This article introduces a study to facilitate the early detection of

preclinical Alzheimer’s disease using consumer-grade technologies.

It uses a smartphone app and a hybrid smartwatch to monitor cognition

over time by collecting a comprehensive set of technologyreported,

patient-reported, and performance-reported outcomes.

This project focuses on continuous and passive data collection,

highlighting the potential of ubiquitous devices to monitor cognitive

changes in naturalistic settings. This article details the tools,

study design, and metrics used to provide a scalable early mental

health monitoring solution. Preliminary findings suggest the effectiveness

of the data collection tools, demonstrating the promise of

ubiquitous computing in advancing mental health research and facilitating

early intervention strategies. The study has the potential

to significantly impact public health by providing a scalable and

non-invasive method for early detection of cognitive impairment,

thereby enabling timely interventions and better management of

Alzheimer’s disease. Additionally, it follows an open data policy,

ensuring that the collected data is available to other researchers for

further analysis and use, promoting transparency and collaboration

in the scientific community. This research underscores the need

for innovative approaches to develop reliable, scalable solutions for

early detection and monitoring of cognitive changes, contributing

significantly to the growing field of ubiquitous technologies for

mental health.