirmed that the residuals for all three ANOVA models were approximately normal. Levene's tests confirmed homogeneity of variance across groups and conditions for ECC (F\u0026thinsp;=\u0026thinsp;0.563, p\u0026thinsp;=\u0026thinsp;0.786), CON (F\u0026thinsp;=\u0026thinsp;1.058, p\u0026thinsp;=\u0026thinsp;0.391), and NET (F\u0026thinsp;=\u0026thinsp;0.334, p\u0026thinsp;=\u0026thinsp;0.938). These findings support the appropriateness of parametric ANOVA despite the violation of multivariate normality observed in the correlation analysis. When significant main effects were detected, post-hoc pairwise comparisons with Bonferroni correction were performed to control for Type I error inflation. Effect sizes were calculated using partial eta-squared (η\u0026sup2;) for ANOVA main effects and interactions, and Cohen's d with 95% confidence intervals for pairwise comparisons.\u003c/p\u003e \u003cp\u003eTo examine the relationship between cognitive performance and biomechanical measures, we first assessed multivariate normality using the Shapiro-Wilk test. Given that the assumption of multivariate normality was violated (W\u0026thinsp;=\u0026thinsp;0.921, p\u0026thinsp;=\u0026thinsp;0.001), Spearman's rank correlation coefficients (rho) were computed between all seven cognitive variables and each of the three KAI phases. The strength of correlations was interpreted using Cohen's guidelines (small: r\u0026thinsp;\u0026ge;\u0026thinsp;0.10, medium: r\u0026thinsp;\u0026ge;\u0026thinsp;0.30, large: r\u0026thinsp;\u0026ge;\u0026thinsp;0.50). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCognitive performance distinguishes ACLR from control groups\u003c/h2\u003e \u003cp\u003eTo identify cognitive variables that distinguish ACLR individuals from matched controls, we conducted an exploratory binomial logistic regression with group membership as the outcome variable. The initial model incorporated all the cognitive performance measures. Following backward elimination, three cognitive measures emerged as significant predictors of group status: CIS, V-SRT, and V-CRT. These variables significantly enhanced the model's predictive power relative to the null model (χ\u0026sup2;(3)\u0026thinsp;=\u0026thinsp;55.090, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), demonstrating their collective ability to reliably differentiate between the ACLR and Control groups. The final model accounted for 25.8% of the variance in group status (Nagelkerke R\u0026sup2; = 0.258).\u003c/p\u003e \u003cp\u003eAll three retained cognitive variables exhibited significant predictive capacity for group status. The V-SRT Score (β\u0026thinsp;=\u0026thinsp;1.205, Odds Ratio\u0026thinsp;=\u0026thinsp;3.338, z\u0026thinsp;=\u0026thinsp;5.145, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) revealed that for each unit increase, the odds of belonging to the Control group (versus the ACLR group) increased by a factor of 3.338, indicating that slower visual simple reaction times were associated with control group membership. Conversely, the V-CRT Score (β = -4.520, Odds Ratio\u0026thinsp;=\u0026thinsp;0.011, z = -5.025, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) indicated that each unit increase was associated with a 98.9% decrease in the odds of being in the Control group (1\u0026ndash;0.011), suggesting that higher V-CRT scores (indicating faster complex reaction times) were associated with ACLR group membership. Lastly, the CIS (β = -0.038, Odds Ratio\u0026thinsp;=\u0026thinsp;0.963, z = -2.164, p\u0026thinsp;=\u0026thinsp;0.030) showed that for every unit increase in the interference score, the odds of belonging to the Control group decreased by 3.7% (1\u0026ndash;0.963), indicating poorer interference control in the ACLR group.\u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e SHOULD APPEAR APPROXIMATELY HERE\u003c/p\u003e \u003cp\u003eGiven the exploratory nature of this analysis and the modest events-per-variable ratio (EPV\u0026thinsp;=\u0026thinsp;5.3, below the recommended threshold of 10), being inconclusive [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We confirmed these findings using Mann-Whitney U test and a rank biserial r to quantify the magnitude of group differences (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). ACLR participants demonstrated significantly faster V-SRT than controls, with a moderate-to-large effect size. ACLR participants also showed faster V-CRT than controls, indicating a moderate effect. However, ACLR participants exhibited a non-significant poorer interference control compared to controls. These findings indicate that ACLR individuals possess a distinct cognitive profile characterized by enhanced visual reactive capabilities compared to matched controls.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics and Group Comparisons for Cognitive Performance Variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACLR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRank Biserial r\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.61\u0026thinsp;\u0026plusmn;\u0026thinsp;10.