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Side-Shift Therapy as Translation-Dominant 3D Auto-Correction in Adolescent Idiopathic Scoliosis: A
1Royal National Orthopaedic Hospital (RNOH), Stanmore, UK.
Abstract:
Adolescent idiopathic scoliosis (AIS) is a three-dimensional deformity characterised by coronal curvature, axial rotation, and altered trunk translation. Radiographic indices (e.g., Cobb angle) quantify structure but incompletely represent functional correctability, a core target of physiotherapeutic scoliosis-specific exercises (PSSE). Side-Shift Therapy (SST), originating from the RNOH tradition (Mehta, 1985) and operationalised via a mobility classification (Betts et al., 2014), is a voluntary lateral translation of the trunk relative to the pelvis (not a side-bend). This paper presents a biomechanics-first teaching framework linking functional spinal unit (FSU) mechanics, thoracic ring behaviour, motor control sequencing, and growth-modulation hypotheses. FSUs are modelled as 6-DOF systems with translation-rotation coupling; in simplified terms, Ktt represents resistance to translation and Ktr represents how rotation changes when translation is performed (short note: coupling = rotation response during translation). Load-path optimisation is framed via an apical bending moment approximation M ≈ Fg·dlat (dlat = lateral moment arm). Time-under-load is explained using viscoelastic forms where eta captures time-dependence. In growing spines, a Hueter-Volkmann-type simplification gdot = g0 - ksigma is used to describe stress-sensitive growth (k = stress sensitivity). Clinically, original mobility Types (1: beyond midline; 2: to midline; 3: cannot reach midline) are retained with reported substantial reliability (kappa ≈ 0.76-0.77). A multiplanar update adds transverse modifiers for Types 1-2: A (rotation reduces), B (rotation persists), C (rotation worsens), providing a pragmatic proxy for coupling behaviour and thoracic compliance. The framework supports stratified PSSE prescription and outlines testable targets for reliability, responsiveness, and predictive validity.
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