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Artificial Intelligence-Based Computerized Digit Vigilance Test in Community-Dwelling Older Adults: Development and

Gong-Hong Lin¹, Dorothy Bai², Yi-Jing Huang^3,4

¹International Ph.D. Program in Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei, Taiwan.

JMIR Medical Informatics

|December 2, 2025

Summary

This summary is machine-generated.

This study introduces an AI-Computerized Digit Vigilance Test (AI-CDVT) for older adults, enhancing sustained attention assessment. The AI-CDVT shows good validity and reliability, offering a more comprehensive evaluation than traditional methods.

Keywords:

age-friendly artificial intelligence assessment attention cognition

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Area of Science:

Gerontology
Cognitive Neuroscience
Artificial Intelligence

Background:

The standard Computerized Digit Vigilance Test (CDVT) measures sustained attention via reaction time and accuracy.
However, it omits critical attentional indicators like eye blinks, yawns, and head/eye movements.
This limitation may result in an incomplete assessment of sustained attention.

Purpose of the Study:

To develop an artificial intelligence (AI)-based Computerized Digit Vigilance Test (AI-CDVT) tailored for older adults.
To enhance the assessment of sustained attention by incorporating additional behavioral features.
To validate the AI-CDVT's performance in a geriatric population.

Main Methods:

Video recordings of participants undergoing the CDVT were analyzed using OpenFace AI software to extract facial and head movement data.
An AI-based scoring model was developed utilizing the Extreme Gradient Boosting regressor.
The AI-CDVT's validity was assessed against established cognitive tests (MoCA, SCW, CTT), and its test-retest reliability was determined using intraclass correlation coefficients (ICCs).

Main Results:

The AI-CDVT demonstrated moderate to good correlations with established cognitive measures: Pearson r values ranged from -0.42 to 0.61.
The test-retest reliability of the AI-CDVT was found to be good, with an intraclass correlation coefficient (ICC) of 0.78.
The study included 153 participants, providing a robust sample for analysis.

Conclusions:

An AI-CDVT was successfully developed, integrating AI-driven analysis of video-recorded behaviors for a comprehensive attention score.
The AI-CDVT exhibits promising validity and test-retest reliability for assessing sustained attention in older adults.
This AI-enhanced tool offers a more complete and accurate measure of sustained attention compared to traditional methods.