UX/UI Case Study

Moov – Designing trust in social fitness

Moov – Designing trust in social fitness

Moov – Designing trust in social fitness

Designing a social fitness app that helps people find sports partners, clubs, and events – and makes joining feel less intimidating.

Designing a social fitness app that helps people find sports partners, clubs, and events – and makes joining feel less intimidating.

Designing a social fitness app that helps people find sports partners, clubs, and events – and makes joining feel less intimidating.

ROLE

ROLE

ROLE

UX/UI Designer

UX Researcher

UX/UI Designer

UX Researcher

UX/UI Designer

UX Researcher

PRODUCT

PRODUCT

PRODUCT

Moov Concept
Mobile App

Moov Concept
Mobile App

Moov Concept
Mobile App

TIMELINE

TIMELINE

TIMELINE

August-September 2025

(6 Weeks)

August-September 2025

(6 Weeks)

August-September 2025

(6 Weeks)

TOOLS

TOOLS

TOOLS

Figma, FigJam,

Google Forms, Lovable

Figma, FigJam,

Google Forms, Lovable

Figma, FigJam,

Google Forms, Lovable

"Moov" centralizes sports clubs, events, and training partners, giving people a clear way to explore options and join without the uncertainty of group chats or social media.

"Moov" centralizes sports clubs, events, and training partners, giving people a clear way to explore options and join without the uncertainty of group chats or social media.

The Context

The Context

Staying active is often more fun with others. But joining a new sports group doesn’t always feel easy. People usually rely on social media posts, group chats, or word of mouth to find clubs and training partners. Even when opportunities are there, uncertainty around pace, group dynamics, and who will show up often stops them from taking the first step.

The problem isn’t motivation. It’s confidence. When expectations are unclear, joining something new can feel intimidating, especially for newcomers.

Staying active is often more fun with others. But joining a new sports group doesn’t always feel easy. People usually rely on social media posts, group chats, or word of mouth to find clubs and training partners. Even when opportunities are there, uncertainty around pace, group dynamics, and who will show up often stops them from taking the first step.

The problem isn’t motivation. It’s confidence. When expectations are unclear, joining something new can feel intimidating, especially for newcomers.

How can we help people feel confident joining social fitness activities before they commit?

The Solution Preview

The Solution Preview

The Solution Preview

Moov brings social fitness into one clear place, helping people discover clubs, events, and training partners with confidence.

By making expectations visible upfront and adding subtle trust cues, Moov reduces uncertainty and helps people decide whether something feels like a good fit before joining.

Moov brings social fitness into one clear place, helping people discover clubs, events, and training partners with confidence.

By making expectations visible upfront and adding subtle trust cues, Moov reduces uncertainty and helps people decide whether something feels like a good fit before joining.

Moov brings social fitness into one clear place, helping people discover clubs, events, and training partners with confidence.

By making expectations visible upfront and adding subtle trust cues, Moov reduces uncertainty and helps people decide whether something feels like a good fit before joining.

Research & Discovery

Research & Discovery

Research & Discovery

To understand where hesitation starts, I explored how people currently find and join social fitness activities. The research combined qualitative insights with broader validation to uncover patterns around uncertainty, confidence, and decision-making.

Rather than focusing on a single method, I used a mix of approaches to capture both how people feel and how common those experiences are.

To understand where hesitation starts, I explored how people currently find and join social fitness activities. The research combined qualitative insights with broader validation to uncover patterns around uncertainty, confidence, and decision-making.


Rather than focusing on a single method, I used a mix of approaches to capture both how people feel and how common those experiences are.

To understand where hesitation starts, I explored how people currently find and join social fitness activities. The research combined qualitative insights with broader validation to uncover patterns around uncertainty, confidence, and decision-making.


Rather than focusing on a single method, I used a mix of approaches to capture both how people feel and how common those experiences are.

