Hiba Arnaout, Anmol Goel, H. Andrew Schwartz, Steffen T. Eberhardt, Dana Atzil-Slonim, Gavin Doherty, Brian Schwartz, Wolfgang Lutz, Tim Althoff, Munmun De Choudhury, Hamidreza Jamalabadi, Raj Sanjay Shah, Flor Miriam Plaza-del-Arco, Dirk Hovy, Maria Liakata, Iryna Gurevych. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Smart home technologies, like cameras, door locks, and speakers, are increasingly used in our everyday lives. However, their continuous data collection and internet connectivity pose various security risks. While research on smart home security has mainly focused on technological aspects, human experience and societal factors also play a crucial role. Various human and social factors, such as user experience with smart home devices, security design processes, and government regulations, are intertwined and influence each other, affecting smart home security. It is therefore important to understand and consider these interconnected factors in technology design to secure homes that contain increasingly connected devices. This scoping review provides an overview of current human-centered studies (N=102) on smart home security, which aims to help researchers and practitioners better navigate this field. We present a conceptual framework that outlines key challenges in ensuring smart home security with a synthesis of insights on contributing human factors. We then summarize general security design principles and map existing user-centred security approaches in smart homes, and highlight research directions for future investigation. Beyond mapping existing studies, the review reveals a growing emphasis on engaging multiple stakeholders, especially smart home users, in shaping human-centered security.

Machine learning-augmented applications have the potential to be powerful tools for decision-making in healthcare. However, healthcare is a complex domain that presents many challenges. These challenges, such as medical errors, clinician–patient relationships and treatment preferences, must be addressed to ensure fairness in ML-augmented healthcare applications. To better understand the influence these challenges have on fairness, 16 experienced engineers and designers with domain knowledge in healthcare technology were interviewed about how they would prioritise fairness in 3 healthcare scenarios (well-being improvement, chronic illness management, acute illness treatment). Using a template analysis, this work identifies the key considerations in the creation of fair ML for healthcare. These considerations clustered into categories related to technology, healthcare context and user perspectives. To explore these categories, we propose the stakeholder fairness conceptual model. This framework aids designers and developers in understanding the complex considerations that stem from the building, management and evaluation of ML-augmented healthcare applications, and how they affect the expectations of fairness. This work then discusses how this model may be applied when the health technology is directly provisioned to users, without a healthcare provider managing its use or adoption. This article contributes to the understanding of fairness requirements in healthcare, including the effect of healthcare errors, clinician-application collaboration and how the evaluation of healthcare technology becomes part of the fairness design process.

Conversational agents (CAs) are a tempting type of computer interface for assisting people’s mental health due to their ability to simulate human-like interactions, however their integration within the broader social context of mental health management remains largely under-explored. Recognizing that managing one’s mental health is often a social rather than individual activity involving close persons such as partners, family, and friends, our research takes a social-orientation to mental health management. Utilizing design cards that depict fictional, yet plausible CA concepts, we present the analysis of an interview study with 24 young adults to understand their views on CAs for both their own use and for a close person. Participants viewed CAs as potentially valuable complements to human support, but expressed concerns about over-reliance and replacement. Our analysis reveal key tensions, design considerations, and opportunities for integrating CAs into mental health ecosystems in ways that respect and enhance existing social support structures.

As AI systems become increasingly integrated into our lives, the need to support appropriate human understanding of AI continues to grow. With new AI capabilities being deployed in different contexts, human-centered explainability is crucial to ensure people can interact with novel AI systems safely and effectively. To address evolving explainability needs, the field of Explainable AI (XAI) has produced numerous frameworks. But what do these frameworks entail and how can they be used in practice? What drives their development? As AI systems continue to grow in complexity, it is important to understand and reflect upon the value of these frameworks and their potential to address upcoming human-centered needs for XAI. Towards this, we performed a scoping review following the PRISMA-ScR procedure, gathering and analyzing a corpus of 73 papers to understand how XAI frameworks can support different stages of human-centered XAI design. We present a unified model and a set of guiding questions to help identify, compare and select relevant XAI frameworks across various design stages, making it easier for designers and researchers to apply human-centered approaches in real-world XAI contexts. We also analyze how frameworks are developed and evaluated, highlighting gaps and opportunities to improve both methodological as well as existing HCXAI practices.

