Is there a difference in knee biomechanics due to fatigue of the quadriceps versus hamstrings during landing on a single limb? a randomized comparative laboratory study

Abstract

Background

The quadriceps and hamstring muscles play a central role in absorbing impact forces and maintaining dynamic knee stability during landing tasks. Together, these muscle groups also strongly influence knee joint kinematics and kinetics. This study aimed to compare the effects of induced fatigue of the quadriceps and hamstring muscles on the kinematics and kinetics of the knee joint during a single-leg landing in a forward-drop jump task.

Methods

Of the 108 athletes (aged 20–25 years) randomized to either the quadriceps or hamstring fatigue group, 93 completed the study and were analyzed. The peak knee flexion angle, vertical ground reaction force (vGRF), anterior tibial shear force, and knee flexion-extension and abduction-adduction moments were measured before and after inducing fatigue in the quadriceps or hamstrings during a forward-drop jump with a single-leg landing.

Results

Hamstring fatigue significantly reduced the peak knee flexion angle (p < 0.001) compared with quadriceps fatigue. Also, hamstring fatigue significantly increased the vGRF and anterior tibial shear force (p < 0.001) more than quadriceps fatigue. The peak flexion moment demonstrated a significant Group × Time interaction (p = 0.022), decreasing significantly following hamstring fatigue (p < 0.001). Conversely, the peak extension moment showed a significant Group × Time interaction (p < 0.001), decreasing significantly following quadriceps fatigue (p < 0.001). The knee abduction and adduction moments were significantly affected (p < 0.001) by quadriceps fatigue more than by hamstring fatigue.

Conclusion

Hamstring fatigue was associated with greater changes in knee flexion angle, vertical ground reaction force, anterior tibial shear force, and peak flexion moment, whereas quadriceps fatigue induced more disturbances in knee abduction and adduction moments. These findings highlight distinct, muscle-specific changes in surrogate biomechanical measures that reflect different mechanical pathways associated with ACL loading.

Clinical trial registration number

The trial was registered on 5/5/2023, Iranian Registry of Clinical Trial, Identifier: (IRCT20230329057784N1).

Introduction

The quadriceps and hamstrings co-contraction maintain the knee joint’s dynamic stability in the frontal plane [1]. These muscles help prevent excessive knee abduction or adduction loading, which puts the knee joint under loading patterns associated with ACL injury mechanical pathways [2]. Furthermore, the quadriceps and hamstring muscles play a crucial role as absorbers of energy upon impact and protect the ACL during landing activities [3]. Evidence from the literature indicates that an increased knee abduction moment disproportionately elevates axial forces on the lateral compartment compared to the medial side. This imbalance leads to intensified lateral compressive forces and increased internal rotation. Concurrently, the lateral collateral ligament may become lax, whereas the medial ligaments are placed under greater tension. Collectively, these biomechanical alterations substantially elevate the strain placed on the ACL during sports-pivot landing activities [4].

Ireland suggested that unilateral landing is riskier for ACL integrity due to the decreased base of support and increased demands on muscles and while increasing knee valgus loading [5]. In a comparison between bilateral and unilateral landing, Pappas et al. found that unilateral landing exposed the knee joint to high-risk kinematics changes, including knee abduction, decreased knee flexion, and decreased hip adduction [6]. Unilateral jump landing activity is considered one of the leading causes of noncontact ACL injuries in which abnormal ground reaction force patterns occur during the landing phase [7]. Fatigue can be defined as a reduction in the force-generation capability of a muscle to produce a consistent muscle contraction in response to neural stimulation and might be caused by deficits in peripheral and central systems [8]. Evaluation of muscle fatigue and its consequences on the joint’s functions and mechanics becomes important for clinicians and athletes to understand the biomechanical consequences of fatigue. Hamstring and quadriceps muscles contribute to the dynamic stability of the knee joint; therefore, assessing the fatigue status of these muscles and their effects on lower limb functions, especially during competitions, is necessary [9]. Neuromuscular fatigue has become one of the potential risk factors for noncontact injuries of the knee joint. Since the anterior cruciate ligament injuries are likely to be significantly correlated with the noncontact knee injuries, therefore prevention modalities should be targeted to mitigate fatigue-induced alterations in landing biomechanics [10]. Based on epidemiological research, knee injury, especially during sports activities, occur at the beginning or end of the season or toward the end of the matches in football players, when ACL injuries are more likely to occur [11]. A previous study found that fatigue-induced quadriceps muscles substantially affect gait kinetics and kinematics [12]. Sturnieks et al. stated that the early stance knee flexion angle decreased significantly, and knee adduction moments increased after a bilateral quadriceps fatigue protocol [13]. Since fatigue could worsen the knee joint’s biomechanics, identifying and clarifying the possible underlying changes in knee kinematics and kinetics has become necessary to build a preventive strategy aiming to optimize movement patterns and decrease high-load joint mechanics during jump-landing tasks. Although prior researches have examined lower-extremity fatigue during bilateral landings or cutting tasks, an important biomechanical gap remains regarding the isolated, comparative effects of quadriceps versus hamstring fatigue during a single-leg forward-drop landing. Single-leg landings place distinct mechanical demands on the knee: the quadriceps must eccentrically absorb impact forces, while the hamstrings provide posterior dynamic stability to limit anterior tibial translation. Broad or multi-joint fatigue protocols tend to obscure these muscle-specific roles because participants adopt global lower-limb compensation strategies. Moreover, it is still unclear whether quadriceps and hamstring fatigue differentially influence sagittal-plane impact forces versus frontal-plane dynamic moment control during rapid single-leg deceleration. Clarifying these distinct fatigue-related failure modes is clinically important for developing targeted neuromuscular training programs that address muscle-specific deficits rather than relying on generalized fatigue-prevention approaches. This study explored how quadriceps-induced and hamstring-induced fatigue differently affect knee joint kinematics and kinetics during a forward-drop jump with a single-leg landing, under the assumption that each muscle group’s fatigue would uniquely influence these biomechanical measures.

