Dyskinesias in Parkinson's disease (PD) patients are a common side effect of long-term dopaminergic therapy and are associated with motor dysfunctions, including gait and balance deficits. Although promising compounds have been developed to treat these symptoms, clinical trials have failed. This failure may, at least partly, be explained by the lack of objective and continuous assessment strategies. This study tested the clinical validity and ecological effect of an algorithm that detects and quantifies dyskinesias of the legs using a single ankle-worn sensor.Twenty-three PD patients (seven with leg dyskinesias) and 13 control subjects were investigated in the lab. Participants performed purposeful daily activity-like tasks while being video-taped. Clinical evaluation was performed using the leg dyskinesia item of the Unified Dyskinesia Rating Scale. The ecological effect of the developed algorithm was investigated in a multi-center, 12-week, home-based sub-study that included three patients with and seven without dyskinesias.In the lab-based sub-study, the sensor-based algorithm exhibited a specificity of 98%, a sensitivity of 85%, and an accuracy of 0.96 for the detection of dyskinesias and a correlation level of 0.61 (p 0.001) with the clinical severity score. In the home-based sub-study, all patients could be correctly classified regarding the presence or absence of leg dyskinesias, supporting the ecological relevance of the algorithm.This study provides evidence of clinical validity and ecological effect of an algorithm derived from a single sensor on the ankle for detecting leg dyskinesias in PD patients. These results should motivate the investigation of leg dyskinesias in larger studies using wearable sensors.
