The interdisciplinary field of Human-Computer Interaction (HCI) thrives on productive engagement with different domains, yet this engagement often breaks due to idiosyncratic writing styles and unfamiliar concepts. Inspired by the dialogic model of abstract metaphors, as well as the potential of Large Language Models (LLMs) to produce on-demand support, we investigate the use of metaphors to facilitate engagement between Science and Technology Studies (STS) and System HCI. Our reflective-style survey with early-career HCI researchers (N=48) reported that limited prior exposure to STS research can hinder perceived openness of the work, and ultimately interest in reading. The survey also revealed that metaphors enhance likelihood to continue reading STS papers, and alternative perspectives can build critical thinking skills to mitigate potential risks of LLM-generated metaphors. We lastly offer a specified model of metaphor exchange (within this generative context) that incorporates alternative perspectives to construct shared understanding in interdisciplinary engagement.

OBJECTIVES: Digital health research involves collecting vast amounts of personal health data, making data management practices complex and challenging to convey during informed consent. MATERIALS AND METHODS: We conducted eight semi-structured focus groups to explore whether dataflow diagrams (DFD) can complement informed consent and improve participants' understanding of data management and associated risks (N = 34 participants). RESULTS: Our analysis found that DFDs could supplement text-based information about data management and sharing practices, such as by helping raise new questions that prompt conversation between prospective participants and members of a research team. Participants in the study emphasized the need for clear, simple, and accessible diagrams that are participant centered. Third-party access to data and sharing of sensitive health data were identified as high-risk areas requiring thorough explanation. Participants generally agreed that the design process should be led by the research team, but it should incorporate many diverse perspectives to ensure the diagram was meaningful to potential participants who are likely unfamiliar with data management. Nearly all participants rejected the idea that artificial intelligence could identify risks during the design process, but most were comfortable with it being used as a tool to format and simplify the diagram. In short, DFDs may complement standard text-based informed consent documents, but they are not a replacement. DISCUSSION: Prospective research participants value diverse ways of learning about study risks and benefits. Our study highlights the value of incorporating information visualizations, such as DFDs, into the informed consent procedures to participate in research. CONCLUSION: Future research should explore other ways of visualizing consent information in ways that help people to overcome digital and data literacy barriers to participating in research. However, creating a DFD requires significant time and effort from research teams. To alleviate these costs, research sponsors can support the creation of shared infrastructure, communities of practice, and incentivize researchers to develop better consent procedures.

OBJECTIVE: Digital behavior change interventions (DBCIs) are feasibly effective tools for addressing physical activity. However, in-depth understanding of participants' long-term engagement with DBCIs remains sparse. Since the effectiveness of DBCIs to impact behavior change depends, in part, upon participant engagement, there is a need to better understand engagement as a dynamic process in response to an individual's ever-changing biological, psychological, social, and environmental context. METHODS: The year-long micro-randomized trial (MRT) HeartSteps II provides an unprecedented opportunity to investigate DBCI engagement among ethnically diverse participants. We combined data streams from wearable sensors (Fitbit Versa, i.e., walking behavior), the HeartSteps II app (i.e. page views), and ecological momentary assessments (EMAs, i.e. perceived intrinsic and extrinsic motivation) to build the idiographic models. A system identification approach and a fluid analogy model were used to conduct autoregressive with exogenous input (ARX) analyses that tested hypothesized relationships between these variables inspired by Self-Determination Theory (SDT) with DBCI engagement through time. RESULTS: Data from 11 HeartSteps II participants was used to test aspects of the hypothesized SDT dynamic model. The average age was 46.33 (SD=7.4) years, and the average steps per day at baseline was 5,507 steps (SD=6,239). The hypothesized 5-input SDT-inspired ARX model for app engagement resulted in a 31.75 % weighted RMSEA (31.50 % on validation and 31.91 % on estimation), indicating that the model predicted app page views almost 32 % better relative to the mean of the data. Among Hispanic/Latino participants, the average overall model fit across inventories of the SDT fluid analogy was 34.22 % (SD=10.53) compared to 22.39 % (SD=6.36) among non-Hispanic/Latino Whites, a difference of 11.83 %. Across individuals, the number of daily notification prompts received by the participant was positively associated with increased app page views. The weekend/weekday indicator and perceived daily busyness were also found to be key predictors of the number of daily application page views. CONCLUSIONS: This novel approach has significant implications for both personalized and adaptive DBCIs by identifying factors that foster or undermine engagement in an individual's respective context. Once identified, these factors can be tailored to promote engagement and support sustained behavior change over time.

Self-tracking can help people understand their medical condition and the factors that influence their symptoms. However, it is unclear how tracking technologies should be tailored to help people cope with the progression of a degenerative disease. To understand how smartphone apps and other tracking technologies can support people in coping with an incurable illness, we interviewed both people with Parkinson's Disease (n=17) and care partners (n=6) who help people with Parkinson's manage their lives. We describe how symptom trackers can help people identify and solve problems to improve their quality of life, the role symptom trackers can play in helping people combat their own tendencies towards avoidance and denial, and the complex role of care partners in defining and tracking ambiguous symptoms. Our findings yield insights that can guide the design of tracking technologies to help people with Parkinson's Disease accept and plan for their condition.

