arxiv: 2605.13261 · v1 · submitted 2026-05-13 · 💻 cs.HC · cs.AI

Recognition: no theorem link

"It became a self-fulfilling prophecy": How Lived Experiences are Entangled with AI Predictions in Menstrual Cycle Tracking Apps

Wendy Zhou , Pelin Karaturhan , Alexandra Weilenmann , Jichen Zhu

Authors on Pith no claims yet

Pith reviewed 2026-05-14 18:30 UTC · model grok-4.3

classification 💻 cs.HC cs.AI

keywords menstrual cycle tracking appsAI predictionshuman-AI entanglementlived experienceself-fulfilling prophecyuser interviewsdesign implicationsnon-normative users

0 comments

The pith

Users of menstrual cycle tracking apps interpret their own bodies and feelings through AI predictions, even when those predictions rest on incomplete or inaccurate logs.

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

Menstrual cycle tracking apps increasingly offer AI forecasts of periods, ovulation, and mood changes based on user-entered data. The paper shows that people begin to read their actual physical sensations and emotional states in terms of what the app predicts, creating a loop where the forecast influences what users notice and report. Because logging is often incomplete, the AI outputs are frequently off, yet the apps provide no built-in way for users to see or question this influence. Non-normative cycle patterns leave users feeling especially isolated from the app's normative framing. A reader would care because these apps are now part of daily health routines for millions, quietly shaping personal knowledge of the body with little visibility into the feedback process.

Core claim

The paper establishes that users' lived experiences become entangled with AI predictions in MCTAs: people understand their bodies and mental states in light of the app's forecasts, yet these forecasts are often faulty because of imperfect logging practices; the interface features and explanations do not support awareness or critical engagement with this entanglement; and non-normative users experience a sense of isolation within it.

What carries the argument

Human-AI entanglement, the process by which users adjust their interpretation of personal experiences to align with AI-generated predictions and explanations in menstrual cycle tracking apps.

If this is right

Predictive features should be designed to surface the effects of missing or inconsistent logs so users can see where forecasts may be unreliable.
Explanations attached to AI outputs need to include prompts that encourage users to compare predictions against their own logged sensations rather than accept them at face value.
Interfaces should explicitly accommodate cycle patterns that fall outside statistical norms to reduce the reported sense of isolation.
Design changes that increase awareness of entanglement would also reduce the risk that faulty predictions reinforce themselves through changed user behavior.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same entanglement pattern may appear in other personal-data apps that combine self-reports with algorithmic forecasts, such as sleep or mood trackers.
Improving the ease and accuracy of data entry could weaken the self-fulfilling loop by producing more reliable predictions that require less interpretive adjustment from users.
Future studies could test whether adding uncertainty visualizations around AI forecasts measurably increases users' willingness to question or override the app's suggestions.

Load-bearing premise

The 14 interviews and group autoethnography capture a representative range of user experiences with these apps without major selection or interpretation bias.

What would settle it

A larger, demographically broader interview or diary study that finds most users do not reinterpret their physical sensations or moods to match AI predictions, even when logging data is known to be incomplete.

Figures

Figures reproduced from arXiv: 2605.13261 by Alexandra Weilenmann, Jichen Zhu, Pelin Karaturhan, Wendy Zhou.

**Figure 1.** Figure 1: Four screenshots from the Flo menstrual cycle tracking app showing: (a) a list of suggested symptoms and moods to log for the day including items like ’Backache,’ ’Cramps,’ and ’Nausea’; (b) the main logging interface with categories for logging daily experiences and menstrual flow; (c) a chatbot interface displaying AI-driven predictions and personalized recommendations; and (d) an AI explanation screen d… view at source ↗