Research approach

Research approach
  1. Competitive analysis

  1. Competitive analysis

Competitive analysis reveals structural gaps in existing solutions

Competitive analysis reveals structural gaps in existing solutions

  1. Survey

  1. Survey

The survey validates recurring patterns

The survey validates recurring patterns

The survey validates recurring patterns

  1. User Interviews

  1. User Interviews

Interviews surface emotional hesitation

Interviews surface emotional hesitation

How people currently discover clubs and partners

Secondary research & competitive analysis

I reviewed platforms people already use to find or coordinate fitness activities, including Strava, Meetup, Instagram, and Smatch, as well as messaging tools. What became clear: - Discovery is scattered across multiple platforms - Some tools focus on performance tracking, others on events, but none cover the full journey - Most platforms lack signals that help users assess fit, vibe, or trust before joining Key takeaway: No single platform helps people find partners, clubs, and events in a structured and reassuring way.

What people said about joining something new

User interviews

I spoke with people who regularly exercise as well as those trying to get started. Conversations focused on recent experiences of joining new clubs, sessions, or training partners. What interviewees shared: - Information is hard to find or scattered - Seeing who else is going provides reassurance - Mismatched pace or goals quickly lead to frustration Key takeaway: Joining is less about motivation and more about emotional safety, visibility, and knowing what to expect.

How common these struggles really are

Survey insights & Affinity Map

To understand whether interview insights were isolated, I ran a survey to capture broader patterns around discovery, motivation, and hesitation. Survey results: - 67% struggle to discover clubs or events in their area -72% say pace or level mismatches discourage them from joining - 83% feel more motivated when training with others Key takeaway: People want to be active together, but unclear expectations and poor discovery lead to drop-off right before commitment.

How people currently discover clubs and partners

Secondary research & competitive analysis

I reviewed platforms people already use to find or coordinate fitness activities, including Strava, Meetup, Instagram, and Smatch, as well as messaging tools. What became clear: - Discovery is scattered across multiple platforms - Some tools focus on performance tracking, others on events, but none cover the full journey - Most platforms lack signals that help users assess fit, vibe, or trust before joining Key takeaway: No single platform helps people find partners, clubs, and events in a structured and reassuring way.

What people said about joining something new

User interviews

I spoke with people who regularly exercise as well as those trying to get started. Conversations focused on recent experiences of joining new clubs, sessions, or training partners. What interviewees shared: - Information is hard to find or scattered - Seeing who else is going provides reassurance - Mismatched pace or goals quickly lead to frustration Key takeaway: Joining is less about motivation and more about emotional safety, visibility, and knowing what to expect.

How common these struggles really are

Survey insights & Affinity Map

To understand whether interview insights were isolated, I ran a survey to capture broader patterns around discovery, motivation, and hesitation. Survey results: - 67% struggle to discover clubs or events in their area -72% say pace or level mismatches discourage them from joining - 83% feel more motivated when training with others Key takeaway: People want to be active together, but unclear expectations and poor discovery lead to drop-off right before commitment.

How people currently discover clubs and partners

Secondary research & competitive analysis

I reviewed platforms people already use to find or coordinate fitness activities, including Strava, Meetup, Instagram, and Smatch, as well as messaging tools. What became clear: - Discovery is scattered across multiple platforms - Some tools focus on performance tracking, others on events, but none cover the full journey - Most platforms lack signals that help users assess fit, vibe, or trust before joining Key takeaway: No single platform helps people find partners, clubs, and events in a structured and reassuring way.

What people said about joining something new

User interviews

I spoke with people who regularly exercise as well as those trying to get started. Conversations focused on recent experiences of joining new clubs, sessions, or training partners. What interviewees shared: - Information is hard to find or scattered - Seeing who else is going provides reassurance - Mismatched pace or goals quickly lead to frustration Key takeaway: Joining is less about motivation and more about emotional safety, visibility, and knowing what to expect.

How common these struggles really are

Survey insights & Affinity Map

To understand whether interview insights were isolated, I ran a survey to capture broader patterns around discovery, motivation, and hesitation. Survey results: - 67% struggle to discover clubs or events in their area -72% say pace or level mismatches discourage them from joining - 83% feel more motivated when training with others Key takeaway: People want to be active together, but unclear expectations and poor discovery lead to drop-off right before commitment.