Materials and methods

Study design

This research involved a randomized, parallel-group laboratory study with repeated measures, enrolling 140 male athletes from football and basketball between April and August 2024. The study was conducted at the biomechanics laboratory within the College of Applied Medical Sciences at Prince Sattam Bin Abdulaziz University, Saudi Arabia. A flowchart of participants’ withdrawal and exclusion is shown in Fig. 1. The participants were allocated and randomized into two groups: the quadriceps and hamstring groups. The three-dimensional (3D) kinematic and kinetic data of the knee joint were analyzed before and after the fatigue-induction protocol for the quadriceps and hamstring muscles during a forward-drop jump with a single-leg landing. All participants were fully informed about the study before it began, and each of them signed a university-approved consent form prior to participation. They also consented to having their data published in an anonymized format in an open-access online journal. This manuscript does not contain any information that could identify them personally. This randomized parallel-group trial was conducted and reported in accordance with the CONSORT 2025 statement guidelines. This study was approved by the Standing Committee for Bioethics Research of Prince Sattam Bin Abdulaziz University (SCBR-002-2023). The study was registered with a clinical registry (registration number: IRCT20230329057784N1). All procedures were conducted in accordance with the principles of the Declaration of Helsinki (1964).

Fig. 1.

Participants’ withdrawal and dropout

Participants

Inclusion and exclusion criteria

Participants were eligible for inclusion if they met the following criteria: (1) male athletes aged between 20 and 25 years; (2) participation in football or basketball as their primary sport; (3) a minimum of five years of continuous experience in their respective sport; (4) regular participation in organized training sessions at least twice per week; and (5) normal hamstring flexibility, defined as a sit-and-reach test score of ≥ 30 cm [14]. The demographic and basic characteristics of participants are outlined in (Table 1). Participants were excluded if they reported any cardiovascular, respiratory, or neurological disorders or had a history of lower-extremity injuries (including ACL injuries). Also, any participant with a condition that might alter their natural lower-limb movement or compromise their safety during the sessions was excluded. Finally, participants were excluded if they scored below 30 cm on the sit-and-reach test or if they couldn’t complete the fatigue protocol and biomechanical testing.

Table 1.

Demographic characteristics of participants in both groups

	Quadriceps group (n = 45) Mean ± SD (95% CI)	Hamstring group (n = 48) Mean ± SD (95% CI)	p
Age (years)	22.6 ± 1.58(22.12–23.07)	22.25 ± 1.64(21.77–22.72)	0.30†
Sport-practice duration (years)	6.93 ± 1.13 (6.59–7.27)	7.17 ± 1.43 (6.74–7.60)	0.31†
Weight (Kg)	70.71 ± 3.1 (69.77–71.64)	72.25 ± 4.33 (71.25–73.85)	0.07†
Height (cm)	175.66 ± 5.26 (174.08-177.24)	176.51 ± 4.85 (175.05-177.96)	0.57†
BMI (Kg/m2)	22.97 ± 1.69 (22.46–23.48)	23.34 ± 1.98 (22.75–23.94)	0.41†
Sit to reach test (cm)	55.45 ± 4.55 (54.08–56.81)	53.55 ± 5.67 (51.96–55.46)	0.08†
No. (%)
Sport distribution
Football players	30 (66.7)	26 (54.2)	0.218‡
Basketball players	15 (33.3)	22 (45.8)

Data are presented as mean ± SD

BMI Body mass index, CI Confidence interval

†: Nonsignificant differences (Independent sample t-test), ‡: Nonsignificant differences (Chi-Square test)

Randomization

After confirming eligibility, each of the 108 athletes was assigned a unique identification number. Randomization was carried out by an independent researcher who was not involved in the study procedures. Using sealed, opaque envelopes containing pre-labeled cards (Quadriceps or Hamstring), a simple 1:1 allocation sequence was implemented. Each athlete selected one sealed envelope to determine group assignment, ensuring allocation concealment. The examiner responsible for outcome assessments did not participate in the randomization process and remained blinded to group allocation. Athletes were instructed not to disclose their assigned group during assessments.

Outcome measures

The primary outcome measures were evaluated immediately before and after the protocol in both the quadriceps fatigue and hamstring fatigue groups. The measures of interest were the peak knee flexion angle (in degrees) and peak vertical ground reaction force (vGRF) (expressed in BW) at initial contact. The anterior tibial shear force was measured in BW, and the knee flexion-extension moments and knee abduction-adduction moments were normalized to body weight and measured in (Nm/kg).

Procedures

The 3D motion analysis

The 3D kinematic and kinetic data of the knee joint were collected before and after the induced fatigue protocol, which was applied to the quadriceps and hamstring muscles during single-leg landing in the forward-drop jump task. The 3D motion analysis system consisted of the 12 infrared cameras (100 Hz) of the Vicon motion capture system (Oxford Metrics Limited, UK), and two ground reaction forces were collected synchronously at 2000 Hz with an AMTI GEN–5 force plate (Watertown, MA, USA). Tracing of the body segments’ motion was conducted by attaching bilaterally 24 retro-reflective markers (14-mm diameter) bilaterally to the following anatomical landmarks. Reflective markers were placed according to the standard Plug-in Gait marker protocol.

