Suitability of Automated Writing Measures for Clinical Trial Outcome in Writer's Cramp.

Abstract

Background

Writer's cramp (WC) dystonia is a rare disease that causes abnormal postures during the writing task. Successful research studies for WC and other forms of dystonia are contingent on identifying sensitive and specific measures that relate to the clinical syndrome and achieve a realistic sample size to power research studies for a rare disease. Although prior studies have used writing kinematics, their diagnostic performance remains unclear.

Objective

This study aimed to evaluate the diagnostic performance of automated measures that distinguish subjects with WC from healthy volunteers.

Methods

A total of 21 subjects with WC and 22 healthy volunteers performed a sentence-copying assessment on a digital tablet using kinematic and hand recognition softwares. The sensitivity and specificity of automated measures were calculated using a logistic regression model. Power analysis was performed for two clinical research designs using these measures. The test and retest reliability of select automated measures was compared across repeat sentence-copying assessments. Lastly, a correlational analysis with subject- and clinician-rated outcomes was performed to understand the clinical meaning of automated measures.

Results

Of the 23 measures analyzed, the measures of word legibility and peak accelerations distinguished subjects with WC from healthy volunteers with high sensitivity and specificity and demonstrated smaller sample sizes suitable for rare disease studies, and the kinematic measures showed high reliability across repeat visits, while both word legibility and peak accelerations measures showed significant correlations with the subject- and clinician-rated outcomes.

Conclusions

Novel automated measures that capture key aspects of the disease and are suitable for use in clinical research studies of WC dystonia were identified. © 2022 International Parkinson and Movement Disorder Society.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1002/mds.29237

Publication Info

Bukhari-Parlakturk, Noreen, Michael W Lutz, Hussein R Al-Khalidi, Shakthi Unnithan, Joyce En-Hua Wang, Burton Scott, Pichet Termsarasab, Lawrence G Appelbaum, et al. (2023). Suitability of Automated Writing Measures for Clinical Trial Outcome in Writer's Cramp. Movement disorders : official journal of the Movement Disorder Society, 38(1). pp. 123–132. 10.1002/mds.29237 Retrieved from https://hdl.handle.net/10161/28285.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Bukhari-Parlakturk

Noreen Bukhari-Parlakturk

Assistant Professor of Neurology

I have a long standing interest in developing disease-modifying therapies for movement disorders, a major unmet clinical need. I work at the interface of neuroscience and neurology to apply mechanistic understanding of neurological disease to develop targeted neuromodulatory therapies and in the process further disease mechanisms and medical therapy.

Lutz

Michael William Lutz

Professor in Neurology

Developing and using computational biology methods to understand the genetic basis of disease with a focus on Alzheimer’s Disease.   Recent work has focused on identification and validation of clinically-relevant biomarkers for Alzheimer’s disease and Alzheimer’s disease with Lewy bodies.


Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.