Product category

A health data app should connect passive signals to daily decisions.

daygauge connects health data with places, habits, experiments and weekly attribution so users can understand how they live.

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Why people search this

Start with the signal your own data can support.

Most health data apps collect charts. The user still has to work out what mattered.

The daygauge approach is to connect health signals with behaviour context: when the user slept, how movement was distributed, where routine changed, what they logged and what shifted afterwards.

Quick answer

daygauge starts with passive data: sleep duration, sleep timing, steps, active minutes, resting heart rate, HRV and coarse place windows.

Search questions answered

What this page covers.

What should a health data app track?
How can Apple Health data become daily insights?
Can wearable data explain habits?
What is the difference between health analytics and medical advice?
How should missing data affect confidence?

How daygauge would use this

From research context to product evidence.

Signal

daygauge starts with passive data: sleep duration, sleep timing, steps, active minutes, resting heart rate, HRV and coarse place windows.Sensitive modules such as CGM, cycle context, labs and supplements stay opt-in and are treated as context, not default score drivers.

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.

Source mix: Apple Health supplied sleep, movement and recovery; coarse location supplied place windows.

Weekly review preview

Data used

daygauge starts with passive data: sleep duration, sleep timing, steps, active minutes, resting heart rate, HRV and coarse place windows.

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

Source mix: Apple Health supplied sleep, movement and recovery; coarse location supplied place windows.

Evidence 2

Missing data: HRV absent today, so recovery confidence drops to low rather than inventing certainty.

Evidence 3

Weekly attribution: movement spread and consistent sleep timing explained most of the lift.

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 daygauge to explain your sleep, movement and routine data?

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