Squat training using virtual target trajectories with structured variability matches traditional training in performance accuracy

Strength training often prioritizes exact repetition, potentially constraining movement exploration and adaptability. Optimal Movement Variability Hypothesis posits that structured variability builds robust coordination and improves responsiveness to perturbations, yet whether trainees can accurately execute squats under such variability remains unclear. Grounded in motor-learning and complex-systems accounts, we tested whether young adults can track variable targets with and without structure, while squatting. Participants controlled a virtual avatar with deep/shallow squats and lateral leans to follow onscreen targets. Sixty-seven individuals were randomized to three conditions: non-variable (repeated positions; n = 18), structured variability (pink noise positions; n = 24), and non-structured variability (white-noise positions; n = 25). Performance was assessed across five sets of 30 targets. Accuracy was quantified as target-avatar spatial error; temporal organization was indexed with detrended fluctuation analysis (DFA). The structured and non-variable groups exhibited lower spatial error than the non-structured group, particularly in later sets. DFA was highest in the structured group, intermediate in the non-structured group, and lowest in the non-variable group, indicating more persistent temporal organization under structured variability. Findings indicate that structured-variability squats match traditional accuracy while promoting more organized temporal structure, supporting integration into training and the feasibility of gamified resistance protocols to enhance robustness and skill.