Investment decisions are often driven by time pressure and emotion, leaving investors vulnerable to cognitive biases. Mobile trading apps intensify these tendencies, yet existing interventions rely on external constraints that fail to foster lasting behavioral change. We investigate how reflection-centered approaches support mindful decision-making across a spectrum of investor expertise. We present MindStock, a technology probe providing principle-anchored feedback by integrating user-defined principles with behavioral data mirroring trading patterns. In a 6-week field study with 16 investors, we found that meaningful reflection comes from the tension between principles and behavioral data. Principles give context to otherwise opaque metrics, while data keeps principles from drifting into vague self-assurances. This pattern varied by experience: novices gravitated toward normative rule-setting, while experienced investors used the system to test and refine their own assumptions. We contribute design implications for supporting reflection in high-stakes decision-making contexts.

Short-form videos have become one of the most addictive forms of digital media, keeping users endlessly scrolling with little self-regulation. Most existing interventions, screen-time limits or warning messages, are imposed from the outside and often lead to resistance or are simply ignored. To explore a more empowering approach, we designed a self-regulatory intervention that allows users to set their own viewing goals, reflect on what they watch, and receive periodic feedback as well as a summary of their viewing behaviors. We conducted a 4-week field study with 20 participants, spanning baseline, intervention, and withdrawal phases. Participants became more conscious of their viewing and more self-disciplined, finding control over both the viewing quantity and quality. However, sustaining self-regulation proved challenging once the intervention was withdrawn. These findings highlight both the promise and fragility of self-regulatory strategies and point to new design opportunities for interventions that support sustainable self-regulation in short-form videos.

Figure 1: Data manipulation is an approach that involves altering the value or structure of self-tracking data.With five data manipulation typology (Insert, Delete, Transform, Increase, and Reduce), we applied data manipulation through exploratory workshops and a one-week field trial.The above cards, which served as key materials for this study, provide an overview of the different types of data manipulation, along with definitions and examples for each type.