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Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI. We present an 8-week exploratory study with 20 college students, demonstrating the MindScape apps efficacy in enhancing positive affect (7%), reducing negative affect (11%), loneliness (6%), and anxiety and depression, with a significant week-over-week decrease in PHQ-4 scores (-0.25 coefficient). The study highlights the advantages of contextual AI journaling, with participants particularly appreciating the tailored prompts and insights provided by the MindScape app. Our analysis also includes a comparison of responses to AI-driven contextual versus generic prompts, participant feedback insights, and proposed strategies for leveraging contextual AI journaling to improve well-being on college campuses. By showcasing the potential of contextual AI journaling to support mental health, we provide a foundation for further investigation into the effects of contextual AI journaling on mental health and well-being.

MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being. We argue that integrating behavioral sensing in LLMs will likely lead to a new frontier in AI. In this Late-Breaking Work paper, we discuss the MindScape contextual journal App design that uses LLMs and behavioral sensing to generate contextual and personalized journaling prompts crafted to encourage self-reflection and emotional development. We also discuss the MindScape study of college students based on a preliminary user study and our upcoming study to assess the effectiveness of contextual AI journaling in promoting better well-being on college campuses. MindScape represents a new application class that embeds behavioral intelligence in AI.

Increasing availability of personal data opened new possibilities for technologies that support individuals' reflection, increase their self-awareness, and inform their future choices. Personal informatics, chiefly concerned with investigating individuals' engagement with personal data, has become an area of active research within Human-Computer Interaction. However, more recent research has argued that personal informatics solutions often place high demands on individuals and require knowledge, skills, and time for engaging with personal data. New advances in Machine Learning (ML) and Artificial Intelligence (AI) can help to reduce the cognitive burden of personal informatics and identify meaningful trends using analytical engines. Furthermore, introducing ML and AI can enable systems that provide more direct support for action, for example through predictions and recommendations. However, there are many open questions as to the design of personal informatics technologies that incorporate ML and AI. In this workshop, we will bring together an interdisciplinary group of researchers in personal informatics, ML, and AI to outline the design space for intelligent personal informatics solutions and develop an agenda for future research in this area.

Current technologies designed for mental health support often have low adoption rates and may not fit people's routines. However, recent literature demonstrates that individuals managing mental illness often incorporate a variety of technologies into their self-management routines, including texting, music, wearables, online communities, games, and social media. Therefore, adopting a perspective focused on understanding the technology ecosystem of mental health management may be a better approach than focusing on singular platforms and applications. In this CHI workshop, we will examine a constellation of digital and non-digital mental health support resources. We will discuss the needs and practices of individuals managing mental illness, how these needs relate to future ecosystems of technologies and services, and the potentials and repercussions of current technologies. By convening a group of interdisciplinary researchers and practitioners, we will collectively address these topics to create resources attentive to individuals' lived experiences with mental illness, goals with respect to management and recovery, and interactions with existing forms of resources, care, and technology.

Background: Integrating stress-reduction interventions into the workplace may improve the health and well-being of employees, and there is an opportunity to leverage ubiquitous everyday work technologies to understand dynamic work contexts and facilitate stress reduction wherever work happens. Sensing-powered just-in-time adaptive intervention (JITAI) systems have the potential to adapt and deliver tailored interventions, but such adaptation requires a comprehensive analysis of contextual and individual-level variables that may influence intervention outcomes and be leveraged to drive the system's decision-making. Objective: This study aims to identify key tailoring variables that influence momentary engagement in digital stress reduction microinterventions to inform the design of similar JITAI systems. Methods: To inform the design of such dynamic adaptation, we analyzed data from the implementation and deployment of a system that incorporates passively sensed data across everyday work devices to send just-in-time stress reduction microinterventions in the workplace to 43 participants during a 4-week deployment. We evaluated 27 trait-based factors (ie, individual characteristics), state-based factors (ie, workplace contextual and behavioral signals and momentary stress), and intervention-related factors (ie, location and function) across 1585 system-initiated interventions. We built logistical regression models to identify the factors contributing to momentary engagement, the choice of interventions, the engagement given an intervention choice, the user rating of interventions engaged, and the stress reduction from the engagement. Results: We found that women (odds ratio [OR] 0.41, 95% CI 0.21-0.77; P=.03), those with higher neuroticism (OR 0.57, 95% CI 0.39-0.81; P=.01), those with higher cognitive reappraisal skills (OR 0.69, 95% CI 0.52-0.91; P=.04), and those that chose calm interventions (OR 0.43, 95% CI 0.23-0.78; P=.03) were significantly less likely to experience stress reduction, while those with higher agreeableness (OR 1.73, 95% CI 1.10-2.76; P=.06) and those that chose prompt-based (OR 6.65, 95% CI 1.53-36.45; P=.06) or video-based (OR 5.62, 95% CI 1.12-34.10; P=.12) interventions were substantially more likely to experience stress reduction. We also found that work-related contextual signals such as higher meeting counts (OR 0.62, 95% CI 0.49-0.78; P<.001) and higher engagement skewness (OR 0.64, 95% CI 0.51-0.79; P<.001) were associated with a lower likelihood of engagement, indicating that state-based contextual factors such as being in a meeting or the time of the day may matter more for engagement than efficacy. In addition, a just-in-time intervention that was explicitly rescheduled to a later time was more likely to be engaged with (OR 1.77, 95% CI 1.32-2.38; P<.001). Conclusions: JITAI systems have the potential to integrate timely support into the workplace. On the basis of our findings, we recommend that individual, contextual, and content-based factors be incorporated into the system for tailoring as well as for monitoring ineffective engagements across subgroups and contexts. [ABSTRACT FROM AUTHOR]

