MoodTune
product Design | spring 2024
Harmonizing both Mind and Music
Timeline
CLIENT
3 Months
EmiT Lab
My role
Tools used
Product Strategy
User Research
User Experience
Usability Testing
Figma
AfterEffects
Lucid
Background
In response to rising mental health concerns in a digitally connected world, MoodTune was conceptualized as a distinct platform dedicated to leveraging music analytics for mental health support. This initiative stems from the limitations in existing music services’ ability to actively engage with users’ emotional states and a broader industry trend towards personalized health and wellness solutions.
MoodTune’s development is driven by two core observations:
Current music platforms lack a dynamic response to users' emotional and mental health needs, offering generic listening experiences without tailoring to the nuanced emotional states of their users.
There's an underserved demand for integrating mental health support with daily technology use, particularly in ways that harness the therapeutic potential of music.
Identifying this void, MoodTune aims to introduce a novel approach to music consumption that prioritizes mental health by offering personalized musical experiences designed to reflect and positively influence users' emotional states.
To kickstart the user research, I worked with the Siebel Center of Design to conduct an in-depth survey with 100 University of Illinois students.
user research
92%
of students revealed they turn to music as a form of stress relief during exam periods.
However, on average, students spend approximately 15 seconds in-app attempting to tailor their song search to mood, creating user grievances and engagement hurdles.
79%
of students expressed a desire for increased personalization regarding new music discovery on existing platforms
70%
of students ranked real-time mood analytics and music recommendations as their top preference in MoodTune’s core functionality.
Results were shared in an aggregated format, ensuring individual responses could not be identified
Competitor Analysis
In framing MoodTune’s position within the market, I analyzed how existing platforms integrate mental health and music, focusing on to user engagement and personalization:
Calm focuses on meditation and mindfulness, with music as one of several tools offered to improve mental well-being. Their approach is broad, targeting general stress and anxiety relief without deep personalization in music selection.
CALM
HEADSPACE
Similar to Calm, Headspace provides guided meditation sessions with some music-based elements aimed at relaxation and sleep. While effective for mindfulness, it lacks the direct focus on leveraging music analytics for emotional state detection and personalized intervention.
PANDORA
Pandora uses the Music Genome Project to create personalized stations based on user preferences. While it offers a music-centric experience, Pandora does not specifically target mental health nor adapt music recommendations based on emotional well-being insights.
SPOTIFY
Spotify provides extensive music discovery and personalization features, including mood-based playlists. However, its recommendations are not directly tied to real-time emotional state analysis or designed with the explicit goal of mental health support.
KEY
TAKEAWAYS
Niche for MoodTune: There’s a clear gap for a service like MoodTune that intricately ties music selection directly to the emotional and mental state of the user, beyond generic mood-based playlists.
Personalization and Engagement: Effective competitor platforms use various levels of personalization, but lack real-time, dynamic adaptation to users’ emotional needs. MoodTune’s focus on personalized mental health support through music fills this void.
Integration with Mental Health: While some competitors incorporate elements beneficial to mental health, none offer a comprehensive, music-first approach specifically designed to assess and respond to users’ mental states through analytics.
user flow
In order to demonstrate the primary function of MoodTune, I utilized Lucid to create a sequence of steps a user would likely encounter when choosing different emotions in-app to receive haptic feedback.
As the user interacts with the system, it performs functions like mood selection, receiving song or podcast recommendations, and playing them with a database element for storing data.
information architecture
Additionally, to promote a coherent experience across different screens, I mapped the IA of MoodTune, expanding on the different screens to be created in my prototype.
Low-Fidelity Prototype and Revisions
Using the information architecture as a guide, I used modern pen-and-paper to create multiple rapid iterations of MoodTune’s different screens.
To synthesize an intuitive and effective design, I connected my screens into a wireframe with additional ‘Log In’ and ‘Home’ screens, ensuring consistency in flow screen-to-screen.
I first created 4 screens to perform function designed above. The screens shift from the ‘home’ to ‘input’ mood screen where users can either select/type their mood. I then combined the ‘results’ into one screen.
high-fidelity prototype
Welcome to MoodTune. Try clicking ‘Log in’ and inputting your mood as ‘Happy’ to see your song recommendations for today.
IMPACT
After printing paper screens of my high-fidelity prototype, I went back to the Siebel Center of Design to conduct MoodTune’s usability testing on the same 100 individuals who were initially interviewed. Here’s what I discovered:
Increased Personalization Satisfaction
Positive Feedback on Mental Health Support
Behavioral Changes in Music Consumption
Due to privacy reasons, please contact me if you’re interested in learning more about this project's quantifiable impact!