collaborators

13 papers

cs.HC2026

Designing and Evaluating Granular Consent for Data Sharing in Cardiac Disease Prevention

Pavithren V S Pakianathan, Rania Islambouli, Magenta Jade Shipsey +3

Dynamic consent can promise end users with greater control, but little is known about how older adults with chronic conditions navigate the tradeoff between control and burden in g…

cs.HC2026

Designing for the Moment: How One-Minute Interventions Fit or Falter Across Domains

Zahra Hassanzadeh, Anne Hsu, Rachel Kornfield +8

This paper explores the design space for one-minute digital interventions that prompt immediate action without onboarding or sensing. By embracing Fogg's Behavior Model and four de…

cs.HC2026

AktivTalk: Digitizing the Talk Test for Voice-Based Exercise Intensity Self-Assessment and Exploring Automated Classification from Speech

Rania Islambouli, Laura Geiger, Daniela Wurhofer +3

Monitoring exercise intensity is critical for safe and effective physical activity, particularly for individuals with cardiovascular disease, where overexertion can pose serious ri…

cs.HC2026

Exploring Self-Tracking Practices of Older Adults with CVD to Inform the Design of LLM-Enabled Health Data Sensemaking

Duosi Dai, Pavithren V S Pakianathan, Gunnar Treff +3

Wearables and mobile health applications are increasingly adopted for self-management of chronic illnesses; yet the data feels overwhelming for older adults with cardiovascular dis…

cs.HC2026

Structured Exploration vs. Generative Flexibility: A Field Study Comparing Bandit and LLM Architectures for Personalised Health Behaviour Interventions

Dominik P. Hofer, Haochen Song, Rania Islambouli +5

Behaviour Change Techniques (BCTs) are central to digital health interventions, yet selecting and delivering effective techniques remains challenging. Contextual bandits enable sta…

cs.LG2026

Tailored Behavior-Change Messaging for Physical Activity: Integrating Contextual Bandits and Large Language Models

Haochen Song, Dominik Hofer, Rania Islambouli +6

Contextual multi-armed bandit (cMAB) algorithms offer a promising framework for adapting behavioral interventions to individuals over time. However, cMABs often require large sampl…