Presents a brief historical overview of research in individual differences in the context of virtual environments. Highlights the notion of structure in the perception of individual users of an information system and the role of individuals' abilities to recognize and use such structures to perform various information-intensive tasks. (Author/LRW)

The ability to regulate our emotions plays an important role in our psychological and physical health. Regulating emotions influences how and when emotions are expressed. We performed a large scale, longitudinal observational study to investigate the effect of emotion regulation ability on expressed affect. We found that expression of negative affect increased throughout the day. For people who suppress emotion this increase is slower that for those who do not. For those with stronger cognitive reappraisal abilities, though not significant, there was a trend for higher positive affect and negative affect increased significantly less steeply, suggesting that they might experience more positive and less negative affect. These results reflect some of the first results based on large scale, continuous tracking of behavioral expression of emotion longitudinally. Our results demonstrate the need to carefully consider the time of day and emotion regulation ability, in addition to gender and age, when attempting to automatically infer affective states for facial behavior. [ABSTRACT FROM AUTHOR]

Background: Today, college students are dealing with depression at some of the highest rates in decades. As the primary mental health service provider, university counseling centers are limited in their capacity and efficiency to provide mental health care due to time constraints and reliance on students' self-reports. A mobile behavioral-sensing platform may serve as a solution to enhance the efficiency and accessibility of university counseling services.Objective: The main objectives of this study are to (1) understand the usefulness of a mobile sensing platform (ie, iSee) in improving counseling services and assisting students' self-management of their depression conditions, and (2) explore what types of behavioral targets (ie, meaningful information extracted from raw sensor data) and feedback to deliver from both clinician and students' perspectives.Methods: We conducted semistructured interviews with 9 clinicians and 12 students with depression recruited from a counseling center at a large Midwestern university. The interviews were 40-50 minutes long and were audio recorded and transcribed. The interview data were analyzed using thematic analysis with an inductive approach. Clinician and student interviews were analyzed separately for comparison. The process of extracting themes involved iterative coding, memo writing, theme revisits, and refinement.Results: From the clinician perspective, the mobile sensing platform helps to improve counseling service by providing objective evidence for clinicians and filling gaps in clinician-patient communication. Clinicians suggested providing students with their sensed behavioral targets organized around personalized goals. Clinicians also recommended delivering therapeutic feedback to students based on their sensed behavioral targets, including positive reinforcement, reflection reminders, and challenging negative thoughts. From the student perspective, the mobile sensing platform helps to ease continued self-tracking practices. Students expressed their need for integrated behavioral targets to understand correlations between behaviors and depression. They also pointed out that they would prefer to avoid seeing negative feedback.Conclusions: Although clinician and student participants shared views on the advantages of iSee in supporting university counseling, they had divergent opinions on the types of behavioral targets and feedback to be provided via iSee. This exploratory work gained initial insights into the design of a mobile sensing platform for depression management and informed a more conclusive research project for the future. [ABSTRACT FROM AUTHOR]

