David Coyle

Mansura A Khan, Khalil Muhammad, Barry Smyth et al.

Food-choices and eating-habits directly contribute to our long-term health. This makes the food recommender system a potential tool to address the global crisis of obesity and malnutrition. Over the past decade, artificial-intelligence and medical researchers became more invested in researching tools that can guide and help people make healthy and thoughtful decisions around food and diet. In many typical (Recommender System) RS domains, smart nudges have been proven effective in shaping users' consumption patterns. In recent years, knowledgeable nudging and incentifying choices started getting attention in the food domain as well. To develop smart nudging for promoting healthier food choices, we combined Machine Learning and RS technology with food-healthiness guidelines from recognized health organizations, such as the World Health Organization, Food Standards Agency, and the National Health Service United Kingdom. In this paper, we discuss our research on, persuasive visualization for making users aware of the healthiness of the recommended recipes. Here, we propose three novel nudging technology, the WHO-BubbleSlider, the FSA-ColorCoading, and the DRCI-MLCP, that encourage users to choose healthier recipes. We also propose a Topic Modeling based portion-size recommendation algorithm. To evaluate our proposed smart-nudges, we conducted an online user study with 96 participants and 92250 recipes. Results showed that, during the food decision-making process, appropriate healthiness cues make users more likely to click, browse, and choose healthier recipes over less healthy ones.

Mansura A. Khan, Ellen Rushe, Barry Smyth et al.

Food choices are personal and complex and have a significant impact on our long-term health and quality of life. By helping users to make informed and satisfying decisions, Recommender Systems (RS) have the potential to support users in making healthier food choices. Intelligent users-modeling is a key challenge in achieving this potential. This paper investigates Ensemble Topic Modelling (EnsTM) based Feature Identification techniques for efficient user-modeling and recipe recommendation. It builds on findings in EnsTM to propose a reduced data representation format and a smart user-modeling strategy that makes capturing user-preference fast, efficient and interactive. This approach enables personalization, even in a cold-start scenario. This paper proposes two different EnsTM based and one Hybrid EnsTM based recommenders. We compared all three EnsTM based variations through a user study with 48 participants, using a large-scale,real-world corpus of 230,876 recipes, and compare against a conventional Content Based (CB) approach. EnsTM based recommenders performed significantly better than the CB approach. Besides acknowledging multi-domain contents such as taste, demographics and cost, our proposed approach also considers user's nutritional preference and assists them finding recipes under diverse nutritional categories. Furthermore, it provides excellent coverage and enables implicit understanding of user's food practices. Subsequent analysis also exposed correlation between certain features and a healthier lifestyle.

David Coyle, Mark Matthews, Gavin Doherty et al.

Using the metrics of the World Health Organisation, the Global Burden of Disease Study has found that mental health difficulties are currently the leading cause of disability in developed countries [1]. Projections also indicate that the global burden of mental health difficulties will continue to rise in the coming decades. The human and economic costs of this trend will be substantial. In this paper we discuss how effectively designed interactive systems, developed through collaborative, interdisciplinary efforts, can play a significant role in helping to address this challenge. Our discussion is grounded in a description of four exploratory systems, each of which has undergone initial clinical evaluations. Directions for future research on mental health technologies are also identified.

David Coyle

In cognitive neuroscience the sense of agency is defined as the as the experience of controlling ones own actions and, through this control, affecting the external world. At CHI 2012 I presented a paper entitled I did that! Measuring Users Experience of Agency in their own Actions [1]. This extended abstract draws heavily on that paper, which described an implicit measure called intentional binding. This measure, developed by researchers in cognitive neuroscience, has been shown to provide a robust implicit measure for the sense of agency. My interest in intentional binding stemmed from prior HCI literature, (e.g. the work of Shneiderman) which emphasises the importance of the sense of control in human-computer interactions. The key question behind the CHI 2012 paper was: can we apply intention binding to provide an implicit measure for the experience of control in human-computer interactions? In investigating this question, replication was a key element of the experimental process.

David Coyle, Gavin Doherty

Effective communication and strong therapeutic relationships are critical to successful mental health interventions. For example, in 1957 Carl Rogers, a pioneer of person-centred therapy, proposed that an empowering relationship could, in and of itself, create the necessary and sufficient conditions for positive therapeutic outcomes [1]. Whilst modern psychological theories no longer favour an exclusive focus on relationships, positive relationships and the dynamics of client-therapist communication remain cornerstones of mental health intervention theories. A more recent meta-review concluded that across all interventions models, irrespective of the theoretical approach, the quality of the relationship between therapists and clients is the second leading determinant of successful clinical outcomes [2]. Over the past ten years we (David Coyle and Gavin Doherty) have designed and evaluated a wide range to systems that provide support for psychological (or talk- based) mental health interventions [3]. Here we briefly consider two recent examples. In each case our aim was to enhance communication and reshape clinical practice in a manner that empowers patients. gNats Island is a computer game that supports face-to-face interventions for adolescents [4]. MindBalance is an online treatment programme for adults experiencing difficulties with depression [5].