Trunk Constraints Increase Knee Joint Kinetics During Sidestep Cutting in Female Athletes: Implications for Anterior Cruciate Ligament Injury Risk

Trunk Constraints Increase Knee Joint Kinetics During Sidestep Cutting in Female Athletes: Implications for Anterior Cruciate Ligament Injury Risk | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Trunk Constraints Increase Knee Joint Kinetics During Sidestep Cutting in Female Athletes: Implications for Anterior Cruciate Ligament Injury Risk Daniel Kadlec, Matthew J. Jordan, Jacqueline Alderson, Sophia Nimphius This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6487198/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract This study investigated the effects of task constraints applied at the trunk and preparatory step on lower-body joint kinetics associated with ACL injury risk during sidestepping in female athletes. Twenty-one trained female athletes performed six sidestep conditions: pre-planned and unplanned sidesteps, each with and without trunk (holding ~ 5–7.5% body mass at chest level) and preparatory-step (ducking under an adjustable rope at eye height) constraints. Relative joint power at the hip, knee, ankle, and entry velocity, and sidestep angle, were analysed using linear mixed models and statistical parametric mapping. In pre-planned sidesteps, trunk constraints significantly increased negative peak knee joint power compared to unconstrained conditions ( p = 0.02) and increased negative knee joint power during early stance (23–27%, p < 0.001). Preparatory-step constraints did not alter knee joint power but significantly increased hip joint power relative to trunk-constrained and unconstrained conditions between 23–35% of stance (p < 0.001). Unplanned sidesteps showed no significant kinetic differences among conditions. Implementing trunk constraints during pre-planned sidesteps increases mechanical demands on the knee joint, facilitating progressive overload and enhancing ACL injury resilience. These findings inform practical training strategies to increase tissue capacity and prepare female athletes for high-risk sidestepping scenarios, potentially contributing to effective ACL injury prevention interventions. Health sciences/Health care Health sciences/Risk factors ACL biomechanics training task constraints joint power Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Non-contact anterior cruciate ligament (ACL) injuries during sidestep cutting maneuvers typically occur within the first 50 ms after initial contact when the imposed mechanical demands exceed the tissue capacities. 1 , 2 Combined externally applied knee flexion, valgus and internal rotation moments can elevate ACL strain up to the point of tissue failure. 3 , 4 The mechanical knee joint demands are well documented, demonstrating how joint kinematics affect externally applied knee joint moments. 5 – 7 Factors such as single joint alignments (e.g., knee flexion during initial contact, lateral trunk sway, hip abduction) 5 , 8 , 9 and entry velocity 10 can increase knee joint moments during sidesteps. These insights have subsequently been used to inform training interventions aiming to reduce ACL injury risk by recommending to minimise or avoid these specific joint angles or segment interactions when sidestepping. 8 , 11 – 14 However, despite extensive research, ACL injuries are not declining 15 , questioning whether avoiding specific high-risk movements sufficiently prepares athletes, especially considering that such compromised positions can arise in the context of complex in-game scenarios. In other words, increasing an athlete’s load tolerance during high-impact activities (e.g., sidesteps with suboptimal body positions) and preparing them for such “worst-case” scenarios may be more effective for reducing ACL injury risk. 16 Traditionally, knee joint moments have served as primary measures to infer ACL loading and subsequent injury risk during sidestepping tasks. However, relying exclusively on isolated plane-specific moments neglects the interdependency of multiplanar loading conditions and disregards the critical factor of load application rate. Joint power, defined as the product of joint moments and angular velocities, inherently accounts for both the magnitude and rate of mechanical energy absorption (negative power) or generation (positive power) at the knee joint. Evaluating joint power thus provides additional complementary insights into knee loading conditions that traditional moment analysis alone cannot capture. Resistance training enhances muscular strength and tissue integrity, improving the ability to handle mechanical demands during high-impact activities. 17 – 19 However, access and opportunity to dedicated and supervised resistance training are often limited, especially outside of male professional sports. Therefore, alternative training methods that practitioners can implement during on-field sessions may provide an alternative approach to increasing injury resiliency and robustness. An emerging approach based on dynamical systems theory is to manipulate constraints (i.e. organism, task and environment) to change the execution strategy to provide a mechanical overload. 20 – 23 For example, changing the execution strategy with task constraints alters joint kinetics during vertical jumping 24 , sprinting 25 and isometric mid-thigh pulls 26 , thus changing the strain on certain muscle groups and joint structures. Executing countermovement jumps with different execution strategies increased net ankle joint moments by ~ 50% during propulsion. 24 Further, sprinting over mini-hurdles increased hip joint angular velocity and hip extension moments just before ground contact compared to unconstrained sprints. 25 Altering the position during an isometric mid-thigh pull increased peak hip extension moments up to ten-fold. 26 During sidestep cutting, trunk positioning, particularly increased transverse and frontal plane trunk rotation opposite to the cutting direction, and reacting to external stimuli (e.g., a generic visual cue or opponent movement), elevate multiplanar knee joint moments 5 , 27 , 28 . Deliberately manipulating these factors through external constraints could intentionally alter athlete execution strategy, influencing both joint kinematics and kinetics. Such targeted constraints could promote specific mechanical adaptations by redistributing joint loading patterns or increasing mechanical demand at selected joints, potentially facilitating soft tissue adaptation and increasing tissue resiliency in the long term. 16 For example, constraints designed to lower an athlete’s center of mass (COM) during the penultimate step, prior to sidestep execution, may encourage athletes to increase sagittal plane hip and knee flexion. Observable kinematic changes to specific constraints compared to unconstrained task execution may shift eccentric load absorption away from the knee and towards the larger hip musculature during the initial stance. Such redistribution could reduce excessive mechanical demands placed directly upon knee joint structures, including the ACL, during early stance periods when sidestepping. Conversely, intentionally disrupting trunk control by imposing a task constraint (e.g., holding an external weight of approximately 5-7.5% body mass at chest level during sidestepping) aims to increase mechanical demand at the knee by altering trunk stability and upper-body posture 29 . Observable kinematic changes may include altered trunk alignment and upper limb positioning. Such alterations increase the mechanical demands on the knee, particularly during the early stance phase of the sidestep cutting. Such heightened demands could promote progressive loading of the knee’s surrounding musculature and ligamentous tissues (including the ACL), offering potential stimulus for adaptation and improved tolerance to similar high-risk situations encountered during gameplay. Critically, constraints-induced modifications in sidestepping mechanics can vary considerably among athletes, resulting in multiple viable execution strategies 5 . Therefore, explicitly examining the resultant kinetic effects of these constraints is necessary to better understand their practical utility in training and injury prevention settings. Thus, we aim to quantify how imposed constraints affect lower-body joint kinetics associated with ACL injury risk during sidestepping tasks. Thus, the primary objective of this study was to examine the effects of trunk and penultimate-step constraints on lower-body joint kinetics compared to unconstrained sidesteps, during both pre-planned and unplanned sidestep cutting. We hypothesised that: (1) imposing a trunk constraint (external weight held at chest level) would increase the mechanical demands on the knee joint, reflected by greater negative joint power; and (2) applying a penultimate-step constraint (ducking under an adjustable rope during the penultimate step) would lower knee joint demands while increasing hip joint eccentric demands relative to unconstrained conditions. We additionally hypothesised that these joint kinetic changes would be consistent across both pre-planned and unplanned sidestep cutting. Results A significant main effect for condition on sidestep angle (F(5, 23.59) = 3.32, p = 0.02) and entry velocity (F(5, 19.9) = 33.3, p < 0.001) was found. Post hoc analyses showed sidestep angle was lower for PP prep compared to UP trunk ( p = 0.03; d = 0.73 [95% CI = 0.36–1.09]). The horizontal velocity of the COM and initial contact (V@IC) was significantly higher in all pre-planned compared to all unplanned conditions by 0.67–0.9 m·s − 1 ( p 2.2), with no significant differences among pre-planned ( p > 0.28) and unplanned conditions ( p = 1.00) (Table 1 ). Table 1 Sidestepping kinematics Condition Sidestep angle (°) V@IC (m·s − 1 ) PP free 38.1 ± 4.5 3.46 ± 0.30 # * † PP trunk 38.3 ± 4.6 3.54 ± 0.37 # * † PP prep 37.6 ± 4.6 * 3.65 ± 0.48 # * † UP free 40.3 ± 5.8 2.78 ± 0.25 UP trunk 40.9 ± 4.7 2.79 ± 0.28 UP prep 39.5 ± 6.3 2.75 ± 0.28 Data are presented as mean ± SD. # = significant difference compared to UP free ( p < 0.001); * = significant difference compared to UP trunk ( p < 0.001); † = significant difference compared to UP prep ( p < 0.001); V@IC = horizontal velocity of the COM at initial contact. COM = Center of mass. No significant differences were found in peak JP HIP ( p = 0.68) between all conditions. A main effect for condition on JP KNEE (F(5, 21.68) = 3.58, p = 0.02) was observed (Fig. 1 ). Post hoc analyses showed JP KNEE was lower for PP trunk compared to UP free ( p = 0.01; d = 0.68 [95% CI = 0.32–1.04]) and compared to PP free ( p = 0.02; d = 0.54 [95% CI = 0.18–0.90]). Similarly, a significant main effect was found for JP ANKLE (F(5, 20.01) = 3.23, p = 0.03), but post hoc analysis failed to reveal between-condition differences ( p = 0.06) (Online supplement – Table 1 ). Exploratory analysis of the random effects revealed significant individual differences in baseline measures, as indicated by the random intercepts' SD for the peak negative joint power. The model accounted for individual variability between PP free and all other conditions (random slopes) (Online supplement – Table 2). Correlations between the random intercepts and slopes were examined to understand the relationship between each participant's baseline level and their response to the conditions and varied widely among all conditions (JP HIP : r = -0.54 to 0.82; JP KNEE : r = -0.33 to 0.75, JP ANKLE : -0.23 to 0.92) (Online supplement – Table 3). The time-normalised mean and SD for net hip, knee, and ankle joint power (W·kg⁻¹) across the stance phase are depicted in Fig. 2 (top panel). SPM repeated-measures ANOVA revealed significant differences among pre-planned conditions for hip joint power (JP HIP ) between 23–35% of stance ({F}=10.044, p < 0.001) and for knee joint power (JP KNEE ) between 23–27% ({F}=10.116, p < 0.001) (Fig. 2 - middle panel). No significant differences in ankle joint power (JP ANKLE ) or between any conditions during unplanned sidesteps were detected (Online Supplement – Fig. 1 ). Post-hoc pairwise contrasts demonstrated significant differences during early stance (Fig. 3 - bottom panel). The PP prep condition elicited significantly greater positive JP HIP values compared to PPfree (21–38%, {t}=4.973, p < 0.001). However, no significant differences emerged between PP free and PP trunk or between PP trunk and PP prep for hop joint power (JP HIP ) after the Bonferroni correction. For knee joint power, the PP trunk condition showed significantly greater negative knee joint power (JP KNEE ) compared to PP free from 23–27% stance ({t}=4.966, p < 0.001). No other significant pairwise differences emerged in the knee joint power post-hoc analyses after correction. Additional exploratory analyses were performed on resultant and decomposed knee joint moments during preplanned conditions to supplement the primary joint power findings. Only the resultant knee joint moment differed significantly among preplanned conditions between 24–33% stance ({F}=7.976, p = 0.002) (Fig. 3 - middle panel). Subsequent pairwise contrasts revealed that the PPtrunk condition had significantly greater resultant knee moments compared to PPprep from 23–36% stance ({t}=4.389, p < 0.001) (Fig. 3 - bottom panel), aligning temporally with the previously observed elevated knee joint power (JP KNEE ) in this timeframe. No significant differences between any conditions during unplanned sidesteps were detected (Online Supplement – Fig. 2 ). SPM analysis of decomposed knee joint moments demonstrated no significant global differences among conditions during early stance in the sagittal (X) or transverse (Z) planes (Fig. 4 – middle panel. However, a significant global difference was detected in the frontal (Y) plane between 55–73% stance ({F}=9.385, p < 0.001), a timeframe outside our primary interest of early stance. Despite the lack of global significance during early stance, pairwise post-hoc contrasts identified localised significant differences between PP trunk and PP prep conditions in the sagittal plane between 27–31% stance ({t}=4.785, p = 0.001) and between PP free and PP trunk conditions in the frontal plane between 52–69% stance ({t}=4.953, p < 0.001) (Fig. 4 – bottom panel). Given the exploratory nature of these decomposed moment analyses and the absence of global significance during early stance, these findings should be interpreted cautiously. No significant differences between any conditions during unplanned sidesteps were detected (Online Supplement – Fig. 3 ). Discussion The study investigated how task constraints affect lower body joint kinetics associated with ACL injury risk during early stance in pre-planned and unplanned sidesteps in female athletes. Our first hypothesis was partially supported with the pre-planned & trunk constrained condition (PP trunk ) significantly increasing negative peak knee joint power (JP KNEE ) compared to the unplanned & unconstrained condition (UP free ) and preplanned & unconstrained condition (PP free ) (Fig. 2 ) and increased negative joint power (JP KNEE ) during early stance (23–27%, p < 0.001) compared to pre-planned & unconstrained condition (PP free ) (Fig. 3 ). Contrary to our initial hypothesis, the pre-planned & preparatory step constrained condition (PP prep ) failed to decrease negative peak knee joint power (JP KNEE ). Instead, positive joint power at the hip increased during early stance compared to the pre-planned & unconstrained condition (PP free ) (21–38%, p < 0.001). Our final hypothesis was rejected as neither unplanned & trunk constrained (UP trunk ) nor the unplanned & preparatory step constrained condition (UP prep ) affected joint power during unplanned sidesteps (UP free ). Our results indicate that while trunk constraints in pre-planned sidesteps increase knee joint demands, a similar effect is not observed in unplanned sidesteps with or without constraints. These findings can inform training interventions to progressively overload the knee surrounding musculature during early stance and prepare athletes for high-impact sidestepping demands. The observed increase in negative knee joint power during pre-planned sidesteps with trunk constraints supports our hypothesis that holding an external mass (~ 5–7.5% of body mass) at chest level elevates mechanical demand at the knee. This finding aligns with existing literature showing altered trunk loading conditions, such as holding sports equipment, and elevating knee joint moments 29 . Although we did not explicitly quantify trunk kinematics, mechanically, adding mass at the trunk will likely shift the body’s centre of mass superiorly and anteriorly, thereby altering ground reaction force orientation relative to the knee joint centre and increasing resultant joint power demands. The increase in negative knee joint power suggests elevated eccentric demands placed upon the knee-extensor musculature and associated soft tissues, highlighting the potential utility of such constraints to progressively overload the knee joint structures. This highlights the importance of trunk control when sidestepping and its influence on lower limb kinetics associated with ACL injury risk. 5 , 27 , 30 These findings reinforce the importance of enhancing athletes’ ability to tolerate imposing demands through targeted resistance, plyometric training and methods to increase trunk capacity (i.e., muscular strength) and facilitate dynamic control when sidestepping (i.e., the ability to control the trunk in different execution strategies) 14 , 19 , 27 Contrary to our hypothesis, implementing a constraint designed to implicitly lower the center of mass (COM) prior to sidestepping did not reduce negative knee joint power. Instead, it resulted in increased positive hip joint power during early stance. This unexpected finding suggests a redistribution of mechanical demand toward the hip musculature rather than the intended reduction of knee joint loading. While our results indicate that the penultimate step constraint influenced joint power distribution, the lack of change in knee joint power highlights the complexity of manipulating movement strategies via constraints alone. One plausible explanation is that athletes accommodated this constraint predominantly through altered hip joint mechanics rather than meaningfully offloading the knee joint. Therefore, imposing such constraints may selectively target hip musculature and joint demands, but might not directly reduce ACL-relevant knee joint kinetics during sidesteps. Further research should investigate whether repeated exposure to similar constraints leads to beneficial long-term adaptations in joint power distribution and improved load tolerance across both the hip and knee joints, potentially offering a complementary training stimulus alongside direct knee-targeted interventions. The constraints failed to change joint power for any lower-limb joints during unplanned sidesteps. This absence of effects may be due to the significantly lower horizontal velocity of the COM at initial contact compared to the pre-planned conditions. Given that higher travel velocities increase peak joint moments during early stance 10 , sidestepping with a reduced horizontal velocity of the COM at initial contact seems ineffective in imposing greater demands to overload any joint capacities 31 . Further, JP KNEE remained unchanged when comparing UP trunk and UP free . As such, holding an external mass in front of the chest when sidestepping may not increase knee joint demands until the athlete reaches a certain horizontal velocity at initial contact. Contrary to previous research, unplanned conditions failed to increase mechanical knee joint demands compared to pre-planned conditions. 12 , 28 , 32 , 33 Although approach velocity was standardised up to 2 m before the targeted sidestepping area, participants likely decelerated to a greater extent during the step(s) prior to sidestepping in unplanned conditions to allow enough time to perceive the generic stimulus and react accordingly. The magnitude of deceleration might reflect the participants' perceived ability to tolerate the imposing mechanical demands nested in the affordance-based control framework. 34 , 35 This framework proposes that skilled performance necessitates that individuals’ motor capacities (e.g., muscle strength) are scaled or calibrated relative to the task and environment. 34 Hence, insufficient capacity may limit the ability to execute desired movements (e.g., sidestepping at high velocities) and compromise performance. 36 – 38 Therefore, training to improve sidestepping performance while tolerating the imposing demands should focus on improving the capacity (i.e. with resistance and plyometric training) and the ability to efficiently utilise this capacity when sidestepping (i.e. with skill training). 39 , 40 Understanding this trade-off-like behaviour between the horizontal velocity at initial contact and the mechanical demands around isolated lower-limb joints is crucial when aiming to overload certain structures and plan design training sessions 10 . It is important to acknowledge that the results and interpretation of group-average data may not apply to single athletes. Considerable variability in the magnitudes of negative peak joint power values (Fig. 2 ) and individual responses to constraints were observed (Online supplemental). This indicates that some participants are more prone to change their execution strategy when utilising the current task constraint than others. Covariates, moderators and mediators like anthropometric differences, muscle strength and activation patterns, training age, and injury history, among others, can contribute to this variable response and should be quantified and reported when possible. 41 Understanding individual responses to constraints is crucial to eliciting desired changes in execution strategy. Future research is necessary to understand how individuals alter their execution strategies in response to various constraints. Our exploratory analysis of continuous resultant knee joint moments provided additional context regarding the underlying mechanics contributing to the observed differences in resultant knee joint power. Specifically, the resultant knee joint moment was different between 24–33% of stance and ( p = 0.002) and notably greater in the pre-planned & trunk constrained (PP trunk ) compared to the pre-planned & preparatory step constrained (PP prep ) during early stance (21–37%, p < 0.001) in the sagittal plane, coinciding precisely with the elevated resultant knee joint power (JP KNEE ) observed in the same timeframe for the pre-planned & trunk constrained (PP trunk ) condition. This temporal alignment reinforces the interpretation that constraints altering trunk positioning can influence mechanical demands at the knee, potentially increasing risk factors associated with ACL injury. Interestingly, the subsequent examination of decomposed knee joint moments (X, Y, and Z components separately) showed no between-group effects during early stance in the preplanned condition and the subsequent pairwise post-hoc comparisons is to be interpreted with caution because decomposing a vector inflates the number of hypothesis tests and ignores the fact that the three components are not independent but merely different projections of the same physical quantity. In the context of these findings, joint power analyses emerge as particularly informative, explicitly incorporating the temporal component of load application rates. Resultant joint power metrics provide valuable insight into dynamic sports movements by quantifying energy flow rates through joints, a perspective often missing from isolated joint moment analyses 42 . However, r esultant mechanical joint power is the dotproduct of its components and therefore a scalar product. As such, the lack of directional specificity underscores the importance of concurrently examining decomposed joint moments to capture multiplanar loading nuances comprehensively. Collectively, these exploratory analyses highlight the complementary nature of joint powers and moments. Future research employing musculoskeletal modelling approaches may offer further refinement on how external joint kinetics relate to ACL strain. Despite the potential benefits discussed in this research, the current study is not without limitations. First, our cohort was limited to female Australian Rules Football players, restricting the generalisability of findings to other sports and populations. Next, we focused exclusively on the dominant leg for sidestepping, leaving questions about whether non-dominant leg mechanics might differ. Further, in the current biomechanical model, we assigned the load for the trunk-constrained conditions to the participant’s mass instead of the isolated trunk segment. This simplification may not fully capture subtle changes in trunk position and orientation when sidestepping and thus may underestimate the outcomes. Lastly, joint kinematics were not considered due to our targeted focus on joint kinetics. Given the strong theoretical and empirical linkage between kinetic factors, such as joint moments and joint powers, and internal joint stress and ACL loading, kinetic variables were prioritised as they provide more direct insights into the mechanisms of injury risk and internal mechanical demands. Nonetheless, including joint kinematics in future research could provide additional context regarding observable movement strategies underpinning the kinetic findings presented here. In conclusion, the current study demonstrates that imposing task constraints during pre-planned sidesteps can significantly alter mechanical demands on the knee joint during early stance in female athletes. Specifically, holding a weighted implement at chest level (trunk constraint) increased negative peak knee joint power compared to unplanned, unconstrained sidesteps. Conversely, no significant changes were observed during unplanned conditions, likely due to a lower entry velocity. These findings support the use of task constraints in athletic training and rehabilitation to progressively expose athletes to higher knee loading, potentially enhancing tissue resilience and better preparing them for the mechanical demands encountered during competition and prepare athletes for “worst-case” scenarios. Understanding how constraints alter lower-body joint loading can help design effective drills to overload single-joint capacities. Materials and Methods Experimental approach to the problem This crossover study examined the effects of two task constraints on knee joint kinetics during pre-planned and unplanned sidestepping compared to unconstrained sidesteps. Six conditions were tested: pre-planned & unconstrained (PP free ), unplanned & unconstrained (UP free ), pre-planned & trunk constrained (PP trunk ), unplanned & trunk constrained (UP trunk ), pre-planned & preparatory step constrained (PP prep ), unplanned & preparatory step constrained (UP prep ). Outcome measures included entry velocity (m·s − 1 ) and sidestep angle (°), peak negative joint power (W·kg − 1 ) for the ankle (JP ANKLE ), knee (JP KNEE ), and hip (JP HIP ) for the cutting step, and continuous joint powers for the ankle, knee and hip throughout the stance phase. Traditionally, knee joint moments have been extensively employed as proxy measures to infer mechanical