Security attacks are rising, as evidenced by the number of reported vulnerabilities. Among them, unknown attacks, including new variants of existing attacks, technical blind spots or previously undiscovered attacks, challenge enduring security. This is due to the limited number of techniques that diagnose these attacks and enable the selection of adequate security controls. In this paper, we propose an automated technique that detects and diagnoses unknown attacks by identifying the class of attack and the violated security requirements, enabling the selection of adequate security controls. Our technique combines anomaly detection to detect unknown attacks with abductive reasoning to diagnose them. We first model the behaviour of the smart home and its requirements as a logic program in Answer Set Programming (ASP). We then apply Z-Score thresholding to the anomaly scores of an Isolation Forest trained using unlabeled data to simulate unknown attack scenarios. Finally, we encode the network anomaly in the logic program and perform abduction by refutation to identify the class of attack and the security requirements that this anomaly may violate. We demonstrate our technique using a smart home scenario, where we detect and diagnose anomalies in network traffic.We evaluate the precision, recall and F1-score of the anomaly detector and the diagnosis technique against 18 attacks from the ground truth labels provided by two datasets, CICIoT2023 and IoT-23. Our experiments show that the anomaly detector effectively identifies anomalies when the network traces are strong indicators of an attack. When provided with sufficient contextual data, the diagnosis logic effectively identifies true anomalies, and reduces the number of false positives reported by anomaly detectors. Finally, we discuss how our technique can support the selection of adequate security controls.

Music-based reminiscence has the potential to positively impact the psychological well-being of older adults. However, the aging process and physiological changes, such as memory decline and limited verbal communication, may impede the ability of older adults to recall their memories and life experiences. Given the advanced capabilities of generative artificial intelligence (AI) systems, such as generated conversations and images, and their potential to facilitate the reminiscing process, this study aims to explore the design of generative AI to support music-based reminiscence in older adults. This study follows a user-centered design approach incorporating various stages, including detailed interviews with two social workers and two design workshops (involving ten older adults). Our work contributes to an in-depth understanding of older adults’ attitudes toward utilizing generative AI for supporting music-based reminiscence and identifies concrete design considerations for the future design of generative AI to enhance the reminiscence experience of older adults.

Anxiety disorders are the most common mental health problem, and cognitive-behavioral therapy is one of the most widely used, evidence-based treatments. While several mobile apps for anxiety that integrate cognitive-behavioral therapy (CBT) techniques exist, ma- jor challenges remain concerning uptake and engagement. Personalization is one strategy that can be used to improve client engagement, and integrating therapist input is one mechanism for such personalization. This study aims to understand therapist practices and identify new possibilities for delivering intervention content between face-to-face CBT therapy sessions. It comprised semi-structured interviews, followed by a series of ideation activities, and thematic analysis of the data. The results showed the central role of clients in shaping the content of therapy sessions, their challenges with homework practice, and therapists’ diverse practices. Analysis of the ideation activities elaborated the potential role of therapists in the personalization of apps for anxiety. We conclude with takeaways for designers of personalized mental health mobile applications.

The use of smartphone-based Serious Games in mental health care is an emerging and promising research field. Combining the intrinsic characteristics of games (e.g., interactiveness, immersiveness, playfulness, user-tailoring and engaging nature) with the capabilities of smartphones (e.g., versatility, ubiquitous connectivity, built-in sensors and anywhere-anytime nature) yields great potential to deliver innovative psychological treatments, which are engaging, effective, fun and always available. This article presents a scoping review, based on the PRISMA (scoping review extension) guidelines, of the field of smartphone-based serious games for mental health care. The review combines an analysis of the technical characteristics, including game design, smartphone and game-specific features, with psychological dimensions, including type and purpose of use, underlying psychological frameworks and strategies. It also explores the integration of psychological features into Serious Games and summarizes the findings of evaluations performed. A systematic search identified 40 smartphone-based Serious Games for mental health care. The majority consist of standalone and self-administrable interventions, applying a myriad of psychological strategies to address a wide range of psychological symptoms and disorders. The findings explore the potential of Serious Games as treatments and for enhancing patient engagement; we conclude by proposing several avenues for future research in order to identify best practices and success factors. Supplementary Information: The online version contains supplementary material available at 10.1007/s11042-024-18971-w.

Digital mental health interventions (DMHIs) have potential to provide effective and accessible care to entire populations, but low client uptake and engagement are significant problems. Few prior studies explore the lived experiences of non-engagers, because reaching this population is inherently difficult. We present an observational inquiry into the barriers to sign-up and early use of a DMHI, along with reasons for initial interest in the DMHI. We collected 205 online questionnaire responses and 20 interviews from self-referring participants across four healthcare ecosystems in the UK and US. Questionnaire results revealed that uncertainty about DMHI usefulness and usability were the main barriers to uptake, whereas forgetting about it, not finding time for it and not finding it useful were the main barriers to early engagement. Participants reported multiple reasons for considering the DMHI, reflecting the contextual, subjective nature of mental health. Our thematic analysis generated themes around (1) the need for human connection, (2) the impact of self-stigma on help-seeking, (3) the lack of knowledge around DMHIs and psychological therapy, (4) the desire for personally relevant care, and (5) the fluctuating, perennial nature of mental health. We discuss implications for DMHI design, implementation and future research, as well as transdisciplinary opportunities.

