Who Gets to Interpret the Workout? User Tensions with AI-Generated Fitness Feedback

2026-04-26

AI Summary

This study investigates interpretive conflicts and experiential tensions arising when users interact with generative AI feedback on fitness tracking platforms. Drawing on 297 Reddit threads and 5,692 associated comments, the research combines qualitative content analysis with large-scale text mining to systematically identify four core tensions for the first time: misaligned evaluative logics, disrupted narrative continuity, insufficient emotional attunement, and neglect of individual differences. Findings reveal that users consistently resist standardized AI interpretations that disregard their specific contexts, affective states, and training histories. The study underscores the necessity of preserving user interpretive openness and agency in self-tracking practices, offering critical theoretical and practical insights for designing human-centered AI systems in fitness contexts.

Technology Category

• Humans and AI: User Experience and Usability
• Knowledge Representation and Reasoning: Preferences
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Fitness tracking platforms increasingly integrate generative AI to interpret activity data, such as Strava's Athlete Intelligence. These integrations raise questions about how athletes engage with AI-supported fitness self-tracking. We analyzed 297 Reddit threads and 5,692 comments from r/Strava following the company's launch of AI features to examine user reactions to AI-generated fitness feedback. Our findings revealed four recurring tensions: (1) numerical evaluation versus contextual understanding; (2) isolated session summaries versus ongoing training narratives; (3) a fixed AI tone versus diverse emotional states; and (4) a single AI voice versus different athletic types. Across these tensions, users resisted AI feedback that constrained interpretations of their own lived experiences. These findings shed light on the implicit challenges of integrating AI into self-tracking platforms. We conclude with implications for the design of AI-supported self-tracking systems that preserve interpretive openness and user agency.

Problem

• AI-generated feedback
• fitness self-tracking
• user agency
• interpretive tension
• athlete experience

Innovation

• generative AI
• fitness self-tracking
• user agency
• interpretive openness
• human-AI interaction