Effects of cognitive demands on whole-body biomechanics during changes-of-direction task performance in female football

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This study investigated the effects of increasing movement options during CODs in response to a real opponent on whole-body biomechanics in female football players. Twenty-nine female football players (15 with high and 14 with low expertise) performed 90° CODs in response to a real opponents’ action under four conditions: anticipated with one option (ANT-1), unanticipated with two (UNANT-2), three (UNANT-3) or four (UNANT-4) movement options. Three-dimensional motion analysis captured whole-body biomechanics at initial contact and during weight acceptance. No significant condition effects were observed for peak knee mechanics. However, at initial contact the pelvis was significantly less tilted and rotated towards the running direction in the UNANT-4 condition than in ANT-1. The hip was significantly more abducted and internally rotated in all unanticipated CODs. Furthermore, trunk rotation to the cutting leg was reduced in all unanticipated conditions compared to ANT-1. No significant differences were found between expertise groups. Increasing cognitive demands in a simulated match-play scenario primarily influenced proximal segment biomechanics during CODs in female football players. We therefore recommend integrating whole-body control and cognitively demanding stimuli into testing and injury prevention strategies. Health sciences/Anatomy/Musculoskeletal system Biological sciences/Psychology Anterior cruciate ligament Cognition Female football kinematics Figures Figure 1 Figure 2 Figure 3 1. Introduction Anterior cruciate ligament (ACL) injuries are a major concern in sports due to their high prevalence rates and long-term health consequences for athletes. 1 In female football, approximately 0.7 ACL injuries per team and season can be expected, 1 which corresponds to an injury risk 4 to 6 times higher than in male football players. 2 Notably, the ACL injury risk is comparable across performance levels in female football, with similar incidence rates in amateur-level and professional players. 3 Understanding the underlying biomechanical risk factors is crucial for the design and implementation of effective injury prevention strategies, 4 such as neuromuscular training. The majority of ACL injuries in female football are non-contact (54%) or indirect contact (34%) injuries. 5 More specifically, these injuries occur without direct player or opponent contact to the injured knee. These injuries predominantly arise in complex defensive playing situations, like pressing and tackling, which require rapid deceleration and change-of-direction (COD) maneuvers to follow the offensive player. 5,6 Biomechanical analyses of ACL injury events provide valuable insights into movement patterns associated with an elevated injury risk. A video analysis of 29 ACL injuries in female football identified knee valgus alignment in 88% of the ACL injury events accompanied by an abducted hip and increased hip internal rotation from the initial contact (IC) to the moment of injury occurrence. 5 The authors further observed that players often show lateral trunk flexion to the injured limb and trunk rotation toward the uninjured limb, i.e., in the intended running direction. 5 These findings underscore the importance of considering whole-body kinematics when investigating ACL injury paradigms with the goal to develop targeted prevention strategies. In recent years, laboratory-based ACL injury risk assessment approaches have expanded beyond focusing on biomechanical aspects by including neurocognitive aspects, especially when addressing non-contact ACL injuries. Notably, ACL injuries often occur during complex match-play situations, where athletes must react rapidly to an opponent’s action. 7 The shift of attentional focus from the player’s own COD action towards an external stimulus may impair an player’s temporo-spatial perception and may result in insufficient movement control. 8 Additionally, deceiving actions, such as feints, further increase the cognitive demands by requiring defenders to inhibit an intended, pre-planned or even an already initiated motor response. 7,8 The inherent time constraints of these match-play situations challenge appropriate feed-forward control strategies, which are essential to stabilize the whole body and particularly the knee joint. 9 Given that ACL injuries typically occur within 50 ms after initial ground contact, reactive mechanisms, such as muscle reflexes, are often insufficient to mitigate the underlying injury risk. 8,10 Thus, understanding the impact of elevated cognitive demands during complex match-play situations on whole-body kinematics appears crucial to better comprehend the mechanisms underlying ACL injuries. 7,11,12 Hughes and Dai (2021) proposed a hypothetical model illustrating how cognitive demands may affect motor control in highly dynamic match-play situations. 12 The model emphasizes decision-making processes by distinguishing between anticipated (pre-planned) CODs and unanticipated movements, with the latter requiring reactive adjustments in response to external stimuli, such as an opponent’s action. 12 Accordingly, decision-making processes are mainly affected by the number and complexity of movement options, as well as the available time to react (ATR). 12 Emerging evidence from laboratory-based studies indicate that cognitive demands, being operationalized as unanticipated versus anticipated CODs, modulate knee joint mechanics and whole-body kinematics. 11,13 More specifically, higher cognitive demands have been associated with increased knee abduction moments, 11,13 greater lateral trunk flexion to the cutting leg and rotation to the intended running direction, 14,15 as well as increased hip abduction and internal rotation during weight acceptance. 16,17 Several research groups have investigated the influence of ATR, defined as the time from stimulus to IC, on biomechanical parameters associated with ACL injury risk. 14,18 Findings revealed that an ATR of around 600 ms is required to differentiate between anticipated and unanticipated CODs, following an approach run at a speed of 4–5 m/s. 14,18,19 Previous research primarily relied on artificial stimuli, such as light signals or arrows, to create unanticipated conditions with 2 (e.g., left or right) or 3 (e.g., left, right or straight) possible movement options. 12,19 However, only few studies have examined the effects of decision-making using ecologically valid stimuli to enhance task complexity and better simulate match-play scenarios. 12,16,20 For instance, Lee et al. (2013) compared the effects of ecologically valid stimuli (i.e., 1 vs. 2 video-animated opponents) versus an artificial stimulus (i.e., arrow) on knee mechanics and trunk kinematics in football players of low versus high performance and training caliber while performing COD tasks with 2 options (right/left). 15 Higher knee abduction moments were found in unanticipated versus anticipated tasks, with the highest moments and most unfavorable trunk kinematics in arrow-induced CODs compared to the condition with video-animated opponents. 15 Furthermore, only the condition with video-animated opponents was able to discriminate between performance levels, with low-level players demonstrating a less favorable COD strategy. 15 Further research using video-animated or real opponent stimuli confirmed higher knee abduction moments 16 and hip abduction angles 16,20 in unanticipated CODs with 2 options versus anticipated CODs. In summary, ecologically valid stimuli seem to increase cognitive demands in match-play situations, especially in players with limited sport-specific expertise and potentially less developed visual-perceptual skills. 15 Despite increasing interest in the role of cognitive demands on ACL injury risk, no studies are currently available that have examined the effects of cognitive demands, by systematically increasing the number of movement options in a complex match-play scenario. Finally, the available research has primarily focused on knee and hip biomechanics, despite the multi-segmental nature of ACL injuries involving the trunk, pelvis, and foot. 19 Therefore, the primary aim of this study was to investigate the effects of increasing cognitive demands on whole-body biomechanics associated with an ACL injury risk during COD task performance in female football players. We systematically increased cognitive demands across 4 levels by varying the number of movement options in a complex football-specific laboratory setting. A secondary aim was to examine whether football-specific expertise moderates the influence of cognitive demands on biomechanical responses by comparing female football players of high versus low training and performance caliber. Based on the relevant literature, 15,16,20 we hypothesized that the gradual increase of cognitive demands during COD task performance may amplify knee joint mechanics associated with ACL injuries (e.g., knee abduction angles and moments), as well as whole-body kinematics such as trunk and hip alignment. Furthermore, we hypothesized that higher football-specific expertise may reduce the influence of increasing cognitive demands on ACL injury-associated biomechanics. 15 2. Methods Using a within-subject repeated-measures design, this study examined the effects of 4 levels of cognitive demands on biomechanics during COD task performance. The study protocol was preregistered on the Open Science Framework and is available at [ https://doi.org/10.17605/OSF.IO/4Z5R8 ]. 2.1. Participants Sample size estimation for the primary research question was based on the effect sizes for knee abduction moments and trunk kinematics (f = 0.27–0.29) of a study with related study design. 15 The a priori power analysis 21 revealed a minimum required sample size of 21 participants to achieve 80% statistical power at an alpha level of 0.05. To account for potential data loss and facilitate analyses addressing the secondary objective, a total sample size of 30 participants was targeted. Accordingly, 30 female athletes volunteered to participate in this study. One participant had to be excluded due to an adverse event during an approach run, resulting in a final sample of 29 participants. Participants completed a questionnaire that included questions on anthropometrics, their current health status assessed via the Physical Activity Readiness Questionnaire (PAR-Q), previous lower limb injuries and their sporting background, including football-expertise. The participants were categorized into 2 groups according to their training and performance caliber. The high expertise group (HE) included 15 players aged 22.9 ± 3.8 years (body height = 168.5 ± 4.7 cm, mass = 63.9 ± 5.1 kg) and the low expertise group (LE) included 14 participants aged 22.5 ± 1.8 years (body height = 168.6 ± 5.6 cm, mass = 63.6 ± 5.6 kg). HE participants competed in the 1st to 4th German football leagues, with an average of 14.7 ± 3.8 years of club-level playing experience and 8.1 ± 5.1 hours of play per week. In contrast, LE participants had limited football experience, having completed only 1 year of a university football course and averaging 0.5 ± 0.7 playing hours per week. However, 6 of the LE participants were engaged in other team sports such as basketball or volleyball. All participants were free of lower limb injuries within the past 3 months prior to the start of the study. Previous ACL injuries were not an exclusion criterion if they had occurred or were surgically treated at least 24 months prior to study participation. Three participants had previously suffered an ACL injury (HE = 2; NE = 1), two of whom had been treated surgically. Prior to testing, all participants were informed about potential risks and provided written informed consent. The study was conducted in accordance with the latest version of the Declaration of Helsinki, and the protocol was approved by the local ethics committee of University of Freiburg, Germany (approval number 24-1142-S2). 2.2. Experimental setup and conditions Approximately 60% of ACL injuries in female football players occur in defensive playing situations like pressing. 