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.81\u0026thinsp;\u0026plusmn;\u0026thinsp;17.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e21.05\u0026thinsp;\u0026plusmn;\u0026thinsp;17.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e17.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-SRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-CRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV-SRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV-CRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. CIS\u0026thinsp;=\u0026thinsp;Cognitive Interference Score; TMT\u0026thinsp;=\u0026thinsp;Trail Making Test difference score (B-A); DS\u0026thinsp;=\u0026thinsp;Digit Span total score; A-SRT\u0026thinsp;=\u0026thinsp;Auditory Simple Reaction Time; A-CRT\u0026thinsp;=\u0026thinsp;Auditory Complex Reaction Time; V-SRT\u0026thinsp;=\u0026thinsp;Visual Simple Reaction Time; V-CRT\u0026thinsp;=\u0026thinsp;Visual Complex Reaction Time. Higher scores indicate better performance for all variables except TMT (where lower scores indicate better executive function performance) and simple reaction times. Rank biserial correlation represents the effect size for Mann-Whitney U tests.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e SHOULD APPEAR APPROXIMATELY HERE\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBiomechanical Differences Between Groups\u003c/h2\u003e \u003cp\u003eWe conducted a 2 (Group: ACLR, Control) \u0026times; 4 (Condition: Standard, Choice, Visual, Audio) factorial analysis of variance (ANOVA) for each KAI phase. This analysis examined whether group and condition factors independently or interactively influenced knee biomechanics during drop-jump landings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEccentric Knee Angular Impulse\u003c/h2\u003e \u003cp\u003eThe ANOVA conducted for KAI\u003csup\u003eECC\u003c/sup\u003e revealed a significant main effect of Group (F(1, 248)\u0026thinsp;=\u0026thinsp;8.71, p\u0026thinsp;=\u0026thinsp;0.003, η\u0026sup2; = .034) and Condition (F(3, 248)\u0026thinsp;=\u0026thinsp;5.01, p\u0026thinsp;=\u0026thinsp;0.002, η\u0026sup2; = .057). The interaction between Group and Condition was not significant (F(3, 248)\u0026thinsp;=\u0026thinsp;0.42, p\u0026thinsp;=\u0026thinsp;0.738, η\u0026sup2; = .005), indicating that both groups responded similarly to the different task conditions. Levene's test confirmed homogeneity of variance across groups and conditions (F(7, 248)\u0026thinsp;=\u0026thinsp;0.56, p\u0026thinsp;=\u0026thinsp;0.786).\u003c/p\u003e \u003cp\u003ePost-hoc pairwise comparisons with Bonferroni correction indicated that the ACLR group (M\u0026thinsp;=\u0026thinsp;0.324, SD\u0026thinsp;=\u0026thinsp;0.078) exhibited significantly lower KAI\u003csup\u003eECC\u003c/sup\u003e compared to the Control group (M\u0026thinsp;=\u0026thinsp;0.350, SD\u0026thinsp;=\u0026thinsp;0.072). This suggests that ACLR participants generated less KAI\u003csup\u003eECC\u003c/sup\u003e during the landing phase across all conditions, representing a small-to-moderate effect size difference (Mean Difference = -0.027, p\u0026thinsp;=\u0026thinsp;0.003, Cohen's d = -0.37).\u003c/p\u003e \u003cp\u003eRegarding condition effects, post-hoc comparisons revealed that the Auditory-cued condition (M\u0026thinsp;=\u0026thinsp;0.359, SD\u0026thinsp;=\u0026thinsp;0.073) produced significantly higher KAI\u003csup\u003eECC\u003c/sup\u003e than both the Standard condition (M\u0026thinsp;=\u0026thinsp;0.318, SD\u0026thinsp;=\u0026thinsp;0.083; p\u0026thinsp;=\u0026thinsp;0.009, Cohen's d\u0026thinsp;=\u0026thinsp;0.57) and the Choice condition (M\u0026thinsp;=\u0026thinsp;0.322, SD\u0026thinsp;=\u0026thinsp;0.074; p\u0026thinsp;=\u0026thinsp;0.025, Cohen's d\u0026thinsp;=\u0026thinsp;0.51). No significant differences emerged between other condition pairs (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.20).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConcentric and Net Knee Angular Impulse\u003c/h2\u003e \u003cp\u003eThe analysis of KAI\u003csup\u003eCON\u003c/sup\u003e revealed non-significant main effects for Group (F(1, 248)\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;=\u0026thinsp;0.447, η\u0026sup2; = .002) and Condition (F(3, 248)\u0026thinsp;=\u0026thinsp;0.03, p\u0026thinsp;=\u0026thinsp;0.993, η\u0026sup2; \u0026lt; .001). The interaction between Group and Condition was also non-significant (F(3, 248)\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;0.768, η\u0026sup2; = .005). Levene's test indicated homogeneity of variance (F(7, 248)\u0026thinsp;=\u0026thinsp;1.06, p\u0026thinsp;=\u0026thinsp;0.391).