The retro-reflective markers were placed bilaterally on the anterior superior iliac spine (ASIS), posterior superior iliac spine (PSIS), the right and left greater trochanter, the medial/lateral epicondyle of the knee joint, the lateral and medial malleolus, the left and right thigh, and the left and right shin. The markers were also attached bilaterally to the second metatarsal heads, fifth metatarsal heads, and calcaneus. The segment dimensions were automatically scaled from the static calibration trial based on participant-specific anthropometric measurements entered into Vicon Nexus. The hip joint centers were calculated using the Davis anthropometric method as implemented in the Plug-in Gait model, while other joint centers were calculated from the standard Plug-in Gait biomechanical model. To identify the initial contact (IC), the motion lab operator used the vertical ground reaction force (vGRF). Raw three-dimensional (3D) marker trajectories and ground reaction force data were filtered separately with a fourth-order, zero-lag low-pass Butterworth filter with a cutoff frequency of 15 Hz in Vicon Nexus (version 2.11; Oxford Metrics Ltd., Oxford, UK) before initial contact detection and inverse dynamics analysis. Applying the same cutoff frequency to both kinematic and kinetic data is a widely accepted practice in biomechanics, as it helps minimize impact-related artifacts during high-intensity landing tasks. Initial contact (IC) was identified from the filtered vertical ground reaction force (vGRF) signal using a 30 N threshold [15], and the landing phase was analyzed from IC to maximum knee flexion. Within this analysis window, peak biomechanical variables—including peak vGRF, peak knee flexion angle, anterior tibial shear force, and peak knee flexion-extension and abduction-adduction moments—were extracted using Polygon software (version 4.3). Kinematic and kinetic data were processed using the standard Plug-In Gait model in Vicon Nexus and then analyzed in Polygon software. Anterior tibial shear force was defined as the anteriorly directed component of the proximal tibial joint reaction force, calculated through the Plug-In Gait inverse dynamics pipeline and expressed relative to the tibial local coordinate system. Knee flexion angle was reported in degrees (°), while vertical ground reaction force and anterior tibial shear force were normalized to body weight (BW). Knee joint moments were normalized to body mass (Nm/kg) before statistical analysis. For frontal-plane kinetics, external knee abduction moments are reported as positive values, indicating a valgus-directed external load. External knee adduction moments are reported as negative values, reflecting a varus-directed external load.

A forward-drop jump with a single-leg landing task

The participant was allowed first to practice a 10-minute warm-up period (5 min of running at a preferred speed plus 5 min of self-stretching of the upper and lower limbs). The participant was then familiarized with the forward drop jump task and was permitted to practice before data collection. After a five-minute rest interval, the participant was asked to complete three successful pre-fatigue trials for data collection. The average of the three trials was analyzed. Participants stood on a 30-cm high platform positioned 20 cm from the force plate. From this stationary position, they performed a controlled forward-drop jump, landing on their dominant leg on the force plate. To standardize the task, no running or self-selected approach was allowed, and all participants completed the procedure under identical testing conditions. They landed on the forefoot (metatarsal heads) with a dominant single-leg (Fig. 2). The single-leg landing was chosen since this movement induces a rapid deceleration, which is a common part of the ACL injury mechanism that would happen during sports [16]. The dominant leg was determined by the participant’s tendency to use a leg for kicking a ball or the leg the participant preferred to use when jumping.

Fig. 2.

Schematic illustration showing how the participant performs the task (single-leg landing) during a forward drop jump.

A trial was considered successful when the participant performed a forward‑drop jump from the platform, landed on the dominant limb with initial forefoot (metatarsal) contact, and maintained a stable landing without slipping, losing balance, or taking corrective steps. An investigator visually checked the data collection for compliance. In addition, trials with incorrect landing characteristics or loss of balance were discarded and repeated. Moreover, the objective verification of landing on the metatarsals was checked post hoc for all trials using data acquisition and capture in Vicon Nexus, by examining the sagittal ankle plantarflexion at the moment of initial contact (kinematic verification). In addition, the initial force application under the metatarsals was verified using the force plate (kinetic verification). Trial that did not meet these conditions were discarded and repeated. After three successful consecutive trials for the pre-fatigue assessment, participants in both groups completed an induced-fatigue protocol designed to induce quadriceps fatigue in the quadriceps group or hamstring fatigue in the hamstring group.

