Injury risk analysis of movement restriction and body asymmetry in sports injury prediction

Abstract

Background

Sports injuries remain a major concern for athletes and fitness enthusiasts, affecting performance and long-term well-being. Effective prediction and prevention strategies are critical for reducing injury incidence. This study investigates the predictive roles of movement restriction and body asymmetry in sports injury risk.

Methods

Sports injuries remain a major concern for athletes and fitness enthusiasts, affecting performance and long-term well-being. Effective prediction and prevention strategies are critical for reducing injury incidence. This study investigates the predictive roles of movement restriction and body asymmetry in sports injury risk.

Results

Significant restrictions in knee and shoulder mobility were associated with increased injury risk. Notably, asymmetries in lower limb strength and flexibility were linked to higher rates of unilateral injuries. For example, a 10% increase in movement restriction corresponded to a 15% higher injury risk, while a 20% strength imbalance predicted a 30% increase. The leg strength imbalance ratio showed a moderate positive correlation with injury rates (r = 0.35, p = 0.03), while the gait symmetry index was negatively correlated (r = -0.42, p < 0.01).

Conclusions

Joint mobility and interlimb balance play critical roles in injury susceptibility. Targeted interventions such as personalized exercise regimens and movement retraining may help mitigate these biomechanical risk factors. Future research should explore sport-specific injury mechanisms and refine predictive models using real-time monitoring and machine learning approaches.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13102-025-01465-z.

Introduction

In recent years, the growing intensity of competitive sports and the increasing popularity of recreational fitness have made sports injuries an urgent concern in the fields of sports medicine, rehabilitation, and training [1, 2]. Injuries can interrupt athletic careers, reduce quality of life, and hinder physical development, regardless of whether one is a professional athlete or an amateur participant [3, 4].

Previous research has identified several intrinsic factors associated with injury risk, including joint movement limitations and asymmetries in musculoskeletal function. For example, Murtagh et al. (2023) demonstrated that reduced joint mobility was significantly associated with higher injury rates in elite athletes [5]. Similarly, Pappas et al. (2024) showed that limb asymmetry affected running mechanics, leading to abnormal loading and elevated injury risk [6]. However, these studies often investigated such factors in isolation rather than in combination.

Movement restriction reflects musculoskeletal dysfunction [7], such as limited range of motion, reduced muscular strength, or ligament stiffness [8]. These limitations can compromise motor control, disturb kinematic chains, and force athletes into compensatory movement patterns that increase injury susceptibility [9].

Meanwhile, body asymmetry—often stemming from uneven development, training imbalances, or anatomical variation—can become problematic when it surpasses a certain threshold [10, 11]. Although some degree of asymmetry is natural, excessive disparities (e.g., >10–15% in lower limb strength) may cause abnormal gait patterns or uneven load transfer during movement, increasing the risk of overuse or acute injuries. For example, significant upper limb asymmetry can disrupt throwing or swinging efficiency [12].

Therefore, this study seeks to clarify the interplay between movement restriction and body asymmetry in relation to sports injuries [13], offering an integrated perspective that supports training optimization, injury risk assessment, and rehabilitation planning [14].

Methodology

Related works

Previous studies have explored various aspects of sports injury prediction, including movement restriction and body asymmetry. For instance, Bishop et al. (2018) conducted a systematic review on the effects of inter-limb asymmetries on physical and sports performance, highlighting the importance of addressing asymmetries to optimize performance and reduce injury risk [15]. Similarly, Ding et al. (2022) reviewed the effectiveness of warm-up intervention programs in preventing sports injuries among children and adolescents, emphasizing the role of preparatory activities in minimizing injury risks [16]. However, despite these advancements, there remains a gap in comprehensively assessing the specific roles of movement restriction and body asymmetry in predicting sports injuries.

Common types of sports injuries

In sports, injuries are a common phenomenon, and there are various reasons for this. Understanding common types of sports injuries and their causes is crucial for prevention, treatment, and rehabilitation. Figure 1 shows a pyramid structure diagram covering seven common types of sports injuries and their approximate proportions among all sports injuries.