loading and subsequent ACL injury risk during sidestep-cutting. ACL loading is inherently multiplanar: forces applied predominantly in one plane inevitably produce secondary rotations and translations due to the knee’s complex articular geometry and the inherent asymmetry of tibiofemoral surfaces. However, focusing predominantly on single-plane knee joint moments and treating them as independent entities inherently simplifies the complex mechanical environment in which ACL injuries occur, potentially obscuring critical interactions among frontal, transverse, and sagittal plane loads 43 . Specifically, isolated planar analyses neglect the interdependency of moments across anatomical planes. For instance, knee flexion angle modulates the mechanical effects of frontal (valgus) and transverse (rotational) moments. Consequently, identical peak knee valgus moments can result in markedly different ACL strains depending on the accompanying kneeflexion angle and moment, because ligament orientation and lever arms both change with flexion. Further, the evaluation of joint moments alone disregards the rate of loading, an essential determinant of ligamentous injury, as established extensively within the fields of materials science and tissue biomechanics 44 , 45 . Higher strain rates accelerate tissue failure and reduce the load magnitude required to induce ligamentous rupture over time, highlighting the necessity of evaluating load application rates alongside magnitudes. Joint powers, defined as the product of joint moments and angular velocities, explicitly incorporate this velocity component and thereby quantify not just the magnitude of loading but, crucially, the rate of energy absorption (negative power) or generation (positive power), by the structures (muscles, tendons, ligaments, joint, fascia) surrounding the knee. As such, joint power analyses afford additional and complementary biomechanical insights, potentially overcoming critical limitations inherent in isolated joint moment assessments and facilitating interpretation between condition differences 46 Participants Twenty-one female Australian Rules Football (ARF) players (n = 21; age: 23.5 ± 4.5 y, height: 170.6 ± 5.8 cm, mass: 67.5 ± 6.6 kg, ARF experience: 5.8 ± 4.5 y, resistance training experience: 3.5 ± 2.5 y) participated in this study. Participants were: (1) free of current lower limb injury and currently competing; (2) had not suffered an injury to the lower limb in the past 12 months requiring surgery; (3) free of any neuromuscular or musculoskeletal disorders that affected the lower limb and (4) had at least 12 months of ARF and resistance training experience. All participants were familiar with sidestepping maneuvers. This study was approved by the Edith Cowan University Human Research Ethics Committee (approval number: #22459). All methods were performed in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants before inclusion in the study. A prior power analysis determined a minimum sample size of 20 participants using G*Power (Version 3.1.9.6, University of Dusseldorf, Dusseldorf, Germany) based on a moderate effect size (Cohen’s f of 0.5) between three different conditions (for the pre-planned and unplanned conditions, respectively), a power of 0.95, a type 1 error of 0.05, a correlation among repeated measures of 0.5 and a nonsphericity correction of 0.5. The effect size was based on the change in joint kinetics with constraints compared to unconstrained movements in vertical jumps, sprints and isometric mid-thigh pulls previously reported. 24 – 26 Sidestep protocol Participants performed three to five pre-planned sidesteps and crossover steps, respectively, with only the right limb as warm-up. For clarity, participants would always sidestep to the left and crossover cut to the right. Although only the sidestepping trials to the left (off the right stance leg) were analysed, crossover cuts served as dummy trials to prevent pre-emption. A 7–10 m run-up was used to achieve a consistent approach velocity between 3.5–4.5 m·s − 1 before executing the sidestep on the force plate. 10 , 28 , 47 A 45° sidestep angle was indicated using adhesive tape lines marked on the force plate, but was not examined by trial, as the actual sidestep angle is often much lower upon execution. 10 Data collection started with the PP free condition, followed by either PP trunk or PP prep , which were allocated in counterbalanced order. Next, the UP free condition was performed, followed by either the UP trunk or UP prep conditions, which were also allocated in counterbalanced order. For all unplanned conditions, a 30 cm arrow displayed on a screen 3 m beyond the force plate indicated the cut direction after participants triggered timing gates 2 m before the force plate at a height of 0.8 m and width of 1.5 m. 28 , 48 This distance ensured participants had adequate time to react, but the tasks remained unplanned. 11 Participants were instructed to maintain a consistent approach velocity throughout each trial and focus on the screen to avoid force plate targeting with the execution leg. For the trunk constraint, participants held an external load at chest level throughout the task. Participants under 70 kg used a 4 kg load, and participants above 70 kg used a 6 kg load (~ 5-7.5% of body mass). The selection of this load range was guided by practicality and feasibility considerations. During pilot testing, heavier weights (≥ 7.5% of body mass) excessively restricted trunk motion and led to conservative movement strategies, detracting from realistic sidestepping patterns. As such, this current mass range was sufficient to not compromise participants’ ability to maintain typical sidestepping movement characteristics, thus preserving ecological validity. This extra mass was added to the body mass for all subsequent calculations. To simplify the calculations, we added the mass to the participant, as the majority of the mass is already distributed to the trunk. Further amendments to the biomechanical model (adding the external mass to the trunk segment) did not make a difference beyond the accuracy reported in the current model. 49 For the preparatory step constraint, participants were ducked under an adjustable rope positioned at the participant’s eye level 50 cm before the force plate, which corresponded to the penultimate foot placement prior to the sidestepping task (Fig. 5 ). Approximately 60 seconds of rest between each trial was provided to minimise fatigue. Trials were repeated if approach velocity deviated, foot placement was incorrect, or the incorrect direction was performed. Testing concluded after three valid sidestepping trials per condition. Data collection started with the PP free condition, followed by either PP trunk or PP prep , which were allocated in counterbalanced order. Next, the UP free condition was performed, followed by either the UP trunk or UP prep conditions, which were also allocated in counterbalanced order. For all unplanned conditions, a 30 cm arrow displayed on a screen 3 m beyond the force plate indicated the cut direction after participants triggered timing gates 2 m before the force plate at a height of 0.8 m and width of 1.5 m. 28 , 48 This distance ensured participants had adequate time to react, but the tasks remained unplanned. 11 Participants were instructed to maintain a consistent approach velocity throughout each trial and focus on the screen to avoid force plate targeting with the execution leg. Data Collection Three-dimensional motion data were synchronously collected at 250 Hz using an 8-camera Vicon MX-series system (Vicon Peak Ltd., Oxford, UK). Ground reaction forces were synchronously collected at 1000 Hz using five 600 x 900 mm force plates (Kistler, Type 9290AD, Sindelfingen, Germany). Thirty-eight retroreflective markers were affixed following the University of Western Australia lower-body and torso marker set and model (Version 5). 11 , 50 Single markers were attached to the left and right calcanei, left and right head of the first and fifth metatarsals, left and right anterior and posterior superior iliac spines, sternal notch, xiphoid process, seventh cervical vertebrae and twelfth thoracic vertebrae. Marker clusters for the creation of segment technical coordinate systems were attached to the left and right thigh and shank. To define the ankle joint, single markers were attached to the medial and lateral malleoli, and to define knee width for the functional joint center method below, single markers were positioned on the left and right medial and lateral epicondyles in the static calibration trials (removed for dynamic trials). Functional knee and hip tasks were performed to identify knee and hip joint axes and centers. 50 Kinematics of the hip, knee, and ankle followed ISB standards. 51 Joint kinetics were expressed in the anatomical coordinate system of the distal segment. A total of 378 trials were subsequently analysed (Twenty-one participants * six conditions * three trials). Data were processed using Vicon Nexus (Version 2.10, Vicon Motion Systems, UK) and analysed with Visual 3D software (Version 2020, C-motion, Inc., Rockville.MD). Kinematic data were low-pass-filtered at a cut-off frequency of 15 Hz, using a fourth-order, zero-lag, Butterworth recursive filter, determined via residual analysis. 52 Instantaneous resultant net joint power was calculated from joint angular velocities multiplied by net joint moments (P = M·ω) and summed for all planes. Continuous joint power for the ankle, knee and hip was calculated during the execution step from initial contact to toe-off and linearly registered to 101 data points and used for the subsequent analysis. Peak negative joint power for the ankle, knee and hip was extracted from initial contact to weight acceptance (first local minimum in the ground reaction force or 30% of stance). 32 , 53 Sidestep angle was calculated using the x- and y-coordinates of the stance foot ankle joint center at initial contact (x1 and y1) and the coordinates of the contralateral ankle joint center at initial contact (x2 and y2) using Eq. (1). The entry velocity was defined as the horizontal velocity of the COM at initial contact of the execution step (V@IC). Sidestep angle = \(\:{tan}^{-1}\left(\frac{a}{b}\right)\) ; where a = |x2 − x1| and b = |y2 − y1| Statistical Analysis Mean and standard deviation (SD) were calculated for all variables. Repeated measures correlations (RMcorr package) were used to evaluate the within-participant relationship between dependent variables to justify separate linear mixed-effects models (LMM). 54 No very large correlation (r = 0.7–0.9, which explains more than 50% of variance) was found, so separate univariate mixedeffects models were deemed appropriate. 55 LMMS analysed between-condition differences in all dependent variables using the residual maximum likelihood to estimate variance components. This approach enables the use of individual trials as distinct data points while accounting for within-participant effects and the correlated nature inherent in clustered data. Each condition was treated as fixed effect, with participant intercepts and by-participant random slopes for condition effects incorporated as random effects [dependent variable ~ 1 + condition + (1 + condition | participant)]. When the fixed effect of condition was significant, pair-wise contrasts were Bonferroniadjusted for the 15 possible comparisons among the six conditions (α/15 = 0.0033). Random effects were explored to evaluate within-participant response to all conditions and treated as an exploratory analysis, as this was not part of the initial research questions. Assumptions of homoscedasticity and normality were checked via residual plots and confirmed with Shapiro-Wilk and Levene’s tests. Cohen’s d effect sizes were calculated and interpreted as trivial ( 0.8). 56 Significance was calculated using the gamlj package (Version 2.5.5), which applies Satterthwaite’s method to estimate degrees of freedom and generate p-values in R Studio (Version 1.4.11.06, R Core Team 2018, http://www.R-project.org/ ). Statistical parametric mapping (SPM) was used to compare stance phase joint power differences for the ankle, knee and hip separately between constrained and unconstrained conditions. 