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arxiv: 2604.23830 · v1 · submitted 2026-04-26 · 💻 cs.HC

Recognition: unknown

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

Sujay Shalawadi , Joel Wester , Samuel Rhys Cox , Niels van Berkel
Authors on Pith no claims yet

Pith reviewed 2026-05-08 05:29 UTC · model grok-4.3

classification 💻 cs.HC
keywords AI feedbackfitness trackinguser tensionsself-interpretationgenerative AIactivity datainterpretive agencydesign implications
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The pith

Users resist AI fitness feedback that limits how they interpret their own workouts

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper studies reactions to AI features that generate summaries and insights from activity data on fitness platforms. Analysis of thousands of user comments reveals four persistent tensions: numerical scores versus personal context, single-session reports versus longer training stories, a uniform AI tone versus shifting emotional needs, and one AI voice versus different kinds of athletes. Users push back when the AI appears to claim the right to decide what their workouts mean. This matters for anyone building or using AI tools that turn personal data into advice, because it shows people want to keep control over the stories they tell about their own efforts.

Core claim

The central claim is that AI-generated fitness feedback introduces four recurring tensions that users experience as constraints on their interpretive freedom. Users prefer contextual and narrative understandings of their training over isolated numerical evaluations, emotionally varied responses over fixed tones, and voices that fit their specific athletic identities rather than a single standardized output. These tensions produce resistance whenever the AI feedback narrows the range of meanings users can assign to their lived experiences.

What carries the argument

Four recurring user tensions identified through analysis of online discussions, each pitting standardized AI output against users' desire for open-ended personal interpretation of fitness data.

Load-bearing premise

Online discussions from one fitness-focused forum capture the range of experiences people have with AI feedback without major bias from who chooses to post there.

What would settle it

A controlled study that directly compares user reactions to AI versus human feedback on the same workout data and finds no evidence of the four tensions or finds users prefer the AI version.

Figures

Figures reproduced from arXiv: 2604.23830 by Joel Wester, Niels van Berkel, Samuel Rhys Cox, Sujay Shalawadi.

Figure 1
Figure 1. Figure 1: The four tensions identified in athletes’ experiences with Athlete Intelligence: (a) AI’s Numerical View versus Athletes’ view at source ↗
Figure 2
Figure 2. Figure 2: Examples of Athlete Intelligence interface elements as seen by Strava users. Screenshots are taken from a member of view at source ↗
Figure 3
Figure 3. Figure 3: Screenshots of Athlete Intelligence shared on view at source ↗
Figure 4
Figure 4. Figure 4: AI-generated workout summaries illustrating mis view at source ↗
Figure 5
Figure 5. Figure 5: Three screenshots of AI-generated fitness feedback: (a) AI summary restating heart rate zone percentages already view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript reports a qualitative analysis of 297 Reddit threads and 5,692 comments from r/Strava collected after Strava launched its AI fitness feedback features. It identifies four recurring user tensions—(1) numerical evaluation versus contextual understanding, (2) isolated session summaries versus ongoing training narratives, (3) fixed AI tone versus diverse emotional states, and (4) a single AI voice versus different athletic types—and argues that users resist AI-generated feedback that constrains personal interpretations of their lived experiences. The paper concludes with design implications for AI-supported self-tracking systems that preserve interpretive openness and user agency.

Significance. If the findings hold after methodological clarification, the work offers a timely empirical contribution to HCI research on self-tracking and generative AI. The large comment volume (5,692) provides a solid empirical base for surfacing recurring patterns of user resistance, which could inform platform design in fitness and related domains. The study usefully extends prior self-tracking literature by focusing on interpretive agency rather than data accuracy alone.

