EcoSanté is a mobile lifestyle intervention that encourages individual behavior change while also helping participants understand the deep connections between daily lifestyle choices and our collective impact on the planet. Informed by research on “small” intervention approaches, we sent participants daily behavioral challenges that demonstrated connections between personal health and environmental impact at large. Through a 20-day mobile intervention study, 139 participants uploaded 1,920 submissions documenting their attempts to engage in these challenges. We found that participants’ self-reported healthy eating behavior and general self-efficacy improved significantly immediately after the intervention. Moreover, 30 days after the intervention, participants’ self-reported eating, exercise, and general self-efficacy all significantly improved compared to the beginning of the study. Participants had a more negative reaction when being asked to come up with their own challenges. Based on quantitative and qualitative findings, we provide implications for future researcher on mobile behavior intervention research.
{"title":"EcoSanté Lifestyle Intervention: Encourage Reflections on the Connections between Health and Environment","authors":"P. Kuo, Michael S. Horn","doi":"10.1145/3609325","DOIUrl":"https://doi.org/10.1145/3609325","url":null,"abstract":"EcoSanté is a mobile lifestyle intervention that encourages individual behavior change while also helping participants understand the deep connections between daily lifestyle choices and our collective impact on the planet. Informed by research on “small” intervention approaches, we sent participants daily behavioral challenges that demonstrated connections between personal health and environmental impact at large. Through a 20-day mobile intervention study, 139 participants uploaded 1,920 submissions documenting their attempts to engage in these challenges. We found that participants’ self-reported healthy eating behavior and general self-efficacy improved significantly immediately after the intervention. Moreover, 30 days after the intervention, participants’ self-reported eating, exercise, and general self-efficacy all significantly improved compared to the beginning of the study. Participants had a more negative reaction when being asked to come up with their own challenges. Based on quantitative and qualitative findings, we provide implications for future researcher on mobile behavior intervention research.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42218606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We explore a range of different metaphors used for Voice User Interfaces (VUIs) by designers, end-users, manufacturers, and researchers using a novel framework derived from semi-structured interviews and a literature review. We focus less on the well-established idea of metaphors as a way for interface designers to help novice users learn how to interact with novel technology, and more on other ways metaphors can be used. We find that metaphors people use are contextually fluid, can change with the mode of conversation, and can reveal differences in how people perceive VUIs compared to other devices. Not all metaphors are helpful, and some may be offensive. Analyzing this broader class of metaphors can help understand, perhaps even predict problems. Metaphor analysis can be a low-cost tool to inspire design creativity and facilitate complex discussions about sociotechnical issues, enabling us to spot potential opportunities and problems in the situated use of technologies.
{"title":"Metaphors in Voice User Interfaces: A Slippery Fish","authors":"Smit Desai, M. Twidale","doi":"10.1145/3609326","DOIUrl":"https://doi.org/10.1145/3609326","url":null,"abstract":"We explore a range of different metaphors used for Voice User Interfaces (VUIs) by designers, end-users, manufacturers, and researchers using a novel framework derived from semi-structured interviews and a literature review. We focus less on the well-established idea of metaphors as a way for interface designers to help novice users learn how to interact with novel technology, and more on other ways metaphors can be used. We find that metaphors people use are contextually fluid, can change with the mode of conversation, and can reveal differences in how people perceive VUIs compared to other devices. Not all metaphors are helpful, and some may be offensive. Analyzing this broader class of metaphors can help understand, perhaps even predict problems. Metaphor analysis can be a low-cost tool to inspire design creativity and facilitate complex discussions about sociotechnical issues, enabling us to spot potential opportunities and problems in the situated use of technologies.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45978271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Extant literature has proposed an important role for trust in moderating people’s willingness to disclose personal information, but there is scant HCI literature that deeply explores the relationship between privacy and trust in apparent privacy paradox circumstances. Attending to this gap, this paper reports a qualitative study examining how people account for continuing to use services that conflict with their stated privacy preferences, and how trust features in these accounts. Our findings undermine the notion that individuals engage in strategic thinking about privacy, raising important questions regarding the explanatory power of the well-known privacy calculus model and its proposed relationship between privacy and trust. Finding evidence of hopeful trust in participants’ accounts, we argue that trust allows people to morally account for their ‘paradoxical’ information disclosure behavior. We propose that effecting greater alignment between people’s privacy attitudes and privacy behavior—or ‘un-paradoxing privacy’—will require greater regulatory assurances of privacy.
