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: The worldwide aging trend requires conceptually new prevention, care, and innovative living solutions to support human-based care using smart technology, and this concerns the whole world. Enabling access to active and healthy aging through personalized digital coaching services like physical activity coaching, cognitive training, emotional well-being, and social connection for older adults in real life could offer valuable advantages to both individuals and societies. A starting point might be the analysis of the perspectives of different professionals (eg, geriatricians) on such technologies. The perspectives of experts in the sector may allow the individualization of areas of improvement of clinical interventions, supporting the positive perspective pointed out by the intrinsic capacity framework. Objective: The overall aim of this study was to explore the cross-national perspectives and experiences of different professionals in the field of intrinsic capacity, and how it can be supported by eHealth interventions. To our knowledge, this is the first study to explore geriatric care providers’ perspectives about technology-based interventions to support intrinsic capacity. Methods: A survey involving 20 geriatricians or clinical experts in the fields of intrinsic capacity and active and healthy aging was conducted in Italy, France, Germany, and Japan between August and September 2021. Results: The qualitative findings pointed out relevant domains for eHealth interventions and provided examples for successful practices that support subjective well-being under the intrinsic capacity framework (the benefits offered by personalized interventions, especially by promoting health literacy but avoiding intrusiveness). Moreover, eHealth interventions could be used as a bridge that facilitates and enables social engagement; an instrument that facilitates communication between doctors and patients; and a tool to enrich the monitoring actions of medical staff. Conclusions: There is an unexplored and significant role for such geriatric perspectives to help the development process and evaluate the evidence-based results on the effectiveness of technologies for older people. This is possible only when clinicians collaborate with data scientists, engineers, and developers in order to match the complex daily needs of older adults.

The aim of this paper is to describe the results collected with the Italian study conducted within eWare project, aimed at supporting the autonomy and health of the older people affected by dementia and their informal caregivers, through the use of an innovative system based on a social robot and a sensorized environmental infrastructure. Nine dyads of older participants with their caregivers were enrolled in testing the system for 6 months. The results show a positive impact of the system in supporting the achievement of personal goals of the participants, as well as in supporting the quality of life of the informal caregivers. Nevertheless, the impact of the system in reducing the caregivers’ burden needs to be deeply investigated. This research highlights the potential of the eWare system but modifications will have to be made, especially on the interactivity capabilities, in order to meet the peculiar needs and wishes of older people with dementia and to favor the long-term use of the system.

Background: Parkinson disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms. Recently, dance has started to be considered an effective intervention for people with PD. Several findings in the literature emphasize the necessity for deeper exploration into the synergistic impacts of dance therapy and exergaming for PD management. Moreover, socially engaging robotic platforms equipped with advanced interaction and perception features offer potential for monitoring patients’ posture and enhancing workout routines with tailored cues. Objective: This paper presents the results of the Social Robotics for Active and Healthy Ageing (SI-Robotics) project, aimed at designing an innovative rehabilitation program targeted at seniors affected by (early-stage) PD. This study therefore aims to assess the usefulness of a dance-based rehabilitation program enriched by artificial intelligence–based exergames and contextual robotic assistance in improving motor function, balance, gait, and quality of life in patients with PD. The acceptability of the system is also investigated. Methods: The study is designed as a technical feasibility pilot to test the SI-Robotics system. For this study, 20 patients with PD were recruited. A total of 16 Irish dance–based rehabilitation sessions of 50 minutes were conducted (2 sessions per week, for 8 wks), involving 2 patients at a time. The designed rehabilitation session involves three main actors: (1) a therapist, (2) a patient, and (3) a socially interacting robot. To stimulate engagement, sessions were organized in the shape of exergames where an avatar shows patients the movements they should perform to correctly carry out a dance-based rehabilitation exercise. Results: Statistical analysis reveals a significant difference on the Performance-Oriented Mobility Assessment scale, both on balance and gait aspects, together with improvements in Short Physical Performance Battery, Unified Parkinson Disease Rating Scale–III, and Timed Up and Go test, underlying the usefulness of the rehabilitation intervention on the motor symptoms of PD. The analysis of the Unified Theory of Acceptance and Use of Technology subscales provided valuable insights into users’ perceptions and interactions with the system. Conclusions: This research underscores the promise of merging dance therapy with interactive exergaming on a robotic platform as an innovative strategy to enhance motor function, balance, gait, and overall quality of life for patients grappling with PD.