\u003c/p\u003e \u003cp\u003eFor KAI\u003csup\u003eNET\u003c/sup\u003e, the ANOVA revealed non-significant main effects for Group (F(1, 248)\u0026thinsp;=\u0026thinsp;1.44, p\u0026thinsp;=\u0026thinsp;0.231, η\u0026sup2; = .006) and Condition (F(3, 248)\u0026thinsp;=\u0026thinsp;1.75, p\u0026thinsp;=\u0026thinsp;0.158, η\u0026sup2; = .021). The interaction between Group and Condition was not significant (F(3, 248)\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;=\u0026thinsp;0.998, η\u0026sup2; \u0026lt; .001).\u003c/p\u003e \u003cp\u003eFIGURE \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e SHOULD APPEAR APPROXIMATELY HERE\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Differential Sensitivity of KAI Phases to Condition Effects in ACLR and Control Participants. Data presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. ACLR (open circles), n\u0026thinsp;=\u0026thinsp;16; Control (filled circles), n\u0026thinsp;=\u0026thinsp;16. (A) Eccentric KAI shows significant group and condition effects. (B) Concentric KAI shows no significant effects. (C) Net KAI shows no significant effects. KAIECC\u0026thinsp;=\u0026thinsp;Eccentric Knee Angular Impulse; KAICON\u0026thinsp;=\u0026thinsp;Concentric Knee Angular Impulse; KAINET\u0026thinsp;=\u0026thinsp;Net Knee Angular Impulse.\u003c/p\u003e \u003cp\u003eThese results demonstrate that KAI\u003csup\u003eECC\u003c/sup\u003e exhibited sensitivity to both Group and Condition effects, with ACLR participants consistently generating lower impulse across all task conditions. External auditory cues resulted in increased eccentric knee control during landing compared to self-initiated movements. In contrast, KAI\u003csup\u003eCON\u003c/sup\u003e and KAI\u003csup\u003eNET\u003c/sup\u003e remained relatively consistent across groups and conditions, suggesting that biomechanical adaptations in these ACLR sample individuals are phase-specific rather than global.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRelationship Between Cognitive Performance and Biomechanical Measures\u003c/h2\u003e \u003cp\u003eTo examine whether cognitive performance is related to biomechanical outcomes, we computed Spearman's rank correlation coefficients (W\u0026thinsp;=\u0026thinsp;0.921, p\u0026thinsp;=\u0026thinsp;0.001) between the seven cognitive variables and each KAI phase across all participants (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman Rank Correlations Between Cognitive Variables and Knee Angular Impulse Phases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eKAI\u003csup\u003eECC\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eKAI\u003csup\u003eCON\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eKAI\u003csup\u003eNET\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.046*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-SRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.007**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-CRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026nbsp;.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV-SRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV-CRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e SHOULD APPEAR APPROXIMATELY HERE\u003c/p\u003e \u003cp\u003eCritically, the three cognitive variables that distinguished groups in the logistic regression (V-SRT, V-CRT, CIS) showed minimal and non-significant correlations with KAI\u003csup\u003eECC\u003c/sup\u003e. This pattern suggests that the cognitive measures distinguishing ACLR from control groups and eccentric knee biomechanics represent largely independent constructs. Furthermore, the absence of significant Group \u0026times; Condition interactions across all KAI phases (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.74) indicates that ACLR and control participants responded similarly to varying task demands despite their distinct cognitive profiles.\u003c/p\u003e \u003cp\u003eThese findings suggest that while ACLR individuals exhibit both cognitive and biomechanical differences compared to controls, these differences appear to reflect parallel adaptations rather than integrated or causally linked processes. The near-zero correlations between the group-distinguishing cognitive variables and the group-differentiating biomechanical measure (KAI\u003csup\u003eECC\u003c/sup\u003e) provide strong evidence for this dissociation. This implies that cognitive and motor control adaptations following ACL reconstruction may occur through separate, albeit concurrent, mechanisms.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the impact of cognitive performance on motor control during multiple drop-jump tasks in individuals with ACLR and healthy controls. Our findings revealed several key points that contribute to our understanding of both cognitive and biomechanical aspects of ACL injury:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCognitive performance effectively differentiated between participants with and without ACLR. Specifically, better scores in visual reaction time tests (both simple and complex) and poor cognitive interference are related to the ACLR group, highlighting a differentiated cognitive component present in participants with ACLR.