{"title":"Un-Paradoxing Privacy: Considering Hopeful Trust","authors":"Bran Knowles, Stacey M. Conchie","doi":"10.1145/3609329","DOIUrl":"https://doi.org/10.1145/3609329","url":null,"abstract":"Extant literature has proposed an important role for trust in moderating people’s willingness to disclose personal information, but there is scant HCI literature that deeply explores the relationship between privacy and trust in apparent privacy paradox circumstances. Attending to this gap, this paper reports a qualitative study examining how people account for continuing to use services that conflict with their stated privacy preferences, and how trust features in these accounts. Our findings undermine the notion that individuals engage in strategic thinking about privacy, raising important questions regarding the explanatory power of the well-known privacy calculus model and its proposed relationship between privacy and trust. Finding evidence of hopeful trust in participants’ accounts, we argue that trust allows people to morally account for their ‘paradoxical’ information disclosure behavior. We propose that effecting greater alignment between people’s privacy attitudes and privacy behavior—or ‘un-paradoxing privacy’—will require greater regulatory assurances of privacy.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41750774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brain-computer interface (BCI) systems hold the potential to foster human flourishing and self-actualization. However, we believe contemporary BCI system design approaches unnecessarily limit these potentialities as they are approached from a traditional interaction perspective, producing command-response experiences. This article proposes to go beyond “interaction” and toward a paradigm of human-computer integration. The potential of this paradigm is demonstrated through three prototypes: Inter-Dream, a system that integrates with the brain's autonomic physiological processes to drive users toward healthy sleep states; Neo-Noumena, a system that integrates with the user's affective neurophysiology to augment the interpersonal communication of emotion; and PsiNet, a system that integrates interpersonal brain activity to amplify human connection. Studies of these prototypes demonstrate the benefits of the integration paradigm in realizing the multifaceted benefits of BCI systems, and this work presents the brain-computer integration framework to help guide designers of future BCI integrations.
{"title":"Brain-Computer Integration: A Framework for the Design of Brain-Computer Interfaces from an Integrations Perspective","authors":"N. Semertzidis, Fabio Zambetta, F. Mueller","doi":"10.1145/3603621","DOIUrl":"https://doi.org/10.1145/3603621","url":null,"abstract":"Brain-computer interface (BCI) systems hold the potential to foster human flourishing and self-actualization. However, we believe contemporary BCI system design approaches unnecessarily limit these potentialities as they are approached from a traditional interaction perspective, producing command-response experiences. This article proposes to go beyond “interaction” and toward a paradigm of human-computer integration. The potential of this paradigm is demonstrated through three prototypes: Inter-Dream, a system that integrates with the brain's autonomic physiological processes to drive users toward healthy sleep states; Neo-Noumena, a system that integrates with the user's affective neurophysiology to augment the interpersonal communication of emotion; and PsiNet, a system that integrates interpersonal brain activity to amplify human connection. Studies of these prototypes demonstrate the benefits of the integration paradigm in realizing the multifaceted benefits of BCI systems, and this work presents the brain-computer integration framework to help guide designers of future BCI integrations.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49211422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Uchidiuno, Jaemarie Solyst, Jonaya Kemper, Erik Harpstead, Ross M. Higashi, Jessica Hammer
Creating technology products using codesign techniques often results in higher end-user engagement compared to expert-driven designs. Codesign sessions are typically structured in flexible and informal ways to achieve equal design partnerships especially in adult-child interactions. This generally leads to better design output, however, it may also increase the enactment of socially constructed stereotypes and biases in ways that negatively affect the experiences of racial minorities and girls/women in design spaces. We codesigned a video game with a K-5 afterschool program located in a working-class, rural, predominantly white county over 20 weeks. We uncover ways that the codesign process and different activity types can create a permissive environment for enacting behaviors that are harmful to minorities. We discuss ways to manage and restructure codesign programs to be more conducive for children and adults from diverse backgrounds, ultimately leading to healthier design partnerships.
