David Kirk

Algorithmically curated social media feeds can provoke perceived anxiety in everyday use, yet little is known about how AI can provide effective support in these contexts. We present TriggerDetector, an LLM-powered system that infers plausible anxiety triggers from user-encountered posts and offers coping suggestions. Following a validation study, we evaluate the system through an in-the-wild deployment with 241 users engaging with their own anxiety-inducing posts. Results show that many users reported reduced anxiety after use (p <.001, Cohen’s d = 0.87), though outcomes varied substantially across interaction instances (each corresponding to one uploaded post), including instances where support was ineffective or increased anxiety. Further analysis indicates that effectiveness depends less on trigger identification and more on how AI-generated suggestions are interpreted and enacted. Interviews (n = 10) reveal how value misalignment, limited actionability, and problematic interpretations shape these outcomes. We contribute empirical insights into when and how AI-mediated support is effective, and derive design implications for safer and more context-sensitive support.

This work involves the building occupants of a smart building during a period of hybrid working, with the purpose of co-designing data-rich workplaces that support wellbeing. Through the design of a custom card-kit based on PROWELL Model of Workplace Wellbeing Assessment, this work provides insights from an online 90 min co-design workshop with six building occupants utilizing the card-kit to speculate on the design of data-driven physical interventions that support workplace wellbeing. Transcript data from the video-recorded workshop were thematically analyzed, producing findings namely framing novel socio-technical dimensions for biophilic and biomimetic designs in the built environment. Contributing to discourses on Human-Building Interaction (HBI) research, findings were synthesized into a design agenda and considerations for supporting wellbeing in the hybrid workplace that utilizes physical feedback and passive sensing. Composed under the premise of co-creating smart environments together with their occupants, the proposed agenda highlights areas of critical research interest for HBI, Biophilic Design and Soft Robotics in the built environment.

Algorithmically curated social media feeds in China are widely described as “producing anxiety,” yet the experiential mechanisms underlying this phenomenon remain unclear. We examine how anxiety emerges and is managed in everyday encounters with personalised feeds on RedNote (Xiaohongshu). Drawing on a survey (n = 435) and a 7-day diary study with follow-up interviews (n = 25), we identify three recurring pathways of anxiety elicitation: upward contrastive comparison, downward assimilative comparison, and affective resonance with personally salient concerns. While upward comparison has been widely documented, downward assimilative comparison—where users identify with others’ setbacks and anticipate similar risks—played a substantial role. Notably, many participants did not avoid anxiety-inducing content but engaged with it for self-improvement or preparatory coping. We argue that such vigilant engagement reflects the interplay between algorithmic curation, platform affordances, and culturally embedded norms of self-improvement, extending dominant comparison-based accounts of social media anxiety.

BACKGROUND Parkinson's clinical trials depend on patient-reported outcomes, often overlooking the vital role of carers in collaboratively tracking symptom progression. This is a potential limitation for decentralized clinical trials aimed at measuring real-world, free-living symptoms with sensors, such as wearables and cameras in the home. OBJECTIVE The primary objective of our study was to inform the design of a multimodal sensor platform for decentralised clinical trials. METHODS A qualitative study was conducted with an inductive approach using semistructured interviews with a cohort of people with Parkinson's. RESULTS This study of 18 participants (14 people diagnosed with Parkinsons, 4 spouses/informal carers) found that carers, household members, and peers take a central role in helping people with Parkinson’s make sense of and manage their symptoms. Our participants relied on others to help with completing tasks and understanding their symptoms through comparison to others, using their Carer-as-Sensor. While our participants mostly viewed their relationships with others positively, this could lead to negative impacts on oneself. Participants could prioritize household needs over their health by not taking medication or risking a chance of falling, or even avoiding being around others to prevent their Parkinson's being on display to reduce carer burden. CONCLUSIONS Our results argue that an 'outsider' and 'insider' approach to reporting symptoms can identify symptoms that are not noticed by people with Parkinson's, or withheld from carers. These form household-centred recommendations more broadly for the design of tracking and annotation strategies in the context of decentralised clinical trials and new innovations in AI to support the capture of nuanced and subtle changes in symptoms.