57 Only results for the first 30% of stance were interpreted as per our primary research questions, as this reflects the time periods of peak ACL loading and ACL injury occurrence (REF). A SPM one-way repeated measures analysis of variance (ANOVA) determined between-condition differences across all pre-planned and unplanned conditions with corrected α to account for three joints and two conditions (with α/6 = 0.0083). Although the current method of ANOVA post-hoc analysis using the paired SPM T-test and Bonferroni correction is likely too simple, where an interaction effect was observed, post-hoc analysis was completed to further explore the capability of SPM analysis method for future hypothesis generation. Due to the current post-hoc limitations, we compared the effects of these constraints separately within pre-planned and unplanned sidesteps to facilitate interpretation in addition to the discrete measures. As such, for post-hoc analyses, the alpha value was set at 0.0083/3 = 0.0028 for the number of post-hoc tests (between-condition comparisons) with 2-tailed inference analysis. The scalar output statistics (SPM{F} and SPM{T}) were calculated separately at each individual data point and are referred to as a statistical parametric map with the calculation of the SPM{F} and SPM{T} indicating the magnitude of the difference between data. Where the scalar output statistic crossed the critical threshold ({F} and {T}), the null hypothesis was rejected. Because of the smoothness of force-time curves and the inter-dependence of neighboring points, multiple adjacent points of the SPM{F} or SPM{T} curve often exceed the critical threshold and are referred to as “suprathreshold clusters”. To supplement the jointpower findings we performed two additional, exploratory SPM analyses covering (i) the resultant kneejoint moment and (ii) the decomposed knee moments about the sagittal (X), frontal (Y) and transverse (Z) axes. For the resultant moment, a oneway repeatedmeasures SPM{F} ANOVA was run separately for the preplanned and unplanned tasks (Bonferroniadjusted omnibus α = 0.025; posthoc α = 0.0083). For the decomposed moments, six ANOVAs were needed (3 axes × 2 tasks), giving an omnibus α = 0.0083 and a posthoc α = 0.0028. Any suprathreshold clusters are reported as exploratory observations. All SPM analyses were completed in the Spyder IDE (Version 5.5.4) distribution of Python (Version 3.10.11) using the open-source package “spm1d” ( http://www.spm1d.org/ ). Statistical significance was set at α ≤ 0.05. Declarations Acknowledgments Author DK and the project were supported by an Industry Engagement Doctoral Grant with Edith Cowan University and VALD Performance. VALD Performance had no role in study design, data collection, analysis, or interpretation. Authors’ contributions DK and SN designed the study, recruited and managed data collection and conducted the analysis. All authors contributed to the manuscript and interpretation of the results. All authors read and approved the final manuscript. Data availability statement The dataset used (filtered and time-normalised relative joint power and relative knee joint moments, including the SPM code) can be provided from the corresponding author upon request. Competing interests The authors declare that they have no competing interests Supplementary material Online supplementary material is provided References Dai, B., Herman, D., Liu, H., Garrett, W. E. & Yu, B. Prevention of ACL injury, part I: injury characteristics, risk factors, and loading mechanism. Res. Sports Med. 20 , 180–197 (2012). Lloyd, D. G., Buchanan, T. S. & Besier, T. F. 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C., Lloyd, D. G., Lay, B. S., Bourke, P. D. & Alderson, J. A. Effects of different visual stimuli on postures and knee moments during sidestepping. Med. Sci. Sports Exerc. 45 , 1740–1748 (2013). Chaudhari, A. M., Hearn, B. K. & Andriacchi, T. P. Sport-dependent variations in arm position during single-limb landing influence knee loading: implications for anterior cruciate ligament injury. Am. J. Sports Med. 33 , 824–830 (2005). David, S., Komnik, I., Peters, M., Funken, J. & Potthast, W. Identification and risk estimation of movement strategies during cutting maneuvers. J. Sci. Med. Sport . 20 , 1075–1080 (2017). Rolley, T. et al. Anticipatory effects on side-step cutting biomechanics in Women’s Australian Football League players. BMJ Open. Sport Exerc. Med. 9 , (2023). Besier, T. F., Lloyd, D. G., Ackland, T. R. & Cochrane, J. L. Anticipatory effects on knee joint loading during running and cutting maneuvers. Med. Sci. Sports Exerc. 33 , 1176–1181 (2001). Donnelly, C. J. et al. Changes in knee joint biomechanics following balance and technique training and a season of Australian football. Br. J. Sports Med. 46 , 917–922 (2012). Fajen, B. R., Riley, M. A. & Turvey, M. T. Information, affordances, and the control of action in sport. Int. J. Sport Psychol. 40 , 79–107 (2009). Fajen, B. R. Perceiving possibilities for action: on the necessity of calibration and perceptual learning for the visual guidance of action. Perception 34 , 717–740 (2005). McBride, J. M. & Nimphius, S. Biological system energy algorithm reflected in sub-system joint work distribution movement strategies: influence of strength and eccentric loading. Sci. Rep. 10 , 12052 (2020). Puniello, M. S., McGibbon, C. A. & Krebs, D. E. Lifting strategy and stability in strength-impaired elders. Spine 26 , 731–737 (2001). van Knobelsdorff, M. H., van Bergen, N. G., van der Kamp, J., Seifert, L. & Orth, D. 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The non-sagittal knee moment vector identifies ‘at risk’ individuals that the knee abduction moment alone does not. Sports Biomech. 22 , 80–90 (2023). Zitnay, J. L. et al. Accumulation of collagen molecular unfolding is the mechanism of cyclic fatigue damage and failure in collagenous tissues. Sci. Adv. 6 , eaba2795 (2020). Edwards, W. B. Modeling Overuse Injuries in Sport as a Mechanical Fatigue Phenomenon. Exerc. Sport Sci. Rev. 46 , 224 (2018). Kotsifaki, A. et al. Symmetry in Triple Hop Distance Hides Asymmetries in Knee Function After ACL Reconstruction in Athletes at Return to Sports. Am. J. Sports Med. 03635465211063192 10.1177/03635465211063192 (2021). Sankey, S. P. et al. How reliable are knee kinematics and kinetics during side-cutting manoeuvres? Gait Posture . 41 , 905–911 (2015). Lee, M. J. C., Lloyd, D. G., Lay, B. S., Bourke, P. D. & Alderson, J. A. Different visual stimuli affect body reorientation strategies during sidestepping. Scand. J. Med. Sci. Sports . 27 , 492–500 (2017). de Leva, P. Adjustments to Zatsiorsky-Seluyanov’s segment inertia parameters. J. Biomech. 29 , 1223–1230 (1996). Besier, T. F., Sturnieks, D. L., Alderson, J. A. & Lloyd, D. G. Repeatability of gait data using a functional hip joint centre and a mean helical knee axis. J. Biomech. 36 , 1159–1168 (2003). Wu, G. & Cavanagh, P. R. ISB recommendations for standardization in the reporting of kinematic data. J. Biomech. 28 , 1257–1261 (1995). Winter, D. A. Biomechanics and Motor Control of Human Movement (Wiley, 2009). Besier, T. F., Lloyd, D. G., Cochrane, J. L. & Ackland, T. R. External loading of the knee joint during running and cutting maneuvers. Med. Sci. Sports Exerc. 33 , 1168–1175 (2001). Bakdash, J. Z. & Marusich, L. R. Repeated Measures Correlation. Front. Psychol. 8 , 456 (2017). Mukaka, M. A guide to appropriate use of Correlation coefficient in medical research. Malawi Med. J. J. Med. Assoc. Malawi . 24 , 69–71 (2012). Lakens, D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front. Psychol. 4 , 863 (2013). Pataky, T. C., Vanrenterghem, J. & Robinson, M. A. Zero- vs. one-dimensional, parametric vs. non-parametric, and confidence interval vs. hypothesis testing procedures in one-dimensional biomechanical trajectory analysis. J. Biomech. 48 , 1277–1285 (2015). Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Sep, 2025 Reviews received at journal 15 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviews received at journal 03 Jul, 2025 Reviewers agreed at journal 22 Jun, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 29 Apr, 2025 Editor invited by journal 29 Apr, 2025 Submission checks completed at journal 26 Apr, 2025 First submitted to journal 19 Apr, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6487198","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":450171375,"identity":"b5171a16-7b22-4357-b6c2-148df1af5ff3","order_by":0,"name":"Daniel Kadlec","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDCCA2wMDDwFDAz8EC4zsVoMGBgkG0jWYnCAWC18B9gSP7wxsJE3Pt78TIKhwjqxgf2MAV4tkgfYDkvOMUgz3HbmmJkEw5n0xAaeHPxaDA6wN0jzGBxOMLuRwybB2HY4sYGBsJbm3zwG/xOM578BavkH1ML/hpAWtmNAWw4kGEjwALU0ALVIELBF8jBbmuUcg2TDGWfSjC0SjqUbt0k8K8Crhe94m/GNNxV28vzthx/e+FBjLdvPn7wBrxbUiEgAYjb86kfBKBgFo2AUEAMAYL9Bb7lv268AAAAASUVORK5CYII=","orcid":"","institution":"Edith Cowan University","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Kadlec","suffix":""},{"id":450171376,"identity":"98b16515-481b-4af5-9e04-e2c5ae3f831f","order_by":1,"name":"Matthew J. Jordan","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"J.","lastName":"Jordan","suffix":""},{"id":450171377,"identity":"f8ab0b8b-31fd-4263-a050-1f8d0a5073a9","order_by":2,"name":"Jacqueline Alderson","email":"","orcid":"","institution":"The University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Jacqueline","middleName":"","lastName":"Alderson","suffix":""},{"id":450171378,"identity":"97954b6a-a699-444e-b49e-7551f00c044c","order_by":3,"name":"Sophia Nimphius","email":"","orcid":"","institution":"Edith Cowan University","correspondingAuthor":false,"prefix":"","firstName":"Sophia","middleName":"","lastName":"Nimphius","suffix":""}],"badges":[],"createdAt":"2025-04-20 03:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6487198/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6487198/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-38368-z","type":"published","date":"2026-04-14T15:59:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81976340,"identity":"80834a01-ad8e-41d6-bb38-4eabeef9a6c8","added_by":"auto","created_at":"2025-05-05 13:46:42","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":330102,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot and individual dots depicting peak negative joint power for A) the hip joint, B) the knee joint and C) the ankle joint. \u003csup\u003e#\u003c/sup\u003e = significant difference between UP\u003csub\u003efree\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e = 0.01) and PP\u003csub\u003efree \u003c/sub\u003e(\u003cem\u003ep\u003c/em\u003e = 0.02) compared to PP\u003csub\u003etrunk\u003c/sub\u003e. Colored dots depict individual participants.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6487198/v1/921f440f0c07da6c7bc38a0e.jpeg"},{"id":81977494,"identity":"898800a9-99bc-4475-83a8-b5245094e8a4","added_by":"auto","created_at":"2025-05-05 14:02:42","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":279528,"visible":true,"origin":"","legend":"\u003cp\u003eTop panel - Mean and SD time series for the hip, knee and ankle joint power normalised to body mass. Middle panel - SPM repeated measures ANOVA results for all pre-planned conditions. Inference curves with suprathreshold clusters (shaded) with the critical threshold (dashed line). Bottom panel - Follow-up SPM T-test with Bonferroni correction between all pre-planned conditions. Inference curves with suprathreshold clusters (shaded) and critical threshold (dashed line). ANOVA = analysis of variance; SPM = statistical parametric mapping.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6487198/v1/7d8bb68415843cc36808ce0d.jpeg"},{"id":81976341,"identity":"ee1335bb-5592-454b-85e0-1a44a59bdb1b","added_by":"auto","created_at":"2025-05-05 13:46:42","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":156637,"visible":true,"origin":"","legend":"\u003cp\u003eTop panel - Mean and SD time series for the resultant knee joint moment normalised to body mass. Middle panel - SPM repeated measures ANOVA results for all pre-planned conditions. Inference curves with suprathreshold clusters (shaded) with the critical threshold (dashed line). Bottom panel - Follow-up SPM T-test with Bonferroni correction between all pre-planned conditions. Inference curves with suprathreshold clusters (shaded) and critical threshold (dashed line). ANOVA = analysis of variance; SPM = statistical parametric mapping.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6487198/v1/705c8459f20fe1c07300db47.jpeg"},{"id":81976344,"identity":"a6076d8f-e5c2-4283-ad7b-38aa37d7b05d","added_by":"auto","created_at":"2025-05-05 13:46:42","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":284548,"visible":true,"origin":"","legend":"\u003cp\u003eTop panel - Mean and SD time series for the decomposed knee joint moment normalised to body mass. Middle panel - SPM repeated measures ANOVA results for all pre-planned conditions. Inference curves with suprathreshold clusters (shaded) with the critical threshold (dashed line). Bottom panel - Follow-up SPM T-test with Bonferroni correction between all pre-planned conditions. Inference curves with suprathreshold clusters (shaded) and critical threshold (dashed line). ANOVA = analysis of variance; SPM = statistical parametric mapping.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6487198/v1/cf949fa08cdab29dffc0efbf.jpeg"},{"id":81977495,"identity":"c61f6b7b-652f-4505-9278-09610d6c1caf","added_by":"auto","created_at":"2025-05-05 14:02:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":260543,"visible":true,"origin":"","legend":"\u003cp\u003eA) Visual representation of the preparatory step constraint and B) trunk constraint (bottom panel) during the preparatory steps (left column) and execution step (right column). The red highlights indicate the constraints.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6487198/v1/30d9223a14965338ef4e8ad8.png"},{"id":107352240,"identity":"560d3a1b-1419-4c7d-9080-6d87d73ef2fa","added_by":"auto","created_at":"2026-04-20 16:13:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1751208,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6487198/v1/90ced809-dcdf-4e6e-aa07-3423de20c120.pdf"},{"id":81977071,"identity":"cb6b0333-f080-4fe1-81d7-da2043cbb661","added_by":"auto","created_at":"2025-05-05 13:54:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":693591,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-6487198/v1/6fba31000e887c676a5e8e7c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trunk Constraints Increase Knee Joint Kinetics During Sidestep Cutting in Female Athletes: Implications for Anterior Cruciate Ligament Injury Risk","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-contact anterior cruciate ligament (ACL) injuries during sidestep cutting maneuvers typically occur within the first 50 ms after initial contact when the imposed mechanical demands exceed the tissue capacities.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Combined externally applied knee flexion, valgus and internal rotation moments can elevate ACL strain up to the point of tissue failure.