How people currently discover clubs and partners

Secondary research & competitive analysis

I reviewed platforms people already use to find or coordinate fitness activities, including Strava, Meetup, Instagram, and Smatch, as well as messaging tools. What became clear: - Discovery is scattered across multiple platforms - Some tools focus on performance tracking, others on events, but none cover the full journey - Most platforms lack signals that help users assess fit, vibe, or trust before joining Key takeaway: No single platform helps people find partners, clubs, and events in a structured and reassuring way.

What people said about joining something new

User interviews

I spoke with people who regularly exercise as well as those trying to get started. Conversations focused on recent experiences of joining new clubs, sessions, or training partners. What interviewees shared: - Information is hard to find or scattered - Seeing who else is going provides reassurance - Mismatched pace or goals quickly lead to frustration Key takeaway: Joining is less about motivation and more about emotional safety, visibility, and knowing what to expect.

How common these struggles really are

Survey insights & Affinity Map

To understand whether interview insights were isolated, I ran a survey to capture broader patterns around discovery, motivation, and hesitation. Survey results: - 67% struggle to discover clubs or events in their area -72% say pace or level mismatches discourage them from joining - 83% feel more motivated when training with others Key takeaway: People want to be active together, but unclear expectations and poor discovery lead to drop-off right before commitment.

Key Insights Summary

Key Insights Summary

Synthesizing interviews, survey results, and competitive analysis revealed five core insights:

Synthesizing interviews, survey results, and competitive analysis revealed five core insights:

Synthesizing interviews, survey results, and competitive analysis revealed five core insights:

  1. Discovery is fragmented and hard to navigate

  2. Matching pace and goals is critical for enjoyment

  3. Clubs can feel intimidating, especially for newcomers

  4. Social connection is a strong motivator

  5. Transparency builds trust and reduces hesitation

  1. Discovery is fragmented and hard to navigate

  2. Matching pace and goals is critical for enjoyment

  3. Clubs can feel intimidating, especially for newcomers

  4. Social connection is a strong motivator

  5. Transparency builds trust and reduces hesitation

  1. Discovery is fragmented and hard to navigate

  2. Matching pace and goals is critical for enjoyment

  3. Clubs can feel intimidating, especially for newcomers

  4. Social connection is a strong motivator

  5. Transparency builds trust and reduces hesitation

These insights directly shaped Moov’s direction…

These insights directly shaped Moov’s direction…

These insights directly shaped Moov’s direction…

… a central discovery hub that prioritizes clarity, reassurance, and confidence before commitment.

… a central discovery hub that prioritizes clarity, reassurance, and confidence before commitment.

… a central discovery hub that prioritizes clarity, reassurance, and confidence before commitment.

What Moov needed to offer to succeed

What Moov needed to offer to succeed

What Moov needed to offer to succeed

Based on research insights, Moov needed to reduce uncertainty before commitment and support confident decision-making without pressure. The goal was not to motivate people more, but to remove the friction that stopped them from taking the first step

Based on research insights, Moov needed to reduce uncertainty before commitment and support confident decision-making without pressure. The goal was not to motivate people more, but to remove the friction that stopped them from taking the first step

Based on research insights, Moov needed to reduce uncertainty before commitment and support confident decision-making without pressure. The goal was not to motivate people more, but to remove the friction that stopped them from taking the first step

Centralized discovery
Centralized discovery

Bring clubs, events, and training partners into one clear place

Bring clubs, events, and training partners into one clear place

Clear expectations
Clear expectations

Make pace, level, activity type, and structure visible before joining.

Make pace, level, activity type, and structure visible before joining.

Reduced intimidation
Reduced intimidation

Help users feel more comfortable by showing who else is attending

Help users feel more comfortable by showing who else is attending

Support different confidence levels
Support different confidence levels

Work equally well for newcomers and experienced athletes.

Work equally well for newcomers and experienced athletes.

Trust before commitment
Trust before commitment

Build reassurance through small, visible signals.

Build reassurance through small, visible signals.

These requirements guided how information was structured and how decisions were supported throughout the app.

