Case Study · UX Research
Strava App
UX Research & Usability Evaluation
Role
UX Researcher
Timeline
December 2025
Methods
Cognitive Walkthrough · Usability Testing · Interviews
Participants
6 Active Users
Overview
Strava is one of the most popular fitness tracking platforms, used by runners, cyclists, and athletes worldwide. It combines performance analytics, route planning, training tools, and a social fitness network into a single app.
But this breadth introduces a fundamental tension: the more a platform tries to do, the harder it can be for any individual user to achieve their specific goal. This study set out to evaluate where Strava excels — and where its complexity becomes friction.
“How well does the Strava app enable users to achieve their goals within the app?”
Central Research Question
Approach
I ran remote moderated usability tests via Lookback and Teams with six active Strava users across varying experience levels, sports backgrounds, and device types. Sessions were conducted in December 2025 and focused on four core feature areas: analytics, route planning, training and tracking, and social interaction.
Cognitive Walkthrough
Systematic expert evaluation stepping through key flows to surface usability issues and form hypotheses before user testing.
Usability Testing
Moderated remote sessions observing real users completing goal-oriented tasks across the four focus areas.
Interviews
Pre- and post-task interviews to capture mental models, motivations, and frustrations in the participants' own words.
Participants
P1 · 26 yrs
Running
2 yrs on Strava
Apple Watch
P2 · 31 yrs
Cycling
5 yrs on Strava
Garmin
P3 · 38 yrs
Triathlon
8 yrs on Strava
Garmin
P4 · 34 yrs
Running · Hyrox
1 yr on Strava
Apple Watch
P5 · 42 yrs
Cycling
12 yrs on Strava
Wahoo
P6 · 45 yrs
Running
0.5 yrs on Strava
Apple Watch
Finding
01
Performance & Analytics
Users found the analytics too complex, poorly prioritised, and lacking the explanations needed to act on data.
The analytics screens surface a large volume of similar-looking metrics with no clear hierarchy and minimal context for what each number means. Less experienced users knew data existed but couldn't interpret it. Even experienced users reported skipping metrics they didn't understand rather than exploring them. The result: rich data that users had stopped trusting.
“Cognitive overload from metric density erodes trust in the data itself.”
Finding
02
Route Planning
A high-value feature that went largely unused — participants preferred Komoot or AllTrails due to high friction and poor mobile usability.
Route planning had the highest task abandonment rate in testing. Users were frequently unsure how to start, the interface offered no onboarding guidance, and the mobile experience was significantly degraded compared to desktop. Several participants described a workaround pattern: plan elsewhere, execute on Strava, sync afterwards.
“Users were outsourcing a core Strava feature to competitors because of UX friction.”
Finding
03
Training & Tracking
The app's most-used feature — but unreliable stop-and-go behaviour and GPS inconsistencies eroded confidence over time.
Activity tracking is central to why most participants use Strava, yet they reported GPS inaccuracies, sync delays, and occasional data loss. These issues were tolerated because there was no perceived alternative — but they described a gradual erosion of trust in the platform. Athletes managing structured training loads felt this most acutely.
“Reliability issues in the core feature have an outsized effect on overall platform trust.”
Finding
04
Social Features
Social features were strongly polarising — motivating for competitive users, anxiety-inducing for recreational athletes.
Kudos, segment leaderboards, and activity feeds were consistently described in opposing terms: motivating or pressure-inducing, connecting or comparing. The platform offers limited control over what social content is surfaced and at what prominence. Users who wanted to opt out of the competitive layer had no clear mechanism — short of hiding activities altogether.
“Social features need to be opt-in at a granular level, not opt-out.”
Recommendations
Individualizable analytics dashboard
High ImpactLet users define their primary goal upfront and surface only the 3–5 metrics most relevant to it. Use progressive disclosure for deeper data — so the interface stays approachable for beginners without losing depth for experienced athletes.
Simplified route planning for mobile
High ImpactGuided entry points, a native drawer interface for map interaction, and an onboarding flow for first-time route creators. Reduce the steps to start and clarify the visual relationship between the map and the route settings panel.
Technical reliability fixes
MediumAddress GPS accuracy inconsistencies, sync delays, and stop-and-go tracking issues. These affect the most-used feature and have a disproportionate impact on overall platform trust — especially for athletes managing structured training.
Descriptive feature and section titles
Quick WinReplace generic labels with titles that explain what a section does and why it matters. Small copy changes that significantly reduce cognitive load — especially for users who are still learning the platform.
Unified time interval display
Quick WinStandardise how time intervals are presented across all analytics views. Inconsistent formats were a recurring source of confusion — a single display convention removes a small but persistent friction point.
Content differentiation by experience level
MediumAdapt the depth and language of analytics content to the user's experience level. What reads as useful detail to a competitive cyclist reads as jargon to someone who started running six months ago.
Optional social and comparison features
MediumAllow users to disable leaderboards, hide comparative metrics, and control activity visibility — without removing the social layer for users who value it. Opt-in competition is more sustainable than opt-out.
Improved clubs and events discovery
BacklogClubs and local events surfacing were hard to find and felt disconnected from the main experience. Better discovery and integration would increase retention among users whose primary motivation is community.
Reflection
Strava offers a lot. But it doesn't enable all users equally well. The clearest takeaway from the research: less complexity, sharper prioritisation, and stronger personalisation have the greatest improvement potential — and these three things are deeply connected.
The social findings challenged my initial assumptions. I expected social features to be broadly positive — instead they revealed how the same design decision produces opposite experiences depending on why someone uses the app. That's a harder design problem than a missing feature or a broken flow.
With more time, I would have tested prototyped versions of the high-impact recommendations — particularly the individualizable dashboard — to validate whether the direction actually resolves the cognitive overload, or just moves it somewhere else.
Next Project
Somnus
Clinical Study Management Platform