Fig. 1.

Pyramid structure of common sports injuries

This pyramid diagram illustrates the hierarchical distribution of seven common types of sports injuries, ranked by their approximate proportions among all sports injuries. The base of the pyramid represents the most frequent injuries (e.g., muscle strain), while the apex represents the least common but often more severe injuries (e.g., muscle cramps). Each layer provides a visual representation of the relative frequency of each injury type, aiding in understanding which injuries are most prevalent and require greater attention in prevention strategies.

Muscle strain

Approximately 25% of total sports injuries, one of the most common sports injuries, usually caused by direct or indirect force acting on muscles, resulting in excessive activity or passive stretching of the muscles. This type of injury commonly occurs in activities requiring explosive power such as running, jumping, and weightlifting [17–19].

Ligament injury

Approximately 20% of total sports injuries, often caused by improper posture or overload, where joint sprains or sudden muscle contractions trigger the injury. Since ligaments are strong fibrous tissues connecting bones, damage can lead to decreased joint stability, pain, swelling, and a sense of instability in the joint. Knee and ankle ligament injuries are particularly common [20].

Muscle contusion

Approximately 15% of total sports injuries, mostly caused by collisions with equipment or each other, characterized by swelling, subcutaneous bleeding, and pain. Although less severe than fractures, if not promptly treated, they can worsen over time [21, 22].

Joint sprain

Approximately 15% of total sports injuries, refers to injuries caused by strong rotation or twisting of a joint, commonly occurring in the waist, ankle, knee, shoulder, wrist, elbow, and other areas [23].

Fracture

Approximately 10% of total sports injuries, one of the most severe and well-known injuries in sports, typically caused by direct or indirect violent impact, such as in contact sports like basketball and football. Pain, swelling, and limitation of movement, even deformity, appear at the fracture site [24].

Stress fracture

Approximately 10% of total sports injuries, primarily due to long-term repetitive motion or stress causing small bone cracks. This type of injury is common in runners, basketball players, and others who engage in the same activity for extended periods. The symptoms are subtle and neglecting them can lead to more serious fractures over time [25, 26].

Muscle cramp

Approximately 5% of total sports injuries, muscle cramps refer to sudden muscle contractions that cannot relax, which may be caused by cold stimulation, excessive loss of electrolytes, rapid or continuous muscle contractions, or fatigue. Muscle cramps are common in prolonged activities such as swimming and long-distance running [27].

Common causes of sports injuries

By analyzing the common types of sports injuries and the sports in which they occur, we summarize the three main factors contributing to sports injuries as shown in Fig. 2:

Fig. 2.

Three major factors contributing to sports injuries

This figure presents a breakdown of the three primary categories contributing to sports injuries: technical factors, preparation factors, and environmental and equipment factors. Each category is further subdivided into specific elements (e.g., insufficient training level, inadequate warm-up, uneven playing fields) that collectively contribute to the occurrence of sports injuries. This visual aids in identifying key areas where interventions could be most effective in reducing injury rates.

Technical factors contributing to sports injuries encompass several key areas. When an athlete’s skill level and physical reserves fall short of the sport’s demands, challenges arise in performing high-intensity or complex movements. This mismatch can overwhelm athletes, making them susceptible to injuries [28]. Execution errors also play a critical role; incorrect techniques, such as improper foot positioning during landing while running, lead to uneven force distribution across joints and muscles, potentially causing long-term damage [29].

Physical fitness levels significantly impact an athlete’s injury risk. If strength, flexibility, endurance, and other vital physical attributes are lacking, athletes face higher chances of experiencing fatigue and discomfort during rigorous activities [30]. Consequently, these conditions elevate the likelihood of sustaining injuries. Adequate preparation before engaging in sports is essential. Without proper warm-up routines and stretching exercises prior to intense activity, muscles and joints remain stiff, increasing vulnerability to harm from sudden exertions [31].

