Healing Heads is a migraine management tool that helps migraine patients manage their condition. The project aims to alleviate the burden migraine patients face, improve their quality of life and overall well-being.
This case study is from my capstone project, which I completed as a final requirement from my masters degree in UX Design at Maryland Institute College of Art.
A migraine is much more than a bad headache. This neurological disease can cause debilitating throbbing pain that can leave patients in bed for days. Sound, light, movement and other triggers may cause symptoms like pain, nausea, visual disturbances, numbness and tingling, irritability, tiredness and many more.
It is estimated that 1 out of 7 people suffer from migraines and it was found to be the sixth highest cause of years lost due to disability (YLD). It’s globally prevalent between 20 to 64 years old and it peaks between 30-39 years old. This overlaps with the prime working age population (25-54).
I envisioned my output to be a functional app for users to input their information. In return, Healing Heads will utilize this data to help users in migraine management.
A unique value proposition of this app is the integration of AI and Machine Learning into its features. The overall concept of this integration revolves around data gathering, finding patterns and insights, and synthesizing this data into predictions and recommendations. The app will try to find patterns in user inputted data and in external data. It will then give its insights, analysis and inform them which days have a higher chance of experiencing an attack.
To approach the problem, I drafted a timeline of activities and methodologies for this 8 week duration.
The main beneficiary and primary users of Healing Heads are migraine sufferers. There is currently no cure for migraine, so the main benefit is for this tool to help them manage their condition.
My secondary beneficiaries are doctors and healthcare professionals. They can benefit from this project by receiving accurate patient data from this app, fostering communication with their patients, and overall helping them make better clinical decisions in their practice.
Research played a pivotal role in helping me gather insights, validating concepts, empathizing with users, and making informed decisions. Some of the research methods I conducted were: Desk research (Gathering of statistics and data, reading through journals and scholarly articles, listening to podcasts of doctors and healthcare professionals, reading community forums and entries from migraine and headache groups), competitive analysis and conducting user interviews
I interviewed 6 migraine patients with the following objectives:
I interviewed 2 subject matter experts with the following objectives:
Unexpected migraine attacks have disrupted their work, travel or social gatherings. They worry about neglecting work, chores and responsibilities during an attack.
Hard to look at the phone for a prolonged period of time due to light sensitivity during a migraine attack.
Participants hesitate to visit doctors because migraine is an invisible condition. Their pain might be dismissed, minimized or not taken seriously.
Participants do not have a consolidated headache journal. They have attempted to start one in the past.
Participants do not want people fussing over them during an attack. They prefer to rest and recover on their own.
Synthesizing my results with an affinity map helped me find themes and insights from my user interviews.
I created a total of three personas two user types: the migraine sufferer and the doctor.
Given the limited timeframe, I had to refine the MVP and prioritize certain features and flows. I focused on Michelle’s persona, journey map and user story.
Napkin sketches have often been my go-to companion when I find that spur of the moment inspiration. I drafted some crazy 8s at a cafe. I then proceeded to make a sitemap for a high level overview of the site structure. This provided the foundation as I proceeded to creating my wireflows and wireframes.
While logging attack details, some of the important information I wanted to capture in the app were: Attack duration, intensity, pain spot, symptoms and triggers. I wanted to give the users an option to record their answers during an attack.
However, initial feedback gathered found that there was a confusion regarding the purpose of this microphone icon.Users will have to keep on going back and forth between interacting with the app then recording their answers, and it didn’t make sense.
So I separated logging migraine attack methods into two. The first one being logging attack with audio and the second one being logging attack with manual input.
Usability testing findings found that there was a confusion in the introductory screen of logging migraine attacks. Users would have to select “audio” or “manual” then click the “get started” button. User feedback gathered that it was an unnecessary extra step that could have been merged into one CTA button. So I merged them into one in the revised introductory screen.
My design system consisted of a style guide and components. My choice of using dark mode was made to address light sensitivity. I also chose monochromatic tones to create a sense of calmness and serenity. The shades of blue also reflect calmness, healing, soothing, knowledge and wisdom.
The objective of this flow is for initial assessment and to understand the user’s background, to tailor fit the content for them, and to train models from the data gathered.
The objective of this flow is understanding the user’s unique condition and help them recognize triggers by identifying patterns from their inputs. Other objectives are to provide users with the data to back their claims when they seek help, and help doctors make better diagnosis and treatment plans with accurate data.
This flow lets users input their everyday lifestyle, and habits. This flow can help users understand the correlation between lifestyle and migraines, to help them consolidate their records, and to monitor the effectiveness of treatment. Some of the important information I wanted to capture were: Lifestyle factors such as sleep and water intake, stress levels and triggers.
With the data collected from the three user flows, the data model generates predictions.