Predictive Effect of Handwriting Impairments and Adaptive Function on Autism Diagnosis

Background: Children with Autism Spectrum Disorder (ASD) frequently exhibit impairments in handwritten letter formation. Why are these deficits potentially relevant in the current age of digital literacies? Research suggests that writing letters by hand helps typically developing children learn to build stable representations in memory and conceptualize information. Children with ASD often exhibit deficits in conceptual adaptive behavior relative to their cognitive skill level, and these deficits may provide important clinical prognostic information. Thus, investigating the interplay of handwriting and conceptual adaptive skills may benefit researchers and clinicians in assessing autistic symptom severity.

Objectives: To determine if handwriting letter formation moderates the relationship between adaptive functioning and ASD diagnosis.

Methods: Participants included 159 children aged 8-16 years (85 ASD; 119 male; M_age=10.43). Three conditions of the Minnesota Handwriting Assessment (MHA) were completed on a digital tablet: participants were presented with one sentence that would be copied (copy), traced (trace), and traced as quickly as possible (fast-trace). Handwriting data was analyzed using Large Deformation Diffeomorphic Metric Mapping (LDDMM) and a letter-form score was given to quantify the participant’s deviation from a template for each condition. Higher scores indicate poor performance. Caregivers of participants completed the Adaptive Behavior Assessment System, Second Edition (ABAS-II). The Conceptual domain of the ABAS-II (ABAS-c) was used for analysis, consisting of three sub-categories: communication, functional academics, and self-direction. Lower scores indicate poor adaptive functioning.

A multiple regression analysis was used to determine how each MHA condition predicted diagnosis. The best predictor was then used in a two-step moderated regression analysis to first examine the effect of the ABAS-c and this predictor separately, and then to measure the combined effect of those predictors on diagnosis.

Results: Multiple regression indicated that, among the three handwriting conditions, fast-trace (FT) letter-form score was the best predictor of ASD diagnosis (see Table 1). A regression model including ABAS-c and FT letter-form scores was significant, with the ABAS-c being a slightly better predictor of ASD diagnosis than FT (see Table 2). Given these results, a moderated regression was used to determine the effect of FT letter-form on the relationship between adaptive functioning and ASD. We observed that the combined effect (i.e, product) of FT letter-form and ABAS-c accounted for significantly more variance in ASD diagnosis than just ABAS-c or FT letter-form alone (see Table 2), suggesting that FT letter-formation moderated the relationship between adaptive functioning and ASD.

Conclusions: Children with ASD struggle with letter formation even when the visual-spatial transformation demands of printing are minimized, as in the FT condition. The moderation analysis further suggests that considering poor adaptive functioning and poor FT letter-formation together was more informative of ASD diagnosis than considering only adaptive functioning or FT letter-formation alone. Therefore, it may be useful to collect handwriting measures as part of an ASD diagnostic evaluation. Handwriting assessments are easy and quick to administer, and as opposed to most parent-report adaptive function measures, are direct measures from the child. Thus, they could provide for a more complete diagnostic picture.