5 Therefore, the experimental setup was designed to simulate a match-like defensive scenario. Participants performed COD tasks, in which they adopted the role of a defender against a real opponent in ball possession. Each movement task included a submaximal approach run of 5 m at 4.0 ± 0.3 m/s, followed by a 90° COD to the left or the right in response to the opponent who determined the direction of the participant by kicking a ball to one side. To ensure reliable and consistent testing conditions, 2 experienced male footballers competing at regional level were selected as opponents and were equally assigned to participants from the HE and LE groups. The participants performed CODs under experimental conditions with increasing cognitive demands, presented in block-randomized order for each participant. In the anticipated (ANT-1) condition (Fig. 1 A), participants performed a COD to the left or the right, which was indicated by a hand signal of the opponent prior to the approach run and therefore resulted in 1 option for the participant. The opponent then performed an inside kick to the right or the left at a standardized time point during the participants’ approach run. In the unanticipated condition with 2 options (UNANT-2, Fig. 1 B), participants initiated the approach run without prior knowledge of the cutting direction, requiring them to react rapidly as the opponent kicked the ball to either side at the same standardized time point as in the ANT-1 condition. The unanticipated condition with 3 options (UNANT-3, Fig. 1 C) further increased the cognitive demand by introducing an additional third movement option. Here, the opponent either passed the ball left or right or stopped it by placing the foot on top of the ball, requiring the participant to decelerate and stop quickly in front of the ball. The unanticipated condition with 4 options (UNANT-4, Fig. 1 D) provided the highest number and complexity of options by containing all previous movement options along with a deceptive feint in several trials. Performing the feint, the opponent initially moved the foot toward the ball, indicating a pass to one direction before quickly switching the supporting leg and kicking the ball to the opposite side. To ensure temporal comparability across conditions, the feint was initiated slightly earlier, ensuring that ball kicking occurred at a timepoint comparable to the other conditions. According to recent evidence, 19 we aimed for an ATR between 300–600 ms. Accordingly, the opponent provided the stimulus by kicking or stopping the ball when the participant was 1.5 m from the center of the force plate. The selected approach run speed of 4 m/s resulted in an ATR of approximately 375 ms. Participants performed CODs on a standard laboratory floor wearing neutral indoor football shoes (Mundial Goal, Adidas, Herzogenaurach, Germany). Prior to data collection, participants completed a standardized warm-up protocol to prepare for fast, dynamic movements. Subsequently, participants performed at least 3 familiarization trials in each experimental condition. To prevent modification in stride length or movement patterns, participants were not instructed to target the force plate during COD execution. However, complete foot contact of the cutting leg on the force plate was required for valid trials. During familiarization, the preferred cutting leg was determined, and only CODs of this leg were taken for further data analysis. For clarity, only the 90° CODs with the preferred leg were analyzed. The other movement options, i.e., CODs to the non-preferred side, CODs after the feint and the stopping maneuvers, served solely to increase cognitive demands but were not included in the final analysis. Each participant performed at least 14 trials per condition, 7 of which were CODs with the preferred cutting leg. If fewer than 5 valid trials were recorded due to missing the force plate or deviating from the desired approach speed, further trials were performed until meeting the required number of 5 valid trials. 2.3. Data collection and analysis The approach run speed was measured by 2 timing gates (Witty Gate, Microgate, Mahopac, NY, USA), placed at a distance of 3.5 m and 1.5 m from the center of the force plate. Three-dimensional motion data of the participant, the opponent and the ball were collected at 200 Hz using a marker-based motion analyses system with 12 cameras (Vicon Motion Systems Ltd., Oxford, Great Britain). Ground reaction forces (GRF) were recorded at 1000 Hz using a ground-embedded force plate measuring 0.9 m x 0.6 m (AMTI BP600900, Watertown, MA USA). Motion capture data and GRF were synchronized (Vicon D-Link) to allow inverse dynamic calculations. A customized marker set was used to analyze players’ trunk, lower limbs and foot kinematics, and to track the movement initiation of the ball and opponent. Based on a previously established marker set, 14 37 markers were placed on the participant’s head (4 markers), trunk (suprasternal notch, xiphoid process, T6 vertebra), pelvis (anterior and posterior superior iliac spines), legs (lateral thigh and shank, medial and lateral epicondyle of the knee, tuberositas tibiae, medial and lateral malleolus) and shoe (3-marker clusters on the forefoot and rearfoot). To track the ball movement, a 3-marker cluster was attached to the ball. Additionally, the opponent was equipped with 18 markers, distributed across specific landmarks on the whole body and the shoes. To ensure reliability for marker placement, the same experienced researcher placed all markers across participants. Marker trajectories and GRF signals were pre-processed with regards to labelling and gap-filling and finally filtered with a low-pass Butterworth filter (4th order, 20 Hz cut-off frequency) in Vicon Nexus. A static calibration trial was conducted with the participant standing in a predefined neutral position within a foot calibration rig to determine segment length and joint centers. Ankle and knee joint centers were defined as the midpoint between the medial and lateral malleoli and medial and lateral epicondyles, respectively. 14,22 Hip joint centers were functionally determined using a standardized dynamic movement protocol (“star-arc movement”). 23 Segment coordinate systems and calculations of joint angles and joint moments were established using a custom-written script in BodyBuilder (Vicon Motion Systems Ltd., Oxford, UK). Briefly, vertical axes for the thigh and shank were defined from distal to proximal joint centers, with the mediolateral axes being defined through the medial and lateral epicondyle and malleolus markers, and the anteroposterior axes resulting from the cross product. Pelvis and trunk segment axes were defined to match with previous publications. 14,24 Joint angles were computed using a YXZ Euler rotation sequence. Knee joint rotations were defined as flexion-extension around the Y-axis, adduction-abduction around the subsequently X’-axis, and internal-external rotation around the Z’’-axis. 14 External knee joint moments were then calculated using a standard inverse dynamics approach and normalized to each participant’s body mass. The IC was set to the frame in which vertical GRF exceeded a value of 20 N. All further processing was performed using custom scripts in Matlab (R2022b, The MathWorks Inc.). The discrete biomechanical variables were selected based on their association with the ACL injury mechanism. 5,11 At IC, knee flexion and foot progression angles as well as pelvis, hip and trunk angles in the frontal and transverse plane were extracted, reflecting preparatory movements for initiating the COD. 14 Furthermore, the peak knee abduction angle and moment were extracted during weight acceptance (WA), as this early phase of stance can be considered relevant for ACL injuries. 8 WA was defined as the time from IC to maximum knee flexion. 25 A detailed description of the joint angles and moment directions will be provided in the results section. The control parameters approach speed and ATR were analyzed to assess their consistency within and between subjects. As it was uncertain whether participants primarily reacted to opponent or ball movements, we opted to report both measures. ATR Opp was defined as the time from initial movement of the opponent's kicking foot until IC, while ATR Ball was defined as the time from initial ball movement until IC. 2.4. Statistical analyses Statistical analyses of discrete data were computed in R software (version 2023.12.0 + 369). For each participant and experimental condition, the dependent biomechanical variables were averaged across 5 trials. Normal distribution of data was confirmed using the Shapiro-Wilk test and by visual inspection of Q-Q-Plots. Homogeneity of variance was confirmed using the Levene's test. A mixed 4x2 ANOVA was used to examine the effects of the within-subject factor condition (4 levels: 1, 2, 3, 4 options) and the between-subject-factor expertise (2 levels: high, low). Statistical significance was set at p < 0.05, and significant main group and condition effects and interactions thereof were followed up with paired t-tests using Bonferroni correction. Effect sizes of the ANOVA analyses were reported as partial eta squared (η p 2 ) and can be interpreted as small ( 0.14) effects. 26 Cohen’s d was calculated to assess the effect sizes of paired t-tests and can be interpreted as small ( 0.8) effects. 26 Additionally, a statistical parametric mapping (SPM) approach was performed to analyze continuous biomechanical data using the spm1d package in MATLAB (R2022b, The MathWorks Inc.). The WA phase was used for analyses. A one-way repeated-measures ANOVA was performed to assess differences in biomechanical variables between the 4 experimental conditions. If ANOVA tests reached significance, Bonferroni adjusted paired t-tests were calculated. To assess the reliability of the experimental conditions, intraclass correlation coefficients (ICCs) and their 95% confident intervals (CI) were calculated for the control parameters approach speed, ATR Ball and ATR Opp using a two-way random effects model (ICC(2, k)). 27 ICC estimates were interpreted as poor ( 0.9) reliability. 27 3. Results 3.1. Control parameters The approach speed remained within the required range of 4.0 ± 0.3 m/s (Table 1 ), with no significant effects of condition (p = 0.117, η p 2 = 0.07) or expertise (p = 0.181, η p 2 = 0.07). Similarly, both time-to-react variables, i.e., ATR Opp and ATR Ball , did not differ between conditions (p = 0.703, η p 2 = 0.02 and p = 0.073, η p 2 = 0.08, respectively) or between expertise levels (p = 0.21, η p 2 = 0.06 and p = 0.144, η p 2 = 0.08, respectively) (Table 1 ). All control parameters showed good between-condition reliability with ICCs of 0.89 [95% CI: 0.82, 0.94] for approach speed, 0.76 [95% CI: 0.63, 0.89] for ATR Opp and 0.79 [95% CI: 0.68, 0.86] for ATR Ball . Table 1 Means ± SDs of the control parameters approach speed, ATR Opp and ATR Ball for the 4 experimental conditions. Expertise ANT-1 UNANT-2 UNANT-3 UNANT-4 Approach speed [m/s] HE 3.99 ± 0.21 4.05 ± 0.24 4.00 ± 0.21 4.02 ± 0.21 LE 3.90 ± 0.12 3.92 ± 0.18 3.90 ± 0.21 3.94 ± 0.19 ATR Opp [s] HE 0.70 ± 0.06 0.71 ± 0.07 0.72 ± 0.07 0.70 ± 0.06 LE 0.74 ± 0.09 0.74 ± 0.09 0.75 ± 0.11 0.74 ± 0.09 ATR Ball [s] HE 0.34 ± 0.07 0.36 ± 0.07 0.37 ± 0.08 0.36 ± 0.05 LE 0.39 ± 0.10 0.41 ± 0.10 0.40 ± 0.11 0.40 ± 0.10 ANT = anticipated, ATR = available time to react, HE = high expertise, LE = low expertise, UNANT = unanticipated 3.2. Discrete biomechanical variables No significant main condition effects were observed for any of the knee joint related discrete variables, including peak knee abduction moment, peak knee abduction angle, and knee flexion angle at IC (see Table 2 ). Table 2 Knee kinetics and kinematics during change-of-direction task performance: means ± SDs and statistics. Expertise ANT-1 UNANT-2 UNANT-3 UNANT-4 Condition Effect (p-value, η p 2 ) Expertise Effect (p-value, η p 2 ) Condition*Expertise Effect (p-value, η p 2 ) Peak knee abduction moment [Nm/kg] HE 0.88 ± 0.42 0.90 ± 0.33 0.86 ± 0.33 0.96 ± 0.38 0.075 (0.081) 0.716 (0.005) 0.180 (0.058) LE 0.78 ± 0.29 0.83 ± 0.33 0.91 ± 0.37 0.90 ± 0.38 Peak knee abduction angle [°] HE 9.5 ± 5.0 10.5 ± 5.5 9.5 ± 5.0 9.8 ± 4.9 0.516 (0.028) 0.729 (0.005) 0.081 (0.079) LE 10.6 ± 3.8 10.0 ± 3.8 10.2 ± 3.6 10.7 ± 3.6 Knee flexion angle at IC [°] HE 28.2 ± 6.8 29.9 ± 7.3 30.1 ± 9.3 29.5 ± 6.7 0.625 (0.021) 0.146 (0.077) 0.817 (0.011) LE 25.7 ± 6.9 25.6 ± 7.5 26.4 ± 6.4 25.4 ± 6.9 ANT = anticipated, IC = initial contact, HE = high expertise, LE = low expertise, UNANT = unanticipated In contrast, significant condition effects were observed for proximal joint and segment kinematics. Regarding hip and pelvis kinematics at IC (Table 3 ), significant main condition effects were found for hip rotation (p = 0.034, η p 2 = 0.101), frontal pelvis tilt (p = 0.004, η p 2 = 0.149) and pelvis rotation (p = 0.012, η p 2 = 0.125). Post-hoc analyses revealed that the pelvis was significantly less tilted and rotated towards the running direction in the UNANT-4 condition, i.e., in unanticipated CODs with the highest cognitive demand, than in ANT-1 (p = 0.006, d = -0.686 and p = 0.041, d = 0.543, respectively). Post-hoc analyses for hip rotation did not reach the level of statistical significance. Lateral trunk flexion at IC was not significantly affected by the condition (Table 3 ). However, there was a significant condition effect on trunk rotation at IC (p < 0.001, ηp 2 = 0.249). Post-hoc analyses revealed that the trunk was significantly more rotated to the cutting leg in the ANT-1 condition compared to UNANT-2 (p = 0.023, d = -0.585), UNANT-3 (p = 0.002, d = -0.745) and UNANT-4 (p = 0.007, d = -0.669). No significant condition effects were found for foot progression angle at IC (Table 3 ). Table 3 Kinematics of the pelvis, hip, trunk and foot at initial contact (IC) during change-of-direction task performance: means ± SDs and statistics. Expertise ANT-1 UNANT-2 UNANT-3 UNANT-4 Condition Effect (p-value, η p 2 ) Expertise Effect (p-value, η p 2 ) Condition*Expertise Effect (p-value, η p 2 ) Frontal pelvis tilt [°] (- = towards running direction, i.e., iliac crest higher on the side of the cutting leg) HE -12.4 ± 4.6 -11.7 ± 5.2 -11.2 ± 4.0 -11.2 ± 5.0 0.004 (0.149) 0.086 (0.105) 0.770 (0.014) LE -9.9 ± 5.4 -8.3 ± 5.6 -7.9 ± 4.9 -7.7 ± 5.4 Pelvis rotation [°] ( + = towards running direction) HE 22.8 ± 9.2 22.1 ± 7.3 21.6 ± 7.4 20.9 ± 6.9 0.012 (0.125) 0.457 (0.021) 0.291 (0.045) LE 23.3 ± 12.3 18.9 ± 11.3 18.8 ± 8.4 17.2 ± 10.8 Hip abduction/adduction [°] ( + = abduction) HE 8.3 ± 5.5 9.6 ± 4.7 9.6 ± 4.9 9.6 ± 5.6 0.405 (0.035) 0.689 (0.006) 0.652 (0.020) LE 9.8 ± 4.3 10.0 ± 5.4 10.2 ± 3.8 9.9 ± 5.0 Hip rotation [°] ( + = internal rotation) HE 2.6 ± 11.1 4.5 ± 11.5 5.8 ± 11.6 6.0 ± 12.7 0.034 (0.101) 0.373 (0.030) 0.716 (0.016) LE 7.1 ± 8.6 7.9 ± 9.8 8.3 ± 8.6 8.9 ± 7.4 Lateral trunk flexion [°] ( + = to the cutting leg) HE 16.0 ± 5.4 15.8 ± 4.3 15.5 ± 4.5 16.5 ± 5.2 0.541 (0.026) 0.069 (0.117) 0.974 (0.003) LE 12.6 ± 6.5 12.1 ± 5.9 11.8 ± 5.7 12.5 ± 7.1 Trunk rotation [°] (- = to the cutting leg) HE -17.3 ± 7.7 -13.9 ± 5.7 -13.6 ± 6.2 -14.1 ± 6.9 < 0.001 (0.249) 0.181 (0.065) 0.561 (0.025) LE -13.3 ± 10.0 -11.7 ± 7.6 -9.7 ± 6.3 -10.3 ± 7.3 Foot progression [°] ( + = internal rotation) HE 11.0 ± 10.5 11.9 ± 9.2 10.7 ± 10.5 10.3 ± 10.9 0.312 (0.043) 0.169 (0.078) 0.478 (0.030) LE 17.8 ± 9.7 15.2 ± 8.3 13.9 ± 9.1 14.6 ± 6.5 IC = initial contact, HE = high expertise, LE = low expertise No significant main group effects or condition-by-group interaction effects were observed for none of the selected biomechanical variables. 3.3. Continuous biomechanical variables The SPM-analyses revealed no significant main condition effects for the parameters knee abduction moment and knee flexion angle during WA. Significant differences in knee abduction angle were found from 39% − 48% of WA (Fig. 2 , p = 0.0458, F = 3.975). However, Bonferroni-corrected post-hoc analyses did not reach the level of statistical significance. Regarding hip kinematics, significant differences were found for the hip abduction angle (Fig. 3 , 63% − 100% of WA, p = 0.035, F = 3.540) and the hip rotation angle (Fig. 3 , 8% − 32% of WA, p = 0.032, F = 3.866), again without reaching significance through post-hoc tests. 4. Discussion In female football, ACL injuries most frequently occur during cognitively demanding match situations—particularly when players execute rapid COD maneuvers in response to an opponent’s action. 5,6 Evidence from previous laboratory-based studies suggests that cognitively demanding unanticipated CODs can modulate biomechanical parameters associated with ACL injuries. 11,13 However, to date, the impact of systematically elevating cognitive demand—by progressively increasing both the number and complexity of available movement options—on these underlying mechanisms remains poorly understood. In this study, we aimed to address this research gap by creating complex match-play scenarios in which female athletes executed a 90° COD task in response to a real opponent, while systematically elevating cognitive demand across 4 levels by increasing the number of available movement options. It is noteworthy to add that we only analyzed 90° CODs to one side, while the remaining movement options served to increase the cognitive demands. The secondary aim was to determine whether football-specific expertise influences biomechanical responses to progressively elevated cognitive demands. Contrary to our expectations, no significant main condition effects were found for peak knee abduction angles, knee flexion angles at IC, or for peak knee abduction moments. However, a trend toward increased peak knee abduction moments were found with increasing cognitive demands. This is in line with findings from a recently published meta-analyses, 19 which compared anticipated and unanticipated CODs in physically active individuals. Although this meta-analysis reported no significant pooled effect of anticipation, more than half of the included studies revealed higher knee abduction moments in unanticipated conditions. 19 For instance, Bill et al. (2022) showed higher knee abduction moments when participants reacted to a movement of a real opponent compared to anticipated trials. 16 In another study, Lee et al. (2013) showed that unanticipated trials elicited by video-animated opponents produced elevated knee abduction moments, but interestingly even higher moments were observed in arrow‐cued COD maneuvers. 15 The authors suggested that ecologically valid match-play stimuli, such as video-based opponents, may prolong the athlete’s ATR by providing earlier visual cues about the intended direction. 15 Conversely, the cognitive demands required to interpret complex visual information and to generate an appropriate motor response are likely to be higher than for simple artificial stimuli. 12 One possible explanation for the absence of significant main condition effects at the knee joint level in our study may therefore lie in the characteristics of the ATR. Previous studies identified an ATR of around 600 ms to be required to differentiate unanticipated from anticipated CODs at an approach speed of 4.0 m/s. 19 Accordingly, we ensured consistency of ATR and approach speed across the conditions, which was reflected by good ICCs and no condition effects for all control parameters. However, while ATR Ball remained within the predefined range of 300–600 ms, ATR Opp –defined as the time from the initial movement of the opponent's kicking foot to the participant's IC on the force plate–was between 700–750 ms. Accordingly, the resulting ATR in the respective trials may have varied depending on whether the participants focused on the opponent's body movement or the ball trajectory to obtain visual cues for the requested movement option. It can therefore be speculated that the prolonged ATR Opp may have diminished condition effects on several biomechanical outcomes, including knee joint mechanics. No statistically significant main condition effects were found for hip abduction/adduction angles at IC. However, significant main condition effects were observed for hip abduction during WA, and for the internal rotation angle during both IC and WA. Even though post-hoc analyses did not reach the level of statistical significance, visual inspection of the continuous waveforms (Fig. 3 ) suggests increased hip abduction and internal rotation across all unanticipated conditions compared to ANT-1. These findings are consistent with outcomes from previous studies 16,17,20,28 and gain additional relevance when considered alongside the findings from video analyses that revealed pronounced hip abduction and internal rotation during ACL injury occurrence. 5,6 Since the hip and knee joint are mechanically linked via the femur, alterations of hip joint kinematics can directly contribute to altered or even detrimental knee mechanics. 29 In addition, pelvis kinematics were significantly influenced by cognitive demands. In the cognitively most demanding UNANT-4 condition, the pelvis was significantly less laterally tilted towards the new running direction, i.e., with the iliac crest being higher toward the new running direction, compared to the ANT-1 condition. This is in line with a finding from a previous study 30 which suggests that greater lateral pelvis tilt in anticipated CODs facilitates lateral foot placement, thereby enabling the generation of higher push-off forces, while reducing the need for pronounced hip abduction angles in the cutting leg. 30 This proposed mechanism aligns with our findings of increased hip abduction during WA in unanticipated conditions. Furthermore, the pelvis rotation to the new running direction was significantly reduced in UNANT-4 compared to ANT-1, which indicates delayed reorientation in the most demanding cognitive condition. In this sense, reduced pelvis rotation may increase the need for high hip internal rotation to achieve the desired direction change; 31 a pattern reflected in our results showing a main condition effect on hip rotation during WA. In summary, these findings highlight the role of pelvis kinematics and control for modulating hip and knee joint mechanics during COD performance. 29 At the trunk level, a significantly more pronounced rotation to the cutting leg was observed in the ANT-1 condition compared to the other cognitive demand conditions. Interestingly, we found no significant main effects of condition on lateral trunk flexion, which is not in line with previous studies. 14,15,30 Given the large mass of the trunk and its influence on the body’s center of mass, lateral trunk flexion is discussed to increase external knee abduction moments by shifting the ground reaction force vector laterally relative to the knee joint. 30,31 In this context, Powers et al. (2010) emphasized the contributing role of proximal segments and joints, especially in the frontal plane, with respect to ACL injuries. 29 With this in mind, the absence of effects on lateral trunk flexion in the current study might help to explain why knee abduction biomechanics also were not affected by increasing cognitive demands. Furthermore, the trunk and hip joint are characterized by a great range of motion, whereas knee frontal plane mechanics are inherently limited. Given this, potential effects of increasing cognitive demands may have primarily emerged at the proximal joints, while subtle changes of knee joint mechanics may have been too small or covered by measurement inaccuracies. It is important to note that pelvis and trunk rotation were quantified using different reference systems. Pelvis rotation was quantified relative to the global coordinate system, whereas trunk rotation was computed relative to the pelvis. This methodological distinction may explain the seemingly contradictory finding that, in the anticipated condition, the pelvis was more rotated to the new running direction, whereas the trunk was more rotated towards the cutting leg. However, this in line with Byrne et al. (2022), who also reported global pelvis and intersegmental trunk angles. 30 When reporting intersegmental trunk angles, pelvis kinematics should always be reported alongside to allow interpretation of the contribution of pelvis and trunk segments to whole-body biomechanics. 30 Additionally, this information may assist practitioners to decide whether to prioritize pelvis or trunk control or to consider both when developing ACL injury prevention strategies. In summary, our findings suggest that increasing cognitive demands during COD task performance primarily affect proximal kinematic strategies, particularly at the hip, pelvis, and trunk. As the post-hoc analyses did not reveal significant differences between the 3 unanticipated conditions, our measurement paradigm may be considered only partially successful in differentiating between increasing levels of cognitive demands. However, while these analyses did not reach the level of statistical significance, visual inspection indicated gradual effects on knee abduction moments, pelvis kinematics and hip with increasing cognitive demands. Regarding the pelvis, only the UNANT-4 condition had a significant effect on kinematics, indicating that complex conditions with a high number of movement options are required to affect the movement control of female athletes in a sport-specific scenario. Based on findings of Lee et al. (2013), we hypothesized that higher football-specific expertise would reduce the biomechanical effects of increasing cognitive demands. 15 However, we did not find an effect of football-specific expertise for any of the biomechanical variables examined in this study. One possible explanation is that the groups might have been relatively similar with regard to their expertise in performing unanticipated CODs in team sports. Six of the LE participants played handball (n = 3) or rugby (n = 3) and may have benefited from their experience in these sports in terms of anticipating and interpreting an opponent’s movements. Furthermore, our measurement setup included no football-specific elements apart from the opponent kicking a ball. Future studies investigating the effects of expertise should tailor the setup precisely to the target sport, e.g., by including a sport-specific task for the participant such as kicking or throwing a ball. Additionally, the use of artificial turf and football-specific footwear instead of a standard laboratory floor and neutral shoes could favor the investigation of expertise on biomechanical outcomes. Although the sample size was based on an a priori power analysis, it was relatively small in light of the multiple comparisons, which is reflected in non-significant post-hoc analyses. Additionally, due to the ecological valid setup with a real opponent, the precise ATR of the respective participants—whether based on the ball or the opponent—remains unknown. Future studies might overcome this limitation for instance through eye-tracking. Finally, beyond sport-specific expertise, individual cognitive abilities such as reaction time and processing speed may be crucial for unanticipated COD task performance 32 and should therefore also be considered in future research. 