Self-management of Chronic Diseases and Conditions

Permanent URI for this collectionhttps://hdl.handle.net/10125/112482

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    Intersection of TikTok/Douyin and Dementia: A Scoping Review of Research to Date (2016-May 2025)
    (2026-01-06) Wang, Yunwen; Chaif, Rim H.; Jia, Danny Yihan; Liu, Hanbo; Izhar, Nazra; Wen, Huaimin; Ha, Huong; Olatunji, Shirley; Ding, Hechen; Li, Xiaoqin
    Short-form video platforms like TikTok play a special cultural role for this decade, enabling research in a range of fields, including health and information sciences. With 55 million Alzheimer's and other dementia cases worldwide, research has started to include TikTok and Douyin as a tool, a site, or a context for understanding and improving dementia-related lived experiences. This review of 15 articles is the first to assess the scope of TikTok/Douyin research (2016-May 2025) on dementia since the apps were launched, identifying the role of TikTok/Douyin in dementia research, shedding light on where the current science is and in which directions future research can better leverage TikTok/Douyin. Implications of findings for dementia care practice and the design of information technology in healthcare are also discussed.
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    The Effect of Type of Explanation on Algorithm Appreciation: The Role of Risk Perceptions in Healthcare Decision-Making
    (2026-01-06) Zhang, Rongen "Sophia"; Werder, Karl; Negi, Kartikeya; Ramesh, Balasubramaniam
    This study examines the impact of various types of Artificial Intelligence (AI) explanations—local, counterfactual, and global—on individuals' algorithm appreciation in healthcare decision-making. Using a scenario-based experiment involving 611 participants, we take a risk perspective to examine how eXplainable (XAI) system credibility (risk probability) and perceived condition severity (risk severity) mediate the relationship between explanation type and algorithm appreciation. We also explore how decision-makers’ risk-taking propensity (risk perception) moderates these relationships. Participants assessed diabetes risk predictions for a hypothetical relative based on explanations generated by an XAI system. Findings reveal that explanation type significantly influences algorithm appreciation through the perceived severity of the condition, but not through the credibility of the XAI system. Importantly, the effects of explanation type vary with participants' risk-taking propensity. Hence, this research highlights the need for personalized XAI strategies to maximize algorithm appreciation in high-risk healthcare decision-making contexts involving non-expert decision-makers.
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    Sweet Talks: An Initial Design Science Research Approach to Enhance Trust in Conversational Agents for Diabetes Self-Management
    (2026-01-06) Schejok, Lucas; Strunk, Jobin; Bockshecker, Alina; Smolnik, Stefan
    Conversational Agents (CAs) are a promising solution to support people with chronic diseases in the self-management of their condition. Diabetes patients, who have to perform various complex and demanding self-management activities on a daily basis, can especially benefit from this technology. However, a lack of trust in CAs hinders their acceptance and adoption. Despite its high importance, systematically derived design knowledge regarding trust in CAs is scarce. This research paper aims to pro-vide a first step toward a design theory for enhancing the trustworthiness in CAs. A design science research approach is followed to identify design requirements and derive design principles based on expert interviews with diabetes patients and a literature analysis. The findings of this research paper help to enhance the trustworthiness of CAs to increase their acceptance and adoption, thereby contributing to greater engagement in self-management activities.