Exploring Human-Data Interaction in Clinical Decision-making Using Scenarios: Co-design Study

When caring for patients with chronic conditions like Chronic Obstructive Pulmonary Disease (COPD), healthcare professionals (HCPs) rely on multiple data sources to make decisions. Collating and visualizing this data, for example on clinical dashboards, holds potential to support timely and informed decision-making. Most studies about data supported decision-making (DSDM) technologies for healthcare have focused on their technical feasibility or quantitative effectiveness. While these studies are an important contribution to the literature, they do not further our limited understanding of how HCPs engage with these technologies and how they can be designed to support specific contexts of use. To progress our knowledge of this area, we must work with HCPs to explore this space and the real-world complexities of healthcare work and service structures. This research aimed to qualitatively explore how DSDM technologies could support HCPs in their decision-making about COPD care. We created a scenario-based research tool, called Respire, that visualized HCPs’ data needs about their COPD patients and services. We used Respire with HCPs to uncover rich and nuanced findings about human-data interaction in this context, focusing on the real-world challenges that HCPs face when carrying out their work and making decisions. We engaged nine respiratory HCPs from two collaborating healthcare organizations to design Respire. We then used Respire as a tool to investigate human-data interaction in the context of decision-making about COPD care. The study followed a co-design approach that had three stages and spanned two years. The first stage involved five workshops with the HCPs to identify data-interaction scenarios which would support their work. The second stage involved creating Respire, an interactive scenario-based web application that visualized HCPs’ data needs, incorporating feedback from the HCPs. The final stage involved 11 one-to-one sessions with HCPs to use Respire, focusing on how they envisaged it could support their work and decisions about care. We found that: (1) HCPs trust data differently depending on where it came from and who recorded it; (2) sporadic and subjective data generated by patients has value but creates challenges for decision-making; and (3) HCPs require support interpreting and responding to new data and its use cases. Our study uncovers important lessons for the design of DSDM technologies to support healthcare contexts. We show that while DSDM technologies have potential to support patient care and healthcare delivery, important sociotechnical and human data interaction challenges influence how these technologies should be designed and deployed. Exploring these considerations during the design process can ensure DSDM technologies are designed with a holistic view of how decision-making and engagement with data occurs in healthcare contexts

Working Together in a PhamilySpace: Facilitating Collaboration on Healthy Behaviors Over Distance

Studies have shown that interpersonal relationships such as families and friends are an important source of support and encouragement to those who seek to engage in healthier habits. However, challenges related to geographic distance may hinder those relationships from fully collaborating and engaging in healthy living together. To explore this domain, we developed and deployed a lightweight photo-based application called PhamilySpace with a week-long intervention. Our goal is to examine family members\u27 and friends\u27 engagement and awareness on healthy behaviors while living apart. Our analysis of the semi-structured interviews, pre/post-intervention instruments, and application logs suggests three main benefits of interventions for health promotion in this context: (1) increased awareness on acts of health; (2) reciprocal sharing of health information supports social accountability over distance; and (3) positive dialogue around health enhances support on healthy living. By providing insights into distributed family/friends interactions and experiences with the application, we identify benefits, challenges, and opportunities for future design interventions that promote healthy behaviors

A Design Exploration of Health-Related Community Displays

The global population is ageing, leading to shifts in healthcare needs. It is well established that increased physical activity can improve the health and wellbeing of many older adults. However, motivation remains a prime concern. We report findings from a series of focus groups where we explored the concept of using community displays to promote physical activity to a local neighborhood. In doing so, we contribute both an understanding of the design space for community displays, as well as a discussion of the implications of our work for the broader CSCW community. We conclude that our work demonstrates the potential for developing community displays for increasing physical activity amongst older adults

HYPOalert: Designing Mobile Technology for Hypoglycemic Detection and Monitoring--Based on Human Breath

Hypoglycemia (HYPO) is characterized by low blood glucose (BG)--leading to complications such as sweating, weakness, passing-out, coma, and even death. Effective HYPO management is required to avoid complications and to increase quality of life. Recently, a noninvasive smart breathing sensor was developed for detection of HYPO in human breath (HYPOalert). The device has the ability to deliver data (via Bluetooth) to a mobile application--with the intent to support Type 1 and 2 diabetics with the self-management of their hypoglycemia. This paper presents the first two (prototype) design iterations of research and testing of HYPOalert. Twelve Type 1 and 2 diabetics were interviewed to deduce user requirements and to understand their perception and level of interest in the proposed mobile system. Outcomes informed a human-centered design process of the interactive prototype, currently under final testing. Results were positive--showing that users were very interested in HYPOalert's use of visualization, as well as its HYPO monitoring and alert system that supports diabetes patients' healthy lifestyle management

Just-in-Time Prompts for Running, Walking, and Performing Strength Exercises in the Built Environment: 4-Week Randomized Feasibility Study

