Farzanehfar Parisa, Woodrow Holly, Horne Malcolm
Parkinson's Laboratory, Florey Institute of Neurosciences and Mental Health, Parkville, VIC, Australia.
Department of Clinical Neurosciences, St. Vincent's Hospital Fitzroy, Fitzroy, VIC, Australia.
Front Aging Neurosci. 2022 Mar 23;14:852992. doi: 10.3389/fnagi.2022.852992. eCollection 2022.
The aim was to examine the role of sensor measurement in identifying and managing fluctuations in bradykinesia of Parkinson's Disease.
Clinical scales and data from wearable sensors obtained before and after optimization of treatment from 107 participants who participated in a previous study was used. Fluctuators were identified by a levodopa response or wearing off in their sensor data and were subdivided according to whether the sensor's bradykinesia scores were in target range, representing acceptable bradykinesia for part of the dose (Controlled Fluctuator: = 22) or above target for the whole dose period (Uncontrolled Fluctuator; = 28). Uncontrolled Non-fluctuators ( = 24) were cases without a levodopa response or wearing-off and sensor bradykinesia scores above target throughout the day (un-controlled). Controlled Non-fluctuators ( = 33) were below target throughout the day (controlled) and used as a reference for good control (MDS-UPDRS III = 33 ± 8.6 and PDQ39 = 28 ± 18).
Treating Fluctuators significantly improved motor and quality of life scores. Converting fluctuators into Controlled Non-fluctuators significantly improved motor, non-motor and quality of life scores and a similar but less significant improvement was obtained by conversion to a Controlled Fluctuator. There was a significantly greater likelihood of achieving these changes when objective measurement was used to guide management.
The sensor's classification of fluctuators bore a relation to severity of clinical scores and treatment of fluctuation improved clinical scores. The sensor measurement aided in recognizing and removing fluctuations with treatment and resulted in better clinical scores, presumably by assisting therapeutic decisions.
旨在研究传感器测量在帕金森病运动迟缓波动的识别与管理中的作用。
使用了参与先前一项研究的107名参与者在治疗优化前后获得的临床量表和可穿戴传感器数据。通过左旋多巴反应或传感器数据中的药效减退来识别波动者，并根据传感器的运动迟缓评分是否在目标范围内进行细分，目标范围代表部分剂量下可接受的运动迟缓（可控波动者：n = 22）或整个剂量期高于目标范围（不可控波动者；n = 28）。不可控非波动者（n = 24）是指无左旋多巴反应或药效减退且全天传感器运动迟缓评分高于目标范围的病例（不可控）。可控非波动者（n = 33）全天评分低于目标范围（可控），用作良好控制的参考（MDS-UPDRS III = 33 ± 8.6，PDQ39 = 28 ± 18）。
治疗波动者可显著改善运动和生活质量评分。将波动者转变为可控非波动者可显著改善运动、非运动和生活质量评分，转变为可控波动者也有类似但不太显著的改善。当使用客观测量来指导管理时，实现这些变化的可能性显著更高。
传感器对波动者的分类与临床评分的严重程度相关，波动的治疗可改善临床评分。传感器测量有助于在治疗中识别和消除波动，并带来更好的临床评分，可能是通过辅助治疗决策实现的。
