Self-tracking has grown in popularity over recent years, leading to a rise in users who monitor data for improving their health. However, decreasing self-motivation due to different user circumstances such as priorities, lifestyles, and habits affects how users conduct self-tracking in apps and hamper their progress towards achieving health goals. We investigated factors that contribute to increasing and maintaining self-motivation while self-tracking for health goals, as well as the potential applications of these factors in health apps. We conducted semi-structured interviews with 15 participants to gain insights into their self-tracking habits and health app usage. Our thematic analysis suggests that users value convenient self-tracking methods and seeing notable progress changes toward a health goal. These findings reveal design opportunities for streamlining app features, reframing progress indicators in data visualizations, and accommodating user priorities.

Play-to-Earn (P2E) crypto-games recently emerged as a gig opportunity despite the absence of regulations. As these platforms continue to grow, there is a need to understand the interactions involved to protect vulnerable stakeholders. This paper describes how an unintended social dynamic became a strategy guiding players to navigate an unregulated space. First, we inquired through surveys (N = 69) and interviews (N = 9) to understand stakeholder motivations and practices in this space. Second, we analyzed data and then conceptualized eight themes (e.g., Management, Social, Gaming). Then, we uncovered four types of relationships (e.g., Manager-Scholar, Manager-Investor-Scholar, Coach-Mentee, Scholar-Turned-Manager) that shaped the behaviours of the different users on the platform. Lastly, we present design implications and recommendations to guide the design of P2E crypto games and the gig-focused communities that thrive around them. Our results contribute to ongoing discussions in designing digital gig economies and crypto-based games.

Manual schedule creation often involves satisfying numerous unique and conflicting constraints, which becomes more cognitively demanding when creating a common academic schedule with other individuals. Poor decision making caused by cognitive overload can result in unsuitable schedules. This study proposes the use of Boolean satisfiability (SAT) solvers in an academic scheduling system to help students balance scheduling preferences and satisfy necessary constraints. Based on the availability of courses and the scheduling preferences of users, the system automatically resolves conflicts and presents possible schedules. In a controlled experiment with 42 undergraduate students, cognitive demand was reduced by eliminating menial decisions, which significantly optimized the creation of a common schedule among peers. We found that human errors and emotional stress were diminished, and schedules created using the system were more satisfactory to participants. Finally, we present recommendations and design implications for future academic scheduling systems.

Navigation applications are becoming ubiquitous in our daily navigation experiences. With the intention to circumnavigate congested roads, their route guidance always follows the basic assumption that drivers always want the fastest route. However, it is unclear how their recommendations are followed and what factors affect their adoption. We present the results of a semi-structured qualitative study with 17 drivers, mostly from the Philippines and Japan. We recorded their daily commutes and occasional trips, and inquired into their navigation practices, route choices and on-the-fly decision-making. We found that while drivers choose a recommended route in urgent situations, many still preferred to follow familiar routes. Drivers deviated because of a recommendation's use of unfamiliar roads, lack of local context, perceived driving unsuitability, and inconsistencies with realized navigation experiences. Our findings and implications emphasize their personalization needs, and how the right amount of algorithmic sophistication can encourage behavioral adaptation.