Adaptability training/methods

In tennis, knowing the correct shot to play and executing the required stroke are very different concepts. Error based learning is a potential means that the motor system will use to adapt a movement (Wolpert et al., 2011). The predicted outcome (a forehand that went in) will be compared to the actual outcome (a forehand that was out) and adjustments can be made on a trial by trial basis. The disparity between predicted outcome and actual outcome can provide valuable insight into the best method to adapt to a situation (Mckeown, 2012). Trial by trial adjustments in motor

systems help individuals adapt to unique circumstances and contrast the view that a pre-determined motor pattern is a pre-requisite for increased expertise. In their review, Davids, Glazier, Araujo, and Bartlett (2003) detail a number of studies which promote the benefit of movement variability and denounce the ideology of a single motor pattern.

Use-dependent learning refers to changes in the motor system that occur through repetition of movement without outcome information (Wolpert et al., 2011). This biases the system towards performing one specific movement pattern in comparison to the movement patterns learned during error based learning (Wolpert et al., 2011). The repetitive, outcome irrelevant principles of use-dependent learning are aligned with current tennis development processes that focus on repetitive task performance. This reductionist approach oversimplifies learning and development in sport by not providing adequate stimulus for athletes (Davids et al., 2013). An applied example is provided by examining the ball toss in tennis (Reid, Whiteside, & Elliott, 2010). In an attempt to create a repeatable, consistent ball toss participants practiced this component in isolation, a common method used by coaches in the field. The ball toss was no more consistent when practiced in isolation with no significant differences between the standard deviation of ball position at the ball zenith (flat serve 5.1±1.8cm, ball toss only 9.0±3.4cm). If anything, the ball toss only condition trended towards being less consistent when compared to performing the serve as normal (Reid et al., 2010). The efficacy of this approach therefore has to be questioned, as not only does it create athletes who are less fluid and adaptive to the current situation it also does not appear to fulfil its theoretical purpose in creating consistency. The adaptability that is required for repeated trials is however different to the immediate perception and action required to adapt to changing constraints.

The viability and validity of adopting a constraints led approach was examined in two different fields, rugby union and physical education (Passos et al., 2008; Renshaw et al., 2010). Both studies suggest the need for increasing variability in training

practices. Variability allows for an adaptive response by the individual as during skilled performance the consistency of performance is what is critical not the consistency of underlying movement patterns (Passos et al., 2008; Renshaw et al., 2010). An assumption is generally held that to achieve consistent performance the underlying movement pattern must be consistent and this assumption has driven training practices (Ranganathan & Newell, 2013). Specifically relating to tennis, biomechanical variability in the tennis serve was found to be functional, with the authors recommending coaches include deliberate perturbations of the serve into training to help develop movement coordination and perception action coupling (Whiteside, Elliott, Lay, & Reid, 2015). The underlying theory behind this recommendation is that if a player is faced with a broad range of movement and skill contexts they will become more proficient at adapting their performance to successfully negotiate the variability that has been shown to exist in tennis (Whiteside et al., 2015).

Differential learning promotes variability in training by constantly changing the movement pattern, avoiding repetitions and incorporates principles of discovery learning (Schőllhorn et al., 2006). Soccer passing and shooting was used to compare differential learning and traditional learning (minimal inter-trial variability, focus on ‘ideal’ movement pattern) (Schőllhorn et al., 2006). After 12 training sessions (~20-40 minutes), there was a significant improvement in the passing score of the differential training group (p=0.009) but not the traditional group (p=0.49). All participants in the differential training group improved their score whilst only 50% of the traditional group improved their score from pre to post-test (Schőllhorn et al., 2006). The significant improvement from pre to post-test in the differential learning group was maintained when investigating shooting at goal (differential group p=0.02, traditional p=0.41) (Schőllhorn et al., 2006). The benefits of differential learning extend to speed skating with a significant difference between the differential learning and control groups (Savelsbergh, Kamper, Rabius, De Koning, & Schőllhorn, 2010). There was no difference between the traditional and differential learning group, however the

differential learning group operated with a number of techniques that would have been considered ‘incorrect’ via the traditional approach. Therefore the result can be construed in a positive context as there was no performance decrement despite learning ‘incorrectly’ (Savelsbergh et al., 2010). The introduction of increased variability in training can account for the problems associated with creating a ‘one size fits all’ optimal movement pattern. The focus is shifted to athlete-environment interaction and allows the athlete to identify their own unique movement solution via exploration (Davids et al., 2013; Savelsbergh et al., 2010; Schőllhorn et al., 2006).

An additional method to promote variability and adaptability, is the use of a training environment that has high contextual interference (CI) (Brady, 2008). This can be achieved by using a varied order of practice and increasing the cognitive demands placed on the individual (Memmert, Hagemann, Althoetmar, Geppert, & Seiler, 2009). Practicing a task under high contextual interference has been demonstrated across a range of sporting and skill environments (e.g. tennis, golf, basketball) and can improve learning and produce performers who are more adaptable to a transfer task (Babo, Azevedo Neto, & Teixeira, 2008; Brady, 2008; Broadbent, Causer, Ford, & Williams, 2015; Porter & Magill, 2010; Van Merrienboer, Kester, & Paas, 2006). The anticipation of tennis shots was assessed via a random and blocked practice schedule (Broadbent et al., 2015). Transfer of learning was also assessed with a field based test as training was conducted in a simulated laboratory setting. The random practice group significantly outperformed the blocked group with response accuracy on the laboratory retention test (p<0.05, 71.7 ± 5.3% and 63.3 ± 6.0%, respectively). The decision time on the transfer test was significantly faster for the random practice group compared to the blocked group (p<0.05, 98 ± 89ms and 238 ± 118ms, respectively), demonstrating the benefits that an environment of high CI can provide (Broadbent et al., 2015).

Adaptability can be summarised in terms of how an individual optimises their perceptual motor performance. The practical value of adaptability, or any novel training stimuli, lies within its ability to generalise and provide a transfer effect. That is, how the

knowledge, skills and abilities gained can be used in different, novel circumstances and how adaptability training impacts on competitive tennis performance (Issurin, 2013; Wolpert et al., 2011). Although the limitations around exercise specificity are acknowledged, discovering a novel training stimuli such as adaptability, is beneficial as varied and innovative exercises are a sought after method of improving training stimulation (Issurin, 2013).