Manipulation of task constraints is the most effective motor learning method for reducing risk factors for ACL injury during side-step cutting in both male and female athletes.

Abstract

This study compared the efficacy of linear, non-linear and differential methods on variables related to ACL injury risk of a side-step cutting task in male and female basketball players. Thirty males and thirty females practiced basketball skills in sixty 90-minute sessions across 5 months. Ten players trained in each of the LP, NLP and DL female/male groups separately. Before and after the intervention, each player was tested on a side-step cutting task. A repeated 3 × 2 × 2 factorial ANOVA with repeated measures was performed for each biomechanical variable. Variables (trunk, hip, and knee flexion angle, knee valgus angle, ankle dorsiflexion angle, hip, knee, and ankle ROM, peak VGRF and knee extension/flexion, knee moment and ankle dorsiflexion moment) all revealed significant test by group interactions (P < 0.05) but no significant group by sex interactions (P > 0.05). In both sex, biomechanical changes were better in the NLP, followed by the DL and LP. It is argued that the advantage of the NLP method results from increased exploration of movement solutions induced by the manipulation of task constraints. Therefore, according to the NLP, it is possible to manipulate the constraints without feedback and the model/pattern can keep the athlete away from possible risks.