Mobile applications, wearables, in-home medical devices and other peripherals are reshaping consumer participation in healthcare. While much research has focused on patient facing mobile technologies, there remains limited evidence of the implications of technologies on models of care and health service delivery. In this paper, we present an evaluation of the clinician experiences of using a bidirectional healthcare platform, Activate TKR, for orthopaedic patients undergoing a Total Knee Replacement (TKR). The platform provides mobile-enabled media rich content to support patients through their journey, while facilitating data sharing and remote monitoring by clinicians. Results show that physiotherapists experienced the greatest enhancements in care delivery through access to patient generated information which improved appointments, status and progress discussion, and tailoring of physiotherapy. Surgeons found value in the mobile-enabled information patients accessed in preparation for surgery, enhancing their overall TKR experience.

This paper describes the continued development of a system to provide early assessment of speech development issues in children and better triaging to professional services. Whilst corpora of children's speech are increasingly available, recognition of disordered children's speech is still a data-scarce task. Transfer learning methods have been shown to be effective at leveraging out-of-domain data to improve ASR performance in similar data-scarce applications. This paper combines transfer learning, with previously developed methods for constrained decoding based on expert speech pathology knowledge and knowledge of the target text. Results of this study show that transfer learning with out-of-domain adult speech can improve phoneme recognition for disordered children's speech. Specifically, a Deep Neural Network (DNN) trained on adult speech and fine-tuned on a corpus of disordered children's speech reduced the phoneme error rate (PER) of a DNN trained on a children's corpus from 16.3% to 14.2%. Furthermore, this fine-tuned DNN also improved the performance of a Hierarchal Neural Network based acoustic model previously used by the system with a PER of 19.3%. We close with a discussion of our planned future developments of the system.

Background: Smartphones have reached levels of popularity and penetration where they are now suitable for use in population health interventions. A key feature of smartphones is push notification or in app messaging service, which can be used to alert users to messages or instructions pertaining to an installed app. Little evidence exists as to the persuasive power of these messages.Method: We conducted a 24-week live user evaluation of push notifications used in a behavior-based mobile app for a meal replacement program to understand the role of push notifications in persuading users to engage with self-monitoring tasks.Results: User perception of the prompts were verified through questionnaires, which in conjunction with the interaction logs show that users were tolerant of multiple daily prompts. The decline in compliance to the tasks set, however, shows that while the participants did not object to receiving prompts, they were less likely to respond to them as the study progressed.Conclusions: Push notifications and user tasks are appropriate mechanisms to engage users with mobile technology in the short term.

A proof of concept system is developed to provide a broad assessment of speech development issues in children. It has been designed to enable non-experts to complete an initial screening of children's speech with the aim of reducing the workload on Speech Language Pathology services. The system was composed of an acoustic model trained by neural networks with split temporal context features and a constrained HMM-encoded with the knowledge of Speech Language Pathologists. Results demonstrated the system was able to improve PER by 33% compared with standard HMM decoders, with a minimum PER of 19.03% achieved. Identification of Phonological Error Patterns with up to 94% accuracy was achieved despite utilizing only a small corpus of disordered speech from Australian children. These results indicate the proposed system is viable and the direction of further development are outlined in the paper. Copyright

The quantity of accessible information has been growing rapidly and far exceeded human processing capabilities. The sheer abundance of information often prevents users from discovering the desired information, or aggravates making informed and correct choices. This highlights the pressing need for intelligent personalized applications that simplify information access and discovery by taking into account users' preferences and needs. One type of personalized application that has recently become tremendously popular in research and industry is recommender systems. These provide to users personalized recommendations about information and products they may be interested to examine or purchase. Extensive research into recommender systems has yielded a variety of techniques, which have been published at a variety of conferences and adopted by numerous Web-sites. This tutorial will provide the participants with broad overview and thorough understanding of algorithms and practically deployed Web and mobile applications of personalized technologies.