**Figure 2.** Figure 2: Three smartphone screenshots from the Flo app shown to interview participants, displaying: AI-based cycle and period predictions with calendar views, personal symptom insights and predictions with icons and descriptions, and fertility prediction features with explanatory text. explained, “I assume that if I don’t enter things every month when I get my period, then it will know less or make a worse predicti… view at source ↗

read the original abstract

In menstrual cycle tracking apps (MCTAs), AI-based predictions and insights have become increasingly popular. These features enable users to receive personalized information about their bodies and mental states. However, there is currently little research on how these predictive AI features and explanations affect users' lived experiences. This paper examines human-AI entanglement in MCTAs through 14 semi-structured user interviews and a group autoethnography. These methods uncover the processes leading to this phenomenon. Our results reveal that: (1) users understand their lived experiences in light of AI predictions, although these predictions can be faulty due to imperfect logging practices, (2) the user interface features and AI explanations do not support awareness or critical engagement with this entanglement and meaning-making, and (3) non-normative MCTA users report a sense of isolation in this entangled interaction. Based on our findings, we propose design implications for predictive AI features and explanations.

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.

Desk Editor's Note private letter to a colleague

AI predictions in menstrual trackers get woven into how users read their own bodies, with weak UI support for questioning them, but the 14-interview sample leaves the scope unclear.

read the letter

The main takeaway is that users of these apps start interpreting their cycles, moods, and symptoms through the AI forecasts, even when logging gaps make the forecasts unreliable. The interfaces give no real help for spotting that loop or stepping outside it, and non-normative users end up feeling cut off from others. That entanglement process is the piece the paper actually adds to the HCI tracking literature. The interviews and autoethnography surface specific examples of how predictions override or reshape daily experience, and the design suggestions for better explanations and critical prompts follow directly from those reports. The work is grounded enough in user accounts to be worth reading for anyone building or studying health apps. The clear limitation is the sample. Fourteen interviews plus group autoethnography is thin for claims about widespread meaning-making, and the abstract gives no recruitment details, demographics, or screening criteria. Self-selection on a personal topic like this could easily tilt the stories toward users already frustrated with the predictions. Without that information it is hard to know whether the isolation effect or the faulty-prediction loop shows up for typical users or mainly for the reflective ones who volunteer. This is for HCI researchers and designers focused on personal health data and predictive interfaces. A reader working on similar apps would pick up concrete points about where current explanations fall short. It is solid enough on its own terms to go to peer review, with the main request being more transparency on who participated and how the themes were derived.

Referee Report

3 major / 2 minor

Summary. This manuscript examines human-AI entanglement in menstrual cycle tracking apps (MCTAs) via 14 semi-structured user interviews and a group autoethnography. It claims that users interpret their lived bodily and mental experiences through AI predictions (which are often inaccurate due to imperfect logging), that current UI features and explanations fail to support awareness or critical engagement with this process, and that non-normative users experience isolation in these interactions. Design implications for predictive AI features are proposed based on the findings.

Significance. If the results hold, the work makes a meaningful contribution to HCI research on AI in personal health tracking by illuminating how algorithmic predictions shape self-understanding in intimate domains. The qualitative approach yields rich, inductive insights into meaning-making and isolation that complement quantitative studies of app accuracy. The design implications provide concrete directions for improving transparency and user agency, which could influence future MCTA development and broader discussions of algorithmic entanglement in sensitive contexts.

major comments (3)

[Methods] Methods section: The recruitment channels, screening criteria, and detailed participant demographics (e.g., age, cycle regularity, prior app experience) are not reported. This is load-bearing for the central claims, as self-selection among users already attuned to or frustrated by predictions could artifactually amplify reports of entanglement and isolation among non-normative users.
[Methods] Methods section: The thematic analysis process is described at a high level only, with no details on how themes were identified, whether multiple researchers coded the data, inter-rater reliability, or how contradictory cases were resolved. This weakens support for the reported processes of user meaning-making and the UI's failure to enable critical engagement.
[Results] Results section: The evidence for claim (2) that UI features do not support critical engagement rests on participant reports, but lacks direct mapping to specific UI elements (e.g., prediction visualizations or explanation text) or accompanying figures/screenshots. This makes it harder to assess the generalizability of the design implications.

minor comments (2)

