Remote identification of the severity of abnormalities of motor assessments of people with Parkinson’s disease by visual observation of accelerometry output

Published: 10 September 2024| Version 1 | DOI: 10.17632/d75z4db4hs.1
Contributors:
Abdelwahab Elshourbagy,
,
,
,
,

Description

Parkinson's disease is one of the most common neurodegenerative disorders, caused by the progressive deterioration of dopaminergic cells in the substantia nigra pars compacta. It is the second leading cause of death in the United States, following Alzheimer's disease. The diagnosis of Parkinson's disease primarily relies on physical and neurological examinations, supported by laboratory data and structured interviews. Motor assessments are typically performed through the visual observation of trained raters, who evaluate patients from a distance as they perform motor tasks (Goetz CG, et al. Mov Disord 2008). However, quantifying the severity of motor symptoms by the naked human eye presents significant challenges and limitations. The utilization of advanced technologies is considered the most reliable solution in the era of technology to address these challenges and provide more precise and objective assessments. To reduce the uncertainty in the motor assessment of people with Parkinson’s disease by visual observation from several feet away, we developed a low-cost, quantitative, continuous measurement of movements in the extremities of individuals with Parkinson’s disease (McKay GN, et al. MethodsX 2019). A low-cost, quantitative, continuous measurement of movements in the extremities of people with Parkinson's disease was conducted by trained raters on 10 individuals with Parkinson’s disease aged 58-80 (65.52 + 9.26 years). Representations of the output signals and their transforms (Harrigan TP, et al., Data Brief 2022) were evaluated by 35 trained raters. Signals and fast Fourier and continuous wavelet transforms were presented to trained raters, without clinical assessments, to be scored for halts, interruptions, amplitude decrements, and slowing. This scoring utilized a scheme (Hernandez ME, et al., MethodsX 2022) similar to the schemes used for rating clinical assessments based on visual observation (Goetz CG, et al. Mov Disord 2008; McKay GN, et al. MethodsX 2019). An online procedure allowed 35 trained raters to complete structured ratings, including halts, amplitude decrements, and slowing of the signals and transforms (Hernandez ME, et al., MethodsX 2022). The scores for movements with no, minimal, or mild impairments were more challenging to classify compared to those indicating moderate or worse impairments. This poster was presented at NYU Psychiatry Research Day, NYU Langone Health, New York, New York, USA, on October 25, 2023.

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Quantifying the severity of motor symptoms by the naked human eye presents significant challenges and limitations. The utilization of advanced technologies is considered the most reliable solution in the era of technology to address these challenges and provide more precise and objective assessments. To reduce the uncertainty in the motor assessment of people with Parkinson’s disease by visual observation from several feet away, we developed a low-cost, quantitative, continuous measurement of movements in the extremities of individuals with Parkinson’s disease (McKay GN, et al. MethodsX 2019). A low-cost, quantitative, continuous measurement of movements in the extremities of people with Parkinson's disease was conducted by trained raters on 10 individuals with Parkinson’s disease aged 58-80 (65.52 + 9.26 years). Representations of the output signals and their transforms (Harrigan TP, et al., Data Brief 2022) were evaluated by 35 trained raters. Signals and fast Fourier and continuous wavelet transforms were presented to trained raters, without clinical assessments, to be scored for halts, interruptions, amplitude decrements, and slowing. This scoring utilized a scheme (Hernandez ME, et al., MethodsX 2022) similar to the schemes used for rating clinical assessments based on visual observation (Goetz CG, et al. Mov Disord 2008; McKay GN, et al. MethodsX 2019). Score only abnormalities observed. If there are none, score 0. Read the descriptions before and during the scoring. Abnormalities to be scores are in three groups: a) interruptions, b) slowing, and c) amplitude reductions. Score each abnormality. a. Interruptions or freezing 1 to 2 interruptions, score 1a. 3 to 5 interruptions, score 2a. 5 or more interruptions or a freeze, a sustained absence of repetitions, score 3a. Worse, score 4 a. b) Slowing a) Minimal slowing, score 1b. b) Mild slowing, score 2b. c) Moderate slowing, score 3b. d) Worse slowing, score 4b c) Amplitude reductions a) End of sequence, score 1c. b) Middle of sequence, score 2c. c) Beginning of sequence, score 3c d) Worse, score 4c. If the individual cannot perform the desired movement and there is no or negligible evidence of any movement in any of the six panels of transforms, then an overall score of 4 is merited even if there is no score of 4a, 4b, or 4c. An online procedure allowed 35 trained raters to complete structured ratings, including halts, amplitude decrements, and slowing of the signals and transforms (Hernandez ME, et al., MethodsX 2022). The scores for movements with no, minimal, or mild impairments were more challenging to classify compared to those indicating moderate or worse impairments.

Institutions

New York University, University of Illinois at Urbana-Champaign, Johns Hopkins University, Misr University for Science and Technology, Zagazig University

Categories

Neuroscience, Clinical Neurology, Developmental Neuroscience, Fast Fourier Transform, Motion Analysis, Neurologic Finding, Accelerometer, Fourier Transform, Research Interview, Technology, Performance Rating Error, Continuous Wavelet Transform, Fourier Analysis, Motion Acquisition, Choice of Technology, Experimental Neurology, Semi-Structured Interview

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