Introduction: Metaverse technology is spurring a transformation in healthcare and has the potential to cause a disruptive shift in rehabilitation interventions. The technology will surely be a promising field offering new resources to improve clinical outcomes, compliance, sustainability, and patients' interest in rehabilitation. Despite the growing interest in technologies for rehabilitation, various barriers to using digital services may continue to perpetuate a digital divide. This article proposes a framework with five domains and elements to consider when designing and implementing Metaverse-based rehabilitation services to reduce potential inequalities and provide best patient care.Methods: The framework was developed in two phases and was informed by previous frameworks in digital health, the Metaverse, and health equity. The main elements were extracted and synthesized via consultation with an interdisciplinary team, including a knowledge user.Results: The proposed framework discusses equity issues relevant to assessing progress in moving toward and implementing the Metaverse in rehabilitation services. The five domains of the framework were identified as equity, health services integration, interoperability, global governance, and humanization.Discussion: This article is a call for all rehabilitation professionals, along with other important stakeholders, to engage in developing an equitable, decentralized, and sustainable Metaverse service and not just be a spectator as it develops. Challenges and opportunities and their implications for future directions are highlighted.

Background: The integration of artificial intelligence (AI) into health sciences students’ education holds significant importance. The rapid advancement of AI has opened new horizons in scientific writing and has the potential to reshape human-technology interactions. AI in education may impact critical thinking, leading to unintended consequences that need to be addressed. Understanding the implications of AI adoption in education is essential for ensuring its responsible and effective use, empowering health sciences students to navigate AI-driven technologies’ evolving field with essential knowledge and skills. Objective: This study aims to provide details on the study protocol and the methods used to investigate the usability and efficacy of ChatGPT, a large language model. The primary focus is on assessing its role as a supplementary learning tool for improving learning processes and outcomes among undergraduate health sciences students, with a specific emphasis on chronic diseases. Methods: This single-blinded, crossover, randomized, controlled trial is part of a broader mixed methods study, and the primary emphasis of this paper is on the quantitative component of the overall research. A total of 50 students will be recruited for this study. The alternative hypothesis posits that there will be a significant difference in learning outcomes and technology usability between students using ChatGPT (group A) and those using standard web-based tools (group B) to access resources and complete assignments. Participants will be allocated to sequence AB or BA in a 1:1 ratio using computer-generated randomization. Both arms include students’ participation in a writing assignment intervention, with a washout period of 21 days between interventions. The primary outcome is the measure of the technology usability and effectiveness of ChatGPT, whereas the secondary outcome is the measure of students’ perceptions and experiences with ChatGPT as a learning tool. Outcome data will be collected up to 24 hours after the interventions. Results: This study aims to understand the potential benefits and challenges of incorporating AI as an educational tool, particularly in the context of student learning. The findings are expected to identify critical areas that need attention and help educators develop a deeper understanding of AI’s impact on the educational field. By exploring the differences in the usability and efficacy between ChatGPT and conventional web-based tools, this study seeks to inform educators and students on the responsible integration of AI into academic settings, with a specific focus on health sciences education. Conclusions: By exploring the usability and efficacy of ChatGPT compared with conventional web-based tools, this study seeks to inform educators and students about the responsible integration of AI into academic settings. Trial Registration: ClinicalTrails.gov NCT05963802; https://clinicaltrials.gov/study/NCT05963802 International Registered Report Identifier (IRRID): PRR1-10.2196/51873

Introduction Despite the increased interest in telerehabilitation (TR), virtual reality (VR) and outcome measures for stroke rehabilitation, surprisingly little research has been done to map and identify the most common outcome measures used in TR. For this review, we conducted a systematic search of the literature that reports outcome measures used in TR or VR for stroke rehabilitation. Our specific objectives included: 1) to identify the outcome measures used in TR and VR studies; and 2) to describe which parts of the International Classification of Functioning are measured in the studies. Methods We conducted a comprehensive search of relevant electronic databases (e.g. PubMed, the Cumulative Index to Nursing and Allied Health Literature, Embase, PSYCOINFO, The Cochrane Central Register of Controlled Trial and the Physiotherapy Evidence Database). The scoping review included all study designs. Two reviewers conducted pilot testing of the data extraction forms and independently screened all the studies and extracted the data. Disagreements about inclusion or exclusion were resolved by consensus or by consulting a third reviewer. Results In total, 28 studies were included in this scoping review. The results were synthesized and reported considering the implications of the findings within the clinical practice and policy context. Discussion This scoping review identified a wide range of outcome measures used in VR and TR studies and helped identify gaps in current use of outcome measures in the literature. The review also informs researchers and end users (i.e. clinicians, policymakers and researchers) regarding the most appropriate outcome measures for TR or VR.