JMIR Rehabilitation and Assistive Technologies

The global incidence of spinal cord injury (SCI) is between 10 and 80 new cases per million people each year. This equates to between 250,000 and 500,000 injuries worldwide per year. In the United Kingdom, approximately 4400 people per year sustain an SCI. People with tetraplegia report upper limb function as their highest priority for improvement after SCI. Using immersive virtual reality (VR) headsets, physical rehabilitation exercises can be completed in engaging digital environments. Immersive VR therefore has the potential to increase the amount of therapy undertaken, leading to improvements in arm and hand function. There is little evidence supporting immersive VR as exercise in SCI, especially while patients with SCI are undergoing acute rehabilitation. In SCI research, co-design of new interventions is not a widely adopted approach, yet people with tetraplegia want to contribute with their expert knowledge on their experiences of SCI.

While smart speakers are emerging as a novel health care technology, people with Parkinson's Disease (PwPD) and speech and language therapists (SaLTs) have reported difficulties using smart speakers with speech and voice impairments in research. To date, PwPD have identified frustration with having to repeat themselves to be understood, devices timing out before they had finished speaking, and being unable to have a conversation with smart speakers. SaLTs have reported technical and practical challenges in implementing voice-assisted technology tools. Both PwPD and SaLTs indicated a lack of knowledge about what smart speakers could do, as well as concerns about privacy and the listening nature of the devices.

Older adults in rural areas of Peru encounter many challenges in accessing critical public services sections, such as public health services, education services, and social assistance public services, due to low levels of digital literacy, lack of technology access, and no formalized and secure identification. This inhibits entry into digital health, education, and social assistance systems and increases their risk of vulnerability and social exclusion.

Large language models (LLMs) have demonstrated potential in automating the analysis of unstructured clinical data, yet their application in rehabilitation therapy for work injury cases remains underexplored.

Telerehabilitation is a safe and effective means of delivering physiotherapy services, but implementation in clinical practice has not been widespread.

Fibromyalgia (FM) causes widespread pain, fatigue, and cognitive abnormalities. Cervical pain is a common and debilitating symptom.

Telerehabilitation has been widely adapted to meet the growing rehabilitation demand, but it is often limited by unstable internet connection, poor audiovisual resolution, and difficult virtual assessment. The Shoulder Telehealth Assessment Tool (STAT), a comprehensive, patient-led, pre-consultation shoulder physical examination pictorial guide, was developed to address these limitations by easing the communication of instruction during the consultation, and potentially removing the need for video calls.

Speech recognition technology is widely used by D/deaf and hard-of-hearing (DHH) individuals in everyday communication, but its clinical applications remain underexplored. Communication barriers in healthcare can compromise safety, understanding, and autonomy for DHH individuals.

The ageing population has resulted in more people living for longer with musculoskeletal conditions who are in need of hip and knee replacement surgery. Lengthening waiting lists are increasingly a challenge for patients and healthcare services.

The Human Activity Profile (HAP) questionnaire is widely used to assess functional capacity in patients with chronic diseases. However, its remote administration via telephone has not been validated in individuals with cardiovascular disease (CVD), despite increasing need for accessible assessment methods, particularly in contexts involving mobility limitations or reduced access to in-person care.

Artificial intelligence (AI)–based gait analysis systems are increasingly applied in rehabilitation settings for objective and quantitative assessment of gait function. However, despite their potential, clinical adoption remains limited due to insufficient consideration of usability, user experience, and integration into actual clinical workflows.

The manufacture of load-bearing prosthetic lower limb sockets is traditionally reliant on skilled technicians working with qualified clinicians, creating bespoke solutions. While this approach is effective and, in some situations necessary, the appeal of a sustainable, efficient, and digitalized production solution has made a statement in recent decades that cannot be ignored. The focus of additive manufacturing (AM) is typically on low weight bearing prostheses, which can be misleading for clinics attempting to adopt AM solutions for clientele with weight bearing and/or activity level needs.