Abstract: In a multifinger cyclic force production task, the finger force variance measured across trials can be decomposed into two components, one that affects the combined force output (“bad variance”) and one that does not (“good variance”). Previous studies have found similar time patterns of “bad variance” and force rate leading to an approximately linear relationship between them. Based on this finding and a recently developed model of multifinger force production, we expected the “bad variance” during cyclic force production to increase monotonically with the rate of force change, both within a cycle and across trials at different frequencies. Alternatively, “bad variance” could show a dependence on task frequency, not on actual force derivative values. Healthy subjects were required to produce cyclic force patterns to prescribed targets by pressing on unidimensional force sensors, at a frequency set by a metronome. The task was performed with only the index finger, and with all four fingers. In the task with all four fingers, the “good variance” increased approximately linearly with an increase in the force magnitude. The “bad variance” showed within-a-cycle modulation similar to that of the force rate. However, an increase in the frequency did not lead to an increase in the “bad variance” that could be expected based on the natural relationships between action frequency and the rate of force change modulation. The results have been interpreted in the framework of an earlier model of multifinger force production where “bad variance” is a result of variance of the timing parameter. The unexpected lack of modulation of the “bad variance” with frequency suggests a drop in variance of the timing parameter with increased frequency. This mechanism may serve to maintain a constant acceptable level of variance under different conditions.

Abstract: Motor learning is central to sports, medicine, and other health professions as it entails learning through practice. To achieve proficiency in a complex motor task, many hours of practice are required. Therefore, finding ways to speed up the learning process is important. This study examines the impact of different training approaches on learning three-ball cascade juggling. Participants were assigned to one of two groups: practicing by gradually increasing difficulty and elements of the juggling movement (“learning in parts”) or training on the complete skill from the start (“all-at-once”). Results revealed that although the all-at-once group in the early stages of learning showed greater improvement in performance, the “learning in parts” group managed to catch up, even over a relatively short period of time. The lack of difference in performance between the groups at the end of the training session suggests that the choice of training regime (between all-at-once and learning in parts), at least in the short term, can be selected based on other factors such as the learner's preference, practical considerations, and cognitive style.

Abstract: The purpose was to evaluate the dynamic knee control during a drop jump test following injury of the anterior cruciate ligament injury (ACL) using finite helical axes. Persons injured 17-28 years ago, treated with either physiotherapy (ACLPT, n = 23) or reconstruction and physiotherapy (ACLR, n = 28) and asymptomatic controls (CTRL, n = 22) performed a drop jump test, while kinematics were registered by motion capture. We analysed the Preparation phase (from maximal knee extension during flight until 50 ms post-touchdown) followed by an Action phase (until maximal knee flexion post-touchdown). Range of knee motion (RoM), and the length of each phase (Duration) were computed. The finite knee helical axis was analysed for momentary intervals of ~15 degrees of knee motion by its intersection (DeltaAP position) and inclination (DeltaAP Inclination) with the knee's Anterior-Posterior (AP) axis. Static knee laxity (KT100) and self-reported knee function (Lysholm score) were also assessed. The results showed that both phases were shorter for the ACL groups compared to controls (CTRL-ACLR: Duration 35+/-8 ms, p = 0.000, CTRL-ACLPT: 33+/-9 ms, p = 0.000) and involved less knee flexion (CTRL-ACLR: RoM 6.6+/-1.9 degrees , p = 0.002, CTRL-ACLR: 7.5 +/-2.0 degrees , p = 0.001). Low RoM and Duration correlated significantly with worse knee function according to Lysholm and higher knee laxity according to KT-1000. Three finite helical axes were analysed. The DeltaAP position for the first axis was most anterior in ACLPT compared to ACLR (DeltaAP position -1, ACLPT-ACLR: 13+/-3 mm, p = 0.004), with correlations to KT-1000 (rho 0.316, p = 0.008), while the DeltaAP inclination for the third axis was smaller in the ACLPT group compared to controls (DeltaAP inclination -3 ACLPT-CTRL: -13+/-5 degrees , p = 0.004) and showed a significant side difference in ACL injured groups during Action (Injured-Non-injured: 8+/-2.7 degrees , p = 0.006). Small DeltaAP inclination -3 correlated with low Lysholm (rho 0.391, p = 0.002) and high KT-1000 (rho -0.450, p = 0.001). Conclusions Compensatory movement strategies seem to be used to protect the injured knee during landing. A decreased DeltaAP inclination in injured knees during Action suggests that the dynamic knee control may remain compromised even long after injury.