Roger Bartlett | PubMed Health

PubMed2005

Movement variability cannot be determined reliably from no-marker conditions.

We sought to quantify the proportion of total variability due to movement variability in digitizing marker and no-marker conditions in treadmill running. Manually digitized markers gave variabilities in joint angles within 0.4 degrees of those from auto-tracking. Marker conditions allowed reliable estimation of movement variability by an experienced operator; the contribution of movement variability to total variability, expressed by omega-squared, had averages across the four operators of 97% for the knee angle and 93% for the hip. The no-marker condition did not allow reliable estimation of movement variability-the above figures fell, respectively, to 72% and 48%-and cannot be recommended. The group design in the marker condition produced omega-squared values of 97% knee and 91% hip, showing excellent objectivity (inter-operator reliability). However, without markers, these values fell to 47% and 15%, respectively, indicating that movement variability cannot be assessed objectively in no-marker conditions.

Adult↗

PubMed2003

Movement systems as dynamical systems: the functional role of variability and its implications for sports medicine.

In recent years, concepts and tools from dynamical systems theory have been successfully applied to the study of movement systems, contradicting traditional views of variability as noise or error. From this perspective, it is apparent that variability in movement systems is omnipresent and unavoidable due to the distinct constraints that shape each individual's behaviour. In this position paper, it is argued that trial-to-trial movement variations within individuals and performance differences observed between individuals may be best interpreted as attempts to exploit the variability that is inherent within and between biological systems. That is, variability in movement systems helps individuals adapt to the unique constraints (personal, task and environmental) impinging on them across different timescales. We examine the implications of these ideas for sports medicine, by: (i) focusing on intra-individual variability in postural control to exemplify within-individual real-time adaptations to changing informational constraints in the performance environment; and (ii) interpreting recent evidence on the role of the angiotensin-converting enzyme gene as a genetic (developmental) constraint on individual differences in physical performance. The implementation of a dynamical systems theoretical interpretation of variability in movement systems signals a need to re-evaluate the ubiquitous influence of the traditional 'medical model' in interpreting motor behaviour and performance constrained by disease or injury to the movement system. Accordingly, there is a need to develop new tools for providing individualised plots of motor behaviour and performance as a function of key constraints. Coordination profiling is proposed as one such alternative approach for interpreting the variability and stability demonstrated by individuals as they attempt to construct functional, goal-directed patterns of motor behaviour during each unique performance. Finally, the relative contribution of genes and training to between-individual performance variation is highlighted, with the conclusion that dynamical systems theory provides an appropriate multidisciplinary theoretical framework to explain their interaction in supporting physical performance.

Adaptation, Physiological↗