PLoS ONE (Jan 2024)

Beyond traditional training: Integrating data from semi-immersive VR dual-task intervention in Parkinsonian Syndromes. A study protocol.

  • Francesca Bruni,
  • Valentina Mancuso,
  • Chiara Stramba-Badiale,
  • Marco Stramba-Badiale,
  • Giuseppe Riva,
  • Karine Goulene,
  • Pietro Cipresso,
  • Elisa Pedroli

DOI
https://doi.org/10.1371/journal.pone.0294199
Journal volume & issue
Vol. 19, no. 2
p. e0294199

Abstract

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Completing cognitive and motor tasks simultaneously requires a high level of cognitive control in terms of executive processes and attentional abilities. Most of the daily activities require a dual-task performance. While walking, for example, it may be necessary to adapt gait to obstacles of the environment or simply participate in a conversation; all these activities involve more than one ability at the same time. This parallel performance may be critical in the cognitive or motor load, especially for patients with neurological diseases such as Parkinsonian Syndromes. Patients are often characterized by a crucial impairment in performing both tasks concurrently, showing a decrease in attention skills and executive functions, thus leading to increased negative outcomes. In this scenario, the accurate assessment of the components involved in dual-task performance is crucial, and providing an early specific training program appears to be essential. The objective of this protocol is to assess cognitive and motor components involved in dual-task performance and create a training program based on ecological activities focusing on executive and motor functions. Thus, we will employ Virtual Reality to provide semi-immersive, multisensory, ecological, standardized, and realistic experiences for rehabilitative purposes in patients with Parkinsonian Syndromes, considering its high prevalence in aging and the incidence of motor and cognitive dysfunctions in this population. Moreover, we propose to integrate the great amount of different data provided by dual-task and Virtual Reality system, using machine learning techniques. These integrations may increase the treatment's reliability in terms of better prognostic indexes and individualized training.