Self-protection abilities during exercise cannot be overlooked. Athletes who do not adopt appropriate protective measures, such as learning how to roll safely to cushion falls, expose themselves to heightened risks of severe injuries [32]. Environmental conditions and equipment suitability are equally crucial. Uneven playing surfaces, extreme weather conditions, and surrounding obstacles can adversely affect performance and safety. For instance, slippery surfaces pose significant fall hazards. Moreover, inadequate or damaged sports equipment escalates injury risks. Running in poorly fitted shoes can lead to plantar fasciitis, while compromised protective gear fails to offer necessary joint protection, making injuries more likely [33, 34].

In summary, technical factors, preparation adequacy, and environmental and equipment considerations all contribute critically to the incidence of sports injuries. To mitigate these risks, athletes are advised to enhance their training standards, refine correct techniques, ensure thorough warm-ups, select suitable environments for practice, and utilize appropriate equipment.

Study design

In this study, a total of 150 athletes from various sports disciplines carefully selected to ensure comprehensive coverage of strength, endurance, skill, and mixed sports domains, thus ensuring the broad applicability and depth of the analysis. Specific sports include, but are not limited to, track and field (sprints, long jump), swimming (freestyle, breaststroke), basketball, football, gymnastics, and weightlifting. These athletes were selected based on their competitive level (all national-level athletes) to ensure a certain level of professionalism and representativeness in their respective sports. Additionally, to account for potential differences due to gender, we ensured an equal gender ratio, with 50% male and 50% female participants.

Data collection methods

Background information on athletes

Our study included 150 athletes from various sports disciplines including track and field (sprints, long jump), swimming (freestyle, breaststroke), basketball, football, gymnastics, and weightlifting. Each participant provided comprehensive personal history including their sport-specific training regimens, previous injury records, role within their respective sports (e.g., defender in football), current health status, and specific characteristics of their training such as intensity, frequency, duration, and types of exercises performed. A structured self-report questionnaire, developed specifically for this study, was used to collect participants’ demographic information, training history, and injury records. The English version of this questionnaire is provided as Supplementary File 1. To enhance external validity, strict inclusion and exclusion criteria were applied:

Inclusion criteria

1. Active competitors at the national level with a minimum of 5 years of professional training in their respective sports.

2. Aged 18–30 years, with no restrictions based on gender (50% male, 50% female).

3. No history of major musculoskeletal injuries (e.g., fractures, ligament ruptures, or surgeries) within the 6 months prior to enrollment, confirmed via medical records and self-reported questionnaires.

4. Participation in regular training (≥ 15 h/week) and competitions during the study period.

Exclusion criteria

1. Athletes presenting with unresolved injuries, chronic musculoskeletal conditions such as osteoarthritis, or those undergoing active rehabilitation at the time of enrollment were excluded from participation. This exclusion aimed to eliminate confounding effects from pre-existing physical limitations that could influence biomechanical assessments or injury risk outcomes during the study period.

2. Individuals who failed to complete baseline biomechanical evaluations or demonstrated an inability to adhere to the 12-month follow-up protocol were also excluded. Compliance with the full assessment schedule was deemed critical to maintaining data integrity and ensuring longitudinal tracking of injury-related variables.

Recruitment process

Participants were recruited through collaboration with local sports clubs, universities, and national sports associations. Initially, a pool of approximately 500 potential participants was identified based on eligibility criteria which included being active competitors at the national level without restrictions due to recent injuries. Of these, 150 athletes agreed to participate, representing a 30% acceptance rate. The recruited population demonstrated homogeneity across certain characteristics such as age range (18–30 years old), gender distribution (50% male and 50% female), and competitive experience (all participants were nationally ranked).

Biomechanical testing

For biomechanical testing, advanced isokinetic dynamometers (model: Biodex System 4 Pro) and ground reaction force platforms (model: Kistler Force Plate 9286AA) were utilized to conduct isometric and isokinetic strength tests on key muscle groups such as quadriceps, hamstrings, pectoralis major, and latissimus dorsi. These tests aimed at assessing force output, explosive power, and endurance. A plantar pressure distribution system (model: Novel Pedar-X) monitored ground reaction forces during standing, walking, running, and specific action execution to analyze gait patterns and load distribution.

