FACEmemory®, an Innovative Online Platform for Episodic Memory Pre-Screening: Findings from the First 3,000 Participants

Abstract

Background: The FACEmemory® online platform comprises a complex memory test and sociodemographic, medical, and family questions. This is the first study of a completely self-administered memory test with voice recognition, pre-tested in a memory clinic, sensitive to Alzheimer's disease, using information and communication technologies, and offered freely worldwide.

Objective: To investigate the demographic and clinical variables associated with the total FACEmemory score, and to identify distinct patterns of memory performance on FACEmemory.

Methods: Data from the first 3,000 subjects who completed the FACEmemory test were analyzed. Descriptive analyses were applied to demographic, FACEmemory, and medical and family variables; t-test and chi-square analyses were used to compare participants with preserved versus impaired performance on FACEmemory (cut-off = 32); multiple linear regression was used to identify variables that modulate FACEmemory performance; and machine learning techniques were applied to identify different memory patterns.

Results: Participants had a mean age of 50.57 years and 13.65 years of schooling; 64.07% were women, and 82.10% reported memory complaints with worries. The group with impaired FACEmemory performance (20.40%) was older, had less schooling, and had a higher prevalence of hypertension, diabetes, dyslipidemia, and family history of neurodegenerative disease than the group with preserved performance. Age, schooling, sex, country, and completion of the medical and family history questionnaire were associated with the FACEmemory score. Finally, machine learning techniques identified four patterns of FACEmemory performance: normal, dysexecutive, storage, and completely impaired.

Conclusions: FACEmemory is a promising tool for assessing memory in people with subjective memory complaints and for raising awareness about cognitive decline in the community.

Keywords: Alzheimer’s disease; community; computerized assessment; early detection; memory; memory impairment; new technologies; patient engagement; subjective memory complaints.

Fig. 1

UMAP [39] bidimensional projection of the variables used to perform the clustering. The intensity represents the probability of cluster membership given by the GMM.

Fig. 2

Four patterns of memory performance identified by machine learning analysis. LN1, names recalled in learning 1; LN2, names recalled in learning 2; LO1, occupations recalled in learning 1; LO2, occupations recalled in learning 2; RLN, names in long-term recall; RLO, occupations in long-term recall; REN, names correctly recognized; REO, occupations correctly recognized; FR, face recognition.