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIndividuals with ACLR surgery demonstrate lower KAI\u003csup\u003eECC\u003c/sup\u003e during the landing phase in the drop jump. This difference suggests that these individuals may employ unique protective strategies for their knees, particularly during the high-impact moment of landing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCognitive demands significantly affect knee mechanics during jumping. Audio-cued jumps led to increased eccentric knee control during landing compared to planned jumps, suggesting an adaptive neuromechanical strategy where increased attentional demands result in extended processing time during the eccentric phase.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eA critical and theoretically significant finding of this study is that cognitive and biomechanical differences in ACLR individuals appear to represent parallel rather than integrated adaptations. The three cognitive variables that distinguished the groups (V-SRT, V-CRT, CIS) showed minimal, non-significant correlations with KAI\u003csup\u003eECC\u003c/sup\u003e, the only biomechanical measure that showed group differences. Furthermore, the absence of significant interactions across all KAI phases (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.74) indicates that ACLR and control participants responded similarly to varying task demands despite their distinct cognitive profiles. This dissociation may suggest that ACL reconstruction may trigger adaptations in multiple systems, cognitive processing, and motor control, through separate, albeit concurrent, mechanisms rather than through a single integrated pathway. From a rehabilitation perspective, this finding implies that cognitive and biomechanical interventions may need to target these systems independently rather than assuming that improvements in one domain will automatically transfer to the other.\u003c/p\u003e \u003cp\u003eThe differentiated cognitive profile observed in ACLR participants reveals a complex adaptation pattern in neurocognitive function. While the ACLR group demonstrated superior performance in both simple and complex visual reaction time tasks, they showed impaired cognitive interference control. This pattern of enhanced reactive capabilities alongside reduced interference control suggests potential compensatory mechanisms in the central nervous system following ACL injury. Previous studies have typically reported global cognitive deficits in ACLR populations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], making our finding of enhanced reaction times particularly noteworthy. This enhancement might reflect neural reorganization following injury, potentially as an adaptation to maintain rapid response capabilities despite altered proprioceptive feedback. However, the increased susceptibility to cognitive interference could indicate a trade-off in attentional resources, where improved reactive speed comes at the cost of reduced ability to filter irrelevant information, similar to findings in other injury adaptation contexts [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, examining the biomechanical aspects of our findings, the observed reduction in KAI\u003csup\u003eECC\u003c/sup\u003e during landing in ACLR individuals provides insight into long-term movement adaptations following reconstruction. This decreased eccentric loading suggests a persistent protective strategy, even in individuals who have completed rehabilitation and returned to sport. Similar protective mechanisms have been documented in previous studies [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], but our findings specifically identify the eccentric phase as the target of this adaptation. The selective nature of this modification (occurring only during the eccentric phase without significant changes in concentric or net impulse) suggests a sophisticated neural control strategy rather than global movement inhibition. This specificity might represent an unconscious optimization between protecting the reconstructed ligament and maintaining functional performance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurther analysis of the sensorimotor aspects revealed that audio-cued jumps elicited increased eccentric knee control compared to planned jumps, providing important insights about sensorimotor integration in dynamic tasks. This enhancement of eccentric control under audio cueing may reflect not only the influence of sensory modality but also the temporal and rhythmic properties of auditory cues. Recent evidence suggests that the temporal structure of auditory cues, particularly rhythmic properties, plays a critical role in regulating movement timing and neuromuscular coordination during landing tasks [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The additional processing time required for auditory stimuli, combined with their temporal