1. Introduction
Running has become one of the most popular aerobic exercises worldwide due to its accessibility, minimal equipment requirements, and substantial health benefits [1]. With the vigorous development of global running events—including marathons, half marathons, and various short-distance road races—participation in running continues to rise. However, this growing engagement is accompanied by persistently high rates of lower-limb injuries. Epidemiological studies reveal that nearly half of runners globally experience lower-limb injuries at some point, adversely affecting both their performance and overall exercise experience, as well as placing additional strain on public health resources and healthcare costs [2,3]. Among the myriad factors contributing to running-related injuries, exercise-induced fatigue stands out as a critical determinant, exerting a profound impact on neuromuscular function and lower-limb biomechanical characteristics [4,5]. Understanding how fatigue modifies biomechanical and muscle activation patterns movement is essential for developing targeted training and rehabilitation strategies to mitigate injury risk and enhance performance.
During the landing phase of running, when the foot makes contact with the ground, fatigue significantly alters lower-limb kinematics and kinetics, increasing mechanical stress on joints and soft tissues [6]. Ground reaction forces during landing can reach two to seven times body weight, and these forces are closely associated with injury incidence [7,8]. Previous studies have demonstrated that exercise-induced fatigue leads to increased joint flexion angles, higher impact forces, and elevated joint moments, which may contribute to impaired shock absorption and joint stability [7,9]. These alterations have been observed in multiple lower-limb joints, including the hip, knee, and ankle, where fatigue-induced changes in joint mechanics can result in compensatory movement patterns that further elevate injury risk [10]. However, most existing research has focused on the effects of localized muscle fatigue, investigating how fatigue in individual muscle groups, such as the hamstrings, quadriceps, and gastrocnemius, affects landing biomechanics [11]. Although these studies provide valuable insights into muscle-specific adaptations, they do not fully elucidate whether whole-limb fatigue alters lower-limb biomechanical characteristics and muscle activation patterns during running [11].
For runners, fatigue does not occur in isolated muscles but rather affects the lower limb as a whole, yet little is known about how global lower-limb fatigue influences biomechanical risk factors for injury [12]. Fatigue-induced changes in joint kinematics and kinetics are accompanied by alterations in muscle activation patterns, but the precise neuromuscular control strategies during the landing phase remain debated [13]. Some studies suggest that agonist–antagonist coactivation ratios remain stable under fatigue due to central neural mechanisms that regulate motor output [14], whereas others argue that antagonist activation decreases to compensate for reduced agonist force production, potentially compromising joint stability [15]. The lack of consensus in the literature highlights the need for further research to determine how whole-limb fatigue affects lower-limb biomechanics and neuromuscular control strategies during running.
Therefore, the purpose of this study is to investigate the biomechanical and neuromuscular adaptations of the lower limbs during the landing phase of running under whole-limb fatigue conditions. Using three-dimensional motion capture, force plate analysis, and surface electromyography (sEMG), this study aims to quantify changes in joint kinematics, kinetics, and muscle activation patterns before and after fatigue. By analyzing neuromuscular regulation strategies, particularly agonist and antagonist muscle activation, this study seeks to provide novel insights into fatigue-induced injury mechanisms and inform injury prevention strategies for runners.
2. Methods
2.1. Participants
Based on a priori power analysis using G*Power 3.0.10 (effect size dz = 0.6, power (1-β) = 0.85, α = 0.05), a minimum of 27 participants was required. Accordingly, thirty healthy male university students were recruited between June 2023 and January 2024 (Table 1).
Participants were included based on the following criteria:
Physically healthy, with no chronic diseases.
No history of lower-limb or foot injuries in the past six months.
Normal foot arch, assessed using the navicular drop test (a difference ≤ 10 mm between seated and standing navicular height) [16].
Regular running-based exercise routine, with an average weekly running distance of no less than 20 km.
Right-leg dominance, determined by the preferred foot for a soccer-kicking task.
Participants were excluded if they failed to complete the full experimental protocol due to unforeseen circumstances or sustained injuries during testing. Additionally, any data loss or invalid data led to exclusion from the final analysis.
