Workout Playlist | Add-a-Feature — Ali Cook

Workout Playlist | Add-on Feature

This project explores an add-on feature for Spotify designed to better support the workout experience.

Spotify offers powerful music discovery and personalization, but it isn’t built for the workout moment.

When songs don’t match effort level, users lose focus, energy, and momentum—often skipping tracks to compensate.

This feature aligns music with workout intensity, reducing disruption and helping them maintain momentum without constant adjustment.

UX/UI Designer/Researcher
6 weeks
iOS mobile app

Figma
Lyssna
View prototype

Understanding the Problem

Music shapes the workout experience—but when it doesn’t align with effort level, it creates friction.

Users often rely on familiar playlists to maintain control, yet misalignment between song intensity and workout effort still occurs, disrupting focus and momentum. To compensate, users skip tracks mid-workout, introducing repeated manual interaction when their attention should be elsewhere.

These interruptions aren’t minor—they directly impact workout quality. Focus breaks, energy drops, and momentum becomes harder to sustain.

While Spotify excels at discovery and personalization, it isn’t designed for alignment with physical effort during workouts—revealing an opportunity to better support workout flow without disrupting the familiarity users rely on.

Research

To understand how people use music during workouts, I conducted 5 semi-structured interviews with individuals who regularly exercise and listen to music.

Participants varied across:

- Solo gym sessions
- Home workouts
- Instructor-led classes
  (in-person and virtual)
- Strength training
- Cardio
  (interval sprints, jogging, and walking)
- Spotify (primarily)
- Alternative platforms
  (including one that supports downloaded MP3’s)

Secondary Research

To better understand how existing products support the workout experience, I conducted a competitive analysis across music streaming and fitness platforms, including Spotify, Apple Music, Amazon Music, PowerAmp, and Peloton.

This helped identify where current solutions succeeded—and where they fell short.

Findings indicated that existing products tended to prioritize either workout-aware music adaptation or user control and familiarity, rather than combining both.

Key Insights

Interviews focused on identifying behavioral patterns, decision-making, and friction points within the workout experience.

Findings were synthesized through affinity mapping.

Music directly impacts performance

When music aligns with effort, it supports focus and energy. When it doesn’t, it becomes a distraction.

Mismatch leads to frequent skipping and flow disruption

4/5 participants skipped songs during workouts
often mid-set, disrupting momentum
some users rushed through sets just to skip a song
behavior was frustrating but normalized

Familiarity is preferred—but not enough

Users relied on their own playlists for control, but still experienced intensity mismatch and repetition from shuffle suggesting that personalization alone doesn’t address workout-specific needs.

Together, these patterns revealed a core tension between the familiarity users wanted, and the manual interaction required when music no longer matched their effort.

Defining the User

Built from *repeated behaviors observed across interviews**, not demographics.*

Alex Martinez

Music-Driven Flow Regulator

Core Tension

Wants uninterrupted workout flow, but must adjust music mid-workout, breaking focus.

The Problem

During solo workouts, users frequently encounter song–intensity mismatches that require manual adjustment mid-exertion, disrupting focus and breaking momentum.

These interruptions are frequent and normalized, yet not adequately addressed by current music platforms.

The Opportunity

How might we improve intensity and mood alignment within familiar listening experiences to reduce mismatch and support focus and momentum during workouts?

Ideation & Direction

Exploration and Design Direction

I explored multiple approaches to reduce song–intensity mismatch while preserving the familiarity users rely on. This exploration focused on apps and platforms that emphasized a shift toward music adapting to the workout.

While each product approached music adaptation differently, I realized that no single approach fully addressed the problem. Users need familiarity and control and reduced interaction during workouts.

From this insight, a hybrid direction emerged:

Structure the workout upfront

Allow users to use their own playlists

Adapt music to match intensity over time

Users can apply this system to their existing playlists, preserving familiarity and control.

This direction laid the foundation for a more structured, low-friction workout experience.

Defining the Structure

Core User Flow & Wireframes

The core user flow and wireframes established the navigation and structure before visual design.

