Pavithren V S Pakianathan

Duosi Dai, Pavithren V S Pakianathan, Gunnar Treff et al.

Wearables and mobile health applications are increasingly adopted for self-management of chronic illnesses; yet the data feels overwhelming for older adults with cardiovascular disease (CVD). This study explores how they make sense of self-tracked data and identifies design opportunities for Large Language Model (LLM)-enabled support. We conducted a seven-day diary study and follow-up interviews with eight CVD patients aged 64-82. We identified six themes: navigating emotional complexity, owning health narratives, prioritizing bodily sensations, selective engagement with health metrics, negotiating socio-technical dynamics of sharing, and cautious optimism toward AI. Findings highlight that self-tracking is affective, interpretive, and socially situated. We outline design directions for LLM-enabled data sensemaking systems: supporting emotional engagement, reinforcing patient agency, acknowledging embodied experiences, and prompting dialogue in clinical and social contexts. To support safety, expert-in-the-loop mechanisms are essential. These directions articulate how LLMs can help translate data into narratives and carry implications for human-data interaction and behavior-change support.

Pavithren V S Pakianathan, Rania Islambouli, Diogo Branco et al.

Individuals are increasingly generating substantial personal health and lifestyle data, e.g. through wearables and smartphones. While such data could transform preventative care, its integration into clinical practice is hindered by its scale, heterogeneity and the time pressure and data literacy of healthcare professionals (HCPs). We explore how large language models (LLMs) can support sensemaking of patient-generated health data (PGHD) with automated summaries and natural language data exploration. Using cardiovascular disease (CVD) risk reduction as a use case, 16 HCPs reviewed multimodal PGHD in a mixed-methods study with a prototype that integrated common charts, LLM-generated summaries, and a conversational interface. Findings show that AI summaries provided quick overviews that anchored exploration, while conversational interaction supported flexible analysis and bridged data-literacy gaps. However, HCPs raised concerns about transparency, privacy, and overreliance. We contribute empirical insights and sociotechnical design implications for integrating AI-driven summarization and conversation into clinical workflows to support PGHD sensemaking.

Pavithren V S Pakianathan, Simon Perrault

Over the past few years, older adults in Singapore have been massively connecting to the Internet using Smartphone. However due to the ever-changing nature of Technology and Cybersecurity landscape, an older adult's limited technical and Privacy and Security (P & S) knowledge, experience and declining cognitive and physical abilities puts them at higher risks. Furthermore mainstream smartphone applications, which are generally not designed with older adults in mind, could result in mismatched mental models thereby creating usability issues. We interviewed 10 older adults above 65 and 10 adults assisting them based in Singapore to investigate how smartphone P & S can be redesigned inclusively by addressing the needs of older adults and people who support them. Our results show that socio-cultural factors affected the process of getting or providing P & S help, culture and attitude affected learning behaviours and older adults expressed heterogeneous P & S preferences based on contextual factors and level of convenience, however there are opportunities for the mechanisms to be senior-friendly. Due to the complex relationship between an older adult's milieu and technology, we aim to utilize a technology probe to investigate further and contribute towards an inclusive P & S model.