Motion capture technology

High-precision three-dimensional motion capture systems (model: Vicon Vantage V5) recorded movement trajectories, speeds, accelerations, and angle changes of athletes’ bodies while executing standardized actions like sprint starts, jumps, shooting, and kicking. This provided visual evidence for analyzing body asymmetry, offering clear insights into the dynamics of athlete movements and their implications on performance and injury risk. By detailing the precise models and characteristics of these instruments, this description allows for replication of the study by other researchers aiming to explore similar questions in the field of sports science.

Reliability and standardization of range of motion testing and symmetry assessment

In this study, to ensure the reliability and standardization of range of motion testing and symmetry assessment, a series of measures were taken. Firstly, industry-recognized high-quality measurement devices such as the Biodex System 4 Pro isokinetic dynamometer, Kistler Force Plate 9286AA ground reaction force platform, Novel Pedar-X plantar pressure distribution system, and Vicon Vantage V5 three-dimensional motion capture system were selected, with all instruments undergoing regular calibration and maintenance to maintain their accuracy. Secondly, detailed operational guidelines were established, specifying the correct posture for athletes, equipment setting parameters, and action execution instructions, thereby reducing variability caused by operator differences. Furthermore, all researchers involved in data collection received professional training to ensure they could proficiently use various measuring tools and accurately execute test procedures. The effectiveness and reliability of the methods were further verified through internal repeated measurements and comparisons with results from other laboratories. Finally, statistical methods were applied for quality control of data analysis, including calculating intraclass correlation coefficients (ICC) and using Bland-Altman plots to analyze differences between different time points or different devices, thus assessing the consistency and reliability of the data. These steps collectively provided a solid foundation for the research and enhanced the credibility of the results.

Data analysis and statistical methods

Upon completion of data collection, two primary statistical analysis methods were adopted to systematically explore potential relationships between movement restriction, body asymmetry, and sports injuries.

Descriptive statistical analysis was conducted to organize collected biomechanical indicators and motion capture data. Means, standard deviations, maximum values, and minimum values were calculated to understand the overall athletic ability and body symmetry among athletes. This step involved compiling data on key biomechanical indicators from 150 athletes across various sports disciplines, providing foundational insights into the sample’s characteristics.

Correlation analysis

Pearson’s correlation coefficient was used for correlation analysis focusing on associations between biomechanical parameters such as the left-right leg strength imbalance ratio, gait symmetry index, peak ground reaction force (right foot), peak ground reaction force (left foot), knee flexion angle (running), and sports injury incidence rates. Specifically, the analysis aimed to determine whether variations in force output, gait asymmetry, and action fluidity significantly influence the risk of sports injuries. By calculating correlation coefficients for these five indicators derived from descriptive statistics, the study sought to identify significant correlations that could inform preventive strategies and training adjustments.

In summary, this detailed approach to data analysis and statistical methods ensured a thorough examination of the complex relationships between biomechanical factors and sports injury risks, laying a solid foundation for both understanding existing dynamics and guiding future research directions.

Multivariate regression analysis and Structural equation Modeling (SEM)

To further investigate the causal relationships and interactions between variables, multivariate regression analysis and structural equation modeling (SEM) were applied. These advanced statistical techniques allowed for the adjustment of confounding factors such as age, gender, and sport type, ensuring more accurate assessments of the impact of biomechanical indicators on injury risk. SEM was used to explore indirect effects and pathways through which biomechanical discrepancies might influence injury risk.

Adjustment for confounding factors

In the multivariate regression models, age, gender, and sport type were included as covariates to control for potential confounding effects. By incorporating these variables as covariates, the study ensured that the main effects of interest (such as left-right leg strength imbalance ratio and gait symmetry index) were independent of these confounders. This approach enhances the reliability and validity of the findings by accounting for extraneous variables that could otherwise distort the results.

Assumption tests

Prior to conducting Pearson correlation analysis and regression analysis, normality tests and homogeneity of variance tests were performed. All continuous variables were subjected to the Shapiro-Wilk test for normality to ensure that they followed a normal distribution (p > 0.05). Levene’s test was used to confirm homogeneity of variances (p > 0.05), ensuring that the data met the assumptions required for these statistical methods. Ensuring these assumptions are met is critical for the validity of the subsequent analyses.

