The term Fairness is used in the field of Machine Learning to refer to a suite of evaluation techniques, measures, and process adjustments focused on the reduction of bias in statistical models. With ...many different potential definitions and methodologies for implementing fairness, some contradictory in goal or incompatible in approach, there are challenges in the selection of the most appropriate definition(s) to meet the ethical expectations society or end users may have for a model. This is further confounded by the potentially complex engineering challenges in applying the selected definition. The goal of designing and developing a fair system requires interdisciplinary collaboration between user experience researchers, focused on the socio-technical challenges of application usage and adoption, and data scientists and machine learning software developers, directly involved in the technical engineering and optimisation of such models. How these two groups interact and how their areas of specialisation can support and influence the other when developing fair AI is unknown. This leaves open questions about how these stakeholders view fairness, how it can be operationalised in their current work, and what are the obstacles/opportunities for improving the treatment of fairness in practice. In order to answer these questions, 18 subject matter experts from the fields of human-computer interaction (HCI) or Machine Learning (ML) were interviewed about their experience with fairness, their perception of current fairness adoption, and the challenges they would anticipate in a set of example scenarios. This work identifies some of the current challenges to developing fair artificial intelligence (AI). In dealing with these challenges, we propose approaches that include expanding the responsibilities of HCI and ML specialists, and identifying areas where joint responsibility is required.
Designing technologies that users will be interested in, start using, and keep using has long been a challenge. In the health domain, the question of technology acceptance is even more important, as ...the possible intrusiveness of technologies could lead to patients refusing to even try them. Developers and researchers must address this question not only in the design and evaluation of new health care technologies but also across the different stages of the user's journey. Although a range of definitions for these stages exists, many researchers conflate related terms, and the field would benefit from a coherent set of definitions and associated measurement approaches.
This review aims to explore how technology acceptance is interpreted and measured in mobile health (mHealth) literature. We seek to compare the treatment of acceptance in mHealth research with existing definitions and models, identify potential gaps, and contribute to the clarification of the process of technology acceptance.
We searched the PubMed database for publications indexed under the Medical Subject Headings terms "Patient Acceptance of Health Care" and "Mobile Applications." We included publications that (1) contained at least one of the terms "acceptability," "acceptance," "adoption," "accept," or "adopt"; and (2) defined the term. The final corpus included 68 relevant studies.
Several interpretations are associated with technology acceptance, few consistent with existing definitions. Although the literature has influenced the interpretation of the concept, usage is not homogeneous, and models are not adapted to populations with particular needs. The prevalence of measurement by custom surveys suggests a lack of standardized measurement tools.
Definitions from the literature were published separately, which may contribute to inconsistent usage. A definition framework would bring coherence to the reporting of results, facilitating the replication and comparison of studies. We propose the Technology Acceptance Lifecycle, consolidating existing definitions, articulating the different stages of technology acceptance, and providing an explicit terminology. Our findings illustrate the need for a common definition and measurement framework and the importance of viewing technology acceptance as a staged process, with adapted measurement methods for each stage.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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.
Mood tracking is commonly employed within a range of mental health interventions. Physical activity and sleep are also important for contextualizing mood data but can be difficult to track manually ...and rely on retrospective recall. Smartwatches could enhance self-monitoring by addressing difficulties in recall of sleep and physical activity and reducing the burden on patients in terms of remembering to track and the effort of tracking. This feasibility study will explore the acceptance of a smartwatch app for self-monitoring of mood, sleep, and physical activity, in an internet-based cognitive-behavioral therapy (iCBT) for depression offered in a routine care setting.
Seventy participants will be randomly allocated to (i) iCBT intervention plus smartwatch app or (ii) iCBT intervention alone. Patient acceptance will be measured longitudinally using a theory-based acceptance questionnaire to understand and compare the evolution of acceptance of the technology-delivered self-report in the two groups. A post-treatment interview will explore participants subjective experience of using the smartwatch. Engagement with the intervention, including self-report, and clinical outcomes, will be measured across both groups to assess for any differences.
This is the first study investigating the evolution of patient acceptance of smartwatch self-report in an iCBT delivered intervention in a clinical sample. Through an engaging and convenient means of capturing ecologically valid mood data, the study has the potential to show that smartwatches are an acceptable means for patient self-monitoring within iCBT interventions for depression and support potential use-cases for smartwatches in the context of mental health interventions in general.
Prospectively registered at ClinicalTrials.gov (NCT04568317).
This multicentre prospective observational study sought to determine the prevalence and the factors associated with high-risk gastric contents in women admitted to the maternity unit for childbirth, ...and to identify the clinical situations in which ultrasound assessment of gastric contents would be most helpful (i.e. when the prevalence of high-risk gastric contents is close to 50%).
Ultrasound assessments of gastric contents were performed within the first hour after admission to the maternity unit. The prevalence of high-risk gastric contents was calculated and variables associated with high-risk gastric contents were identified using logistic regression analyses.
A total of 1003 parturients were analysed. The prevalence of high-risk gastric contents was 70% (379/544; 95% confidence interval: 66–74%) in women admitted in spontaneous labour and 65% (646/1003; 95% confidence interval: 61–67%) in the whole cohort. Lower gestational age, increased fasting duration for solids, and elective Caesarean delivery were independently associated with reduced likelihood of high-risk gastric contents. In women admitted in spontaneous labour and in the whole cohort, the prevalence of high-risk gastric contents ranged from 85% to 86% for fasting duration for solids <6 h, 63%–68% for fasting 6–8 h, 54%–55% for fasting 8–12 h, and 47%–51% for fasting ≥12 h.
Around two-thirds of parturients had high-risk gastric contents within the first hour after admission to the maternity unit. Our results suggest that gastric emptying for solids continues in labouring women, and that gastric ultrasound would be most helpful when fasting duration is ≥8 h.
HCI and Affective Health Sanches, Pedro; Janson, Axel; Karpashevich, Pavel ...
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems,
05/2019
Conference Proceeding
Odprti dostop
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.
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.
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.
Design for the Long Now is a project inspired by the Long Now Foundation, a nonprofit promoting long-term thinking and responsibility in the face of rapidly accelerating technology and culture. This ...one-day workshop will provide a space for HCI researchers and technology designers to reflect on the timelines that govern their work and explore where and how ethical issues emerge. In drawing on real-world ethical dilemmas that participants have experienced, we hope to better understand how power dynamics and hierarchies can constrain or accelerate ethical decision making and consider the wider context and systems surrounding these processes. Through playful, creative experimentation, community building, and the use of non-violent communication and systems thinking tools, this workshop aims to build participants’ agency and inspire collective action. We also hope to promote change in the design practices and norms that perpetuate harm.
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