JMIR Rehabilitation and Assistive Technologies

Spinal cord injury (SCI) causes debilitating secondary conditions such as severe muscle deterioration, cardiovascular, and metabolic dysfunctions, significantly impacting patients' quality of life. Functional Electrical Stimulation (FES) combined with cycling exercise (FES-cycling) has shown promise in improving muscle function and health in individuals with SCI.

Health care is shifting toward 5 proactive approaches: personalized, participatory, preventive, predictive, and precision-focused services (P5 medicine). This patient-centered care leverages technologies such as artificial intelligence (AI)–powered robots, which can personalize and enhance services for users with disabilities. These advancements are crucial given the World Health Organization’s projection of a global shortage of up to 10 million health care workers by 2030.

Web-based concussion self-management education programs for adolescents can improve functional outcomes, reduce concussion symptoms, and increase self-efficacy. However, there are a limited number of studies examining the perceptions and acceptance of these programs and the use of these tools in the adult concussion population.

Stationary bikes are used in numerous rehabilitation settings with most offering limited functionalities and types of training. Smart technologies, such as artificial intelligence and robotics, bring new possibilities to achieve rehabilitation goals. However, it is important that these technologies meet the needs of users in order to improve their adoption in current practice.

Evidence suggests that individuals with motor neuron disease (MND), a terminal illness, find enjoyment and social connection through video games. However, MND-related barriers can make gaming challenging, exacerbating feelings of boredom, stress, isolation, and loss of control over daily life.

Cognitive frailty is known to be associated with both nutrition and cognitive training. However, effective treatments that engage older adults with cognitive frailty in both the Mediterranean diet and cognitive training are lacking.

People with severe or profound intellectual disability and visual impairment tend to have serious problems in orientation and mobility and need assistance for their indoor traveling. The use of technology solutions may be critically important to help them curb those problems and achieve a level of independence.

Memory and learning deficits are among the most impactful and longest-lasting symptoms experienced by people with chronic traumatic brain injury (TBI). Despite the persistence of post-TBI memory deficits and their implications for community reintegration, memory rehabilitation is restricted to short-term care within structured therapy sessions. Technology shows promise to extend memory rehabilitation into daily life and to increase the number and contextual diversity of learning opportunities. Ecological momentary assessment and intervention frameworks leverage mobile phone technology to assess and support individuals’ behaviors across contexts and have shown benefit in other chronic conditions. However, few studies have used regular outreach via text messaging for adults with chronic TBI, and none have done so to assess and support memory.

Visual disability is a growing problem for many middle-aged and older adults. Conventional mobility aids, such as white canes and guide dogs, have notable limitations that have led to increasing interest in electronic travel aids (ETAs). Despite remarkable progress, current ETAs lack empirical evidence and realistic testing environments and often focus on the substitution or augmentation of a single sense.

People with intellectual disabilities in residential or outpatient facilities for people with disabilities run the risk of being digitally excluded by not having opportunities for taking advantage of digitalization possibilities.

Telemonitoring (TM), as part of telehealth, allows physiotherapists to monitor and coach their patients using remotely collected data. The use of TM requires a different approach compared to face-to-face treatment. Although a telehealth capability framework exists for healthcare professionals, it remains unclear what specific capabilities are required to use TM during physiotherapy treatments.

Parkinson’s Disease (PD) is reported to be among the most prevalent neurodegenerative diseases globally, presenting ongoing challenges and increasing burden on healthcare systems. In an effort to support PD patients, their carers, and the wider healthcare sector to manage this incurable condition, the focus has begun to shift away from traditional treatments. One of the most contemporary treatments includes prescribing Assistive Technologies (ATs), which are viewed as a way to promote independent living and deliver remote care. However, the uptake of these ATs is varied with some users not ready or willing to accept all forms of AT and others only willing to adopt low-technology solutions. Consequently, to manage both the demands on resources and the efficiency with which ATs are deployed, new approaches are needed to automatically assess or predict a user’s likelihood to accept and adopt a particular AT before it is prescribed. Classification algorithms can be employed to automatically consider the range of factors impacting AT adoption likelihood, thereby potentially supporting more effective AT allocation. From a computational perspective, different classification algorithms and selection criteria offer various opportunities and challenges to address this need.