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(01) Full Case Study

Designing for Rest

The deep-sleep feature for Oura Ring that turns nightly data into guidance people can act on.

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Project type

Product Design, Mobile UX, Health & Wellness

Project role

Product Designer, UX Researcher, UI Designer

Date

2024

01

Overview

A deep sleep feature for the Oura Ring built around the science of rest.

A deep sleep feature for the Oura Ring built around the science of rest.

Oura tracks sleep stages, deep sleep, and readiness with quiet precision. But users were left holding a score, not a next step, rich data that rarely translated into changed behaviour.

Oura tracks sleep stages, deep sleep, and readiness with quiet precision. But users were left holding a score, not a next step, rich data that rarely translated into changed behaviour.

02

Understanding the disconnect

"Users receive insights but lack actionable next steps."

"Users receive insights but lack actionable next steps."

Research approach

Research approach

I conducted mixed-method research to understand the gap between data and behaviour change. This included 5 in-depth interviews with active sleep trackers (ages 23–38, diverse professions), a survey of 17 wearable users, and competitive analysis of WHOOP, Fitbit, and Apple Watch. I synthesized findings into an affinity map to identify patterns across motivations, barriers, and opportunities.

I conducted mixed-method research to understand the gap between data and behaviour change. This included 5 in-depth interviews with active sleep trackers (ages 23–38, diverse professions), a survey of 17 wearable users, and competitive analysis of WHOOP, Fitbit, and Apple Watch. I synthesized findings into an affinity map to identify patterns across motivations, barriers, and opportunities.

Survey · Question 01

What would make sleep data more actionable for you?

47.1%

Personalized recommendations ranked first, while lifestyle tips, the most generic option, came last at 5.9%. Guidance had to be shaped around the user's own patterns to count as actionable.

Personalized recommendations ranked first, while lifestyle tips, the most generic option, came last at 5.9%. Guidance had to be shaped around the user's own patterns to count as actionable.

Personalized recommendations

47.1%

Goal tracking

29.4%

Integration with other health data

17.6%

Lifestyle tips

5.9%

View complete survey results

01

Finding 01

Finding 01

Research

Users wanted guidance “front and centre the first thing you see when you open the app.”

Decision

Surface micro-actions as a dropdown on the main dashboard easy to check off or snooze.

Surface micro-actions as a dropdown on the main dashboard easy to check off or snooze.

Impact

Guidance became unavoidable instead of buried three taps deep.

Guidance became unavoidable instead of buried three taps deep.

02

Finding 02

Finding 02

Research

Quiz-like, guided inputs were strongly preferred for creating a goal.

Decision

A short guided set-up with personalized feedback and visible progress tracking.

A short guided set-up with personalized feedback and visible progress tracking.

Impact

Goal-setting felt easy and motivating rather than like data entry.

Goal-setting felt easy and motivating rather than like data entry.

03

Finding 03

Finding 03

Research

People wanted digestible, plain-text reporting, not raw metrics or graphs.

Decision

Lead with plain-language insights and suggestions; keep the charts secondary.

Lead with plain-language insights and suggestions; keep the charts secondary.

Impact

Reporting read like a coach, not a spreadsheet.

Reporting read like a coach, not a spreadsheet.

04

Finding 04

Research

Daily check-ins were desired, but must never feel overwhelming.

Decision

Optional check-ins with a customizable frequency.

Optional check-ins with a customizable frequency.

Impact

Engagement without the fatigue that kills a daily habit.

Engagement without the fatigue that kills a daily habit.

What emerged

What emerged

Across all research, one theme dominated: users had rich data but no path forward. They wanted guidance that was actionable, personalized to their life, and delivered in plain language, not more metrics. These insights directly shaped the design phase, driving every decision toward simplicity, personalization, and micro-actions over data visualization.

Across all research, one theme dominated: users had rich data but no path forward. They wanted guidance that was actionable, personalized to their life, and delivered in plain language, not more metrics. These insights directly shaped the design phase, driving every decision toward simplicity, personalization, and micro-actions over data visualization.

03

Process

Process

Strategic goals & prioritization

Strategic goals & prioritization

Research revealed three aligned priorities: users needed clarity and actionability, business needed engagement and trust, both required simplicity over complexity. This shaped feature prioritization.

Research revealed three aligned priorities: users needed clarity and actionability, business needed engagement and trust, both required simplicity over complexity. This shaped feature prioritization.

Aligned goals

Business

Drive retention & engagement

User

Progress tracking & insights

Shared

Clarity + Actionability + Trust

Feature prioritization

P4

Partnerships · Research mode

P3

Smart home

P2

Social · Education

P1

Sleep goal tracking

Sleep goal tracking

AI coaching

AI coaching

Behavior tracking

Behavior tracking

Simplified dashboard

Simplified dashboard

From P1 to Two Flows

From P1 to Two Flows

With P1 features locked : goal tracking, AI coaching, behavior insights, simplified dashboard, the design challenge became singular: how do these features work together?

