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Discriminative ability of instrumented cognitive-motor assessments to distinguish fallers from non-fallers

  • Julia Seinsche,
  • Elena Kyprianou,
  • Eling D. de Bruin,
  • Enrico Saibene,
  • Francesco Rizzo,
  • Ilaria Carpinella,
  • Lisa Lutz,
  • Maurizio Ferrarin,
  • Riccardo Villa,
  • Savvina Chrysostomou,
  • Sotiria Moza,
  • Eleftheria Giannouli

摘要

In older populations, sensitive fall risk assessment tools are important to timely intervene and prevent falls. Instrumented assessments have shown to be superior to standardized fall risk assessments such as the Timed Up and Go Test (TUG) and should capture both motor and cognitive functions. Therefore, the aim was to test novel instrumented assessments with and without a cognitive component. One hundred thirty-seven older adults aged 73.1 ± 7.3 years, 38 categorized as fallers and 99 as non-fallers, conducted five instrumented assessments on the Dividat Senso, a pressure sensitive stepping platform, and three standardized geriatric assessments (TUG, TUG-dual task, 30-s Sit-to-Stand Test (STS)). T-tests were applied to compare the test performance of fallers versus non-fallers. Furthermore, logistic regression analyses and area under the curve (AUC) analyses were performed. Statistically significant differences between fallers and non-fallers were found in the Go/No-Go test (p = .001, d = .72), the TUG (p = .014, d = .48), and the STS (p = .008, d = .51). Only the Go/No-Go test contributed significantly to all regression models. Significant AUC values were found for the Reaction Time Test (RTT) (AUC = .628, p = .023), Go/No-Go (AUC = .673, p = .002), TUG (AUC = .642, p = .012), and STS (AUC = .690, p = .001). The Go/No-Go test measuring inhibition showed the best discriminative ability suggesting added value of instrumented assessments with a cognitive component for clinical fall risk assessment in relatively healthy older adults. The study should be extended with a frailer population, in which TUG and the other instrumented assessments are possibly good predictors as well.