Self-monitoring of mood and lifestyle habits is the cornerstone of many therapies, but it is still hindered by persistent issues including inaccurate records, gaps in the monitoring, patient burden, and perceived stigma. Smartwatches have the potential to deliver enhanced self-reports, but their acceptance in clinical mental health settings is unexplored and rendered difficult by a complex theoretical landscape and need for a longitudinal perspective. We present the Mood Monitor smartwatch application for mood and lifestyle habits self-monitoring. We investigated patient acceptance of the app within a routine 8-week digital therapy. We recruited 35 patients of the UK’s National Health Service and evaluated their acceptance through three online questionnaires and a post-study interview. We assessed the clinical feasibility of the Mood Monitor by comparing clinical, usage, and acceptance metrics obtained from the 35 patients with a smartwatch with those from an additional 34 patients without a smartwatch (digital treatment as usual). Findings showed that the smartwatch app was highly accepted by patients, revealed which factors facilitated and impeded this acceptance, and supported clinical feasibility. We provide guidelines for the design of self-monitoring on a smartwatch and reflect on the conduct of human-computer interaction research evaluating user acceptance of mental health technologies.

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 approaches for qualitative data analysis. Its increased use demonstrates a change in research practices, and with that the emergence of new local standards. We need to understand and reflect upon these emerging local practices, including departures from what is advocated as quality TA practice more generally. Toward this, we conducted a scoping review of a decade of CHI publications (2012 – 2021) that researched healthcare and termed their analysis approach “thematic analysis”; 78 papers reporting a total of 100 TAs were included. We contribute a description of 1) the contexts in which TA is being used, 2) the TA approaches being conducted, and 3) how TA is being reported. Drawing on this, we discuss opportunities to improve research practice when using TA in healthcare HCI.

Abstract The response to the spread of the COVID-19 pandemic has involved the creation of complex, novel public health technologies deployed at an unprecedented scale. One such technology intervention is the deployment of digital COVID certificates, applications that confirm a person’s COVID-19 immunity status via vaccination, negative tests or having contracted the disease. These certificates have formed part of governmental strategies to manage the resumption of travel and social activities. The potential impact of these technologies on daily life has led to perceived concerns regarding the fairness of the restrictions associated with these systems. These fairness concerns are intuitively understood by users, and challenges exist in conceptualizing them in a manner that is addressable with the tools available to application and system designers. In order to improve our understanding of the fairness concerns of users and how we might conceptualize them, we analysed 27 semi-structured interviews with Irish participants regarding the use of digital immunity certificates. Our results suggest that the user perceptions of the fairness of such solutions are primarily thought about at the group level, with the concerns of Anti-discrimination and Egalitarianism being the most frequently discussed. Based on our analysis, we further identify societal and technical accessibility as important factors when attempting to address these fairness concerns.

Recent advances in AI and machine learning (ML) promise significant transformations in the future delivery of healthcare. Despite a surge in research and development, few works have moved beyond demonstrations of technical feasibility and algorithmic performance. However, to realize many of the ambitious visions for how AI can contribute to clinical impact requires the closer design and study of AI tools or interventions within specific health and care contexts. This article outlines our collaborative, human-centered approach to developing an AI application that predicts treatment outcomes for patients who are receiving human-supported, internet-delivered Cognitive Behavioral Therapy (iCBT) for symptoms of depression and anxiety. Intersecting the fields of HCI, AI, and healthcare, we describe how we addressed the specific challenges of (1) identifying clinically relevant AI applications ; and (2) designing AI applications for sensitive use contexts like mental health. Aiming to better assist the work practices of iCBT supporters, we share how learnings from an interview study with 15 iCBT supporters surfaced their practices and information needs and revealed new opportunities for the use of AI. Combined with insights from the clinical literature and technical feasibility constraints, this led to the development of two clinical outcome prediction models. To clarify their potential utility for use in practice, we conducted 13 design sessions with iCBT supporters that utilized interface mock-ups to concretize the AI output and derive additional design requirements. Our findings demonstrate how design choices can impact interpretations of the AI predictions as well as supporter motivation and sense of agency. We detail how this analysis and the design principles derived from it enabled the integration of the prediction models into a production interface. Reporting on identified risks of over-reliance on AI outputs and needs for balanced information assessment and preservation of a focus on individualized care, we discuss and reflect on what constitutes a responsible, human-centered approach to AI design in this healthcare context.

Touchless input could transform clinical activity by allowing health professionals direct control over medical imaging systems in a sterile manner. Currently, users face the issues of being unable to directly manipulate imaging in aseptic environments, as well as needing to touch shared surfaces in other hospital areas. Unintended input is a key challenge for touchless interaction and could be especially disruptive in medical contexts. We evaluated four clutching techniques with 34 health professionals, measuring interaction performance and interviewing them to obtain insight into their views on clutching, and touchless control of medical imaging. As well as exploring the performance of the different clutching techniques, our analysis revealed an appetite for reliable touchless interfaces, a strong desire to reduce shared surface contact, and suggested potential improvements such as combined authentication and touchless control. Our findings can inform the development of novel touchless medical systems and identify challenges for future research.

