Background: The literature on upper limb robot-assisted therapy showed that robot-measured metrics can simultaneously predict registered clinical outcomes. However, only a limited number of studies correlated pre-treatment kinematics with discharge motor recovery. Given the importance of predicting rehabilitation outcomes for optimizing physical therapy, a predictive model for motor recovery that incorporates multidirectional indicators of a patient’s upper limb abilities is needed.Objective: The aim of this study was to develop a predictive model for rehabilitation outcome at discharge (i.e., muscle strength assessed by the Motricity Index of the affected upper limb) based on multidirectional 2D robot-measured kinematics.Methods: Re-analysis of data from 66 subjects with subacute stroke who underwent upper limb robot-assisted therapy with an end-effector robot was performed. Two least squares error multiple linear regression models for outcome prediction were developed and differ in terms of validation procedure: the Split Sample Validation (SSV) model and the Leave-One-Out Cross-Validation (LOOCV) model. In both models, the outputs were the discharge Motricity Index of the affected upper limb and its sub-items assessing elbow flexion and shoulder abduction, while the inputs were the admission robot-measured metrics.Results: The extracted robot-measured features explained the 54% and 71% of the variance in clinical scores at discharge in the SSV and LOOCV validation procedures respectively. Normalized errors ranged from 22% to 35% in the SSV models and from 20% to 24% in the LOOCV models. In all models, the movement path error of the trajectories characterized by elbow flexion and shoulder extension was the significant predictor, and all correlations were significant.Conclusion: This study highlights that motor patterns assessed with multidirectional 2D robot-measured metrics are able to predict clinical evalutation of upper limb muscle strength and may be useful for clinicians to assess, manage, and program a more specific and appropriate rehabilitation in subacute stroke patients.

Idiopathic chronic neck pain is a highly disabling musculoskeletal condition. Immersive virtual reality shows a promising efficacy in the treatment of chronic cervical pain through the mechanism of distraction from the pain. This case report describes the management of C.F., a fifty-seven-year-old woman, who suffered from neck pain for fifteen months. She had already undergone a cycle of physiotherapy treatments including education, manual therapy, and exercises, following international guidelines. The patient’s poor compliance did not allow adherence to the exercise’s prescription. Home exercise training through virtual reality was therefore proposed to the patient to improve her adherence to the treatment plan. The personalization of the treatment allowed the patient to resolve in a short time period her problem and return to live with her family peacefully.

BACKGROUND: Musculoskeletal pain can be defined as a consequence of a trauma or as a disorder of varying nature, which can be manifested in bones, muscles, tendinous and ligamentous structures, including those of the wrist and hand. However, such pain can mimic pathologies that need further diagnostic investigations; as a consequence, healthcare practitioners have to be able to detect their signs and symptoms, in order to refer the patient to the most appropriate clinician. The current literature lacks of reviews that analyze red flags in the field of wrist and hand pain. The reviewers would like to look for any correlations that could be useful in a correct screening process by clinicians. METHODS: This scoping review will be performed in accordance with the PRISMA extensions for Scoping Reviews (PRISMA-ScR). Studies will be included if they meet the following inclusion criteria: population characterized by wrist and hand pain with or without comorbidities either at the present or in the past. No limits of age, gender and sport activity will be applied. No study design, publication type, and data restrictions will be applied. MEDLINE, Web of Science, Cochrane Library, CINAHL, Embase, PEDro databases will be searched up to February 2023. Two reviewers will independently screen all title, abstracts and full-text studies for inclusion. A data collection form will be developed by the research team to extract the characteristics of the studies included. A tabular and accompanying narrative summary of the information will be provided. CONCLUSIONS: This will be the first scoping review to provide a comprehensive overview of the topic. The results will add meaningful information for future research and clinical practice. The results of this research will be published in a peer-reviewed journal and will be presented at relevant (inter)national scientific events.

Background: Telemedicine is an effective, widely used strategy in the field of cystic fibrosis management. The objective of this scoping review is to summarize and analyze the scientific literature with the special focus on the tools and the strategies used in patients with a chronic disease, such as cystic fibrosis. Methods: This scoping review will be performed in accordance with the Joanna Briggs Institute methodology. In this context, the planned scoping review is a research synthesis that will map the literature on the applications of telemedicine and telemonitoring to the management of cystic fibrosis, with the aim to identify key concepts in the research and work to be conducted that may impact clinical practice. Studies will be included if they meet the following population, concept, and context criteria: all patients with cystic fibrosis receiving treatment with the tools of telemedicine and telemonitoring. No study design, publication type, or data restrictions will be applied. MEDLINE, Scopus, CINHAL, Pedro, Embase, Web of Science, ACM Digital Library, Health Technology Assessment Database (HTA), and Cochrane Central will be searched up to September 2022. Discussion: To the best of our knowledge, this will be the first scoping review to provide a comprehensive overview of the topic. The results could add meaningful information for future research and, especially, for clinical practice, when implementing telerehabilitation in cystic fibrosis treatment. Furthermore, we expect that our work may identify possible knowledge gaps on the topic. The results of this research will be published in a peer-reviewed journal and will be presented at relevant international scientific events, such as in congress or meetings.

(1) Background: In neurorehabilitation, Wearable Powered Exoskeletons (WPEs) enable intensive gait training even in individuals who are unable to maintain an upright position. The importance of WPEs is not only related to their impact on walking recovery, but also to the possibility of using them as assistive technology; however, WPE-assisted community ambulation has rarely been studied in terms of walking performance in real-life scenarios. (2) Methods: This study proposes the integration of an Inertial Measurement Unit (IMU) system to analyze gait kinematics during real-life outdoor scenarios (regular, irregular terrains, and slopes) by comparing the ecological gait (no-WPE condition) and WPE-assisted gait in five able-bodied volunteers. The temporal parameters of gait and joint angles were calculated from data collected by a network of seven IMUs. (3) Results: The results showed that the WPE-assisted gait had less knee flexion in the stance phase and greater hip flexion in the swing phase. The different scenarios did not change the human–exoskeleton interaction: only the low-speed WPE-assisted gait was characterized by a longer double support phase. (4) Conclusions: The proposed IMU-based gait assessment protocol enabled quantification of the human–exoskeleton interaction in terms of gait kinematics and paved the way for the study of WPE-assisted community ambulation in stroke patients.