Core User Flow

Based from research insights and the persona
Translated user needs and goals into logical steps and decision points that map the user’s journey

Wireframes

Visually translated the core user flow into a sequence of screens used to define the structure, hierarchy, and flow
Helped map how users move through the setup process—from selecting workout inputs to generating a playlist aligned with intensity over time

How it Works

Select Your Workout

• Activity
• Duration
• Intensity pattern

Choose Your Music

• Let Spotify choose
• Use an existing playlist
• Customize with AI Playlist

Start Your Workout

Songs are automatically arranged to match intensity over time

Below are two key screens that show the structure of the system and key decision-making points

Intensity Selection Screen

Represents core concept
Shows intensity model
(warm up/peak/cool down)
Connects directly to value:
music adapting to workout phases

What Makes It Work

Aligns music to workout phases

warm up, peak, cool down

Reduces mid-workout interaction

by structuring playlists upfront

Preserves Familiarity

through existing playlists

Early thinking

Design Decisions

Designed for familiarity

Built within Spotify’s existing playlist flow to feel intuitive and easy to adopt

Setup Over Interruption

Moves decision-making upfront to reduce interaction during workouts

Flexible, Not Prescriptive

Supports both user-selected playlists and system-generated options

Low-fidelity screens depicting the core user flow

Pick Your Music Screen

Shows key decision point
Communicates flexibility + user control
Offers options instead of forcing behavior

Aligning music to workout phases:
Image shows how songs are arranged chronologically by BPM and workout phase

What Changed Through Wireframing

Early concepts explored multiple ways to structure the experience, but wireframes made it clear that:

too many options upfront created hesitation
the flow needed clearer sequencing
structure mattered more than adding features

This led to a more guided, step-by-step setup flow that reduces decision-making during workouts.

Wireframing clarified not just how the feature should work—but what to remove to keep the experience focused

Usability Testing

Evaluating the Setup Experience

Five users with varying workout and music habits
Remote moderated usability sessions with think-aloud protocol
Create a workout playlist for a
45-minute run

Navigation, clarity of inputs, and
overall usability
Task completion, user understanding, and confidence in the result

What Was Tested

Testing focused on the Workout Playlist setup experience, including:

Selecting activity, duration, and intensity
Choosing a music source (Spotify, existing playlist, or AI)
Generating a playlist aligned with workout phases

Iterations

Usability testing revealed a few key friction points—primarily around feature clarity, decision overload, and interaction efficiency.

Iterations focused on high-priority issues identified during testing that directly impacted overall functionality and the user experience. At a high-level, iterations focused on reducing cognitive load, improving clarity, and supporting real-time flexibility during workouts.

Clarifying Feature Recognition and Reducing Overload

Problem

20% of participants selected “Playlist” instead of
“Smart Fitness Playlist”
Multiple participants hesitated between similar playlist options before starting the workout

Iteration

Renamed feature → Workout Playlist
Grouped playlist options into clearer categories
Maintained visibility (avoided hiding features behind extra steps)

Intended Impact

Improve feature recognition
Reduce decision fatigue
Create a clearer path to start workout

Improving Time Selection Efficiency

Problem

Time duration picker worked, but interaction was unclear
Participants were split between expecting manual input (50%) and scrolling/dragging (50%)
Scrolling to precise values was perceived as inefficient

Iteration

Introduced preset time intervals (quick selection)
Added optional custom duration entry for flexibility
Increased increments (5 min intervals) to reduce effort

Intended Impact

Enable faster selection
Reduce interaction effort
Support different input preferences

Supporting Real-Time Workout Flexibility

Problem

20% of participants raised a high-impact concern: workout intensity may change unexpectedly during a workout
Without real-time adjustment, the planned workout could become misaligned with actual effort, causing the music to no longer match the user’s intensity

Iteration

Added in-playlist intensity adjustment
Lightweight control (bottom sheet)
- Take it easier
- Keep current
- Push harder

Intended Impact

Maintain workout flow
Reduce the need to restart or navigate away
Keep music aligned with real-time effort

Playlist Options Screen

Before

After

Time Duration Screen

Before

Intensity adjustment option

After

Workout Playlist transforms how users experience music during workouts by aligning songs to intensity—while preserving control through familiar playlists or guided options.

Following usability testing and iterative refinement, the final Workout Playlist experience simplifies setup while adding flexibility for changing effort during workouts.

Final Solution

Users can apply this system to their existing playlists, preserving familiarity and control.

Core Experience