User acceptance is key for the successful uptake and use of health technologies, but also impacted by numerous factors not always easily accessible nor operationalised by designers in practice. This work seeks to facilitate the application of acceptance theory in design practice through the Technology Acceptance (TAC) toolkit: a novel theory-based design tool and method comprising 16 cards, 3 personas, 3 scenarios, a virtual think-space, and a website, which we evaluated through workshops conducted with 21 designers of health technologies. Findings showed that the toolkit revised and extended designers’ knowledge of technology acceptance, fostered their appreciation, empathy and ethical values while designing for acceptance, and contributed towards shaping their future design practice. We discuss implications for considering user acceptance a dynamic, multi-stage process in design practice, and better supporting designers in imagining distant acceptance challenges. Finally, we examine the generative value of the TAC toolkit and its possible future evolution.

The COVID-19 pandemic has led governments worldwide to introduce various measures restricting human activity and mobility. Along with the administration of COVID-19 vaccinations and rapid testing, socio-technological solutions such as digital COVID-19 certificates have been considered as a strategy to lessen these restrictions and allow the resumption of routine activities. Using a mixed-methods approach – a survey (n=1008) and 27 semi-structured interviews – this study explores the attitudes of residents in the Republic of Ireland towards the idea of introducing digital COVID-19 certificates. We examine the topics of acceptability, fairness, security and privacy of COVID-related personal data, and practical considerations for implementation. Our study reveals the conditional and contextual nature of the acceptability of digital certificates, identifying specific factors that affect it, associated data practices, and related public concerns and expectations of such technologies.

Online mental health interventions are increasingly important in providing access to, and supporting the effectiveness of, mental health treatment. While these technologies are effective, user attrition and early disengagement are key challenges. Evidence suggests that integrating a human supporter into such services mitigates these challenges, however, it remains under-studied how supporter involvement benefits client outcomes, and how to maximize such effects. We present our analysis of 234,735 supporter messages to discover how different support strategies correlate with clinical outcomes. We describe our machine learning methods for: (i) clustering supporters based on client outcomes; (ii) extracting and analyzing linguistic features from supporter messages; and (iii) identifying context-specific patterns of support. Our findings indicate that concrete, positive and supportive feedback from supporters that reference social behaviors are strongly associated with better outcomes; and show how their importance varies dependent on different client situations. We discuss design implications for personalized support and supporter interfaces.

Internet-delivered Cognitive Behavioural Therapy (iCBT) is an effective treatment for depression and anxiety disorders. However longitudinal qualitative research into the client's subjective experience of this form of treatment ?in the wild' is relatively scarce. We present an analysis of secondary outcomes in a naturalistic RCT conducted within the UK's Improving Access to Psychological Therapies programme. We evaluated clients' expectations, experience, and context of usage of iCBT, across three timepoints. Results are discussed in terms of the creation of a therapeutic space online, the impact of hope, expectations and personal factors on the therapeutic experience, iCBT as "therapy on the go" and developing skills for life. While iCBT on the whole provides a positive, supportive and therapeutic experience for clients, the study identified managing expectations, polarized preferences, momentary help-seeking and long-term support as important aspects of the experience to consider in future design.

Mobile technologies are valuable tools for the self-report of mental health and wellbeing. These systems pose many unique design challenges which have received considerable attention within HCI, including the engagement of users. However, less attention has been paid to the use of personal devices in public health. Integrating self-reported data within the context of clinical care suggests the need to design interfaces to support data management, sense-making, risk-assessment, feedback and patient-provider relationships. This paper reports on a qualitative design study for the clinical interface of a mobile application for the self-report of psychological wellbeing and depression during pregnancy. We examine the design tensions which arise in managing the expectations and informational needs of pregnant women, midwives, clinical psychologists, GPs and other health professionals with respect to a broad spectrum of wellbeing. We discuss strategies for managing these tensions in the design of technologies required to balance personal information with public health.

High prevalence of mental illness and the need for effective mental health care, combined with recent advances in AI, has led to an increase in explorations of how the field of machine learning (ML) can assist in the detection, diagnosis and treatment of mental health problems. ML techniques can potentially offer new routes for learning patterns of human behavior; identifying mental health symptoms and risk factors; developing predictions about disease progression; and personalizing and optimizing therapies. Despite the potential opportunities for using ML within mental health, this is an emerging research area, and the development of effective ML-enabled applications that are implementable in practice is bound up with an array of complex, interwoven challenges. Aiming to guide future research and identify new directions for advancing development in this important domain, this article presents an introduction to, and a systematic review of, current ML work regarding psycho-socially based mental health conditions from the computing and HCI literature. A quantitative synthesis and qualitative narrative review of 54 papers that were included in the analysis surfaced common trends, gaps, and challenges in this space. Discussing our findings, we (i) reflect on the current state-of-the-art of ML work for mental health, (ii) provide concrete suggestions for a stronger integration of human-centered and multi-disciplinary approaches in research and development, and (iii) invite more consideration of the potentially far-reaching personal, social, and ethical implications that ML models and interventions can have, if they are to find widespread, successful adoption in real-world mental health contexts.

