ArticleJMIR medical informatics2025
Artificial Intelligence-Based Computerized Digit Vigilance Test in Community-Dwelling Older Adults: Development and Validation Study.
Article in JMIR medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Pilot Randomized, Double-Blind, Placebo-Controlled Parallel Group Trial Evaluating the Effect of 8 Week-Consumption of Guava Jelly Drink in Improving Cognition and Mental Well-Being in Working-Age Adults.Foods (Basel, Switzerland) · 2026Article
- TeamSTEPPS Teamwork and Nurses' Vigilance in Critical Care: A Cross-Sectional Correlational Study.Journal of nursing management · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The Computerized Digit Vigilance Test (CDVT) is a well-established measure of sustained attention. However, the CDVT only measures the total reaction time and response accuracy and fails to capture other crucial attentional features such as the eye blink rate, yawns, head movements, and eye movements. Omitting such features might provide an incomplete representative picture of sustained attention. Objective: This study aimed to develop an artificial intelligence (AI)-based Computerized Digit Vigilance Test (AI-CDVT) for older adults. Methods: Participants were assessed by the CDVT with video recordings capturing their head and face. The Montreal Cognitive Assessment (MoCA), Stroop Color Word Test (SCW), and Color Trails Test (CTT) were also administered. The AI-CDVT was developed in three steps: (1) retrieving attentional features using OpenFace AI software (CMU MultiComp Lab), (2) establishing an AI-based scoring model with the Extreme Gradient Boosting regressor, and (3) assessing the AI-CDVT's validity by Pearson r values and test-retest reliability by intraclass correlation coefficients (ICCs). Results: In total, 153 participants were included. Pearson r values of the AI-CDVT with the MoCA were -0.42, -0.31 with the SCW, and 0.46-0.61 with the CTT. The ICC of the AI-CDVT was 0.78. Conclusions: We developed an AI-CDVT, which leveraged AI to extract attentional features from video recordings and integrated them to generate a comprehensive attention score. Our findings demonstrated good validity and test-retest reliability for the AI-CDVT, suggesting its potential as a reliable and valid tool for assessing sustained attention in older adults.
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