Back

A Meta-Analytical Review of Executive Function Skills in Adults who Stutter

Ofoe, L. C.; Ntourou, K.; Clifton, S.; Coalson, G. A.

2025-09-04 pathology
10.1101/2025.09.02.25334917 medRxiv
Show abstract

PurposeExecutive function has been identified as a potential area of vulnerability in individuals who stutter. The present study identified and analyzed the data across empirical studies of the executive function skills of adults who do (AWS) and do not stutter (AWNS). MethodElectronic databases, literature reviews, and reference sections of articles and dissertations were searched to identify candidate studies that examined behavioral measures of working memory, inhibition, and/or cognitive flexibility. A total of 39 studies met the eligibility criteria for this meta-analysis. A random-effects model was applied to estimate the pooled effect sizes (Hedges g) and 95% confidence intervals. ResultsAWS were significantly less accurate than AWNS on measures of working memory (Hedges g = - 0.41, p < .001), including nonword repetition (Hedges g = -.57, p < .001), forward digit span (Hedges g = -0.23, p = .02), backward digit span (Hedges g = -.38, p = .004) and operation span tasks (Hedges g = - .37, p = .018). AWS performed comparably to AWNS on inhibition measures (Hedges g = -0.10, p = .37). An insufficient number of published studies were available to conduct a meaningful analysis of cognitive flexibility. ConclusionsPresent findings suggest that AWS, as a group, exhibit weaknesses in one component of executive function - working memory - compared to AWNS. Additional research is necessary to determine potential differences in inhibition and cognitive flexibility in AWS.

Published in Journal of Speech, Language, and Hearing Research (predicted rank #1) · training set

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.