AI Solutionism and Digital Self-Tracking with Wearables

TL;DR

This paper critiques the growing automation in digital self-tracking and wearables, arguing that AI-driven feedback can erode user agency and capacity for independent interpretation. Grounded in qualitative observations from Oura Ring users, it identifies hurdles such as impaired information literacy and alienation from data, including evidence of learned helplessness. The authors advocate a balanced, user-centered design that favors slower, reflective approaches and a mix of manual and automated tracking, with attention to governance and value judgments in defining health. The work highlights the practical implications for designing digital health technologies that empower users rather than render them bystanders to their own data, with potential policy and design guidance for future interventions.

Abstract

Self-tracking technologies and wearables automate the process of data collection and insight generation with the support of artificial intelligence systems, with many emerging studies exploring ways to evolve these features further through large-language models (LLMs). This is done with the intent to reduce capture burden and the cognitive stress of health-based decision making, but studies neglect to consider how automation has stymied the agency and independent reflection of users of self-tracking interventions. In this position paper, we explore the consequences of automation in self-tracking by relating it to our experiences with investigating the Oura Ring, a sleep wearable, and navigate potential remedies.