2.2. Experimental Procedure
The experimental procedure included pre-fatigue measurements, a fatigue intervention protocol, and post-fatigue measurements. All tests were conducted in the Human Performance and Rehabilitation Laboratory at the university, following the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Soochow University (SUDA20211227H03). Participants were recruited through university-wide advertisements, provided with detailed study information, and gave written informed consent before participation. To ensure safety, a preliminary health screening was conducted, and during the fatigue protocol, heart rate and perceived exertion (Borg RPE scale) were monitored. The protocol was terminated if participants exhibited signs of excessive distress or an inability to maintain running pace. A trained researcher was present throughout to oversee safety and provide immediate assistance if needed.
2.2.1. Experimental Equipment
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(1). Motion Capture System: An eight-camera Vicon MX13 infrared motion capture system (Vicon, Oxford, UK) was used to collect kinematic data at a sampling frequency of 200 Hz. Sixteen reflective markers were placed on anatomical landmarks of the lower limbs following the Vicon lower-limb model.
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(2). Force Platform: A Kistler 3D force platform (Kistler, Winterthur, Switzerland) with an external signal amplifier was used to measure ground reaction forces (GRF) at 1000 Hz.
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(3). Surface Electromyography (sEMG): Muscle activity of the rectus femoris (RF), biceps femoris (BF), tibialis anterior (TA), and gastrocnemius (GAS) was recorded using a multi-channel surface EMG system (Biovision, Wiesbaden, Germany) at a sampling frequency of 1024 Hz. Disposable Ag/AgCl electrodes were placed parallel to muscle fibers with a 2–3 cm inter-electrode distance.
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(4). Treadmill: A Zebris FDM series treadmill (Zebris Medical GmbH, Isny im Allgäu, Germany) was used for the fatigue protocol, equipped with integrated sensors to measure gait parameters.
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(5). Heart Rate Monitor: A Xiaomi sports watch (Wuhan, China) was used to monitor participants’ heart rates during the fatigue intervention.
2.2.2. Experimental Protocol
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(1). Pre-test preparation: Participants were tested individually to ensure consistency in experimental conditions and minimize fatigue effects. After recording anthropometric data (e.g., height, weight, leg length), participants were familiarized with the laboratory environment and experimental equipment. Participants wore tight-fitting shorts and heart rate monitors and were instructed to remove upper body hair at electrode placement sites. Skin preparation included abrasion with fine sandpaper and cleaning with alcohol wipes to reduce impedance.
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(2). Warm-up: A general warm-up consisting of joint mobilization (30 min) and treadmill running at 8 km/h for 5–10 min was performed, followed by a 3 min rest. sEMG electrodes and motion capture markers were then applied.
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(3). Fatigue protocol: A treadmill-based constant-speed running fatigue protocol was implemented to induce exercise-induced fatigue, based on the protocol proposed by Quammen et al. [17]. Participants ran at a constant speed of 14 km/h until exhaustion, defined by meeting all three criteria:
Heart rate reaching ≥ 90% of age-predicted maximum heart rate.
Participant self-reported exhaustion using a Rating of Perceived Exertion (RPE) ≥ 19.
Participant’s voluntary termination of the task.
Note: During the fatigue protocol, gait data were not recorded. The termination of the fatigue test was based solely on heart rate, RPE, and voluntary exhaustion criteria.
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(4). Biomechanical and sEMG data collection:
Pre-fatigue measurement:
After the warm-up, participants performed a 10 m straight-line running trial at a self-selected speed (8–12 km/h), based on their habitual training pace. To control speed, a metronome was set to match the step cadence of each participant during their self-selected trial.
A 20 m acceleration zone was provided before the 10 m test zone to ensure participants reached a steady state running speed before data collection.
The force platform was placed in the center of the laboratory, and a 10 m deceleration zone was designated after the test zone to allow natural stopping without affecting running mechanics (Figure 1).
Participants were instructed to step naturally onto the force platform without adjusting their stride, ensuring that their dominant right foot landed in the center.
Surface electromyography (sEMG) signals were recorded using bipolar Ag/AgCl electrodes placed over the rectus femoris, biceps femoris, tibialis anterior, and gastrocnemius according to SENIAM guidelines. To minimize cross-talk, electrodes were positioned along the muscle belly in the direction of the muscle fibers, maintaining an inter-electrode distance of 20 mm. Before placement, the skin was abraded and cleaned with alcohol to reduce impedance. Muscle activation sites were identified through manual palpation and confirmed by voluntary contractions. To ensure reproducibility and reduce variability, the same researcher applied all electrodes, and placement was standardized across participants using anatomical landmarks.
Successful trials were defined as full-foot contact within the platform boundaries. Three valid trials were recorded with a 10 s rest interval between runs.
