Loneliness is a growing public health concern, particularly among older adults, and has been linked to adverse physical and mental health outcomes. This study presents a machine learning approach to predict levels of loneliness using behavioural and emotional data collected from 124 participants through a mobile phone application over a 71-day period. The dataset includes 27 features derived from self-logged information such as wellbeing scores, mood fluctuations, and time spent in various home locations. Feature selection was applied to identify the most discriminative indicators, with classification and regression models evaluated using both Support Vector Machine (SVM), and Random Forest (RF). We applied feature selection to identify the most discriminative indicators and evaluated both Support Vector Machine (SVM) and Random Forest (RF) models for classification and regression. The highest classification accuracy—69.19% on a 7-point loneliness scale—was achieved using a five-fold SVM with the top 13 features. In the regression task, the best performance was observed using 26 features, resulting in a minimum Mean Squared Error (MSE) of 0.6752. These findings indicate that a selected subset of behavioural and emotional features can offer a meaningful estimation of loneliness levels. This has potential to inform the design of real-time, personalised digital tools aimed at identifying and supporting individuals at risk of loneliness.

Pain is an essential indicator of health and guides clinical treatments. Logging pain is important in supporting this. However, there is little research into pre-adolescent children's pain logging tools. Utilising the Bluebells method to engage children as co-designers, we gathered children's perspectives on pain-logging tools; in the first workshop by using tangible design approaches to support creative thinking, and in the second workshop by discussing developed prototypes based on the children's designs. Our findings highlight design concepts that the research team – despite many years of pain-related research – had not considered in the context of paediatric logging, namely a) prioritizing children's privacy in social settings while using pain-logging tools; b) emphasizing personalization to boost engagement; and c) logging general well-being of children alongside pain intensity to collect more insightful data. These findings thus demonstrate the value of co-designing pain-logging technologies with children.

There are many design techniques to support the co-design of tangible technologies. However, few of these design methods allow the involvement of users at scale and across diverse geographic locations. While popular in psychology, the story completion method (SCM) has only recently started to be adopted within the HCI community. We explore whether SCM can generate meaningful design insights from large, diverse study populations for the design of Tangible User Interfaces (TUIs). Based on the results of two questionnaire studies using SCM, we conclude that the method can be used to generate meaningful design insights. Drawing on a systematic review of 870 TUI papers, we then contextualise the strengths and weaknesses of SCM against commonly used design methods, before reflecting on our experience of using the method across two distinct domains. We discuss the advantages of the method (particularly in terms of the scale and diversity of participation) and the challenges (particularly around constructing meaningful story stems, and developing the correct level of scaffolding to support creativity). We conclude that SCM is particularly suitable to be used in the early stages of the design process to understand the socio-cultural context of deployment.

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. However, over nine months of interviews and design workshops with 15 people with T1D, we had to re-assess our assumptions about user needs. Our participants reported confidence in their personal knowledge and rejected machine learning based decision support when coping with routine situations, but highlighted the need for technological support in the context of unfamiliar or unexpected situations (holidays, illness, etc.). However, these are the situations where prior data are often lacking and drawing data-driven conclusions is challenging. Reflecting this challenge, we provide suggestions on how machine learning and other artificial intelligence approaches, e.g., expert systems, could enable decision-making support in both routine and unexpected situations.

Co-design is a widely applied design process with well-documented values, including mutual learning and collective creativity. However, the real-world challenges of conducting multidisciplinary co-design research to inform the design of self-care technologies are not well established. We provide a qualitative account of a multidisciplinary project that aimed to co-design machine learning applications for Type 1 Diabetes (T1D) self-management. Through interviews, we identify not only perceived social, technological and strategic benefits of co-design but also organisational, translational and pragmatic design challenges: participants with T1D experienced difficulties in co-designing systems that met their individual self-care needs as part of group activities; HCI and AI researchers described challenges resulting from applying co-design outcomes to data-driven ML work; and industry collaborators highlighted academic data sharing regulations as cross-organisational challenges that can impede co-design efforts. Based on this understanding, we discuss opportunities for supporting multidisciplinary collaborations and aligning individual health needs with collaborative co-design activities.

For those with chronic conditions, such as Type 1 diabetes, smartphone apps offer the promise of an affordable, convenient, and personalized disease management tool. However, despite significant academic research and commercial development in this area, diabetes apps still show low adoption rates and underwhelming clinical outcomes. Through user-interaction sessions with 16 people with Type 1 diabetes, we provide evidence that commonly used interfaces for diabetes self-management apps, while providing certain benefits, can fail to explicitly address the cognitive and emotional requirements of users. From analysis of these sessions with eight such user interface designs, we report on user requirements, as well as interface benefits, limitations, and then discuss the implications of these findings. Finally, with the goal of improving these apps, we identify 3 questions for designers, and review for each in turn: current shortcomings, relevant approaches, exposed challenges, and potential solutions.

Type 1 diabetes is a potentially life-threatening chronic condition that requires frequent interactions with diverse data to inform treatment decisions. While mobile technologies such as blood glucose meters have long been an essential part of this process, designing interfaces that explicitly support decision-making remains challenging. Dual-process models are a common approach to understanding such cognitive tasks. However, evidence from the first of two studies we present suggests that in demanding and complex situations, some individuals approach disease management in distinctive ways that do not seem to fit well within existing models. This finding motivated, and helped frame our second study, a survey (n=192) to investigate these behaviors in more detail. On the basis of the resulting analysis, we posit Fluid Contextual Reasoning to explain how some people with diabetes respond to particular situations, and discuss how an extended framework might help inform the design of user interfaces for diabetes management.