5. Conclusions In summary, our findings indicate that increased cognitive demands during COD task performance in female football players predominantly influence proximal kinematics at the hip, pelvis and trunk, rather than knee joint mechanics. This supports the relevance of proximal control strategies in injury-related scenarios characterized by elevated cognitive demands. Moreover, the acceptable ICCs for approach speed as well as ATR Opp and ATR Ball indicate that the test setup successfully created a reliable and ecologically valid scenario within a controlled laboratory environment. Based on our findings, we recommend that practitioners and coaches integrate both whole-body control and cognitively demanding decision-making tasks into testing and injury prevention strategies for female football players regardless of their expertise level. Declarations Competing interests The authors declare no competing interests. Author Contribution All authors listed have made a substantial, direct and intellectual contribution to the work and approved it for publication. Clara Ebner (CE), Urs Granacher (UG) and Dominic Gehring (DG) conceived the idea for the study design. CE performed the data collection, analyzed the resulting dataset and wrote the first draft of the manuscript. UG and DG revised the manuscript. All authors read and approved the final version. Acknowledgement The research was funded by the Federal Institute of Sport Science (Bundesinstitut für Sportwissenschaft, BISp), Germany [Grant Number: ZMI4-070601/24-25] and through a PhD scholarship of Clara Ebner, granted by the Cusanuswerk-Bischöfliche Studienförderung. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.We would like to thank Roland Blechschmied for the illustration of the experimental conditions (Figure 1). 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Effect of gender on trunk and pelvis control during lateral movements with perturbed landing. Eur J Sport Sci . 16(2),182–189, doi:10.1080/17461391.2014.992478 (2016). Worthen-Chaudhari L, Bing J, Schmiedeler JP, Basso DM. A new look at an old problem: Defining weight acceptance in human walking. Gait Posture . 39(1), 588–592, doi:10.1016/j.gaitpost.2013.09.012 (2014). Cohen J. Statistical Power Analysis for the Behavioral Sciences. routledge. (1988). Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med . 15(2), 155–163, doi:10.1016/j.jcm.2016.02.012 (2016). Kim JH, Lee KK, Kong SJ, An KO, Jeong JH, Lee YS. Effect of anticipation on lower extremity biomechanics during side-and cross-cutting maneuvers in young soccer players. American Journal of Sports Medicine . 42(8), 1985–1992, doi:10.1177/0363546514531578 (2014). Powers CM. The influence of abnormal hip mechanics on knee injury: A biomechanical perspective. Journal of Orthopaedic and Sports Physical Therapy . 40(2), 42–51, doi:10.2519/jospt.2010.3337 (2010). Byrne S, Lay B, Staynor J, Alderson J, Donnelly CJ. The effect of planning time on penultimate and ultimate step kinematics and subsequent knee moments during sidestepping. Scand J Med Sci Sports . 32(9), 1366–1376, doi:10.1111/sms.14194 (2022). Donelon TA, Dos’Santos T, Pitchers G, Brown M, Jones PA. Biomechanical Determinants of Knee Joint Loads Associated with Increased Anterior Cruciate Ligament Loading During Cutting: A Systematic Review and Technical Framework. Sports Med Open . 6(1), doi:10.1186/s40798-020-00276-5 (2020). Bertozzi F, Fischer PD, Hutchison KA, Zago M, Sforza C, Monfort SM. Associations Between Cognitive Function and ACL Injury-Related Biomechanics: A Systematic Review. Sports Health: A Multidisciplinary Approach . doi:10.1177/19417381221146557 (2023). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 24 Jun, 2025 Reviews received at journal 23 Jun, 2025 Reviews received at journal 19 Jun, 2025 Reviews received at journal 13 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor assigned by journal 12 Jun, 2025 Editor invited by journal 09 Jun, 2025 Submission checks completed at journal 07 Jun, 2025 First submitted to journal 04 Jun, 2025 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. 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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-6818040","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":470791740,"identity":"6f65a5af-10dc-4593-bf6c-4ef8a9154fa8","order_by":0,"name":"Clara Ebner","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABTUlEQVRIie2RMUvDQBTH33FwWa7NmlDrZ3gQSAkU+1UaChlbpCB1Swm0SzSr+RYBQdysBNql3ZUOdurkoLikEtRramsSizgK5g/He3f3fve/uwdQqNBflLQJTTHIYrfahEMAypOc2DmEfiEU1xnfTLVfIUzZIbBGYD8iO3T5FEG9XZPOlqer63qnAVxfLAA7slQaV3qvUPWyiBIyzXfB6hruRJ+XppbhAq9hE9DwnbKlzs5B87M2SMUVOIRmcGexORmEyIWLYr7FiCHX1b4LZjDKIdILieHdDB6W7Hi1RYQLNrbITR7hmviZkXBhDEppRGzpqh0Jl/xbeJceYKuLU4tWxFuQh+wkQZSw3DL6tqJdZF3k4fCSPPaO2jgZk2fxYygNnSs1EojszW7v7bhe9fKNSa73GZMW0PQOGSh76tMF38+LfyYKFSpU6D/oA4j3ZeJl2LZIAAAAAElFTkSuQmCC","orcid":"","institution":"University of Freiburg","correspondingAuthor":true,"prefix":"","firstName":"Clara","middleName":"","lastName":"Ebner","suffix":""},{"id":470791741,"identity":"2d2641a2-9c18-4bff-bc51-c8bf47f18a7e","order_by":1,"name":"Urs Granacher","email":"","orcid":"","institution":"University of Freiburg","correspondingAuthor":false,"prefix":"","firstName":"Urs","middleName":"","lastName":"Granacher","suffix":""},{"id":470791742,"identity":"0ef44040-8a33-413c-b9ee-466548623476","order_by":2,"name":"Dominic Gehring","email":"","orcid":"","institution":"University of Freiburg","correspondingAuthor":false,"prefix":"","firstName":"Dominic","middleName":"","lastName":"Gehring","suffix":""}],"badges":[],"createdAt":"2025-06-04 08:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6818040/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6818040/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-25069-2","type":"published","date":"2025-10-23T16:17:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84681803,"identity":"e497ce72-2342-4406-82f4-dbd704063c55","added_by":"auto","created_at":"2025-06-16 08:29:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":479867,"visible":true,"origin":"","legend":"\u003cp\u003eExemplified illustration of one movement option for each experimental condition. Further movement options can be found in the description of the conditions in the text. The blue figures represent the opponent, the green figures the participant during COD task performance. The red arrows indicate the analyzed movement option. (A) ANT-1: the grey figures represent the starting positions of the subject and the opponent, with the latter pointing in the desired cutting direction. (B) UNANT-2: the grey figures represent the time of the stimulus provided by the opponent by kicking the ball. (C) UNANT-3: the grey figures represent the time of the stimulus provided by the opponent by stopping the ball. (D) UNANT-4: the grey figures represent the time of the feint, initiated by the opponent shortly before he kicked the ball to the other side.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6818040/v1/07b0f885a1c54870f8335dca.png"},{"id":84681804,"identity":"a63e518d-9ec7-4744-bfa4-f206b729f083","added_by":"auto","created_at":"2025-06-16 08:29:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":190847,"visible":true,"origin":"","legend":"\u003cp\u003eTop row: Means (± SD shaded areas) of knee abduction angle and moment during the weight acceptance phase. ANT = anticipated, UNANT = unanticipated. Bottom row: Statistical parametric mapping (SPM) results for the main condition effects.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6818040/v1/d74f19bebc86146fe89c5096.png"},{"id":84682479,"identity":"55922f8f-9f18-417d-8cbb-05b93f8dd445","added_by":"auto","created_at":"2025-06-16 08:37:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":198743,"visible":true,"origin":"","legend":"\u003cp\u003eTop row: Means (± SD shaded areas) of hip abduction and rotation angle during the weight acceptance phase. ANT = anticipated, UNANT = unanticipated. Bottom row: Statistical parametric mapping (SPM) results for the main condition effects.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6818040/v1/ff0d960437e390abfc637603.png"},{"id":94490210,"identity":"d1d0c892-5df6-4727-9ff8-2fb4a6eaa542","added_by":"auto","created_at":"2025-10-27 17:08:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1719417,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6818040/v1/da4a1df0-1d3e-434f-a94a-4a9a8d11488e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of cognitive demands on whole-body biomechanics during changes-of-direction task performance in female football","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAnterior cruciate ligament (ACL) injuries are a major concern in sports due to their high prevalence rates and long-term health consequences for athletes.\u003csup\u003e1\u003c/sup\u003e In female football, approximately 0.7 ACL injuries per team and season can be expected,\u003csup\u003e1\u003c/sup\u003e which corresponds to an injury risk 4 to 6 times higher than in male football players.\u003csup\u003e2\u003c/sup\u003e Notably, the ACL injury risk is comparable across performance levels in female football, with similar incidence rates in amateur-level and professional players.\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eUnderstanding the underlying biomechanical risk factors is crucial for the design and implementation of effective injury prevention strategies,\u003csup\u003e4\u003c/sup\u003e such as neuromuscular training. The majority of ACL injuries in female football are non-contact (54%) or indirect contact (34%) injuries.\u003csup\u003e5\u003c/sup\u003e More specifically, these injuries occur without direct player or opponent contact to the injured knee. These injuries predominantly arise in complex defensive playing situations, like pressing and tackling, which require rapid deceleration and change-of-direction (COD) maneuvers to follow the offensive player.\u003csup\u003e5,6\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBiomechanical analyses of ACL injury events provide valuable insights into movement patterns associated with an elevated injury risk. A video analysis of 29 ACL injuries in female football identified knee valgus alignment in 88% of the ACL injury events accompanied by an abducted hip and increased hip internal rotation from the initial contact (IC) to the moment of injury occurrence.\u003csup\u003e5\u003c/sup\u003e The authors further observed that players often show lateral trunk flexion to the injured limb and trunk rotation toward the uninjured limb, i.e., in the intended running direction.\u003csup\u003e5\u003c/sup\u003e These findings underscore the importance of considering whole-body kinematics when investigating ACL injury paradigms with the goal to develop targeted prevention strategies.\u003c/p\u003e \u003cp\u003eIn recent years, laboratory-based ACL injury risk assessment approaches have expanded beyond focusing on biomechanical aspects by including neurocognitive aspects, especially when addressing non-contact ACL injuries. Notably, ACL injuries often occur during complex match-play situations, where athletes must react rapidly to an opponent\u0026rsquo;s action.\u003csup\u003e7\u003c/sup\u003e The shift of attentional focus from the player\u0026rsquo;s own COD action towards an external stimulus may impair an player\u0026rsquo;s temporo-spatial perception and may result in insufficient movement control.\u003csup\u003e8\u003c/sup\u003e Additionally, deceiving actions, such as feints, further increase the cognitive demands by requiring defenders to inhibit an intended, pre-planned or even an already initiated motor response.\u003csup\u003e7,8\u003c/sup\u003e The inherent time constraints of these match-play situations challenge appropriate feed-forward control strategies, which are essential to stabilize the whole body and particularly the knee joint.\u003csup\u003e9\u003c/sup\u003e Given that ACL injuries typically occur within 50 ms after initial ground contact, reactive mechanisms, such as muscle reflexes, are often insufficient to mitigate the underlying injury risk.\u003csup\u003e8,10\u003c/sup\u003e Thus, understanding the impact of elevated cognitive demands during complex match-play situations on whole-body kinematics appears crucial to better comprehend the mechanisms underlying ACL injuries.