Three trials were recorded with a 10 s rest interval. Kinematic data (lower-limb joint angles) and kinetic data (GRF) were captured simultaneously with EMG signals from the RF, BF, TA, and GAS muscles.
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ii.. Post-fatigue measurement: Immediately following the fatigue intervention, participants repeated the running trials under the same conditions as the pre-fatigue test. The same cadence (set by the metronome in the pre-fatigue test) was used to regulate running speed, ensuring a consistent running pattern before and after fatigue. All biomechanical and sEMG data were collected using the same protocol.
2.3. Data Analysis
2.3.1. Biomechanical Parameters
The ground contact phase during running was divided into three periods based on force plate data:
Pre-landing phase: 100 ms before ground contact.
Initial contact (LR1): from ground contact (vertical GRF >10 N) to 50 ms post-contact.
Late contact (LR2): from 50 ms to 200 ms post-contact.
Ground reaction forces (GRFs): five components of GRFs were analyzed: vertical (vGRF), anterior–posterior (aGRF and pGRF), and medial–lateral (mGRF and lGRF).
Joint angles and moments: at contact and takeoff moments. Peak joint moments (normalized to body weight) were extracted for comparison. Angles and moments were both measured in the lower-limb sagittal plane (X-axis).
2.3.2. EMG Parameters
EMG signals were bandpass filtered (10–480 Hz) and analyzed for the following:
Root mean square (RMS): reflecting muscle activation intensity.
Mean power frequency (MPF): indicating muscle fatigue.
Coactivation ratio (CAR): calculated as follows:
The CAR assessed the coordination strategy between agonist and antagonist muscles.
2.4. Statistical Analysis
To assess normality, the Shapiro–Wilk test was used in combination with the visual inspection of Q-Q plots. Since none of the variables exhibited severe skewness, no data transformation was applied. Paired t-tests were conducted for normally distributed data, while the Wilcoxon signed-rank test was used for non-normally distributed variables. To control for multiple comparisons, the Benjamini–Hochberg false discovery rate (FDR) correction was applied.
3. Results
3.1. Results of Lower-Limb Biomechanical Characteristics
3.1.1. Results of Hip, Knee, and Ankle Joint Angles During the Landing Phase
The Shapiro–Wilk test indicated that the joint angle data during landing followed a normal distribution.
A paired t-test was conducted to compare lower-limb joint angle changes during different landing phases before and after fatigue. After applying the Benjamini–Hochberg false discovery rate (FDR) correction, the hip joint angle at the initial contact (IC) phase was significantly lower post-fatigue (FDR-adjusted p = 0.023, t = 3.452), while its range of motion (ROM) significantly increased (FDR-adjusted p = 0.0115, t = 2.164). The knee joint angle at the IC (FDR-adjusted p = 0.0157, t = 3.377) and takeoff moment (TM) (FDR-adjusted p = 0.0088, t = 5.426) was significantly lower post-fatigue, whereas the knee ROM increased significantly (FDR-adjusted p = 0.0063, t = 6.213). The ankle ROM also showed a significant increase (FDR-adjusted p = 0.0077, t = 2.216). The detailed results are shown in Table 2 and Figure 2.
3.1.2. Results of Hip, Knee, and Ankle Joint Moments During the Landing Phase
The Shapiro–Wilk test indicated that the joint moments data during landing followed a normal distribution.
A paired t-test showed that the peak sagittal plane moment of the knee joint increased significantly post-fatigue (FDR-adjusted p = 0.0092, t = 3.121), while no significant changes were observed in the hip and ankle moments (p > 0.05). The detailed results are shown in Table 3.
3.1.3. Results of Ground Reaction Force During Landing Pre- and Post-Fatigue
The Shapiro–Wilk test indicated that the ground reaction force data during landing followed a normal distribution.
A paired t-test was conducted to compare ground reaction force changes during different landing phases before and after fatigue. The second peak of vertical ground reaction force (vGRF2) significantly increased post-fatigue (FDR-adjusted p = 0.0086, t = 6.722). Other GRF components did not show significant changes (p > 0.05). The detailed results are shown in Table 4 and Figure 3.
3.2. Surface Electromyography (sEMG) Results of Lower-Limb Muscles
3.2.1. RMS Results of Lower-Limb Muscles During Pre-Landing and Landing Phase
The Shapiro–Wilk test indicated that the RMS data during landing followed a normal distribution.