Results

Descriptive analysis of Biomechanical indicators

Analysis of 150 athletes revealed key biomechanical characteristics (Table 1; Fig. 3). The leg strength imbalance ratio averaged 1.05 (SD = 0.12), indicating a 5% inter-limb strength discrepancy in the cohort. Quartile analysis showed 25% of athletes exhibited near-symmetry (Q1 ≤ 0.97), while the upper quartile (Q3 ≥ 1.10) demonstrated clinically significant asymmetry. Gait symmetry averaged 92.1% (SD = 4.2%), with 34% of athletes falling below the 90% threshold associated with elevated injury risk. Systematic right-foot dominance was observed in ground reaction forces (Right: 1202 N ± 153 N vs. Left: 1183 N ± 142 N; Δ = 19 N, *d*=0.13). Knee flexion angles during running clustered tightly around the biomechanically optimal range (Mean = 60.2°±5.1°, IQR = 6.7°), with 68% of measurements between 56.8° and 63.5°.

Table 1.

Distribution characteristics of biomechanical indicators (n = 150)

Indicator	Mean	SD	Q1	Median	Q3	IQR	Min	Max
Leg Strength Imbalance Ratio	1.05	0.12	0.97	1.04	1.10	0.13	0.85	1.30
Gait Symmetry Index (%)	92.1	4.2	89.3	92.5	95.0	5.7	85.0	98.0
R. Peak GRF (N)	1202	153	1098	1205	1308	210	900	1500
L. Peak GRF (N)	1183	142	1080	1180	1285	205	880	1450
Knee Flexion Angle (°)	60.2	5.1	56.8	60.5	63.5	6.7	50.0	70.0

Fig. 3.

Leg strength Imbalance ratio and gait symmetry index distributions

Injury risk associations

Bivariate correlations established significant injury relationships (Table 2; Fig. 4). Leg strength imbalance showed moderate positive correlation with injury incidence (*r*=0.35, *p*=0.03), while gait symmetry demonstrated a stronger protective effect (*r*=−0.42, *p*=0.004). In multivariate modeling adjusting for age, gender, and sport type (Table 3), each 0.1-unit increase in leg imbalance elevated injury odds by 15% (aOR = 1.15, 95% CI:1.03–1.29, *p*=0.016). Conversely, every 5% improvement in gait symmetry reduced injury odds by 22% (aOR = 0.78, 95% CI:0.65–0.93, *p*=0.006). Structural equation modeling revealed that gait asymmetry indirectly increased injury risk through elevated knee joint loading (β = 0.41, *p*<0.001), accounting for 13.5% of total injury risk (95% CI:0.08–0.19).

Table 2.

Bivariate correlations with injury incidence

Indicator	*r*	*p*-value
Leg Strength Imbalance	0.35*	0.03
Gait Symmetry Index	−0.42**	0.004
R. Peak GRF	0.28	0.12
L. Peak GRF	0.25	0.21
Knee Flexion Angle	−0.09	0.45

***p < 0.05, **p < 0.01*

Fig. 4.

Multivariate predictors of injury risk

Table 3.

Multivariate predictors of injury risk

Predictor	aOR	95% CI	*p*-value
Leg Imbalance (per 0.1 unit)	1.15	[1.03, 1.29]	0.016
Gait Symmetry (per 5%)	0.78	[0.65, 0.93]	0.006
Age (per year)	1.05	[0.97, 1.14]	0.210
Gender (Male)	1.51	[0.98, 2.32]	0.062

Risk stratification and sport-specific trends

Threshold analysis identified high-risk subgroups (Table 4). Athletes with leg imbalance > 1.10 had 2.15× higher injury risk (95% CI:1.42–3.25), while those with gait symmetry < 90% showed 2.83× increased risk (95% CI:1.91–4.18). The 12.7% of athletes exceeding both thresholds exhibited synergistic risk amplification (RR = 4.10, 95% CI:2.75–6.12). Exploratory sport-specific analysis revealed distinct biomechanical profiles: Strength athletes demonstrated the highest leg imbalance (1.12 ± 0.15), endurance athletes displayed superior gait symmetry (94%±3%), and team sport athletes generated the highest ground reaction forces (1250 N ± 160 N). Stratified regression confirmed stronger imbalance-injury associations in strength athletes (aOR = 1.24, *p*=0.01) versus endurance athletes (aOR = 1.08, *p*=0.18).

