Effects of lead leg selection on bilateral landing force-time characteristics: Return to sport testing implications.

We quantified the effect of lead leg selection on landing force-time characteristics during a vertical drop landing (VDL) initiated with a step-off. Plyometric-trained subjects (male: n = 8; female: n = 5; age =23 ± 3.3 years; body mass =74.4 ± 11.2 kg) performed 6 step-off-initiated VDLs from a 45-cm box (n = 3/lead leg). Pooled and lead leg stratified between-limb comparisons of limb-specific initial ground contact timing, peak vertical ground reaction force (Fzpeak ), and early landing-phase impulse (Impulse100ms ) were assessed by a two-factor, within-subject ANOVA, and limb symmetry indexes were calculated (α = 0.05). Pooled data showed that the lead leg made contact prior to the trail leg and contributed a greater fraction to Fzpeak compared with the trail leg. Stratifying trials by lead leg selection led to significant between-limb differences in Fzpeak (p < 0.05) and Impulse100ms (p < 0.01) with the right leg leading but not with the left leg leading. Lead leg selection in step-off-initiated VDLs influenced landing performance and limb symmetry indexes of variables associated with lower limb injury risk, suggesting the need to control for lead leg selection in these tasks. A step-off may not be a suitable technique to initiate landing tasks when assessing limb symmetry.

Forecasting neuromuscular recovery after anterior cruciate ligament injury: Athlete recovery profiles with generalized additive modeling.

A retrospective analysis of longitudinally collected athlete monitoring data was conducted to generate a model of neuromuscular recovery after anterior cruciate ligament (ACL) injury and reconstruction (ACLR). Neuromuscular testing data including countermovement jump (CMJ) force-time asymmetries and knee extensor strength (maximum voluntary contractionext ) asymmetries (between-limb asymmetry index-AI) were obtained from athletes with ACLR using semitendinosus (ST) autograft (n = 29; AI measurements: n = 494), bone patellar tendon bone autograft (n = 5; AI measurements: n = 88) and noninjured controls (n = 178; AI measurements: n = 3188). Explosive strength measured as the rate of torque development was also calculated. CMJ force-time asymmetries were measured over discrete movement phases (eccentric deceleration phase, concentric phase). Separate additive mixed effects models (additive mixed effects model [AMM]) were fit for each AI with a main effect for the surgical technique and a smooth term for the time since surgery (days). The models explained between 43% and 91% of the deviance in neuromuscular recovery after ACLR. The mean time course was generated from the AMM. Comparative neuromuscular recovery profiles of an athlete with an accelerated progression and an athlete with a delayed progression after a serious multiligament injury were generated. Clinical Significance: This paper provides a new perspective on the utility of longitudinal athlete monitoring including routine testing to develop models of neuromuscular recovery after ACLR that can be used to characterize individual progression throughout rehabilitation.