After FDR correction, significant increases were observed in the rectus femoris during LR2 (FDR-adjusted p = 0.0057, t = 4.751), the biceps femoris during PR (FDR-adjusted p = 0.0077, t = 3.013), the tibialis anterior during LR1 (FDR-adjusted p = 0.0051, t = 5.142), and the gastrocnemius during LR2 (FDR-adjusted p = 0.0057, t = 3.414). No significant changes were observed in other phases (p > 0.05). The detailed results are shown in Table 5 and Figure 4.
3.2.2. MPF Results of Lower-Limb Muscles During Pre-Landing and Landing Phase
The Shapiro–Wilk test indicated that the MPF data during landing followed a normal distribution.
Significant decreases were observed in the rectus femoris during LR2 (FDR-adjusted p = 0.0082, t = 4.342), the biceps femoris during PR (FDR-adjusted p = 0.0081, t = 7.321) and LR1 (FDR-adjusted p = 0.0046, t = 5.214), the tibialis anterior during PR (FDR-adjusted p = 0.0038, t = 6.322), LR1 (FDR-adjusted p = 0.0172, t = 8.521), and LR2 (FDR-adjusted p = 0.0077, t = 5.348), and the gastrocnemius during LR1 (FDR-adjusted p = 0.0046, t = 8.686). No significant changes were observed in other phases (p > 0.05). The detailed results are shown in Table 6 and Figure 5.
3.2.3. Coactivation Ratio Results During Pre-Landing and Landing Phases
The Shapiro–Wilk test indicated that the coactivation ratio data during landing followed a normal distribution.
A paired t-test showed that the coactivation ratio (CAR) of the biceps femoris to rectus femoris (BF:RF) significantly decreased post-fatigue in the LR1 phase (FDR-adjusted p = 0.0033, t = 4.238) and LR2 phase (FDR-adjusted p = 0.0057, t = 4.981). Similarly, the CAR of the tibialis anterior to gastrocnemius (TA:GAS) significantly decreased post-fatigue in the LR2 phase (FDR-adjusted p = 0.0057, t = 4.842). No significant changes were observed in other phases (p > 0.05). The detailed results are shown in Table 7 and Figure 6.
4. Discussion
This study investigated the biomechanical and neuromuscular changes in the lower limbs during the landing phase of running under fatigue conditions. Using a methodological approach that included three-dimensional motion capture, force platform analysis, and surface electromyography, the kinematic, kinetic, and neuromuscular characteristics were analyzed across different phases of landing. The results showed significant post-fatigue changes in joint angles, joint moments, and muscle activation patterns, particularly in the knee and hip joints, as well as in the coactivation ratios of key muscle groups. These findings provide insights into the mechanisms of neuromuscular adaptation to fatigue and offer practical implications for training optimization and injury prevention in running.
Exercise-induced fatigue leads to changes in the sagittal plane’s kinematic characteristics of the lower limbs during running. Specifically, fatigue results in reduced hip and knee joint angles at the moment of landing, as well as increased joint range of motion throughout the landing phase. Increased flexion in the hip and knee joints can lower the body’s center of gravity, thereby enhancing balance and stability during running. The reduced knee joint angle at the moment of landing, followed by increased flexion, extends the moment arm of the quadriceps, allowing for greater knee extension moments. This corresponds with the observed increase in peak knee joint moments in the study results. Other studies have suggested that increased knee flexion may serve as a compensatory mechanism post-fatigue to adequately absorb ground reaction forces, protect the joint, and maintain consistent power output [12]. Drerrik [18] proposed that a reduced knee flexion angle could potentially decrease the effective mass of the body or lower limbs. During the landing phase, the ankle joint primarily transitions from dorsiflexion to plantarflexion. Experimental results indicate minimal changes in ankle joint angles pre- and post-fatigue, but ankle function decreases as dorsiflexion, dorsiflexion moments, and joint energy absorption diminish. Exercise-induced fatigue also alters the kinetic characteristics of the lower limbs during running. Fatigue leads to an increase in peak knee joint moments and vertical ground reaction forces (vGRFs). Previous studies have highlighted that the repetitive impact forces experienced by lower-limb joints during sustained running are a primary contributor to overuse injuries in the lower extremities [19]. The increase in peak knee joint moments may be associated with a greater flexion of the hip and knee joints at heel strike. For the hip joint, the increased flexion extends the hip forward, thereby enhancing hip extensor moments. However, the present study found no significant differences in hip joint peak moments pre- and post-fatigue, which contrasts with some prior findings [20]. Kernozek et al. [21] reported that while peak knee joint moments tend to increase post-fatigue, hip joint moments often decrease, potentially due to reduced ground reaction forces affecting hip moment generation. In this study, the lack of significant changes in hip joint moments may be attributed to differences in fatigue protocols. Unlike Kernozek et al., who implemented a knee extensor-specific fatigue protocol, this study employed a whole-limb fatigue model, which may have led to different neuromuscular compensation strategies. Additionally, the timing of the measurements conducted immediately after fatigue induction might have influenced the observed outcomes, as fatigue progression could vary across different muscle groups. Similarly, no significant differences were observed in peak ankle joint moments pre- and post-fatigue, consistent with prior research. However, ankle function declines with reduced dorsiflexion, dorsiflexion moments, and joint energy absorption [22]. Consequently, higher impact loads may be transferred to the knee joint for primary regulation.
