The continuum of family dynamics experienced during driving cessation may help clinicians better understand and respond to complex family needs. Interventions should be tailored to families' distinctive needs with consideration of their unique contextual factors influencing dynamics, to provide sensitive and responsive support for families managing driving cessation.
Current neurorehabilitation models primarily rely on extended hospital stays and regular therapy sessions requiring close physical interactions between rehabilitation professionals and patients. The current COVID-19 pandemic has challenged this model, as strict physical distancing rules and a shift in the allocation of hospital resources resulted in many neurological patients not receiving essential therapy. Accordingly, a recent survey revealed that the majority of European healthcare professionals involved in stroke care are concerned that this lack of care will have a noticeable negative impact on functional outcomes. COVID-19 highlights an urgent need to rethink conventional neurorehabilitation and develop alternative approaches to provide high-quality therapy while minimizing hospital stays and visits. Technology-based solutions, such as, robotics bear high potential to enable such a paradigm shift. While robot-assisted therapy is already established in clinics, the future challenge is to enable physically assisted therapy and assessments in a minimally supervized and decentralized manner, ideally at the patient’s home. Key enablers are new rehabilitation devices that are portable, scalable and equipped with clinical intelligence, remote monitoring and coaching capabilities. In this perspective article, we discuss clinical and technological requirements for the development and deployment of minimally supervized, robot-assisted neurorehabilitation technologies in patient’s homes. We elaborate on key principles to ensure feasibility and acceptance, and on how artificial intelligence can be leveraged for embedding clinical knowledge for safe use and personalized therapy adaptation. Such new models are likely to impact neurorehabilitation beyond COVID-19, by providing broad access to sustained, high-quality and high-dose therapy maximizing long-term functional outcomes.
Holding onto a quick fix is a pivotal phase whereby supports, such as engagement in realistic goal setting, are essential to facilitate family members' resolution of driving disruption issues. Family members who see no way out might not actively seek help and these points to a need for long-term and regular follow-ups. Future research can explore ways to support family members at these key times. Implications for rehabilitation Health professionals need to facilitate the process of fostering hope in family members to set realistic expectations of return to driving and the duration of driving disruption. It is necessary to follow-up with family members even years after ABI as the issue of driving disruption could escalate to be a crisis and family members might not actively seek help. Health professionals can consider both practical support for facilitating transport and emotional support when addressing the issue of driving disruption with family members.
Biology education relies heavily on visual representations, many of which contain
arrow symbols. Many different styles of arrows are used, with no correlation to their
underlying meaning. This creates a problem for students, who struggle to interpret
and learn from figures containing arrows in biology.
Assessment of human movement performance in activities of daily living (ADL) is a key component in clinical and rehabilitation settings. Motion capture technology is an effective method for objective assessment of human movement. Existing databases capture human movement and ADL performance primarily in the Western population, and there are no Asian databases to date. This is despite the fact that Asian anthropometrics influence movement kinematics and kinetics. This paper details the protocol in the first phase of the largest Asian normative human movement database. Data collection has commenced, and this paper reports 10 healthy participants. Twelve tasks were performed and data was collected using Qualisys motion capture system, force plates and instrumented table and chair. In phase two, human movement of individuals with stroke and knee osteoarthritis will be captured. This can have great potential for benchmarking with the normative human movement captured in phase one and predicting recovery and progression of movement for patients. With individualised progression, it will offer the development of personalised therapy protocols in rehabilitation.
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