JMIR Rehabilitation and Assistive Technologies
Development and evaluation of rehabilitation, physiotherapy and assistive technologies, robotics, prosthetics and implants, mobility and communication tools, home automation, and telerehabilitation.
Editor-in-Chief:
Sarah Munce, MSc, PhD, University of Toronto, Canada
CiteScore 4.2
Recent Articles
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.
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.
Educational multimedia is a cost-effective and straightforward way to administer large-scale information interventions to patient populations in musculoskeletal health care. While an abundance of health research informs the content of these interventions, less guidance exists about optimizing their design.
Work burden increases for physiotherapists in the primary health care sector as the prevalence of musculoskeletal disorders (MSDs) increases. Digital health technologies (DHTs) are proposed as a viable solution to secure the sustainability of the health care system and have shown promising results in a range of conditions. However, little is known about use of DHTs among physiotherapists in the primary health care sector in Norway.
Globally, one in three people live with health conditions that could be improved with rehabilitation. Ideally this is provided by trained professionals delivering evidence based dose, intensity and content of rehabilitation, for optimal recovery. The widely acknowledged inability of global healthcare providers to deliver recommended levels of rehabilitation, creates an opportunity for technological innovation. Design processes that lack close consideration of users’ needs and budgets, however, mean that many rehabilitation technologies are neither useful, nor used. To address this problem our multi-disciplinary research group have established a co-creation centre for rehabilitation technology that places the end user at the centre of the innovation process.
Low back pain (LBP) is a significant public health problem that can result in physical disability and financial burden for the individual and society. Physical therapy is effective for managing LBP and includes evaluation of posture and movement, interventions directed at modifying posture and movement, and prescription of exercises. However, physical therapists have limited tools for objective evaluation of low back posture and movement and monitoring of exercises, and this evaluation is limited to the time frame of a clinical encounter. There is a need for a valid tool that can be used to evaluate low back posture and movement and monitor exercises outside the clinic. To address this need, a fabric-based, wearable sensor, Motion Tape (MT), was developed and adapted for a low back use case. MT is a low-profile, disposable, self-adhesive, skin-strain sensor developed by spray coating piezoresistive graphene nanocomposites directly onto commercial kinesiology tape.
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