Aisling Ann O’Kane
Biographic Data
| ID | 7397291 |
|---|---|
| NAME | Aisling Ann O’Kane |
| GIVEN NAMES | Aisling Ann |
| FAMILY NAME | O’Kane |
| SIGNATURE | O’KANE A A |
| AFFILIATIONS | University College London |
| ORCID | 0000-0001-8219-8126 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Leveraging everyday mobile communication platforms for inclusive and accessible user studies
Traditionally conducted in person, user research is increasingly shifting to remote methods that leverage everyday communication technologies. Although video conferencing platforms such as Zoom have proved successful in supporting distributed user research, they also pose challenges for low resource settings, including high costs, scalability concerns, and the need for stable internet connectivity. In response, we explore how widely adopted alter…
Co-designing opportunities for Human-Centred Machine Learning in supporting Type 1 diabetes decision-making
Type 1 Diabetes (T1D) self-management requires hundreds of daily decisions. Diabetes technologies that use machine learning have significant potential to simplify this process and provide better decision support, but often rely on cumbersome data logging and cognitively demanding reflection on collected data. We set out to use co-design to identify opportunities for machine learning to support diabetes self-management in everyday settings. Howeve…
Turning to Peers
People are increasingly involved in the self-management of their own health, including chronic conditions. With technology advances, the choice of self-management practices, tools, and technologies has never been greater. The studies reported here investigated the information seeking practices of two different chronic health populations in their quest to manage their health conditions. Migraine and diabetes patients and clinicians in the UK and t…
No prominent works on this page.
Turning to Peers
People are increasingly involved in the self-management of their own health, including chronic conditions. With technology advances, the choice of self-management practices, tools, and technologies has never been greater. The studies reported here investigated the information seeking practices of two different chronic health populations in their quest to manage their health conditions. Migraine and diabetes patients and clinicians in the UK and t…
Co-designing opportunities for Human-Centred Machine Learning in supporting Type 1 diabetes decision-making
Type 1 Diabetes (T1D) self-management requires hundreds of daily decisions. Diabetes technologies that use machine learning have significant potential to simplify this process and provide better decision support, but often rely on cumbersome data logging and cognitively demanding reflection on collected data. We set out to use co-design to identify opportunities for machine learning to support diabetes self-management in everyday settings. Howeve…
Leveraging everyday mobile communication platforms for inclusive and accessible user studies
Traditionally conducted in person, user research is increasingly shifting to remote methods that leverage everyday communication technologies. Although video conferencing platforms such as Zoom have proved successful in supporting distributed user research, they also pose challenges for low resource settings, including high costs, scalability concerns, and the need for stable internet connectivity. In response, we explore how widely adopted alter…
Computer Science (3 works) · Human–computer interaction (2 works) · Internet privacy (2 works) · Psychology (2 works) · Artificial Intelligence (1 works) · Chronic condition (1 works) · Data science (1 works) · Decision support system (1 works) · Diabetes Management and Education (1 works) · Diabetes Management and Research (1 works)