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The mechanical knee joint demands are well documented, demonstrating how joint kinematics affect externally applied knee joint moments.\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Factors such as single joint alignments (e.g., knee flexion during initial contact, lateral trunk sway, hip abduction)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and entry velocity\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e can increase knee joint moments during sidesteps. These insights have subsequently been used to inform training interventions aiming to reduce ACL injury risk by recommending to minimise or avoid these specific joint angles or segment interactions when sidestepping.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e However, despite extensive research, ACL injuries are not declining\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, questioning whether avoiding specific high-risk movements sufficiently prepares athletes, especially considering that such compromised positions can arise in the context of complex in-game scenarios. In other words, increasing an athlete\u0026rsquo;s load tolerance during high-impact activities (e.g., sidesteps with suboptimal body positions) and preparing them for such \u0026ldquo;worst-case\u0026rdquo; scenarios may be more effective for reducing ACL injury risk.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTraditionally, knee joint moments have served as primary measures to infer ACL loading and subsequent injury risk during sidestepping tasks. However, relying exclusively on isolated plane-specific moments neglects the interdependency of multiplanar loading conditions and disregards the critical factor of load application rate. Joint power, defined as the product of joint moments and angular velocities, inherently accounts for both the magnitude and rate of mechanical energy absorption (negative power) or generation (positive power) at the knee joint. Evaluating joint power thus provides additional complementary insights into knee loading conditions that traditional moment analysis alone cannot capture.\u003c/p\u003e \u003cp\u003eResistance training enhances muscular strength and tissue integrity, improving the ability to handle mechanical demands during high-impact activities.\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e However, access and opportunity to dedicated and supervised resistance training are often limited, especially outside of male professional sports. Therefore, alternative training methods that practitioners can implement during on-field sessions may provide an alternative approach to increasing injury resiliency and robustness. An emerging approach based on dynamical systems theory is to manipulate constraints (i.e. organism, task and environment) to change the execution strategy to provide a mechanical overload.\u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e For example, changing the execution strategy with task constraints alters joint kinetics during vertical jumping\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, sprinting\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and isometric mid-thigh pulls\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, thus changing the strain on certain muscle groups and joint structures. Executing countermovement jumps with different execution strategies increased net ankle joint moments by ~\u0026thinsp;50% during propulsion.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Further, sprinting over mini-hurdles increased hip joint angular velocity and hip extension moments just before ground contact compared to unconstrained sprints.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Altering the position during an isometric mid-thigh pull increased peak hip extension moments up to ten-fold.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDuring sidestep cutting, trunk positioning, particularly increased transverse and frontal plane trunk rotation opposite to the cutting direction, and reacting to external stimuli (e.g., a generic visual cue or opponent movement), elevate multiplanar knee joint moments \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Deliberately manipulating these factors through external constraints could intentionally alter athlete execution strategy, influencing both joint kinematics and kinetics. Such targeted constraints could promote specific mechanical adaptations by redistributing joint loading patterns or increasing mechanical demand at selected joints, potentially facilitating soft tissue adaptation and increasing tissue resiliency in the long term.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e For example, constraints designed to lower an athlete\u0026rsquo;s center of mass (COM) during the penultimate step, prior to sidestep execution, may encourage athletes to increase sagittal plane hip and knee flexion. Observable kinematic changes to specific constraints compared to unconstrained task execution may shift eccentric load absorption away from the knee and towards the larger hip musculature during the initial stance. Such redistribution could reduce excessive mechanical demands placed directly upon knee joint structures, including the ACL, during early stance periods when sidestepping.\u003c/p\u003e \u003cp\u003eConversely, intentionally disrupting trunk control by imposing a task constraint (e.g., holding an external weight of approximately 5-7.5% body mass at chest level during sidestepping) aims to increase mechanical demand at the knee by altering trunk stability and upper-body posture \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Observable kinematic changes may include altered trunk alignment and upper limb positioning. Such alterations increase the mechanical demands on the knee, particularly during the early stance phase of the sidestep cutting. Such heightened demands could promote progressive loading of the knee\u0026rsquo;s surrounding musculature and ligamentous tissues (including the ACL), offering potential stimulus for adaptation and improved tolerance to similar high-risk situations encountered during gameplay.\u003c/p\u003e \u003cp\u003eCritically, constraints-induced modifications in sidestepping mechanics can vary considerably among athletes, resulting in multiple viable execution strategies\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Therefore, explicitly examining the resultant kinetic effects of these constraints is necessary to better understand their practical utility in training and injury prevention settings. Thus, we aim to quantify how imposed constraints affect lower-body joint kinetics associated with ACL injury risk during sidestepping tasks. Thus, the primary objective of this study was to examine the effects of trunk and penultimate-step constraints on lower-body joint kinetics compared to unconstrained sidesteps, during both pre-planned and unplanned sidestep cutting. We hypothesised that: (1) imposing a trunk constraint (external weight held at chest level) would increase the mechanical demands on the knee joint, reflected by greater negative joint power; and (2) applying a penultimate-step constraint (ducking under an adjustable rope during the penultimate step) would lower knee joint demands while increasing hip joint eccentric demands relative to unconstrained conditions. We additionally hypothesised that these joint kinetic changes would be consistent across both pre-planned and unplanned sidestep cutting.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA significant main effect for condition on sidestep angle (F(5, 23.59)\u0026thinsp;=\u0026thinsp;3.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) and entry velocity (F(5, 19.9)\u0026thinsp;=\u0026thinsp;33.3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was found. \u003cem\u003ePost hoc\u003c/em\u003e analyses showed sidestep angle was lower for PP\u003csub\u003eprep\u003c/sub\u003e compared to UP\u003csub\u003etrunk\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03; \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.73 [95% CI\u0026thinsp;=\u0026thinsp;0.36\u0026ndash;1.09]). The horizontal velocity of the COM and initial contact (V@IC) was significantly higher in all pre-planned compared to all unplanned conditions by 0.67\u0026ndash;0.9 m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003ed\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;2.2), with no significant differences among pre-planned (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.28) and unplanned conditions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eSidestepping kinematics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSidestep angle (\u0026deg;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV@IC (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003csub\u003efree\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30 \u003csup\u003e#\u003c/sup\u003e * \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003csub\u003etrunk\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e38.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 \u003csup\u003e#\u003c/sup\u003e * \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePP\u003csub\u003eprep\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e37.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48 \u003csup\u003e#\u003c/sup\u003e * \u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUP\u003csub\u003efree\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e40.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUP\u003csub\u003etrunk\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e40.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUP\u003csub\u003eprep\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e39.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\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\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. \u003csup\u003e#\u003c/sup\u003e = significant difference compared to UP\u003csub\u003efree\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); * = significant difference compared to UP\u003csub\u003etrunk\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); \u003csup\u003e\u0026dagger;\u003c/sup\u003e = significant difference compared to UP\u003csub\u003eprep\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); V@IC\u0026thinsp;=\u0026thinsp;horizontal velocity of the COM at initial contact. COM\u0026thinsp;=\u0026thinsp;Center of mass.\u003c/p\u003e \u003cp\u003eNo significant differences were found in peak JP\u003csub\u003eHIP\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.68) between all conditions. A main effect for condition on JP\u003csub\u003eKNEE\u003c/sub\u003e (F(5, 21.68)\u0026thinsp;=\u0026thinsp;3.58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). \u003cem\u003ePost hoc\u003c/em\u003e analyses showed JP\u003csub\u003eKNEE\u003c/sub\u003e was lower for PP\u003csub\u003etrunk\u003c/sub\u003e compared to UP\u003csub\u003efree\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.68 [95% CI\u0026thinsp;=\u0026thinsp;0.32\u0026ndash;1.04]) and compared to PP\u003csub\u003efree\u003c/sub\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02; \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.54 [95% CI\u0026thinsp;=\u0026thinsp;0.18\u0026ndash;0.90]). Similarly, a significant main effect was found for JP\u003csub\u003eANKLE\u003c/sub\u003e (F(5, 20.01)\u0026thinsp;=\u0026thinsp;3.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), but \u003cem\u003epost hoc\u003c/em\u003e analysis failed to reveal between-condition differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06) (Online supplement \u0026ndash; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eExploratory analysis of the random effects revealed significant individual differences in baseline measures, as indicated by the random intercepts' SD for the peak negative joint power. The model accounted for individual variability between PP\u003csub\u003efree\u003c/sub\u003e and all other conditions (random slopes) (Online supplement \u0026ndash; Table\u0026nbsp;2). Correlations between the random intercepts and slopes were examined to understand the relationship between each participant's baseline level and their response to the conditions and varied widely among all conditions (JP\u003csub\u003eHIP\u003c/sub\u003e: r = -0.54 to 0.82; JP\u003csub\u003eKNEE\u003c/sub\u003e: r = -0.33 to 0.75, JP\u003csub\u003eANKLE\u003c/sub\u003e: -0.23 to 0.92) (Online supplement \u0026ndash; Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eThe time-normalised mean and SD for net hip, knee, and ankle joint power (W\u0026middot;kg⁻\u0026sup1;) across the stance phase are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (top panel). SPM repeated-measures ANOVA revealed significant differences among pre-planned conditions for hip joint power (JP\u003csub\u003eHIP\u003c/sub\u003e) between 23\u0026ndash;35% of stance ({F}=10.044, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and for knee joint power (JP\u003csub\u003eKNEE\u003c/sub\u003e) between 23\u0026ndash;27% ({F}=10.116, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e - middle panel). No significant differences in ankle joint power (JP\u003csub\u003eANKLE\u003c/sub\u003e) or between any conditions during unplanned sidesteps were detected (Online Supplement \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePost-hoc pairwise contrasts demonstrated significant differences during early stance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e - bottom panel). The PP\u003csub\u003eprep\u003c/sub\u003e condition elicited significantly greater positive JP\u003csub\u003eHIP\u003c/sub\u003e values compared to PPfree (21\u0026ndash;38%, {t}=4.973, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, no significant differences emerged between PP\u003csub\u003efree\u003c/sub\u003e and PP\u003csub\u003etrunk\u003c/sub\u003e or between PP\u003csub\u003etrunk\u003c/sub\u003e and PP\u003csub\u003eprep\u003c/sub\u003e for hop joint power (JP\u003csub\u003eHIP\u003c/sub\u003e) after the Bonferroni correction. For knee joint power, the PP\u003csub\u003etrunk\u003c/sub\u003e condition showed significantly greater negative knee joint power (JP\u003csub\u003eKNEE\u003c/sub\u003e) compared to PP\u003csub\u003efree\u003c/sub\u003e from 23\u0026ndash;27% stance ({t}=4.966, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No other significant pairwise differences emerged in the knee joint power post-hoc analyses after correction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditional exploratory analyses were performed on resultant and decomposed knee joint moments during preplanned conditions to supplement the primary joint power findings. Only the resultant knee joint moment differed significantly among preplanned conditions between 24\u0026ndash;33% stance ({F}=7.976, p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e - middle panel). Subsequent pairwise contrasts revealed that the PPtrunk condition had significantly greater resultant knee moments compared to PPprep from 23\u0026ndash;36% stance ({t}=4.389, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e - bottom panel), aligning temporally with the previously observed elevated knee joint power (JP\u003csub\u003eKNEE\u003c/sub\u003e) in this timeframe. No significant differences between any conditions during unplanned sidesteps were detected (Online Supplement \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSPM analysis of decomposed knee joint moments demonstrated no significant global differences among conditions during early stance in the sagittal (X) or transverse (Z) planes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026ndash; middle panel. However, a significant global difference was detected in the frontal (Y) plane between 55\u0026ndash;73% stance ({F}=9.385, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a timeframe outside our primary interest of early stance. Despite the lack of global significance during early stance, pairwise post-hoc contrasts identified localised significant differences between PP\u003csub\u003etrunk\u003c/sub\u003e and PP\u003csub\u003eprep\u003c/sub\u003e conditions in the sagittal plane between 27\u0026ndash;31% stance ({t}=4.785, p\u0026thinsp;=\u0026thinsp;0.001) and between PP\u003csub\u003efree\u003c/sub\u003e and PP\u003csub\u003etrunk\u003c/sub\u003e conditions in the frontal plane between 52\u0026ndash;69% stance ({t}=4.953, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026ndash; bottom panel). Given the exploratory nature of these decomposed moment analyses and the absence of global significance during early stance, these findings should be interpreted cautiously. No significant differences between any conditions during unplanned sidesteps were detected (Online Supplement \u0026ndash; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study investigated how task constraints affect lower body joint kinetics associated with ACL injury risk during early stance in pre-planned and unplanned sidesteps in female athletes. Our first hypothesis was partially supported with the pre-planned \u0026amp; trunk constrained condition (PP\u003csub\u003etrunk\u003c/sub\u003e) significantly increasing negative peak knee joint power (JP\u003csub\u003eKNEE\u003c/sub\u003e) compared to the unplanned \u0026amp; unconstrained condition (UP\u003csub\u003efree\u003c/sub\u003e) and preplanned \u0026amp; unconstrained condition (PP\u003csub\u003efree\u003c/sub\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and increased negative joint power (JP\u003csub\u003eKNEE\u003c/sub\u003e) during early stance (23\u0026ndash;27%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to pre-planned \u0026amp; unconstrained condition (PP\u003csub\u003efree\u003c/sub\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Contrary to our initial hypothesis, the pre-planned \u0026amp; preparatory step constrained condition (PP\u003csub\u003eprep\u003c/sub\u003e) failed to decrease negative peak knee joint power (JP\u003csub\u003eKNEE\u003c/sub\u003e). Instead, positive joint power at the hip increased during early stance compared to the pre-planned \u0026amp; unconstrained condition (PP\u003csub\u003efree\u003c/sub\u003e) (21\u0026ndash;38%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Our final hypothesis was rejected as neither unplanned \u0026amp; trunk constrained (UP\u003csub\u003etrunk\u003c/sub\u003e) nor the unplanned \u0026amp; preparatory step constrained condition (UP\u003csub\u003eprep\u003c/sub\u003e) affected joint power during unplanned sidesteps (UP\u003csub\u003efree\u003c/sub\u003e). Our results indicate that while trunk constraints in pre-planned sidesteps increase knee joint demands, a similar effect is not observed in unplanned sidesteps with or without constraints. These findings can inform training interventions to progressively overload the knee surrounding musculature during early stance and prepare athletes for high-impact sidestepping demands.\u003c/p\u003e \u003cp\u003eThe observed increase in negative knee joint power during pre-planned sidesteps with trunk constraints supports our hypothesis that holding an external mass (~\u0026thinsp;5\u0026ndash;7.5% of body mass) at chest level elevates mechanical demand at the knee. This finding aligns with existing literature showing altered trunk loading conditions, such as holding sports equipment, and elevating knee joint moments\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Although we did not explicitly quantify trunk kinematics, mechanically, adding mass at the trunk will likely shift the body\u0026rsquo;s centre of mass superiorly and anteriorly, thereby altering ground reaction force orientation relative to the knee joint centre and increasing resultant joint power demands. The increase in negative knee joint power suggests elevated eccentric demands placed upon the knee-extensor musculature and associated soft tissues, highlighting the potential utility of such constraints to progressively overload the knee joint structures. This highlights the importance of trunk control when sidestepping and its influence on lower limb kinetics associated with ACL injury risk.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e These findings reinforce the importance of enhancing athletes\u0026rsquo; ability to tolerate imposing demands through targeted resistance, plyometric training and methods to increase trunk capacity (i.e., muscular strength) and facilitate dynamic control when sidestepping (i.e., the ability to control the trunk in different execution strategies)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eContrary to our hypothesis, implementing a constraint designed to implicitly lower the center of mass (COM) prior to sidestepping did not reduce negative knee joint power. Instead, it resulted in increased positive hip joint power during early stance. This unexpected finding suggests a redistribution of mechanical demand toward the hip musculature rather than the intended reduction of knee joint loading. While our results indicate that the penultimate step constraint influenced joint power distribution, the lack of change in knee joint power highlights the complexity of manipulating movement strategies via constraints alone. One plausible explanation is that athletes accommodated this constraint predominantly through altered hip joint mechanics rather than meaningfully offloading the knee joint. Therefore, imposing such constraints may selectively target hip musculature and joint demands, but might not directly reduce ACL-relevant knee joint kinetics during sidesteps. Further research should investigate whether repeated exposure to similar constraints leads to beneficial long-term adaptations in joint power distribution and improved load tolerance across both the hip and knee joints, potentially offering a complementary training stimulus alongside direct knee-targeted interventions.\u003c/p\u003e \u003cp\u003eThe constraints failed to change joint power for any lower-limb joints during unplanned sidesteps. This absence of effects may be due to the significantly lower horizontal velocity of the COM at initial contact compared to the pre-planned conditions. Given that higher travel velocities increase peak joint moments during early stance\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, sidestepping with a reduced horizontal velocity of the COM at initial contact seems ineffective in imposing greater demands to overload any joint capacities\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Further, JP\u003csub\u003eKNEE\u003c/sub\u003e remained unchanged when comparing UP\u003csub\u003etrunk\u003c/sub\u003e and UP\u003csub\u003efree\u003c/sub\u003e. As such, holding an external mass in front of the chest when sidestepping may not increase knee joint demands until the athlete reaches a certain horizontal velocity at initial contact.\u003c/p\u003e \u003cp\u003eContrary to previous research, unplanned conditions failed to increase mechanical knee joint demands compared to pre-planned conditions.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Although approach velocity was standardised up to 2 m before the targeted sidestepping area, participants likely decelerated to a greater extent during the step(s) prior to sidestepping in unplanned conditions to allow enough time to perceive the generic stimulus and react accordingly. The magnitude of deceleration might reflect the participants' perceived ability to tolerate the imposing mechanical demands nested in the affordance-based control framework.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e This framework proposes that skilled performance necessitates that individuals\u0026rsquo; motor capacities (e.g., muscle strength) are scaled or calibrated relative to the task and environment.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Hence, insufficient capacity may limit the ability to execute desired movements (e.g., sidestepping at high velocities) and compromise performance.\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Therefore, training to improve sidestepping performance while tolerating the imposing demands should focus on improving the capacity (i.e. with resistance and plyometric training) and the ability to efficiently utilise this capacity when sidestepping (i.e. with skill training).\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Understanding this trade-off-like behaviour between the horizontal velocity at initial contact and the mechanical demands around isolated lower-limb joints is crucial when aiming to overload certain structures and plan design training sessions \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is important to acknowledge that the results and interpretation of group-average data may not apply to single athletes. Considerable variability in the magnitudes of negative peak joint power values (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and individual responses to constraints were observed (Online supplemental). This indicates that some participants are more prone to change their execution strategy when utilising the current task constraint than others. Covariates, moderators and mediators like anthropometric differences, muscle strength and activation patterns, training age, and injury history, among others, can contribute to this variable response and should be quantified and reported when possible.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Understanding individual responses to constraints is crucial to eliciting desired changes in execution strategy. Future research is necessary to understand how individuals alter their execution strategies in response to various constraints.\u003c/p\u003e \u003cp\u003eOur exploratory analysis of continuous resultant knee joint moments provided additional context regarding the underlying mechanics contributing to the observed differences in resultant knee joint power. Specifically, the resultant knee joint moment was different between 24\u0026ndash;33% of stance and (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and notably greater in the pre-planned \u0026amp; trunk constrained (PP\u003csub\u003etrunk\u003c/sub\u003e) compared to the pre-planned \u0026amp; preparatory step constrained (PP\u003csub\u003eprep\u003c/sub\u003e) during early stance (21\u0026ndash;37%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the sagittal plane, coinciding precisely with the elevated resultant knee joint power (JP\u003csub\u003eKNEE\u003c/sub\u003e) observed in the same timeframe for the pre-planned \u0026amp; trunk constrained (PP\u003csub\u003etrunk\u003c/sub\u003e) condition. This temporal alignment reinforces the interpretation that constraints altering trunk positioning can influence mechanical demands at the knee, potentially increasing risk factors associated with ACL injury.\u003c/p\u003e \u003cp\u003eInterestingly, the subsequent examination of decomposed knee joint moments (X, Y, and Z components separately) showed no between-group effects during early stance in the preplanned condition and the subsequent pairwise post-hoc comparisons is to be interpreted with caution because decomposing a vector inflates the number of hypothesis tests and ignores the fact that the three components are not independent but merely different projections of the same physical quantity.