The induced fatigue protocol

The quantification of the peak torque of the quadriceps and hamstring muscles (baseline peak torque) before initiating the fatigue protocol facilitated the determination of strength degradation and eventually indicated fatigue occurrence. To help control for circadian and metabolic influences on neuromuscular performance, all testing sessions were held during daytime hours (10:00–16:00). Before each session, participants were asked to follow simple, consistent pre-test routines: get 7–8 h of sleep, maintain their usual eating and hydration habits, and avoid any vigorous exercise for 48 h beforehand. The participants were also instructed to avoid caffeine, alcohol, and energy supplements for at least 12 h prior to testing. We verbally confirmed adherence to these guidelines before beginning the baseline maximal voluntary contractions. The peak torque of the quadriceps and hamstring muscles was determined using an isokinetic dynamometer (CSMI Humac 2009, Cybex II, II+, version 129, USA) to measure maximal voluntary concentric contraction (MVCC) at 120°·s⁻¹ and the range of motion at which the peak torque was measured ranged from 10 to 90 degrees of knee flexion. The participant was positioned in a sitting position for quadriceps peak torque measurement and in a prone position for hamstring peak torque measurement. The angle between the inclination of the seat and the horizontal position was set at 25 degrees. The chest and thighs were secured with straps to the dynamometer seat. The center of the knee joint was adjusted with the center of the dynamometer force cell. The type of fatigue was acute-induced fatigue, in which isokinetic concentric exercises were performed to reach the fatigue level of the quadriceps and hamstring muscles. For inducing fatigue to the quadriceps, the dominant leg (the leg with which the participant would kick a ball or preferred to jump) was determined. Then, a series of five alternating quadriceps MVCCs was conducted. The highest value of the peak torque for the quadriceps muscle was recorded. Two minutes of rest were provided for the participants before the fatigue protocol. Muscle fatigue was defined as a reduction in peak torque to less than 50% of the participant’s maximum voluntary concentric contraction (MVCC), sustained across five consecutive cycles on the isokinetic dynamometer. This 50% threshold is a well-established benchmark in dynamometric fatigue research, ensuring a meaningful level of acute performance fatigability without inducing contractile exhaustion or undue strain. Requiring the torque drop to persist for five repetitions helped confirm that the decline reflected genuine neuromuscular fatigue rather than momentary fluctuations, brief submaximal effort, or early termination of the task. Participants performed cycles of three consecutive MVCC repetitions of knee extension at 300°·s⁻¹ [17] with a 30-secend rest interval to elicit and confirm this state, with the target fatigue state officially established once the peak torque remained below the 50% threshold for five consecutive cycles. Immediately after participants reached the fatigue threshold—defined as peak torque falling below 50% of their baseline MVCC for five consecutive cycles—they were moved from the isokinetic dynamometer to the force-plate testing area. The transition was kept tight, with the first post-fatigue landing trial initiated within roughly 30 to 45 s. To ensure all testing occurred while participants were still in a fatigued state and before meaningful recovery could take place, all three post-fatigue single-leg landing trials were completed within an overall window of approximately 3 to 5 min. The same steps of MVCC and rest were applied to participants in the hamstring group to induce hamstring fatigue; however, the MVCC was in the knee flexion direction. The number of MVCCs the participant could perform had no limit [18]. Immediately after fatigue occurred, the 3D kinematic and kinetic data of the knee joint in both groups were obtained again. The participants were asked to perform three-consecutive forward-drop jumps with single-leg landings as post-fatigue data. The average of the three trials was considered. Although a 50% reduction in peak torque was used as the fatigue threshold for both groups, reaching this mechanical criterion does not necessarily indicate equivalent physiological or metabolic fatigue between the quadriceps and hamstrings. Differences in muscle-fiber composition, physiological cross-sectional area, and recovery characteristics between the anterior and posterior thigh muscles mean that the same level of torque decline may reflect distinct underlying neuromuscular fatigue states.

Sample size calculation

The analysis utilized G*Power software, version 3.0.10, from Neu-Isenburg, Germany. An a priori power analysis was performed using G*Power (version 3.1.9.7) for an F-test under a repeated-measures ANOVA (within–between interaction) framework. Assuming a medium effect size (Cohen’s  f=0.20) [18], an alpha level of 0.05, statistical power of 0.80, two groups, two repeated measurements, a correlation among repeated measures of  r=0.60, and a nonsphericity correction of  ϵ=1.00, the required total sample size was estimated at  N=34(17 participants per group), yielding an actual power of 0.808. We recruited 108 athletes to allow for possible dropouts, difficulties completing the fatigue protocol, and motion-capture problems during data collection. After applying data-quality procedures, 15 participants were excluded due to incomplete landing trials or technical artifacts, resulting in a final analyzed sample of 93 healthy athletes (45 in the quadriceps-fatigue group and 48 in the hamstring-fatigue group). This final sample size substantially exceeds the minimum requirement and provides post-hoc power greater than 0.95 for all primary outcomes and regression analyses.

Statistical analysis

The analysis was conducted using the SPSS version 23 software package. Statistical analyses were conducted using a complete-case analysis. Only those participants who completed pre- and post-fatigue biomechanical measurements were included in the final analysis. To assess the possibility of attrition bias, we compared baseline demographic characteristics—age, weight, height, BMI, sport-practice duration, and sit-and-reach scores—as well as sport distribution between participants who completed the study (n = 93) and those who did not (n = 15). Independent t-tests and Chi-Square analyses showed no meaningful differences between the two groups. Because the reasons for non-completion were unrelated to protocol tolerance or physical performance—and instead stemmed from external scheduling conflicts or motion-capture software artifacts—the missing data were considered Missing Completely at Random (MCAR). This supports the use of a complete-case analysis for all reported outcomes.

We used a two-way mixed-design ANOVA (group × time) to examine how the factors changed over time and how they interacted, taking into account both the within-subject and between-subject differences across all measurement points. The normality of the data distribution was assessed using the Shapiro-Wilk test, while the homogeneity of variances was examined using Levene’s test. A multiple linear regression model was used to examine how anterior tibial shear force (dependent variable) relates to its post-fatigue predictors—peak knee flexion angle and vGRF. The analysis reports adjusted , unstandardized ( B) and standardized () coefficients, standard errors,  t-values,  p-values, 95% confidence intervals. A multicollinearity was evaluated using Variance Inflation Factors (VIF < 2.0 considered acceptable). These findings are interpreted as associative and exploratory rather than causal. The significance threshold was set at an alpha level of 0.05, indicating that results with a p-value less than 0.05 were considered statistically significant.

