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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Bukhari-Parlakturk, N; Lutz, MW; Al-Khalidi, HR; Unnithan, S; Wang, JE-H; Scott, B; Termsarasab, P; Appelbaum, LG; Calakos, N

Published Date

  • January 2023

Published In

Volume / Issue

  • 38 / 1

Start / End Page

  • 123 - 132

PubMed ID

  • 36226903

Pubmed Central ID

  • PMC9851940

Electronic International Standard Serial Number (EISSN)

  • 1531-8257

Digital Object Identifier (DOI)

  • 10.1002/mds.29237


  • eng

Conference Location

  • United States