Sensitive opt-in

Cycle context should explain patterns only when the user opts in.

daygauge can connect cycle phase with sleep, recovery and energy patterns privately, without fertility, pregnancy or hormone inference.

Join waitlist See score anatomy Read evidence model

Why people search this

Start with the signal your own data can support.

Many users want wearable data interpreted in relation to cycle phase because HRV, temperature, sleep and energy can shift across the month.

This is a major Pro opportunity, but it must be handled as sensitive opt-in context with no leaderboard use and no reproductive inference.

Quick answer

daygauge can use user-enabled cycle context beside sleep timing, resting HR, HRV, temperature and self-reported symptoms.

Search questions answered

What this page covers.

Can cycle phase affect HRV?
Can wearables show menstrual patterns?
Should cycle data affect recovery?
How should cycle insights stay private?
Can daygauge predict fertility?

How daygauge would use this

From research context to product evidence.

Signal

daygauge can use user-enabled cycle context beside sleep timing, resting HR, HRV, temperature and self-reported symptoms.The app must not predict fertility, pregnancy, ovulation, hormone disorders or cycle abnormalities.

Confidence

Missing or sensitive data lowers confidence instead of creating false certainty.If the signal is not measured, explicitly imported or user-approved, daygauge should say so in the evidence.

Weekly review

Pro keeps the weekly baseline review: what changed, what moved with it, and whether the pattern repeated.This is where daygauge should beat a generic wearable dashboard: better explanation, clearer baselines and safer boundaries.

Example evidence

What a user should expect to see in the app.

Cycle context enabled: recovery proxy compared with same-phase baseline, not generic monthly average.

Weekly review preview

Data used

daygauge can use user-enabled cycle context beside sleep timing, resting HR, HRV, temperature and self-reported symptoms.

Confidence

Confidence rises when the same pattern repeats against your own baseline and drops when key signals are missing.

Next move

daygauge would suggest one small experiment, then watch whether the evidence repeats over the next week.

Boundary

Research context only. daygauge does not diagnose, treat, prevent or predict disease risk. Personal medical concerns belong with a qualified clinician.

Evidence 1

Cycle context enabled: recovery proxy compared with same-phase baseline, not generic monthly average.

Evidence 2

Sleep evidence: luteal-context nights averaged 34 minutes less sleep than user's own follicular-context baseline.

Evidence 3

Privacy: cycle context excluded from local rank, circles and share cards.

Safety line

Research context only. daygauge does not diagnose, treat, prevent or predict disease risk. Personal medical concerns belong with a qualified clinician.

Research context

Sources daygauge can cite without overclaiming.

These sources are used as context for product wording and evidence labels. They should not be turned into personal disease-risk estimates.

Research context only. daygauge does not diagnose, treat, prevent or predict disease risk. Personal medical concerns belong with a qualified clinician.

Product boundaries

What daygauge should not claim.

No diagnosis, treatment, prevention or personal disease-risk prediction.
No hidden inference from sensitive data such as fertility, hormones, glucose, labs, cycle context or exposure tests.
No guilt language, food moralising, overtraining incentives or leaderboard use for sensitive topics.
No claim that a single habit caused a result. daygauge can show patterns, confidence and possible confounders.

Related daygauge guides.

Early access

Want private cycle-aware evidence without reproductive guesses?

Join the TestFlight waitlist and tell us which pattern you want daygauge to explain first.

iOS TestFlight first · paid app, one plan · evidence context, not medical advice