Software-based agents are becoming increasingly ubiquitous and automated. However, current technology and algorithms are still fallible, which considerably affects users' trust and interaction with such agents. In this article, we investigate two factors that can engender user trust in agents: reliability and attractiveness of agents. We show that agent reliability is not more important than agent attractiveness. Subjective user ratings of agent trust and perceived accuracy suggest that attractiveness may be even more important than reliability. [ABSTRACT FROM AUTHOR]

When information is known only to friends in a social network, traditional crowdsourcing mechanisms struggle to motivate a large enough user population and to ensure accuracy of the collected information. We thus introduce friendsourcing, a form of crowdsourcing aimed at collecting accurate information available only to a small, socially-connected group of individuals. Our approach to friendsourcing is to design socially enjoyable interactions that produce the desired information as a side effect. We focus our analysis around Collabio, a novel social tagging game that we developed to encourage friends to tag one another within an online social network. Collabio encourages friends, family, and colleagues to generate useful information about each other. We describe the design space of incentives in social tagging games and evaluate our choices by a combination of usage log analysis and survey data. Data acquired via Collabio is typically accurate and augments tags that could have been found on Facebook or the Web. To complete the arc from data collection to application, we produce a trio of prototype applications to demonstrate how Collabio tags could be utilized: an aggregate tag cloud visualization, a personalized RSS feed, and a question and answer system. The social data powering these applications enables them to address needs previously difficult to support, such as question answering for topics comprehensible only to a few of a user's friends. [ABSTRACT FROM AUTHOR]

Buxton and Myers (1986) compared a specialized document navigation device and a scrollbar. Their device tracked finger movements along two touch-sensitive areas: one for absolute movement and one for relative movement. They found that their system was faster when navigating a 5-page document. We identified finger-tracking issues with the Buxton and Myers device for larger documents (100 pages) and developed an alternative device employing a range sensor and rotary encoder to track finger movement. We ran a new experiment comparing the traditional scrollbar, the Buxton and Myers design, and our new design. Both the Buxton and Myers design and our new design were poorly received by users compared to the scrollbar. Our results indicate that in a large document, factors beyond finger tracking accuracy influence the performance of a device providing absolute movement. From these results, we identify possible improvements necessary to implement effective and practical absolute navigation devices. [ABSTRACT FROM AUTHOR]

The dominant paradigm for searching and browsing large data stores is text-based: presenting a scrollable list of search results in response to textual search term input. While this works well for the Web, there is opportunity for improvement in the domain of personal information stores, which tend to have more heterogeneous data and richer metadata. In this paper, we introduce FacetMap, an interactive, query-driven visualization, generalizable to a wide range of metadata-rich data stores. FacetMap uses a visual metaphor for both input (selection of metadata facets as filters) and output. Results of a user study provide insight into tradeoffs between FacetMap's graphical approach and the traditional text-oriented approach. [ABSTRACT FROM AUTHOR]

Creativity support tools is a research topic with high risk but potentially very high payoff. The goal is to develop improved software and user interfaces that empower users to be not only more productive but also more innovative. Potential users include software and other engineers, diverse scientists, product and graphic designers, architects, educators, students, and many others. Enhanced interfaces could enable more effective searching of intellectual resources, improved collaboration among teams, and more rapid discovery processes. These advanced interfaces should also provide potent support in hypothesis formation, speedier evaluation of alternatives, improved understanding through visualization, and better dissemination of results. For creative endeavors that require composition of novel artifacts (e.g., computer programs, scientific papers, engineering diagrams, symphonies, artwork), enhanced interfaces could facilitate exploration of alternatives, prevent unproductive choices, and enable easy backtracking. This U.S. National Science Foundation sponsored workshop brought together 25 research leaders and graduate students to share experiences, identify opportunities, and formulate research challenges. Two key outcomes emerged: (a) encouragement to evaluate creativity support tools through multidimensional in-depth longitudinal case studies and (b) formulation of 12 principles for design of creativity support tools. [ABSTRACT FROM AUTHOR]

Calendar applications for small handheld devices are growing in popularity. This led us to develop DateLens, a novel calendar interface for PDAs designed to support complex tasks. It uses a fisheye representation coupled with compact overviews to give the big picture in a small space. The interface also gives users control over the visible time period, as well as supporting integrated search to discover patterns and outliers. Designed with device scalability in mind, DateLens currently runs on desktop computers as well as PDAs. Two user studies were conducted to examine the viability of DateLens as a replacement for traditional calendar visualizations. In the first study, non-PDA users performed complex tasks significantly faster with DateLens than with the Microsoft Pocket PC 2002™ calendar (using a PDA emulator). In addition, they rated DateLens as being easier to use than the default calendar application for a majority of the tasks. In the second study, the participants were expert Pocket PC users and the software was run on their own devices. Again, DateLens performed significantly faster for the complex tasks, and there were satisfaction differences favoring each calendar for different kinds of tasks. From these studies, it is clear that DateLens is superior for more complex tasks such as those associated with longer time periods. For daily event tracking, users familiar with the default Pocket PC calendar strongly preferred its daily view and behaviors. [ABSTRACT FROM AUTHOR]