Results

Of the 140 athletes screened for eligibility, 108 met the inclusion criteria and were randomized. Fifteen participants (13.9%) did not complete the study and were excluded, leaving a final complete-case sample of 93 athletes. In the quadriceps group, nine participants were lost due to follow-up or scheduling issues (n = 4) or excluded because of marker-tracking and technical artifacts (n = 5). In the hamstring group, six participants withdrew after beginning external training programs (n = 2) or choosing to discontinue participation (n = 4). The final analyzed sample included 56 football players and 37 basketball players, allocated to the quadriceps group (n = 45; 30 football, 15 basketball) and the hamstring group (n = 48; 26 football, 22 basketball). Independent-samples t-tests and chi-square analyses showed no significant differences between completers (n = 93) and non-completers (n = 15) in age (22.42 ± 1.61 vs. 22.13 ± 1.55 years, p = 0.52), BMI (23.16 ± 1.84 vs. 22.85 ± 1.72 kg/m², p = 0.54), sit-and-reach flexibility (54.47 ± 5.21 vs. 53.80 ± 4.96 cm, p = 0.64), or sport distribution (p = 0.71). The quadriceps and hamstring groups were also comparable at baseline, with no significant differences in demographic characteristics or sport distribution (Table 1).

There was a significant Group × Time interaction for peak knee flexion angle (F(1, 91) = 265.92, p < 0.001, ηp² = 0.745). Change-score comparisons (Δ Post–Pre) showed that the reduction in knee flexion was markedly greater after hamstring fatigue (Δ = − 7.82°; Pre: 34.82° ± 8.45°, Post: 27.00° ± 6.87°) than after quadriceps fatigue (Δ = − 2.13°; Pre: 35.13° ± 8.27°, Post: 33.00° ± 8.28°). The between-group difference in change was 5.69° (95% CI: 4.62° to 6.76°; p < 0.001; Cohen’s d = 2.37), indicating a substantially stiffer landing posture following hamstring fatigue (Table 2).

Table 2.

Changes in peak knee biomechanical variables during single-leg landing before and after quadriceps and hamstring fatigue

Variable	Quadriceps group (n = 45) Mean ± SD (95% CI)			Hamstring group (n = 48) Mean ± SD (95% CI)			Group X Time interaction
	Pre	Post	P	Pre	Post	P	P	η2Partial
Peak Knee flexion angle, degrees )⁰(	35.13 ± 8.27 (32.64–37.62)	33 ± 8.28 (30.51–35.48)	P < 0.001*	34.82 ± 8.45 (32.28–36.36)	27 ± 6.87 (25.60-29.73)	P < 0.001*	p < 0.001†	0.745
Peak vGRF, (BW)	3.90 ± 0.28 (3.81–3.98)	4.20 ± 0.31 (4.1–4.29)	P < 0.001*	3.70 ± 0.26 (3.62–3.78)	4.80 ± 0.42 (4.67–4.93)	P < 0.001*	P < 0.001†	0.930
Anterior tibial shear force, (BW)	0.25 ± 0.01 (0.24–0.25)	0.33 ± 0.09 (0.30-0.35)	P < 0.001*	0.26 ± 0.09 (0.25–0.27)	0.41 ± 0.06 (0.39–0.43)	P < 0.001*	P < 0.001†	0.251
Peak knee flexion moment, (Nm/kg)	0.45 ± 0.19 (0.39–0.51)	0.43 ± 0.19 (0.37–0.49)	P = 0.616	0.43 ± 0.20 (0.37–0.49)	0.32 ± 0.17 (0.27–0.37)	P < 0.001*	P = 0.022†	0.057
Peak knee extension moment, (Nm/kg)	0.65 ± 0.21 (0.58–0.71)	0.56 ± 0.18 (0.50–0.61)	P < 0.001*	0.64 ± 0.20 (0.58–0.70)	0.64 ± 0.18 (0.58–0.70)	P = 0.496	P < 0.001†	0.751
knee abduction moment, (Nm/kg)	0.68 ± 0.28 (0.59–0.76)	0.45 ± 0.31 (0.35–0.54)	P < 0.001*	0.67 ± 0.25 (0.59–0.74)	0.66 ± 0.22 (0.59–0.72)	P = 0.093	P < 0.001†	0.906
knee adduction moment, (Nm/kg)	0.32 ± 0.28 (0.23-0.40)	0.19 ± 0.18 (0.13–0.24)	P < 0.001*	0.36 ± 0.29 (0.28–0.45)	0.35 ± 0.25 (0.27–0.43)	P = 0.93	P < 0.001†	0.388

Data are presented as mean ± SD, CI: Confidence interval, vGRF: Vertical ground reaction force, BW: Body weight, Group Time interactions were derived from two-way mixed ANOVA with degrees of freedom DF = (1, 91), *: Significant difference between pre– to post-fatigue within the group, †: Significant interaction between pre- to post fatigue in both quadriceps and hamstring groups

A significant Group × Time interaction was found for peak vGRF (F(1, 91) = 1207.00, p < 0.001, ηp² = 0.930). Pre-to-post change-score analysis showed that hamstring fatigue produced a much larger increase in impact forces (Δ = +1.10 BW; Pre: 3.70 ± 0.26 BW, Post: 4.80 ± 0.42 BW) compared with quadriceps fatigue (Δ = +0.30 BW; Pre: 3.90 ± 0.28 BW, Post: 4.20 ± 0.31 BW). The between-group difference in change was 0.80 BW (95% CI: 0.72 to 0.88 BW; p < 0.001; Cohen’s d = 3.20), indicating a substantially greater rise in landing impact forces following hamstring fatigue (Table 2).

For anterior tibial shear force, we observed a significant Group × Time interaction (F(1, 91) = 30.49, p < 0.001, ηp² = 0.251). The increase from pre- to post-fatigue was notably larger in the hamstring-fatigue group (Δ = +0.15 BW; Pre: 0.26 ± 0.09 BW, Post: 0.41 ± 0.06 BW) compared with the quadriceps-fatigue group (Δ = +0.08 BW; Pre: 0.25 ± 0.01 BW, Post: 0.33 ± 0.09 BW). The between-group difference in change was 0.07 BW (95% CI: 0.04 to 0.10 BW; p < 0.001; Cohen’s d = 0.99), indicating greater anterior knee-loading surrogate responses following hamstring fatigue (Table 2).

