GARD is an addiction recovery assistant app prototype created as part of my graduate studies in HCI and Design.
I was tasked with creating a user experience of my choice sourced out of the following formula:
(thematic context) + (purpose) + (target outcome)
The specific formula I created was:
(addiction recovery) + (connection and community) + (staying alive and moving towards wholeness)
While researching the issue of addiction and addiction recovery I discovered that:
- 1 in 10 US adults will struggle with drug use disorder at some point in their lives.
- 700,000 Americans died from overdosing on a drug from 1999 to 2017.
- 2M+ Americans receive treatment for drug and alcohol addiction each year.
40-60% of those receiving treatment for drug addiction will relapse. Alcoholics are between 50 and 90 percent.
These facts, coupled with my own experiences in working with people struggling with addiction, led me to ask “How might I lower the relapse rate by helping people recovering from addiction to stay connected to a healthy community and moving towards wholeness?“
GARD is a mobile app designed with three primary goals in mind:
GARD dynamically protects users from isolation and encourages relationship growth using ML.
ML-powered and location-aware suggestions give users early warning to avoid trouble.
Users gain points and rank by making positive choices around connection and avoidance.
The starting point was my own experience working with people struggling with addiction. My initial research was focused on interrogating the hypothesis that the majority of people who enter a drug and alcohol rehab will relapse soon after. The research confirms this hypothesis although it should be noted that research surrounding drug and alcohol addiction is rarely as precise as one might wish due to the high level of variability found in the people being studied (e.g. when people relapse they aren’t going to stay involved in any research study they may have been a part of).
Although outside of the scope of research for this project, I did speak informally to several people currently in a drug and alcohol rehab program asking them why they thought the relapse rate was so high. The consistent answer revolved around leaving healthy community.
Key decisions made:
- Machine learning would need to be part of the solution allowing for seemingly disparate data points to be connected.
- The solution needed to be available to users at all times, therefore a mobile app made the most sense as even the poorest people struggling with addiction can get a free Obama phone.
- The core functionality of the app needed to revolve around keeping the user connected to healthy community in a way not afforded by other social networking platforms.
Sketches and Wireframes
My sketches, while admittedly very messy, played an essential role in allowing me to visually brainstorm efficiently before trying to lay out the key elements in wireframes.
Key decisions made:
- Privacy norms would not be upheld. While the user would have initial control over the entire experience, this control would be something they could (and likely should) cede over to a trusted mentor. For example, I envisioned that the user could allow their mentor to set a password unknown to the user which would keep them from deleting the app or changing key settings. This is the idea behind the meaning of the name GARD (Romanian for “fence”). That while the user is healthy they can, together with their trusted community, build a set of digital fences that will come into play should they start to stray from the path of sobriety.
- Move away from the standard nomenclature of “friends” to denote social connection. GARD uses the term “Circles” to denote these social connections for the primary reason that the app is knowingly going to break privacy norms for the sake of accountability and I wanted an easy way to separate weak and strong-tie relationships. GARD calls strong-tie relationships “Inner Circle” relationships and weak-tie “Outer Circle” relationships.
- To design the app with Android Material Design in mind because of my familiarity with the platform.
Key decisions made:
- To use a black and white color scheme. While certainly a recent design trend I thought it spoke well to the life and death nature of the subject matter too.
- Based on a peer review, I changed the ML training icon from a brain to the sliders seen in the current iteration.
- Peers and instructors were initially confused by the Android FAB button. I chose to keep it based on my understanding that they were iOS users and therefore largely unfamiliar with the Android platform.
- To move from 5 navigation bar items to 4. This was a matter of realizing that all the content found under Actions could be placed in the Feed.
“10 Percent of US Adults Have Drug Use Disorder at Some Point in Their Lives.” National Institutes of Health (NIH), 18 Nov. 2015, https://www.nih.gov/news-events/news-releases/10-percent-us-adults-have-drug-use-disorder-some-point-their-lives.
Drug Rehab Statistics, Rehabilitation Stats. http://luxury.rehabs.com/drug-rehab/statistics/. Accessed 20 Feb. 2021.
“Addiction Statistics | Drug & Substance Abuse Statistics.” American Addiction Centers, https://americanaddictioncenters.org/rehab-guide/addiction-statistics. Accessed 20 Feb. 2021.
McCance-Katz – The National Survey on Drug Use and Health 2017. Pdf. https://www.samhsa.gov/data/sites/default/files/nsduh-ppt-09-2018.pdf. Accessed 20 Feb. 2021.
“U.S. Drug Treatment Statistics.” Michael’s House Treatment Centers, https://www.michaelshouse.com/drug-rehab/us-drug-treatment-statistics/. Accessed 20 Feb. 2021.