Abstract
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.
Relevance. Empirical statistical models can be used to track changes in rowing technique, and
discriminate between different rowing groups. This may impact rowing training, and
rehabilitation.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Clinical BiomechanicsAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Measuring spinal motion in rowers: the use of an electromagnetic device.Clin. Biomech. 2000; 15: 772-776
- Error and data coding in the multi-dimensional analysis of human movement signals.Proc. Inst. Mech. Eng. Part H––J. Eng. Med. 1993; 207: 103-110
- Multivariate Analysis.Academic Press, London1979 (p. 231)
Wahba, G., 1990. Spline Models in Statistics. CBMS-NSF Regional Conference Series, Siam, PA
Article info
Publication history
Accepted:
April 8,
2003
Received:
September 17,
2002
Identification
Copyright
© 2003 Elsevier Science Ltd. Published by Elsevier Inc. All rights reserved.