Presents a brief historical overview of research in individual differences in the context of virtual environments. Notion of structure in the perception of individual users of an information system; Role of individuals' abilities in recognizing and using such structures; Recurring themes of studies pertaining to individuals' use of such structures.

This special issue turns the spotlight on business intelligence (BI) as an area of inquiry and explores beyond the current standard practices. The articles in the issue describe the processes that practitioners currently follow in this area and how new BI techniques and capabilities will help users understand and act on widely disparate types of data. The articles also report on how organizations make decisions around datasets, what purposes visualizations are used for, and what different representations people use to show and explore data. [ABSTRACT FROM PUBLISHER]

Co-located collaboration can be extremely valuable during complex visual analytics tasks. We present an exploratory study of a system designed to support collaborative visual analysis tasks on a digital tabletop display. Fifteen participant pairs employed Cambiera, a visual analytics system, to solve a problem involving 240 digital documents. Our analysis, supported by observations, system logs, questionnaires, and interview data, explores how pairs approached the problem around the table. We contribute a unique, rich understanding of how users worked together around the table and identify eight types of collaboration styles that can be used to identify how closely people work together while problem solving. We show how the closeness of teams' collaboration and communication influenced how they performed on the task overall. We further discuss the role of the tabletop for visual analytics tasks and derive design implications for future co-located collaborative tabletop problem solving systems. [ABSTRACT FROM AUTHOR]

Computing and visualizing sets of elements and their relationships is one of the most common tasks one performs when analyzing and organizing large amounts of data. Common representations of sets such as convex or concave geometries can become cluttered and difficult to parse when these sets overlap in multiple or complex ways, e.g., when multiple elements belong to multiple sets. In this paper, we present a design study of a novel set visual representation, LineSets, consisting of a curve connecting all of the set's elements. Our approach to design the visualization differs from traditional methodology used by the InfoVis community. We first explored the potential of the visualization concept by running a controlled experiment comparing our design sketches to results from the state-of-the-art technique. Our results demonstrated that LineSets are advantageous for certain tasks when compared to concave shapes. We discuss an implementation of LineSets based on simple heuristics and present a study demonstrating that our generated curves do as well as human-drawn ones. Finally, we present two applications of our technique in the context of search tasks on a map and community analysis tasks in social networks. [ABSTRACT FROM AUTHOR]

MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being. We argue that integrating behavioral sensing in LLMs will likely lead to a new frontier in AI. In this Late-Breaking Work paper, we discuss the MindScape contextual journal App design that uses LLMs and behavioral sensing to generate contextual and personalized journaling prompts crafted to encourage self-reflection and emotional development. We also discuss the MindScape study of college students based on a preliminary user study and our upcoming study to assess the effectiveness of contextual AI journaling in promoting better well-being on college campuses. MindScape represents a new application class that embeds behavioral intelligence in AI.

Traditional 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.

