With the increasing size and complexity of personal information and data landscapes, there is a need for guidance and support in the appropriate management of a deceased person’s postmortem privacy and digital legacy. However, most people engage poorly with existing mechanisms for specifying and planning for access and suitable usage of their own data. We report on two studies exploring the ways in which contextual factors such as the accessor and the data type may affect the appropriateness of personal data flows differently during life and after death. Our findings indicate that suitable data access after death is highly individual and contextual, with differences in appropriateness between during-life and after-death data flows significantly affected by the accessor and the data type in question. We identify that ambiguous accessor motivation, failure to communicate intent, changing temporal context and latent data values further complicate the act of digital legacy planning. Our findings also provide further evidence for the existence of a postmortem privacy paradox in which reported user behaviors do not reflect intent. With this in mind, we offer design recommendations for the integration of digital legacy planning functionality within Personal Information Management (PIM) and Group Information Management (GIM) systems.

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual's current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing.

Cardiovascular diseases (CVDs) are a major global health challenge, compounded by a significant systemic treatment gap: the underutilization of cardiac rehabilitation (CR). Central to this issue is the crucial yet underperforming referral process to CR due to various factors, including patients’ lack of information. By co-designing a CR referral assistant with cardiac patients and healthcare professionals, this work explores how technology can support patient journeys from the acute hospital to CR, overcoming existing healthcare system barriers. This work (1) contributes a map of patients’ evolving needs tailored to their pathway, (2) provides design implications for interactive digital health technologies aiming to facilitate patient transitions across established healthcare system boundaries, and (3) discusses the potential of technology as a “patient companion” during their transition to CR.

Typing on mobile devices is a common and complex task. The act of typing itself thereby encodes rich information, such as the typing method, the context it is performed in, and individual traits of the person typing. Researchers are increasingly using a selection or combination of experience sampling and passive sensing methods in real-world settings to examine typing behaviours. However, there is limited understanding of the effects these methods have on measures of input speed, typing behaviours, compliance, perceived trust and privacy. In this paper, we investigate the tradeoffs of everyday data collection methods. We contribute empirical results from a four-week field study (N=26). Here, participants contributed by transcribing, composing, passively having sentences analyzed and reflecting on their contributions. We present a tradeoff analysis of these data collection methods, discuss their impact on text-entry applications, and contribute a flexible research platform for in the wild text-entry studies.

In our data-centric world, most services rely on collecting and using personal data. The EU's General Data Protection Regulation (GDPR) aims to enhance individuals’ control over their data, but its practical impact is not well understood. We present a 10-participant study, where each participant filed 4-5 data access requests. Through interviews accompanying these requests and discussions scrutinising returned data, it appears that GDPR falls short of its goals due to non-compliance and low-quality responses. Participants found their hopes to understand providers’ data practices or harness their own data unmet. This causes increased distrust without any subjective improvement in power, although more transparent providers do earn greater trust. We propose designing more effective, data-inclusive and open policies and data access systems to improve both customer relations and individual agency, and also that wider public use of GDPR rights could help with delivering accountability and motivating providers to improve data practices.

Academics and community organisations are increasingly adopting co-research practices where participants contribute to qualitative data collection, analysis, and dissemination. These qualitative practices can often lack transparency that can present a problem for stakeholders (such as funding agencies) who seek evidence of the rigour and accountability in these decision-making processes. When qualitative research is done digitally, paradata is available as interaction logs that reveal the underlying processes, such as the time spent engaging with different segments of an interview. In practice, paradata is seldom used to examine the decisions associated with undertaking qualitative research. This paper explores the role of paradata arising from a four-month engagement with a community-led charity that used a digital platform to support their qualitative co-research project. Through observations of platform use and reflective post-deployment interviews, our findings highlight examples of paradata generated through digital tools in qualitative research, e.g., listening coverage, engagement rate, thematic maps and data discards. From this, we contribute a conceptualisation of paradata and discuss its role in qualitative research to improve process transparency, enhance data sharing, and to create feedback loops with research participants.

There is growing evidence that digital peer-support networks can have a positive influence on behaviour change and wellbeing outcomes for people who harm themselves and others. However, making and sustaining such networks are subject to ethical and pragmatic challenges, particularly for perpetrators of domestic violence whom pose unique risks when brought together. In this work we report on a ten-month study where we worked with six support workers and eighteen perpetrators in the design and deployment of Fragments of the Past; a socio-material system that connects audio messages with tangible artefacts. We share how crafting digitally-augmented artefacts - ‘fragments’ - of experiences of desisting from violence can translate messages for motivation and rapport between peers, without subjecting the process to risks inherent with direct inter-personal communication. These insights provide the basis for practical considerations for future network design with challenging populations.