structure, might facilitate more complete motor planning and enhance eccentric control mechanisms. This finding aligns with recent work showing that slower processing can sometimes lead to more controlled movement execution [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven these findings, the role of cognitive function emerges as a critical consideration in ACL injury risk and rehabilitation. The distinctive cognitive profile observed in ACLR participants (characterized by superior reaction times but impaired interference control) may represent more than just a post-injury adaptation. This pattern could potentially identify individuals at higher risk for non-contact ACL injuries, particularly in situations requiring sustained attention amid distractions, which is common in sport environments. Recent studies have shown that neurocognitive deficits precede and may predict ACL injury risk [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with specific impairments in visuospatial attention and processing speed increasing injury odds by up to 3-fold [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The relationship between cognitive processing and movement control aligns with emerging evidence that decreased neurocognitive performance correlates with higher-risk biomechanical patterns during dynamic tasks [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Furthermore, studies have demonstrated that athletes with lower cognitive performance scores show decreased dynamic postural control and increased landing forces, particularly during dual-task conditions [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe parallel nature of cognitive and biomechanical adaptations has important clinical implications for ACL rehabilitation. Current rehabilitation protocols typically focus predominantly on restoring physical function, with cognitive factors receiving less systematic attention. Our findings suggest that rehabilitation programs should incorporate both cognitive training (e.g., improving interference control, enhancing rapid decision-making under pressure) and biomechanical retraining (e.g., enhancing landing mechanics, increasing eccentric knee loading capacity) as complementary rather than redundant components. The dissociation between cognitive and biomechanical measures implies that addressing movement patterns alone may not resolve cognitive adaptations, and vice versa. Specifically, interventions targeting visual processing speed and interference control, perhaps through sport-specific reactive drills, dual-task training, or neurocognitive exercises, may be warranted alongside traditional strength and movement retraining. Additionally, the enhanced reactive capabilities observed in ACLR individuals, while potentially compensatory, could be leveraged as a strength in return-to-sport programming if appropriately channeled [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile this study provides novel insights through its comprehensive assessment of both cognitive and biomechanical parameters across multiple jump conditions, certain limitations must be considered when interpreting these results. First, the logistic regression analysis, while revealing meaningful cognitive distinctions between groups, had a modest events-per-variable ratio (EPV\u0026thinsp;=\u0026thinsp;5.3, below the recommended 10), being inconclusive [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and should therefore be interpreted as exploratory and hypothesis-generating rather than definitive. The confirmation of these findings through independent statistical tests (Mann-Whitney U tests and effect size calculations) provides additional confidence, but replication in larger samples is needed. However, considering our sample size, future studies would include larger samples to confirm null findings, particularly for KAICON and KAINET, where no group differences emerged.\u003c/p\u003e \u003cp\u003eSecond, our sample exhibited heterogeneity in injury characteristics, including variation in the number of ACL tears (12 single tears, 4 multiple tears), graft types (patellar, hamstring, gracilis, synthetic), time since surgery (range: 17\u0026ndash;64 months, M\u0026thinsp;=\u0026thinsp;32.81\u0026thinsp;\u0026plusmn;\u0026thinsp;15.77 months), and sport backgrounds (10 different sports). While this heterogeneity enhances external validity by representing the diverse ACLR population, it may have increased within-group variability and reduced statistical power to detect effects. Future research with larger, more homogeneous samples could clarify whether specific injury or surgical characteristics moderate the cognitive-biomechanical relationships observed here.