Perinatal depression (PND) affects up to 15% of women within the United Kingdom and has a lasting impact on a woman's quality of life, birth outcomes and her child's development. Suicide is the leading cause of maternal mortality. However, it is estimated that at least 50% of PND cases go undiagnosed. This paper presents the results of the first feasibility study to examine the potential of mobile devices to engage women in antenatal mental health screening. Using a mobile application, 254 women attending 14 National Health Service midwifery clinics provided 2,280 momentary and retrospective reports of their wellbeing over a 9-month period. Women spoke positively of the experience, installing and engaging with this technology regardless of age, education, wellbeing, number of children, marital or employment status, or past diagnosis of depression. 39 women reported a risk of depression, self-harm or suicide; two-thirds of whom were not identified by screening in-clinic.

Depression is an affective disorder with distinctive autobiographical memory impairments, including negative bias, overgeneralization and reduced positivity. Several clinical therapies address these impairments, and there is an opportunity to develop new supports for treatment by considering depression-associated memory impairments within design. We report on interviews with ten experts in treating depression, with expertise in both neuropsychology and cognitive behavioral therapies. The interviews explore approaches for addressing each of these memory impairments. We found consistent use of positive memories for treating all memory impairments, the challenge of direct retrieval, and the need to support the experience of positive memories. We aim to sensitize HCI researchers to the limitations of memory technologies, broaden their awareness of memory impairments beyond episodic memory recall, and inspire them to engage with this less explored design space. Our findings open up new design opportunities for memory technologies for depression, including positive memory banks for active encoding and selective retrieval, novel cues for supporting generative retrieval, and novel interfaces to strengthen the reliving of positive memories.

In the last decade, the number of articles on HCI and health has increased dramatically. We extracted 139 papers on depression, anxiety and bipolar health issues from 10 years of SIGCHI conference proceedings. 72 of these were published in the last two years. A systematic analysis of this growing body of literature revealed that most innovation happens in automated diagnosis, and self-tracking, although there are innovative ideas in tangible interfaces. We noted an overemphasis on data production without consideration of how it leads to fruitful interventions. Moreover, we see a need to promote ethical practices for involvement of people living with affective disorders. Finally, although only 16 studies evaluate technologies in a clinical context, several forms of support and intervention illustrate how rich insights are gained from evaluations with real patients. Our findings highlight potential for growth in the design space of affective health technologies.

Health and wellbeing applications increasingly raise ethical issues for design. User-centred and participatory design approaches, while grounded in everyday wisdom, cannot be expected to address ethical reflection consistently, as multiple value systems come into play. We explore the potential of phronesis, a concept from Aristotelian virtue ethics, for mHealth design. Phronesis describes wisdom and judgment garnered from practical experience of specific situations in context. Applied phronesis contributes everyday wisdom to challenging issues for vulnerable target users. Drawing on research into mHealth technologies for psychological wellbeing, we explore how phronesis can inform ethical design. Using a case study on an app for self-reporting symptoms of depression during pregnancy, we present a framework for incorporating a phronetic approach into design, involving: (a) a wide feedback net to capture phronetic input early in design; (b) observing the order of feedback, which directly affects value priorities in design; (c) ethical pluralism recognising different coexisting value systems; (d) acknowledging subjectivity in the disclosure and recognition of individual researcher and participant values. We offer insights into how a phronetic approach can contribute everyday wisdom to designing mHealth technologies to help designers foster the values that promote human flourishing.

Interactivity is central to digital products, systems and experiences. It has long been studied from different disciplinary perspectives but while applications continue to be described as ‘more’ or ‘less’ interactive, there is a lack of consensus on its meaning. One approach to improving understanding is to focus on the different ways we talk about it rather than pursuing a single all-purpose definition. This study examines how interactivity is treated in a sample of Human Computer Interaction (HCI) research papers (N = 117). We use a combined content and discourse analysis approach to explore the range of definitions and technical configurations associated with the concept and unpick the wider discourses around it. We find it frequently described as a characteristic of technologies, an instrumental approach that suits quantitative measurement, but is acknowledged to miss other qualities. We also find qualitative user assessments that measure a spectrum of low to high interactivity, which are interpreted in different ways due to a lack of explicit definitions. The analysis reveals rich discursive material where different layered conceptions of interactivity arise, which we describe as ‘modes’ of interactivity. The dominant mode in HCI is ‘empowerment’, also found most frequently in public discourse. We offer modes of interactivity as a design and evaluation tool for more conscious consideration of this multidimensional concept.

