Towards Inclusive Futures for Worker WellbeingThe global COVID-19 pandemic has motivated new collaborations across borders and boundaries, while also emphasizing the importance of supporting the continued wellbeing of hybrid, remote, and in-person workers. Work in CSCW and HCI has demonstrated that identity, culture, and organizational factors all have a strong influence on how people come to understand wellbeing. However, more work is needed to understand how to support workplace wellbeing for location-independent teams, particularly given the importance of these diverse factors. In this study, we ask the question: how did organizational, cultural, and individual factors influence how workers understood their workplace wellbeing needs during the move to remote work? We take a look at the broader human infrastructure supporting hybrid and remote work, including both workers who engage in technology-mediated labor, and the essential workers who support it. Through linguistic analyses of 13,265 diary entries collected throughout the COVID-19 pandemic and in-depth interviews with 26 employees from around the world, we find substantial diversity in wellbeing needs and conceptualizations. We build on these findings to provide recommendations for how technology design can better support new and diverse forms of worker wellbeing.2024SPSachin R Pendse et al.Session 1a: Work and TechnologyCSCW
Improving Work-Nonwork Balance with Data-Driven Implementation Intention with Mental ContrastingWork-nonwork balance is an important aspect of workplace well-being with associations to improved physical and mental health, job performance, and quality of life. However, realizing work-nonwork balance goals is challenging due to competing demands and limited resources within organizational and interpersonal contexts. These challenges are compounded by technologies that blur the boundaries of work and nonwork in the always-on work cultures. At an individual level, such challenges can be subsided through the effective application of self-regulation techniques, such as implementation intentions and mental contrasting (IIMC). Further supporting these techniques through reflection on personal data, we implement the idea of data-driven IIMC into a self-tracking and behavior planning system and evaluate it in a three-week between-participant study with 43 information workers who used our system for improving work-nonwork balance. We find evidence that reflection on personal data improves awareness of behavior plan compliance and rescheduling, which are important in realizing work-nonwork balance goals. We also observe the value of micro-reflection, reflection on limited data of the very recent past, for IIMC. Our findings highlight opportunities for automation in data collection and sense-making and for further exploring the role of data-driven IIMC as boundary negotiating artifacts in support of work-nonwork balance goals.2024YSYasaman S. Sefidgar et al.Session 3b: Work, Non-Work, and Social TechnologiesCSCW
Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic ProcrastinationTraditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and potential limitations of LLMs in this context remain underexplored. To address this, we conducted interviews and focus group discussions with 15 university students and 6 experts, during which a technology probe for generating personalized advice for managing procrastination was presented. Our results highlight the necessity for LLMs to provide structured, deadline-oriented steps and enhanced user support mechanisms. Additionally, our results surface the need for an adaptive approach to questioning based on factors like busyness. These findings offer crucial design implications for the development of LLM-based tools for managing procrastination while cautioning the use of LLMs for therapeutic guidance.2024ABAnanya Bhattacharjee et al.University of TorontoHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationCHI
MeetingCoach: An Intelligent Dashboard for Supporting Effective & Inclusive MeetingsVideo-conferencing is essential for many companies, but its limitations in conveying social cues can lead to ineffective meetings. We present MeetingCoach, an intelligent post-meeting feedback dashboard that summarizes contextual and behavioral meeting information. Through an exploratory survey (N=120), we identified important signals (e.g., turn taking, sentiment) and used these insights to create a wireframe dashboard. The design was evaluated with in situ participants (N=16) who helped identify the components they would prefer in a post-meeting dashboard. After recording video-conferencing meetings of eight teams over four weeks, we developed an AI system to quantify the meeting features and created personalized dashboards for each participant. Through interviews and surveys (N=23), we found that reviewing the dashboard helped improve attendees' awareness of meeting dynamics, with implications for improved effectiveness and inclusivity. Based on our findings, we provide suggestions for future feedback system designs of video-conferencing meetings.2021SSSamiha Samrose et al.University of RochesterRemote Work Tools & ExperienceNotification & Interruption ManagementCHI
