Nowadays, people struggle to find a balance with their smartphone use. Existing tools for digital-self control have proven ineffective in the long term due to their over-restrictive nature. In this paper, we developed Brainy, a gamified mobile application that encourage users towards healthy digital habits using a virtual brain pet that reflect the state of the user’s brain concerning their digital wellbeing. Through a 10-day in-the-wild study with 17 users, we found that the app effectively enhanced users’ awareness of their smartphone usage patterns and its consequences on their brains. Gamification elements, such as the daily wellbeing bar, were well-perceived by users, helping them reflect and motivating towards healthier habits thus showing promising opportunities for supporting digital wellbeing. Our findings open the way for the use of gamification elements in digital wellbeing apps to better support people.

Recent advancements in AI, particularly Generative AI (GenAI) and Large Language Models (LLMs), have facilitated the integration of AI techniques into digital wellbeing applications, i.e., digital tools that aim at helping people’s wellbeing as a sum of mental and emotional wellness. These AI-powered systems hold the potential to foster healthier habits by collecting and analyzing user behavioral data to provide personalized and dynamic solutions tailored to each user’s needs and lifestyle, therefore improving the efficacy with respect to traditional non-AI interventions. Yet, their development presents significant challenges, including ethical concerns, privacy risks, and the potential for over-reliance on automated interventions. In this paper, we conduct a systematic literature review to examine the key characteristics, challenges, and opportunities in the existing research about AI-powered digital wellbeing tools. Based on our findings, we propose a design framework that outlines 6 critical dimensions and 23 sub-dimensions, spacing from user data and privacy to intervention strategies and personalization, offering practical guidance for researchers and practitioners developing AI-powered digital wellbeing applications. The framework emphasizes the importance of developing tailored and adaptive user-centered interventions adhering to scientific principles, psychological models and responsible data collection. We discuss the applicability and utility of our framework in evaluating and guiding the integration of AI in digital wellbeing applications.

The HCI research community has traditionally considered digital wellbeing an end-user responsibility, designing tools for digital self-control that support them to self-regulate their usage of apps and Web sites. Yet, these attempts are often ineffective in the long term, as many tech companies still adopt “attention-capture” designs that compromise users’ sense of agency and self-control. Taking a complementary perspective, this article presents a set of eight heuristics to create user interfaces that preserve and respect user attention by design . The heuristics stem from a systematic literature review and are grounded in the three fundamental psychological needs defined by the self-determination theory, i.e., autonomy, competence, and relatedness. In addition to being informed by theory and research, each heuristic is accompanied by practical strategies and real-world examples, offering designers actionable guidelines to value people’s attention in user interfaces.

Recent research has identified the detrimental consequences stemming from the pervasive and excessive use of digital devices, prompting the emergence of the concept of digital wellbeing. This workshop serves as a platform for both researchers and practitioners to convene and delve into discussions surrounding the imperative task of educating young generations on digital wellbeing. Participants will engage in a collaborative exploration of innovative strategies and tools aimed at fostering a more mindful and conscious engagement with digital platforms. Through shared insights and collective expertise, the workshop aims at paving the way for a healthier and more balanced relationship with technology among today’s teenagers.

User interfaces heavily rely on attention-capture design patterns, e.g., infinite scroll and other variable-rewarding mechanisms, that erode users’ sense of autonomy and undermine their digital wellbeing. Instead of having users rely on external self-regulation tools, this paper advocates that tech companies and designers should prioritize users’ digital wellbeing by design. To take a first step in this direction, we present Digital Wellbeing Lens, a Figma plugin that guides designers in creating user interfaces that respect and preserve user time and attention. The plugin allows the continuous evaluation of prototypes against attention-capture patterns, calculating a digital wellbeing score and suggesting suitable design alternatives. Besides introducing the plugin, we demonstrate its practical application through a use case involving the design of a mobile social media app, and we report on the results of a first exploratory study with four designers, discussing the opportunities and challenges of embracing this paradigm shift.

Many tech companies exploit psychological vulnerabilities to design digital interfaces that maximize the frequency and duration of user visits. Consequently, users often report feeling dissatisfied with time spent on such services. Prior work has developed typologies of damaging design patterns (or dark patterns) that contribute to financial and privacy harms, which has helped designers to resist these patterns and policymakers to regulate them. However, we are missing a collection of similar problematic patterns that lead to attentional harms. To close this gap, we conducted a systematic literature review for what we call ‘attention capture damaging patterns’ (ACDPs). We analyzed 43 papers to identify their characteristics, the psychological vulnerabilities they exploit, and their impact on digital wellbeing. We propose a definition of ACDPs and identify eleven common types, from Time Fog to Infinite Scroll. Our typology offers technologists and policymakers a common reference to advocate, design, and regulate against attentional harms.