OBJECTIVE: To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones. METHODS: Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app. RESULTS: We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0-7). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score ≥3.5) and unstable (SRM score <3.5) states with high accuracy (precision: 0.85 and recall: 0.86). CONCLUSIONS: Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.

The shift towards multicore processing has led to a much wider population of developers being faced with the challenge of exploiting parallel cores to improve software performance. Debugging and optimizing parallel programs is a complex and demanding task. Tools which support development of parallel programs should provide salient information to allow programmers of multicore systems to diagnose and distinguish performance problems. Appropriate design of such tools requires a systematic analysis of the problems which might be identified, and the information used to diagnose them. Building on the literature, we put forward a potential taxonomy of parallel performance problems, and an observational model which links measurable performance data to these problems. We present a validation of this model carried out with parallel programming experts, identifying areas of agreement and disagreement. This is accompanied with a survey of the prevalence of these problems in software development. From this we can identify contentious areas worthy of further exploration, as well as those with high prevalence and strong agreement, which are natural candidates for initial moves towards better tool support.

In this paper, we argue that atypical cognitive, perceptual and behavioral characteristics associated with serious mental illnesses should be taken into consideration when designing health technologies. While applications have been developed to assist in the treatment of these illnesses, the specific psychological characteristics of these disorders have rarely been considered extensively in the design process. Here, we explore how an understanding of the low-level characteristics of bipolar disorder, combined with a clinically-validated treatment and patients' lived experience, can inform mHealth design. We present a novel method -- in situ design -- to support ecologically valid design, and demonstrate its use through the co-development with 9 individuals with bipolar disorder of MoodRhythm, a mobile application designed to track and stabilize daily routines. We provide evidence that mHealth design elements tailored to the characteristics and needs of individuals with bipolar disorder can result in engaging interactions.

Wizard of OZ (WOZ) is a well-established method for simulating the functionality and user experience of future systems. Using a human wizard to mimic certain operations of a potential system is particularly useful in situations where extensive engineering effort would otherwise be needed to explore the design possibilities offered by such operations. The WOZ method has been widely used in connection with speech and language technologies, but advances in sensor technology and pattern recognition as well as new application areas such as human-robot interaction have made it increasingly relevant to the design of a wider range of interactive systems. In such cases achieving acceptable performance at the user interface level often hinges on resource intensive improvements such as domain tuning, which are better done once the overall design is relatively stable. While WOZ is recognised as a valuable prototyping technique, surprisingly little effort has been put into exploring it from a methodological point of view. Starting from a survey of the literature, this paper presents a systematic investigation and analysis of the design space for WOZ for language technology applications, and proposes a generic architecture for tool support that supports the integration of components for speech recognition and synthesis as well as for machine translation. This architecture is instantiated in WebWOZ - a new web-based open-source WOZ prototyping platform. The viability of generic support is explored empirically through a series of evaluations. Researchers from a variety of backgrounds were able to create experiments, independent of their previous experience with WOZ. The approach was further validated through a number of real experiments, which also helped to identify a number of possibilities for additional support, and flagged potential issues relating to consistency in Wizard performance.

In recent years there has been a shift in microprocessor manufacture from building single-core processors towards providing multiple cores on the same chip. This shift has meant that a much wider population of developers are faced with the task of developing parallel software: a difficult, time consuming and expensive process. With the aim of identifying issues, emerging practices and design opportunities for support, we present in this paper a qualitative study in which we interviewed a range of software developers, in both industry and academia. We then perform a systematic analysis of the data and identify several cross-cutting themes. These analysis themes include the practical relevance of the probe effect, the significance of orchestration models in development and the mismatch between currently available tools and developers' needs. We also identify an important characteristic of parallel programming, where the process of optimisation goes hand in hand with the process of debugging, as opposed to clearer distinctions which may be made in traditional programming. We conclude with reflection on how the study can inform the design of software tools to support developers in the endeavour of parallel programming.

Gaining an understanding of user needs is a central component of HCI design approaches such as user-centred design and participatory design. In some settings, such as mental health care, access to end-users is often constrained. This is a particular difficulty given that the experience of those with mental illness can be difficult for researchers to understand, and is further complicated by its associated stigma. In addition, the therapeutic setting is outside the common experience of most people and protected from outside intrusion. Although role-play has been used in varied ways in HCI, rarely has it been defined with sufficient clarity to enable others to deploy it in a nuanced manner. We argue that role-play is particularly suited for use in mental healthcare settings and, when used judiciously, can address some of the difficulties associated with working in this setting. This paper details a range of role-play formats appropriated from therapeutic role-play, drawing upon the HCI and mental health literature, therapist input and our experience of using role-play for a number of purposes at different stages of the development process. We consider how and why role-play can be used to generate empathy, gain understanding of therapy, provide feedback on designs before clinical use and help train therapists in using technology in the treatment room.

