Helping Older Adults with Ongoing Mobile Technology Use: Challenges and Design Opportunities

Despite increasing mobile technology adoption among older adults, challenges hinder their effective and continued application use. This dissertation explores the factors influencing older adults' ongoing mobile technology use and investigates ways to enhance their mobile technology experiences. To better explore these challenges during older adults ongoing mobile technology use, we introduce the Senior Technology Learning Preferences Model for Mobile Technologies (STELE), which identifies two primary pathways for tech engagement: self-reliant support, where older adults learn independently through exploration and resources such as online tutorials, and social support, where they seek help from knowledgeable others. STELE explains that an older adult’s choice between these pathways is shaped by their identity as a tech learner, their past learning experiences, and their access to a supportive social network. Expanding on this, we analyze the relational dynamics of tech caregiving across three cultural contexts, identifying key challenges such as repeated requests, emotional labor, and the strategies learners and caregivers use to maintain positive interactions. To further understand older adults' support preferences, we develop and validate the Mobile Tech Support Questionnaire (MTSQ), which measures older adults’ preference for and perceived quality of support during continued mobile technology use. Our findings reveal that preferences for self-reliant and social support are not mutually exclusive; older adults often rely on both, depending on the situation. The availability and reliability of support influence how frequently older adults seek that type of support and how easy they find the experience. We also investigate how feelings of confidence and anxiety during mobile use, as well as mobile device proficiency, play a significant role in shaping support preferences and perceptions. Findings suggest that higher proficiency and confidence predict a preference for self-reliant tech support, while proficiency also influences perceptions of its quality. These findings highlight the importance of considering individual learning styles and personalization when designing tech support tools for older adults. Since effective tech support of any kind begins with clear problem communication, we investigate how older adults formulate their technology-related queries in unstructured contexts. A diary study reveals key communication challenges, including verbosity, incompleteness, over-specification, and under-specification. Using a few-shot prompt-chaining approach with GPT-4o, we evaluate how reformulating queries improves solution accuracy. Results show that even minor changes to the structure of a query significantly improve the accuracy of solutions generated by AI and retrieved by search engines. Evaluations by older adults and tech helpers further highlight the potential of this approach in real-life scenarios. By integrating empirical research with practical design considerations, this dissertation provides a deeper understanding of how older adults learn and use mobile technology. We lay the foundation for developing more inclusive, socially aware, and effective support systems that empower older adults to navigate mobile technologies with confidence.