4 research outputs found
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The InclusiveMag Method: A Start Towards More Inclusive Software for Diverse Populations
How can software practitioners assess whether their software supports diverse users? Although there are empirical processes that can be used to find “inclusivity bugs” piecemeal, what is often needed is a systematic inspection method to assess software’s support for diverse populations. To help fill this gap, this thesis introduces InclusiveMag, a generalization of GenderMag that can be used to generate systematic inclusiveness methods for a particular dimension of diversity. We then present 1) a multicase study covering eight diversity dimensions, of eight teams’ experiences applying InclusiveMag to eight under-served populations and their “mainstream” counterparts and 2) the start of the application of InclusiveMag to making software more inclusive to individuals of low socioeconomic status, through means of a systematic mapping study
Designing for Lived Health: Engaging the Sociotechnical Complexity of Care Work
As healthcare is increasingly shaped by everyday interaction with data and technologies, there is a widespread interest in creating information systems that help people actively participate in managing their own health and wellness. To date, personal health technologies are largely designed as large-scale “patient-centered” systems, grounded in a biomedical model of care and clinical processes and/or commercial “self-care” technologies, that seek to facilitate individual behavior change through activities like fitness tracking. Through investigating the lived experience of chronic illness—multiple, messy, and often the site of uncomfortable dependencies—my thesis empirically and theoretically engages the limitations of such popular design narratives to address sociotechnical complexities in personal health management. My findings, drawn from people’s care practices across three distinct field sites, argue for a need to contend with lived health: the ways in which everyday health and wellness activities are connected to wider ecologies of care that include the emotional labor of family and friends, entanglements of data, machineries and bodies, localized networks of resources and expertise, and contested forms of information work. My thesis contributes to the literature of Information and Computer Science in the fields of Human-Computer Interaction and Computer-Supported Cooperative Work by offering an alternative analytical lens for designing health systems that support a wider range of people’s social and emotional needs.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146030/1/eskaziu_1.pd
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Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence
Health coaching is a promising approach to support self-management of chronic conditions like type 2 diabetes; however, there aren’t enough coaching practitioners to support those in need. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have the potential to enable innovative, automated health coaching interventions, but important gaps remain in applying AI and ML to coaching interventions. This thesis aims to identify computational approaches and interactive technologies that enable automated health coaching systems. First, I utilized computational approaches that leverage individuals’ self-tracking and health data and used an expert system to translate ML inferences into personalized nutrition goal recommendations. The system, GlucoGoalie, was evaluated in multiple studies including a 4-week deployment study which demonstrated the feasibility of the approach.
Second, I compared human-powered and automated/chatbot approaches to health coaching in a 3-week study which found that t2.coach — a scripted, theoretically-grounded chatbot designed through an iterative, user-centered process — cultivated a coach-like experience that had many similarities to the experience of messaging with actual health coaches, and outlined directions for automated, conversational coaching interventions. Third, I examined multiple AI approaches to enable micro-coaching dialogs — brief coaching conversations related to specific meals, to support achievement of nutrition goals — including a knowledge-based system for natural language understanding, and a data-driven, reinforcement learning approach for dialog management. Together, the results of these studies contribute methods and insights that take steps towards more intelligent conversational coaching systems, with resonance to research in informatics, human-computer interaction, and health coaching