Analysis of the usability of an interactive system requires both an understanding of how the system is to be used and a means of assessing the system against that understanding. Such analytic assessments are particularly important in safety-critical systems as latent vulnerabilities may exist which have negative consequences only in certain circumstances. Many existing approaches to assessment use tasks or scenarios to provide explicit representation of their understanding of use. These normative user behaviours have the advantage that they clarify assumptions about how the system will be used but have the disadvantage that they may exclude many plausible deviations from these norms. Assessments of how a design fails to support these user behaviours can be a matter of judgement based on individual experience rather than evidence. We present a systematic formal method for analysing interactive systems that is based on constraints rather than prescribed behaviour. These constraints capture precise assumptions about what information resources are used to perform action. These resources may either reside in the system itself or be external to the system. The approach is applied to two different medical device designs, comparing two infusion pumps currently in common use in hospitals. Comparison of the two devices is based on these resource assumptions to assess consistency of interaction within the design of each device.

Online mental health interventions can benefit people experiencing a range of psychological difficulties, but attrition is a major problem in real-world deployments. We discuss strategies to reduce attrition, and present SilverCloud, a platform designed to provide more engaging online experiences. The paper presents the results of a practice-based clinical study in which 45 clients and 6 therapists used an online Cognitive Behavioural Therapy programme for depression. Pre and post-treatment assessments, using the Beck Depression Inventory, indicate a statistically significant improvement in depressive symptoms, with a large effect size, for the moderate-to-severe clinical sub-sample receiving standalone online treatment (n=18). This group was the primary target for the intervention. A high level of engagement was also observed compared to a prior online intervention used within the same service. We discuss strategies for design in this area and consider how the quantitative and qualitative results contribute towards our understanding of engagement.

In a dynamic environment where context changes frequently, users’ privacy requirements can also change. To satisfy such changing requirements, there is a need for continuous analysis to discover new threats and possible mitigation actions. A frequently changing context can also blur the boundary between public and personal space, making it difficult for users to discover and mitigate emerging privacy threats. This challenge necessitates some degree of self-adaptive privacy management in software applications.

The need to provide effective mental health treatments for adolescents has been described as a global public health challenge [27]. In this paper we discuss the exploratory evaluations of the first adolescent intervention to fully integrate a computer game implementing Cognitive Behavioural Therapy. Three distinct studies are presented: a detailed evaluation in which therapists independent of the design team used the game with 6 adolescents experiencing clinical anxiety disorders; a study in which a member of the design team used the game with 15 adolescents; and finally a study assessing the acceptability of the game and intervention with 216 practicing therapists. Findings are presented within the context of a framework for the design and evaluation of complex health interventions. The paper provides an in-depth insight into the use of therapeutic games to support adolescent interventions and provides stronger evidence than previously available for both their effectiveness and acceptability to stakeholders.

Mental illness is a significant and growing problem throughout the world. Many mental health problems have their root in childhood, and early intervention is recommended. Engaging young people in psychotherapeutic activities is challenging, and treatment adherence is often poor. This paper presents a series of studies carried out as part of the development of a mobile and online symptom tracking tool for adolescents with mental health problems. Teenagers use the system to record symptoms on their mobile phones and can view this information in a clinical setting with a therapist. We focus on a clinical pilot of the system with ten users in public mental health clinics. As well as indicating that the mobile diary tool can increase client adherence to therapeutic activities, the study yields insight into the factors influencing the success of the design and informs the design of other systems to be used as adjuncts to therapy.

It is increasingly recognised that technology has the potential to significantly improve access, engagement, effectiveness and affordability of treatment for mental health problems. The development of such technology has recently become the subject of Human–Computer Interaction research. As an emerging area with a unique set of constraints and design concerns, there is a need to establish guidelines which encapsulate the knowledge gained from existing development projects. We present an initial set of design guidelines extracted from the literature and from a series of development projects for software to support mental health interventions. The first group of guidelines pertain to the design process itself, addressing the limitations in access to clients in mental healthcare settings, and strategies for collaborative design with therapists. The second group considers major design factors in the development of these technologies, including therapeutic models, client factors, and privacy. The third group concerns conduct of the evaluation process, and the constraints on evaluating mental healthcare technologies. We motivate and explain these guidelines with reference to concrete design projects and problems.