Post-fatigue, the second peak of vertical ground reaction forces significantly increased compared to pre-fatigue, while the first vGRF peak and forces in other directions remained unchanged. These results align with findings by Zadpoor and Breine [23,24]. The significant increase in vGRF and loading rates during the landing phase following fatigue suggests insufficient mechanical adjustments by the knee joint. This could impair the ability of knee extensors to buffer ground impact effectively, thereby imposing substantial mechanical stress on the musculoskeletal system and increasing the risk and incidence of lower-limb overuse injuries. The discrepancy in vGRF responses to fatigue suggests that running experience, fatigue onset timing, and biomechanical adaptations might play crucial roles in modulating impact forces. The sEMG findings highlighted distinct neuromuscular adaptations in response to fatigue. Increased activation levels of the rectus femoris and biceps femoris during the pre-activation phase underscore the critical role of pre-activation in joint stabilization. This heightened activation aligns with previous studies [25] emphasizing the importance of pre-activation in mitigating joint instability. During the early and late landing phases, a reduction in the coactivation ratio of the biceps femoris, acting as an antagonist during knee extension, was observed. This adjustment may reflect a compensatory strategy by the central nervous system (CNS) to offset diminished quadriceps strength and maintain net torque output. This observation aligns with findings from Hodder et al., who reported increased biceps femoris activity during knee flexion post-fatigue, suggesting an adaptive response to maintain joint stability and function [26]. Additionally, Harper and Thompson found that older adults exhibited enhanced antagonist muscle coactivation following a fatiguing protocol, indicating an age-related compensatory mechanism to preserve knee extension torque [27].
For the ankle joint, fatigue induced the increased activation of both the tibialis anterior and gastrocnemius. The tibialis anterior’s strong pre-activation and early-phase activation highlight its role in maintaining dorsiflexion and preparing the foot for controlled ground contact. Concurrently, the gastrocnemius’ enhanced activation during the push-off phase likely compensates for reduced knee extensor strength and aids in recruiting additional muscle fibers to sustain plantarflexion. Notably, the reduced coactivation ratio of the tibialis anterior as an antagonist suggests a CNS-driven adaptation to optimize force output while preserving stability. These findings are consistent with previous research [28,29] that demonstrated reduced antagonist coactivation as a neuromuscular strategy to address task-specific demands under fatigue.
The coordination of agonist and antagonist muscles is a complex process involving the motor cortex, cerebellum, and spinal motor neurons. This coordination is essential for smooth and efficient movement and is modulated by mechanisms such as reciprocal inhibition and coactivation. Reciprocal inhibition allows for the relaxation of antagonist muscles when agonists contract, facilitating movement, while coactivation involves the simultaneous activation of both muscle groups to stabilize joints during certain tasks [30]. Under fatigue conditions, neuromuscular regulation adapts to maintain performance [31]. Initially, the central nervous system (CNS) may increase the activation of both agonist and antagonist muscles, a strategy known as the “common drive”, to preserve joint stability and force output. However, as fatigue progresses, this strategy may shift. Recent studies suggest that the CNS adopts differentiated control strategies for agonist and antagonist muscles during fatigue progression. Initially, a coordinated “common drive” mechanism enhances the activity of both muscle groups. Later, despite continued increases in muscle activity, neural–muscular coupling remains stable, indicating a complex adaptation process [32]. Additionally, research has shown that during fatiguing contractions, there is a unique modulation of synaptic input from Ia afferents to antagonist motor neurons, suggesting separate control mechanisms for agonist and antagonist muscles. This modulation reflects the CNS’s ability to adjust muscle activity to compensate for fatigue-induced changes, ensuring the maintenance of net torque and functional performance [33]. These findings highlight the complexity of neuromuscular adaptations under fatigue, indicating that the CNS employs dynamic strategies beyond the traditional “common drive” theory. The reduction in antagonist coactivation observed post-fatigue may serve to optimize force production by the agonist muscles, thereby sustaining performance despite the onset of fatigue. Understanding these mechanisms is crucial for developing interventions aimed at enhancing motor performance and preventing fatigue-related injuries.
