Neuromuscular activity of the lower-extremities during running, landing and changing-of-direction movements in individuals with anterior cruciate ligament reconstruction: a review of electromyographic studies

Postural Control During Single-Leg Landing in Female Athletes After Anterior Cruciate Ligament Reconstruction

Background

Secondary anterior cruciate ligament (ACL) injury is a complication of ACL reconstruction (ACLR), which may result from altered neuromuscular control affecting anticipatory postural adjustments (APAs) and compensatory postural adjustments (CPAs) required for maintaining balance during movement. However, it remains unclear how APAs and CPAs differ in single-leg landings post-ACLR compared to healthy subjects.

Purpose

The purpose of this study was to clarify the differences in muscle activities of APAs and CPAs, lower limb kinematics, and kinetics between athletes with a history of ACLR and healthy athletes during single-leg landing.

Study design

Cross-sectional study.

Methods

Eighteen female athletes were recruited and divided into ACLR (n = 9) and control groups (n = 9). The experimental task involved a single-leg landing from a 30 cm box. Joint angles and moments were determined using a 3-dimensional motion analysis system, while muscle activity was assessed using surface electromyography. Analysis intervals were divided into two phases: the APA phase (-150 ms to 50 ms) and the CPA phase (50 ms to 250 ms), with initial contact (0 ms) as the reference point. Muscle activity onset time was defined as the time when the baseline exceeded by the sum of mean values and 2 standard deviations.

Results

No significant differences were observed in muscle activity or onset time between the ACLR and control groups. However, an increased hip external rotation moment was observed during the CPA phase in the ACLR group.

Conclusion

These findings suggest that APAs and CPAs of athletes who returned to sports more than 1 year post-ACLR may be similar. The increased hip external rotation moment in the ACLR group during the CPA phase could represent a specific compensatory strategy to decrease the hip internal rotation angle post-ACLR.

Level of Evidence

III.

A new neural network model that detects graft ruptures and contralateral anterior cruciate ligament injuries

PURPOSE

The purpose of this study was to develop a neural network model for predicting second anterior cruciate ligament (ACL) injury risk following ACL reconstruction using patient features from medical records.

METHODS

Of 486 consecutive patients who underwent primary unilateral ACL reconstruction, 386 patients (198 women, 188 men) with a mean age of 25.1 ± 11.6 years were included in this study. Fifty-eight features, including demographic data, surgical, preoperative and postoperative data, were retrospectively collected from medical records, and features with an incidence of less than 5% were excluded. Finally, 14 features were used for the analysis. The multilayer perceptron was composed of four hidden layers with a rectified linear unit as activation and was trained to maximise the area under the receiver-operating characteristic curve (auROC). Subsequently, validation was carried out through a rigorous threefold cross-validation process. To ascertain the most efficacious combination of features with the highest auROC, a single feature with the least impact on auROC maximisation was systematically eliminated from the comprehensive variable set, ultimately resulting in the retention of a mere two variables.

RESULTS

The median follow-up period was 50.5 (24-142) months. Fifty-seven knees had a second ACL injury, with a graft rupture rate of 7.7% and a contralateral injury rate of 6.9%. The maximum auROC for predicting graft rupture was 0.81 with two features: young age and hamstring graft. Meanwhile, the maximum auROC for predicting contralateral ACL injury was 0.74 with seven features, including young age, presence of medial meniscus tear, small body mass index, hamstring graft, female sex and medial meniscus repair or treatment.

CONCLUSION

A neural network model with patient features from medical records detected graft ruptures and contralateral ACL injuries with acceptable accuracy. This model can serve as a new, useful tool in clinical practice to inform decisions about ACL reconstruction and retuning to sports postoperatively.

LEVEL OF EVIDENCE

Level IV.

Mechanical energy flow analysis in athletes with and without anterior cruciate ligament reconstruction during single-leg drop landing

Techniques that reduce mechanical energy have been linked to lower chances of experiencing an Anterior Cruciate Ligament (ACL) injury. Although there is evidence that movement patterns are altered in athletes who have undergone Anterior Cruciate Ligament Reconstruction (ACLR), energy transfer mechanisms have not been examined. This study aimed to compare energy flow mechanisms during single-leg drop landing between athletes with and without history of ACLR. A total of 20 female athletes were included in this study. Ten participants underwent ACLR 12 months ago (mean age, 21.57 ± 0.41 years) and 10 were healthy controls (mean age, 20.89 ± 0.21 years). Participants executed the single-leg drop landing (SLL) maneuver by descending from a 30 cm wooden box and landing on the tested leg on an embedded force plate. Information collected during the SLL trials was refined using rigid-body analysis and inverse dynamics within Nexus software, ultimately allowing construction of skeletal models of the athletes. Ankle and knee mechanical energy expenditure (MEE) was higher in the control participants during landing. However, the result for the hip MEE demonstrated that MEE of the control group was significantly lower compared with the ACLR group, but MEE of the control subjects was higher as compared to ACLR group (p ˂ 0.05). Results suggest the avoidant use of the quadriceps muscle post ACLR leads to knee-avoidant mechanics and loss of knee joint power generation during a SLL task.