Ethical requirements, severe constraints on access to end users and the necessity of real-world clinical evaluations represent significant challenges to designers of new technologies in mental healthcare (MHC) settings. This paper describes the collaborative approaches, incorporating HCI methods with input for MHC professionals and MHC theory, which were applied in the development of Personal Investigator (PI), a 3D computer game developed to support adolescent mental health interventions. Different stages in the evaluation of PI are discussed and the lessons learned through a multi-site clinical evaluation are presented. This evaluation has provided strong initial evidence that games such as PI offer the potential to improve adolescent engagement in talk-based interventions. It has also provided an insight into factors which should be considered in future designs in the MHC domain, e.g. the need to incorporate high levels of adaptability in future systems. Based on the difficulties encountered and lessons learned critical aims for future research are outlined.

The cost to society of mental illness is substantial. A large scale international study has identified mental illnesses as the second leading cause of disability and premature mortality in the developed world [Murray, C.L., Lopez, A.D. (Eds.), 1996. The Global Burden of Disease: A comprehensive assessment of mortality and disability from disease, injuries, and risk factors in 1990 and projected to 2020. Harvard University, Cambridge, MA]. Unfortunately, research also suggests that the majority of people suffering from treatable mental health disorders do not have access to the required treatment. Furthermore, even when treatment is accessible many sufferers are unable to successfully engage with professional services [Surgeon General, 1999. Mental Health: A Report of the Surgeon General – Executive Summary, Department of Health and Human Services, Washington, DC, Retrieved August 2006, from http://www.surgeongeneral.gov/library/mentalhealth/home.html; WHO World Mental Health Survey Consortium, 2004. Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization World Mental Health Surveys. Journal of the American Medical Association, 291(21)]. Computer assisted mental health interventions have the potential to help in addressing this imbalance. However, a review of literature shows that to date this potential has been largely unexplored. One of the primary reasons for this is that few researchers from a HCI or technical background have engaged in this area. The primary purpose of this paper is to provide a foundation and set an agenda for future research on the design of technology for talk-based mental health interventions. Theoretical approaches to the treatment of mental illness are reviewed, as is previous research on the use of technology in this area. Several significant factors effecting design and evaluation are identified and based on these factors a broad set of design guidelines are proposed to aid the development of new technologies. Of the issues identified, ethical requirements along with the sensitivity and stigma associated with mental illness pose particular challenges to HCI professionals. These factors place strict limitations on access to mental health care (MHC) settings by non-MHC professionals and create difficulties for the direct application of traditional HCI methods, such as participatory, user-centred and iterative design. To overcome these difficulties this paper proposes a model for collaborative design and evaluation, involving both HCI and MHC professionals. The development of adaptable technologies is an important element of the proposed approach. The final contribution of the paper is to suggest future research directions and identify ways in which HCI researchers can contribute to this work.

Advances in graphing applications, plug-ins and toolkits means that integrating charts and graphs into software is easier than ever before. However, selecting the optimal graphing technique for a workers task remains a difficult challenge. Information visualization experts draw on research from cognitive engineering, perceptual psychology and human computer interaction when designing displays. For the increasing number of developers who are integrating visual displays into applications, there is for a lack of a general methodology that pulls together key activities from these diverse fields. In the absence of such a methodology, it is very difficult for software developers to identify if their choice of representation satisfies both the user's tasks and perceptual limitations. We describe the approach taken in the redesign of an interactive chart used in a high volume manufacturing environment. We show how analyses of the work domain, the data and the users' tasks are all crucial steps in the design process

In high volume manufacturing (HVM), system control is shared between automation and human workers. The social organisation of workers plays an important role in supporting human decisions. Advances in the application of automation to a system, may change the social organisation associated with its operation. It establishes new work roles with broader information demands that require advanced decision support systems (DSS). Visualisation tools have been shown to improve decision-making in many situations. While guidelines exist for the visual representation of quantitative data, no methodology exists for displaying complex information structures. We apply cognitive work analysis (CWA) to a semiconductor HVM plant to derive a model of its information structures. This is a first step towards the creation of an interactive visual DSS. A number of modifications to CWA techniques are made to accommodate the complexity of HVM

Abstract. Formal approaches to the design of interactive systems rely on reasoning about properties of the system at a very high level of abstraction. Specifications to support such an approach typically provide little scope for reasoning about presentations and the representation of information in the presentation. In contrast, psychological theories such as distributed cognition place a strong emphasis on the role of representations, and their perception by the user, in the cognitive process. However, the post-hoc techniques for the observation and analysis of existing systems which have developed out of the theory do not help us in addressing such issues at the design stage. Mn this paper we show how a formalisation can be used to investigate the representational aspects of an interface. Our goal is to provide a framework to help identify and resolve potential problems with the representation of information, and to support understanding of representational issues in design. We present a model for linking properties at the abstract and perceptual levels, and illustrate its use in a case study of a ight deck instrument. There is a widespread consensus that proper tool support is a prerequisite for the adoption of formal techniques, but the use of such tools can have a profound effect on the process itself. In order to explore this issue, we apply a higher-order logic theorem prover to the analysis.