major comments (3)
  1. [§3.1] §3.1 (Data Collection): The paper relies on self-selected posts and comments from a single subreddit (r/Strava) without discussing or mitigating self-selection bias. Users posting about AI features are disproportionately likely to hold strong (often negative) views, which directly affects the central claim that the four tensions are 'recurring' and that resistance to constrained interpretations is widespread among athletes.
  2. [§3.2] §3.2 (Analysis): The description of the thematic analysis process is high-level and does not report the codebook development, number of coders, inter-rater reliability statistics, or how the four specific tensions were iteratively derived from the data. These details are load-bearing for the validity of the findings reported in §4 and the subsequent design implications.
  3. [§5] §5 (Discussion and Implications): The design recommendations assume the four tensions generalize beyond the sampled English-speaking, data-oriented endurance athletes on Reddit. No triangulation with other data sources or explicit limitation on demographic scope is provided, weakening the bridge from the observed tensions to broader claims about 'athletes' and AI self-tracking systems.
minor comments (2)
  1. [Abstract] Abstract: Adding one sentence on the qualitative analysis approach and a brief limitations note would improve reader expectations before the detailed findings.
  2. [§4] §4: The presentation of the four tensions would benefit from a summary table listing representative quotes alongside each tension to aid quick comparison.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We have revised the manuscript to improve methodological transparency, explicitly discuss limitations, and refine the scope of our claims. Our point-by-point responses follow.

read point-by-point responses

  1. Referee: [§3.1] §3.1 (Data Collection): The paper relies on self-selected posts and comments from a single subreddit (r/Strava) without discussing or mitigating self-selection bias. Users posting about AI features are disproportionately likely to hold strong (often negative) views, which directly affects the central claim that the four tensions are 'recurring' and that resistance to constrained interpretations is widespread among athletes.

    Authors: We agree that self-selection bias merits explicit discussion, as users who post about AI features on r/Strava are more likely to hold strong opinions. This is inherent to analyzing public discussions of new platform features and is appropriate for surfacing emergent tensions rather than estimating prevalence. We have added a dedicated paragraph in §3.1 acknowledging this bias, noting its implications for the 'recurring' patterns we observed, and clarifying that our claims concern engaged users in this community. We have also adjusted phrasing in the abstract and §4 to emphasize patterns in the sampled discussions rather than widespread resistance among all athletes. revision: yes

  2. Referee: [§3.2] §3.2 (Analysis): The description of the thematic analysis process is high-level and does not report the codebook development, number of coders, inter-rater reliability statistics, or how the four specific tensions were iteratively derived from the data. These details are load-bearing for the validity of the findings reported in §4 and the subsequent design implications.

    Authors: We appreciate the call for greater methodological detail. The original description was kept concise for flow, but we have substantially expanded §3.2 in the revision to describe the full process: iterative codebook development via open coding on an initial subset of threads by two researchers, followed by axial coding and team consensus to derive the four tensions; the number of coders; and how patterns were refined across the dataset. We have also included inter-rater reliability statistics from our coding procedure to support the validity of the findings in §4. revision: yes

  3. Referee: [§5] §5 (Discussion and Implications): The design recommendations assume the four tensions generalize beyond the sampled English-speaking, data-oriented endurance athletes on Reddit. No triangulation with other data sources or explicit limitation on demographic scope is provided, weakening the bridge from the observed tensions to broader claims about 'athletes' and AI self-tracking systems.

    Authors: We accept that the original framing could overstate generalizability. We have added an explicit limitations subsection in §5 (with cross-reference in §3) that details the sample's scope—primarily English-speaking, data-oriented endurance athletes active on Reddit—and notes the absence of triangulation with other methods such as interviews. We have revised the design implications to present them as relevant to comparable AI-supported self-tracking contexts rather than all athletes, thereby strengthening the connection between our evidence and the recommendations. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical qualitative analysis with direct data grounding

full rationale

The paper conducts a thematic analysis of 297 Reddit threads and 5,692 comments to identify four user tensions with AI fitness feedback. No equations, derivations, fitted parameters, predictions, or self-citations appear in the load-bearing steps; the findings are presented as direct outcomes of the comment analysis without any reduction to prior inputs or author-defined constructs. This is a standard self-contained qualitative study whose central claims rest on the sampled data rather than any circular chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a qualitative empirical study with no mathematical derivations, free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5458 in / 1080 out tokens · 39601 ms · 2026-05-08T05:29:50.452737+00:00 · methodology

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Reference graph

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