Table 4.

Injury risk stratification

Risk Profile	Threshold	Injury Rate	RR [95% CI]
Leg Imbalance Only	> 1.10	38.2%	2.15 [1.42–3.25]
Gait Asymmetry Only	< 90%	44.7%	2.83 [1.91–4.18]
Combined Risk	Both	62.5%	4.10 [2.75–6.12]

Model performance and secondary findings

The primary logistic regression demonstrated excellent fit (AIC = 142.3, Pseudo R²=0.28). Secondary analysis revealed knee flexion angle moderated GRF effects in endurance athletes: Each 5° reduction below 60° increased GRF-related injury risk by 18% (*p*=0.04). The SEM pathway (gait asymmetry → knee loading → injury) remained significant after controlling for sport type (Sobel Z = 4.12, *p*<0.001), explaining 22% of variance in knee joint loading (η²=0.22).

Discussion

This study reveals that both movement restriction and body asymmetry are significantly associated with increased risks of sports injuries. Specifically, the lower limb strength imbalance ratio exhibited a positive correlation with injury incidence (r = 0.35, P < 0.05), while the gait symmetry index showed a negative correlation (r = −0.42, P < 0.01). These findings suggest that biomechanical imbalances—whether in muscular output or movement coordination—may disrupt normal loading patterns, resulting in heightened injury susceptibility.

Gait asymmetry, a key variable examined in this study, refers to the uneven distribution of movement parameters between the left and right limbs during locomotion. Pronounced asymmetry often leads to disproportionate loading, where one leg endures greater mechanical stress. Over time, this uneven loading can manifest as overuse injuries affecting joints, tendons, and soft tissues. Prior research supports these mechanisms: individuals with greater gait asymmetry have shown higher peak loading rates and impulse on the dominant limb [35]. Ahmadi and Yalfani (2022) linked asymmetries in ground reaction forces to an elevated incidence of knee osteoarthritis [36], while Willwacher et al. (2022) reported that irregular loading patterns contribute to patellofemoral pain syndrome [37].

Symmetrical gait mechanics promote the even distribution of forces, reducing excessive localized stress and lowering the risk of musculoskeletal damage. Furthermore, gait symmetry improves overall balance, neuromuscular efficiency, and energy utilization during athletic activities. A systematic review by Gao (2022) confirmed that interventions targeting gait symmetry in runners significantly reduced lower limb injury rates [38]. Miller et al. (2021) similarly found that athletes undergoing gait retraining reported less knee pain and better running economy [39].

Recent advancements in optimizing landing mechanics have demonstrated their potential in mitigating such risks. Xu et al. (2024) proposed novel strategies to reduce lower limb injury risk through optimized landing techniques, emphasizing the importance of biomechanical adjustments in injury prevention [40]. Additionally, the biomechanical patterns before and after fatigue play a crucial role in predicting ACL force accurately and effectively, as highlighted by Xu et al. (2023) [41]. Furthermore, Zhang et al. (2025) investigated the effect of restricted ankle dorsiflexion on knee injury risk during landing, shedding light on the relationship between specific joint limitations and injury susceptibility [42]. These findings collectively underscore the significance of optimizing landing mechanics and understanding biomechanical factors in reducing lower limb injuries.

In addition to gait factors, this study identified movement restrictions—especially in the shoulder and knee joints—as critical contributors to injury risk. Such restrictions may arise from limited joint range of motion, muscle stiffness, or impaired neuromuscular coordination. These deficits not only impair athletic performance but may also alter kinematic chains and cause compensatory movement strategies. For example, athletes with reduced hip or ankle mobility may exhibit increased frontal plane knee movement during running or landing, elevating injury potential. Logistic regression results further demonstrated that every 10% increase in joint mobility restriction was associated with a 15% increase in injury odds, reinforcing its clinical relevance.