\u003csup\u003e7,11,12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHughes and Dai (2021) proposed a hypothetical model illustrating how cognitive demands may affect motor control in highly dynamic match-play situations.\u003csup\u003e12\u003c/sup\u003e The model emphasizes decision-making processes by distinguishing between anticipated (pre-planned) CODs and unanticipated movements, with the latter requiring reactive adjustments in response to external stimuli, such as an opponent\u0026rsquo;s action.\u003csup\u003e12\u003c/sup\u003e Accordingly, decision-making processes are mainly affected by the number and complexity of movement options, as well as the available time to react (ATR).\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eEmerging evidence from laboratory-based studies indicate that cognitive demands, being operationalized as unanticipated versus anticipated CODs, modulate knee joint mechanics and whole-body kinematics.\u003csup\u003e11,13\u003c/sup\u003e More specifically, higher cognitive demands have been associated with increased knee abduction moments,\u003csup\u003e11,13\u003c/sup\u003e greater lateral trunk flexion to the cutting leg and rotation to the intended running direction,\u003csup\u003e14,15\u003c/sup\u003e as well as increased hip abduction and internal rotation during weight acceptance.\u003csup\u003e16,17\u003c/sup\u003e Several research groups have investigated the influence of ATR, defined as the time from stimulus to IC, on biomechanical parameters associated with ACL injury risk.\u003csup\u003e14,18\u003c/sup\u003e Findings revealed that an ATR of around 600 ms is required to differentiate between anticipated and unanticipated CODs, following an approach run at a speed of 4\u0026ndash;5 m/s.\u003csup\u003e14,18,19\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious research primarily relied on artificial stimuli, such as light signals or arrows, to create unanticipated conditions with 2 (e.g., left or right) or 3 (e.g., left, right or straight) possible movement options.\u003csup\u003e12,19\u003c/sup\u003e However, only few studies have examined the effects of decision-making using ecologically valid stimuli to enhance task complexity and better simulate match-play scenarios.\u003csup\u003e12,16,20\u003c/sup\u003e For instance, Lee et al. (2013) compared the effects of ecologically valid stimuli (i.e., 1 vs. 2 video-animated opponents) versus an artificial stimulus (i.e., arrow) on knee mechanics and trunk kinematics in football players of low versus high performance and training caliber while performing COD tasks with 2 options (right/left).\u003csup\u003e15\u003c/sup\u003e Higher knee abduction moments were found in unanticipated versus anticipated tasks, with the highest moments and most unfavorable trunk kinematics in arrow-induced CODs compared to the condition with video-animated opponents.\u003csup\u003e15\u003c/sup\u003e Furthermore, only the condition with video-animated opponents was able to discriminate between performance levels, with low-level players demonstrating a less favorable COD strategy.\u003csup\u003e15\u003c/sup\u003e Further research using video-animated or real opponent stimuli confirmed higher knee abduction moments\u003csup\u003e16\u003c/sup\u003e and hip abduction angles\u003csup\u003e16,20\u003c/sup\u003e in unanticipated CODs with 2 options versus anticipated CODs. In summary, ecologically valid stimuli seem to increase cognitive demands in match-play situations, especially in players with limited sport-specific expertise and potentially less developed visual-perceptual skills.\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDespite increasing interest in the role of cognitive demands on ACL injury risk, no studies are currently available that have examined the effects of cognitive demands, by systematically increasing the number of movement options in a complex match-play scenario. Finally, the available research has primarily focused on knee and hip biomechanics, despite the multi-segmental nature of ACL injuries involving the trunk, pelvis, and foot.\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTherefore, the primary aim of this study was to investigate the effects of increasing cognitive demands on whole-body biomechanics associated with an ACL injury risk during COD task performance in female football players. We systematically increased cognitive demands across 4 levels by varying the number of movement options in a complex football-specific laboratory setting. A secondary aim was to examine whether football-specific expertise moderates the influence of cognitive demands on biomechanical responses by comparing female football players of high versus low training and performance caliber.\u003c/p\u003e \u003cp\u003eBased on the relevant literature,\u003csup\u003e15,16,20\u003c/sup\u003e we hypothesized that the gradual increase of cognitive demands during COD task performance may amplify knee joint mechanics associated with ACL injuries (e.g., knee abduction angles and moments), as well as whole-body kinematics such as trunk and hip alignment. Furthermore, we hypothesized that higher football-specific expertise may reduce the influence of increasing cognitive demands on ACL injury-associated biomechanics.\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eUsing a within-subject repeated-measures design, this study examined the effects of 4 levels of cognitive demands on biomechanics during COD task performance. The study protocol was preregistered on the Open Science Framework and is available at [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17605/OSF.IO/4Z5R8\u003c/span\u003e\u003cspan address=\"10.17605/OSF.IO/4Z5R8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eSample size estimation for the primary research question was based on the effect sizes for knee abduction moments and trunk kinematics (f\u0026thinsp;=\u0026thinsp;0.27\u0026ndash;0.29) of a study with related study design.\u003csup\u003e15\u003c/sup\u003e The a priori power analysis\u003csup\u003e21\u003c/sup\u003e revealed a minimum required sample size of 21 participants to achieve 80% statistical power at an alpha level of 0.05. To account for potential data loss and facilitate analyses addressing the secondary objective, a total sample size of 30 participants was targeted.\u003c/p\u003e \u003cp\u003eAccordingly, 30 female athletes volunteered to participate in this study. One participant had to be excluded due to an adverse event during an approach run, resulting in a final sample of 29 participants. Participants completed a questionnaire that included questions on anthropometrics, their current health status assessed via the Physical Activity Readiness Questionnaire (PAR-Q), previous lower limb injuries and their sporting background, including football-expertise.\u003c/p\u003e \u003cp\u003eThe participants were categorized into 2 groups according to their training and performance caliber. The high expertise group (HE) included 15 players aged 22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 years (body height\u0026thinsp;=\u0026thinsp;168.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 cm, mass\u0026thinsp;=\u0026thinsp;63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 kg) and the low expertise group (LE) included 14 participants aged 22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 years (body height\u0026thinsp;=\u0026thinsp;168.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6 cm, mass\u0026thinsp;=\u0026thinsp;63.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6 kg).\u003c/p\u003e \u003cp\u003eHE participants competed in the 1st to 4th German football leagues, with an average of 14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 years of club-level playing experience and 8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 hours of play per week. In contrast, LE participants had limited football experience, having completed only 1 year of a university football course and averaging 0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 playing hours per week. However, 6 of the LE participants were engaged in other team sports such as basketball or volleyball.\u003c/p\u003e \u003cp\u003eAll participants were free of lower limb injuries within the past 3 months prior to the start of the study. Previous ACL injuries were not an exclusion criterion if they had occurred or were surgically treated at least 24 months prior to study participation. Three participants had previously suffered an ACL injury (HE\u0026thinsp;=\u0026thinsp;2; NE\u0026thinsp;=\u0026thinsp;1), two of whom had been treated surgically.\u003c/p\u003e \u003cp\u003e Prior to testing, all participants were informed about potential risks and provided written informed consent. The study was conducted in accordance with the latest version of the Declaration of Helsinki, and the protocol was approved by the local ethics committee of University of Freiburg, Germany (approval number 24-1142-S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental setup and conditions\u003c/h2\u003e \u003cp\u003eApproximately 60% of ACL injuries in female football players occur in defensive playing situations like pressing.\u003csup\u003e5\u003c/sup\u003e Therefore, the experimental setup was designed to simulate a match-like defensive scenario. Participants performed COD tasks, in which they adopted the role of a defender against a real opponent in ball possession. Each movement task included a submaximal approach run of 5 m at 4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 m/s, followed by a 90\u0026deg; COD to the left or the right in response to the opponent who determined the direction of the participant by kicking a ball to one side. To ensure reliable and consistent testing conditions, 2 experienced male footballers competing at regional level were selected as opponents and were equally assigned to participants from the HE and LE groups.\u003c/p\u003e \u003cp\u003e The participants performed CODs under experimental conditions with increasing cognitive demands, presented in block-randomized order for each participant. In the anticipated (ANT-1) condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), participants performed a COD to the left or the right, which was indicated by a hand signal of the opponent prior to the approach run and therefore resulted in 1 option for the participant. The opponent then performed an inside kick to the right or the left at a standardized time point during the participants\u0026rsquo; approach run. In the unanticipated condition with 2 options (UNANT-2, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), participants initiated the approach run without prior knowledge of the cutting direction, requiring them to react rapidly as the opponent kicked the ball to either side at the same standardized time point as in the ANT-1 condition. The unanticipated condition with 3 options (UNANT-3, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) further increased the cognitive demand by introducing an additional third movement option. Here, the opponent either passed the ball left or right or stopped it by placing the foot on top of the ball, requiring the participant to decelerate and stop quickly in front of the ball. The unanticipated condition with 4 options (UNANT-4, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) provided the highest number and complexity of options by containing all previous movement options along with a deceptive feint in several trials. Performing the feint, the opponent initially moved the foot toward the ball, indicating a pass to one direction before quickly switching the supporting leg and kicking the ball to the opposite side. To ensure temporal comparability across conditions, the feint was initiated slightly earlier, ensuring that ball kicking occurred at a timepoint comparable to the other conditions. According to recent evidence,\u003csup\u003e19\u003c/sup\u003e we aimed for an ATR between 300\u0026ndash;600 ms. Accordingly, the opponent provided the stimulus by kicking or stopping the ball when the participant was 1.5 m from the center of the force plate. The selected approach run speed of 4 m/s resulted in an ATR of approximately 375 ms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eParticipants performed CODs on a standard laboratory floor wearing neutral indoor football shoes (Mundial Goal, Adidas, Herzogenaurach, Germany). Prior to data collection, participants completed a standardized warm-up protocol to prepare for fast, dynamic movements. Subsequently, participants performed at least 3 familiarization trials in each experimental condition.\u003c/p\u003e \u003cp\u003eTo prevent modification in stride length or movement patterns, participants were not instructed to target the force plate during COD execution. However, complete foot contact of the cutting leg on the force plate was required for valid trials. During familiarization, the preferred cutting leg was determined, and only CODs of this leg were taken for further data analysis. For clarity, only the 90\u0026deg; CODs with the preferred leg were analyzed. The other movement options, i.e., CODs to the non-preferred side, CODs after the feint and the stopping maneuvers, served solely to increase cognitive demands but were not included in the final analysis.