During the last years, the significant increase in the number of patients in need of rehabilitation has generated an unsustainable economic impact on healthcare systems, implying a reduction in therapeutic supervision and support for each patient. To address this problem, this paper proposes a tele-rehabilitation system based on serious games and in-cloud data analytics services, in accordance with Industry 4.0 design principles regarding modularity, service orientation, decentralization, virtualization and real-time capability. The system, specialized for post-stroke patients, comprises components for real-time acquisition of patient's motor data and a decision support service for their analysis. Raw data, reports, and recommendations are made available on the cloud to clinical operators to remotely assess rehabilitation outcomes and dynamically improve therapies. Furthermore, the results of a pilot study on the clinical impact deriving from the adoption of the proposed solution, and of a qualitative analysis about its acceptance, are presented and discussed.

Background: Overground Robot-Assisted Gait Training (o-RAGT) appears to be a promising stroke rehabilitation in terms of clinical outcomes. The literature on surface ElectroMyoGraphy (sEMG) assessment in o-RAGT is limited. This paper aimed to assess muscle activation patterns with sEMG in subjects subacute post stroke after training with o-RAGT and conventional therapy. Methods: An observational preliminary study was carried out with subjects subacute post stroke who received 15 sessions of o-RAGT (5 sessions/week; 60 min) in combination with conventional therapy. The subjects were assessed with both clinical and instrumental evaluations. Gait kinematics and sEMG data were acquired before (T1) and after (T2) the period of treatment (during ecological gait), and during the first session of o-RAGT (o-RAGT₁). An eight-channel wireless sEMG device acquired in sEMG signals. Significant differences in sEMG outcomes were found in the BS of TA between T1 and T2. There were no other significant correlations between the sEMG outcomes and the clinical results between T1 and T2. Conclusions: There were significant functional gains in gait after complex intensive clinical rehabilitation with o-RAGT and conventional therapy. In addition, there was a significant increase in bilateral symmetry of the Tibialis Anterior muscles. At this stage of the signals from the tibialis anterior (TA), gastrocnemius medialis (GM), rectus femoris (RF), and biceps femoris caput longus (BF) muscles of each lower extremity. sEMG data processing extracted the Bilateral Symmetry (BS), the Co-Contraction (CC), and the Root Mean Square (RMS) coefficients. Results: Eight of 22 subjects in the subacute stage post stroke agreed to participate in this sEMG study. This subsample demonstrated a significant improvement in the motricity index of the affected lower limb and functional ambulation. The heterogeneity of the subjects’ characteristics and the small number of subjects was associated with high variability research, functional gait recovery was associated with minimal change in muscle activation patterns.

In rehabilitation, the upper limb function is generally assessed using clinical scales and functional motor tests. Although the Box and Block Test (BBT) is commonly used for its simplicity and ease of execution, it does not provide a quantitative measure of movement quality. This study proposes the integration of an ecological Inertial Measurement Units (IMUs) system for analysis of the upper body kinematics during the execution of a targeted version of BBT, by able-bodied persons with subjects with Parkinson’s disease (PD). Joint angle parameters (mean angle and range of execution) and hand trajectory kinematic indices (mean velocity, mean acceleration, and dimensionless jerk) were calculated from the data acquired by a network of seven IMUs. The sensors were applied on the trunk, head, and upper limb in order to characterize the motor strategy used during the execution of BBT. Statistics revealed significant differences (p < 0.05) between the two groups, showing compensatory strategies in subjects with PD. The proposed IMU-based targeted BBT protocol allows to assess the upper limb function during manual dexterity tasks and could be used in the future for assessing the efficacy of rehabilitative treatments.

Pervasive health technologies can increase the effectiveness of personal health monitoring and training, but more user studies are necessary to understand the interest for these technologies, and how they should be designed and implemented. In the present study, we evaluated eWALL, a user-centered pervasive health technology consisting of a platform that monitors users’ physical and cognitive behavior, providing feedback and motivation via an easy-to-use, touch-based user interface. The eWALL was placed for one month in the home of 48 subjects with a chronic condition (chronic obstructive pulmonary disease—COPD or mild cognitive impairment—MCI) or with an age-related impairment. User acceptance, platform use, and potential clinical effects were evaluated using surveys, data logs, and clinical scales. Although some features of the platform need to be improved before reaching technical maturity and making a difference in patients’ lives, the real-life evaluation of eWALL has shown how some features may influence patients’ intention to use this promising technology. Furthermore, this study made it clear how the free use of different health apps is modulated by the real needs of the patient and by their usefulness in the context of the patient’s clinical status.

Objective: To evaluate the psychological state of healthcare workers (HCWs) in the field of rehabilitation during the COVID-19 pandemic. Methods: Cross-sectional observational study. Sample of 334 HCWs including: nurses, medical doctors, therapists, scientists, and clerical workers working at the IRCCS San Raffaele Roma rehabilitation hospital during the second wave of the COVID-19 pandemic. Anonymous web-based questionnaire included 14-item Resilience Scale, Brief-COPE, Hospital Anxiety Depression Scale, Fear of COVID-19 Scale. Occupational and sociodemographic characteristics. Results: High levels of resilience, low levels of anxiety, depression, and fear were observed in the study population; the most frequently used coping strategies in the Brief-COPE were acceptance, planning, and active coping. Specifically, 87% of the participants reported a moderate to high level of resilience, with the highest level observed in nurses while physicians show the lowest level. HCWs showed symptoms of anxiety (29%), depressive symptoms (10%), and fear caused by the COVID-19 pandemic (44%). Statistically significant differences were observed between different occupations for fear (p <0.05) and resilience (p <0.01). Levels of anxiety and fear appeared to be higher in female and younger workers. The latter group - who also reported higher levels of depression - showed lower levels of resilience. Conclusions: In our study hospital and non-hospital workers show different emotional, cognitive, and behavioural resources when facing stressful situations, like in the case of the SARS-CoV-2 pandemics. Our results support the role of resilience and the proper use of problem-focused and emotion-focused coping strategies as protective factors from psychological distress.

Frailty is a geriatric multidimensional syndrome characterized by a loss of physiologic reserves and disproportionate vulnerability to external stressors and associated with increased risk of multiple negative health outcomes. Since frailty can be prevented, controlled, and even reverted in its early stages, identifying the main factors involved in its development is crucial to implement preventive and/or restorative interventions. The aim of this study was to assess the impact of a broad range of parameters, including host factors, lifestyle, diet, and environmental and occupational conditions, on the development of frailty in later life. A cross-sectional study was conducted on 221,896 individuals aged 60 and over classified as non-frail (119,332, 53.8%), pre-frail (93,180, 42.0%), and frail (9384, 4.2%) according to the frailty phenotype. Using principal component analysis and machine learning to streamline the data, significant associations were found between frailty risk and air quality, diet, smoking, working conditions, and heavy alcohol consumption. Early-life factors, including breastfed as a baby and maternal smoking around birth, also emerged as predictors of frailty, which was further characterized by clinical indicators like polypharmacy, levels of C-reactive protein and other biomarkers of inflammageing. This study provided robust and original evidence on the association between a large battery of potential risk factors, from early to later stages of life, and the occurrence of frailty in older age. These results will contribute to the development of effective prevention strategies and facilitate the early detection of individuals at high risk of developing frailty.