[Abstract] Abstract: Adding one sentence on the analysis approach (e.g., thematic analysis) would help readers immediately contextualize the strength of the findings.
[Discussion] Discussion: The design implications could be more tightly linked to specific interview excerpts or autoethnographic observations to increase their actionability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which has identified important opportunities to strengthen the transparency of our methods and the evidentiary basis for our results. We have prepared revisions that directly address each major comment while preserving the integrity of our qualitative findings on human-AI entanglement in menstrual cycle tracking apps.

read point-by-point responses

Referee: [Methods] Methods section: The recruitment channels, screening criteria, and detailed participant demographics (e.g., age, cycle regularity, prior app experience) are not reported. This is load-bearing for the central claims, as self-selection among users already attuned to or frustrated by predictions could artifactually amplify reports of entanglement and isolation among non-normative users.

Authors: We agree that detailed reporting of recruitment and demographics is necessary to allow readers to evaluate potential selection effects. The revised manuscript will add a new subsection in Methods that specifies recruitment via targeted posts on Reddit (r/menstruation, r/TwoXChromosomes), Facebook groups focused on menstrual health, and university mailing lists; screening required at least three months of MCTA use with exposure to AI predictions; and a table presenting anonymized participant details including age range (18-42), self-reported cycle regularity (regular/irregular), duration of app use, and prior experience with predictive features. This information will enable assessment of whether the sample over-represents users already critical of predictions while still reflecting the diversity of experiences reported, including both normative and non-normative cycles. revision: yes
Referee: [Methods] Methods section: The thematic analysis process is described at a high level only, with no details on how themes were identified, whether multiple researchers coded the data, inter-rater reliability, or how contradictory cases were resolved. This weakens support for the reported processes of user meaning-making and the UI's failure to enable critical engagement.

Authors: We acknowledge the need for greater methodological transparency. The revised Methods section will expand the description to state that we followed Braun and Clarke's six-phase reflexive thematic analysis. Two authors independently open-coded all 14 interview transcripts and the autoethnographic notes, then met weekly to compare codes, resolve discrepancies through discussion, and iteratively develop themes. No quantitative inter-rater reliability metric was computed, consistent with reflexive TA; instead, we will include a brief account of how contradictory cases (e.g., participants who expressed strong trust in predictions despite inaccuracies) were incorporated into the final themes of entanglement and limited critical engagement. Example codebook excerpts will be added to the supplementary materials. revision: yes
Referee: [Results] Results section: The evidence for claim (2) that UI features do not support critical engagement rests on participant reports, but lacks direct mapping to specific UI elements (e.g., prediction visualizations or explanation text) or accompanying figures/screenshots. This makes it harder to assess the generalizability of the design implications.

Authors: We agree that tighter linkage between participant accounts and concrete UI elements would improve the paper. The revised Results section will include a new figure showing representative screenshots (or generic mock-ups) of common MCTA prediction visualizations, insight cards, and explanation tooltips drawn from the apps mentioned by participants. Each example will be annotated with direct participant quotes and mapped to the specific design implication it supports (e.g., absence of uncertainty indicators in period predictions). Additional verbatim excerpts referencing particular interface components will be inserted to ground the claim that current features do not scaffold critical awareness. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on primary qualitative data collection

full rationale

The paper derives its findings through thematic analysis of 14 semi-structured interviews and a group autoethnography, which are independent primary data sources rather than any fitted parameters, equations, or self-referential derivations. No load-bearing steps reduce to self-citation chains, ansatzes, or renaming of known results; the reported entanglement between user experiences and AI predictions emerges directly from participant accounts without statistical forcing or definitional loops. This is the expected outcome for an inductive HCI study whose central claims are falsifiable via additional user data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on standard qualitative HCI assumptions rather than new parameters or entities.

axioms (1)

domain assumption Semi-structured interviews and group autoethnography can surface reliable accounts of user meaning-making
Invoked implicitly when treating interview data as evidence of entanglement processes

pith-pipeline@v0.9.0 · 5473 in / 1086 out tokens · 37001 ms · 2026-05-14T18:30:17.203764+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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