RESUMO
OBJECTIVE: Determine the effectiveness of a medial off-loader brace with sensor monitoring capabilities and associated phone application in improving outcomes for individuals with knee osteoarthritis (OA). METHODS: Randomized clinical trial of participants with knee OA, aged 40-75 with two groups: 1) brace-only 2) brace+sensor (sensor providing walking time, knee range of motion and 7-day activity streak). Both groups received a prefabricated custom-fitted medial off-loader brace and 12-week self-guided exercise therapy program. Baseline and post-intervention assessments included subjective and objective outcomes. RESULTS: 60 participants were recruited (n = 30/group). The brace+sensor group demonstrated higher study retention(88.89%) compared to the brace-only group(73.33%). Significant improvement in KOOS knee pain and other KOOS sub-scores compared to baseline was observed for both groups. However, only the brace+sensor group improved beyond the established minimal clinically important difference for KOOS pain(11.31+/-13.87). KOOS ADL was also significantly improved in the brace+sensor group compared to brace only group(p = 0.049). Both treatment groups had significant improvement in functional outcomes(10 m walk, 5x sit-to-stand, p < 0.05). Only the brace+sensor group had significant improvements in the 6-minute walk test(p = 0.02) and reduction in participant weight(p = 0.01) at 12 weeks. CONCLUSION: Incorporating wearable technology in standard bracing for individuals with knee OA has potential in improving clinical outcomes.
RESUMO
Introduction: Myoelectric pattern recognition systems have shown promising control of upper limb powered prostheses and are now commercially available. These pattern recognition systems typically record from up to 8 muscle sites, whereas other control systems use two-site control. While previous offline studies have shown 8 or fewer sites to be optimal, real-time control was not evaluated. Methods: Six individuals with no limb absence and four individuals with a transradial amputation controlled a virtual upper limb prosthesis using pattern recognition control with 8 and 16 channels of EMG. Additionally, two of the individuals with a transradial amputation performed the Assessment for Capacity of Myoelectric Control (ACMC) with a multi-articulating hand and wrist prosthesis with the same channel count conditions. Results: Users had significant improvements in control when using 16 compared to 8 EMG channels including decreased classification error (p = 0.006), decreased completion time (p = 0.019), and increased path efficiency (p = 0.013) when controlling a virtual prosthesis. ACMC scores increased by more than three times the minimal detectable change from the 8 to the 16-channel condition. Discussion: The results of this study indicate that increasing EMG channel count beyond the clinical standard of 8 channels can benefit myoelectric pattern recognition users.