A key challenge in online communities is that of keeping a community active and alive. All online communities work hard to keep their members through various initiatives, such as personalisation and recommendation technologies. In online communities aimed at supporting behavioural change, that is, in domains such as diet, lifestyle, or the environment, the main reason for participation is not to connect with real-world friends for sharing and communicating, but to meet and gain support from like-minded people in an online environment. Introducing personalisation and recommendation features in these networks is challenging, as traditional approaches leverage the densely populated friendship relations found in typical social networks, and these are not present in these new community types. We address this challenge by looking beyond the articulated friendships of a community for evidence of relationships. In particular, we look at the interactions of members of an online community with other members and resources. In this article, we present a social behaviour model and apply it to two types of recommendation systems, a people recommender and a content recommender system. We evaluate our systems using the interaction logs of an online diet and lifestyle community in which 5,000 Australians participated in a 12-week programme. Our results show that our social behaviour-based recommendation algorithms outperform baselines, friendship-based, and link-prediction algorithms.

The explosion of apps for the medical and wellness sectors has been noted by many. Consumer apps, which provide innovative solutions for self management for a range of health problems have flooded the market, due to high consumer demand. More recently we have seen increased presence of truly medical applications for clinical professionals and patients of serious conditions. In the majority of cases consumer based mHealth apps allow people to do old things in new ways, such as recording health measures digitally rather than on paper. Medical apps, aimed at increasing the quality and efficiency of existing health care delivery models, provide clinical staff with convenient tools and easy to access resources and communication mechanisms. Finally, in rare and exciting cases we are seeing mHealth applications that are doing things in entirely new ways to drive real innovation in health care delivery through mobile devices. This tutorial will inform participants about the breadth of mHealth applications that are transforming the Health Services sector and put forward a strong case for HCI and efficacy research.

The emergence of what is called the social web and the continuing stream of new applications and community-based platforms including Facebook, Twitter, LinkedIn and others had a substantial impact on recommender systems research and practice over the last years in different ways.

Recommendations to connect like-minded people can result in increased engagement amongst members of online communities, thus playing an important role in their sustainability. We have developed a suite of algorithms for friend recommendations using a social trust model called STrust. In STrust, the

No abstract available.

Personalized systems and recommender systems exploit implicitly and explicitly provided user information to address the needs and requirements of those using their services. User preference information, often in the form of interaction logs and ratings data, is used to identify similar users, whose opinions are leveraged to inform recommendations or to filter information. In this work we explore a different dimension of information trends in user bias and reasoning learned from ratings provided by users to a recommender system. Our work examines the characteristics of a dataset of 100,000 user ratings on a corpus of recipes, which illustrates stable user bias towards certain features of the recipes (cuisine type, key ingredient, and complexity). We exploit this knowledge to design and evaluate a personalized rating acquisition tool based on active learning, which leverages user biases in order to obtain ratings bearing high-value information and to reduce prediction errors with new users.

Personalized technologies aim to enhance user experience by taking into account users’ interests, preferences, and other relevant information. Persuasive technologies aim to modify user attitudes, intentions, or behavior through computer-human dialogue and social influence. While both personalized and persuasive technologies influence user interaction and behavior, we posit that this influence could be significantly increased if the two technologies were combined to create personalized and persuasive systems. For example, the persuasive power of a one-size-fits-all persuasive intervention could be enhanced by considering the users being influenced and their susceptibility to the persuasion being offered. Likewise, personalized technologies could cash in on increased success, in terms of user satisfaction, revenue, and user experience, if their services used persuasive techniques. Hence, the coupling of personalization and persuasion has the potential to enhance the impact of both technologies. This new, developing area clearly offers mutual benefits to both research areas, as we illustrate in this special issue.

People’s daily lives have become increasingly sedentary, with extended periods of time being spent in front of a host of electronic screens for learning, work, and entertainment. We present research into the use of an adaptive persuasive technology, which introduces bursts of physical activity into a traditionally sedentary activity: computer game playing. Our game design approach leverages the playfulness and addictive nature of computer games to motivate players to engage in mild physical activity. The design allows players to gain virtual in-game rewards in return for performing real physical activity captured by sensory devices. This article presents a two-stage analysis of the activity-motivating game design approach applied to a prototype game. Initially, we detail the overall acceptance of active games discovered when trialing the technology with 135 young players. Results showed that players performed more activity without negatively affecting their perceived enjoyment of the playing experience. The analysis did discover, however, a lack of balance between the amounts of physical activity carried out by players with various gaming skills, which prompted a subsequent investigation into adaptive techniques for balancing the amount of physical activity performed by players. An evaluation of additional 90 players showed that adaptive techniques successfully overcame the gaming skills dependence and achieved more balanced activity levels. Overall, this work positions activity-motivating games as an approach that can potentially change the way players interact with computer games and lead to healthier lifestyles.