A significant Group × Time interaction was identified for peak knee flexion moment (F(1, 91) = 5.45, p = 0.022, ηp² = 0.057). Hamstring fatigue led to a clear reduction in flexion moment (Δ = − 0.11 Nm/kg; Pre: 0.43 ± 0.20, Post: 0.32 ± 0.17 Nm/kg; p < 0.001), whereas quadriceps fatigue produced no meaningful change (Δ = − 0.02 Nm/kg; Pre: 0.45 ± 0.19, Post: 0.43 ± 0.19 Nm/kg; p = 0.616). This resulted in a between-group difference in change of 0.09 Nm/kg (95% CI: 0.01 to 0.17 Nm/kg; p = 0.022; Cohen’s d = 0.48). Conversely, a significant Group × Time interaction was also observed for peak extension moment (F(1, 91) = 274.16, p < 0.001, ηp² = 0.751). Quadriceps fatigue caused a substantial reduction in extension moment (Δ = − 0.09 Nm/kg; Pre: 0.65 ± 0.21, Post: 0.56 ± 0.18 Nm/kg; p < 0.001), while hamstring fatigue showed no change (Δ = 0.00 Nm/kg; Pre: 0.64 ± 0.20, Post: 0.64 ± 0.18 Nm/kg; p = 0.496) resulting in a between-group difference in change of 0.09 Nm/kg (95% CI: 0.08 to 0.10 Nm/kg; p < 0.001; Cohen’s d = 2.02) Table 2.

Significant Group × Time interactions were observed for both peak knee abduction moment (F(1, 91) = 872.09, p < 0.001, ηp² = 0.906) and peak knee adduction moment (F(1, 91) = 57.77, p < 0.001, ηp² = 0.388). Quadriceps fatigue led to clear reductions in abduction moment (Δ = − 0.23 Nm/kg; Pre: 0.68 ± 0.28, Post: 0.45 ± 0.31 Nm/kg; p < 0.001) and adduction moment (Δ = − 0.13 Nm/kg; Pre: 0.32 ± 0.28, Post: 0.19 ± 0.18 Nm/kg; p < 0.001). In contrast, hamstring fatigue produced no meaningful changes in either abduction moment (Δ = − 0.01 Nm/kg; p = 0.093) or adduction moment (Δ = − 0.01 Nm/kg; p = 0.93). Between-group comparisons of pre-to-post change confirmed that frontal-plane joint moments were uniquely and substantially altered following quadriceps fatigue (Abduction: Mean difference = 0.22 Nm/kg, 95% CI: 0.20 to 0.24 Nm/kg, p < 0.001, Cohen’s d = 3.10; Adduction: Mean difference = 0.12 Nm/kg, 95% CI: 0.09 to 0.15 Nm/kg, p < 0.001, Cohen’s d = 1.15) Table 2.

In our exploration of the predictors of anterior tibial shear force in the study, we employed a multiple linear regression model to estimate the contributions of peak knee flexion degrees and peak vGRF in predicting how these variables collectively impact anterior tibial shear force after quadriceps and hamstring fatigue. In the quadriceps-fatigue group, the multiple linear regression model was statistically significant, F(2, 42) = 119.92, p < 0.001, explaining 84.4% of the variance in post-fatigue anterior tibial shear force (Adjusted R² = 0.844, R² = 0.851). Peak knee flexion angle emerged as a strong independent predictor (B = -0.011, SE = 0.001, t = -11.89, p < 0.001, 95% CI [− 0.013, − 0.010], VIF = 1.08), indicating that each 1° reduction in knee flexion was associated with a 0.011 BW increase in anterior tibial shear force (equivalent to 0.11 BW for a 10° decrease). In contrast, vGRF did not significantly predict anterior tibial shear force (B = -0.048, SE = 0.026, t = -1.87, p = 0.069, 95% CI [− 0.099, 0.004]), reflecting a non-significant change of 0.048 BW for every 1 BW increase in ground reaction force as shown (Fig. 3).

Fig. 3.

Bivariate linear regression scatter plots illustrate the relationship between peak knee flexion angle (degrees) and post-fatigue anterior tibial shear force (BW) for the (Left) quadriceps-fatigue group (R² = 0.839) and (Right) hamstring-fatigue group (R² = 0.692). Each plot includes the trend line, the unadjusted regression equation, and the simple linear regression fit. BW: Body weight

In the hamstring-fatigue group, the multiple linear regression model was statistically significant, F(2, 45) = 52.62, p < 0.001, explaining 68.7% of the variance in post-fatigue anterior tibial shear force (Adjusted R² = 0.687, R² = 0.701). Peak knee flexion angle was a strong independent predictor (B = -0.008, SE = 0.001, t = -8.71, p < 0.001, 95% CI [− 0.009, − 0.006], VIF = 1.12), indicating that each 1° reduction in knee flexion was associated with a 0.008 BW increase in anterior tibial shear force (equivalent to 0.08 BW for a 10° decrease). In contrast, vGRF did not significantly predict anterior tibial shear force (B = 0.016, SE = 0.014, t = 1.10, p = 0.275, 95% CI [− 0.013, 0.044]), reflecting a non-significant increase of 0.016 BW for every 1 BW rise in ground reaction force, as shown (Fig. 3).

Discussion

This study investigated the differential impacts of quadriceps and hamstring fatigue on knee biomechanics during a single-leg landing task. The findings highlight distinct biomechanical alterations in these muscle groups following fatigue, with significant implications for surrogate biomechanical markers associated with ACL loading. It is also important to note that these neuromuscular and biomechanical responses were observed only in young male participants. Because males and females differ in ACL injury patterns, baseline movement mechanics, and fatigue responses, these findings cannot be generalized to female athletes.

