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Motor Assessment With the STEGA iPad App to Measure Handwriting in Children.
Philip, Benjamin A; Li, Fuhai; Hawkins-Chernof, Elizabeth; Chen, Ling; Swamidass, Victoria; Zwir, Igor.
Afiliación
  • Philip BA; Benjamin A. Philip, PhD, is Assistant Professor, Program in Occupational Therapy, School of Medicine, Washington University in St. Louis, St. Louis, MO; bphilip@wustl.edu.
  • Li F; Fuhai Li, PhD, is Assistant Professor, Institute for Informatics, Department of Pediatrics, School of Medicine, Washington University in St. Louis, St. Louis, MO.
  • Hawkins-Chernof E; Elizabeth Hawkins-Chernof, OTD, is Assistant Professor, Program in Occupational Therapy, Maryville University, St. Louis, MO. At the time the research was conducted, Hawkins-Chernof was Lecturer, Program in Occupational Therapy, School of Medicine, Washington University in St. Louis, St. Louis, MO.
  • Chen L; Ling Chen, MD, PhD, is Assistant Professor, Division of Biostatistics, School of Medicine, Washington University in St. Louis, St. Louis, MO.
  • Swamidass V; Victoria Swamidass, MBA, is CEO, PlatformSTL, St. Louis, MO.
  • Zwir I; Igor Zwir, PhD, is Associate Professor, Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain; Associate Professor, Department of Psychiatry, School of Medicine, Washington University in St. Louis, St. Louis, MO; and Associate Professor, Institute of Neuro
Am J Occup Ther ; 77(3)2023 May 01.
Article en En | MEDLINE | ID: mdl-37310748
ABSTRACT
IMPORTANCE Handwriting and the fine motor control (hand and fingers) underlying it are key indicators of numerous motor disorders, especially among children. However, current assessment methods are expensive, slow, and subjective, leading to a lack of knowledge about the relationship between handwriting and motor control.

OBJECTIVE:

To develop and validate the iPad precision drawing app Standardized Tracing Evaluation and Grapheme Assessment (STEGA) to enable rapid quantitative assessment of fine motor control and handwriting.

DESIGN:

Cross-sectional, single-arm observational study.

SETTING:

Academic research institution.

PARTICIPANTS:

Fifty-seven typically developing right-handed children ages 9 to 12 yr with knowledge of cursive. OUTCOMES AND

MEASURES:

Predicted quality, measured as the correlation between handwriting letter legibility (Evaluation Tool of Children's Handwriting-Cursive [ETCH-C]) and predicted legibility (calculated from STEGA's 120 Hz, nine-variable data).

RESULTS:

STEGA successfully predicted handwriting (r2 = .437, p < .001) using a support vector regression method. Angular error was the most important aspect of STEGA performance. STEGA was much faster to administer than the ETCH-C (M = 6.7 min, SD = 1.3, versus M = 19.7 min, SD = 5.2). CONCLUSIONS AND RELEVANCE Assessment of motor control (and especially pen direction control) may provide a meaningful, objective way to assess handwriting. Future studies are needed to validate STEGA with a wider age range, but the initial results indicate that STEGA can provide the first rapid, quantitative, high-resolution, telehealth-capable assessment of the motor control that underpins handwriting. What This Article Adds The ability to control pen direction may be the most important motor skill for successful handwriting. STEGA may provide the first criterion standard for the fine motor control skills that underpin handwriting, suitable for rehabilitation research and practice.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Aplicaciones Móviles Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Am J Occup Ther Año: 2023 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Aplicaciones Móviles Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Am J Occup Ther Año: 2023 Tipo del documento: Article