Public media and researchers in different areas have recently focused on perhaps unexpected problems that derive from an excessive and frequent use of technology, giving rise to a new kind of psychological “digital” wellbeing. Such a novel and pressing topic has fostered, both in the academia and in the industry, the emergence of a variety of digital self-control tools allowing users to self-regulate their technology use through interventions like timers and lock-out mechanisms. While these emerging technologies for behavior change hold great promise to support people’s digital wellbeing, we still have a limited understanding of their real effectiveness, as well as of how to best design and evaluate them. Aiming to guide future research in this important domain, this article presents a systematic review and a meta-analysis of current work on tools for digital self-control. We surface motivations, strategies, design choices, and challenges that characterize the design, development, and evaluation of digital self-control tools. Furthermore, we estimate their overall effect size on reducing (unwanted) technology use through a meta-analysis. By discussing our findings, we provide insights on how to (i) overcome a limited perspective that exclusively focuses on technology overuse and self-monitoring tools, (ii) evaluate digital self-control tools through long-term studies and standardized measures, and (iii) bring ethics in the digital wellbeing discourse and deal with the business model of contemporary tech companies.

In the contemporary attention economy, tech companies design the interfaces of their digital platforms by adopting attention-capture dark patterns to drive their behavior and maximize time spent and daily visits. Two popular examples are viral recommendations and content autoplay on social networks. As these patterns exploit people’s psychological vulnerabilities and may contribute to technology overuse and problematic behaviors, there is the need of promoting the design of technology that better align with people’s digital wellbeing. This workshop seeks to advance this timely and urgent need, by inviting researchers and practitioners in interdisciplinary domains to engage in conversation around the design of interfaces that allow people to take advantage of digital platforms in a meaningful and conscious way.

Nowadays, end users can exploit end-user development (EUD) platforms to personalise their Internet of Things (IoT) ecosystems, typically through trigger–action rules. Unfortunately, within such platforms, users are forced to adopt a unique, vendor-centric abstraction: to define triggers and actions, they must specifically refer to every single device or online service needed to execute the intended behaviours. As a consequence, little social and practical benefits of EUD in this domain have emerged so far. In this paper, we build on the idea that other abstractions besides the vendor-centric one are possible, and that the growth of end-user personalisation in the IoT may depend on their identification. Specifically, we report on the results of a 1-week-long diary study during which 24 participants were free to collect trigger–action rules arising during their daily activities. First, we demonstrate that users would adopt different abstractions by personalising devices, information and people-related behaviours, where the individual is at the centre of the interaction. Then, we show that the adopted abstraction may depend on different factors, ranging from the user profile, e.g. their programming experience, to the context in which the personalisation is introduced. While users are inclined to personalise physical objects in the home, for example, they often go ‘beyond devices’ in the city, where they are more interested in the underlying information. Finally, we discuss the retrieved results by identifying new design opportunities to improve the relationship between users and the IoT.

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.

People, especially young adults, often use their smartphones out of habit: They compulsively browse social networks, check emails, and play video-games with little or no awareness at all. While previous studies analyzed this phenomena qualitatively , e.g., by showing that users perceive it as meaningless and addictive, yet our understanding of how to discover smartphone habits and mitigate their disruptive effects is limited. Being able to automatically assess habitual smartphone use, in particular, might have different applications, e.g., to design better “digital wellbeing” solutions for mitigating meaningless habitual use. To close this gap, we first define a data analytic methodology based on clustering and association rules mining to automatically discover complex smartphone habits from mobile usage data. We assess the methodology over more than 130,000 phone usage sessions collected from users aged between 16 and 33, and we show evidence that smartphone habits of young adults can be characterized by various types of links between contextual situations and usage sessions, which are highly diversified and differently perceived across users. We then apply the proposed methodology in Socialize, a digital wellbeing app that (i) monitors habitual smartphone behaviors in real time and (ii) uses proactive notifications and just-in-time reminders to encourage users to avoid any identified smartphone habits they consider as meaningless. An in-the-wild study with 20 users (ages 19–31) demonstrates that Socialize can assist young adults in better controlling their smartphone usage with a significant reduction of their unwanted smartphone habits.

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.

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.

Nowadays, users can personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules. As the number of supported technologies grows, however, so does the design space, i.e., the combinations between different triggers and actions: without proper support, users often experience difficulties in discovering rules and their related functionality. In this paper, we introduce TAPrec, an End-User Development platform that supports the composition of trigger-action rules with dynamic recommendations. By exploiting a hybrid and semantic recommendation algorithm, TAPrec suggests, at composition time, either a) new rules to be used or b) actions for auto-completing a rule. Recommendations, in particular, are computed to follow the user's high-level intention, i.e., by focusing on the rules' final purpose rather than on low-level details like manufacturers and brands. We compared TAPrec with a widely used trigger-action programming platform in a study on 14 end users. Results show evidence that TAPrec is appreciated and can effectively simplify the personalization of connected entities: recommendations promoted creativity by helping users personalize new functionality that are not easily noticeable in existing platforms.