\u003c/p\u003e \u003cp\u003eThird, the cross-sectional design precludes determination of whether the observed cognitive and biomechanical differences preceded injury, resulted from injury and reconstruction, or reflect ongoing compensatory adaptations. Longitudinal research tracking individuals from pre-injury through return-to-sport could clarify the temporal relationships and causal mechanisms underlying these observations. Additionally, while we carefully controlled for time since return-to-sport (minimum 6 months), participants were not formally matched by sport type or competitive level, which may have contributed to heterogeneity in both cognitive and biomechanical performance.\u003c/p\u003e \u003cp\u003eFourth, although our findings suggest parallel rather than integrated cognitive-biomechanical adaptations, we cannot rule out the possibility that more complex, non-linear relationships exist that were not captured by correlation analyses. Advanced analytical approaches such as machine learning or dynamical systems analysis might reveal subtle interactions between cognitive and motor systems that are not apparent in traditional statistical frameworks.\u003c/p\u003e \u003cp\u003eBased on these findings and limitations, several key research directions warrant investigation. Longitudinal studies are urgently needed to examine whether the observed cognitive profile represents a pre-existing risk factor for ACL injury or develops as a consequence of injury. This aligns with recent calls for prospective studies investigating cognitive function as a predictor of injury risk [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, investigation of targeted interventions incorporating both cognitive and motor training could help optimize injury prevention strategies, particularly given evidence that dual-task training can improve both cognitive performance and movement control [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Moreover, examination of these cognitive-motor interactions under more complex, sport-specific conditions would enhance ecological validity and clinical applicability, potentially leading to more effective screening tools for injury risk. Future research should also explore the development of cognitive training protocols specifically designed to enhance interference control while maintaining quick reaction times, as this combination appears particularly relevant to injury risk and prevention.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides novel evidence that individuals with ACLR exhibit distinct cognitive and biomechanical adaptations that appear to develop through parallel rather than integrated mechanisms. Three cognitive variables, V-SRT, V-CRT and CIS successfully distinguished ACLR from control groups, with ACLR individuals demonstrating enhanced visual reactive capabilities alongside impaired interference control. Biomechanically, ACLR individuals showed persistent reductions in eccentric knee angular impulse during landing, suggesting protective movement strategies that remain even after return to sport. Critically, minimal correlations between group-distinguishing cognitive variables and eccentric knee biomechanics, combined with the absence of interactions, indicate that these cognitive and biomechanical differences represent independent adaptations rather than causally linked processes.\u003c/p\u003e \u003cp\u003eThese findings have important implications for ACL rehabilitation and injury prevention. The parallel nature of cognitive and biomechanical adaptations suggests that rehabilitation protocols should incorporate targeted interventions for both systems independently. Cognitive training focused on improving interference control and maintaining rapid decision-making, combined with biomechanical retraining to optimize eccentric loading strategies, may be more effective than assuming improvements in one domain will transfer to the other. Future research should examine whether targeted dual-domain interventions, addressing cognitive and biomechanical systems as separate but complementary targets, can reduce re-injury risk and improve return-to-sport outcomes in ACLR populations. Longitudinal studies are particularly needed to determine whether the observed cognitive profile represents a pre-existing injury risk factor or a post-injury adaptation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by XXX, XXX, XXX, XXX, XXX, and XXX. The first draft of the manuscript was written by XXX, XXX, XXX, XXX, XXX, XXX, XXX, and XXX, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles outlined in the Declaration of Helsinki. Approval was granted by the XXXXXXXX Institutional Review Board (approval number 19-242 EP 1808).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used and analyzed during the current study is available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMejane, J., Faubert, J., Romeas, T., \u0026amp; Labbe, D. R. (2019). 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Single-versus dual-task functional movement paradigms: a biomechanical analysis. \u003cem\u003eJournal of sport rehabilitation, 30\u003c/em\u003e(5), 774-785.