These requirements guided how information was structured and how decisions were supported throughout the app.

Who Moov is designed for

Who Moov is
designed for

Rather than designing for a single type of athlete, Moov supports people who approach social fitness with different levels of confidence. These personas help ground decisions in real needs while keeping the focus on shared goals.

Rather than designing for a single type of athlete, Moov supports people who approach social fitness with different levels of confidence. These personas help ground decisions in real needs while keeping the focus on shared goals.

Rather than designing for a single type of athlete, Moov supports people who approach social fitness with different levels of confidence. These personas help ground decisions in real needs while keeping the focus on shared goals.

The structure and flow

The structure and flow

The structure and flow

Turning insights into a clear flow

With requirements and personas defined, the next step was shaping a structure that helps users move from discovery to decision without feeling overwhelmed.

Moov is organized around a simple journey: exploring what’s available close by, understanding whether something feels like a good fit, and only then deciding to join.

Understanding how both roles move through the flow was essential. I mapped the journey from discovering an event to confirming tickets to ensure the feature supported forward momentum rather than adding friction.


The flow highlights two perspectives working together toward a shared decision:

Turning insights into a clear flow


With requirements and personas defined, the next step was shaping a structure that helps users move from discovery to decision without feeling overwhelmed.


Moov is organized around a simple journey: exploring what’s available close by, understanding whether something feels like a good fit, and only then deciding to join.

Core structure

  • Onboarding to understand interests and preferences

  • A central discovery hub for clubs, events, and training partners

  • Detail views that clearly show pace, level, and attendance

  • A low-pressure action to join or request a buddy

Understanding how both roles move through the flow was essential. I mapped the journey from discovering an event to confirming tickets to ensure the feature supported forward momentum rather than adding friction.


The flow highlights two perspectives working together toward a shared decision:

Core structure


  • Onboarding to understand interests and preferences

  • A central discovery hub for clubs, events, and training partners

  • Detail views that clearly show pace, level, and attendance

  • A low-pressure action to join or request a buddy

Low-Fidelity Exploration

Low-Fidelity Exploration

Low-Fidelity Exploration

To explore how Moov should work at a structural level, I started with low-fidelity sketches and wireframes. At this stage, I focused on testing key structural decisions such as:

  • Home feed vs search as separate entry points

  • Discovery of clubs, events, and training partners

  • Filtering by sport, level, and preferences

  • Detail pages that surface key information before joining

  • A clear but low-pressure path toward joining or finding a buddy

These wireframes helped define the core structure of the app and highlighted where users might need more clarity before committing.

To explore how Moov should work at a structural level, I started with low-fidelity sketches and wireframes. At this stage, I focused on testing key structural decisions such as:


  • Home feed vs search as separate entry points

  • Discovery of clubs, events, and training partners

  • Filtering by sport, level, and preferences

  • Detail pages that surface key information before joining

  • A clear but low-pressure path toward joining or finding a buddy


These wireframes helped define the core structure of the app and highlighted where users might need more clarity before committing.

To explore how Moov should work at a structural level, I started with low-fidelity sketches and wireframes. At this stage, I focused on testing key structural decisions such as:


  • Home feed vs search as separate entry points

  • Discovery of clubs, events, and training partners

  • Filtering by sport, level, and preferences

  • Detail pages that surface key information before joining

  • A clear but low-pressure path toward joining or finding a buddy


These wireframes helped define the core structure of the app and highlighted where users might need more clarity before committing.

Testing early ideas

Testing early ideas

Testing early ideas

To validate the low-fidelity concepts, I tested the core flows with early prototypes. The focus was on whether the structure felt intuitive and whether users understood how to move from discovery to action.

To validate the low-fidelity concepts, I tested the core flows with early prototypes. The focus was on whether the structure felt intuitive and whether users understood how to move from discovery to action.