Despite the insights provided by this study, several limitations should be acknowledged. First, the fatigue protocol employed was short term and conducted under controlled laboratory conditions, which may not fully replicate the prolonged or repetitive fatigue experienced during real-world running scenarios. This limits the generalizability of the findings to endurance running or outdoor conditions. Additionally, our fatigue protocol was performed on a treadmill under uniform conditions, whereas real-world running includes variable terrains, environmental factors, and pacing strategies, which may elicit different fatigue responses. Future studies should consider incorporating field-based assessments or more ecologically valid fatigue protocols to better reflect real-world running conditions. Second, this study exclusively recruited young, healthy male participants with a specific BMI range and regular running habits, which limits the applicability of the findings to broader populations, including females, older adults, or individuals with varying physical profiles. Given that sex, age, and training background can influence neuromuscular fatigue and biomechanical responses, future research should examine more diverse populations to enhance the generalizability of these findings. Third, the measurements were taken immediately after the fatigue protocol, providing a snapshot of fatigue-induced changes but not accounting for the dynamic progression or recovery of fatigue over time. A longitudinal assessment could offer deeper insights into the temporal effects of fatigue on neuromuscular and biomechanical adaptations. Fourth, this study focused primarily on kinematic and kinetic changes in the sagittal plane, potentially overlooking critical adaptations in the frontal and transverse planes, which are important for stability, injury risk, and load redistribution strategies. Future research should include three-dimensional biomechanical analyses to provide a more comprehensive understanding of multi-planar adaptations to fatigue. Lastly, while surface electromyography (sEMG) provided valuable information on muscle activation patterns, it is limited to superficial muscles and is prone to signal interference, such as cross-talk and variability in electrode placement. Incorporating advanced techniques, such as fine-wire EMG, musculoskeletal modeling, or high-density EMG, could provide a more comprehensive analysis of muscle coordination and joint-specific compensatory mechanisms.
5. Conclusions
After running-induced fatigue, the lower limbs adapt during the landing phase by increasing hip and knee flexion angles at both initial contact and toe-off, as well as enhancing joint range of motion. These adaptations lower the center of gravity and generate greater push-off forces. Muscle fatigue was observed in the rectus femoris, biceps femoris, tibialis anterior, and gastrocnemius, accompanied by increased muscle activation levels. During the pre-activation phase prior to ground contact, the activation strategies of agonist and antagonist muscles did not show significant changes compared to pre-fatigue conditions. However, in the early and late landing phases post-contact, the coactivation levels of antagonist muscles were reduced compared to pre-fatigue. This reduction may be attributed to fatigue disrupting the original balance of reciprocal inhibition, leading to changes in the regulation pathways of agonist motor neurons by higher central nervous systems, thereby altering the activation patterns of antagonist motor neurons.
Conceptualization, B.C. and J.J.; methodology, B.C. and J.J.; software, J.W.; validation, B.C. and J.J. and G.W.; formal analysis, B.C. and JY.J; investigation, B.C.; resources, B.C.; data curation, B.C.; writing—original draft preparation, B.C.; writing—review and editing, G.W.; visualization, B.C. and J.J.; supervision, B.C. and J.J.; project administration, J.W. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the Ethical Review Board: SUDA20211227H03, approval date 27 December 2021.
Written informed consent has been obtained from the patient(s) to publish this paper.
Restrictions apply to the datasets: the datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to
We would like to express our sincere gratitude to all the participants in this study for their valuable support.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 2. Changes in lower-limb joint angles during the landing phase before and after fatigue. * Indicates that the results are statistically significant.
Enlarge this image.Figure 3. Changes in ground reaction force during landing pre- and post-fatigue. * Indicates that the results are statistically significant.
