Children's Coordination of the “Sweet Spot” When Striking a Forehand Is Shaped by the Equipment Used

www.nature.com/scientificreports OPEN Children’s coordination of the “sweet spot” when striking a forehand is shaped by the equipment used Tim Buszard1,2,3*, Alessandro Garofolini1,3, David Whiteside1,2, Damian Farrow1 & Machar Reid2 Children’s movement coordination is signifcantly infuenced by the equipment used when performing multi-articular actions. Previously we reported that scaled equipment (smaller racket and a softer ball), but not full-sized equipment, promoted a functional coupling between upper arm and forearm angles in children performing a forehand. However, it remains unclear whether the shoulder-racket distance—which is controlled by this coupling—is a performance variable. This study therefore advanced previous research by examining whether the shoulder-racket distance is associated with performance. We also improved our understanding of how the shoulder-racket distance is controlled by including the hand-racket segment in our biomechanical model. Twenty-one children performed 40 forehands in a hitting for accuracy task. Participants were randomly divided into two groups—a scaled equipment group and a full-sized equipment group. Results revealed that the shoulder-racket distance was a performance variable, as evidenced by: (a) its variance reduced closer to ball impact, (b) its distance at ball impact, but not at the start of the forward swing, diferentiated good from poor performance, and (c) its distance was similar for both groups, implying that there was a “sweet spot” for striking a ball, regardless of racket size. We also showed that it is the shoulder-racket vector in state-space (i.e., distance and angle) that diferentiates good from poor performance. Finally, the manner in which the shoulder-racket distance was controlled difered between the groups, with scaled equipment promoting a more distal control than full-sized equipment. Implications for skill acquisition are discussed. A central issue for understanding performance of multi-articular actions is how abundant degrees of freedom in the musculoskeletal system are controlled to produce functional movements1,2. In tasks that require precise outcomes, such as in sport, the ability to coordinate muscles, joints and limbs to successfully perform the task is complex and challenging. To the naked eye, professional athletes appear to display repeatable and consistent movements, yet variability in movement is an inherent feature of successful performance3,4. Tese seemingly con- tradictory statements are at the heart of motor control science; that is, to understand how motor control systems constrain the many possible joint confgurations to stabilize the salient task-dependent performance variables 5. Performance variables (ofen referred to as essential variables) are invariant features of the motor system that can be produced by a number of joint confgurations (referred to as elementary variables). Te elementary variables can either form a synergy, meaning movements at each joint co-vary, or not form a synergy 5. Synergies are formed by means of self-organisation and are governed by the equation of constraints acting on the system6. Indeed, the formation of synergies that produce functional outcomes is a hallmark of skilful performance7. Understanding how constraints—categorised as task, environmental, and organismic 8—interact to infuence the formation of synergies is therefore essential. One of the most common constraints in sport is the equipment used by the performer. Diferences in size and/ or mass change the object’s inertial properties and this subsequently modifes the confuence of constraints9. Te use of inappropriately sized equipment that leads to qualitative changes in coordination is perhaps most appar- ent in children’s sport, whereby children are ofen expected to execute skills with equipment that is designed for 1Institute for Health and Sport (IHES), Victoria University, Melbourne, Australia. 2Game Insight Group, Tennis Australia, Melbourne, Australia. 3These authors contributed equally: Tim Buszard and Alessandro Garofolini. *email: [email protected] Scientifc Reports | (2020) 10:21003 | https://doi.org/10.1038/s41598-020-77627-5 1 Vol.:(0123456789) www.nature.com/scientificreports/ adults10. For example, we recently reported how diferent rackets and tennis balls elicited distinct motor control strategies in children 11. When a full-sized (adult) tennis racket and a standard tennis ball was used, variance in the distance between the shoulder and racket (i.e., the hitting lever) was explained by the angles in the upper arm and forearm, but there was a lack of coupling between these segments. Conversely, when children used a smaller tennis racket and a sofer ball, variance in the distance between the shoulder and the racket was explained by the coupling of the upper arm and forearm angles. In other words, these segments worked in unison, whereby variability in one segment was compensated by variability in the other segment. Tis can be interpreted as the formation of a synergy amongst the elementary