Searching for the Ideal Selection of Cognitive Screening Tasks for Early Detection of Cognitive Decline in Older Individuals With Low Literacy
Year: 2026
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Tests for detecting neurocognitive disorders may not be suitable for individuals with low educational levels. This study presents a battery of cognitive tests and tasks to improve the detection of cognitive decline in older adults with different literacy levels. METHOD: 78 older adults attending primary care services were classified in four groups based on their educational level (low versus high) and cognitive status (healthy versus decline). The evaluation included a selection of cognitive tests and tasks administered in two orders to each half of the sample to reduce sequence of administration bias. RESULTS: Most tests (MMSE, Stroop, Five Digit Test, and BNT-15, as well as many individual tasks) could not differentiate properly between low-educated healthy individuals and highly educated individuals with decline, with the latter even showing better performance despite the decline in some selected tasks. The Phototest emerged as the only effective tool where healthy individuals with low education outperformed highly educated individuals with cognitive decline. A separate comparison of low versus high education groups (healthy versus decline) raised the need for differentiated tasks, based on the education level, to specifically discriminate between healthy individuals and individuals with decline. CONCLUSIONS: Educational level (EL) significantly impacts cognitive performance. High EL individuals show better results and even compensate for cognitive decline when screened with a range of widely used cognitive tasks. Tasks must be adapted to EL to avoid biases and improve accuracy in detecting cognitive decline. Careful selection of tools based on EL is recommended to optimise cognitive assessment.
Source: Psychogeriatrics
DOI: 10.1111/psyg.70225
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,750
OpenAlex ID: W7221503534