{"title":"“What's Your Name Again?”: How Race and Gender Dynamics Impact Codesign Processes and Output","authors":"J. Uchidiuno, Jaemarie Solyst, Jonaya Kemper, Erik Harpstead, Ross M. Higashi, Jessica Hammer","doi":"10.1145/3603624","DOIUrl":"https://doi.org/10.1145/3603624","url":null,"abstract":"Creating technology products using codesign techniques often results in higher end-user engagement compared to expert-driven designs. Codesign sessions are typically structured in flexible and informal ways to achieve equal design partnerships especially in adult-child interactions. This generally leads to better design output, however, it may also increase the enactment of socially constructed stereotypes and biases in ways that negatively affect the experiences of racial minorities and girls/women in design spaces. We codesigned a video game with a K-5 afterschool program located in a working-class, rural, predominantly white county over 20 weeks. We uncover ways that the codesign process and different activity types can create a permissive environment for enacting behaviors that are harmful to minorities. We discuss ways to manage and restructure codesign programs to be more conducive for children and adults from diverse backgrounds, ultimately leading to healthier design partnerships.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46614811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In virtual reality (VR), interactions may fail when users encounter new, unknown, or unexpected objects. We propose using feedforward in VR to help users interact with objects by revealing how such objects work. Feedforward lets users know what to do and how to do it by showing the available actions and outcomes before an interaction. In this paper, we first chart the design space of feedforward in VR and illustrate how to design feedforward for specific VR interactions. We discuss starting the feedforward, previewing actions and outcomes, and returning the virtual world to its state before the feedforward. Second, we implement three real-world VR applications to show how feedforward can be applied to multistep interactions, perceived interactivity, and discoverability. Third, we conduct an evaluation of the design space with 14 VR experts to understand its usefulness. Finally, we summarize the findings of our work on VR feedforward in 15 guidelines.
{"title":"Using Feedforward to Reveal Interaction Possibilities in Virtual Reality","authors":"A. Muresan, Jess McIntosh, K. Hornbæk","doi":"10.1145/3603623","DOIUrl":"https://doi.org/10.1145/3603623","url":null,"abstract":"In virtual reality (VR), interactions may fail when users encounter new, unknown, or unexpected objects. We propose using feedforward in VR to help users interact with objects by revealing how such objects work. Feedforward lets users know what to do and how to do it by showing the available actions and outcomes before an interaction. In this paper, we first chart the design space of feedforward in VR and illustrate how to design feedforward for specific VR interactions. We discuss starting the feedforward, previewing actions and outcomes, and returning the virtual world to its state before the feedforward. Second, we implement three real-world VR applications to show how feedforward can be applied to multistep interactions, perceived interactivity, and discoverability. Third, we conduct an evaluation of the design space with 14 VR experts to understand its usefulness. Finally, we summarize the findings of our work on VR feedforward in 15 guidelines.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49025710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Li Ding, Jack Terwilliger, Aishni Parab, Meng Wang, Lex Fridman, Bruce Mehler, B. Reimer
Non-intrusive, real-time analysis of the dynamics of the eye region allows us to monitor humans’ visual attention allocation and estimate their mental state during the performance of real-world tasks, which can potentially benefit a wide range of human-computer interaction (HCI) applications. While commercial eye-tracking devices have been frequently employed, the difficulty of customizing these devices places unnecessary constraints on the exploration of more efficient, end-to-end models of eye dynamics. In this work, we propose CLERA, a unified model for Cognitive Load and Eye Region Analysis, which achieves precise keypoint detection and spatiotemporal tracking in a joint-learning framework. Our method demonstrates significant efficiency and outperforms prior work on tasks including cognitive load estimation, eye landmark detection, and blink estimation. We also introduce a large-scale dataset of 30k human faces with joint pupil, eye-openness, and landmark annotation, which aims to support future HCI research on human factors and eye-related analysis.
