SponsorLens: Designing a Human-Centered Computational System to Support Peer Mentorship in Substance Use Disorder Recovery
2022-11
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SponsorLens: Designing a Human-Centered Computational System to Support Peer Mentorship in Substance Use Disorder Recovery
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2022-11
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Substance use disorders (SUDs), such as alcohol and drug abuse, are widespread and hazardous to public health. Over the last two decades, nearly one million Americans have died from a drug overdose. In 2020 alone, over ninety thousand died from a drug overdose in the United States. This marks a 31\% increase in overdose deaths from 2019 to 2020. Due to this dramatic increase in overdose deaths, the need for effective treatment is great. However, SUDs have been historically difficult to treat, given their chronic cycles of treatment and relapse. To improve treatment outcomes, new and supplemental approaches are needed. This dissertation aims to position participants as co-designers and subject matter experts in designing technologies that complement, rather than disrupt, the current values, practices, and challenges of individuals in recovery from SUDs. To contribute a rich qualitative understanding of the values, practices, and challenges of individuals in recovery from SUDs, I conducted a series of participatory design workshops with sixteen women living in a sober living environment. While several challenges and practices were highlighted in these workshops, participants independently and unanimously chose to focus their designs on technology to support their relationship with their peer mentor (i.e., sponsor). I then expanded this study to investigate how social computing may support or hinder dyadic mentorship within SUD recovery. I conducted twenty-seven semi-structured interviews with fifteen mentors (i.e., sponsors) and twelve mentees (i.e., sponsees). This study informed the creation of specific design implications to increase mentor (i.e., sponsor) capacity, facilitate mentorship (i.e., sponsorship) initiation, and grow a broader support community for mentees (i.e., sponsees). Finally, to contribute a deeper empirical understanding of social computing interventions for peer mentorship in SUD recovery, I designed and developed a high-fidelity prototype called SponsorLens. SponsorLens functioned as a synchronous communication and scheduling system to increase mentor (\i.e., sponsor) capacity and the frequency of contact between mentors (i.e., sponsors) and mentees (i.e., sponsees). To investigate the feasibility of SponsorLens, I conducted a four-week field deployment study with four dyadic mentorship pairs in recovery from SUDs.
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University of Minnesota Ph.D. dissertation. November 2022. Major: Human Factors/Ergonomics. Advisor: Svetlana Yarosh. 1 computer file (PDF); xi, 244 pages.
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Schmitt, Zach. (2022). SponsorLens: Designing a Human-Centered Computational System to Support Peer Mentorship in Substance Use Disorder Recovery. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/252511.
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