\u003c/p\u003e \u003cp\u003eIn the context of these findings, joint power analyses emerge as particularly informative, explicitly incorporating the temporal component of load application rates. Resultant joint power metrics provide valuable insight into dynamic sports movements by quantifying energy flow rates through joints, a perspective often missing from isolated joint moment analyses \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. However, r\u003cem\u003eesultant mechanical joint power is the dotproduct of its components and therefore a scalar product. As such, the lack of\u003c/em\u003e directional specificity underscores the importance of concurrently examining decomposed joint moments to capture multiplanar loading nuances comprehensively. Collectively, these exploratory analyses highlight the complementary nature of joint powers and moments. Future research employing musculoskeletal modelling approaches may offer further refinement on how external joint kinetics relate to ACL strain.\u003c/p\u003e \u003cp\u003eDespite the potential benefits discussed in this research, the current study is not without limitations. First, our cohort was limited to female Australian Rules Football players, restricting the generalisability of findings to other sports and populations. Next, we focused exclusively on the dominant leg for sidestepping, leaving questions about whether non-dominant leg mechanics might differ. Further, in the current biomechanical model, we assigned the load for the trunk-constrained conditions to the participant\u0026rsquo;s mass instead of the isolated trunk segment. This simplification may not fully capture subtle changes in trunk position and orientation when sidestepping and thus may underestimate the outcomes. Lastly, joint kinematics were not considered due to our targeted focus on joint kinetics. Given the strong theoretical and empirical linkage between kinetic factors, such as joint moments and joint powers, and internal joint stress and ACL loading, kinetic variables were prioritised as they provide more direct insights into the mechanisms of injury risk and internal mechanical demands. Nonetheless, including joint kinematics in future research could provide additional context regarding observable movement strategies underpinning the kinetic findings presented here.\u003c/p\u003e \u003cp\u003eIn conclusion, the current study demonstrates that imposing task constraints during pre-planned sidesteps can significantly alter mechanical demands on the knee joint during early stance in female athletes. Specifically, holding a weighted implement at chest level (trunk constraint) increased negative peak knee joint power compared to unplanned, unconstrained sidesteps. Conversely, no significant changes were observed during unplanned conditions, likely due to a lower entry velocity. These findings support the use of task constraints in athletic training and rehabilitation to progressively expose athletes to higher knee loading, potentially enhancing tissue resilience and better preparing them for the mechanical demands encountered during competition and prepare athletes for \u0026ldquo;worst-case\u0026rdquo; scenarios. Understanding how constraints alter lower-body joint loading can help design effective drills to overload single-joint capacities.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eExperimental approach to the problem\u003c/p\u003e \u003cp\u003eThis crossover study examined the effects of two task constraints on knee joint kinetics during pre-planned and unplanned sidestepping compared to unconstrained sidesteps. Six conditions were tested: pre-planned \u0026amp; unconstrained (PP\u003csub\u003efree\u003c/sub\u003e), unplanned \u0026amp; unconstrained (UP\u003csub\u003efree\u003c/sub\u003e), pre-planned \u0026amp; trunk constrained (PP\u003csub\u003etrunk\u003c/sub\u003e), unplanned \u0026amp; trunk constrained (UP\u003csub\u003etrunk\u003c/sub\u003e), pre-planned \u0026amp; preparatory step constrained (PP\u003csub\u003eprep\u003c/sub\u003e), unplanned \u0026amp; preparatory step constrained (UP\u003csub\u003eprep\u003c/sub\u003e). Outcome measures included entry velocity (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and sidestep angle (\u0026deg;), peak negative joint power (W\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for the ankle (JP\u003csub\u003eANKLE\u003c/sub\u003e), knee (JP\u003csub\u003eKNEE\u003c/sub\u003e), and hip (JP\u003csub\u003eHIP\u003c/sub\u003e) for the cutting step, and continuous joint powers for the ankle, knee and hip throughout the stance phase.\u003c/p\u003e \u003cp\u003eTraditionally, knee joint moments have been extensively employed as proxy measures to infer mechanical loading and subsequent ACL injury risk during sidestep-cutting. ACL loading is inherently multiplanar: forces applied predominantly in one plane inevitably produce secondary rotations and translations due to the knee\u0026rsquo;s complex articular geometry and the inherent asymmetry of tibiofemoral surfaces. However, focusing predominantly on single-plane knee joint moments and treating them as independent entities inherently simplifies the complex mechanical environment in which ACL injuries occur, potentially obscuring critical interactions among frontal, transverse, and sagittal plane loads\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Specifically, isolated planar analyses neglect the interdependency of moments across anatomical planes. For instance, knee flexion angle modulates the mechanical effects of frontal (valgus) and transverse (rotational) moments. Consequently, identical peak knee valgus moments can result in markedly different ACL strains depending on the accompanying kneeflexion angle and moment, because ligament orientation and lever arms both change with flexion.\u003c/p\u003e \u003cp\u003eFurther, the evaluation of joint moments alone disregards the rate of loading, an essential determinant of ligamentous injury, as established extensively within the fields of materials science and tissue biomechanics\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Higher strain rates accelerate tissue failure and reduce the load magnitude required to induce ligamentous rupture over time, highlighting the necessity of evaluating load application rates alongside magnitudes. Joint powers, defined as the product of joint moments and angular velocities, explicitly incorporate this velocity component and thereby quantify not just the magnitude of loading but, crucially, the rate of energy absorption (negative power) or generation (positive power), by the structures (muscles, tendons, ligaments, joint, fascia) surrounding the knee. As such, joint power analyses afford additional and complementary biomechanical insights, potentially overcoming critical limitations inherent in isolated joint moment assessments and facilitating interpretation between condition differences\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003eTwenty-one female Australian Rules Football (ARF) players (n\u0026thinsp;=\u0026thinsp;21; age: 23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5 y, height: 170.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 cm, mass: 67.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6 kg, ARF experience: 5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5 y, resistance training experience: 3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5 y) participated in this study. Participants were: (1) free of current lower limb injury and currently competing; (2) had not suffered an injury to the lower limb in the past 12 months requiring surgery; (3) free of any neuromuscular or musculoskeletal disorders that affected the lower limb and (4) had at least 12 months of ARF and resistance training experience. All participants were familiar with sidestepping maneuvers. This study was approved by the Edith Cowan University Human Research Ethics Committee (approval number: #22459). All methods were performed in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants before inclusion in the study. A prior power analysis determined a minimum sample size of 20 participants using G*Power (Version 3.1.9.6, University of Dusseldorf, Dusseldorf, Germany) based on a moderate effect size (Cohen\u0026rsquo;s \u003cem\u003ef\u003c/em\u003e of 0.5) between three different conditions (for the pre-planned and unplanned conditions, respectively), a power of 0.95, a type 1 error of 0.05, a correlation among repeated measures of 0.5 and a nonsphericity correction of 0.5. The effect size was based on the change in joint kinetics with constraints compared to unconstrained movements in vertical jumps, sprints and isometric mid-thigh pulls previously reported.\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSidestep protocol\u003c/p\u003e \u003cp\u003eParticipants performed three to five pre-planned sidesteps and crossover steps, respectively, with only the right limb as warm-up. For clarity, participants would always sidestep to the left and crossover cut to the right. Although only the sidestepping trials to the left (off the right stance leg) were analysed, crossover cuts served as dummy trials to prevent pre-emption. A 7\u0026ndash;10 m run-up was used to achieve a consistent approach velocity between 3.5\u0026ndash;4.5 m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e before executing the sidestep on the force plate.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e A 45\u0026deg; sidestep angle was indicated using adhesive tape lines marked on the force plate, but was not examined by trial, as the actual sidestep angle is often much lower upon execution.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eData collection started with the PP\u003csub\u003efree\u003c/sub\u003e condition, followed by either PP\u003csub\u003etrunk\u003c/sub\u003e or PP\u003csub\u003eprep\u003c/sub\u003e, which were allocated in counterbalanced order. Next, the UP\u003csub\u003efree\u003c/sub\u003e condition was performed, followed by either the UP\u003csub\u003etrunk\u003c/sub\u003e or UP\u003csub\u003eprep\u003c/sub\u003e conditions, which were also allocated in counterbalanced order. For all unplanned conditions, a 30 cm arrow displayed on a screen 3 m beyond the force plate indicated the cut direction after participants triggered timing gates 2 m before the force plate at a height of 0.8 m and width of 1.5 m.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e This distance ensured participants had adequate time to react, but the tasks remained unplanned.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Participants were instructed to maintain a consistent approach velocity throughout each trial and focus on the screen to avoid force plate targeting with the execution leg.\u003c/p\u003e \u003cp\u003eFor the trunk constraint, participants held an external load at chest level throughout the task. Participants under 70 kg used a 4 kg load, and participants above 70 kg used a 6 kg load (~\u0026thinsp;5-7.5% of body mass). The selection of this load range was guided by practicality and feasibility considerations. During pilot testing, heavier weights (\u0026ge;\u0026thinsp;7.5% of body mass) excessively restricted trunk motion and led to conservative movement strategies, detracting from realistic sidestepping patterns. As such, this current mass range was sufficient to not compromise participants\u0026rsquo; ability to maintain typical sidestepping movement characteristics, thus preserving ecological validity. This extra mass was added to the body mass for all subsequent calculations. To simplify the calculations, we added the mass to the participant, as the majority of the mass is already distributed to the trunk. Further amendments to the biomechanical model (adding the external mass to the trunk segment) did not make a difference beyond the accuracy reported in the current model.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e For the preparatory step constraint, participants were ducked under an adjustable rope positioned at the participant\u0026rsquo;s eye level 50 cm before the force plate, which corresponded to the penultimate foot placement prior to the sidestepping task (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Approximately 60 seconds of rest between each trial was provided to minimise fatigue. Trials were repeated if approach velocity deviated, foot placement was incorrect, or the incorrect direction was performed. Testing concluded after three valid sidestepping trials per condition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData collection started with the PP\u003csub\u003efree\u003c/sub\u003e condition, followed by either PP\u003csub\u003etrunk\u003c/sub\u003e or PP\u003csub\u003eprep\u003c/sub\u003e, which were allocated in counterbalanced order. Next, the UP\u003csub\u003efree\u003c/sub\u003e condition was performed, followed by either the UP\u003csub\u003etrunk\u003c/sub\u003e or UP\u003csub\u003eprep\u003c/sub\u003e conditions, which were also allocated in counterbalanced order. For all unplanned conditions, a 30 cm arrow displayed on a screen 3 m beyond the force plate indicated the cut direction after participants triggered timing gates 2 m before the force plate at a height of 0.8 m and width of 1.5 m.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e This distance ensured participants had adequate time to react, but the tasks remained unplanned.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Participants were instructed to maintain a consistent approach velocity throughout each trial and focus on the screen to avoid force plate targeting with the execution leg.