Large Scale Analysis of Multitasking Behavior During Remote MeetingsVirtual meetings are critical for remote work because of the need for synchronous collaboration in the absence of in-person interactions. In-meeting multitasking is closely linked to people's productivity and wellbeing. However, we currently have limited understanding of multitasking in remote meetings and its potential impact. In this paper, we present what we believe is the most comprehensive study of remote meeting multitasking behavior through an analysis of a large-scale telemetry dataset collected from February to May 2020 of U.S. Microsoft employees and a 715-person diary study. Our results demonstrate that intrinsic meeting characteristics such as size, length, time, and type, significantly correlate with the extent to which people multitask, and multitasking can lead to both positive and negative outcomes. Our findings suggest important best-practice guidelines for remote meetings (e.g., avoid important meetings in the morning) and design implications for productivity tools (e.g., support positive remote multitasking).2021HCHancheng Cao et al.Stanford UniversityRemote Work Tools & ExperienceNotification & Interruption ManagementWorkplace Wellbeing & Work StressCHI
AffectiveSpotlight: Facilitating the Communication of Affective Responses from Audience Members during Online PresentationsThe ability to monitor audience reactions is critical when delivering presentations. However, current videoconferencing platforms offer limited solutions to support this. This work leverages recent advances in affect sensing to capture and facilitate communication of relevant audience signals. Using an exploratory survey (N=175), we assessed the most relevant audience responses such as confusion, engagement, and head-nods. We then implemented AffectiveSpotlight, a Microsoft Teams bot that analyzes facial responses and head gestures of audience members and dynamically spotlights the most expressive ones. In a within-subjects study with 14~groups (N=117), we observed that the system made presenters significantly more aware of their audience, speak for a longer period of time, and self-assess the quality of their talk more similarly to the audience members, compared to two control conditions (randomly-selected spotlight and default platform UI). We provide design recommendations for future affective interfaces for online presentations based on feedback from the study.2021PMPrasanth Murali et al.Northeastern UniversitySocial & Collaborative VRHuman-LLM CollaborationCHI
Understanding Conversational and Expressive Style in a Multimodal Embodied Conversational AgentEmbodied conversational agents have changed the ways we can interact with machines. However, these systems often do not meet users' expectations. A limitation is that the agents are monotonic in behavior and do not adapt to an interlocutor. We present SIVA (a Socially Intelligent Virtual Agent), an expressive, embodied conversational agent that can recognize human behavior during open-ended conversations and automatically align its responses to the conversational and expressive style of the other party. SIVA leverages multimodal inputs to produce rich and perceptually valid responses (lip syncing and facial expressions) during the conversation. We conducted a user study (N=30) in which participants rated SIVA as being more empathetic and believable than the control (agent without style matching). Based on almost 10 hours of interaction, participants who preferred interpersonal involvement evaluated SIVA as significantly more animate than the participants who valued consideration and independence.2021DADeepali Aneja et al.Adobe Research, University of WashingtonFull-Body Interaction & Embodied InputConversational ChatbotsAgent Personality & AnthropomorphismCHI
Optimizing for Happiness and Productivity: Modeling Opportune Moments for Transitions and Breaks at WorkInformation workers perform jobs that demand constant multitasking, leading to context switches, productivity loss, stress, and unhappiness. Systems that can mediate task transitions and breaks have the potential to keep people both productive and happy. We explore a crucial initial step for this goal: finding opportune moments to recommend transitions and breaks without disrupting people during focused states. Using affect, workstation activity, and task data from a three-week field study (N=25), we build models to predict whether a person should continue their task, transition to a new task, or take a break. The R-squared values of our models are as high as 0.7, with only 15% error cases. We ask users to evaluate the timing of recommendations provided by a recommender that relies on these models. Our study shows that users find our transition and break recommendations to be well-timed, rating them as 86% and 77% accurate, respectively. We conclude with a discussion of the implications for intelligent systems that seek to guide task transitions and manage interruptions at work.2020HKHarmanpreet Kaur et al.University of MichiganNotification & Interruption ManagementWorkplace Wellbeing & Work StressCHI