Background: App-based mobile health exercise interventions can motivate individuals to engage in more physical activity (PA). According to the Fogg Behavior Model, it is important that the individual receive prompts at the right time to be successfully persuaded into PA. These are referred to as just-in-time (JIT) interventions. The Playful Active Urban Living (PAUL) app is among the first to include 2 types of JIT prompts: JIT adaptive reminder messages to initiate a run or walk and JIT strength exercise prompts during a walk or run (containing location-based instruction videos). This paper reports on the feasibility of the PAUL app and its JIT prompts. Objective: The main objective of this study was to examine user experience, app engagement, and users' perceptions and opinions regarding the PAUL app and its JIT prompts and to explore changes in the PA behavior, intrinsic motivation, and the perceived capability of the PA behavior of the participants. Methods: In total, 2 versions of the closed-beta version of the PAUL app were evaluated: a basic version (Basic PAUL) and a JIT adaptive version (Smart PAUL). Both apps send JIT exercise prompts, but the versions differ in that the Smart PAUL app sends JIT adaptive reminder messages to initiate running or walking behavior, whereas the Basic PAUL app sends reminder messages at randomized times. A total of 23 participants were randomized into 1 of the 2 intervention arms. PA behavior (accelerometer-measured), intrinsic motivation, and the perceived capability of PA behavior were measured before and after the intervention. After the intervention, participants were also asked to complete a questionnaire on user experience, and they were invited for an exit interview to assess user perceptions and opinions of the app in depth. Results: No differences in PA behavior were observed (Z=−1.433; P = .08), but intrinsic motivation for running and walking and for performing strength exercises significantly increased (Z=−3.342; P < .001 and Z=−1.821; P = .04, respectively). Furthermore, participants increased their perceived capability to perform strength exercises (Z=2.231; P = .01) but not to walk or run (Z=−1.221; P = .12). The interviews indicated that the participants were enthusiastic about the strength exercise prompts. These were perceived as personal, fun, and relevant to their health. The reminders were perceived as important initiators for PA, but participants from both app groups explained that the reminder messages were often not sent at times they could exercise. Although the participants were enthusiastic about the functionalities of the app, technical issues resulted in a low user experience. Conclusions: The preliminary findings suggest that the PAUL apps are promising and innovative interventions for promoting PA. Users perceived the strength exercise prompts as a valuable addition to exercise apps. However, to be a feasible intervention, the app must be more stable

Stress Detection Using Experience Sampling: A Systematic Mapping Study

Stress has been designated the "Health Epidemic of the 21st Century" by the World Health Organization and negatively affects the quality of individuals' lives by detracting most body systems. In today's world, different methods are used to track and measure various types of stress. Among these techniques, experience sampling is a unique method for studying everyday stress, which can affect employees' performance and even their health by threatening them emotionally and physically. The main advantage of experience sampling is that evaluating instantaneous experiences causes less memory bias than traditional retroactive measures. Further, it allows the exploration of temporal relationships in subjective experiences. The objective of this paper is to structure, analyze, and characterize the state of the art of available literature in the field of surveillance of work stress via the experience sampling method. We used the formal research methodology of systematic mapping to conduct a breadth-first review. We found 358 papers between 2010 and 2021 that are classified with respect to focus, research type, and contribution type. The resulting research landscape summarizes the opportunities and challenges of utilizing the experience sampling method on stress detection for practitioners and academics. 2022 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This research was funded by Molde University College, Specialized Univ. Norway, through support of the Open Access fund.Scopus2-s2.0-8512936724

Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization

The interplay between mood and eating has been the subject of extensive research within the fields of nutrition and behavioral science, indicating a strong connection between the two. Further, phone sensor data have been used to characterize both eating behavior and mood, independently, in the context of mobile food diaries and mobile health applications. However, limitations within the current body of literature include: i) the lack of investigation around the generalization of mood inference models trained with passive sensor data from a range of everyday life situations, to specific contexts such as eating, ii) no prior studies that use sensor data to study the intersection of mood and eating, and iii) the inadequate examination of model personalization techniques within limited label settings, as we commonly experience in mood inference. In this study, we sought to examine everyday eating behavior and mood using two datasets of college students in Mexico (N_mex = 84, 1843 mood-while-eating reports) and eight countries (N_mul = 678, 329K mood reports incl. 24K mood-while-eating reports), containing both passive smartphone sensing and self-report data. Our results indicate that generic mood inference models decline in performance in certain contexts, such as when eating. Additionally, we found that population-level (non-personalized) and hybrid (partially personalized) modeling techniques were inadequate for the commonly used three-class mood inference task (positive, neutral, negative). Furthermore, we found that user-level modeling was challenging for the majority of participants due to a lack of sufficient labels and data from the negative class. To address these limitations, we employed a novel community-based approach for personalization by building models with data from a set of similar users to a target user