What went well
What went well
What went well
  • The buddy request flow felt natural

  • Users liked the “Quick Club Search” shortcut

  • Overall navigation was easy to understand

What could be improved
What could be improved
What could be improved
  • Some filters were unclear and affected matching

  • Skill levels caused confusion and needed better explanation

  • Confirmation after actions felt too subtle and easy to miss

Giving Moov its Look and Feel

With the structure in place, I explored a visual direction that reflects what Moov stands for: growth, inclusivity, connection, trust, and joy. The aim was to create an identity that feels energetic and social, without being intimidating. The style guide defined the foundation for how the product looks and feels across screens.

With the structure in place, I explored a visual direction that reflects what Moov stands for: growth, inclusivity, connection, trust, and joy. The aim was to create an identity that feels energetic and social, without being intimidating.


The style guide defined the foundation for how the product looks and feels across screens.


  • A bold, friendly color palette that feels active and approachable

  • A clear typographic system to support hierarchy and readability

  • Consistent components for navigation, cards, and actions

  • Visual references that emphasize movement, community, and real-life activity


This system helped ensure consistency across the app and served as the basis for all high-fidelity designs.

High-Fidelity Exploration

High-Fidelity Exploration

High-Fidelity Exploration

With the structure validated in low fidelity, I moved into high-fidelity design to explore how the experience holds up once real content, hierarchy, and visual direction are applied. This step helped assess whether the core flows still felt clear and approachable at a more realistic level.

I translated the validated structure into detailed screens for onboarding, discovery, and buddy requests, focusing on consistency and readability rather than adding new functionality.

With the structure validated in low fidelity, I moved into high-fidelity design to explore how the experience holds up once real content, hierarchy, and visual direction are applied. This step helped assess whether the core flows still felt clear and approachable at a more realistic level.

I translated the validated structure into detailed screens for onboarding, discovery, and buddy requests, focusing on consistency and readability rather than adding new functionality.

High-Fidelity Testing

High-Fidelity Testing

High-Fidelity Testing

To evaluate the high-fidelity prototype, I ran moderated usability tests with five target users. The sessions focused on whether people could discover activities, assess fit, and complete key flows with confidence. The sessions centered on these core tasks:

  1. Finding and joining a club

  2. Creating a training partner request

  3. Completing onboarding and profile setup

To validate the updated design, I ran a second round of moderated usability tests with five users. This round focused on the two core tasks:


  1. Starting a planning group and inviting friends


  2. Creating and interacting with a poll

What went well
What went well
What went well
  • Onboarding felt intuitive and easy to follow

  • Club pages were clear and inviting

  • Buddy request creation was straightforward

  • Location-based discovery helped users orient quickly

What could be improved
What could be improved
What could be improved
  • Some cards felt visually dense

  • Filters and chips were sometimes unclear

  • Certain elements appeared clickable when they were not

  • Small interaction details reduced confidence

Key takeaway:

All users completed the tested tasks, but feedback highlighted opportunities to improve clarity and reassurance before committing.

Iterations

Final Outcome

Final Outcome

Final Outcome

Moov creates a more approachable way to discover sports clubs, events, and training partners by reducing uncertainty before joining.


By surfacing key details early and adding small trust signals, the experience helps people feel confident taking their first step into social fitness.


Moov creates a more approachable way to discover sports clubs, events, and training partners by reducing uncertainty before joining.


By surfacing key details early and adding small trust signals, the experience helps people feel confident taking their first step into social fitness.


Reflections & Next Steps

Reflections & Next Steps

Reflections & Next Steps

This project reinforced how small moments of friction can disrupt confidence, even when motivation is high. Testing showed that clear structure, feedback, and onboarding mattered more than adding new features.

With more time, I’d explore deeper profiles, lightweight accountability features, and simple tools for organisers to set expectations and support their communities.

This project taught me how powerful it is to test early and design for different participation styles. Even low-fidelity prototypes revealed issues with navigation and feedback that I wouldn’t have caught from static screens alone.


Designing inside an existing system like Ticketmaster’s meant borrowing familiar components while still pushing for new behaviour – a balance between respecting constraints and advocating for user needs.

Let's create something extraordinary

Let's create something extraordinary

If you’d like to collaborate on a digital product, improve a feature, or bring a new idea to life, I’d love to chat.