Muscle strengthening exercises consistently demonstrate improvements in the pain and function of adults with knee osteoarthritis, but individual response rates can vary greatly. Identifying individuals who are more likely to respond is important in developing more efficient rehabilitation programs for knee osteoarthritis. Therefore, the overall goal of this thesis was to identify responders to exercise with a conventional motion capture system and translate these findings into a clinically accessible wearable sensor system.
It was found that a conventional motion capture system, in combination with patient-reported outcome measures (e.g., function) collected at the baseline of an exercise intervention can successfully predict responders to treatment with greater than 85% accuracy (chapter three). To translate these findings to the clinical setting, more accessible wearable sensors (e.g., accelerometers) were examined in the remaining chapters.
Chapter four found that while a single sensor at the lower back could subgroup some gait patterns, it was not sensitive enough to separate other, more similar, gait patterns. Therefore, the reliability of using multiple wearable sensors was examined in chapter five. The lower back, thigh, shank, and foot were all found to be reliable sensor locations for gait analysis and therefore suitable in the final study as potential predictors of response.
Finally, chapter six found that a unique combination of wearable sensor data and patient reported outcome measures could successfully identify responders to an exercise intervention with similar accuracy to the conventional motion capture system. Further, the best limited set of sensors included only the back and thigh. Therefore, these findings suggest the potential development of a simplified two sensor system that can provide clinicians with an efficient and relatively unobtrusive way to use to optimize treatment.