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Maximal force production requires OPTIMAL conditions.

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The OPTIMAL theory of motor learning identifies several motivational and attentional factors that draw out latent motor performance capabilities. One implication of the OPTIMAL theory of motor learning (Wulf &… Click to show full abstract

The OPTIMAL theory of motor learning identifies several motivational and attentional factors that draw out latent motor performance capabilities. One implication of the OPTIMAL theory of motor learning (Wulf & Lewthwaite, 2016) is that standardized motor performance assessments likely do not reflect maximal capabilities unless they are "optimized" with appropriate testing conditions. The present study examined the effects of three key motivational (enhanced expectancies, EE, and autonomy support, AS) and attentional (external focus, EF) variables in the OPTIMAL theory on maximum force production. In Experiment 1, a handgrip strength task was used. EE, AS, and EF were implemented, in a counterbalanced order, on consecutive trial blocks in an optimized group. A control group performed all blocks under neutral conditions. While there were no group differences on Block 1 (baseline), the optimized group outperformed the control group on all other blocks. In Experiment 2, participants performed two one-repetition maximum (1-RM) squat lift tests, separated by one week. Two groups, an optimized group and control group, had similar 1-RM values on the first test performed under neutral conditions. However, on the second test, a group performing under optimized conditions (EE, AS, EF) showed an increase in 1-RM, while there was no change from the first to the second test for a control group. We argue that standard test conditions may not produce true maximal performance. The findings corroborate the importance of key factors in the OPTIMAL theory and should be applied to ensure accurate strength performance assessment.

Keywords: control group; group; optimal theory; motor; performance; force production

Journal Title: Human movement science
Year Published: 2020

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