Introduction: Difficulties in emotional regulation are key to the development and maintenance of chronic pain. Recent evidence shows internet-delivered dialectic behaviour therapy (iDBT) skills training can reduce emotional dysregulation and pain intensity. However, further studies are needed to provide more definitive evidence regarding the efficacy of iDBT skills training in the chronic pain population., Methods and Analysis: A single-case experimental design (SCED) with multiple baselines will be used to examine the efficacy of a 4-week iDBT-Pain skills training intervention (iDBT-Pain intervention) to reduce emotional dysregulation and pain intensity in individuals with chronic pain. The iDBT-Pain intervention encompasses two components: (1) iDBT-Pain skills training sessions (iDBT-Pain sessions) and (2) the iDBT-Pain skills training web application (iDBT-Pain app). Three individuals with chronic pain will be recruited and randomly allocated to different baseline phases (5, 9 or 12 days). Following the baseline phase, participants will receive six 60-90 min iDBT-Pain sessions approximately 4 or 5 days apart, delivered by a psychologist via Zoom. To reinforce learnings from the iDBT-Pain sessions, participants will have unlimited use of the iDBT-Pain app. A 7-day follow-up phase (maintenance) will follow the intervention, whereby the iDBT-Pain sessions cease but the iDBT-Pain app is accessible. Emotional regulation, as the primary outcome measure, will be assessed using the Difficulties in Emotion Regulation Scale. Pain intensity, as the secondary outcome measure, will be assessed using a visual analogue scale. Generalisation measures will assess psychological state factors (depression, anxiety and coping behaviour), alongside sleep quality, well-being and harm avoidance. SCEDs are increasingly considered effective designs for internet-delivered psychological interventions because SCED enables the investigation of interindividual variability in a heterogeneous population such as chronic pain., Ethics and Dissemination: This trial was approved by the University of New South Wales (HC200199). Results will be published in peer-reviewed journals., Trial Registration Number: ACTRN12620000604909., Competing Interests: Competing interests: CW receives consulting fees from Mindstrong Health and Lyra Health. JSu reports other from Microsoft, outside the submitted work., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Mobile mental health interventions have the potential to reduce barriers and increase engagement in psychotherapy. However, most current tools fail to meet evidence-based principles. In this paper, we describe data-driven design implications for translating evidence-based interventions into mobile apps. To develop these design implications, we analyzed data from a month-long field study of an app designed to support dialectical behavioral therapy, a psychotherapy that aims to teach concrete coping skills to help people better manage their mental health. We investigated whether particular skills are more or less effective in reducing distress or emotional intensity. We also characterized how an individual's disorders, characteristics, and preferences may correlate with skill effectiveness, as well as how skill-level improvements correlate with study-wide changes in depressive symptoms. We then developed a model to predict skill effectiveness. Based on our findings, we present design implications that emphasize the importance of considering different environmental, emotional, and personal contexts. Finally, we discuss promising future opportunities for mobile apps to better support evidence-based psychotherapies, including using machine learning algorithms to develop personalized and context-aware skill recommendations.

Background: Today, college students are dealing with depression at some of the highest rates in decades. As the primary mental health service provider, university counseling centers are limited in their capacity and efficiency to provide mental health care due to time constraints and reliance on students' self-reports. A mobile behavioral-sensing platform may serve as a solution to enhance the efficiency and accessibility of university counseling services., Objective: The main objectives of this study are to (1) understand the usefulness of a mobile sensing platform (ie, iSee) in improving counseling services and assisting students' self-management of their depression conditions, and (2) explore what types of behavioral targets (ie, meaningful information extracted from raw sensor data) and feedback to deliver from both clinician and students' perspectives., Methods: We conducted semistructured interviews with 9 clinicians and 12 students with depression recruited from a counseling center at a large Midwestern university. The interviews were 40-50 minutes long and were audio recorded and transcribed. The interview data were analyzed using thematic analysis with an inductive approach. Clinician and student interviews were analyzed separately for comparison. The process of extracting themes involved iterative coding, memo writing, theme revisits, and refinement., Results: From the clinician perspective, the mobile sensing platform helps to improve counseling service by providing objective evidence for clinicians and filling gaps in clinician-patient communication. Clinicians suggested providing students with their sensed behavioral targets organized around personalized goals. Clinicians also recommended delivering therapeutic feedback to students based on their sensed behavioral targets, including positive reinforcement, reflection reminders, and challenging negative thoughts. From the student perspective, the mobile sensing platform helps to ease continued self-tracking practices. Students expressed their need for integrated behavioral targets to understand correlations between behaviors and depression. They also pointed out that they would prefer to avoid seeing negative feedback., Conclusions: Although clinician and student participants shared views on the advantages of iSee in supporting university counseling, they had divergent opinions on the types of behavioral targets and feedback to be provided via iSee. This exploratory work gained initial insights into the design of a mobile sensing platform for depression management and informed a more conclusive research project for the future., (©Jingbo Meng, Syed Ali Hussain, David C Mohr, Mary Czerwinski, Mi Zhang. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.07.2018.)

Effective communication between patients and their clinicians during clinical encounters has a positive impact on health outcomes. Technology has the potential to help transform this synchronous interaction, but researchers are still at early stages of developing interventions to assess and improve patient-clinician communication. In this workshop, we envision opening up a dialogue among researchers and clinicians who wish to discuss directions for future research in this domain. In particular, the workshop will focus on exploring how technologies available today, as well as projected for the future, can support the communication needs of clinicians and patients.