As digital technologies become more prevalent there is a growing awareness of the importance of good security and privacy practices. The tools and techniques used to achieve this are typically designed with the living user in mind, with little consideration of how they should or will perform after the user has died. We report on two workshops carried out with users of password managers to explore their views on the post-mortem sharing, security and privacy of a range of common digital assets. We discuss a post-mortem privacy paradox where users recognise value in planning for their digital legacy, yet avoid actively doing so. Importantly, our findings highlight a tension between the use of recommended security tools during life and facilitating appropriate post-mortem access to chosen assets. We offer design recommendations to facilitate and encourage digital legacy planning while promoting good security habits during life.

Smartphones increasingly serve as the source for, or to aggregate, a considerable amount of data that can be relevant in public health emergencies. Hence the sharing and utilisation of mobile health data, for example to help control the spread of communicable diseases, has become a relevant issue, with the COVID-19 pandemic adding a sudden urgency mirrored in debates around contact tracing apps. Building on exploratory work that indicated user perceptions and values around consent, and the notion that smartphones and mobile health data can be perceived as elements of self-embodiment, we present an online study comparing three scenarios of representative diseases undertaken during the first wave lockdown in the UK. Using a mixed-methods analysis of responses from 86 participants, we identify tensions and mitigations in user values and from those present the description of four characteristic user-groups that can inform considerations for design and development activities in this space.

Questionnaires are among the most common research tools in virtual reality (VR) user studies. Transitioning from virtuality to reality for giving self-reports on VR experiences can lead to systematic biases. VR allows to embed questionnaires into the virtual environment which may ease participation and avoid biases. To provide a cohesive picture of methods and design choices for questionnaires in VR (inVRQ), we discuss 15 inVRQ studies from the literature and present a survey with 67 VR experts from academia and industry. Based on the outcomes, we conducted two user studies in which we tested different presentation and interaction methods of inVRQs and evaluated the usability and practicality of our design. We observed comparable completion times between inVRQs and questionnaires outside VR (nonVRQs) with higher enjoyment but lower usability for \inVRQs. These findings advocate the application of inVRQs and provide an overview of methods and considerations that lay the groundwork for inVRQ design.

Learning about alternatives to violence is an essential part of change work with domestic violence perpetrators. This is complex work, seeking to tackle a sensitive issue by involving the development of deep, embodied learning for perpetrators who may lack perspective on their behaviour. Interactive storytelling has been providing users with the opportunity to explore speculative scenarios in a controlled environment. We discuss the design of Choice-Point: a web-based application that allows perpetrators adopt the role of different fictional characters in an abusive scenario for conveying the essential skill of perspective-taking. We evaluated Choice-Point through trials with three groups of perpetrators, a support group of victim-survivors and an expert critique from support workers. We discuss challenges in using such technologies - such as our system - for engagement; the value of perpetrator agency in supporting non-violent behaviours, and the potential to positively shape perpetrators' journeys to non-violence within social care settings.

This paper provides a critical examination of how digital systems within a charitable organisation in the North of England are being used to both support and challenge male perpetrators of domestic violence. While there exists a range of digital tools to support the victim-survivors of domestic violence, no tools are available to challenge the abusive and harmful behaviours of perpetrators. Through this work, we uncovered the compelling moral responsibilities intrinsic within interactions with technological systems between perpetrators and support workers. As such, we highlight four spaces of negotiation concerning a person's responsibility in changing their abusive behaviour, which we have coined as mechanisms to represent their fundamental and interconnected nature. These mechanisms include self-awareness, acknowledging the extent of harms, providing peer support and respecting authorities. These insights are the basis for offering some practical considerations for HCI scholars, policymakers and intervention designers in their work with perpetrators of violence.

Many online games suffer when players drop off due to lost connections or quitting prematurely, which leads to match terminations or game-play imbalances. While rule-based outcome evaluations or substitutions with bots are frequently used to mitigate such disruptions, these techniques are often perceived as unsatisfactory. Deep learning methods have successfully been used in deep player behavior modelling (DPBM) to produce non-player characters or bots which show more complex behavior patterns than those modelled using traditional AI techniques. Motivated by these findings, we present an investigation of the player-perceived awareness, believability and representativeness, when substituting disconnected players with DPBM agents in an online-multiplayer action game. Both quantitative and qualitative outcomes indicate that DPBM agent substitutes perform similarly to human players and that players were unable to detect substitutions. Notably, players were in fact able to detect substitution with agents driven by more traditional heuristics.

