ArticleInternational journal of environmental research and public health2022
Measuring Equine-Assisted Therapy: Validation and Confirmatory Factor Analysis of an ICF-Based Standardized Assessment-Tool.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
- Effectiveness of hippotherapy on balance performance, neurophysiological parameters and clinical symptoms of multiple sclerosis: Study protocol of a randomized controlled multicenter study (MS-HIPPO II - Movement in Balance).Contemporary clinical trials communications · 2026Article
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3 authors.
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Abstract
The International Classification of Functioning, Disability, and Health (ICF) of the World Health Organization (WHO) was established as an international framework for monitoring rehabilitation outcomes and the impacts of health interventions since, as the term "functioning" implies, it emphasizes a person's "lived health" in addition to their biological health status. Equine-assisted therapy (EAT) represents a holistic intervention approach that aims to improve both biomedical functioning and the patient's lived health in relation to performing activities and participating in social situations. In this study, the psychometric properties of an ICF-based digital assessment tool for the measurement of the rehabilitation impacts of EAT were analyzed via simultaneous confirmatory factor analyses (CFA) and reliability and sensitivity tests. In total, 265 patients from equine-assisted therapy centers in Germany were included for CFA. Change sensitivity was assessed via multi-level analyses based on 876 repeated assessments by 30 therapists. Results show satisfactory model-fit statistics; McDonald's omega (ML) showed excellent scores for the total scale (ω = 0.96) and three subscales (ω = 0.95; ω = 0.95, ω = 0.93). The tool proved itself to be change sensitive and reliable (change sensitivity
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