Objective Older adults are at high risk of falls and this problem calls for efficient and scalable interventions. This study investigated whether a motion capture system paired with balance training exergaming software is a feasible strategy to deliver therapeutic exercise to older adults in an aged care facility. Methods This study analyzed data from a quality improvement rehabilitation initiative. Two convenience samples of older adults were included: a usual care group ( n = 12), admitted to a rehabilitation hospital and receiving standard in-patient therapy 5×/week and the Evolv group ( n = 12), admitted to an aged care facility, prescribed exergaming 3×/week. All participants performed 30-minute exercise sessions based on a fall prevention program over 3 months. The Short Physical Performance Battery (SPPB) and Tinetti Performance Oriented Mobility Assessment test were administered pre- and post-treatment. Results No adverse events were recorded during the interventions. Mean SPPB increase for Evolv participants was 2.25 ± 1.35 ( p < .001, CI for mean = 1.39 to 3.11, d = 1.66), compared with a non-significant change in the usual care group (mean increase = 2.25 ± 3.82, p = .066, CI for mean = −0.18 to 4.68, d = 0.59). Tinetti improvement was significant for the individuals receiving usual care (3.83 ± 2.82, p = .012, CI for mean = 1.01 to 6.66, d = 0.86) but there were no significant between-group differences in outcomes. Conclusions Exergaming with the Evolv system for balance and strength training may be a feasible strategy to improve physical function for older adults recovering in an aged care facility.
ObjectiveSelf-report tools are recommended in research and clinical practice to capture individual perceptions regarding health status; however, only modest correlations are found with performance-based results. The Lower Extremity Functional Scale (LEFS) is one well-validated measure of impairment affecting physical activities that has been compared with objective tests. More recently, mobile gait assessment software can provide comprehensive motion tracking output from ecologically valid environments, but how this data relates to subjective scales is unknown. Therefore, the association between the LEFS and walking variables remotely collected by a smartphone was explored.MethodsProprietary algorithms extracted spatiotemporal parameters detected by a standard integrated inertial measurement unit from 132 subjects enrolled in physical therapy for orthopedic or neurological rehabilitation. Users initiated ambulation recordings and completed questionnaires through the OneStep digital platform. Discrete categories were created based on LEFS score cut-offs and Analysis of Variance was applied to estimate the difference in gait metrics across functional groups (Low-Medium-High).ResultsThe main finding of this cross-sectional retrospective study is that remotely-collected biomechanical walking data are significantly associated with individuals' self-evaluated function as defined by LEFS categorization (n = 132) and many variables differ between groups. Velocity was found to have the strongest effect size.DiscussionWhen patients are classified according to subjective mobility level, there are significant differences in quantitative measures of ambulation analyzed with smartphone-based technology. Capturing real-time information about movement is important to obtain accurate impressions of how individuals perform in daily life while understanding the relationship between enacted activity and relevant clinical outcomes.
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