Examining the Reliability of the Landing Error Scoring System With Raters Using the Standardized Instructions and Scoring Sheet

Intra-rater Reliability of a Qualitative Landing Scale for the Single-Hop Test: A Pilot Study

Background

The test battery classically used for return-to-sport (RTS) decision-making after anterior cruciate ligament (ACL) reconstruction (ACLR) may not be sufficient, as it does not include a qualitative analysis of movement. Therefore, the Landing Error Scoring System (LESS) scale was adapted to a primary functional test in the typical RTS test battery: the single leg hop for distance (SHD).

Hypothesis/ Purpose

The aim of this study was to determine the intra-rater reliability of the LESS scale adapted to the SHD (SHD-LESS scale) in healthy young athletes.

Study Design

Reliability analysis.

Methods

Nineteen healthy individuals (14 men, 5 women; mean age: 22.4 years) participated in the study. Participants performed the SHD tasks on both limbs (dominant and non-dominant) using a standardized protocol in two sessions that were one week apart (single reviewer; 2-dimensional video). Intra-class correlation coefficients (ICC2,1) were used to measure the reproducibility of the scale in the dominant (dom) and non-dominant (nondom) limbs. Additionally, limb data (dom and nondom) were pooled and evaluated collectively with intra-class correlation coefficients. The Kappa coefficient was used to assess the reproducibility of each individual item of SHD-LESS scale.

Results

The intra-rater reliability was good (ICCdom = 0.77; ICCnondom = 0.87; ICCpooled = 0.87) for the overall SHD-LESS scale scores. Agreement of SHD-LESS individual items ranged from 62% to 100%. Dorsiflexion at initial contact (97% agreement; kappa value=0.79) and knee valgus after landing (88% agreement; kappa value=0.65) had excellent agreement and kappa values.

Conclusion

The newly-adapted SHD-LESS scale showed good intra-rater reliability overall. Further studies should evaluate the impact of using the SHD-LESS scale within the RTS test battery on outcomes in patients after ACLR.

Level of Evidence

3.

Reliability of a Qualitative Instrument to Assess High-Risk Mechanisms during a 90° Change of Direction in Female Football Players

Sidestep cuts between 60° and 180° and one-leg landings have been identified as the main mechanisms of ACL injuries in several sports. This study sought to determine intra- and inter-rater reliability of a qualitative tool to assess high-risk movements in a 90° change of direction when the test is applied in a real framework of sport practice. Female footballers from two teams (n = 38) participated in this study and were asked to perform 90° cutting trials to each side, which were simultaneously filmed from a frontal and a sagittal view. A total of 61 cases were selected for 2D qualitative observational analysis by three raters. Poor reliability was found among each pair of raters as well as moderate reliability when the Cutting Movement Assessment Score (CMAS) was given by the same rater at different moments, but with too high a minimum detectable change. On the other hand, raters presented a significant, as well as moderate-to-good intra-rater reliability for most items of the CMAS tool. There was, however, non-significant reliability between observers in rating most check-points of the tool. For these reasons, more objective guidelines and clearer definitions for each criterion within the CMAS, as well as a longer, standardised training period for novel observers, would be highly recommended to improve the reliability of this tool in an applied context with female footballers.

A Machine-Learning Approach to Measure the Anterior Cruciate Ligament Injury Risk in Female Basketball Players

Anterior cruciate ligament (ACL) injury represents one of the main disorders affecting players, especially in contact sports. Even though several approaches based on artificial intelligence have been developed to allow the quantification of ACL injury risk, their applicability in training sessions compared with the clinical scale is still an open question. We proposed a machine-learning approach to accomplish this purpose. Thirty-nine female basketball players were enrolled in the study. Leg stability, leg mobility and capability to absorb the load after jump were evaluated through inertial sensors and optoelectronic bars. The risk level of athletes was computed by the Landing Error Score System (LESS). A comparative analysis among nine classifiers was performed by assessing the accuracy, F1-score and goodness. Five out nine examined classifiers reached optimum performance, with the linear support vector machine achieving an accuracy and F1-score of 96 and 95%, respectively. The feature importance was computed, allowing us to promote the ellipse area, parameters related to the load absorption and the leg mobility as the most useful features for the prediction of anterior cruciate ligament injury risk. In addition, the ellipse area showed a strong correlation with the LESS score. The results open the possibility to use such a methodology for predicting ACL injury.