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People can nowadays take advantage of Digital Self-Control Tools (DSCTs) to regulate their technology use, e.g., by defining a usage timer or by removing some distractive functionality like recommendations or a newsfeed. In this paper, we aim to overcome the limits of these restrictive methods, e.g., their low effectiveness in the long term, by speculating on tools that can teach users how to use technology better. To this end, we present StepByStep, a novel mobile DSCT that proactively assists users in learning how to regulate smartphone use through adaptable and continuously variable interventions. The DSCT is the result of a 2-phase iterative design process. StepByStep starts conversations with users to suggest possible problematic smartphone behaviors, and it establishes personalized "learning paths" via gamification. It then progressively reduces its degree of support until the user acquires a sufficient level of independence, i.e., it is able to sustain a new behavior without the help of the tool. Two exploratory in-the-wild studies with 8 and 41 participants, respectively, show initial evidence that the adopted approach is promising and could effectively assist users into understanding when and how to reduce the time spent on their smartphones, thus avoiding unwanted smartphone habits. • Present a digital self-control that teaches users how to regulate smartphone use. • Describe how the app integrates established psychological theories and constructs. • Evaluate StepByStep in two in-the-wild studies with 8 and 41 participants. [ABSTRACT FROM AUTHOR]

Highlights • Investigate whether a high-level representation can help end-user better create trigger-action rules in the IoT. • Present EUPOnt, a high-level semantic model for end-user development in the IoT. • Propose an end-user development platform to execute high-level rules. • Evaluate the suitability of the EUPont approach with a 30-people user study. Abstract Various programming environments for End-User Development (EUD) allow the composition of Internet of Things (IoT) applications, i.e., connections between IoT objects to personalize their joint behavior. These environments, however, only support a one-to-one mapping between pairs of object instances, and adopt a low level of abstraction that forces users to be aware of every single technology they may encounter in their applications. As a consequence, numerous open questions remain: would a "higher level" of abstraction help users creating their IoT applications more effectively and efficiently compared with the contemporary low-level representation? Which representation would users prefer? How high-level IoT applications could be actually executed? To answer these questions, we introduce EUPont, a high-level semantic model for EUD in the IoT. EUPont allows the creation of high-level IoT applications, able to adapt to different contextual situations. By integrating the ontology in the architecture of an EUD platform, we demonstrate how the semantic capabilities of the model allow the execution of high-level IoT applications. Furthermore, we evaluate the approach in a user study with 30 participants, by comparing a Web interface for composing IoT applications powered by EUPont with the one employed by a widely used EUD platform. Results show that the high-level approach is understandable, and it allows users to create IoT applications more correctly and quickly than contemporary solutions. [ABSTRACT FROM AUTHOR]

A growing - yet debated - discussion around the negative aspects of overusing technology led researchers and practitioners to consider a new kind of psychological digital wellbeing, giving rise to the flourishing of digital self-control tools (DSCTs). As many people nowadays own and use several devices, there is a need to understand better their overlapping use (and non-use) and the relationships to digital wellbeing. This special issue aims to provide the academic and industry research communities a venue for work at the state-of-the-art on digital wellbeing and DSCTs, with a particular focus on digital wellbeing within complex technological and social contexts. [ABSTRACT FROM AUTHOR]

• Propose a literature programming approach for prototyping Internet-of-Things systems. • Present the design and implementation of the IoT notebook, together with its novel features and architecture. • Evaluate the IoT notebook in an exploratory user study with 13 novice IoT developers working for a large IT company. Computational notebooks create narratives that consolidate text, executable code, and visualizations in a single document. They are widely used in data science, enabling data scientists to accurately document and execute the steps of their analyses in an exploratory and iterative manner. Prototyping Internet of Things (IoT) systems is also complex because of IoT heterogeneous and interconnected nature. Indeed, IoT system prototyping spans multiple development and execution environments, and developers, besides focusing on the code, are required to configure various devices. To ascertain if and how computational notebooks' capabilities might be useful in the IoT scenario, this work presents an IoT-tailored notebook architecture aimed at helping developers build and share a computational narrative around the prototyping of IoT systems. To that end, we propose the concept of "IoT notebook", for which we analyze the required features and present its first implementation. We evaluate our proposal by conducting an exploratory user study among non-expert IoT developers working in a large IT company. Finally, we point out the potential of this approach. [ABSTRACT FROM AUTHOR]

• Investigate the habitualness of smarphone app switching behavior, i.e., transitioning from one app to another in the same usage session to consume content. • Present RecApps, an interactive floating widget designed to proactively support the transition from one app to another. • Evaluate RecApps in a three-week long in-the-wild study with 18 smartphone users. • Discuss the design space for proactively supporting app switching behaviors with a reference to different design dimensions, ranging from visibility to cognitive and physical demand. 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. [ABSTRACT FROM AUTHOR]