The exponential growth of the social web poses challenges and new opportunities for recommender systems. The social web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. The Social Web provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.

Previous research has shown that providing family engagement and social support play important roles in weight management success, helping to achieve long-term lifestyle changes. Traditionally, the support provided by online health communities is primarily targeted at individuals and does not involve their families. SOFA (SOcial FAmilies), a novel approach for engaging, motivating, and persuading families to adopt a healthy lifestyle, is proposed. SOFA is an online social network for families coupled with a repository of health-related educational content. This article reports the results of a live user study aimed at investigating how user profile representation and system-assigned tasks influence users engagement with the system and change their attitude toward a healthy lifestyle. The results show that representing family members as individuals increases the number of active members per family as well as their retention, contribution to, and engagement with the network. The results also show that family-based social networks positively change the attitude of family members toward a healthy lifestyle.

Sustaining user participation is a challenge for even the most popular web sites. In this work we report on an effort to increase exposure to, and interaction with, a repository of health based information by coupling the repository with a social networking application. We hypothesize that we can sustain user interaction with a repository and increase nutrition knowledge through content browsing by reporting on the browsing actions of other users in a Social Networking System's activity feeds. We report on the findings of a live user study, which showed that coupling health content with a social networking system successfully increased content browsing and that highly engaged users are seen to have an altered attitude toward control over their health.

Contemporary lifestyle has become increasingly sedentary: little physical (sports, exercises) and much sedentary (TV, computers) activity. The nature of sedentary activity is self-reinforcing, such that increasing physical and decreasing sedentary activity is difficult. We present a novel approach aimed at combating this problem in the context of computer games. Rather than explicitly changing the amount of physical and sedentary activity a person sets out to perform, we propose a new game design that leverages user engagement to generate out of game motivation to perform physical activity while playing. In our design, players gain virtual game rewards in return for real physical activity performed. Here we present and evaluate an application of our design to the game Neverball. We adapted Neverball by reducing the time allocated to accomplish the game tasks and motivated players to perform physical activity by offering time based rewards. An empirical evaluation involving 180 participants shows that the participants performed more physical activity, decreased the amount of sedentary playing time, and did not report a decrease in perceived enjoyment of playing the activity motivating version of Neverball.

The addictive nature of game playing contributes to an increasingly sedentary lifestyle. In this demonstration we showcase PLAY, MATE!, a novel mixed reality game design that motivates players to perform physical activity as part of playing. According to the PLAY, MATE! design, players gain virtual game rewards in return for the real physical activity they perform. We demonstrate the application of the PLAY, MATE! design to an open source game and allow participants to experience physical activity motivating games in person.

As the obesity epidemic takes hold across the world many medical professionals are referring users to online systems aimed at educating and persuading users to alter their lifestyle. The challenge for many of these systems is to increase initial adoption and sustain participation for sufficient time to have real impact on the life of its users. In this work we present some preliminary investigation into the design of a recipe recommender, aimed at educating and sustaining user participation, which makes tailored recommendations of healthy recipes. We concentrate on the two initial dimensions of food recommendations: data capture and food-recipe relationships and present a study into the suitability of varying recommender algorithms for the recommendation of recipes.

In recent years health care professionals have been investigating the use of ICT technologies in order to influence the general public to change their attitude and behaviour toward a healthier lifestyle. We present Mobile Mentor, a platform aimed at supporting individuals on goal driven programs through personalized mobile technology. This demonstration focuses on a weight loss prototype, Weight Management Mentor, which supports self regulation through the collection of real time diet and exercise data, self reflection and awareness through its graphical feedback mechanisms, and interaction with a health practitioner or advisor through a central server.

Social relationships and family involvement play an important role in health management, whereas activity awareness is useful in decision-making and stimulating motivation and action. In this paper, we propose a novel activity awareness user interface for family-oriented healthy living social networks. It is intended to increase family members' interaction with healthy living social networks. A user study showed that the activity awareness interface can add value to specific aspects of interaction with family-based healthy living social applications. The interface increased interaction with the underlying healthy living content and led to higher level of learning about healthy living and impact on specific healthy living activities. There was also significant appreciation of and interaction with the activity awareness user interface elements.

Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation strategy. In this work we investigate the use of aggregated group data in collaborative filtering recipe recommendations. We present results of a study that exploits recipe ratings provided by families of users, in order to evaluate the accuracy of several group recommendation strategies and weighting models, and analyze the impact of switching strategies, data aggregation heuristics, and group characteristics on the performance of recommendations.

Physical activity motivating game design encourages players to perform real physical activity in order to gain virtual game rewards. Previous research into activity motivating games showed that they have the potential to motivate players to perform physical activity, while retaining the enjoyment of playing. However, it was discovered that a uniform motivating approach resulted in different levels of activity performed by players of varying gaming skills. In this work we present and evaluate two adaptive recommendation-based techniques, which aim to balance the amount of physical activity performed by players by adapting the level of motivation to their observed gaming skills. Experimental evaluation showed that the adaptive techniques not only increase the amount of activity performed and retain the enjoyment of playing, but also balance the amount of activity performed by players of varying gaming skills and allow for game difficulty to be set in a player-dependent manner.

The success of social media has resulted in an information overload problem, where users are faced with hundreds of new contributions, edits and communications at every visit. A prime example of this in social networks is the news or activity feeds, where the actions (friending, commenting, photo sharing, etc) of friends on the network are presented to users in order to inform them of the network activity. In this work we endeavour to reduce the burden on individuals of identifying interesting updates in social network news feeds by automatically identifying and recommending relevant items to individuals where item relevance is based on the observed interactions of the individual with the social network. The results of our offline study show that combining short term interest models, exploiting previous viewing behavior of users, and long-term models, exploiting previous viewing of network actions, was the best predictor of feed item relevance.

The exponential growth of the Social Web both poses challenges, and presents opportunities for Recommender System research. The Social Web has turned information consumers into active contributors who generate large volumes of rapidly changing online data. Recommender Systems strive to identify relevant content for users at the right time and in the right context but achieving this goal has become more difficult, in part due to the volume and nature of information contributed through the Social Web.

Presented at the 2009 ACM SIGCHI international conference on supporting group work, May 10–13 2009, Sanibel Island, Florida

Social network sites rely on the contributions of their members to create a lively and enjoyable space. Recent research has focused on using personalization and recommender technologies to encourage participation of existing members. In this work we present an early-intervention approach to encouraging participation and engagement, which makes recommendations to new users during their sign-up process. Our recommender system exploits external social media to produce people and profile entry recommendations for new users. We present results of a live user study, showing that users who received recommendations at sign-up created more social connections, contributed more content, and were on the whole more engaged with the system, contributing more without prompt and returning more often. We further show that recommendations for multiple content types yield significantly better results, in terms of user contribution and consumption; and that recommendations of more active users yield a higher return rate.

No abstract available.

Traditional social networking sites allow users to enter responses to a set of predefined fields when populating their personal profiles. In the system discussed in this work, freeform 'About You' entries allow users to craft their own questions / topics. We found that this kind of flexibility often leads to low content contributions and infrequent updates. The 'About You' recommender system described in this paper differs from many recommender systems in that it recommends content for users to create, rather than consume. We present empirical data from an experiment with 2,000 users of a social networking site during a one month period. Our findings suggest that users who receive recommendations create more entries and update them more over time. Further, using articulated social network information for recommendations performed better than content-based matching.

The goal of this paper is to detail the integration of two "social Web" technologies - social search and social navigation - and to highlight the benefits of such integration on two levels. Firstly, both technologies harvest and harness "community wisdom" and in an integrated system each of the search and navigation components can benefit from the additional community wisdom gathered by the other when assisting users to locate relevant information. Secondly, by integrating search and browsing we facilitate the development of a unique interface that effectively blends search and browsing functionality as part of a seamless social information access service. This service allows users to effectively combine their search and browsing behaviors. In this paper we will argue that this integration provides significantly more than the simple sum of the parts.

The size and diversity of the Web has been the root cause of the poor performance of many retrieval systems, with little navigational support provided by many large online formation repositories. The online information retrieval process cross different repositories shares similarities with content access facilities and user behaviors even when containing inherently different content types. In this work, we introduce our social recommender system called ASSIST. The recommendation framework in ASSIST can be applied to any online information retrieval service with key information access components, search and browsing. ASSIST exploits multiple forms of social implicit feedback in order to generate well-informed user recommendations in the online information retrieval domain.