\u003c/p\u003e \u003cp\u003eEach participant performed at least 14 trials per condition, 7 of which were CODs with the preferred cutting leg. If fewer than 5 valid trials were recorded due to missing the force plate or deviating from the desired approach speed, further trials were performed until meeting the required number of 5 valid trials.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data collection and analysis\u003c/h2\u003e \u003cp\u003eThe approach run speed was measured by 2 timing gates (Witty Gate, Microgate, Mahopac, NY, USA), placed at a distance of 3.5 m and 1.5 m from the center of the force plate.\u003c/p\u003e \u003cp\u003eThree-dimensional motion data of the participant, the opponent and the ball were collected at 200 Hz using a marker-based motion analyses system with 12 cameras (Vicon Motion Systems Ltd., Oxford, Great Britain). Ground reaction forces (GRF) were recorded at 1000 Hz using a ground-embedded force plate measuring 0.9 m x 0.6 m (AMTI BP600900, Watertown, MA USA). Motion capture data and GRF were synchronized (Vicon D-Link) to allow inverse dynamic calculations.\u003c/p\u003e \u003cp\u003eA customized marker set was used to analyze players\u0026rsquo; trunk, lower limbs and foot kinematics, and to track the movement initiation of the ball and opponent. Based on a previously established marker set,\u003csup\u003e14\u003c/sup\u003e 37 markers were placed on the participant\u0026rsquo;s head (4 markers), trunk (suprasternal notch, xiphoid process, T6 vertebra), pelvis (anterior and posterior superior iliac spines), legs (lateral thigh and shank, medial and lateral epicondyle of the knee, tuberositas tibiae, medial and lateral malleolus) and shoe (3-marker clusters on the forefoot and rearfoot). To track the ball movement, a 3-marker cluster was attached to the ball. Additionally, the opponent was equipped with 18 markers, distributed across specific landmarks on the whole body and the shoes. To ensure reliability for marker placement, the same experienced researcher placed all markers across participants.\u003c/p\u003e \u003cp\u003eMarker trajectories and GRF signals were pre-processed with regards to labelling and gap-filling and finally filtered with a low-pass Butterworth filter (4th order, 20 Hz cut-off frequency) in Vicon Nexus. A static calibration trial was conducted with the participant standing in a predefined neutral position within a foot calibration rig to determine segment length and joint centers. Ankle and knee joint centers were defined as the midpoint between the medial and lateral malleoli and medial and lateral epicondyles, respectively.\u003csup\u003e14,22\u003c/sup\u003e Hip joint centers were functionally determined using a standardized dynamic movement protocol (\u0026ldquo;star-arc movement\u0026rdquo;).\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSegment coordinate systems and calculations of joint angles and joint moments were established using a custom-written script in BodyBuilder (Vicon Motion Systems Ltd., Oxford, UK). Briefly, vertical axes for the thigh and shank were defined from distal to proximal joint centers, with the mediolateral axes being defined through the medial and lateral epicondyle and malleolus markers, and the anteroposterior axes resulting from the cross product. Pelvis and trunk segment axes were defined to match with previous publications.\u003csup\u003e14,24\u003c/sup\u003e Joint angles were computed using a YXZ Euler rotation sequence. Knee joint rotations were defined as flexion-extension around the Y-axis, adduction-abduction around the subsequently X\u0026rsquo;-axis, and internal-external rotation around the Z\u0026rsquo;\u0026rsquo;-axis.\u003csup\u003e14\u003c/sup\u003e External knee joint moments were then calculated using a standard inverse dynamics approach and normalized to each participant\u0026rsquo;s body mass. The IC was set to the frame in which vertical GRF exceeded a value of 20 N. All further processing was performed using custom scripts in Matlab (R2022b, The MathWorks Inc.).\u003c/p\u003e \u003cp\u003eThe discrete biomechanical variables were selected based on their association with the ACL injury mechanism.\u003csup\u003e5,11\u003c/sup\u003e At IC, knee flexion and foot progression angles as well as pelvis, hip and trunk angles in the frontal and transverse plane were extracted, reflecting preparatory movements for initiating the COD.\u003csup\u003e14\u003c/sup\u003e Furthermore, the peak knee abduction angle and moment were extracted during weight acceptance (WA), as this early phase of stance can be considered relevant for ACL injuries.\u003csup\u003e8\u003c/sup\u003e WA was defined as the time from IC to maximum knee flexion.\u003csup\u003e25\u003c/sup\u003e A detailed description of the joint angles and moment directions will be provided in the results section.\u003c/p\u003e \u003cp\u003eThe control parameters approach speed and ATR were analyzed to assess their consistency within and between subjects. As it was uncertain whether participants primarily reacted to opponent or ball movements, we opted to report both measures. ATR\u003csub\u003eOpp\u003c/sub\u003e was defined as the time from initial movement of the opponent's kicking foot until IC, while ATR\u003csub\u003eBall\u003c/sub\u003e was defined as the time from initial ball movement until IC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses of discrete data were computed in R software (version 2023.12.0\u0026thinsp;+\u0026thinsp;369). For each participant and experimental condition, the dependent biomechanical variables were averaged across 5 trials. Normal distribution of data was confirmed using the Shapiro-Wilk test and by visual inspection of Q-Q-Plots. Homogeneity of variance was confirmed using the Levene's test. A mixed 4x2 ANOVA was used to examine the effects of the within-subject factor \u003cem\u003econdition\u003c/em\u003e (4 levels: 1, 2, 3, 4 options) and the between-subject-factor \u003cem\u003eexpertise\u003c/em\u003e (2 levels: high, low). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and significant main group and condition effects and interactions thereof were followed up with paired t-tests using Bonferroni correction. Effect sizes of the ANOVA analyses were reported as partial eta squared (η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e) and can be interpreted as small (\u0026lt;\u0026thinsp;0.06), medium (0.06\u0026ndash;0.14) and large (\u0026gt;\u0026thinsp;0.14) effects.\u003csup\u003e26\u003c/sup\u003e Cohen\u0026rsquo;s d was calculated to assess the effect sizes of paired t-tests and can be interpreted as small (\u0026lt;\u0026thinsp;0.5), medium (0.5\u0026ndash;0.8) and large (\u0026gt;\u0026thinsp;0.8) effects.\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAdditionally, a statistical parametric mapping (SPM) approach was performed to analyze continuous biomechanical data using the spm1d package in MATLAB (R2022b, The MathWorks Inc.). The WA phase was used for analyses. A one-way repeated-measures ANOVA was performed to assess differences in biomechanical variables between the 4 experimental conditions. If ANOVA tests reached significance, Bonferroni adjusted paired t-tests were calculated.\u003c/p\u003e \u003cp\u003eTo assess the reliability of the experimental conditions, intraclass correlation coefficients (ICCs) and their 95% confident intervals (CI) were calculated for the control parameters approach speed, ATR\u003csub\u003eBall\u003c/sub\u003e and ATR\u003csub\u003eOpp\u003c/sub\u003e using a two-way random effects model (ICC(2, k)).\u003csup\u003e27\u003c/sup\u003e ICC estimates were interpreted as poor (\u0026lt;\u0026thinsp;0.5), moderate (0.5\u0026ndash;0.75), good (0.75\u0026ndash;0.9), and excellent (\u0026gt;\u0026thinsp;0.9) reliability.\u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Control parameters\u003c/h2\u003e \u003cp\u003eThe approach speed remained within the required range of 4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 m/s (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with no significant effects of condition (p\u0026thinsp;=\u0026thinsp;0.117, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.07) or expertise (p\u0026thinsp;=\u0026thinsp;0.181, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.07). Similarly, both time-to-react variables, i.e., ATR\u003csub\u003eOpp\u003c/sub\u003e and ATR\u003csub\u003eBall\u003c/sub\u003e, did not differ between conditions (p\u0026thinsp;=\u0026thinsp;0.703, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.02 and p\u0026thinsp;=\u0026thinsp;0.073, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.08, respectively) or between expertise levels (p\u0026thinsp;=\u0026thinsp;0.21, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.06 and p\u0026thinsp;=\u0026thinsp;0.144, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.08, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All control parameters showed good between-condition reliability with ICCs of 0.89 [95% CI: 0.82, 0.94] for approach speed, 0.76 [95% CI: 0.63, 0.89] for ATR\u003csub\u003eOpp\u003c/sub\u003e and 0.79 [95% CI: 0.68, 0.86] for ATR\u003csub\u003eBall\u003c/sub\u003e.\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\u003eMeans\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs of the control parameters approach speed, ATR\u003csub\u003eOpp\u003c/sub\u003e and ATR\u003csub\u003eBall\u003c/sub\u003e for the 4 experimental conditions.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpertise\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANT-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUNANT-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUNANT-3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUNANT-4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eApproach speed [m/s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eATR\u003csub\u003eOpp\u003c/sub\u003e [s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eATR\u003csub\u003eBall\u003c/sub\u003e [s]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eANT\u0026nbsp;=\u0026nbsp;anticipated, ATR\u0026nbsp;=\u0026nbsp;available time to react, HE\u0026nbsp;=\u0026nbsp;high expertise, LE\u0026nbsp;=\u0026nbsp;low expertise, UNANT\u0026nbsp;=\u0026nbsp;unanticipated\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Discrete biomechanical variables\u003c/h2\u003e \u003cp\u003eNo significant main condition effects were observed for any of the knee joint related discrete variables, including peak knee abduction moment, peak knee abduction angle, and knee flexion angle at IC (see 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\u003eKnee kinetics and kinematics during change-of-direction task performance: means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs and statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpertise\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANT-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUNANT-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUNANT-3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUNANT-4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCondition Effect\u003c/p\u003e \u003cp\u003e(p-value, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExpertise Effect\u003c/p\u003e \u003cp\u003e(p-value, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCondition*Expertise Effect (p-value, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePeak knee abduction moment\u003c/b\u003e [Nm/kg]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.075 (0.