Reliable detection and continuous monitoring of memory dysfunction in mild cognitive impairment and healthy aging through adaptive computational phenotyping.

With the rising prevalence of age-related memory impairments, efficiently detecting and monitoring decline is increasingly urgent. Unfortunately, traditional assessment methods fall short of these needs, as they typically require in-person administration and cannot be repeated frequently. Here, we demonstrate that remote identification and monitoring of abnormal memory function is possible by combining an online assessment platform with computational phenotyping, allowing repeatable, unsupervised remote observations from patients. Fifty-one well-characterized older individuals, including 24 patients with amnestic mild cognitive impairment and 27 age- and education-matched healthy controls, completed a series of longitudinal, unsupervised, remote weekly 8-minute online memory assessments for up to one year. Weekly test data were fit to a formal model of memory consolidation and forgetting, yielding an individualized index of memory function, the Seattle-Groningen Memory Assessment (SGMA) score. The SGMA score was found to be reliable, with a mean correlation of r = 0.70 across assessments. The score was also found to be stable across different study materials, and only barely affected by practice effects, which averaged to a 0.2% increase per assessment. Finally, the SGMA score was found to be diagnostic, being capable of detecting mild cognitive impairment with up to 87% accuracy. These findings show that model-based, adaptive assessments can support high-frequency, remote detection and scalable longitudinal monitoring of early memory decline, providing a new way to assess memory decline trajectories in healthy aging and dementia.