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

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.

"Obviously, Nothing's Gonna Happen in Five Minutes": How Adolescents and Young Adults Infrastructure Resources to Learn Type 1 Diabetes Management.

Learning personalized self-management routines is pivotal for people with type 1 diabetes (T1D), particularly early in diagnosis. Context-aware technologies, such as hybrid closed-loop (HCL) insulin pumps, are important tools for diabetes self-management. However, clinicians have observed that practices using these technologies involve significant individual differences. We conducted interviews with 20 adolescents and young adults who use HCL insulin pump systems for managing T1D, and we found that these individuals leverage both technological and non-technological means to maintain situational awareness about their condition. We discuss how these practices serve to infrastructure their self-management routines, including medical treatment, diet, and glucose measurement-monitoring routines. Our study provides insights into adolescents' and young adults' lived experiences of using HCL systems and related technology to manage diabetes, and contributes to a more nuanced understanding of how the HCI community can support the contextualized management of diabetes through technology design.

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making.

BACKGROUND: Food practice plays an important role in health. Food practice data collected in daily living settings can inform clinical decisions. However, integrating such data into clinical decision-making is burdensome for both clinicians and patients, resulting in poor adherence and limited utilization. Automation offers benefits in this regard, minimizing this burden resulting in a better fit with a patient's daily living routines, and creating opportunities for better integration into clinical workflow. Although the literature on patient-generated health data (PGHD) can serve as a starting point for the automation of food practice data, more diverse characteristics of food practice data provide additional challenges. OBJECTIVES: We describe a series of steps for integrating food practices into clinical decision-making. These steps include the following: (1) sensing food practice; (2) capturing food practice data; (3) representing food practice; (4) reflecting the information to the patient; (5) incorporating data into the EHR; (6) presenting contextualized food practice information to clinicians; and (7) integrating food practice into clinical decision-making. METHODS: We elaborate on automation opportunities and challenges in each step, providing a summary visualization of the flow of food practice-related data from daily living settings to clinical settings. RESULTS: We propose four implications of automating food practice hereinafter. First, there are multiple ways of automating workflow related to food practice. Second, steps may occur in daily living and others in clinical settings. Food practice data and the necessary contextual information should be integrated into clinical decision-making to enable action. Third, as accuracy becomes important for food practice data, macrolevel data may have advantages over microlevel data in some situations. Fourth, relevant systems should be designed to eliminate disparities in leveraging food practice data. CONCLUSION: Our work confirms previously developed recommendations in the context of PGHD work and provides additional specificity on how these recommendations apply to food practice.

Being (In)Visible: Privacy, Transparency, and Disclosure in the Self-Management of Bipolar Disorder.

Research in personal informatics (PI) calls for systems to support social forms of tracking, raising questions about how privacy can and should support intentionally sharing sensitive health information. We focus on the case of personal data related to the self-tracking of bipolar disorder (BD) in order to explore the ways in which disclosure activities intersect with other privacy experiences. While research in HCI often discusses privacy as a disclosure activity, this does not reflect the ways in which privacy can be passively experienced. In this paper we broaden conceptions of privacy by defining transparency experiences and contributing factors in contrast to disclosure activities and preferences. Next, we ground this theoretical move in empirical analysis of personal narratives shared by people managing BD. We discuss the resulting emergent model of transparency in terms of implications for the design of socially-enabled PI systems. CAUTION: This paper contains references to experiences of mental illness, including self-harm, depression, suicidal ideation, etc.

Activity Theory Analysis of Heart Failure Self-Care.

The management of chronic health conditions such as heart failure is a complex process emerging from the activity of a network of individuals and artifacts. This article presents an Activity Theory-based secondary analysis of data from a geriatric heart failure management study. Twenty-one patients' interviews and clinic visit observations were analyzed to uncover eight configurations of roles and activities involving patients, clinicians, and others in the sociotechnical network. For each configuration or activity pattern, we identify points of tension and propose guidelines for developing interventions for future computer-supported healthcare systems.

Development and Evaluation of a Smartphone-Based Measure of Social Rhythms for Bipolar Disorder.

Dynamic psychological processes are most often assessed using self-report instruments. This places a constraint on how often and for how long data can be collected due to the burden placed on human participants. Smartphones are ubiquitous and highly personal devices, equipped with sensors that offer an opportunity to measure and understand psychological processes in real-world contexts over the long term. In this article, we present a novel smartphone approach to address the limitations of self-report in bipolar disorder where mood and activity are key constructs. We describe the development of MoodRhythm, a smartphone application that incorporates existing self-report elements from interpersonal and social rhythm therapy, a clinically validated treatment, and combines them with novel inputs from smartphone sensors. We reflect on lessons learned in transitioning from an existing self-report instrument to one that involves smartphone sensors and discuss the potential impact of these changes on the future of psychological assessment.