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.716 (0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.180 (0.058)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePeak knee abduction angle\u003c/b\u003e [\u0026deg;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.516 (0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.729 (0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.081 (0.079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eKnee flexion angle at IC\u003c/b\u003e [\u0026deg;]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.625 (0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.146 (0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.817 (0.011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eANT\u0026nbsp;=\u0026nbsp;anticipated, IC\u0026nbsp;=\u0026nbsp;initial contact, HE\u0026nbsp;=\u0026nbsp;high expertise, LE\u0026nbsp;=\u0026nbsp;low expertise, UNANT\u0026nbsp;=\u0026nbsp;unanticipated\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn contrast, significant condition effects were observed for proximal joint and segment kinematics. Regarding hip and pelvis kinematics at IC (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), significant main condition effects were found for hip rotation (p\u0026thinsp;=\u0026thinsp;0.034, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.101), frontal pelvis tilt (p\u0026thinsp;=\u0026thinsp;0.004, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.149) and pelvis rotation (p\u0026thinsp;=\u0026thinsp;0.012, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.125). Post-hoc analyses revealed that the pelvis was significantly less tilted and rotated towards the running direction in the UNANT-4 condition, i.e., in unanticipated CODs with the highest cognitive demand, than in ANT-1 (p\u0026thinsp;=\u0026thinsp;0.006, d = -0.686 and p\u0026thinsp;=\u0026thinsp;0.041, d\u0026thinsp;=\u0026thinsp;0.543, respectively). Post-hoc analyses for hip rotation did not reach the level of statistical significance. Lateral trunk flexion at IC was not significantly affected by the condition (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, there was a significant condition effect on trunk rotation at IC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ηp\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.249). Post-hoc analyses revealed that the trunk was significantly more rotated to the cutting leg in the ANT-1 condition compared to UNANT-2 (p\u0026thinsp;=\u0026thinsp;0.023, d = -0.585), UNANT-3 (p\u0026thinsp;=\u0026thinsp;0.002, d = -0.745) and UNANT-4 (p\u0026thinsp;=\u0026thinsp;0.007, d = -0.669). No significant condition effects were found for foot progression angle at IC (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKinematics of the pelvis, hip, trunk and foot at initial contact (IC) during change-of-direction task performance: means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs and statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpertise\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eANT-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUNANT-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUNANT-3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUNANT-4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCondition Effect\u003c/p\u003e \u003cp\u003e(p-value, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExpertise Effect\u003c/p\u003e \u003cp\u003e(p-value, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCondition*Expertise Effect (p-value, η\u003csub\u003ep\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFrontal pelvis tilt\u003c/b\u003e [\u0026deg;]\u003c/p\u003e \u003cp\u003e(- = towards running direction, i.e., iliac crest higher on the side of the cutting leg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.004 (0.149)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.086 (0.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.770 (0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePelvis rotation\u003c/b\u003e [\u0026deg;]\u003c/p\u003e \u003cp\u003e(\u0026thinsp;+\u0026thinsp;=\u0026thinsp;towards running direction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.012 (0.125)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.457 (0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.291 (0.045)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eHip abduction/adduction\u003c/b\u003e [\u0026deg;]\u003c/p\u003e \u003cp\u003e(\u0026thinsp;+\u0026thinsp;=\u0026thinsp;abduction)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.405 (0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.689 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.652 (0.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eHip rotation\u003c/b\u003e [\u0026deg;]\u003c/p\u003e \u003cp\u003e(\u0026thinsp;+\u0026thinsp;=\u0026thinsp;internal rotation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.034 (0.101)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.373 (0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.716 (0.016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eLateral trunk flexion\u003c/b\u003e [\u0026deg;]\u003c/p\u003e \u003cp\u003e(\u0026thinsp;+\u0026thinsp;=\u0026thinsp;to the cutting leg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.541 (0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.069 (0.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.974 (0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTrunk rotation\u003c/b\u003e [\u0026deg;]\u003c/p\u003e \u003cp\u003e(- = to the cutting leg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-17.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026nbsp;0.001 (0.249)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.181 (0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.561 (0.025)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFoot progression\u003c/b\u003e [\u0026deg;]\u003c/p\u003e \u003cp\u003e(\u0026thinsp;+\u0026thinsp;=\u0026thinsp;internal rotation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0\u0026nbsp;\u0026plusmn;\u0026nbsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.9\u0026nbsp;\u0026plusmn;\u0026nbsp;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.7\u0026nbsp;\u0026plusmn;\u0026nbsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.3\u0026nbsp;\u0026plusmn;\u0026nbsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.312 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.169 (0.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.478 (0.030)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u0026nbsp;\u0026plusmn;\u0026nbsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.2\u0026nbsp;\u0026plusmn;\u0026nbsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9\u0026nbsp;\u0026plusmn;\u0026nbsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.6\u0026nbsp;\u0026plusmn;\u0026nbsp;6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIC\u0026nbsp;=\u0026nbsp;initial contact, HE\u0026nbsp;=\u0026nbsp;high expertise, LE\u0026nbsp;=\u0026nbsp;low expertise\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo significant main group effects or condition-by-group interaction effects were observed for none of the selected biomechanical variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Continuous biomechanical variables\u003c/h2\u003e \u003cp\u003eThe SPM-analyses revealed no significant main condition effects for the parameters knee abduction moment and knee flexion angle during WA. Significant differences in knee abduction angle were found from 39% \u0026minus;\u0026thinsp;48% of WA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, p\u0026thinsp;=\u0026thinsp;0.0458, F\u0026thinsp;=\u0026thinsp;3.975). However, Bonferroni-corrected post-hoc analyses did not reach the level of statistical significance. Regarding hip kinematics, significant differences were found for the hip abduction angle (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, 63% \u0026minus;\u0026thinsp;100% of WA, p\u0026thinsp;=\u0026thinsp;0.035, F\u0026thinsp;=\u0026thinsp;3.540) and the hip rotation angle (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, 8% \u0026minus;\u0026thinsp;32% of WA, p\u0026thinsp;=\u0026thinsp;0.032, F\u0026thinsp;=\u0026thinsp;3.866), again without reaching significance through post-hoc tests.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn female football, ACL injuries most frequently occur during cognitively demanding match situations\u0026mdash;particularly when players execute rapid COD maneuvers in response to an opponent\u0026rsquo;s action.\u003csup\u003e5,6\u003c/sup\u003e Evidence from previous laboratory-based studies suggests that cognitively demanding unanticipated CODs can modulate biomechanical parameters associated with ACL injuries.\u003csup\u003e11,13\u003c/sup\u003e However, to date, the impact of systematically elevating cognitive demand\u0026mdash;by progressively increasing both the number and complexity of available movement options\u0026mdash;on these underlying mechanisms remains poorly understood.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to address this research gap by creating complex match-play scenarios in which female athletes executed a 90\u0026deg; COD task in response to a real opponent, while systematically elevating cognitive demand across 4 levels by increasing the number of available movement options. It is noteworthy to add that we only analyzed 90\u0026deg; CODs to one side, while the remaining movement options served to increase the cognitive demands. The secondary aim was to determine whether football-specific expertise influences biomechanical responses to progressively elevated cognitive demands.\u003c/p\u003e \u003cp\u003eContrary to our expectations, no significant main condition effects were found for peak knee abduction angles, knee flexion angles at IC, or for peak knee abduction moments. However, a trend toward increased peak knee abduction moments were found with increasing cognitive demands. This is in line with findings from a recently published meta-analyses,\u003csup\u003e19\u003c/sup\u003e which compared anticipated and unanticipated CODs in physically active individuals. Although this meta-analysis reported no significant pooled effect of anticipation, more than half of the included studies revealed higher knee abduction moments in unanticipated conditions. \u003csup\u003e19\u003c/sup\u003e For instance, Bill et al. (2022) showed higher knee abduction moments when participants reacted to a movement of a real opponent compared to anticipated trials.\u003csup\u003e16\u003c/sup\u003e In another study, Lee et al. (2013) showed that unanticipated trials elicited by video-animated opponents produced elevated knee abduction moments, but interestingly even higher moments were observed in arrow‐cued COD maneuvers.\u003csup\u003e15\u003c/sup\u003e The authors suggested that ecologically valid match-play stimuli, such as video-based opponents, may prolong the athlete\u0026rsquo;s ATR by providing earlier visual cues about the intended direction.\u003csup\u003e15\u003c/sup\u003e Conversely, the cognitive demands required to interpret complex visual information and to generate an appropriate motor response are likely to be higher than for simple artificial stimuli.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOne possible explanation for the absence of significant main condition effects at the knee joint level in our study may therefore lie in the characteristics of the ATR. Previous studies identified an ATR of around 600 ms to be required to differentiate unanticipated from anticipated CODs at an approach speed of 4.0 m/s.\u003csup\u003e19\u003c/sup\u003e Accordingly, we ensured consistency of ATR and approach speed across the conditions, which was reflected by good ICCs and no condition effects for all control parameters. However, while ATR\u003csub\u003eBall\u003c/sub\u003e remained within the predefined range of 300\u0026ndash;600 ms, ATR\u003csub\u003eOpp\u003c/sub\u003e\u0026ndash;defined as the time from the initial movement of the opponent's kicking foot to the participant's IC on the force plate\u0026ndash;was between 700\u0026ndash;750 ms. Accordingly, the resulting ATR in the respective trials may have varied depending on whether the participants focused on the opponent's body movement or the ball trajectory to obtain visual cues for the requested movement option. It can therefore be speculated that the prolonged ATR\u003csub\u003eOpp\u003c/sub\u003e may have diminished condition effects on several biomechanical outcomes, including knee joint mechanics.\u003c/p\u003e \u003cp\u003eNo statistically significant main condition effects were found for hip abduction/adduction angles at IC. However, significant main condition effects were observed for hip abduction during WA, and for the internal rotation angle during both IC and WA. Even though post-hoc analyses did not reach the level of statistical significance, visual inspection of the continuous waveforms (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) suggests increased hip abduction and internal rotation across all unanticipated conditions compared to ANT-1. These findings are consistent with outcomes from previous studies\u003csup\u003e16,17,20,28\u003c/sup\u003e and gain additional relevance when considered alongside the findings from video analyses that revealed pronounced hip abduction and internal rotation during ACL injury occurrence.\u003csup\u003e5,6\u003c/sup\u003e Since the hip and knee joint are mechanically linked via the femur, alterations of hip joint kinematics can directly contribute to altered or even detrimental knee mechanics.\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn addition, pelvis kinematics were significantly influenced by cognitive demands. In the cognitively most demanding UNANT-4 condition, the pelvis was significantly less laterally tilted towards the new running direction, i.e., with the iliac crest being higher toward the new running direction, compared to the ANT-1 condition. This is in line with a finding from a previous study\u003csup\u003e30\u003c/sup\u003e which suggests that greater lateral pelvis tilt in anticipated CODs facilitates lateral foot placement, thereby enabling the generation of higher push-off forces, while reducing the need for pronounced hip abduction angles in the cutting leg.