Published December 9, 2021 | Version v1
Thesis Open

Understanding functioning and its complexity in persons with spinal cord injury as a first step towards corresponding prediction modelling

  • 1. Uni Luzern


The objective of this doctoral thesis was to inform the development of future prediction
models of functioning in spinal cord injury (SCI) by examining the complexity of functioning
and its predictors in persons with SCI attending first rehabilitation in Switzerland as well as the
current state of prediction research in the field of SCI rehabilitation. To achieve this objective,
three related research studies were conducted.
Study 1 used the International Classification of Functioning, Disability and Health (ICF) as a
conceptual framework and structural equation modelling (SEM) as methodology to
investigate associations between body structures and functions, and activities as well as their
relationship with contextual factors and characteristics of the health condition in persons with
SCI in Switzerland at discharge from first rehabilitation. Findings revealed potential important
direct and indirect effects within the tested association structures. Study 2 used latent process
mixed models (LPMMs) and multinomial logistic regression as methodologies to identify
classes of functioning trajectories and corresponding predictors of class membership in
persons with SCI undergoing first rehabilitation in Switzerland. Results showed four distinct
classes of functioning trajectories and revealed robust predictors for distinguishing between
identified classes. Lastly, study 3 summarized current literature on prediction models of
functioning in the field of SCI rehabilitation in the form of a scoping review. Results showed
that the potential of functioning-based prediction models for use in clinical practice remains
to be explored.
Altogether, the findings of this doctoral thesis will pave the way for discussions and future
research on prediction models of functioning in SCI with the ultimate goal of enhancing clinical


+ ID der Publikation: unilu_57702 + Sprache: Englisch + Letzte Aktualisierung: 2022-03-09 15:28:24



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