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.

Understanding how people are willing to express notification preferences is essential for designing personalized intelligent notification systems. Yet little is known about when, how, and under what conditions individuals choose to provide such input. We conducted semi-structured interviews with 33 participants, using design probes to examine the timing, methods, and concerns surrounding preference expression. Our findings make three contributions. First, we show that willingness to provide feedback depends not only on input ease and function but also on the justifiability of the moment, with requests embedded into notification-handling routines perceived as most natural. Second, we find that sustained engagement requires two forms of clarity: clarity in how to express one’s preferences and clarity in how the system interprets and applies that input. Third, we reveal expectations for notification systems to act as evolving partners that distinguish temporary and situational shifts from longer-term preference changes and support mutual learning over time.

To help users manage the overwhelming influx of smartphone notifications, this study explores how large language models (LLMs) can be leveraged to generate notification summaries. We developed an Android application that integrates ChatGPT to summarize notifications and conducted an in-the-wild deployment to examine how users guided the model. To further understand user expectations for LLM-generated summaries, we interviewed 20 participants following a week-long engagement with the app. Our findings reveal five main strategies that users employed in their prompts for generating summaries. Additionally, interviewees expected summaries to prioritize three types of notifications, preferred three levels of information disclosure influenced by content anticipation and perceived criticality, and used three different approaches to synthesizing notifications based on their interrelationships. Finally, interviewees envisioned notification summarization functioning like a virtual assistant, desiring capabilities beyond simple information condensation, including support for task and information management, revisiting archived content, and tracking activities for reflection.

People nowadays can use multiple devices to interact with notifications, whether via noticing, glancing, reading, or acting upon them. Prior research has focused on actual usage or on device preferences. However, users’ ideal experience of cross-device notification-interaction might differ from their current practices (due to situational limitations) and/or across the four notification-interaction stages. We therefore conducted an experience-sampling method study with multi-device users to investigate these gaps and the influence of device context. Our results reveal that nearly half of the time, the non-phone devices the participants had ranked as their top preferences for notification-interaction were not actually used, due to the devices’ context. Beyond device context, the participants’ choices of devices for notification-interaction were heavily determined by 1) their preferences that particular notification-interaction stages to take place (or not) on particular devices; and 2) the device on which they had undertaken the former stage.