Over the last ten years, HCI researchers have introduced a range of novel ways to support health behavior change, from glanceable displays to sophisticated game dynamics. Yet, this research has not had as much impact as its originality warrants. A key reason for this is that common forms of evaluation used in HCI make it difficult to effectively accumulate-and use-knowledge across research projects. This paper proposes a strategy for HCI research on behavior change that retains the field's focus on novel technical contributions while enabling accumulation of evidence that can increase impact of individual research projects both in HCI and the broader behavior-change science. The core of this strategy is an emphasis on the discovery of causal effects of individual components of behavior-change technologies and the precise ways in which those effects vary with individual differences, design choices, and contexts in which those technologies are used.

Desirable outcomes such as health are tightly linked to behaviors, thus inspiring research on technologies that support people in changing those behaviors. Many behavior-change technologies are designed by HCI experts but this approach can make it difficult to personalize support to each user's unique goals and needs. This paper reports on the iterative design of two complementary support strategies for helping users create their own personalized behavior-change plans via self-experimentation: One emphasized the use of interactive instructional materials, and the other additionally introduced context-aware computing to enable user creation of "just in time" home-based interventions. In a formative trial with 27 users, we compared these two approaches to an unstructured sleep education control. Results suggest great promise in both strategies and provide insights on how to develop personalized behavior-change technologies.

A core tradition of HCI lies in the experimental evaluation of the effects of techniques and interfaces to determine if they are useful for achieving their purpose. However, our individual analyses tend to stand alone, and study results rarely accrue in more precise estimates via meta-analysis: in a literature search, we found only 56 meta-analyses in HCI in the ACM Digital Library, 3 of which were published at CHI (often called the top HCI venue). Yet meta-analysis is the gold standard for demonstrating robust quantitative knowledge. We treat this as a user-centered design problem: the failure to accrue quantitative knowledge is not the users' (i.e. researchers') failure, but a failure to consider those users' needs when designing statistical practice. Using simulation, we compare hypothetical publication worlds following existing frequentist against Bayesian practice. We show that Bayesian analysis yields more precise effects with each new study, facilitating knowledge accrual without traditional meta-analyses. Bayesian practices also allow more principled conclusions from small-n studies of novel techniques. These advantages make Bayesian practices a likely better fit for the culture and incentives of the field. Instead of admonishing ourselves to spend resources on larger studies, we propose using tools that more appropriately analyze small studies and encourage knowledge accrual from one study to the next. We also believe Bayesian methods can be adopted from the bottom up without the need for new incentives for replication or meta-analysis. These techniques offer the potential for a more user- (i.e. researcher-) centered approach to statistical analysis in HCI.

Computing systems that place a high level of burden on their users can have a negative affect on initial adoption, retention, and overall user experience. Through an iterative process, we have developed a model for user burden that consists of six constructs: 1) difficulty of use, 2) physical, 3) time and social, 4) mental and emotional, 5) privacy, and 6) financial. If researchers and practitioners can have an understanding of the overall level of burden systems may be having on the user, they can have a better sense of whether and where to target future design efforts that can reduce those burdens. To help assist with understanding and measuring user burden, we have also developed and validated a measure of user burden in computing systems called the User Burden Scale (UBS), which is a 20-item scale with 6 individual sub-scales representing each construct. This paper presents the process we followed to develop and validate this scale for use in evaluating user burden in computing systems. Results indicate that the User Burden Scale has good overall inter-item reliability, convergent validity with similar scales, and concurrent validity when compared to systems abandoned vs. those still in use.

Exergames, video game systems that require exertion and interaction, have been rising in popularity in the past years. However, research on popular exergames shows mixed health benefits, potentially due to minimal energy expenditure and decreasing use over time. This paper presents a 2x2 experimental study (N = 44), using a popular exergame, where we vary the framing of intention (i.e., "Gameplay" or "Exercise") and feedback (i.e., "Health" or "No health") to explore their single and interactive impacts on perceived exertion, objectively measured energy expenditure, affect, and duration of usage in a single session. Our study showed that participants primed with exercise used the system significantly longer than those primed with game play (M = 49.2 ±2.0 min versus M = 39.3 ±2.0 min). We discuss our results and possible design implications based on our single-session experiment. We conclude with a discussion on the potential impact of focusing on "healthifying" exergames -highlighting an exergames" dual purpose as both a game and exercise - as opposed to gamifying health behaviors.

Many people struggle with their weight and are turning to online communities for social and informational support. The aim of this study is to understand the issues commonly discussed in online weight loss communities. Through observation and content analysis of threads in one specific weight loss community, we identified 17 distinct categories discussed by the participants. We detail four categories specifically: Personal Experience, Consumption Choices, Dieting Strategy, and Exercise. Our analysis describes some key user roles and states that often relate to different phases of a person's weight loss journey. We identify a set of transient states, which are not proper roles but are significant in an online community where individuals are attempting to change their own behaviors. We close with design suggestions for encouraging and maintaining participant engagement in an online health community

Researchers in HCI and behavioral science are increasingly exploring the use of technology to support behavior change in domains such as health and sustainability. This work, however, remain largely siloed within the two communities. We begin to address this silo problem by attempting to build a bridge between the two disciplines at the level of behavioral theory. Specifically, we define core theoretical terms to create shared understanding about what theory is, discuss ways in which behavioral theory can be used to inform research on behavior change technologies, identify shortcomings in current behavioral theories, and outline ways in which HCI researchers can not only interpret and utilize behavioral science theories but also contribute to improving them.

There is a novel opportunity to increase awareness of health activities in a person's daily life. One way to do that is through context-aware self-monitoring. We explore design insights from field work and field testing different methods for self-monitoring. We present an innovative design for establishing context-aware self-monitoring that has the potential to improve health.