Modelling multivariate biomechanical measurements of the spine during a rowing exercise

Objective. To investigate the ability of statistical techniques to detect systematic changes in rowing technique during a rowing session and to discriminate between rowers of different abilities with and without back pain. Design. Statistical techniques were applied to kinematic datasets of elite level rowers, in order to construct an empirical model of the rowing stroke. Background. The size and complexity of datasets generated by biomechanical kinematics evaluations has led to opportunities for analysing pathology whilst introducing substantial challenges for statistical analysis. Methods. Spinal motion and load output of 18 International and National standard competitive rowers were monitored during ergometer rowing sessions. International rower data were used to construct an empirical model of this activity. Linear stroke models were derived using principal components and a generalized cross-validation procedure. Performance characteristics of the identified models were calculated for all rowing groups. The stroke model was applied to distinguishing pattern variations within and between rowers. A multivariate logistic regression analysis was carried out to examine the relationship between stroke model parameters on the incidence of low back pain. Results. 90% of the variability in the data was explained by the first three principal component variables. Stroke models with three basis functions were selected for each variable. The models performed well on the National rowers, providing validation of the models. A 2-variable model showed a significant difference between the rowing stroke characteristics of rowers with and without low back pain (P<0.01). Conclusions. A parsimonious collection of empirical models effectively describes motion and load characteristics of ergometer rowing. Patterns in rowing technique are found to be strongly associated with the incidence lower back pain.