Abstract
The One Card Learning Test (OCL80) from the Cogstate Brief Battery—a digital cognitive test used both in-person and remotely in clinical trials and in healthcare contexts to inform health decisions—has shown high sensitivity to changes in memory in early Alzheimer's disease (AD). However, recent studies suggest that OCL sensitivity to memory impairment in symptomatic AD is not as strong as that for other standardized assessments of memory. This study aimed to improve the sensitivity of the OCL80 to AD-related memory impairment by reducing the test difficultly (i.e., OCL48). Experiment 1 showed performance in healthy adults improved on the OCL48 while the pattern separation operations that constrain performance on the OCL80 were retained. Experiment 2 showed repeated administration of the OCL48 at short retest intervals did not induce ceiling or practice effects. Experiment 3 showed that the sensitivity of the OCL48 to AD-related memory impairment (Glass's Δ = 3.11) was much greater than the sensitivity of the OCL80 (Glass's Δ = 1.94). Experiment 4 used data from a large group of cognitively normal older adults to calibrate performance scores between the OCL80 and OCL48 using equipercentile equating. Together these results showed the OCL48 to be a valid and reliable test of learning with greater sensitivity to memory impairment in AD than the OCL80.
Highlights
The importance of digital technology to decision-making in the field of the neuropsychology of Alzheimer’s disease (AD) is growing rapidly [1,2,3]
This study investigated in cognitively normal (CN) adults whether with reduced difficulty, performance on the OCL would improve while retaining its dependence on pattern separation memory operations
The OCL is a digital tool that has been used extensively to measure change in memory but for which required further optimization to improve its sensitivity to memory impairment in AD
Summary
The importance of digital technology to decision-making in the field of the neuropsychology of Alzheimer’s disease (AD) is growing rapidly [1,2,3]. Such tests make use of computer software to analyze in real time, performance data in order to adjust instructional and training procedures so individuals understand and adhere to the rules and requirements of the tests [7, 8] Such refinement has allowed one of these digital tools, the Cogstate Brief Battery (CBB), to extend aspects of neuropsychological decision-making from one-on-one clinical interviews [9, 10], to supervised settings in large groups [11], high frequency repeated testing [12, 13] and pre-interview triage [14], as well as wide-scale unsupervised internet-based cognitive assessment [15,16,17]. Understanding such cases, as well as the methods and solutions developed for improvement, should contribute to the knowledge base concerning the optimal design characteristics for novel digital assessments tools for application in AD contexts
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