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Many users take advantage of digital self-control tools to self-regulate their device usage through interventions such as timers and lockout mechanisms. One of the major challenges faced by these tools is the user reacting against their self-imposed constraints and abandoning the tool. Although lower-risk interventions would reduce the likelihood of abandonment, previous research on digital self-control tools has left this area of study relatively unexplored. In response, this paper contributes two foundational principles relating risk and effectiveness; four widely applicable novel design patterns for reducing risk of abandonment of digital self-control tools (continuously variable interventions, anti-aging design, obligatory bundling of interventions, and intermediary control systems); and a prototype digital self-control tool that implements these four low-risk design patterns.

While Digital Self-Control Tools (DSCTs) mainly target smartphones, more effort should be put into evaluating multi-device ecosystems to enhance digital wellbeing as users typically use multiple devices at a time. In this paper, we first review more than 300 DSCTs by demonstrating that the majority of them implements a single-device conceptualization that poorly adapts to multi-device settings. Then, we report on the results from an interview and a sketching exercise (N=20) exploring how users make sense of their multi-device digital wellbeing. Findings show that digital wellbeing issues extend beyond smartphones, with the most problematic behaviors deriving from the simultaneous usage of different devices to perform uncorrelated tasks. While this suggests the need of DSCTs that can adapt to different and multiple devices, our work also highlights the importance of learning how to properly behave with technology, e.g., through educational courses, which may be more effective than any lock-out mechanism.

Nowadays, several interfaces for end-user development (EUD) empower end users to jointly program the behavior of their smart devices and online services, typically through trigger-action rules. Despite their popularity, such interfaces often expose too much functionality and force the user to search among a large number of supported technologies disposed of confused grid menus. This paper contributes to the EUD with the aim of interactively assisting end users in composing IF-THEN rules with an optimizer in the loop. The goal, in particular, is to automatically redesign the layout of the EUD interfaces to facilitate the users in defining triggers and actions. For this purpose, we define a predictive model to characterize the composition of trigger-action rules on the basis of their final functionality, we adopt different optimization algorithms to explore the design space, and c) we present EUDoptimizer, the integration of our approach in IFTTT, one of the most popular EUD interfaces. We demonstrate that good layout solutions can be obtained in a reasonable amount of time. Furthermore, an empirical evaluation with 12 end users shows evidence that EUDoptimizer reduces the efforts needed to compose trigger-action rules.

Nowadays, end users can take advantage of end-user development platforms to personalize the Internet of Things. These platforms typically adopt a vendor-centric abstraction, by letting users to customize each of their smart device and/or online service through different trigger-action rules. Despite the popularity of such an approach, several research challenges in this domain are still underexplored. Which “things” would users personalize, and in which contexts? Are there any other effective abstractions besides the vendor-centric one? Would users adopt different abstractions in different contexts? To answer these questions, we report on the results of a 1-week-long diary study during which 24 participants noted down trigger-action rules arising during their daily activities. Results show that users would adopt multiple abstractions by personalizing devices, information, and people-related behaviors where the individual is at the center of the interaction. We found, in particular, that the adopted abstraction may depend on different factors, ranging from the user profile to the context in which the personalization is introduced. While users are inclined to personalize physical objects in the home, for example, they often go “beyond devices” in the city, where they are more interested in the underlying information. Our findings identify new design opportunities in HCI to improve the relationship between the Internet of Things, personalization paradigms, and users.

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance Nest security camera detects a movement, THEN blink the Philips Hue lamp in the kitchen.” Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present , a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user’s need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input, implements a semantic recommendation process that takes into account ( a ) the current user’s intention , ( b ) the connected entities owned by the user, and ( c ) the user’s long-term preferences revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a short-term preference , thus allowing to provide refined recommendations that better align with the original intention. We evaluate by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of in recommending IF-THEN rules that satisfy the current personalization intention of the user.

Users can nowadays take advantage of Digital-Self Control Tools (DSCT) to self-regulate their usage of applications and websites by means of interventions like timers and lockout mechanisms. However, DSCTs mainly focus on the interaction between users and a single device at a time, while people typically use more than one device, and in a concurrent way. This motivates the need of exploring tools that can adapt to multi-device settings. We present FeelHabits, a DSCT that allows users to set up, through a novel approach, multi-device intentions, i.e., contextual time and launch limits for the simultaneous and/or alternating use of the PC and the smartphone. Stemming from the defined intentions, FeelHabits employs different levels of severity to warn the user about a reached limit on the currently used device. A preliminary study on 7 participants suggests that FeelHabits might be effective for reducing some multi-device behaviors, and opens the way for further research.

Despite a large body of literature analyzing mobile device usage, app switching is still an overlooked interaction. To better understand and streamline the app switching experience in modern smartphones, we first explore how to automatically extract and characterize habitual app switching behaviors from smartphone usage data. By applying a data analytic methodology based on association rules to a large dataset of smartphone usage, in particular, we demonstrate that users repeatedly switch between the same applications under different contexts (e.g., location and time). We then implemented the methodology in RecApps, an interactive floating widget that proactively suggests the next apps to be used while the user is interacting with their smartphone. We evaluate RecApps through an in-the-wild study with 18 participants. Findings show that RecApps simplifies and supports the transitions between the users’ favorite apps, while highlighting the need for novel interactions supporting app switching behavior. We use such results to explore trade-offs in the design space for proactively supporting app switching behavior in mobile interaction.

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of IF-THEN rules. Unfortunately, how to make such a personalization effective and appreciated is still largely unknown. On the one hand, contemporary platforms to compose IF-THEN rules adopt representation models that strongly depend on the exploited technologies, thus making end-user personalization a complex task. On the other hand, the usage of technology-independent rules envisioned by recent studies opens up new questions, and the identification of available connected entities able to execute abstract users' needs become crucial. To this end, we present HeyTAP, a conversational and semantic-powered trigger-action programming platform able to map abstract users' needs to executable IF-THEN rules. By interacting with a conversational agent, the user communicates her personalization intentions and preferences. User's inputs, along with contextual and semantic information related to the available connected entities, are then used to recommend a set of IF-THEN rules that satisfies the user's needs. An exploratory study on 8 end users preliminary confirms the effectiveness and the appreciation of the approach, and shows that HeyTAP can successfully guide users from their needs to specific rules.