Summary

Prospective investigation of the interrelationships among clinical risk factors that result in direct and indirect effects on hamstring injuries

Abstract

Introduction

Hamstring injury (HI) poses a significant challenge in field-based team sports. Research identifies Increased athlete age and prior injuries as major risk factors, though results are inconsistent with other commonly measured factors. Recent studies highlight the necessity of investigating the intricate relationships among risk factors contributing to hamstring injuries (HIs). This study aimed to prospectively assess the interrelationships among clinical risk factors that result in direct and indirect effects on HI.

Methods

The data were collected through field-based preseason screening involving ninety-nine professional and semi-professional football athletes from five teams participating in the second and third Greek divisions. First, athletes completed a structured questionnaire regarding demographic and previous injury characteristics as well as the Athlete Burnout Questionnaire (ABQ). Then, anthropometric characteristics, isometric hamstring (HS) and quadriceps strength with the use of a handheld dynamometer, HS endurance (single leg HS bridge), core endurance (prone bridge, side bridge, Biering Sorensen test), and single leg triple hop test, was collected. Throughout the following season, new HIs were recorded. Exploratory factor analysis and partial least squares structural equation modeling were used to examine the direct, indirect, and mediated effects of the factors. Data were analyzed using SPSS version 28 and SmartPLS version 4.1.0.6.

Results

Thirteen athletes sustained sixteen HIs during the season. The results confirmed eight latent factors (age, isometric hamstring strength asymmetries, HS and core endurance, ABQ, previous injuries, height, strength, and new HIs) with their associated 20 measured items. The measured items appropriately loaded onto each latent factor (>0.60), resulting in an adequate level of factor validity (AVE >0.50) and reliability (Cronbach’s Alpha, Reliability ρA, and composite Reliability ρc >0.60). Based on the structural model results, age had the greatest direct influence on new HIs (path coefficient (PC) 0.620, p=0.001). Athletes above 24 years old seem to be more prone to HIs. Furthermore, HS strength asymmetries had a significant direct positive influence on the propensity for new HIs (PC 0.258, p=0.008). Conversely, HS and core endurance were found to indirectly reduce HIs (PC -0,082, p=0,046) and had a negative direct association with HS strength asymmetries (PC -0.319, p=<0.001). Height demonstrated a positive indirect relationship with asymmetries (PC 0.077, p=0.028) and a negative direct relationship with HS and core endurance (PC -0.208, p=<0.035). Likewise, strength exhibited a negative indirect association with asymmetries (PC -0.099, p=0.022) and a positive direct association with HS and core endurance (PC 0.309, p=<0.001). HS and core endurance were also negatively directly affected by the presence of previous injuries (PC -0.210, p=0.017). The moderating analysis indicates that higher values of ABQ strengthens the negative effect of previous injuries on HS and core endurance, and the existence of previous injuries impacts the relationships between HS and core endurance with strength and strength with new HIs. Finally, increasing age strengthens the positive effect of HS strength asymmetries on new HIs.

Conclusions

These findings contribute to a more comprehensive understanding of the interrelationships of critical intrinsic factors influencing HIs and facilitate improved planning for injury prevention strategies.