Healthy Clinical Trial
Official title:
Lower Extremities Biomechanics Responses to Power and Strength Combined Training in Adults: a Randomized Clinical Trial
Anterior cruciate ligament (ACL) is the most frequently injured knee ligament during performance of recreational activities and sports. In the United States, the annual incidence is 68.6 per 100,000 people per year and in Brazil, the estimation of ACL reconstruction increases 64%. There are different biomechanical profiles of risk factors for an ACL injury variable, the ligament dominance, the quadriceps dominance, the trunk dominance, and the leg dominance. Thus, the purpose of this study is to investigate the biomechanics adaptations after power and strength combined training protocol in healthy individuals. A second aim is to determine the effect of the training on knee injury risk factors.
This is a parallel randomized clinical trial comparing the effect of combined training with
power and strength exercises on lower extremity biomechanics in healthy individuals.
The sample size was calculated with G*Power software using the ANOVA: Repeated measures,
within-between interaction, 90% power, alpha 0.05, and 30% drop-out. Data from the tuck jump
test (knee flexion range) by Makaruk (2014) were considered for this calculation with effect
size 0.46. Thus, a total of 32 individuals (16 per group) is required for this study. To
ensure the proper simple size, after collecting the first five participants per group, the
sample size will be checked again.
The participants will be randomized in experimental and no intervention groups inside each
risk profile group. Randomization ratio will be 1:1 and interventions will last 10 weeks,
with two weekly sessions for the exercise arms.
The outcomes will include functional clinical tests, kinematic and kinetic variables during
landing tasks, and strength of knee and hip muscles.
The data analysis will be performed by intention to treat and per protocol. Generalized
estimating equations will be used to identify interaction effects of groups and time followed
by Bonferroni post-hoc. When effect are found, effect size will be estimated. Missing data
will be estimate by statistical analysis.
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