With P1 features locked : goal tracking, AI coaching, behavior insights, simplified dashboard, the design challenge became singular: how do these features work together?

What do I do today?

Setting daily habits

Transforms data into micro-actions: small, easy suggestions users see immediately.

Transforms data into micro-actions: small, easy suggestions users see immediately.

Where am I going?

Setting a deep-sleep goal

Gives users a north star and personalized roadmap.

Understand the problem

→

Set a goal

→

Receive daily guidance

→

Progress toward it

From scores to steps

Make the data do something

Make the data do something

Early concepts simply added more charts and tested cold. Reframing the work around micro-actions changed everything: the same metrics started to feel like coaching instead of homework.
The brief became less about visualising sleep and more about guiding the next small decision at the right moment, in plain language.

Early concepts simply added more charts and tested cold. Reframing the work around micro-actions changed everything: the same metrics started to feel like coaching instead of homework.
The brief became less about visualising sleep and more about guiding the next small decision at the right moment, in plain language.

Hand-drawn Oura app wireframe sketches showing sleep goal and daily routine screens.

04

From research to design

Concept 01

Setting Daily Habits

Small steps tied to the user’s patterns, surfaced at the right moment of the day. Reduces effort, keeps guidance simple, supports daily consistency.

Small steps tied to the user’s patterns, surfaced at the right moment of the day. Reduces effort, keeps guidance simple, supports daily consistency.

Grayscale Oura app wireframes for daily habit guidance and sleep recommendations.

Concept 02

Deep Sleep Goal

One outcome to aim for. Users set a deep-sleep target and the app adapts advice to their trends, direction instead of more charts.

One outcome to aim for. Users set a deep-sleep target and the app adapts advice to their trends, direction instead of more charts.

Grayscale Oura app wireframes showing the deep sleep goal setup and dashboard flow.

05

The work

Two flows, one principle: guide the next step.

Two flows, one principle: guide the next step.

The feature ships as two connected flows. Both keep the data quiet and the next action loud — shown here as annotated walkthroughs.

The feature ships as two connected flows. Both keep the data quiet and the next action loud — shown here as annotated walkthroughs.

Flow A

Setting daily habits

A plateau alert invites small, snooze-able habits that drop straight onto the timeline.

A plateau alert invites small, snooze-able habits that drop straight onto the timeline.

Daily habits — alert → select → confirm

Daily habits — alert → select → confirm

Flow B

Setting a deep-sleep goal

A guided, quiz-like set-up produces a personalized plan, a target, and a check-in rhythm.

A guided, quiz-like set-up produces a personalized plan, a target, and a check-in rhythm.

Guided set-up — expectations → outcome → lifestyle

Guided set-up — expectations → outcome → lifestyle

06

Outcome & reflection

Less data on a screen. More rest in real life.

Less data on a screen. More rest in real life.

Testing & iteration cycle

Pain point

Why it happened

Iteration made

Pain point

Pain point

High misclick rates (62–85%)

Why it happened

Users didn’t know where to go next

Users didn’t know where to go next

Iteration made

Simplified UI with visual hierarchy

Simplified UI with visual hierarchy

Pain point

Pain point

Unclear terminology (“Micro Actions”)

Why it happened

Term wasn’t self-explanatory

Term wasn’t self-explanatory

Iteration made

Added tooltips and refined wording

Added tooltips and refined wording

Pain point

Pain point

Flat information hierarchy

Why it happened

Critical actions buried in clutter

Critical actions buried in clutter

Iteration made

Prioritized next steps

Prioritized next steps

01 / Visualizing

Simplify to engage

Simplify to engage

Stripping the dashboard back to one clear signal improved engagement far more than another graph ever did.

Stripping the dashboard back to one clear signal improved engagement far more than another graph ever did.

02 / Personalized

Tailor to the person

Tailor to the person

Recommendations mapped to individual sleep patterns not generic hygiene rules earned trust.

Recommendations mapped to individual sleep patterns not generic hygiene rules earned trust.

03 / Actionable

Motivation over metrics

Motivation over metrics

Behaviour change depends on motivation, not numbers. Plain-language micro-actions carried the feature.

Behaviour change depends on motivation, not numbers. Plain-language micro-actions carried the feature.

What's next

Pressure-test the micro-action engine over a longer window. Does guidance still feel fresh after week three? And explore an adaptive cadence that quietly recedes once a habit sticks. Building better sleep through small, consistent changes is the whole thesis; the design just has to keep earning its place each night.

What's next

Enrich the context around decisions, destination photos, sample costs, activity previews and grow the AI Concierge from budget tips toward fully personalized trip suggestions.