Abstract Human-Computer Interaction (HCI) researchers have been increasingly interested in investigating self-trackers’ experience with self-tracking tools (STT) to get meaningful insights from their data. However, the literature lacks a coherent, integrated and dedicated source on designing tools that support self-trackers’ sensemaking practices. To address this, we carried out a systematic literature review by synthesizing the findings of 91 articles published before 2021 in HCI literature. We identified four data sensemaking modes that self-trackers go through (i.e., self-calibration, data augmentation, data handling, and realization). We also identified four design implications for designing self-tracking tools that support self-trackers’ data sensemaking practices (i.e., customized tracking experience, guided sensemaking, collaborative sensemaking, and learning sensemaking through self-experimentation). We provide a research agenda with nine directions for advancing HCI studies on data sensemaking practices. With these contributions, we created an analytical information source that could guide designers and researchers in understanding, studying, and designing for self-trackers’ data sensemaking practices.

/pdf/data-sensemaking-in-self-tracking-towards-a-new-generation-63ul18f3.pdf

Data Sensemaking in Self-Tracking: Towards a New Generation of Self-Tracking Tools

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.

Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App

People experiencing severe distress increasingly use Large Language Model (LLM) chatbots as mental health support tools. Discussions on social media have described how engagements were lifesaving for some, but evidence suggests that general-purpose LLM chatbots also have notable risks that could endanger the welfare of users if not designed responsibly. In this study, we investigate the lived experiences of people who have used LLM chatbots for mental health support. We build on interviews with 21 individuals from globally diverse backgrounds to analyze how users create unique support roles for their chatbots, fill in gaps in everyday care, and navigate associated cultural limitations when seeking support from chatbots. We ground our analysis in psychotherapy literature around effective support, and introduce the concept of therapeutic alignment, or aligning AI with therapeutic values for mental health contexts. Our study offers recommendations for how designers can approach the ethical and effective use of LLM chatbots and other AI mental health support tools in mental health care.

pdf/the-typing-cure-experiences-with-large-language-model-4qvjs4qgmn.pdf

The Typing Cure: Experiences with Large Language Model Chatbots for Mental Health Support

BACKGROUND
With the development of emerging technologies, digital behavior change interventions (DBCIs) help to maintain regular physical activity in daily life.


OBJECTIVE
To comprehensively understand the design implementations of habit formation techniques in current DBCIs, a systematic review was conducted to investigate the implementations of behavior change techniques, types of habit formation techniques, and design strategies in current DBCIs.


METHODS
The process of this review followed the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) guidelines. A total of 4 databases were systematically searched from 2012 to 2022, which included Web of Science, Scopus, ACM Digital Library, and PubMed. The inclusion criteria encompassed studies that used digital tools for physical activity, examined behavior change intervention techniques, and were written in English.


RESULTS
A total of 41 identiﬁed research articles were included in this review. The results show that the most applied behavior change techniques were the self-monitoring of behavior, goal setting, and prompts and cues. Moreover, habit formation techniques were identified and developed based on intentions, cues, and positive reinforcement. Commonly used methods included automatic monitoring, descriptive feedback, general guidelines, self-set goals, time-based cues, and virtual rewards.


CONCLUSIONS
A total of 32 commonly design strategies of habit formation techniques were summarized and mapped to the proposed conceptual framework, which was categorized into target-mediated (generalization and personalization) and technology-mediated interactions (explicitness and implicitness). Most of the existing studies use the explicit interaction, aligning with the personalized habit formation techniques in the design strategies of DBCIs. However, implicit interaction design strategies are lacking in the reviewed studies. The proposed conceptual framework and potential solutions can serve as guidelines for designing strategies aimed at habit formation within DBCIs.

Digital Behavior Change Intervention Designs for Habit Formation: Systematic Review.

Personal informatics (PI) systems are widely used in various domains such as mental health to provide insights from self-tracking data for behavior change. Users are highly interested in examining relationships from the self-tracking data, but identifying causality is still considered challenging. In this study, we design DeepStress, a PI system that helps users analyze contextual factors causally related to stress. DeepStress leverages a quasi-experimental approach to address potential biases related to confounding factors. To explore the user experience of DeepStress, we conducted a user study and a follow-up diary study using participants’ own self-tracking data collected for 6 weeks. Our results show that DeepStress helps users consider multiple contexts when investigating causalities and use the results to manage their stress in everyday life. We discuss design implications for causality support in PI systems.

