Background: The Sensory Organisation Test (SOT) of computerised dynamic posturography (CDP) is a well-established clinical test used to measure postural control. Advances in technology have enabled new CDP systems to use immersive virtual reality, such as the Bertec® Balance Advantage®. While the Bertec provides an innovative approach to posturography, the reliability and learning effects of the Bertec in administering the SOT has not been thoroughly investigated.
Research Question: To evaluate the reliability and performance during repeated administration of the Bertec® Balance Advantage® SOT.
Methods: Fourteen healthy adults (age 27.17 ± 5.5years; 10 females) participated. Each participant performed five SOTs over three sessions. The first two sessions were approximately two days apart and the third one month later. In the first two sessions, two SOTs were conducted, and in the third session, one was performed. Composite, equilibrium, and ratio scores were used for analysis.
Results: Poor within-session reliability was found in the first session for the composite score (ICC: 0.73, 95% CI: 0.32-0.91), which improved by the second session (ICC: 0.84, 95% CI: 0.58-0.94). Poor within-session reliability (ICC <0.5) was found for all ratio and equilibrium scores, except for the equilibrium score of condition 3, which demonstrated moderate reliability (ICC: 0.84, 95% CI: 0.57-0.95). Poor between-session reliability was found for all outcomes. There was an increase in the composite and equilibrium scores for conditions 5 and 6 over the 5 tests, which plateaued after the fourth test, and were retained at 1 month.
Significance: The data demonstrate a steady increase in performance with repeated exposure to the Bertec SOT, which was maintained one month later, indicating a learning effect. We recommend that a minimum of two familiarisation sessions should be administered to establish baseline performance and improve reliability.
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http://dx.doi.org/10.1016/j.gaitpost.2022.02.004 | DOI Listing |
BMC Med Inform Decis Mak
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Department of Pediatrics, School of Medicine, Ekbatan Hospital, Hamadan University of Medical Sciences, Hamadan, Iran.
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Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting.
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