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Mobile app-based unsupervised monitoring of cognition holds the promise to facilitate case-finding in clinical care and the individual detection of cognitive impairment in clinical and research settings. In the context of Alzheimer’s disease, this is particularly relevant for patients who seek medical advice due to memory complaints. Here we developed a Remote Digital Memory Composite (RDMC) score from an unsupervised remote and mobile cognitive assessment battery focused on episodic memory and long-term recall and assessed its construct validity, retest reliability and diagnostic accuracy when predicting MCI-grade impairment in a memory clinic sample and healthy controls. A total of 199 study participants were recruited from three cohorts and included as healthy controls (HC; n=97), individuals with subjective cognitive decline (SCD; n= 59) or patients with mild cognitive impairment (MCI; n=43). Study participants performed cognitive assessments in a fully remote and unsupervised setting via a smartphone app for cognitive testing. The derived RDMC score was highly correlated with the PACC5 score across participants and demonstrated good retest reliability. Diagnostic accuracy for discriminating memory impairment from no impairment was high (cross-validated AUC = 0.83, 95% CI [0.66, 0.99]) with a sensitivity of 0.82 and a specificity of 0.71. Our results indicate that unsupervised mobile cognitive assessments in a memory clinic setting using the implementation in the neotiv digital platform results in a good discrimination between cognitively impaired and unimpaired individuals. Thus, it is feasible to complement neuropsychological assessment of episodic memory with unsupervised and remote assessments on mobile devices. This contributes to recent efforts for implementing remotely performed episodic memory assessment for case-finding and monitoring in large research trials and clinical care.
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Berron et al. (2021) studied this question.