Circadian Rhythms and Physiological Synchrony: Evidence of the Impact of Diversity on Small Group CreativityCircadian rhythms determine daily sleep cycles, mood, and cognition. Depending on an individual's circadian preference, or chronotype (i.e.,``early birds'' and ``night owls''), the rhythms shift earlier or later in the day. Early birds experience circadian arousal peaks earlier in the morning than night owls. Prior work has shown that individuals are more effective at analytic tasks during their peak arousal times but are more creative during their off-peak times. We investigate if these findings hold true for small groups. We find that time of day and a group's majority chronotype impact performance on analytic and creative tasks. Physiological synchrony among group members positively predicts group satisfaction. Specifically, homogeneous groups perform worse on all tasks regardless of time of day, but they achieve greater physiological synchrony and feel more satisfied as a group. Based on these findings, we present and advocate for a temporal dimension of group diversity.2019EJEunice Jun et al.Groups and creativityCSCW
Effects of Individual Differences in Blocking Workplace DistractionsInformation workers are experiencing ever-increasing online distractions in the workplace, and software to block distractions is becoming more popular. We conducted an exploratory field study with 32 information workers in their workplace using software to block online distractions for one week. We discovered that with online distractions blocked, participants assessed their focus and productivity to be significantly higher. Those who benefited most were those who reported being less in control of their work, associated with personality traits of lower Conscientiousness and Lack of Perseverence. Unexpectedly, those reporting higher control of work experienced a cost of higher workload with online distractions blocked. Those who reported the greatest increase in focus with distractions blocked were those who were more susceptible to social media distractions. Without distractions, people with higher control of work worked longer stretches without physical breaks, with consequently higher stress. We present design recommendations to promote focus for our observed coping behaviors.2018GMGloria Mark et al.University of California, IrvineNotification & Interruption ManagementWorkplace Wellbeing & Work StressCHI
3rd Symposium on Computing and Mental Health: Understanding, Engaging, and Delighting UsersThe World Health Organization predicts that by the year 2030, mental illnesses will be the leading disease burden globally. Advances in technology create opportunities for close collaboration between computation and mental health researchers. The intersection between ubiquitous computing and sensing, social media, data analytics and emerging technologies offers promising avenues for developing technologies to help those in mental distress. Yet for these to be useful and usable, human-centered design and evaluation will be essential. The third in our series of Symposia on Computing and Mental Health will provide an opportunity for researchers to come together under the auspices of CHI to discuss the design and evaluation of new mental health technologies. Our emphasis is on understanding users and how to increase engagement with these technologies in daily life.2018GWGreg Wadley et al.The University of MelbourneMental Health Apps & Online Support CommunitiesWorkplace Wellbeing & Work StressCHI
Regulating Feelings During Interpersonal Conflicts by Changing Voice Self-perceptionEmotions play a major role in how interpersonal conflicts unfold. Although several strategies and technological approaches have been proposed for emotion regulation, they often require conscious attention and effort. This often limits their efficacy in practice. In this paper, we propose a different approach inspired by self-perception theory: noticing that people are often reacting to the perception of their own behavior, we artificially change their perceptions to influence their emotions. We conducted two studies to evaluate the potential of this approach by automatically and subtly altering how people perceive their own voice. In one study, participants that received voice feedback with a calmer tone during relationship conflicts felt less anxious. In the other study, participants who listened to their own voices with a lower pitch during contentious debates felt more powerful. We discuss the implications of our findings and the opportunities for designing automatic and less perceptible emotion regulation systems.2018JCJean Costa et al.Cornell UniversityVoice User Interface (VUI) DesignExplainable AI (XAI)CHI
Pocket Skills: A Conversational Mobile Web App To Support Dialectical Behavioral TherapyMental health disorders are a leading cause of disability worldwide. Although evidence-based psychotherapy is effective, engagement from such programs can be low. Mobile apps have the potential to help engage and support people in their therapy. We developed Pocket Skills, a mobile web app based on Dialectical Behavior Therapy (DBT). Pocket Skills teaches DBT via a conversational agent modeled on Marsha Linehan, who developed DBT. We examined the feasibility of Pocket Skills in a 4-week field study with 73 individuals enrolled in psychotherapy. After the study, participants reported decreased depression and anxiety and increased DBT skills use. We present a model based on qualitative findings of how Pocket Skills supported DBT. Pocket Skills helped participants engage in their DBT and practice and implement skills in their environmental context, which enabled them to see the results of using their DBT skills and increase their self-efficacy. We discuss the design implications of these findings for future mobile mental health systems.2018JSJessica Schroeder et al.University of WashingtonAgent Personality & AnthropomorphismMental Health Apps & Online Support CommunitiesCHI