Using thematic analysis in healthcare HCI at CHI: A scoping review

R Bowman, C Nadal, K Morrissey, A Thieme… - Proceedings of the …, 2023 - dl.acm.org
CHI papers researching healthcare human-computer interaction (HCI) are increasingly
reporting the use of “thematic analysis”(TA). TA refers to a range of flexible and evolving …

Chronic care in a life transition: Challenges and opportunities for artificial intelligence to support young adults with type 1 diabetes moving to university

S James, M Armstrong, Z Abdallah… - Proceedings of the 2023 …, 2023 - dl.acm.org
Self-managing a chronic condition involves adapting management strategies to life's
continual change. Among these changes, moments of significant life transition can render …

Do people engage cognitively with AI? Impact of AI assistance on incidental learning

KZ Gajos, L Mamykina - … of the 27th International Conference on …, 2022 - dl.acm.org
When people receive advice while making difficult decisions, they often make better
decisions in the moment and also increase their knowledge in the process. However, such …

Sociocultural dimensions of tracking health and taking care

KS Bhat, N Kumar - Proceedings of the ACM on Human-Computer …, 2020 - dl.acm.org
The field of personal health informatics has received increasing attention within the CSCW
and HCI communities as health tracking becomes more affordable, accessible, and …

From reflection to action: Combining machine learning with expert knowledge for nutrition goal recommendations

E G. Mitchell, E M. Heitkemper… - Proceedings of the …, 2021 - dl.acm.org
Self-tracking can help personalize self-management interventions for chronic conditions like
type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy …

Characteristics of smartphone-based dietary assessment tools: A systematic review

LM König, M Van Emmenis, J Nurmi… - Health Psychology …, 2022 - Taylor & Francis
Smartphones have become popular in assessing eating behaviour in real-life and real-time.
This systematic review provides a comprehensive overview of smartphone-based dietary …

A simple modeling framework for prediction in the human glucose–insulin system

M Sirlanci, ME Levine, CC Low Wang… - … Journal of Nonlinear …, 2023 - pubs.aip.org
Forecasting blood glucose (BG) levels with routinely collected data is useful for glycemic
management. BG dynamics are nonlinear, complex, and nonstationary, which can be …

Patient-generated health data: dimensions, challenges, and open questions

MC Figueiredo, Y Chen - Foundations and Trends® in …, 2020 - nowpublishers.com
In this review, we present an overview of patient-generated health data (PGHD) research,
focusing on important aspects that inform and define studies in the area. We start by …

A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking

K Li, I Urteaga, A Shea, VJ Vitzthum… - Journal of the …, 2022 - academic.oup.com
Objective The study sought to build predictive models of next menstrual cycle start date
based on mobile health self-tracked cycle data. Because app users may skip tracking …

[HTML][HTML] Co-designing opportunities for Human-Centred Machine Learning in supporting Type 1 diabetes decision-making

K Stawarz, D Katz, A Ayobi, P Marshall… - International Journal of …, 2023 - Elsevier
Abstract Type 1 Diabetes (T1D) self-management requires hundreds of daily decisions.
Diabetes technologies that use machine learning have significant potential to simplify this …