Points are widely used design elements in gamified systems. Yet how they motivate is still unclear: what motivational meaning or functional significance do users ascribe to points and when? To answer this question, we conducted a semi-structured interview study with 27 users of two popular gamified platforms, Duolingo and Habitica. Through reflexive thematic analysis, we constructed six different types of functionalisation variously proposed in prior gamification and personal informatics work but often not empirically supported. We highlight the importance of functional design detail (such as points should proportionally reward effort) and derive design guidelines.
Sustaining the effectiveness of behavior change technologies remains a key challenge. AI self-modeling, which generates personalized portrayals of one’s ideal self, has shown promise for motivating behavior change, yet prior work largely examines short-term effects. We present one of the first longitudinal evaluations of AI self-modeling in fitness engagement through a two-stage empirical study. A 1-week, three-arm experiment (visual self-modeling (VSM), auditory self-modeling (ASM), Control; N=28) revealed that VSM drove initial performance gains, while ASM showed no significant effects. A subsequent 4-week study (VSM vs. Control; N=31) demonstrated that VSM sustained higher performance levels but exhibited diminishing improvement rates after two weeks. Interviews uncovered a catalyst effect that fostered early motivation through clear, attainable goals, followed by habituation and internalization which stabilized performance. These findings highlight the temporal dynamics of personalized nudging and inform the design of behavior change technologies for long-term engagement.
Prior work has examined how users judge their smartphone use, typically focusing on either usage duration or intention. How these two factors jointly shape such evaluations remains unclear. We conducted a two-week study with 104 participants, who reviewed their screenshots and provided labels of both usage intention and evaluation of time use. Across 73,000 sessions (6.1M screenshots), the relationship between duration and evaluation was initially linear but then bounded: positive evaluations declined and negative ones rose with longer phone use duration but both eventually stabilized, most often judged neutral. Trajectories varied by intention. Entertainment mirrored the overall trend; functional use continually lost positive evaluations, whereas information-seeking became increasingly positive during the first half hour before later declining; messaging-based connections slowly lost positive evaluations, while social media–based connections declined more quickly; finally, “no specific intention” unfolded in phases—from short positive use to regret-prone mid-length episodes to neutral long sessions.
Mental fatigue, a common consequence of cognitively demanding work, impairs concentration and well-being, posing long-term health risks. Distinct from drowsiness, mental fatigue is reliably measured with EEG, yet conventional setups remain too cumbersome for everyday use. To overcome this barrier, this study investigates whether EEG headphones can detect mental fatigue and recovery across two common digital break activities: playing a video game and browsing social media. We conducted an experiment with consecutive task sessions and an intermittent break, collecting self-report, performance, and EEG data. Our results show that EEG headphones can detect mental fatigue and recovery dynamics via relative alpha power, and differentiate recovery effects between break types. Social media proved more restorative than gaming, with effects persisting into the subsequent task. These findings establish needed working principles for using headphone-EEG in naturalistic fatigue and recovery research, providing a foundation for future studies.
People suffer from information overload while using digital devices, yet little is known about the interaction of the overload experiences and everyday web behavior. We conducted a large-scale longitudinal observational study (N=277) over seven months. The study combines over 13M passively observed web traces from desktop computers and mobile devices with four waves of surveys measuring the experienced information overload. Our results demonstrate that repetitive, short-duration use of devices (i.e., high sparseness) in online sessions differentiate highly overloaded individuals from others with a large effect. Furthermore, mobile web duration and session sparseness predict increase in the overload. Overall, our results highlight that the web usage duration, the temporal patterns of usage, and the choice of a device are associated with information overload. By highlighting session sparseness as an actionable behavioral signal, our results inform the development of digital well-being tools that nudge users toward healthier interaction patterns and reduce overload.
The adoption of responsible data science (RDS) practices in AI development remains inadequate despite growing awareness of algorithmic harms. One measure of success is by observing practitioners’ behaviors – namely, their adoption of responsible sequences of behaviors in their model building practice. This paper evaluates two interventions for changing problematic behaviors: (i) a motivational priming intervention that introduces short, relevant stories, and (ii) a fairness toolkit (Aequitas)—to bridge the gap between ethical principles and practitioner behavior. Through a mixed-methods study with data scientists (N=12), we assess how these interventions influence fairness practices, model outcomes, and cognitive load across credit risk and income classification tasks. Results indicate that both interventions were efficient in promoting responsible data science behaviors and improving the delivered models’ fairness, while maintaining baseline accuracy. We argue that effective behavior change interventions must balance technical tooling with motivational scaffolding to provide actionable insights for fostering sustainable RDS practices.
Concerns about smartphone dependency have sparked interest in minimal mobile phones: devices supporting basic communication without social apps, web browsing, or games. These design choices are thought to improve well-being, but have not been tested empirically. We conducted a first-of-its-kind longitudinal experiment examining effects of switching from smartphones to minimal mobile phones on young adults’ psychological well-being over a week (n = 166). To account for individual variation in intrinsic motivation to try minimal phones, a quasi-experimental design compared the outcomes of three groups: 1) high-interest volunteers who were asked to use minimal phones or participants that were randomly assigned to either 2) use minimal phones or 3) continue using their own smartphones. Results showed switching to minimal phones reduced phone and social media use. However, only high-interest volunteers - those intrinsically motivated to participate - showed significant within-person changes in well-being, reporting reduced stress, increased life satisfaction, and less FoMo. No effects on well-being were observed for those assigned to use the phone. Results suggest switching to minimal mobile phones may support some motivated individuals in improving agency and well-being.