End users can program trigger-action rules to personalize the joint behavior of their smart devices and online services. Trigger-action programming is, however, a complex task for non-programmers and errors made during the composition of rules may lead to unpredictable behaviors and security issues, e.g., a lamp that is continuously flashing or a door that is unexpectedly unlocked. In this paper, we introduce EUDebug, a system that enables end users to debug trigger-action rules. With EUDebug, users compose rules in a web-based application like IFTTT. EUDebug highlights possible problems that the set of all defined rules may generate and allows their step-by-step simulation. Under the hood, a hybrid Semantic Colored Petri Net (SCPN) models, checks, and simulates trigger-action rules and their interactions. An exploratory study on 15 end users shows that EUDebug helps identifying and understanding problems in trigger-action rules, which are not easily discoverable in existing platforms.

As smartphone use increases dramatically, so do studies about technology overuse. Many different mobile apps for breaking "smartphone addiction" and achieving "digital wellbeing" are available. However, it is still not clear whether and how such solutions work. Which functionality do they have? Are they effective and appreciated? Do they have a relevant impact on users' behavior? To answer these questions, (i) we reviewed the features of 42 digital wellbeing apps, (ii) we performed a thematic analysis on 1,128 user reviews of such apps, and (iii) we conducted a 3-week-long in-the-wild study of Socialize, an app that includes the most common digital wellbeing features, with 38 participants. We discovered that digital wellbeing apps are appreciated and useful for some specific situations. However, they do not promote the formation of new habits and they are perceived as not restrictive enough, thus not effectively helping users to change their behavior with smartphones.

Nowadays, end users can personalize their smart devices and web applications by defining or reusing IF-THEN rules through dedicated End-User Development (EUD) tools. Despite apparent simplicity, such tools present their own set of issues. The emerging and increasing complexity of the Internet of Things, for example, is barely taken into account, and the number of possible combinations between triggers and actions of different smart devices and web applications is continuously growing. Such a large design space makes end-user personalization a complex task for non-programmers, and motivates the need of assisting users in easily discovering and managing rules and functionality, e.g., through recommendation techniques. In this article, we tackle the emerging problem of recommending IF-THEN rules to end users by presenting RecRules , a hybrid and semantic recommendation system. Through a mixed content and collaborative approach, the goal of RecRules is to recommend by functionality : it suggests rules based on their final purposes, thus overcoming details like manufacturers and brands. The algorithm uses a semantic reasoning process to enrich rules with semantic information, with the aim of uncovering hidden connections between rules in terms of shared functionality. Then, it builds a collaborative semantic graph, and it exploits different types of path-based features to train a learning to rank algorithm and compute top-N recommendations. We evaluate RecRules through different experiments on real user data extracted from IFTTT, one of the most popular EUD tools. Results are promising: they show the effectiveness of our approach with respect to other state-of-the-art algorithms and open the way for a new class of recommender systems for EUD that take into account the actual functionality needed by end users.

The Internet of Things (IoT) is, nowadays, a well recognized paradigm. In this field, End User Development (EUD) is a promising approach that allows users to program their devices and services. The representation models adopted by contemporary EUD interfaces, however, are often highly technology-dependent, and the interaction between users and the IoT ecosystem is put to a hard test. The goal of my research is to explore new approaches and tools for helping end-users to program their technological devices and services. For this purpose, I proposed EUPont, an ontological model able to represent abstract and technology independent trigger-action rules, that can be adapted to different contextual situations. EUPont has been evaluated in terms of understandability, completeness, and usefulness. Currently, I am using the semantic features of the model in different research projects, e.g., to optimize the layout of EUD interfaces, and to design a recommender system of trigger-action rules. Preliminary results are promising, and confirm the benefit of using the semantic information of EUPont for helping end-users to better deal with the forthcoming IoT world.

In the field of Ambient Assisted Living a limited amount of research aims at supporting caregivers that work with people with disabilities in assisted living facilities (ALFs). In fact, research activities on healthcare support systems in AAL mainly focus on improving the quality of life for people in their own homes or supporting nurses and doctors in hospitals. This paper explores and applies the Internet of Things paradigm in the ALFs context. In particular, we present the design, the implementation, and the experimental evaluation of a system capable of supporting the daily activities of healthcare assistants that operate in ALFs for people with physical or cognitive disabilities. The solution combines wearable and mobile technologies to improve assistance requests and anomaly detection. With this healthcare support system, caregivers can be automatically alerted of potentially hazardous situations that happen to the inhabitants while these are out of sight. Furthermore, inhabitants can require assistance instantly and from any point of the facility. We evaluated the system in two ways. We performed a functional test with two professional caregivers, and we deployed the system in an ALF in Italy for 36 hours, collecting the opinions of the involved caregivers and inhabitants.