Eating disorder (ED) recovery extends beyond therapy sessions, unfolding in vulnerable moments embedded in everyday life and relationships. Yet empirical understanding of how these moments arise, how supporters contribute, and how technologies might offer timely, contextual assistance remains limited. To address this gap, we conducted a design session and two-week diary study with 27 individuals with ED and 12 social supporters. Our analysis identified diverse contexts in which patients and supporters perceived support to be needed, and the forms of support they envisioned digital tools could offer. While many needs were mutually recognized, the actual practice of support often involved mismatches, suggesting opportunities for technologies to help mediate supportive engagement. Our study contributes empirical insight into everyday support moments in ED recovery and highlights opportunities to design digital interventions that provide context-sensitive assistance, empower supporters, and extend care beyond clinical settings.

Developing new machine learning applications often requires the collection of new datasets. However, existing datasets may already contain relevant information to train models for new purposes. We propose SoundCollage: a framework to discover new classes within audio datasets by incorporating (1) an audio pre-processing pipeline to decompose different sounds in audio samples, and (2) an automated model-based annotation mechanism to identify the discovered classes. Furthermore, we introduce the clarity measure to assess the coherence of the discovered classes for better training new downstream applications. Our evaluations show that the accuracy of downstream audio classifiers within discovered class samples and a held-out dataset improves over the baseline by up to 34.7% and 4.5%, respectively. These results highlight the potential of SoundCollage in making datasets reusable by labeling with newly discovered classes. To encourage further research in this area, we open-source our code at github.com/nokia-bell-labs/audio-class-discovery.

Digital food content’s popularity is underscored by recent studies revealing its addictive nature and association with disordered eating. Notably, individuals with eating disorders exhibit a positive correlation between their digital food content consumption and disordered eating behaviors. Based on these findings, we introduce FoodCensor, an intervention designed to empower individuals with eating disorders to make informed, conscious, and health-oriented digital food content consumption decisions. FoodCensor (i) monitors and hides passively exposed food content on smartphones and personal computers, and (ii) prompts reflective questions for users when they spontaneously search for food content. We deployed FoodCensor to people with binge eating disorder or bulimia (n=22) for three weeks. Our user study reveals that FoodCensor fostered self-awareness and self-reflection about unconscious digital food content consumption habits, enabling them to adopt healthier behaviors consciously. Furthermore, we discuss design implications for promoting healthier digital content consumption practices for vulnerable populations to specific content types.

We demonstrate StressTrendmeter, a mobile app that targets college students for anonymously sharing the source of stress via the form of hashtags, viewing stress topics based on trends, and providing social support through the empathy button and hashtag-based chat.

People experience various stressful events in their daily lives. Receiving social support, especially from peers who went through a similar experience, helps individuals cope with such stress. We propose StressTrendmeter, a mobile application that targets college students for anonymously sharing the source of stress via the form of hashtags, viewing stress topics based on trends, and providing social support through the empathy button and hashtag-based chat. We deployed StressTrendmeter to 222 students from two universities for five weeks. With hashtags and trending features, students found StressTrendmeter (i)helpful to spontaneously yet concisely articulate their stress topics and (ii) easy to browse through and become aware of issues around the campus. Our study reveals that social sharing with StressTrendmeter brought awareness, resonance, and accountability as students empathized and expressed support. Based on our study, we share design implications for social support systems with community awareness.

Many applications utilize sensors on mobile devices and apply deep learning for diverse applications. However, they have rarely enjoyed mainstream adoption due to many differentindividual conditionsusers encounter. Individual conditions are characterized by users’ unique behaviors and different devices they carry, which collectively make sensor inputs different. It is impractical to train countless individual conditions beforehand and we thus argue meta-learning is a great approach in solving this problem. We presentMetaSensethat leverages “seen” conditions in training data to adapt to an “unseen” condition (i.e., the target user). Specifically, we design a meta-learning framework that learns “how to adapt” to the target via iterative training sessions of adaptation. MetaSense requires very few training examples from the target (e.g., one or two) and thus requires minimal user effort. In addition, we propose asimilar condition detector(SCD) that identifies when the unseen condition has similar characteristics to seen conditions and leverages this hint to further improve the accuracy. Our evaluation with 10 different datasets shows that MetaSense improves the accuracy of state-of-the-art transfer learning and meta learning methods by 15 and 11 percent, respectively. Furthermore, our SCD achieves additional accuracy improvement (e.g., 15 percent for human activity recognition).