Tremor rating scales and laboratory tools for assessing tremor

原发性震颤 评定量表 物理医学与康复 振幅 传感器 心理学 医学 声学 物理 量子力学 发展心理学
作者
Rodger J. Elble,William G. Ondo
出处
期刊:Journal of the Neurological Sciences [Elsevier]
卷期号:435: 120202-120202 被引量:16
标识
DOI:10.1016/j.jns.2022.120202
摘要

The purpose of this review is to characterize and compare validated clinical rating scales and transducers that are used in the clinical assessment of tremor disorders. Tremor is an involuntary oscillatory movement of a body part. Tremor can be characterized in terms of amplitude and frequency of oscillation, and these kinematic properties vary randomly and with activities of daily living. Clinical rating scales are most useful when performing a comprehensive assessment of tremor severity (amplitude), anatomical distribution, activation conditions, and impact on activities of daily living and quality of life. Motion transducers are often used in conjunction with surface electromyography to discern properties of tremor that are important diagnostically. Motion transducers are needed for an accurate determination of tremor frequency and for precise quantification of changes in amplitude and frequency over time. The precision and accuracy of motion transducers exceed that of all clinical rating scales. However, these advantages of transducers are mitigated by the considerable within-subject random variability in tremor amplitude, such that the smallest detectable statistically significant change in tremor amplitude is comparable for scales and transducers. Comprehensive anatomical and behavioral assessment of tremor with transducers is not clinically feasible. Transducers and scales are presently viewed as complementary methods of quantifying tremor amplitude. Transducer measures are logarithmically related to clinical ratings, as predicted by the Weber-Fechner law of psychophysics. This relationship must be considered when interpreting change in clinical ratings, produced by disease or treatment. This article is part of the Special Issue "Tremor" edited by Daniel D. Truong, Mark Hallett, and Aasef Shaikh.
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