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.