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Atlas Mental Health

Atlas Mental Health

Branding and Mobile App Design

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About Atlas Mental Health

In the United States, 1 in 5 college students suffer from depression. Atlas is seeking to help students manage their depression via a mobile application that enables them to track completion of activities of daily living and coaches them through cognitive behavioral therapy (CBT) exercises. We worked with the Atlas team to help them conceptualize the first version of their app.

Project Goals

Design a minimum viable product and brand direction for the Atlas mobile app. The first version should focus on activities of daily living, include flows for two main users, and support future integration of cognitive behavioral therapy exercises.

My Role: Product Designer

  • User Research & Usability Testing

  • UX Design

  • UI Design

 

TL;DR

Delivered an MVP prototype of the Atlas mobile app and a visual style guide. The prototypes will help the Atlas team further develop the rest of the Atlas application as they integrate CBT exercises. The estimated launch time of the Atlas app is 2018.

Click around the final prototypes below (“Main Customer” on left, “Accountability Partner” on right):

 
 

 

Research

The Intersection of Mental Health and Technology

As designers new to the field of mental health, we wanted to get up to speed with current stats and solutions. Mental Health can be defined as, “a state of well-being in which every individual realizes his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to her or his community” (WHO). Mental Health America reports that there is a shortage in mental health providers with a ratio of 4 individuals needing help to 1 provider.

Fortunately, there has been a lot of recent attention on creating digital solutions for mental health. Apple pegged self-care as a top trend for 2017 (Techcrunch) and in recent years, there has been a reduction in stigma around mental health which historically has prevented individuals from seeking help. Digital delivery of these programs can reach people who have taken the first step of acknowledging that they might benefit from an app in this space and according to Statista, 11% of 18-29 years olds use an app to relieve stress.

Cognitive Behavioral Therapy is one type of therapy that has seen great success in helping patients overcome mental health issues. It is also well-suited to technology allowing individuals who may not want to speak to someone face-to-face or are unable to geographically, to record and analyze their negative thought patterns via an app. In the case of Atlas, the app will be used in addition to face-to-face therapy sessions further expanding the patient’s support system.

Completion of Activities of Daily Living and mood tracking are additional inputs that a patient and therapist can use to understand their mental health.

Atlas’ Business Model

It is an incredibly exciting time for technology to be infiltrating the health system. A few key challenges that technology can address include:

  • Accessibility – reaching patients that prefer not to meet face-to-face or who live in remote areas

  • Shortage of Therapists – supplementing in-person therapy making a therapist’s treatment more successful (eg. digitizing exercises, offering different modalities for different types of learners, the use of AI)

  • Cost – many psychologist offices are considered out-of-network and the majority of consumers are not yet ready to pay for healthcare; by partnering with employers and insurance companies (B2B), cost to the patient can be reduced

When designing, it’s important to keep in mind how an end user will first discover your application. In the case of Atlas, our main user will be referred to the app by their therapist. The buddy user would then be referred to the app by the main user:

Atlas Customer Model: Clinic (subscriber) pays for app; main user (patient) is referred to the app by their therapist; a buddy is then invited to the app by the main user

Atlas Customer Model: Clinic (subscriber) pays for app; main user (patient) is referred to the app by their therapist; a buddy is then invited to the app by the main user

Research Questions

Before diving into competitor products, we came up with a few key questions to keep an eye out for in our analysis:

  • How will this app differ from existing task management, habit tracking, and list-making applications?

  • How do we design in a way that engages depressed users and doesn't cause more anxiety?

  • How do we provide a clear, actionable experience for buddies?

Competitive Analysis

We researched a variety of existing applications in the space and conducted feature analysis and usability testing.

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Customer Interviews and Survey Outreach

Due to health privacy laws, it is quite challenging to get in touch with our “main user” (the patient) by going through a therapist. Instead, we conducted outreach and sent out a link to an anonymized survey on depression to college students on various social media forums. We targeted individuals who had depression during college, went to college in the last 10 years, and own a smart phone.

We received 60 survey responses and conducted a handful of phone calls with university students willing to discuss with us their depression.

In summary, we were able to learn about the impact of living with a mental illness, emotions experienced while dealing with depression in college, and the type of support most valued. We found that:

  • The most valuable support comes from a few close friends and/or family members

  • On average, 2-3 people are fully aware of a user's condition and can act as buddies

  • Shame and anxiety are primary inhibitors to reaching out when in need

Persona

We formed a persona based off of our research. Due to the difficulty in speaking with actual end users of the product, we note that this is a provisional persona that will need to be further validated once the product has a user base.

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Ideation

Combining initial feature ideas from the client, customer interviews and competitive analysis, we were able to refine scope for a V1 of the application:

  1. Truncated task management

  2. Mood tracking input & data visualization

  3. Accountability & notification flows

  4. Ability to connect with buddies in-app

  5. Geofencing capabilities & automatic task confirmation

  6. Gamification without punitive measures

Information about the emotional state of our main users helped guide how we approached feature design and UX flows. Since anxiety levels are already high for the end user, triggering further anxiety by presenting too much information, providing punitive feedback, or having unclear next actions needed to be avoided.

We conducted a Design Studio to sketch initial ideas to bring into lo-fidelity designs.

 

Designing a Supportive Brand Experience

The next step was to dive straight in to the feel of the app.  The atmosphere to be created in hi-fidelity designs functions as a main determinant of how users perceive the app.

Following a multi-hour branding workshop with the company's leadership team, style tiles were created to provide options for brand direction. The chosen color palettes evoke grounded, tranquil, or energizing experiences.  The typefaces selected are all sans serif with higher than average x-heights and open, rounded counters.  This achieves a modern, approachable look and adds a friendly personality to the interface. 

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Plant graphics were created for the app home screen to track progress, and their style needed to generate a mood of inspiration and liveliness.  Since the plants "grow" in response to task completion, they were designed with a flat, bright color scheme to inspire light-heartedness and further the app's messaging of support and forward progress.

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Lo-fidelity

The insights we gained from research, the branding workshop and the design studio allowed us to dive into lo-fidelity designs. We conducted 3 rounds of usability testing to guide our design decisions and created a final version of lo-fidelity mockups.

Here are a few examples of iterations throughout the lo-fi phase:

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The following design patterns worked well in lo-fidelity testing and we aimed to carry them thru to hi-fidelity:

  1. Nested or hidden functionality so that the user can focus on relevant tasks

  2. Automated check-in for location-based tasks

  3. Data visualization & collection that is useful for all user types

 

Hi-fidelity

In hi-fidelity, the goal was to create an experience that is light, approachable, and guides the user towards the next action. To achieve this, we kept the following top of mind when designing components:

  1. Hairline strokes

  2. Rounded corners

  3. White space

  4. Splashes of bright color

  5. Gray text for softer impact

  6. Outlined icons for lightness

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Hi-fidelity designs were tested using a clickable prototype. Continued testing of current designs with target demographics & creating additional flows for CBT exercises are two of the top priorities going forward for Atlas.