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首页> 外文期刊>Journal of Parkinson’s disease. >Objective motion sensor assessment highly correlated with scores of global levodopa-induced dyskinesia in Parkinson's disease.
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Objective motion sensor assessment highly correlated with scores of global levodopa-induced dyskinesia in Parkinson's disease.

机译：客观运动传感器评估与帕金森病的全球左佳豚鼠诱导的止吐剂量高度相关。

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Chronic use of medication for treating Parkinson's disease (PD) can give rise to peak-dose dyskinesia. Adjustments in medication often sacrifice control of motor symptoms, and thus balancing this trade-off poses a significant challenge for disease management.To determine whether a wrist-worn motion sensor unit could be used to ascertain global dyskinesia severity over a levodopa dose cycle and to develop a severity scoring algorithm highly correlated with clinician ratings.Fifteen individuals with PD were instrumented with a wrist-worn motion sensor unit, and data were collected with arms in resting and extended positions once every hour for three hours after taking a levodopa dose. Two neurologists blinded to treatment status viewed subject videos and rated global and upper extremity dyskinesia severity based on the modified Abnormal Involuntary Movement Scale (mAIMS). Linear regression models were developed using kinematic features extracted from motion sensor data and extremity, global, or combined (average of extremity and global) mAIMS scores.Dyskinesia occurring during a levodopa dose cycle was successfully measured using a wrist-worn sensor. The logarithm of the power spectrum area between 0.3-3 Hz and the combined clinician scores resulted in the best model performance, with a correlation coefficient between clinician and model scores of 0.81 and root mean square error of 0.55, both averaged across the arms resting and extended postures.One sensor unit worn on either hand can effectively predict global dyskinesia severity during the arms resting or extended positions.
机译：慢性使用药物治疗帕金森病（PD）可以产生峰值剂量障碍。药物调整往往牺牲了对电机症状的控制，因此平衡了这种权衡对疾病管理构成了重大挑战。要确定腕带磨损运动传感器单元是否可用于在左旋多巴剂量周期上确定全球性持久性严重程度。开发与临床医生评级高度相关的严重性评分算法。用腕带的运动传感器单元用PD的仪器用PD的十亿个，并且在服用Levodopa剂量后每小时在一次休息和伸展位置时收集数据。两位神经根学家都蒙蔽了治疗状态，根据改进的异常非自愿运动规模（MAIMS），观看了主题视频和额定的全球和上肢止咳肾上腺症严重程度。使用从运动传感器数据提取的运动学特征和肢体，全局或肢体平均值（极端和全球）的运动分数来开发线性回归模型。使用手腕磨损的传感器成功测量在左旋多巴剂量周期中发生的yyskinesia。功率谱面积为0.3-3 Hz和组合的临床医生得分导致最佳的模型性能，临床医生与型号之间的相关系数0.81，均为0.55的根均方误差0.55，两者在双臂休息时平均平均扩展姿势。在任何一种手上佩戴的传感器单元可以有效地预测在手臂休息或扩展位置期间的全局障碍程度严重程度。

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