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-orthopaedic-and-trauma-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aots","sideBox":"Learn more about [Archives of Orthopaedic and Trauma Surgery](http://link.springer.com/journal/402)","snPcode":"402","submissionUrl":"https://submission.springernature.com/new-submission/402/3","title":"Archives of Orthopaedic and Trauma Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Neuro-motor control, Anterior Cruciate Ligament, Drop-Jump, Knee Angular Impulse, Biomechanics, Cognition","lastPublishedDoi":"10.21203/rs.3.rs-8971155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8971155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eVisual processing speed and cognitive interference control play crucial roles in athletic movements and anterior cruciate ligament (ACL) injury risk. The relationship between these specific cognitive functions and biomechanical performance following ACL reconstruction (ACLR) remains poorly understood. Given this, we aim to investigate cognitive performance differences between ACLR individuals and matched controls during drop-jump tasks and examine knee angular impulse patterns and their relationship to cognitive function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThirty-two females (16 anterior cruciate ligament reconstruction, 16 controls; age 20±1 years) completed cognitive assessments including the Stroop Color and Word Test, Trail Making Test, Digit Span Memory Test, and visual/auditory reaction time tests. Participants performed drop-jumps under four conditions: standard, choice, visual-cued, and audio-cued. Knee angular impulse was calculated for eccentric, concentric, and net phases during landing. Binomial logistic regression identified cognitive predictors distinguishing groups, followed by factorial analyses of variance to assess knee angular impulse differences. Spearman's rank correlation coefficients examined relationships between cognitive performance measures and knee angular impulse phases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThree cognitive predictors distinguished groups: cognitive interference score, visual simple reaction time, and visual complex reaction time (χ²(3)=55.090, p \u0026lt; 0.001). The ACLR group demonstrated faster (shorter) visual reaction times, but impaired interference control compared to controls. ACLR participants showed significantly lower eccentric knee angular impulse compared to controls (p = 0.003, Cohen's d=-0.37), while audio-cued conditions produced higher eccentric\u003c/p\u003e\n\u003cp\u003eknee angular impulse than standard and choice conditions. Despite distinct cognitive profiles,\u003c/p\u003e\n\u003cp\u003eminimal correlations emerged between group-distinguishing cognitive variables and eccentric knee angular impulse, suggesting parallel rather than integrated adaptations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion:\u003c/strong\u003e ACLR individuals exhibit distinct cognitive-biomechanical profiles characterized by enhanced reactive capabilities alongside reduced interference control and persistent protective movement strategies. Results support incorporating cognitive assessment and training into ACL rehabilitation protocols.\u003c/p\u003e","manuscriptTitle":"Visual Processing and Interference Performance Influences on Knee Angular Impulse in ACLR Individuals: A Cognitive-Biomechanical Analysis of Drop-Jumps","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 16:16:15","doi":"10.21203/rs.3.rs-8971155/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-16T11:02:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T19:53:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2139858635149191856028847537038356441","date":"2026-04-09T16:46:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T12:55:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33415503897147498834053102540285478541","date":"2026-03-09T07:53:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-04T15:58:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-27T15:06:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-27T14:27:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Orthopaedic and Trauma Surgery","date":"2026-02-25T20:48:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-orthopaedic-and-trauma-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aots","sideBox":"Learn more about [Archives of Orthopaedic and Trauma Surgery](http://link.springer.com/journal/402)","snPcode":"402","submissionUrl":"https://submission.springernature.com/new-submission/402/3","title":"Archives of Orthopaedic and Trauma Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f3411482-7938-49ba-9608-24471f7e13e3","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T11:54:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 16:16:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8971155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8971155","identity":"rs-8971155","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}