Large language models (LLMs) have been increasingly adopted to support patients' healthcare-seeking in recent years. While prior patient-centered studies have examined the capabilities and experience of LLM-based tools in specific health-related tasks such as information-seeking, diagnosis, or decision-supporting, the inherently longitudinal nature of healthcare in real-world practice has been underexplored. This paper presents a four-week diary study with 25 patients to examine LLMs' roles across healthcare-seeking trajectories. Our analysis reveals that patients integrate LLMs not just as simple decision-support tools, but as dynamic companions that scaffold their journey across behavioral, informational, emotional, and cognitive levels. Meanwhile, patients actively assign diverse socio-technical meanings to LLMs, altering the traditional dynamics of agency, trust, and power in patient-provider relationships. Drawing from these findings, we conceptualize future LLMs as a longitudinal boundary companion that continuously mediates between patients and clinicians throughout longitudinal healthcare-seeking trajectories.

OBJECTIVE: This study examined the use of machine learning (ML) and domain-specific enrichment in patient-generated health data, in the form of free-text meal logs, to classify meals on alignment with different nutritional goals. MATERIALS AND METHODS: We used a dataset of over 3000 meal records collected by 114 individuals from a diverse, low-income community in a major US city using a mobile app. Registered dietitians (RDs) provided expert judgment for meal-goal alignment, used as the "gold-standard" for evaluation. Using text embeddings (TF-IDF and BERT) and domain-specific enrichment information (ontologies, ingredient parsers, and macronutrient contents) as inputs, we evaluated the performance of logistic regression and multilayer perceptron classifiers using accuracy, precision, recall, and F1 score against the gold standard and the individual's self-assessment. RESULTS: On average, individuals who logged meals achieved 0.576 accuracy of meal-goal alignment self-assessments. Even without enrichment, ML outperformed individual's self-assessments, with accuracies within 0.726-0.841 for different goals. The best-performing combination of ML classifier with enrichment achieved even higher accuracies (0.814-0.902). In general, ML classifiers with enrichment of parsed ingredients, food entities, and macronutrients information performed well across multiple nutritional goals, but there was variability in the impact of enrichment and classification algorithm on accuracy of classification for different nutritional goals. CONCLUSION: ML can utilize unstructured free-text meal logs and reliably classify whether meals align with specific nutritional goals, exceeding individuals' self-assessments, especially when incorporating nutrition domain knowledge. Our findings highlight the potential of ML analysis of patient-generated health data to support patient-centered nutrition guidance in precision healthcare.

Computational intelligence is increasingly common in interactive systems in many domains, including health. Health coaching with conversational agents (CA) can reach wide populations, but the level of computational intelligence needed for a positive coaching experience is unclear. We conducted a study with sixteen individuals with diabetes and prediabetes who used a CA for health coaching, T2 Coach. Qualitative interviews revealed that participants saw T2 Coach as reliable in helping them stay on track with self-management, appreciated the flexibility in choosing personally meaningful goals and engaging on their own terms, and felt it provided encouragement and even compared it favorably with human coaches. However, they also noted that coaching experience could be improved with more fluid conversations, more tailoring to their personal preferences and lifestyles, and more sensitivity to specific contexts, all of which require more computational intelligence. We discuss implications and design directions for more intelligent coaching CA in health.

Managing complex chronic illness is challenging due to its unpredictability. This paper explores the potential of voice for automated flare-up forecasts. We conducted a six-week speculative design study with individuals with endometriosis, tasking participants to submit daily voice recordings and symptom logs. Through focus groups, we elicited their experiences with voice capture and perceptions of its usefulness in forecasting flare-ups. Participants were enthusiastic and intrigued at the potential of flare-up forecasts through the analysis of their voice. They highlighted imagined benefits from the experience of recording in supporting emotional aspects of illness and validating both day-to-day and overall illness experiences. Participants reported that their recordings revolved around their endometriosis, suggesting that the recordings' content could further inform forecasting. We discuss potential opportunities and challenges in leveraging the voice as a data modality in human-centered AI tools that support individuals with complex chronic conditions.

