@Article{info:doi/10.2196/60041,
author="O'Laughlin, Kristine D
and Cheng, Britte Haugan
and Volponi, Joshua J
and Lorentz, John David A
and Obregon, Sophia A
and Younger, Jessica Wise
and Gazzaley, Adam
and Uncapher, Melina R
and Anguera, Joaquin A",
title="Validation of an Adaptive Assessment of Executive Functions (Adaptive Cognitive Evaluation-Explorer): Longitudinal and Cross-Sectional Analyses of Cognitive Task Performance",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="21",
volume="27",
pages="e60041",
keywords="executive functions; serious games; validation; computerized assessment; cognitive assessment",
abstract="Background: Executive functions (EFs) predict positive life outcomes and educational attainment. Consequently, it is imperative that our measures of EF constructs are both reliable and valid, with advantages for research tools that offer efficiency and remote capabilities. Objective: The objective of this study was to evaluate reliability and validity evidence for a mobile, adaptive measure of EFs called Adaptive Cognitive Evaluation-Explorer (ACE-X). Methods: We collected data from 2 cohorts of participants: a test-retest sample (N=246, age: mean 35.75, SD 11.74 y) to assess consistency of ACE-X task performance over repeated administrations and a validation sample involving child or adolescent (5436/6052, 89.82{\%}; age: mean 12.78, SD 1.60 years) and adult participants (484/6052, 8{\%}; age: mean 38.11, SD 14.96 years) to examine consistency of metrics, internal structures, and invariance of ACE-X task performance. A subset of participants (132/6052, 2.18{\%}; age: mean 37.04, SD 13.23 years) also completed a similar set of cognitive tasks using the Inquisit platform to assess the concurrent validity of ACE-X. Results: Intraclass correlation coefficients revealed most ACE-X tasks were moderately to very reliable across repeated assessments (intraclass correlation coefficient=0.45-0.79; P<.001). Moreover, in comparisons of internal structures of ACE-X task performance, model fit indices suggested that a network model based on partial correlations was the best fit to the data ($\chi$228=40.13; P=.06; comparative fit index=0.99; root mean square error of approximation=0.03, 90{\%} CI 0.00-0.05; Bayesian information criterion=5075.87; Akaike information criterion=4917.71) and that network edge weights are invariant across both younger and older adult participants. A Spinglass community detection algorithm suggested ACE-X task performance can be described by 3 communities (selected in 85{\%} of replications): set reconfiguration, attentional control, and interference resolution. On the other hand, Pearson correlation coefficients indicated mixed results for the concurrent validity comparisons between ACE-X and Inquisit (r=--.05-.62, P<.001-.76). Conclusions: These findings suggest that ACE-X is a reliable and valid research tool for understanding EFs and their relations to outcome measures. ",
issn="1438-8871",
doi="10.2196/60041",
url="https://www.jmir.org/2025/1/e60041",
url="https://doi.org/10.2196/60041"
}