Place patterns

Home days and office days create different bodies of evidence.

daygauge can compare where work happened with movement spread, long sitting, lunch walks, social windows and evening recovery.

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Why people search this

Start with the signal your own data can support.

Hybrid work changed daily movement without most people seeing the pattern clearly.

daygauge has a strong edge here because it can connect place patterns with movement, sleep, focus and social-time proxies.

Quick answer

daygauge can use coarse place windows, manual work-hours logs, calendar load where permissioned, movement spread and long-sit blocks.

Search questions answered

What this page covers.

Does working from home reduce movement?
Can location explain habits?
How can an app detect office days?
Can daygauge track work hours manually?
How should workplace privacy work?

How daygauge would use this

From research context to product evidence.

Signal

daygauge can use coarse place windows, manual work-hours logs, calendar load where permissioned, movement spread and long-sit blocks.The app should not expose exact workplace addresses, employer data, meeting titles or productivity surveillance.

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.

Office day: 3,100 more steps than WFH baseline but 52 minutes later dinner.

Weekly review preview

Data used

daygauge can use coarse place windows, manual work-hours logs, calendar load where permissioned, movement spread and long-sit blocks.

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

Office day: 3,100 more steps than WFH baseline but 52 minutes later dinner.

Evidence 2

WFH day: longest sit block 2h 40m; movement spread compressed into evening.

Evidence 3

Manual work log: 9.5 hours logged, recovery proxy lower next morning.

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 place patterns in your own 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