Balancing games and producing content that remains interesting and challenging is a main cost factor in the design and maintenance of games. Dynamic difficulty adjustments (DDA) can successfully tune challenge levels to player abilities, but when implemented with classic heuristic parameter tuning (HPT) often turns out to be very noticeable, e.g. as "rubber-banding". Deep learning techniques can be employed for deep player behavior modeling (DPBM), enabling more complex adaptivity, but effects over frequent and longer-lasting game engagements, as well as how it compares to HPT has not been empirically investigated. We present a situated study of the effects of DDA via DPBM as compared to HPT on intrinsic motivation, perceived challenge and player motivation in a real-world MMORPG. The results indicate that DPBM can lead to significant improvements in intrinsic motivation and players prefer game experience episodes featuring DPBM over experience episodes with classic difficulty management.

Questionnaires are among the most common research tools in virtual reality (VR) evaluations and user studies. However, transitioning from virtual worlds to the physical world to respond to VR experience questionnaires can potentially lead to systematic biases. Administering questionnaires in VR (inVRQs) is becoming more common in contemporary research. This is based on the intuitive notion that inVRQs may ease participation, reduce the Break in Presence (BIP) and avoid biases. In this paper, we perform a systematic investigation into the effects of interrupting the VR experience through questionnaires using physiological data as a continuous and objective measure of presence. In a user study (n=50), we evaluated question-asking procedures using a VR shooter with two different levels of immersion. The users rated their player experience with a questionnaire either inside or outside of VR. Our results indicate a reduced BIP for the employed inVRQ without affecting the self-reported player experience.

In increasingly digitalised societies, government initiatives to ensure that public services remain accessible for everyone typically focus on the digital inclusion of older adults. However, by solely viewing older adults as passive recipients or consumers of services, digital inclusion strategies under-emphasise the concept of digital participation. Highlighting the importance of older adults as active contributors in a digital society, we investigated the potential of content creation to increase older adults' digital skills whilst also strengthening their digital participation. Through a workshop and interviews involving three different groups of older content producers, we show that content creation can stimulate older adults' digital participation. We report on challenges faced by the content creators, including time constraints, lack of professional support and the preference to create content collaboratively. We propose that by facilitating collaborative content creation activities, local communities can better support older adults' digital participation and facilitate inclusion across different life domains.

Room-scale mapping facilitates natural locomotion in virtual reality (VR), but it creates a problem when encountering virtual walls. In traditional video games, player avatars can simply be prevented from moving through walls. This is not possible in VR with room-scale mapping due to the lack of physical boundaries. Game design is either limited by avoiding walls, or the players might ignore them, which endangers the immersion and the overall game experience. To prevent players from walking through walls, we propose a combination of auditory, visual, and vibrotactile feedback for wall collisions. This solution can be implemented with standard game engine features, does not require any additional hardware or sensors, and is independent of game concept and narrative. A between-group study with 46 participants showed that a large majority of players without the feedback did pass through virtual walls, while 87% of the participants with the feedback refrained from walking through walls. The study found no notable differences in game experience.

Enabling multiple-purpose robots to follow textual instructions is an important challenge on the path to automating skill acquisition. In order to contribute to this goal, we work with physical exercise instructions as an everyday activity domain where textual descriptions are usually focused on body movements. Body movements are a common element across a broad range of activities that are of interest for robotic automation. Developing a text-to-animation system, as a first step towards understanding language for machines, is an important task. The process requires natural language understanding (NLU) including non-declarative sentences and the extraction of semantic information from complex syntactic structures with a large number of potential interpretations. Despite a comparatively high density of semantic references to body movements, exercise instructions still contain a large amount of underspecified information. Detecting and bridging or filling such underspecified elements is extremely challenging when relying on methods from NLU alone. Humans, however, can often add such implicit information with ease, due to its embodied nature. We present a process that contains a combination of a semantic parser and a Bayesian network. It explicates the information that is contained in textual movement instructions so that an animation execution of the motion-sequences performed by a virtual humanoid character can be rendered. Human computation is then employed to determine best candidates and to further inform the models in order to increase performance adequacy.

Current eye tracking technologies have a number of drawbacks when it comes to practical use in real-world settings. Common challenges, such as high levels of daylight, eyewear (e.g. spectacles or contact lenses) and eye make-up, give rise to noise that undermines their utility as a standard component for mobile computing, design, and evaluation. To work around these challenges, we introduce CrowdEyes, a mobile eye tracking solution that utilizes crowdsourcing for increased tracking accuracy and robustness. We present a pupil detection task design for crowd workers together with a study that demonstrates the high-level accuracy of crowdsourced pupil detection in comparison to state-of-the-art pupil detection algorithms. We further demonstrate the utility of our crowdsourced analysis pipeline in a fixation tagging task. In this paper, we validate the accuracy and robustness of harnessing the crowd as both an alternative and complement to automated pupil detection algorithms, and explore the associated costs and quality of our crowdsourcing approach.