{"title":"CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild","authors":"Li Ding, Jack Terwilliger, Aishni Parab, Meng Wang, Lex Fridman, Bruce Mehler, B. Reimer","doi":"10.1145/3603622","DOIUrl":"https://doi.org/10.1145/3603622","url":null,"abstract":"Non-intrusive, real-time analysis of the dynamics of the eye region allows us to monitor humans’ visual attention allocation and estimate their mental state during the performance of real-world tasks, which can potentially benefit a wide range of human-computer interaction (HCI) applications. While commercial eye-tracking devices have been frequently employed, the difficulty of customizing these devices places unnecessary constraints on the exploration of more efficient, end-to-end models of eye dynamics. In this work, we propose CLERA, a unified model for Cognitive Load and Eye Region Analysis, which achieves precise keypoint detection and spatiotemporal tracking in a joint-learning framework. Our method demonstrates significant efficiency and outperforms prior work on tasks including cognitive load estimation, eye landmark detection, and blink estimation. We also introduce a large-scale dataset of 30k human faces with joint pupil, eye-openness, and landmark annotation, which aims to support future HCI research on human factors and eye-related analysis.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45437283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Introduction to the Special Issue on Human-Centred AI in Healthcare: Challenges Appearing in the Wild","authors":"T. Andersen, F. Nunes, Lauren Wilcox, Enrico W. Coiera, Yvonne Rogers","doi":"10.1145/3589961","DOIUrl":"https://doi.org/10.1145/3589961","url":null,"abstract":"CCS Concepts: • Human-centered computing → HCI theory, concepts and models","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":"30 1","pages":"1 - 12"},"PeriodicalIF":3.7,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49422023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Bødker, Sarah E. Fox, Nicolas Lalone, Megh Marathe, R. Soden
This special issue builds on and expands HCI’s engagement with historical approaches, questioning our field’s ontological orientations and offering new methods for examining the past. The set of articles featured reinvigorates questions on whose technological labor matters, how we might challenge the racist and misogynistic consequences of HCI’s inter-disciplinary inheritances, and offer new modes of liberatory world-making. Taken together, this collection serves as an act of reclamation of the lineage of our field, providing resources and guidance for a more just present and future.
{"title":"(Re)Connecting History to the Theory and Praxis of HCI","authors":"S. Bødker, Sarah E. Fox, Nicolas Lalone, Megh Marathe, R. Soden","doi":"10.1145/3589804","DOIUrl":"https://doi.org/10.1145/3589804","url":null,"abstract":"This special issue builds on and expands HCI’s engagement with historical approaches, questioning our field’s ontological orientations and offering new methods for examining the past. The set of articles featured reinvigorates questions on whose technological labor matters, how we might challenge the racist and misogynistic consequences of HCI’s inter-disciplinary inheritances, and offer new modes of liberatory world-making. Taken together, this collection serves as an act of reclamation of the lineage of our field, providing resources and guidance for a more just present and future.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":"30 1","pages":"1 - 7"},"PeriodicalIF":3.7,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48902273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pratik Ghosh, K. Posner, Stephanie L. Hyland, William C. Van Cleve, M. Bristow, D. Long, Konstantina Palla, B. Nair, C. Fong, Ronald Pauldine, M. Vavilala, Kenton O’Hara
Hypotension during perioperative care, if undetected or uncontrolled, can lead to serious clinical complications. Predictive machine learning models, based on routinely collected EHR data, offer potential for early warning of hypotension to enable proactive clinical intervention. However, while research has demonstrated the feasibility of such machine learning models, little effort is made to ground their formulation and development in socio-technical context of perioperative care work. To address this, we present a study of collaborative work practices of clinical teams during and after surgery with specific emphasis on the organisation of hypotension management. The findings highlight where predictive insights could be usefully deployed to reconfigure care and facilitate more proactive management of hypotension. We further explore how the socio-technical insights help define key parameters of machine learning prediction tasks to align with the demands of collaborative clinical practice. We discuss more general implications for the design of predictive machine learning in hospital care.
{"title":"Framing Machine Learning Opportunities for Hypotension Prediction in Perioperative Care: A Socio-Technical Perspective","authors":"Pratik Ghosh, K. Posner, Stephanie L. Hyland, William C. Van Cleve, M. Bristow, D. Long, Konstantina Palla, B. Nair, C. Fong, Ronald Pauldine, M. Vavilala, Kenton O’Hara","doi":"10.1145/3589953","DOIUrl":"https://doi.org/10.1145/3589953","url":null,"abstract":"Hypotension during perioperative care, if undetected or uncontrolled, can lead to serious clinical complications. Predictive machine learning models, based on routinely collected EHR data, offer potential for early warning of hypotension to enable proactive clinical intervention. However, while research has demonstrated the feasibility of such machine learning models, little effort is made to ground their formulation and development in socio-technical context of perioperative care work. To address this, we present a study of collaborative work practices of clinical teams during and after surgery with specific emphasis on the organisation of hypotension management. The findings highlight where predictive insights could be usefully deployed to reconfigure care and facilitate more proactive management of hypotension. We further explore how the socio-technical insights help define key parameters of machine learning prediction tasks to align with the demands of collaborative clinical practice. We discuss more general implications for the design of predictive machine learning in hospital care.","PeriodicalId":50917,"journal":{"name":"ACM Transactions on Computer-Human Interaction","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45068728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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