- Oral presentation
- Open Access
An algorithm for determining scoliosis curve type according to Schroth
© Schreiber et al; licensee BioMed Central Ltd. 2012
- Published: 27 January 2012
- Cervical Spine
- Thoracic Spine
- Curve Type
- Exercise Prescription
- Curve Location
To describe a refined classification algorithm, based on instructions provided during certification training, to unambiguously guide scoliosis curve type classification in a clinical trial.
Schroth exercises are scoliosis specific [1, 2]. They are the most researched and have been shown to lead to good outcomes. The Schroth classification consists of four mutually exclusive curve type categories (3c, 3cp, 4c and 4cp). Patients with scoliosis are classified according to their clinical presentation by a certified Schroth therapist. Observing the alignment of the following body blocks guides the classification assessment: lumbar spine and pelvis, thoracic spine and rib cage, and the cervical spine, head and shoulder girdle. Classifying patients’ curve types within the four Schroth curve categories determines the appropriate exercise prescription for a patient. An algorithm is needed to minimize errors in classifying different scoliosis patterns and help standardize exercise prescription.
Using the Schroth classification instructions described by Hennes, A. (2008, 2009), two certified Schroth research therapists and a physiotherapy professor developed the proposed algorithm and a set of operational definitions and instructions with respect to:
major curve location
body blocks rotation
relative position of the pelvis and lumbar block
prominent hip location
body weight balance
A refined algorithm for determining the scoliosis curve type according to Schroth is proposed for use in a randomized clinical trial.
Using the proposed algorithm may help prevent treatment failure by minimizing incorrect classification and exercise prescription. The algorithm can help clinicians correctly classify curves, which then allows an appropriate exercise prescription.
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.