Matching game difficulty to player ability is a crucial step toward a rewarding player experience, yet making difficulty adjustments that are effective yet unobtrusive can be challenging. This paper examines the impact of automatic and player-initiated difficulty adjustment on player experience through two studies. In the first study, 40 participants played the casual game THYFTHYF either in motion-based or sedentary mode, using menu-based, embedded, or automatic difficulty adjustment. In the second study, we created an adapted version of the commercially available game fl0w to allow us to carry out a more focused study of sedentary casual play. Results from both studies demonstrate that the type of difficulty adjustment has an impact on perceived autonomy, but other player experience measures were not affected as expected. Our findings suggest that most players express a preference for manual difficulty choices, but that overall game experience was not notably impacted by automated difficulty adjustments.

Strong adaptability is a major requirement and challenge in the physiotherapeutic use of motion-based games for health. For adaptation tool development, tablets are a promising platform due to their similarity in affordance compared to traditional clipboards. In a comparative study, we examined three different input modalities on the tablet that allow for configuring joint angles: direct-touch, classic interface components (e.g. buttons and sliders), and a combination of both. While direct touch emerged as the least preferable modality, the results highlight the benefits of the combination of direct-touch and classic interface components as the most accessible modality for configuring joint angle ranges. Furthermore, the importance of configuring joint angles along three distinct axes and the interesting use-case of configuration tools as communication support emerged.

Exergames are increasingly considered as an exercise instruction modality in health applications. Studies are typically conducted in non-situated contexts and capture short-term effects. We present first results from a medium-scale study conducted over the course of 5 weeks and integrated into a normal rehabilitation program. The study features three groups, comparing manually adjustable exergames with the identical games in adaptive versions and manual physiotherapy interventions without games. The results indicate that the exergames and traditional therapy are comparable regarding measures of competence and enjoyment, while exergames led to significantly higher scores for autonomy, presence, and in a functional reach test. With traditional therapy, scores for tension-pressure and effort-importance were significantly higher. The initial results of the broader study presented in this paper deliver insights regarding motivational aspects of exergames and traditional therapy and point out which motivational aspects could be strengthened in future implementations.

Autonomous manipulation robots can be valuable aids as interactive agents in the home, yet it has proven extremely difficult to program their behavior. Imitation learning uses data on human demonstrations to build behavioral models for robots. In order to cover a wide range of action strategies, data from many individuals is needed. Acquiring such large amounts of data can be a challenge. Tools for data capturing in this domain must thus implement a good user experience. We propose to use human computation games in order to gather data on human manual behavior. We demonstrate the idea with a strategy game that is operated via a natural user interface. A comparison between using the game for action execution and demonstrating actions in a virtual environment shows that people interact longer and have a better experience when playing the game.

Game balancing can help players with different skill levels play multiplayer games together; however, little is known about how the balancing approach affects performance, experience, and self-esteem'especially when differences in player strength result from given abilities, rather than learned skill. We explore three balancing approaches in a dance game and show that the explicit approach commonly used in commercial games reduces self-esteem and feelings of relatedness in dyads, whereas hidden balancing improves self-esteem and reduces score differential without affecting game outcome. We apply our results in a second study with dyads where one player had a mobility disability and used a wheelchair. By making motion-based games accessible for people with different physical abilities, and by enabling people with mobility disabilities to compete on a par with able-bodied peers, we show how to provide empowering experiences through enjoyable games that have the potential to increase physical activity and self-esteem.

Motion-based video games can have a variety of benefits for the players and are increasingly applied in physical therapy, rehabilitation and prevention for older adults. However, little is known about how this audience experiences playing such games, how the player experience affects the way older adults interact with motion-based games, and how this can relate to therapy goals. In our work, we decompose the player experience of older adults engaging with motion-based games, focusing on the effects of manipulations of the game representation through the visual channel (visual complexity), since it is the primary interaction modality of most games and since vision impairments are common amongst older adults. We examine the effects of different levels of visual complexity on player experience, performance, and exertion in a study with fifteen participants. Our results show that visual complexity affects the way games are perceived in two ways: First, while older adults do have preferences in terms of visual complexity of video games, notable effects were only measurable following drastic variations. Second, perceived exertion shifts depending on the degree of visual complexity. These findings can help inform the design of motion-based games for therapy and rehabilitation for older adults.

Herein we describe the QuickWoZ system, a Wizard-of-Oz (WoZ) tool that allows for the remote control of the behavior of animated characters in a 3D environment. The complete scene, character, behaviors and sounds can be defined in simple XML documents, which are parsed at runtime, so that setting up an experiment can be done without programming expertise. Quick selection lists and buttons enable the wizard to easily control the agents' behavior and allow for fast reactions to the subjects' input.