Muscle fatigue can cause a delay in muscle activation, resulting in reduced motor control and increased knee laxity [19] which is a recognized risk factor for knee instability and altered ACL loading characteristics [20]. Although generic lower extremity fatigue models better reflect the sports environment [21, 22], isolating muscle groups clarifies the specific contributions of individual muscles to dynamic stability [18].

Knee flexion angle

Fatigue in both muscle groups reduced the peak knee flexion angle, but the effect was much more pronounced after hamstring fatigue. Hamstring fatigue caused a 7.82° decrease (to 27°), whereas quadriceps fatigue resulted in a smaller 2.13° decrease (to 33°). Reduced knee flexion angles are associated with stiffer landings, increasing the demand on passive structures such as ligaments and elevating markers linked to increased ACL strain [23, 24].

In agreement, Weinhandl et al. [25] reported that hamstring fatigue decreased knee flexion angles and increased surrogate markers associated with ACL loading. However, Blackburn et al. [26] found that greater hamstring stiffness, rather than strength, correlated with a more stable knee joint by reducing anterior tibial translation, suggesting that inadequate hamstring stiffness may elevate biomechanical variables associated with ACL loading constraints. Conversely, Liu et al. [20] reported no significant differences in knee flexion angles before and after fatigue during forehand badminton lunges, attributing the results to their fatigue protocol, which only fatigued the quadriceps, hamstring muscles, and gastrocnemius muscles.

Vertical Ground Reaction Force (vGRF)

Quadriceps and hamstring fatigue increased the peak vGRF, with greater increases after hamstring fatigue (4.80 BW) than after quadriceps fatigue (4.20 BW). Higher vGRF may expose joints to larger forces [18] and less effective shock absorption due to hamstring fatigue [27]. The existence of a difference in vGRF between hamstring and quadriceps fatigue may be attributed to the essential role of the hamstrings in performing eccentric control over the knee joint during dynamic deceleration [28]. When the hamstring are fatigued, their ability to counter the anterior translation of the tibia and decelerate the rapidly extending knee is profoundly affected [29]. To accommodate this loss of active posterior stabilization, the participant adopts a stiffer landing strategy characterized by increased knee extension posture and a reduced joint range of movement [28, 30]. This stiff condition reduces the ability of the lower extremity to absorb the shock efficiently, causing kinetic impact energy to be transmitted directly through the kinetic chain and consequently increasing the peak vGRF [29. 31]. Hence, improvement of hamstring neuromuscular drive plays an essential role in restoring knee flexion excursion and significantly reduces these high-impact loads [32].

Anterior tibial shear force

The anterior tibial shear force increased after both quadriceps and hamstring fatigue, with greater increases in the hamstring group (0.41 BW vs. 0.33 BW). This supports the crucial role of hamstrings in resisting anterior tibial translation and protecting the ACL [33]. Contracting the quadriceps during jumping or cutting causes the tibia to move forward relative to the femur, while the hamstrings stabilize the knee by applying a backward force on the tibia to counteract this movement [34]. The regression analysis in the current study confirmed that decreased a knee flexion angle significantly predicts increased anterior tibial shear force, underscoring the importance of maintaining adequate knee flexion to mitigate anterior tibial shear forces.

Knee flexion and extension moments

The study observed a significant decrease in the peak knee flexion moment after hamstring fatigue, whereas the quadriceps group showed no significant changes. Conversely, the peak extension moment significantly decreased after quadriceps fatigue but not after hamstring fatigue. These findings indicate that hamstring fatigue impairs the ability to generate flexion torque during landing, leading to compensatory strategies that place additional stress on the knee joint [2]. In contrast, quadriceps fatigue reduced peak knee extension moments, reflecting the primary role in knee extension and the absorption of landing forces [19] In line with our study, García-Luna et al. [35] mentioned that decreased and delayed peak knee extension moments after quadriceps fatigue suggest a diminished ability to control the body’s downward vertical velocity after impact and reduced efficiency of the stretch-shortening cycle during propulsion.

Knee abduction and adduction moments

Quadriceps fatigue led to notable reductions in both peak external knee abduction and adduction moments. Rather than indicating a loss of frontal-plane stability, these lower moment values suggest a shift in landing strategy. After quadriceps fatigue, participants showed a reduced ability to eccentrically generate and absorb joint moments, resulting in smaller dynamic moment magnitudes across the frontal plane. This pattern indicates that quadriceps fatigue limits the active torque output needed to manage mediolateral stability during landing [36]. As a result, although lower moment values reduce external torque demands on the joint, they also point to a limited neuromuscular ability to actively stabilize the knee against sudden varus or valgus disturbances during deceleration.

In line with our findings, Nyland et al. [37] reported decreased peak knee abduction moments following quadriceps fatigue, indicating reduced dynamic lateral knee stabilization against varus stresses. Also, Murdock et al. [38] observed higher knee adduction moments in individuals with decreased quadriceps strength. Bedo et al. [39] compared knee kinematics and kinetics during sidestep cutting and single-leg landing before and after a fatigue protocol in handball athletes, reporting that fatigue affected the frontal plane in both tasks and the sagittal plane during single-leg landing. Although joint moments did not differ in their study, reduced knee kinematics during fatigue may lead to high-risk modifications in joint loading.

