What are active hours and why they matter

Active hours measures how many hours in a day a user is physically active (at least some movement). Learn how to interpret MyHealthCop Ai’s active_hours biomarker and use it in your product.

Active hours is the number of hours in a day during which a user has meaningful physical movement (i.e., they were not continuously sedentary for that entire hour). It’s measured as an hour count. Higher active hours usually indicates activity spread across the day — a strong signal for “movement frequency,” not just total volume.

Key Takeaways

  • What it measures: how many hours contain at least some activity (movement spread across the day).
  • Why it matters: frequent movement is often more sustainable (and behaviorally protective) than “one big workout” plus long sedentary time.
  • How to use it: power “move more often” experiences, break-up-sitting nudges, and streaks for consistency.
  • Best practice: interpret relative to baseline and pair with steps/active duration for context.

What Are “Active Hours”?

Active hours answers a simple behavioral question:

“Across the day, how many separate hours did the user move at all?”

This makes it different from metrics like steps or active minutes, which can be dominated by a single session.

Example:

  • A user does a 45-minute run in the morning and sits the rest of the day.
  • Another user does short walks spread throughout the day.

They might have similar total steps, but the second user often has more active hours.

Why Active Hours Matters

Active hours is useful because it captures movement distribution.

From a product perspective, distribution matters because:

  • it’s strongly connected to habit formation (small, repeatable actions)
  • it reduces “all-or-nothing” thinking (“I missed my workout, so today is a write-off”)
  • it supports programs that encourage breaking up long periods of sitting

Product takeaway: Active hours is a great metric for behavior change experiences because it rewards frequency and consistency, not only intensity.

How to Interpret Active Hours

Use these interpretation rules:

  • Higher than baseline: movement is more distributed across the day (more “movement touchpoints”).
  • Lower than baseline: the day likely had longer uninterrupted sedentary blocks.
  • High steps + low active hours: movement may be concentrated into one session.
  • Lower steps + higher active hours: frequent light movement (often good for habit consistency).

Use baselines, not rigid thresholds

A “good” number varies by job, commute, climate, mobility constraints, and lifestyle. A safe default is baseline framing:

  • “Compared to your usual…”

How to Use Active Hours in Your Product

1) “Move more often” challenges

Active hours maps naturally into micro-goals:

  • “Hit 6 active hours today”
  • “Add +1 active hour compared to yesterday”
  • “Active hour streak” (reward consistency)

These goals often feel more achievable than steps targets.

2) Break-up-sitting nudges (timing-aware)

If active hours are low, prompt lightweight actions:

  • a 2–5 minute walk
  • a quick stair break (if appropriate)
  • a standing/walking call suggestion

Avoid over-nudging. Use user preferences, time-of-day, and baseline behavior.

3) Personalize intensity and tone

When active hours drop, users may be stressed, sick, traveling, or overloaded. Good defaults:

  • softer tone
  • smaller asks
  • fewer notifications

4) Explain why activity scores changed

Active hours is a strong “driver” explanation:

  • “Your steps were similar, but you moved in fewer hours today — that’s why your activity frequency score dipped.”

5) Combine with steps and active duration for better guidance

Active hours becomes most actionable when paired with:

  • steps (volume)
  • active_duration (minutes moving)
  • floors_climbed (bonus intensity)
  • intensity durations (low/medium/high)

This lets you say things like:

  • “Your volume was fine, but movement wasn’t spread through the day.”
  • “Let’s add one extra active hour tomorrow with a short walk break.”

Implementation Suggestions for your Products

  1. Make the metric visible as a habit lever
    • Users understand “hours I moved” instantly. Use it as a primary daily habit KPI.
  2. Design for variability and missing coverage
    • Some sources/devices may detect activity differently. Handle null or inconsistent values gracefully.
  3. Trend it over 7–14 days
    • Weekly trends are more stable and reduce day-to-day noise.
  4. Use simple decision rules Examples:
    • If active_hours is trending down → suggest “movement breaks” content
    • If steps are high but active_hours is low → encourage distribution (“spread the movement”)
    • If active_hours increases week-over-week → celebrate consistency (habit reinforcement)
  5. Respect accessibility and context
    • Offer alternatives for users with mobility limitations, and avoid framing sedentary time as failure.