While empirical evidence supports the use of arts-based interventions in promoting healthcare workers’ well-being and personal growth, art prompts are underexplored and underused in narrative medicine. With the COVID-19 pandemic having had a profound impact on the healthcare workforce with an already high burn-out rate, multimodal arts interventions may help address the holistic dimensions of well-being. Narrative medicine is an interdisciplinary field that complements and expands on conventional healthcare training by supporting narrative competence skills and creativity derived from the arts and humanities domains to address the needs of healthcare providers and receivers. 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. Furthermore, we estimate their overall effect size on reducing (unwanted) technology use through a meta-analysis. We surface motivations, strategies, design choices, and challenges that characterize the design, development, and evaluation of digital self-control tools. 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. 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. 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. 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. However, when considering only the emotional dimension of engagement, this correlation is stronger and the analysis of facial action units and head pose (facial movements) are positively correlated with it, while there is an inverse correlation with the gaze, meaning that the more the student’s feels engaged the less are the gaze movements. ![]() Results show that, globally, engagement prediction from students’ facial behavior was weakly correlated to their subjective answers. Then, the collected videos were analyzed automatically with a software that implements the model and provides an interface for the visual analysis of the model outcome. During the experiment we collected videos of students behavior and, at the end of each session, we asked students to answer a questionnaire for assessing their perceived engagement. The aim of the study was to compare the self-evaluation of the engagement perceived by the students with the one assessed by the model. ![]() ![]() In order to test its performance in learning contexts, an experiment, involving students attending an online lecture, was performed. The dataset used to learn the model is the one of the EmotiW 2019 challenge datasets. This article presents a research aiming to investigate how student engagement level can be assessed from facial behavior and proposes a model based on Long Short-Term Memory (LSTM) networks to predict the level of engagement from facial action units, gaze, and head poses. The automatic monitoring and assessment of the engagement level of learners in distance education may help in understanding problems and providing personalized support during the learning process.
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