Enlarge this image.Figure 4. RMS results of lower-limb muscles during landing pre- and post-fatigue. * Indicates that the results are statistically significant.
Enlarge this image.Figure 5. MPF results of lower-limb muscles during landing pre- and post-fatigue. * Indicates that the results are statistically significant.
Enlarge this image.Figure 6. Coactivation ratio at different landing phases pre- and post-fatigue. * Indicates that the results are statistically significant.
Characteristics of participants.
| Characteristic | Value |
|---|---|
| Number | 30 |
| Age (years) | 24.8 ± 0.6 |
| Height (cm) | 173.2 ± 3.3 |
| Weight (kg) | 71.7 ± 4.9 |
| BMI (kg/m2) | 23.24 ± 3.22 |
Results of lower-limb joint angles during landing before and after fatigue.
| Joint | Phase | Pre-Fatigue (°) | Post-Fatigue (°) | t | p |
|---|---|---|---|---|---|
| Hip | IC (°) | 125.23 ± 3.26 | 119.59 ± 4.57 | 2.452 | 0.021 |
| TM (°) | 144.67 ± 2.33 | 150.51 ± 1.52 | 1.371 | 0.074 | |
| ROM (°) | 36.39 ± 3.88 | 40.19 ± 3.42 | 3.164 | 0.001 | |
| Knee | IC (°) | 167.31 ± 5.26 | 159.29 ± 4.32 | 3.377 | 0.013 |
| TM (°) | 156.67 ± 6.13 | 152.75 ± 2.58 | 5.426 | 0.005 | |
| ROM (°) | 50.21 ± 5.24 | 55.79 ± 4.65 | 6.213 | 0.001 | |
| Ankle | IC (°) | 95.11 ± 8.12 | 97.59 ± 1.29 | 2.274 | 0.113 |
| TM (°) | 112.56 ± 10.14 | 116.31 ± 12.81 | 1.022 | 0.251 | |
| ROM (°) | 29.61 ± 7.14 | 31.21 ± 3.51 | 2.216 | 0.003 |
Notes: IC: initial contact; TM: takeoff moment (takeoff instance); ROM: range of motion.
Results of lower-limb joint moments during landing before and after fatigue.
| Joint | Pre-Fatigue (°) | Post-Fatigue (°) | t | p |
|---|---|---|---|---|
| Hip (N·m/kg) | 2.71 ± 1.27 | 2.91 ± 0.78 | 1.542 | 0.057 |
| Knee (N·m/kg) | 2.77 ± 2.27 | 3.58 ± 1.79 | 3.121 | 0.004 |
| Ankle (N·m/kg) | 3.66 ± 0.42 | 3.21 ± 0.69 | 0.981 | 0.623 |
Results of ground reaction force during landing pre- and post-fatigue.
| Metrics | Pre-Fatigue (°) | Post-Fatigue (°) | t | p |
|---|---|---|---|---|
| vGRF1 (BW) | 1.79 ± 0.31 | 1.81 ± 0.72 | 3.712 | 0.101 |
| vGRF2 (BW) | 2.43 ± 0.28 | 2.48 ± 0.31 | 6.722 | 0.006 |
| aGRF (BW) | 0.34 ± 0.17 | 0.35 ± 0.11 | 1.092 | 0.114 |
| pGRF (BW) | 0.58 ± 0.29 | 0.55 ± 0.14 | 3.714 | 0.077 |
| mGRF (BW) | 0.12 ± 0.03 | 0.13 ± 0.09 | 0.628 | 0.697 |
| lGRF (BW) | 0.35 ± 0.15 | 0.29 ± 0.13 | 3.219 | 0.081 |
Notes: vGRF1: first peak of vertical ground reaction force; vGRF2: second peak of vertical ground reaction force; aGRF: anterior ground reaction force; pGRF: posterior ground reaction force; mGRF: medial ground reaction force; lGRF: lateral ground reaction force.
