The objective of this theme is to develop cost-efficient community mobility interventions that optimize outcomes for mobility subgroups of patients with ABI. This will allow us to customize community environment mobility training by identifying, on a continuous basis, specific rehabilitation strategies and interventions that patient subgroups benefit from most.
Moving around in community environments requires the skills to cope with multiple simultaneous dimensions. In the context of this activity, we will develop and test individually-tailored interventions grounded in best evidence in community ambulation, principles of motor learning and participatory action research with end/knowledge-users.
Researchers involved:
Recent research suggests that accounting for the time spent in specific physical and occupational therapy activities, above and beyond patients’ characteristics, has the potential to enhance the predictive value of models explaining rehabilitation outcomes after ABI. Capturing accurate and comprehensive intervention-level data at different times within the continuum of care allows us to identify the mediating effects of specific rehabilitation interventions to the recovery of mobility in different patient subgroups. We will compare how the intervention mapping applies to VR, gait retraining, and community based interventions for mobility and extend to persons with ABI.
Researchers involved:
In this activity we will develop and evaluate the impact of a decision support module, Rehabilitation Application – Intervention Selection Enabler (RAISE), on clinician and patient clinical decision making in terms of mobility and practices. Using the predictive models we will build RAISE for clinicians and conduct the first-ever implementation studies and randomized trials on the acceptability and effectiveness of RAISE for selection of mobility retraining strategies.
Researchers involved:
Biomedical Research and Informatics Living Laboratory
for Innovative Advances of New Technologies
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