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A short digital eye-tracking assessment predicts cognitive status among adults

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Abstract

Current cognitive assessments suffer from limited scalability and high user burden. This study aimed to (1) examine the relationship between a brief eye-tracking-based visual paired-comparison (VPC) and gold standard cognitive assessments, (2) examine longitudinal stability of the VPC task, (3) determine the ability of the VPC task to differentiate between cognitively normal (CN) individuals and individuals with mild cognitive impairment (MCI). Fifty-five adults (n = 44 CN, n = 11 MCI; 56.4 ± 26.7 years) were tested on two occasions, separated by at least 14 days. Visit 1 included VPC, Montreal Cognitive Assessment (MoCA), Digit Symbol Coding test (DSC), and NIH Toolbox Cognitive Battery (NIHTB-CB). Visit 2 included VPC, DSC, NIHTB-CB, and dual-task (DT). Significant differences existed between baseline VPC scores for CN and MCI groups (p < .001). VPC scores remained stable over time in both groups (p < .05). Significant associations existed between VPC and MoCA (p < .01), DSC (p < .001), and various NIHTB-CB subtests at both time points. The VPC test significantly predicts cognitive outcomes (p < .05), with age and VPC being the only significant predictors. Additionally, area under the curve (receiver operator characteristic = 0.80) for VPC scores demonstrated good classification accuracy. VPC reliably predicted cognitive status while remaining stable over time and displayed significant associations with gold standard cognitive assessments. VPC is a less burdensome and more scalable assessment than traditional tests, enabling longitudinal monitoring of cognitive status in resource-limited environments.

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Authors and Affiliations

  1. University of Arkansas, 1 University of Arkansas, Fayetteville, AR, 72701, USA

    Joshua L. Gills, Jordan M. Glenn & Michelle Gray

  2. Neurotrack Technologies, Inc., Redwood City, CA, USA

    Nick T. Bott, Erica N. Madero & Jordan M. Glenn

Authors
  1. Joshua L. Gills
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  2. Nick T. Bott
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  3. Erica N. Madero
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  4. Jordan M. Glenn
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  5. Michelle Gray
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Correspondence to Michelle Gray.

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Gills, J.L., Bott, N.T., Madero, E.N. et al. A short digital eye-tracking assessment predicts cognitive status among adults. GeroScience 43, 297–308 (2021). https://doi.org/10.1007/s11357-020-00254-5

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