Lost in Scheduling? A Reliable Tool for Detecting Subtle Cognitive Decline in Mild Cognitive Impairment and Mild Alzheimer's Disease.

PURPOSE: Neurodegenerative diseases, notably Alzheimer's disease (AD), present a significant public health challenge. This study aims to evaluate the effectiveness of the Ecological Assessment Battery for Numbers (EABN) in detecting subtle cognitive decline in mild cognitive impairment (MCI) and mild AD, focusing on everyday life mathematical situations.

METHOD: This cross-sectional study involved 66 participants (21 mild AD, 23 MCI, and 22 controls). Clinical assessments included the Mini-Mental State Examination (MMSE), the EABN, and the Numerical Activities of Daily Living questionnaire. Statistical analyses utilized nonparametric tests, Spearman rank correlation, and receiver operating characteristic curve analyses.

RESULTS

The EABN demonstrated an area under the curve (AUC) of 0.83 (95% confidence interval [0.73, 0.92]) in distinguishing patients and controls. Patients with pathologically high EABN scores were significantly older, and a positive correlation was observed between EABN and MMSE scores (r = .30; p = .04). The Appointment subtest within EABN emerged as the most discriminative (AUC = 0.86). Among MCI patients with pathological EABN scores, 70% progressed to AD or amyloid angiopathy.

CONCLUSIONS

This study reveals early manifestation of mathematical cognition impairments in cognitive decline, even before patient-reported complaints. The EABN, a practical tool for ecological assessment, could aid in early diagnosis and guide interventions. Integrating mathematical cognition assessment into routine care aligns with recommendations for cognitive rehabilitation in MCI patients, potentially preventing autonomy loss. These findings underscore the significance of a comprehensive approach to patient management, incorporating ecological assessments for nuanced diagnostics and early intervention in neurodegenerative diseases.