Objective: the objective of this review is to analyze the advances in the field of rehabilitation through virtual reality, while taking into account non-immersive systems, as evidence have them shown to be highly accepted by older people, due to the lowest “cibersikness” symptomatology. Data sources: a systematic review of the literature was conducted in June 2019. The data were collected from Cochrane, Embase, Scopus, and PubMed databases, analyzing manuscripts and articles of the last 10 years. Study selection: we only included randomized controlled trials written in English aimed to study the use of the virtual reality in rehabilitation. We selected 10 studies, which were characterized by clinical heterogeneity. Data extraction: quality evaluation was performed based on the Physioterapy Evidence Database (PEDro) scale, suggested for evidence based review of stroke rehabilitation. Of 10 studies considered, eight were randomized controlled trials and the PEDro score ranged from four to a maximum of nine. Data synthesis: VR (Virtual Reality) creates artificial environments with the possibility of a patient interaction. This kind of experience leads to the development of cognitive and motor abilities, which usually positively affect the emotional state of the patient, increasing collaboration and compliance. Some recent studies have suggested that rehabilitation treatment interventions might be useful and effective in treating motor and cognitive symptoms in different neurological disorders, including traumatic brain injury, multiple sclerosis, and progressive supranuclear palsy. Conclusions: as it is shown by the numerous studies in the field, the application of VR has a positive impact on the rehabilitation of the most predominant geriatric syndromes. The level of realism of the virtual stimuli seems to have a crucial role in the training of cognitive abilities. Future research needs to improve study design by including larger samples, longitudinal designs, long term follow-ups, and different outcome measures, including functional and quality of life indexes, to better evaluate the clinical impact of this promising technology in healthy old subjects and in neurological patients.

(1) Background: Parkinson’s disease (PD) is one of the most frequent causes of disability among older people. Recently, virtual reality and exergaming have been emerged as promising tools for gait and balance rehabilitation in PD patients. Our purpose is to evaluate an innovative treatment for older patients with PD, based on non-immersive virtual reality exergames, improving gait and balance and reducing falling risk. (2) Methods: Thirty PD patients were recruited and randomly divided into two groups, to receive a traditional rehabilitation (CG) or a technological rehabilitation (TG). (3) Results: A statistical improvement of balance at the end of treatments was observed in both groups (CG: 12.4 ± 0.7 vs. 13.5 ± 0.8, p = 0.017; TG: 13.8 ± 0.5 vs. 14.7 ± 0.4, p = 0.004), while the overall risk of falling was significantly reduced only in the TG (POMA Total: 24.6 ± 0.9 vs. 25.9 ± 0.7, p = 0.010). The results between groups shows that all POMA scores differ in a statistically significant manner in the TG, emphasizing improvement not only in balance but also in gait characteristics (9.7 ± 0.8 vs. 11.4 ± 0.2, p = 0.003). Moreover, TG also improves the psychological sphere, measured thorough MSC-(17.1 ± 0.4 vs. 16.5 ± 0.4, p = 0.034). Although an improvement in FES-I and Gait Speed can be observed, this increase does not turn out to be significant. (4) Conclusions: Results suggest how non-immersive virtual reality exergaming technology offers the opportunity to effectively train cognitive and physical domains at the same time.

The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer’s disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer’s disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer’s disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer’s disease patients—both at the dementia and pre-dementia phases of the disease—from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer’s disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer’s.

Background: Parkinson’s disease is one of the most frequent causes of disability among the older adults. It is a chronic-progressive neuro-degenerative disease, characterized by several motor disorders. Balance disorders are a symptom that involves the body axis and do not respond to dopaminergic therapy used in Parkinson’s disease. Therefore, physiotherapy becomes an important intervention for the management of motor disorders. Originally, these rehabilitative approaches were based on empirical experiences, but several scientific evidences suggests that neuronal plasticity is exercise-dependent. In this context, robotic rehabilitation plays an important role because it allows to perform task-oriented exercises and to increase the number of repetitions and their intensity. This protocol study aims to evaluate the effectiveness of robotic-based intervention of the older adults with Parkinson’s disease, designed to improve the gait and to reduce the risk of falling. Methods: This study is a single-blinded randomized controlled trial. The primary outcomes are: risk of falling, gait performance and fear of falling measured through Performance-Oriented Mobility Assessment (POMA), instrumental gait analysis and Short Falls Efficacy Scale – International (FES-I), respectively. One hundred ninety-five patients with PD will be recruited and randomly divided into three groups, to receive a traditional rehabilitation program or a robotic rehabilitation using Tymo system or Walker View in addition to the traditional therapy. Assessments will be performed at baseline, at the end of treatment and 6 months, 1 year and 2 years from the end of the treatment. A 10-treatment session will be conducted, divided into 2 training sessions per week, for 5 weeks. The control group will perform traditional therapy sessions lasting 50 min. The technological intervention group will carry out 30 min of traditional therapy and 20 min of treatment with a robotic system. Discussion: The final goals of the present study are to propose a new approach in the PD rehabilitation, focused on the use of robotic device, and to check the results not only at the end of the treatment but also in the long term. Trial registration: NCT04087031, registration date September 12, 2019.

The identification of diagnostic-prognostic biomarkers of dementia has become a global priority due to the prevalence of neurodegenerative diseases in aging populations. The objective of this study was to assess the diagnostic performance of cerebrospinal fluid (CSF) biomarkers across patients affected by either Alzheimer's disease (AD), tauopathies other than AD (TP), or vascular dementia (VD), and cognitively normal subjects (CNS). One hundred fifty-three patients were recruited and tested for classical AD CSF biomarkers- Amyloid-ß42 and tau proteins - and novel candidate biomarkers - neurofilament (NF-) light and microRNA (miR) -21, -125b, -146a, and -222.All dementia patients had significantly higher concentrations of NF-light compared to CNS, with the TP group displaying the highest NF-light values. A significant inverse correlation was also observed between NF-light and cognitive impairment. Of the four miRNAs analyzed, miR-222 levels were significantly increased in VD patients compared to both CNS and AD. In addition, while NF-light showed a better diagnostic performance than miR-222 and classical AD biomarkers in differentiating TP and VD from CNS, classical AD biomarkers revealed higher performance in discriminating AD from non-AD disorders.Overall, our results suggest that CSF NF-light and miR-222 are promising biomarkers that may help to diagnose non-AD disorders.