DeepStress: Supporting Stressful Context Sensemaking in Personal Informatics Systems Using a Quasi-experimental Approach

Computational toys and apps, or coding kits, are the primary learning media for young children to engage in Computational Thinking concepts and skills. However, how coding kits are designed to welcome children of different genders remains unclear, a critical research gap given that computing is mostly a male-dominated field. Drawing on relevant literature, we develop a framework of gender-related design features in toys (i.e., boy-oriented, girl-oriented, or gender-neutral features) and employ it to examine gender-related design features in commercially available coding kits for young children aged seven and under (N=110). The findings reveal the lopsided gender representation in coding kits, e.g., their physical forms and supported coding activities are predominantly boy-oriented. We discuss design and research implications for coding kits to welcome participation from children of different genders, especially young girls whose preferred design features are underrepresented in current designs.

Examining Gender-oriented Design Features in Computational Toys and Kits for Young Children

Background. Adolescents and young adults with type 1 diabetes have high HbA1c levels and often struggle with self-management behaviors and attention to diabetes care. Hybrid closed-loop systems (HCL) like the t:slim X2 with Control-IQ technology (Control-IQ) can help improve glycemic control. The purpose of this study is to assess adolescents’ situational awareness of their glucose control and engagement with the Control-IQ system to determine significant factors in daily glycemic control. Methods. Adolescents (15–25 years) using Control-IQ participated in a 2-week prospective study, gathering detailed information about Control-IQ system engagements (boluses, alerts, and so on) and asking the participants’ age and gender about their awareness of glucose levels 2-3 times/day without checking. Mixed models assessed which behaviors and awareness items correlated with time in range (TIR, 70–180 mg/dl, 3.9–10.0 mmol/L). Results. Eighteen adolescents/young adults (mean age 18 ± 1.86 years and 86% White non-Hispanic) completed the study. Situational awareness of glucose levels did not correlate with time since the last glucose check (
 
 p
 =
 0.8
 
 ). In multivariable modeling, lower TIR was predicted on days when adolescents underestimated their glucose levels (r = −0.22), received more CGM alerts (r = −0.31), and had more pump engagements (r = −0.27). A higher TIR was predicted when adolescents responded to CGM alerts (r = 0.20) and entered carbohydrates into the bolus calculator (r = 0.49). Conclusion. Situational awareness is an independent predictor of TIR and may provide insight into patterns of attention and focus that could positively influence glycemic outcomes in adolescents. Proactive engagements predict better TIR, whereas reactive engagement predicted lower TIR. Future interventions could be designed to train users to develop awareness and expertise in effective diabetes self-management.

https://downloads.hindawi.com/journals/pedi/2023/1888738.pdf

Situational Awareness and Proactive Engagement Predict Higher Time in Range in Adolescents and Young Adults Using Hybrid Closed-Loop

Technology plays an increasingly pivotal role in mediating mental health support in people's everyday lives. However, it is not clear how that mediation is occurring, to what end, and what technologies are implicated. In this study, we examine these questions with a mixed-methods analysis of conversations among participants in several Bipolar Disorder (BD) communities on Reddit. Analyzing posts produced over four years, we identify a wide variety of technologies that people employ to manage their mental conditions, such as communication technologies, online communities and tracking tools. Using this taxonomy of technologies as a framework, we then summarize three technology-mediated management strategies that these technologies enable, including serving as community, episode, and information mediators. We argue that with a comprehensive and nuanced understanding of people's in situ technology use, we can identify research and design opportunities for designing human-centered technologies to help people manage mental health challenges more effectively. 

Technology-Mediated Strategies for Coping with Mental Health Challenges: Insights from People with Bipolar Disorder

Expression-in-action and Expression-on-action: A Systematic Review of Mediums for Expression in Mental Health

Personal informatics (PI) systems have been developed to support reflection. While reflection is considered an indispensable activity in PI use, how and when reflection occurs is still under-studied. In this paper, we present an analysis of the interactive features of 123 commercial PI apps, revealing that reflective practices are unevenly supported. The lack of features that encourage user-driven reflection, scaffolding for setting goals and configuring data collection and presentation, and consideration of wider implications stand to limit meaning-making and frustrate nuanced insight generation based on lived experiences. Based on our findings, we discuss how reflection is currently misrepresented in personal informatics tools, identify and characterize the gaps between theoretical research on reflection and interface features in current apps, and offer suggestions about how reflection could be better supported.

/pdf/reflection-in-theory-and-reflection-in-practice-an-1lpdr5e1.pdf

Reflection in Theory and Reflection in Practice: An Exploration of the Gaps in Reflection Support among Personal Informatics Apps

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