Athletes
Conditions
Keywords
Muscular Fatigue, Lower Limb Kinematics, Single-Leg Drop Landing, Female Athletes
Brief summary
this study will be conducted to investigate the effect of muscular fatigue on lower limb kinematics during single-leg drop landing in female professional volleyball players using a mobile-based motion analysis application.
Detailed description
Muscular fatigue impairs neuromuscular control and alters lower limb kinematics during dynamic tasks such as landing, increasing the risk of injury. For example, repetitive jump-landing tasks, as frequently performed in volleyball, have been shown to induce progressive changes in lower limb biomechanics, including reduced ankle plantarflexion and altered knee alignment, which may compromise energy absorption and joint stability.Female athletes face a higher incidence of knee and ligament injuries than males, including ACL tears and ankle sprains, due to sex-specific biomechanical and neuromuscular characteristics such as reduced hamstring activation, increased joint laxity, and altered landing mechanics .Markerless, smartphone-based systems such as OpenCap provide a validated, cost-effective, and field-based solution for 3D kinematic analysis.By applying OpenCap to evaluate the effects of muscular fatigue on single-leg drop landing in professional female volleyball players, this study aims to detect early deviations in movement patterns that may predispose athletes to injury. These findings can inform preseason screening and guide the development of tailored neuromuscular training programs, contributing to context-specific, sex-informed injury prevention strategies in female sport
Interventions
OTHERfatigue protocol
Before formal testing, participants will perform a five-minute warm-up replicating their usual pre-volleyball training routine. Following the warm-up, the single-leg drop landing (SLDL) task will be clearly explained and demonstrated to all participants.Stand barefoot on both legs on a 30 cm box, then extend the testing leg forward. They will then step off the box using the testing leg, land on the same leg with a toe-heel landing pattern. After familiarization, participants will perform three trials of the SLDL task under non-fatigued conditions.Following the completion of the non-fatigue single-leg drop landing (SLDL) trials, each participant will perform three maximal vertical jumps to assess their maximum touch height. The highest value obtained from these attempts will be recorded as the participant's baseline for subsequent comparisons.Following the completion of the baseline maximal vertical jump assessment, participants will undergo a systematic fatigue induction protocol
Sponsors
Cairo University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Intervention model description
fatigue protocol
Eligibility
Sex/Gender
FEMALE
Age
20 Years to 25 Years
Healthy volunteers
Yes
Inclusion criteria
* Female volleyball players aged 20-25 years * Practice professional volleyball training or competition experience of at least 2 years * typically engage in a high volume of training, with regular participation in at least five training sessions per week * Willingness to adhere to fatigue protocol and provide informed consent
Exclusion criteria
* Current or recent (within the past 6 months) lower limb injury or medical condition * History of lower limb surgeries * Neurological disorders or balance impairments . * Experiencing pain or discomfort during jumping or landing tasks * Body mass index (BMI) greater than 30 kg/m² * Inability to comply with the fatigue protocol or follow instructions for the landing tasks
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| hip range of motion | up to one day | OpenCap will be used to assess hip range of motion. the Video recordings will be analyzed using OpenCap, a validated markerless motion capture system that estimates 3D joint kinematics from synchronized smartphone cameras through an automated pipeline that applies advanced computer vision and machine learning algorithms to extract joint angles efficiently and with high validity |
| knee flexion range of motion | up to one day | OpenCap will be used to assess knee flexion range of motion. the Video recordings will be analyzed using OpenCap, a validated markerless motion capture system that estimates 3D joint kinematics from synchronized smartphone cameras through an automated pipeline that applies advanced computer vision and machine learning algorithms to extract joint angles efficiently and with high validity |
| ankle dorsiflexion range of motion | up to one day | OpenCap will be used to assess ankle dorsiflexion range of motion. the Video recordings will be analyzed using OpenCap, a validated markerless motion capture system that estimates 3D joint kinematics from synchronized smartphone cameras through an automated pipeline that applies advanced computer vision and machine learning algorithms to extract joint angles efficiently and with high validity |
| knee valgus angle | up to one day | OpenCap will be used to assess knee valgus angle. the Video recordings will be analyzed using OpenCap, a validated markerless motion capture system that estimates 3D joint kinematics from synchronized smartphone cameras through an automated pipeline that applies advanced computer vision and machine learning algorithms to extract joint angles efficiently and with high validity |
Contacts
CONTACTmawada mohamed, master
Outcome results
None listed