Abstract
BackgroundAs eye tracking-based assessment of cognition becomes more widely used in older adults, particularly those at risk for dementia, reliable and scalable methods to collect high-quality data are required. Eye tracking-based cognitive tests that utilize device-embedded cameras have the potential to reach large numbers of people as a screening tool for preclinical cognitive decline. However, to fully validate this approach, more empirical evidence about the comparability of eyetracking-based paradigms to existing cognitive batteries is needed.ObjectiveUsing a population of clinically normal older adults, we examined the relationship between a 30-minute Visual Paired Comparison (VPC) recognition memory task and cognitive composite indices sensitive to a subtle decline in domains associated with Alzheimer disease. Additionally, the scoring accuracy between software used with a commercial grade eye tracking camera at 60 frames per second (FPS) and a manually scored procedure used with a laptop-embedded web camera (3 FPS) on the VPC task was compared, as well as the relationship between VPC task performance and domain-specific cognitive function.MethodsA group of 49 clinically normal older adults completed a 30-min VPC recognition memory task with simultaneous recording of eye movements by a commercial-grade eye-tracking camera and a laptop-embedded camera. Relationships between webcam VPC performance and the Preclinical Alzheimer Cognitive Composite (PACC) and National Institutes of Health Toolbox Cognitive Battery (NIHTB-CB) were examined. Inter-rater reliability for manually scored tests was analyzed using Krippendorff’s kappa formula, and we used Spearman’s Rho correlations to investigate the relationship between VPC performance scores with both cameras. We also examined the relationship between VPC performance with the device-embedded camera and domain-specific cognitive performance.ResultsModest relationships were seen between mean VPC novelty preference and the PACC (r=.39, P=.007) and NIHTB-CB (r=.35, P=.03) composite scores, and additional individual neurocognitive task scores including letter fluency (r=.33, P=.02), category fluency (r=.36, P=.01), and Trail Making Test A (–.40, P=.006). Robust relationships were observed between the 60 FPS eye tracker and 3 FPS webcam on both trial-level VPC novelty preference (r=.82, P<.001) and overall mean VPC novelty preference (r=.92 P<.001). Inter-rater agreement of manually scored web camera data was high (kappa=.84).ConclusionsIn a sample of clinically normal older adults, performance on a 30-minute VPC task correlated modestly with computerized and paper-pencil based cognitive composites that serve as preclinical Alzheimer disease cognitive indices. The strength of these relationships did not differ between camera devices. We suggest that using a device-embedded camera is a reliable and valid way to assess performance on VPC tasks accurately and that these tasks correlate with existing cognitive composites.
Highlights
Alzheimer disease (AD) and other forms of dementia, broadly characterized by declines in mental ability severe enough to interfere with daily life, pose serious challenges to patients, caregivers, and healthcare systems worldwide
Modest relationships were seen between mean Visual Paired Comparison (VPC) novelty preference and the Preclinical Alzheimer Cognitive Composite (PACC) (r=.39, P=.007) and NIHTB-CB (r=.35, P=.03) composite scores, and additional individual neurocognitive task scores including letter fluency (r=.33, P=.02), category fluency (r=.36, P=.01), and Trail Making Test A (–.40, P=.006)
Robust relationships were observed between the 60 frames per second (FPS) eye tracker and 3 FPS webcam on both trial-level VPC novelty preference (r=.82, P
Summary
Alzheimer disease (AD) and other forms of dementia, broadly characterized by declines in mental ability severe enough to interfere with daily life, pose serious challenges to patients, caregivers, and healthcare systems worldwide. Detection of AD during the preclinical phase has the potential to decrease medical and long-term care costs by as much as US $7 trillion in the US [2]. Detection of preclinical AD can enable people to seek treatment earlier, address modifiable risk factors, and potentially slow the progression of the disease, preserving cognitive function and reducing population health care costs [1,2]. As eye tracking-based assessment of cognition becomes more widely used in older adults, those at risk for dementia, reliable and scalable methods to collect high-quality data are required. Eye tracking-based cognitive tests that utilize device-embedded cameras have the potential to reach large numbers of people as a screening tool for preclinical cognitive decline. To fully validate this approach, more empirical evidence about the comparability of eyetracking-based paradigms to existing cognitive batteries is needed
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