MenoCare, Menopause Support App | Dhruvi Gopani Case Study

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🏆 Best Design · IterateUX Challenge 2024

Designed menopause support for women who've been googling their symptoms alone at 3AM

A billion people go through menopause. The apps that existed treated it like a footnote. Here's what I built, what I learned, and what I'd change.

My Role

UX Designer & Researcher

Team

With

IterateUX

Timeline

5 weeks

IMPACT

30sec

task completion
(down from 3 min)

task completion

3 → 1

home actions
(simplified)

100%

accessibility first

CONTEXT

Existing menopause apps served tech-savvy users well but alienated women 50+

Women in their 50s were Googling "am I dying or is this menopause" at 3am. They weren't talking to anyone about it. When they tried existing apps, they abandoned them.

What Research Revealed

I interviewed 5 women across different menopause stages (ages 45 - 65).

The pattern: they downloaded apps but found them unusable.

The Core Problem

The apps assumed tech literacy.

Too many features they didn't understand. Icons without labels. Complex navigation. Clinical language. Felt designed for their daughters, not them.

RESEARCH

Five interviews. One Pattern That Changed Everything.

Full disclosure: I came into this knowing almost nothing about menopause. I left understanding why a billion people are underserved.

The biggest barrier wasn't lack of information. Women weren't confused about menopause - they were navigating something alone that nobody around them seemed willing to discuss.

“ I am comfortable talking about it but I sense a lot of hush hush from others. Instead of shying away from the topic it should be openly discussed. It is a normal phenomenon, what’s to hide? ”

— Research Participant, 50s, South Asia

“ I am comfortable talking about it but I sense a lot of hush hush from others. Instead of shying away from the topic it should be openly discussed. It is a normal phenomenon, what’s to hide? ”

— Research Participant, 50s, South Asia

Turning Interviews into Direction

After affinity mapping and synthesis, two distinct behavior patterns emerged:

Mei

42% of our users

Independent • Tech-Savvy

Context: Lives alone, wants quick coping methods and control

"Give me information fast and let me decide what to do with it."

Needs: Quick access to data, customizable tracking, straightforward answers

Sunita

58% of our users

Family-Oriented • Less Tech-Comfortable

Context: Needs discreet solutions, doesn't want to explain to anyone

"Help me without making this a big deal."

Needs: Simple interface, anonymous options, immediate help during symptoms

My Strategic Decision

Design for Sunita first. If she can use it, everyone can. If we design for Mei, we lose 58% of our users. This single insight shaped every interface choice that followed.

PROBLEM FRAMING

Testing validated the problem

I ran usability tests with the mid-fi prototype. Participants over 50 got stuck consistently.

QUANTITATIVE DATA

Task success rate

50%

Avg time on task

3min

Avg misclicks

2.9

QUALITATIVE DATA

Icons were useless: "I don't know what these pictures mean"

Navigation confusing: Kept getting lost

Information overload: Felt overwhlemed

“ I don't know what these pictures mean. ”

— Participant trying to book doctor appointment

“ I couldn’t really tell these were different sections to tap on.”

— Participant trying to book doctor appointment (took 3 minutes)

Mid-fi home screen with unlabeled icons and hard-to-discover tabs for elderly users

The Challenge

How might we make the app usable for women who might open it in a panic during a hot flash?

DESIGN DECISIONS

Three decisions that defined the product

I ran usability tests with the mid-fi prototype. Participants over 50 got stuck consistently.

DECISION 01
AI Companion, Not Chatbot
What I did: Designed conversation flows that speak like a knowledgeable friend, not a medical database.

The craft: I scripted 15+ AI response variations for vulnerable moments like "I think I'm broken."

Why it mattered: Women were already Googling — they needed better answers in a safer space.
💡 This was the decision judges mentioned most in their feedback
DECISION 02
Smartwatch Over New Hardware
What happened: The team was gravitating toward recommending specific wearables. I pushed back.
My reasoning: Cost barrier would kill adoption before users tried the app. They already owned smartwatches, they would be our health sensors.
The solution: Biometric monitoring for breathing and heart rate to detect hot flash onset, with discreet wrist alerts.
💡 The constraint became the feature
DECISION 03
Emergency Alerts Surface Actions
What I designed: The home screen isn't a dashboard — it's a hub where alerts surface alongside immediate actions.

The flow: When a hot flash alert fires, you get:
→ A breathing exercise (immediate relief)
→ A community thread (you're not alone)
→ A doctor booking option (if it's getting worse)
💡 The alert was the trigger. The response was the product.

DECISION 01

AI Companion, Not Chatbot

What I did: Designed conversation flows that speak like a knowledgeable friend, not a medical database.

The craft: I scripted 15+ AI response variations for vulnerable moments like "I think I'm broken."

Why it mattered: Women were already Googling — they needed better answers in a safer space.

💡 This was the decision judges mentioned most in their feedback

DECISION 02

Smartwatch Over New Hardware

What happened: The team was gravitating toward recommending specific wearables. I pushed back.

My reasoning: Cost barrier would kill adoption before users tried the app. They already owned smartwatches, they would be our health sensors.

The solution: Biometric monitoring for breathing and heart rate to detect hot flash onset, with discreet wrist alerts.

