Abstract
Our research team previously developed an accelerometry-based device, which can be worn on the waist during daily life activities and detects the occurrence of dyskinesia in patients with Parkinson’s disease. The goal of this study was to analyze the magnitude of correlation between the numeric output of the device algorithm and the results of the Unified Dyskinesia Rating Scale (UDysRS), administered by a physician. In this study, 13 Parkinson’s patients, who were symptomatic with dyskinesias, were monitored with the device at home, for an average period of 30 minutes, while performing normal daily life activities. Each patient’s activity was simultaneously video-recorded. A physician was in charge of reviewing the recorded videos and determining the severity of dyskinesia through the UDysRS for every patient. The sensor device yielded only one value for dyskinesia severity, which was calculated by averaging the recorded device readings. Correlation between the results of physician’s assessment and the sensor output was analyzed with the Spearman’s correlation coefficient. The correlation coefficient between the sensor output and UDysRS result was 0.70 (CI 95%: 0.33–0.88; p = 0.01). Since the sensor was located on the waist, the correlation between the sensor output and the results of the trunk and legs scale sub-items was calculated: 0.91 (CI 95% 0.76–0.97: p < 0.001). The conclusion is that the magnitude of dyskinesia, as measured by the tested device, presented good correlation with that observed by a physician.
Highlights
Our research team previously developed an accelerometry-based device, which can be worn on the waist during daily life activities and detects the occurrence of dyskinesia in patients with Parkinson’s disease
This study aims at verifying or rejecting the hypothesis that the numerical output of the dyskinesia algorithm is correlated with the severity of dyskinesia, as measured with a clinical scale
Besides inertial signals and synchronized video records, the database included sociodemographic variables and variables related to the Parkinson’s disease for every included patient: year of diagnosis, severity measured with the Hoehn & Yahr scale (H&Y)[15], therapeutic schedule, Freezing of Gait Questionnaire (FOG-Q) score[16] and scores of the Unified Parkinson’s Disease Rating Scale (UPDRS) on and off state[17]
Summary
Our research team previously developed an accelerometry-based device, which can be worn on the waist during daily life activities and detects the occurrence of dyskinesia in patients with Parkinson’s disease. The goal of this study was to analyze the magnitude of correlation between the numeric output of the device algorithm and the results of the Unified Dyskinesia Rating Scale (UDysRS), administered by a physician. Dyskinesias are a consequence of the dopaminergic treatment, and in many cases they can be improved by adjusting the therapeutic schedule This is difficult for physicians to do, as they are fluctuating symptoms, which appear and disappear throughout the daytime, with a hard-to-establish chronology. To obtain detailed information on the time sequence of these symptoms, physicians ask patients to keep written records of the times of the day when dyskinesias occur (patient diaries). Our research team has been developing a waist worn wearable monitor that detects several Parkinson’s symptoms and analyses their evolution over time
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