\u003c/p\u003e \u003cp\u003eData Collection\u003c/p\u003e \u003cp\u003eThree-dimensional motion data were synchronously collected at 250 Hz using an 8-camera Vicon MX-series system (Vicon Peak Ltd., Oxford, UK). Ground reaction forces were synchronously collected at 1000 Hz using five 600 x 900 mm force plates (Kistler, Type 9290AD, Sindelfingen, Germany). Thirty-eight retroreflective markers were affixed following the University of Western Australia lower-body and torso marker set and model (Version 5).\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e Single markers were attached to the left and right calcanei, left and right head of the first and fifth metatarsals, left and right anterior and posterior superior iliac spines, sternal notch, xiphoid process, seventh cervical vertebrae and twelfth thoracic vertebrae. Marker clusters for the creation of segment technical coordinate systems were attached to the left and right thigh and shank. To define the ankle joint, single markers were attached to the medial and lateral malleoli, and to define knee width for the functional joint center method below, single markers were positioned on the left and right medial and lateral epicondyles in the static calibration trials (removed for dynamic trials). Functional knee and hip tasks were performed to identify knee and hip joint axes and centers.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e Kinematics of the hip, knee, and ankle followed ISB standards.\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e Joint kinetics were expressed in the anatomical coordinate system of the distal segment. A total of 378 trials were subsequently analysed (Twenty-one participants * six conditions * three trials).\u003c/p\u003e \u003cp\u003eData were processed using Vicon Nexus (Version 2.10, Vicon Motion Systems, UK) and analysed with Visual 3D software (Version 2020, C-motion, Inc., Rockville.MD). Kinematic data were low-pass-filtered at a cut-off frequency of 15 Hz, using a fourth-order, zero-lag, Butterworth recursive filter, determined via residual analysis.\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e Instantaneous resultant net joint power was calculated from joint angular velocities multiplied by net joint moments (P\u0026thinsp;=\u0026thinsp;M\u0026middot;ω) and summed for all planes. Continuous joint power for the ankle, knee and hip was calculated during the execution step from initial contact to toe-off and linearly registered to 101 data points and used for the subsequent analysis. Peak negative joint power for the ankle, knee and hip was extracted from initial contact to weight acceptance (first local minimum in the ground reaction force or 30% of stance).\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e Sidestep angle was calculated using the x- and y-coordinates of the stance foot ankle joint center at initial contact (x1 and y1) and the coordinates of the contralateral ankle joint center at initial contact (x2 and y2) using Eq.\u0026nbsp;(1). The entry velocity was defined as the horizontal velocity of the COM at initial contact of the execution step (V@IC).\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSidestep angle = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{tan}^{-1}\\left(\\frac{a}{b}\\right)\\)\u003c/span\u003e\u003c/span\u003e; where a = |x2 \u0026minus; x1| and b = |y2 \u0026minus; y1|\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eMean and standard deviation (SD) were calculated for all variables. Repeated measures correlations (RMcorr package) were used to evaluate the within-participant relationship between dependent variables to justify separate linear mixed-effects models (LMM).\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e No very large correlation (r\u0026thinsp;=\u0026thinsp;0.7\u0026ndash;0.9, which explains more than 50% of variance) was found, so separate univariate mixedeffects models were deemed appropriate.\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e LMMS analysed between-condition differences in all dependent variables using the residual maximum likelihood to estimate variance components. This approach enables the use of individual trials as distinct data points while accounting for within-participant effects and the correlated nature inherent in clustered data. Each condition was treated as fixed effect, with participant intercepts and by-participant random slopes for condition effects incorporated as random effects [dependent variable\u0026thinsp;~\u0026thinsp;1\u0026thinsp;+\u0026thinsp;condition + (1\u0026thinsp;+\u0026thinsp;condition | participant)]. When the fixed effect of \u003cem\u003econdition\u003c/em\u003e was significant, pair-wise contrasts were Bonferroniadjusted for the 15 possible comparisons among the six conditions (α/15\u0026thinsp;=\u0026thinsp;0.0033). Random effects were explored to evaluate within-participant response to all conditions and treated as an exploratory analysis, as this was not part of the initial research questions.\u003c/p\u003e \u003cp\u003eAssumptions of homoscedasticity and normality were checked via residual plots and confirmed with Shapiro-Wilk and Levene\u0026rsquo;s tests. Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e effect sizes were calculated and interpreted as trivial (\u0026lt;\u0026thinsp;0.2), small (0.2\u0026ndash;0.49), moderate (0.5\u0026ndash;0.79), and large (\u0026gt;\u0026thinsp;0.8).\u003csup\u003e56\u003c/sup\u003e Significance was calculated using the gamlj package (Version 2.5.5), which applies Satterthwaite\u0026rsquo;s method to estimate degrees of freedom and generate p-values in R Studio (Version 1.4.11.06, R Core Team 2018, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org/\u003c/span\u003e\u003cspan address=\"http://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistical parametric mapping (SPM) was used to compare stance phase joint power differences for the ankle, knee and hip separately between constrained and unconstrained conditions.\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e Only results for the first 30% of stance were interpreted as per our primary research questions, as this reflects the time periods of peak ACL loading and ACL injury occurrence (REF). A SPM one-way repeated measures analysis of variance (ANOVA) determined between-condition differences across all pre-planned and unplanned conditions with corrected α to account for three joints and two conditions (with α/6\u0026thinsp;=\u0026thinsp;0.0083). Although the current method of ANOVA post-hoc analysis using the paired SPM T-test and Bonferroni correction is likely too simple, where an interaction effect was observed, post-hoc analysis was completed to further explore the capability of SPM analysis method for future hypothesis generation. Due to the current post-hoc limitations, we compared the effects of these constraints separately within pre-planned and unplanned sidesteps to facilitate interpretation in addition to the discrete measures. As such, for post-hoc analyses, the alpha value was set at 0.0083/3\u0026thinsp;=\u0026thinsp;0.0028 for the number of post-hoc tests (between-condition comparisons) with 2-tailed inference analysis. The scalar output statistics (SPM{F} and SPM{T}) were calculated separately at each individual data point and are referred to as a statistical parametric map with the calculation of the SPM{F} and SPM{T} indicating the magnitude of the difference between data. Where the scalar output statistic crossed the critical threshold ({F} and {T}), the null hypothesis was rejected. Because of the smoothness of force-time curves and the inter-dependence of neighboring points, multiple adjacent points of the SPM{F} or SPM{T} curve often exceed the critical threshold and are referred to as \u0026ldquo;suprathreshold clusters\u0026rdquo;.\u003c/p\u003e \u003cp\u003eTo supplement the jointpower findings we performed two additional, exploratory SPM analyses covering (i) the resultant kneejoint moment and (ii) the decomposed knee moments about the sagittal (X), frontal (Y) and transverse (Z) axes. For the resultant moment, a oneway repeatedmeasures SPM{F} ANOVA was run separately for the preplanned and unplanned tasks (Bonferroniadjusted omnibus α\u0026thinsp;=\u0026thinsp;0.025; posthoc α\u0026thinsp;=\u0026thinsp;0.0083). For the decomposed moments, six ANOVAs were needed (3 axes \u0026times; 2 tasks), giving an omnibus α\u0026thinsp;=\u0026thinsp;0.0083 and a posthoc α\u0026thinsp;=\u0026thinsp;0.0028. Any suprathreshold clusters are reported as exploratory observations.\u003c/p\u003e \u003cp\u003eAll SPM analyses were completed in the Spyder IDE (Version 5.5.4) distribution of Python (Version 3.10.11) using the open-source package \u0026ldquo;spm1d\u0026rdquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.spm1d.org/\u003c/span\u003e\u003cspan address=\"http://www.spm1d.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Statistical significance was set at α\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor DK and the project were supported by an Industry Engagement Doctoral Grant with Edith Cowan University and VALD Performance. VALD Performance had no role in study design, data collection, analysis, or interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDK and SN designed the study, recruited and managed data collection and conducted the analysis. All authors contributed to the manuscript and interpretation of the results. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used (filtered and time-normalised relative joint power and relative knee joint moments, including the SPM code) can be provided from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnline supplementary material is provided\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDai, B., Herman, D., Liu, H., Garrett, W. E. \u0026amp; Yu, B. Prevention of ACL injury, part I: injury characteristics, risk factors, and loading mechanism. \u003cem\u003eRes. 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Biomech.\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e, 1277\u0026ndash;1285 (2015).\u003c/span\u003e\u003c/li\u003e\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":"ACL, biomechanics, training, task constraints, joint power","lastPublishedDoi":"10.21203/rs.3.rs-6487198/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6487198/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the effects of task constraints applied at the trunk and preparatory step on lower-body joint kinetics associated with ACL injury risk during sidestepping in female athletes. Twenty-one trained female athletes performed six sidestep conditions: pre-planned and unplanned sidesteps, each with and without trunk (holding ~ 5–7.5% body mass at chest level) and preparatory-step (ducking under an adjustable rope at eye height) constraints. Relative joint power at the hip, knee, ankle, and entry velocity, and sidestep angle, were analysed using linear mixed models and statistical parametric mapping.\u003c/p\u003e\n\u003cp\u003eIn pre-planned sidesteps, trunk constraints significantly increased negative peak knee joint power compared to unconstrained conditions (\u003cem\u003ep\u003c/em\u003e = 0.02) and increased negative knee joint power during early stance (23–27%, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Preparatory-step constraints did not alter knee joint power but significantly increased hip joint power relative to trunk-constrained and unconstrained conditions between 23–35% of stance (p \u0026lt; 0.001). Unplanned sidesteps showed no significant kinetic differences among conditions.\u003c/p\u003e\n\u003cp\u003eImplementing trunk constraints during pre-planned sidesteps increases mechanical demands on the knee joint, facilitating progressive overload and enhancing ACL injury resilience. These findings inform practical training strategies to increase tissue capacity and prepare female athletes for high-risk sidestepping scenarios, potentially contributing to effective ACL injury prevention interventions.\u003c/p\u003e","manuscriptTitle":"Trunk Constraints Increase Knee Joint Kinetics During Sidestep Cutting in Female Athletes: Implications for Anterior Cruciate Ligament Injury Risk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 13:46:37","doi":"10.21203/rs.3.rs-6487198/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-26T07:44:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T15:07:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237992135104447517896840535458307508380","date":"2025-09-02T18:04:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-04T02:38:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20862292103769737190613375173248809235","date":"2025-06-23T01:50:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-29T16:01:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T16:00:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-29T05:43:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-26T04:38:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-20T03:20:11+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":"a5f11cd3-9fc2-480c-a8af-9b155d511e3e","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47904186,"name":"Health sciences/Health care"},{"id":47904187,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-20T16:11:20+00:00","versionOfRecord":{"articleIdentity":"rs-6487198","link":"https://doi.org/10.1038/s41598-026-38368-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-14 15:59:24","publishedOnDateReadable":"April 14th, 2026"},"versionCreatedAt":"2025-05-05 13:46:37","video":"","vorDoi":"10.1038/s41598-026-38368-z","vorDoiUrl":"https://doi.org/10.1038/s41598-026-38368-z","workflowStages":[]},"version":"v1","identity":"rs-6487198","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6487198","identity":"rs-6487198","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}