variables to control the hitting lever distance. Tis study was signifcant as it was the frst to investigate the efect of equipment scaling on children’s movement coordination in a multi-articular action. However, despite the compelling diferences in motor control strategies that were observed between scaled and full-sized equipment, at least two issues remain unclear. First, although the upper arm and forearm angles explained the variance in the shoulder-racket distance, we cannot conclude that this distance is a salient performance variable as we did not measure its relationships with performance outcomes. Tere is a wealth of research on the kinematics of the forehand 12–15; yet, little is known about the role of the shoulder-racket distance in the coordination of the stroke. Given that this distance determines the lever’s inertial mass, which infuences the linear velocity required to strike the ball and generate sufcient force, we expected this distance to be associated with performance—particularly the distance at ball impact as this is the most important moment in striking tasks e.g.16. Second, we did not include the hand-racket segment in our model, so we do not know the contribution of this segment in explaining the variance in shoulder-racket distance. Given that the tennis forehand requires proximal to distal sequence of joint rotations15, the hand-racket segment is likely to play a central role in explaining the variance in the shoulder-racket distance closer to ball impact. Te aim of the current study was therefore twofold. First, we aimed to determine whether the distance between the shoulder and the racket is a salient performance variable in a tennis forehand task performed by children. We hypothesised that this would be evidenced by (a) a similar shoulder-racket distance across the swing regardless of equipment, therein demonstrating the invariant nature of the variable; (b) performance diferences based on shoulder-racket distance at ball impact; and (c) less variance in shoulder-racket distance for more accurate shots. We also measured the distribution of the shoulder-racket vector in state-space based on hitting accuracy. State-space is a 2D plane which was defned by two orthogonal global directions with the origin fxed at the shoulder. We expected the distribution of the shoulder-racket vector to represent a smaller and skinnier ellipse for more accurate shots; hence demonstrating the stability of the shoulder-racket distance for successful performance. For less accurate shots, we expected the distribution of the shoulder-racket vector to be large and structured orthogonal to the high scores. Tis would suggest that poor performance at the task is associated with a variable shoulder-racket vector in diferent directions of state-space than successful performance. Te second aim was to assess the contribution of the hand-racket segment in explaining the variance in shoulder-racket distance. We hypothesised that the hand-racket segment would have a greater contribution in explaining the variance in shoulder-racket distance closer to ball impact. We also expected to observe a more even contribution across the three segments (racket, forearm and upper arm) when using scaled equipment than full-sized equipment. Tis was based on our previous fnding whereby scaled equipment promoted co-variation amongst the elementary variables, whereas full-sized equipment did not 11. Finally, we anticipated that variance in the hand-racket segment angle would play a more signifcant role explaining variance in the shoulder-racket distance when using scaled equipment, as this is indicative of a fner level of control 17,18. Results Shoulder-racket distance was consistent between groups (Fig. 1A). Te scaled racket allowed a longer lever dur- ing the swing, but at ball impact both rackets were guided to a distance about 0.3 of arm length. For variability (Fig. 1A), the standard deviation dropped consistently until 75% of the swing time, followed by an increase and a fnal drop. Tis was consistent between groups, although the full-sized group displayed higher variability overall. At the start of the swing, shoulder-racket distance was similar for both low-scoring (≤ 3, LOW) and high- scoring (≥ 7, HIGH) trials (Fig. 1B). At impact, there was a main efect for score (F1,874 = 21.48, p < 0.01) but no main efect for group (F1,874 = 2.63, p = 0.105). Indeed, both the scaled and full-sized groups had a higher shoulder-racket distance in HIGH compared to LOW scores at ball impact (Fig. 1C). Results from the multiple linear regression showed that the predictors (variance in angles for the upper arm, forearm, and hand-racket segments) signifcantly explained the variance in shoulder-racket distance in both groups (p < 0.01). However, the predictors explained 16.5% of the variance in shoulder-racket distance for the full-sized group, and 27.4% for the scaled group. For the full-sized group, the most important predictor was variance in the upper arm angle (86%) followed by variance in the forearm angle (10%) and variance in the angle of the hand-racket segment (4%).

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