💡 The constraint became the feature

DECISION 03

Emergency Alerts Surface Actions

What I designed: The home screen isn't a dashboard — it's a hub where alerts surface alongside immediate actions.

The flow: When a hot flash alert fires, you get:
→ A breathing exercise (immediate relief)
→ A community thread (you're not alone)
→ A doctor booking option (if it's getting worse)

💡 The alert was the trigger. The response was the product.

EXECUTION

Iterations

I went through three major iterations, testing with 4 participants over 50 after each version.

Version 1: Initial Mid-Fi

Mid-fi prototypes with unlabeled icons and hard-to-discover tabs for elderly users

✅ WHAT WORKED

Flat information architecture felt intuitive
Bottom tab navigation felt familiar from other apps

❌ WHAT DIDN'T WORK

Icons had no labels — users didn't know what they meant
Home screen too crowded with 6 cards
Participants couldn't find the AI assistant
Task completion time: 3 minutes average

Version 2: Labels Added

Mid-fi home screen with labelled icons and actionable emergency alerts

✅ WHAT WORKED

Icons now had labels — immediate improvement in comprehension
Larger font sizes (16pt minimum) improved readability
Actionable emergency alerts were better

❌ WHAT DIDN'T WORK

Participants couldn't distinguish critical symptoms from daily stats
AI assistant icon was confused with customer support
Participants got lost between tabs
Task completion time: 1.5 minutes (better but not good enough)

Version 3: Final (Shipped)

High-fi screens with labelled icons, redesigned navigation and actionable CTAs

✅ WHAT WORKED

Prominent critical symptom alert banner
Dedicated sections to book appointments & chat with AI on home screen
Added actionable CTAs visible upon receiving alerts
Reorganised home screen into 2 focused tabs
Task completion time: 28 seconds average
100% task success rate

❌ WHAT DIDN'T WORK

N/A — all usability targets met in final testing

OUTCOMES

What Actually Shipped

🏆

Best Design Award
IterateUX Challenge

100%

task success rate
(up from 50%)

28s

avg task completion
(down from 3 min)

Version 1: Initial Mid-Fi
Mid-fi prototypes with unlabeled icons and hard-to-discover tabs for elderly users
✅ WHAT WORKED
Flat information architecture felt intuitive
Bottom tab navigation felt familiar from other apps
❌ WHAT DIDN'T WORK
Icons had no labels — users didn't know what they meant
Home screen too crowded with 6 cards
Participants couldn't find the AI assistant
Task completion time: 3 minutes average
Version 2: Labels Added
Mid-fi home screen with labelled icons and actionable emergency alerts
✅ WHAT WORKED
Icons now had labels — immediate improvement in comprehension
Larger font sizes (16pt minimum) improved readability
Actionable emergency alerts were better
❌ WHAT DIDN'T WORK
Participants couldn't distinguish critical symptoms from daily stats
AI assistant icon was confused with customer support
Participants got lost between tabs
Task completion time: 1.5 minutes (better but not good enough)
Version 3: Final (Shipped)
High-fi screens with labelled icons, redesigned navigation and actionable CTAs
✅ WHAT WORKED
Prominent critical symptom alert banner
Dedicated sections to book appointments & chat with AI on home screen
Added actionable CTAs visible upon receiving alerts
Reorganised home screen into 2 focused tabs
Task completion time: 28 seconds average
100% task success rate
❌ WHAT DIDN'T WORK
N/A — all usability targets met in final testing

Final Features

Home Screen: 3 primary actions — Critical symptoms alerts, weekly health summary, Education/Awareness, Find a Doctor, Talk to AI
Symptom Tracker: Quick-log buttons, optional details
AI Companion: Conversational interface with pre-filled prompts, customizable appearance
Community: Topic-based groups with one-tap join, anonymous posting option
Emergency Alert (Phone + Smartwatch): One-tap alert, sends cooling tips immediately, logs automatically

High-fi prototypes for smartwatches

The Trade-Off I Made

We lost some visual polish. But we gained usability for the people who actually needed this app. I prioritized accessibility for the person who's struggling — not the person who finds everything easy.

REFLECTION

What I'd do differently

🏳️‍⚧️ Include transgender women in research

Menopause is not exclusively a cisgender female experience. This was our most significant gap — and the first thing I'd address in any follow-up study.

🌍 Build multilingual support from day one

We designed in English only. That excludes huge portions of our target audience.

🤖 Deeper AI conversation logic

In testing, a participant said she was having chest pain. Our AI suggested breathing exercises. I'd build symptom severity triage into the AI's first response — not just coping tips.

What I Learned

✓ Usability testing with the actual age group is non-negotiable

Our assumptions were wrong. All of them. Testing with 50+ users revealed failures we never would have caught designing for ourselves.

✓ Simplicity beats feature bloat every time

Every time I wanted to add something "cool," I asked: "Would Sunita use this during a hot flash?" That question killed half our feature backlog — and made the app better.

✓ Constraints drive better solutions

The smartwatch limitation forced us to a better answer than unlimited budget would have. Designing within real cost constraints pushed us to actually solve the problem.

THE BOTTOM LINE

I designed an app that women 50+ could use during their worst moments — not just their best ones. That meant killing features I liked, simplifying things I thought were "intuitive," and testing until the design actually worked for people who weren't me.