\u003csup\u003e30\u003c/sup\u003e This proposed mechanism aligns with our findings of increased hip abduction during WA in unanticipated conditions. Furthermore, the pelvis rotation to the new running direction was significantly reduced in UNANT-4 compared to ANT-1, which indicates delayed reorientation in the most demanding cognitive condition. In this sense, reduced pelvis rotation may increase the need for high hip internal rotation to achieve the desired direction change;\u003csup\u003e31\u003c/sup\u003e a pattern reflected in our results showing a main condition effect on hip rotation during WA. In summary, these findings highlight the role of pelvis kinematics and control for modulating hip and knee joint mechanics during COD performance.\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAt the trunk level, a significantly more pronounced rotation to the cutting leg was observed in the ANT-1 condition compared to the other cognitive demand conditions. Interestingly, we found no significant main effects of condition on lateral trunk flexion, which is not in line with previous studies.\u003csup\u003e14,15,30\u003c/sup\u003e Given the large mass of the trunk and its influence on the body\u0026rsquo;s center of mass, lateral trunk flexion is discussed to increase external knee abduction moments by shifting the ground reaction force vector laterally relative to the knee joint.\u003csup\u003e30,31\u003c/sup\u003e In this context, Powers et al. (2010) emphasized the contributing role of proximal segments and joints, especially in the frontal plane, with respect to ACL injuries.\u003csup\u003e29\u003c/sup\u003e With this in mind, the absence of effects on lateral trunk flexion in the current study might help to explain why knee abduction biomechanics also were not affected by increasing cognitive demands. Furthermore, the trunk and hip joint are characterized by a great range of motion, whereas knee frontal plane mechanics are inherently limited. Given this, potential effects of increasing cognitive demands may have primarily emerged at the proximal joints, while subtle changes of knee joint mechanics may have been too small or covered by measurement inaccuracies.\u003c/p\u003e \u003cp\u003eIt is important to note that pelvis and trunk rotation were quantified using different reference systems. Pelvis rotation was quantified relative to the global coordinate system, whereas trunk rotation was computed relative to the pelvis. This methodological distinction may explain the seemingly contradictory finding that, in the anticipated condition, the pelvis was more rotated to the new running direction, whereas the trunk was more rotated towards the cutting leg. However, this in line with Byrne et al. (2022), who also reported global pelvis and intersegmental trunk angles.\u003csup\u003e30\u003c/sup\u003e When reporting intersegmental trunk angles, pelvis kinematics should always be reported alongside to allow interpretation of the contribution of pelvis and trunk segments to whole-body biomechanics.\u003csup\u003e30\u003c/sup\u003e Additionally, this information may assist practitioners to decide whether to prioritize pelvis or trunk control or to consider both when developing ACL injury prevention strategies.\u003c/p\u003e \u003cp\u003eIn summary, our findings suggest that increasing cognitive demands during COD task performance primarily affect proximal kinematic strategies, particularly at the hip, pelvis, and trunk. As the post-hoc analyses did not reveal significant differences between the 3 unanticipated conditions, our measurement paradigm may be considered only partially successful in differentiating between increasing levels of cognitive demands. However, while these analyses did not reach the level of statistical significance, visual inspection indicated gradual effects on knee abduction moments, pelvis kinematics and hip with increasing cognitive demands. Regarding the pelvis, only the UNANT-4 condition had a significant effect on kinematics, indicating that complex conditions with a high number of movement options are required to affect the movement control of female athletes in a sport-specific scenario.\u003c/p\u003e \u003cp\u003eBased on findings of Lee et al. (2013), we hypothesized that higher football-specific expertise would reduce the biomechanical effects of increasing cognitive demands.\u003csup\u003e15\u003c/sup\u003e However, we did not find an effect of football-specific expertise for any of the biomechanical variables examined in this study. One possible explanation is that the groups might have been relatively similar with regard to their expertise in performing unanticipated CODs in team sports. Six of the LE participants played handball (n\u0026thinsp;=\u0026thinsp;3) or rugby (n\u0026thinsp;=\u0026thinsp;3) and may have benefited from their experience in these sports in terms of anticipating and interpreting an opponent\u0026rsquo;s movements.\u003c/p\u003e \u003cp\u003eFurthermore, our measurement setup included no football-specific elements apart from the opponent kicking a ball. Future studies investigating the effects of expertise should tailor the setup precisely to the target sport, e.g., by including a sport-specific task for the participant such as kicking or throwing a ball. Additionally, the use of artificial turf and football-specific footwear instead of a standard laboratory floor and neutral shoes could favor the investigation of expertise on biomechanical outcomes.\u003c/p\u003e \u003cp\u003eAlthough the sample size was based on an a priori power analysis, it was relatively small in light of the multiple comparisons, which is reflected in non-significant post-hoc analyses. Additionally, due to the ecological valid setup with a real opponent, the precise ATR of the respective participants\u0026mdash;whether based on the ball or the opponent\u0026mdash;remains unknown. Future studies might overcome this limitation for instance through eye-tracking.\u003c/p\u003e \u003cp\u003eFinally, beyond sport-specific expertise, individual cognitive abilities such as reaction time and processing speed may be crucial for unanticipated COD task performance\u003csup\u003e32\u003c/sup\u003e and should therefore also be considered in future research.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, our findings indicate that increased cognitive demands during COD task performance in female football players predominantly influence proximal kinematics at the hip, pelvis and trunk, rather than knee joint mechanics. This supports the relevance of proximal control strategies in injury-related scenarios characterized by elevated cognitive demands. Moreover, the acceptable ICCs for approach speed as well as ATR\u003csub\u003eOpp\u003c/sub\u003e and ATR\u003csub\u003eBall\u003c/sub\u003e indicate that the test setup successfully created a reliable and ecologically valid scenario within a controlled laboratory environment.\u003c/p\u003e \u003cp\u003eBased on our findings, we recommend that practitioners and coaches integrate both whole-body control and cognitively demanding decision-making tasks into testing and injury prevention strategies for female football players regardless of their expertise level.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors listed have made a substantial, direct and intellectual contribution to the work and approved it for publication. Clara Ebner (CE), Urs Granacher (UG) and Dominic Gehring (DG) conceived the idea for the study design. CE performed the data collection, analyzed the resulting dataset and wrote the first draft of the manuscript. UG and DG revised the manuscript. All authors read and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe research was funded by the Federal Institute of Sport Science (Bundesinstitut f\u0026uuml;r Sportwissenschaft, BISp), Germany [Grant Number: ZMI4-070601/24-25] and through a PhD scholarship of Clara Ebner, granted by the Cusanuswerk-Bisch\u0026ouml;fliche Studienf\u0026ouml;rderung. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.We would like to thank Roland Blechschmied for the illustration of the experimental conditions (Figure 1).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHall\u0026eacute;n A, Tom\u0026aacute;s R, Ekstrand J, et al. UEFA Women\u0026rsquo;s Elite Club Injury Study: a prospective study on 1527 injuries over four consecutive seasons 2018/2019 to 2021/2022 reveals thigh muscle injuries to be most common and ACL injuries most burdensome. \u003cem\u003eBr J Sports Med\u003c/em\u003e. 58(3), 128\u0026ndash;136, doi:10.1136/bjsports-2023-107133 (2024).\u003c/li\u003e\n\u003cli\u003eAchenbach L, Bloch H, Klein C, et al. 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Associations Between Cognitive Function and ACL Injury-Related Biomechanics: A Systematic Review. \u003cem\u003eSports Health: A Multidisciplinary Approach\u003c/em\u003e. doi:10.1177/19417381221146557 (2023).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anterior cruciate ligament, Cognition, Female football, kinematics","lastPublishedDoi":"10.21203/rs.3.rs-6818040/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6818040/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnterior cruciate ligament injuries often occur during change-of-directions (CODs), particularly when combined with cognitively demanding decision-making tasks. This study investigated the effects of increasing movement options during CODs in response to a real opponent on whole-body biomechanics in female football players.\u003c/p\u003e \u003cp\u003eTwenty-nine female football players (15 with high and 14 with low expertise) performed 90\u0026deg; CODs in response to a real opponents\u0026rsquo; action under four conditions: anticipated with one option (ANT-1), unanticipated with two (UNANT-2), three (UNANT-3) or four (UNANT-4) movement options. Three-dimensional motion analysis captured whole-body biomechanics at initial contact and during weight acceptance.\u003c/p\u003e \u003cp\u003eNo significant condition effects were observed for peak knee mechanics. However, at initial contact the pelvis was significantly less tilted and rotated towards the running direction in the UNANT-4 condition than in ANT-1. The hip was significantly more abducted and internally rotated in all unanticipated CODs. Furthermore, trunk rotation to the cutting leg was reduced in all unanticipated conditions compared to ANT-1. No significant differences were found between expertise groups. Increasing cognitive demands in a simulated match-play scenario primarily influenced proximal segment biomechanics during CODs in female football players. We therefore recommend integrating whole-body control and cognitively demanding stimuli into testing and injury prevention strategies.\u003c/p\u003e","manuscriptTitle":"Effects of cognitive demands on whole-body biomechanics during changes-of-direction task performance in female football","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 08:29:19","doi":"10.21203/rs.3.rs-6818040/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-24T06:07:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T04:49:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-19T05:29:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-13T08:29:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301011912768308383193710546148293874713","date":"2025-06-13T05:35:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184341970965719355559266761996881607657","date":"2025-06-13T04:08:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316245450645684327361790335529789803410","date":"2025-06-13T04:00:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-13T03:55:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T03:24:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-09T10:57:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-07T09:25:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-04T08:10:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d317d5d-a752-4855-a20a-9802caca991a","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49997330,"name":"Health sciences/Anatomy/Musculoskeletal system"},{"id":49997331,"name":"Biological sciences/Psychology"}],"tags":[],"updatedAt":"2025-10-27T16:27:54+00:00","versionOfRecord":{"articleIdentity":"rs-6818040","link":"https://doi.org/10.1038/s41598-025-25069-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-23 16:17:08","publishedOnDateReadable":"October 23rd, 2025"},"versionCreatedAt":"2025-06-16 08:29:19","video":"","vorDoi":"10.1038/s41598-025-25069-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-25069-2","workflowStages":[]},"version":"v1","identity":"rs-6818040","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6818040","identity":"rs-6818040","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}