RMS results of lower-limb muscles during landing pre- and post-fatigue.
| Muscle | Phase | Pre-Fatigue | Post-Fatigue | t | p |
|---|---|---|---|---|---|
| Rectus Femoris | PL | 0.55 ± 0.21 | 0.53 ± 0.38 | 2.145 | 0.149 |
| LR1 | 0.51 ± 0.42 | 0.52 ± 0.19 | 1.561 | 0.451 | |
| LR2 | 1.12 ± 0.38 | 1.24 ± 0.34 | 4.751 | 0.006 | |
| Biceps Femoris | PL | 1.21 ± 0.45 | 1.27 ± 0.15 | 3.013 | 0.039 |
| LR1 | 1.25 ± 0.32 | 1.33 ± 0.48 | 0.891 | 0.248 | |
| LR2 | 0.56 ± 0.42 | 0.57 ± 0.29 | 1.164 | 0.552 | |
| Tibialis Anterior | PL | 1.25 ± 0.48 | 1.17 ± 0.42 | 2.486 | 0.253 |
| LR1 | 1.22 ± 0.41 | 1.24 ± 0.57 | 5.142 | 0.002 | |
| LR2 | 0.37 ± 0.12 | 0.41 ± 0.21 | 2.451 | 0.069 | |
| Gastrocnemius | PL | 0.56 ± 0.22 | 0.78 ± 0.18 | 1.428 | 0.043 |
| LR1 | 0.62 ± 0.26 | 0.56 ± 0.29 | 1.012 | 0.149 | |
| LR2 | 1.07 ± 0.35 | 1.42 ± 0.58 | 3.414 | 0.001 |
Notes: PL: pre-landing phase; LR1: early landing phase; LR2: late landing phase; RMS: root mean square.
MPF results of lower-limb muscles during landing pre- and post-fatigue.
| Muscle | Phase | Pre-Fatigue | Post-Fatigue | t | p |
|---|---|---|---|---|---|
| Rectus Femoris | PL | 127.65 ± 26.62 | 135.79 ± 27.43 | 4.532 | 0.067 |
| LR1 | 133.64 ± 23.21 | 131.41 ± 12.47 | 6.632 | 0.124 | |
| LR2 | 171.42 ± 25.67 | 160.23 ± 24.35 | 4.342 | 0.002 | |
| Biceps Femoris | PL | 168.34 ± 27.28 | 150.41 ± 17.29 | 7.321 | 0.001 |
| LR1 | 144.42 ± 21.53 | 140.01 ± 12.56 | 5.214 | 0.005 | |
| LR2 | 130.04 ± 29.12 | 128.98 ± 23.67 | 2.241 | 0.651 | |
| Tibialis Anterior | PL | 164.18 ± 11.93 | 145.82 ± 25.74 | 6.322 | 0.006 |
| LR1 | 153.45 ± 21.78 | 146.98 ± 19.82 | 8.521 | 0.001 | |
| LR2 | 105.58 ± 14.72 | 95.01 ± 12.43 | 5.348 | 0.001 | |
| Gastrocnemius | PL | 105.63 ± 28.47 | 106.39 ± 22.18 | 6.562 | 0.267 |
| LR1 | 109.02 ± 15.24 | 105.39 ± 21.41 | 8.686 | 0.001 | |
| LR2 | 118.09 ± 9.45 | 110.01 ± 12.48 | 7.341 | 0.015 |
Notes: PL: pre-landing phase; LR1: early landing phase; LR2: late landing phase; MPF: mean power frequency.
Coactivation ratio at different landing phases pre- and post-fatigue.
| Phase | Fatigue | CRA | t | p | |
|---|---|---|---|---|---|
| BF:RF | PL | Pre-Fatigue | 0.45 ± 0.19 | 1.002 | 0.321 |
| Post-Fatigue | 0.41 ± 0.21 | ||||
| LR1 | Pre-Fatigue | 0.41 ± 0.04 | 4.238 | 0.006 | |
| Post-Fatigue | 0.39 ± 0.16 | ||||
| LR2 | Pre-Fatigue | 0.50 ± 0.05 | 4.981 | 0.002 | |
| Post-Fatigue | 0.46 ± 0.04 | ||||
| TA:GAS | PL | Pre-Fatigue | 0.44 ± 0.15 | 0.721 | 0.876 |
| Post-Fatigue | 0.56 ± 0.24 | ||||
| LR1 | Pre-Fatigue | 0.50 ± 0.18 | 1.732 | 0.034 | |
| Post-Fatigue | 0.45 ± 0.21 | ||||
| LR2 | Pre-Fatigue | 0.34 ± 0.02 | 4.842 | 0.001 | |
| Post-Fatigue | 0.29 ± 0.08 |
Note: PL: pre-landing phase; LR1: early landing phase; LR2: late landing phase; RF: rectus femoris; BF: biceps femoris; TA: tibialis anterior; GAS: gastrocnemius; CAR: coactivation ratio.
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