Although peak ground reaction forces and knee flexion angles did not show statistically significant correlations with injury rates in this study, they remain valuable biomechanical indicators. These parameters reflect aspects of force absorption, joint stability, and landing strategy, which are known to influence injury mechanisms in specific contexts. Future studies should explore whether these variables interact with asymmetry or mobility factors under different sport-specific conditions [43].

Despite the valuable insights provided, this study has several limitations. First, the lack of dynamic biomechanical modeling—such as joint torque analysis and kinetic chain simulations—limits understanding of the precise mechanisms underlying injury risk. Interactions among foot, ankle, and knee mechanics were not fully explored. Second, the sample was not stratified by sport type, despite varying biomechanical demands across disciplines, which reduces the specificity and generalizability of the findings. Parameters such as arch index and center of pressure trajectory, known to influence lower limb compensation, were not examined in detail. Moreover, environmental factors—including footwear, surface hardness, and training conditions—were not strictly controlled, potentially introducing variability. Finally, the absence of subgroup analysis by athletic discipline may obscure sport-specific injury mechanisms; for instance, strength imbalance may be more relevant to power athletes, while gait symmetry could be critical for endurance runners. Future research should incorporate stratified sampling and sport-specific modeling to refine injury risk assessment and enhance the applicability of preventive strategies.

Looking ahead, future research should integrate more advanced modeling techniques to simulate real-world biomechanical interactions. Incorporating wearable technologies and AI-driven feedback systems can allow for real-time monitoring of movement asymmetries and joint restrictions, enabling personalized training adjustments. Additionally, future studies should adopt stratified designs to compare biomechanical risk factors across sport types, genders, and training levels.

There is also growing interest in the psychological dimension of injury prevention. Athlete self-perception and bodily awareness can influence movement quality and injury outcomes. Integrating subjective assessments—such as proprioceptive confidence or fatigue perception—may enhance injury risk profiling and intervention effectiveness.

In conclusion, this study provides robust evidence that movement restriction and body asymmetry are important predictors of sports injury. Addressing these issues through targeted mobility training, strength balancing, and gait correction can reduce injury risk and improve athletic performance. A more nuanced understanding of the interplay between biomechanics, sport specificity, and technology-enhanced feedback will be essential for the future development of individualized, data-driven injury prevention protocols.

Conclusion

This analysis confirms that leg strength imbalance and gait asymmetry are significant predictors of sports injury risk, highlighting their central role in susceptibility to musculoskeletal harm. The observed associations suggest that routine assessment of these indicators can enhance early identification of at-risk athletes.

To translate these findings into practice, it is recommended to implement periodic screening protocols—using simple strength tests and gait analysis—to detect emerging asymmetries. Targeted interventions (e.g., unilateral strengthening exercises, gait retraining) should be integrated into training regimens, with progress monitored through wearable sensors or motion-capture systems. Future research ought to broaden predictive models by incorporating variables such as joint mobility, muscle force distribution, and neuromuscular control metrics. In addition, longitudinal studies using real-time monitoring platforms can evaluate the effectiveness of personalized interventions and refine thresholds for preventive action. Such an approach promises to improve injury prevention strategies and support individualized training for diverse athlete populations.

Authors’ contributions

Shuang Zhou collected relevant research data.Huan Liu wrote the main manuscript.All authors reviewed the manuscript.

Funding

This work was not supported by any funds.

Data availability

The figures and tables used to support the findings of this study are included in the article.

Declarations

Ethics approval and consent to participate

Informed consents (Consent to Participate and Consent to Publish) were obtained from all participants, including participants are under 16, from a parent and/or legal guardian. Ethics approval was waived by the ethics committee of Shantou University Medical College in accordance with the Declaration of Helsinki.

Consent for publication

Not Applicable.

Competing interests

The authors declare no competing interests.