Publications by authors named "F Tamburella"
: Accurate prediction of neurorehabilitation outcomes following Spinal Cord Injury (SCI) is crucial for optimizing healthcare resource allocation and improving rehabilitation strategies. Artificial Neural Networks (ANNs) may identify complex prognostic factors in patients with SCI. However, the influence of psychological variables on rehabilitation outcomes remains underexplored despite their potential impact on recovery success.
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- Treadmill-based Robotic-Assisted Gait Training (t-RAGT) enhances rehabilitation by using robots to help patients walk, but the role of physiotherapists and the type of feedback provided to patients needs further exploration.
- This study examined the effects of different types of visual feedback (chart, emoticon, game) and levels of physiotherapist-patient interaction (low, medium, high) on patients' attention and emotional engagement using eye-tracking and EEG methods.
- Results indicated that both the type of feedback and the level of interaction influenced patients' visual attention and emotional response, particularly regarding the therapist's involvement and the areas of interest monitored during the t-RAGT sessions.
Article Synopsis
- The study examines the increasing rates of nontraumatic spinal cord injuries (SCI) and how their causes affect patient outcomes, particularly with aging populations.
- It analyzes a group of 1,080 patients, focusing on both traumatic and nontraumatic SCI, and uses various assessment tools to compare rehabilitation results and influencing factors.
- Results indicate notable differences between the two injury types, yet both showed similar improvements in neurological and functional status after rehabilitation, with some advantages seen in traumatic injury patients.
Several technologies have been introduced into neurorehabilitation programs to enhance traditional treatment of individuals with Spinal Cord Injury (SCI). Their effectiveness has been widely investigated, but their adoption has not been properly quantified. The aim of this study is to assess the distribution of conventional (Treatment As Usual-TAU) and technology-aided (Treatment With Technologies-TWT) treatments conveniently grouped based on different therapeutic goals in a selected SCI unit.
View Article and Find Full Text PDFPrediction of neurorehabilitation outcomes after a Spinal Cord Injury (SCI) is crucial for healthcare resource management and improving prognosis and rehabilitation strategies. Artificial neural networks (ANNs) have emerged as a promising alternative to conventional statistical approaches for identifying complex prognostic factors in SCI patients. a database of 1256 SCI patients admitted for rehabilitation was analyzed.
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