Knee flexion angle and anterior tibial shear force

Regression analysis showed that the knee flexion angle, but not the vGRF, was the most critical predictor of anterior tibial shear force in both fatigue groups. Decreased knee flexion increases anterior tibial shear force, confirming its strong link to risk-related biomechanical markers. In the quadriceps-fatigue group, the regression model accounted for 84.4% of the variance in anterior tibial shear force. In the hamstring-fatigue group, the model explained 68.7% of the variance. Across both groups, peak knee flexion angle consistently emerged as the key predictor associated with anterior tibial shear force. The high variance explained (R² = 84.4% and 68.7%) should be interpreted with caution. Because knee flexion angle and proximal tibial shear force are derived from the same landing event, a natural degree of mechanical and mathematical coupling exists between them. As a result, these regression models primarily reflect strong biomechanical co-variation rather than independent causal effects. These regression results should be viewed as exploratory and associative rather than causal, but they clearly show that reduced knee-flexion posture strongly covaries with higher anterior tibial shear forces following fatigue. This is crucial, as decreased knee flexion during landing is associated with greater anterior tibial translation and higher estimated ACL strain mechanical profiles [15, 40]. Although the peak vGRF significantly correlated with anterior tibial shear force, it did not predict changes in the regression models. This indicates that while vGRF influences knee biomechanics, the knee flexion angle plays a more critical role post-fatigue. Previous studies showed that a high vGRF increases knee joint load [27, 41], but this study highlights that the knee flexion angle is a more direct predictor of anterior tibial shear force changes.

Implications for ACL injury prevention

Both quadriceps and hamstring fatigue altered dynamic landing mechanics, with hamstring fatigue producing more pronounced changes in biomechanical markers linked to ACL loading—specifically reduced knee flexion, higher vertical ground reaction forces, and greater anterior tibial shear force. However, these variables represent indirect kinematic and kinetic estimates rather than direct measurements of in vivo ACL tensile force, strain, or actual clinical injury risk. Thus, improving hamstring strength and endurance [2], and monitoring fatigue during training and competition are essential strategies to optimize lower limb kinetics and kinematics associated with ACL loading [42].

Limitations and future research

Several limitations should be acknowledged. First, the sample included only male participants to control for sex-related differences in strength and hormone-mediated joint laxity; however, female athletes exhibit distinct landing mechanics, neuromuscular control patterns, and higher non-contact ACL injury rates, so these findings cannot be generalized to female populations. Second, although a 50% reduction in peak concentric torque was used as a standardized fatigue threshold, identical relative torque drops do not ensure equivalent physiological fatigue states between the quadriceps and hamstrings due to differences in muscle architecture, fiber-type composition, and cross-sectional area. Third, the protocol assessed acute performance fatigability—reflected by reduced torque capacity—rather than underlying neurophysiological mechanisms; without sEMG spectral analysis or voluntary activation testing (e.g., interpolated twitch techniques), the contributions of central motor drive versus peripheral excitation–contraction impairment remain unclear. Fourth, post-fatigue landing trials occurred within a 30–45 s transition window and were completed within 3–5 min, but exact timestamps were not recorded, allowing for subtle recovery effects and potential influence of unstandardized time-of-day or prior-day activity. Fifth, all kinetic and kinematic variables—including anterior tibial shear force, peak knee flexion angle, vGRF, and joint moments—are surrogate indicators of ACL loading rather than direct in vivo measures of ligament strain or tensile force. Sixth, although testing occurred between 10:00–16:00, individual circadian timepoints were not logged, introducing possible diurnal variation in baseline neuromuscular performance. Seventh, the regression analysis was exploratory, and the high R² values largely reflect mechanical and mathematical coupling within biomechanical modeling rather than causal relationships with ACL strain or injury risk. Eighth, the standardized 30-cm forward drop-jump lacks ecological validity; performed without approach speed, perturbations, or cognitive load, it reflects controlled laboratory landings rather than reactive sport scenarios, limiting generalizability to game-like conditions. Ninth, combining basketball and football athletes introduces sport-specific movement variability; although groups were balanced, the total sample (n = 93) was insufficient to examine sport-by-fatigue interactions. Future work should use larger, sport-stratified cohorts to determine whether sport-specific movement patterns influence fatigue-related neuromuscular responses.

Conclusion

The study provides valuable insights into how quadriceps and hamstring fatigue differentially affect knee biomechanics during single-leg landings. Hamstring fatigue may result in greater disruption of risk-related biomechanical markers associated with ACL loading compared with quadriceps fatigue, primarily due to its impact on the knee flexion angle, vGRF, and anterior tibial shear force, whereas quadriceps fatigue induces more disturbance in knee abduction and adduction moments. These findings underscore the importance of targeted conditioning programs to enhance dynamic stability of the knee and mitigate high-impact mechanical stress patterns, particularly in activities involving repetitive landings and high-impact forces.

Authors' contributions

WSM and SMH: Writing – review & editing, Writing – original draft, Visualization, Validation, Resources, Project administration, Formal analysis, Methodology, Investigation, Data curation, Conceptualization. NLR and SMA: Writing – review & editing, Project administration, Methodology, Data curation, Conceptualization. MIM: Visualization, Validation, Project administration, Methodology, Investigation, and Data curation. MMH: Visualization, Validation, Data curation. SMA: Writing – review & editing, Writing – original draft, Visualization, Validation. All authors reviewed and approved the final version of the manuscript.

Funding

This research is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2026/R/1447).

Data availability

The data generated and reported in the present study are available upon reasonable request.

Declarations

Ethics approval and consent to participate

All participants were fully informed about the study before it began, and each of them signed a university-approved consent form prior to participation. They also consented to having their data published in an anonymized format in an open-access online journal. This manuscript does not contain any information that could identify them personally. This study was approved by the Standing Committee for Bioethics Research of Prince Sattam bin Abdulaziz University (SCBR-002-2023). This research was conducted in accordance with the ethical standards established by the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.