A mobile digital device proficiency performance test for cognitive clinical research

TL;DR

This paper introduces the MDAT, an open-source, performance-based mobile device proficiency test designed for cognitive research, validated on a diverse adult population, aiming to standardize digital assessments in neurology.

Contribution

It presents a novel, low-cost, reproducible mobile device proficiency test framework validated with a diverse sample, addressing a gap in digital cognitive assessment standardization.

Findings

The MDAT shows high internal consistency and content validity.

The test is minimally affected by general disability or health perceptions.

It is suitable for diverse adult populations across age and income levels.

Abstract

Mobile device proficiency is increasingly important for everyday living, including to deliver healthcare services. Human-device interactions represent a potential in cognitive neurology and aging research. Although traditional pen-and-paper evaluations serve as valuable tools within public health strategies for population-scale cognitive assessments, digital devices could amplify cognitive assessment. However, even person-centered studies often fail to incorporate measures of mobile device proficiency and research with digital mobile technology frequently neglects these evaluations. Besides that, cognitive screening, a fundamental part of brain health evaluation and a widely accepted strategy to identify high-risk individuals vulnerable to cognitive impairment and dementia, has research using digital devices for older adults in need for standardization. To address this shortfall, the DigiTAU collaborative and interdisciplinary project is creating refined methodological parameters for the investigation of digital biomarkers. With careful consideration of cognitive design elements, here we describe the open-source and performance-based Mobile Device Abilities Test (MDAT), a simple, low-cost, and reproductible open-sourced test framework. This result was achieved with a cross-sectional study population sample of 101 low and middle-income subjects aged 20 to 79 years old. Partial least squares structural equation modeling (PLS-SEM) was used to assess the measurement of the construct. It was possible to achieve a reliable method with internal consistency, good content validity related to digital competences, and that does not have much interference with auto-perceived global functional disability, health self-perception, and motor dexterity. Limitations for this method are discussed and paths to improve and establish better standards are highlighted.