Intelligent systems for self-management can help patients and improve quality of life. However, designing AI-based systems is challenging because designers need to account not only for user needs, but also for capabilities and practical constraints of underlying algorithms. We propose and implement a human-centered AI framework to align human and technological requirements and constraints that can guide design of intelligent systems for personal health. We use concepts from a machine learning technique, reinforcement learning, to elicit user needs, through directed content analysis of user interviews, and uncover practical data constraints, through analysis of "in the wild" user engagement logs from a self-monitoring app. We gather and triangulate human-machine-data requirements for a self-management tool for individuals with endometriosis - a poorly understood, complex chronic condition with no reliable treatment. We present recommendations for developing a system that aligns with needs, capabilities, and constraints from human user, data, and machine learning perspectives.

Conversational interaction, for example through chatbots, is well-suited to enable automated health coaching tools to support self-management and prevention of chronic diseases. However, chatbots in health are predominantly scripted or rule-based, which can result in a stagnant and repetitive user experience in contrast with more dynamic, data-driven chatbots in other domains. Consequently, little is known about the tradeoffs of pursuing data-driven approaches for health chatbots. We examined multiple artificial intelligence (AI) approaches to enable micro-coaching dialogs in nutrition — brief coaching conversations related to specific meals, to support achievement of nutrition goals — and compared, reinforcement learning (RL), rule-based, and scripted approaches for dialog management. While the data-driven RL chatbot succeeded in shorter, more efficient dialogs, surprisingly the simplest, scripted chatbot was rated as higher quality, despite not fulfilling its task as consistently. These results highlight tensions between scripted and more complex, data-driven approaches for chatbots in health.

When people receive advice while making difficult decisions, they often make better decisions in the moment and also increase their knowledge in the process. However, such incidental learning can only occur when people cognitively engage with the information they receive and process this information thoughtfully. How do people process the information and advice they receive from AI, and do they engage with it deeply enough to enable learning? To answer these questions, we conducted three experiments in which individuals were asked to make nutritional decisions and received simulated AI recommendations and explanations. In the first experiment, we found that when people were presented with both a recommendation and an explanation before making their choice, they made better decisions than they did when they received no such help, but they did not learn. In the second experiment, participants first made their own choice, and only then saw a recommendation and an explanation from AI; this condition also resulted in improved decisions, but no learning. However, in our third experiment, participants were presented with just an AI explanation but no recommendation and had to arrive at their own decision. This condition led to both more accurate decisions and learning gains. We hypothesize that learning gains in this condition were due to deeper engagement with explanations needed to arrive at the decisions. This work provides some of the most direct evidence to date that it may not be sufficient to include explanations together with AI-generated recommendation to ensure that people engage carefully with the AI-provided information. This work also presents one technique that enables incidental learning and, by implication, can help people process AI recommendations and explanations more carefully.

Learning macronutrient assessment skills can support improved health outcomes and overall wellbeing. We conducted two Mechanical Turk studies to investigate how users might benefit from the crowd's input in macronutrient assessment education. We first determined whether the wisdom of the crowd alone would provide users with enough insight to arrive at accurate macronutrient estimates. Next, we tested two methods of teaching macronutrient assessment skills (Comparison and Decomposition) and analyzed their effectiveness. Results from these studies indicate that while the crowd alone may not be sufficient to support this type of education, users may yet benefit from access to community-generated photos and labels while they use either the Comparison or Decomposition strategy.

Self-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants' self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.

Health coaching can be an effective intervention to support self-management of chronic conditions like diabetes, but there are not enough coaching practitioners to reach the growing population in need of support. Conversational technology, like chatbots, presents an opportunity to extend health coaching support to broader and more diverse populations. However, some have suggested that the human element is essential to health coaching and cannot be replicated with technology. In this research, we examine automated health coaching using a theory-grounded, wizard-of-oz chatbot, in comparison with text-based virtual coaching from human practitioners who start with the same protocol as the chatbot but have the freedom to embellish and adjust as needed. We found that even a scripted chatbot can create a coach-like experience for participants. While human coaches displayed advantages expressing empathy and using probing questions to tailor their support, they also encountered tremendous barriers and frustrations adapting to text-based virtual coaching. The chatbot coach had advantages in being persistent, as well as more consistently giving choices and options to foster client autonomy. We discuss implications for the design of virtual health coaching interventions.

[This corrects the article DOI: 10.1371/journal.pcbi.1005232.].

In chronic conditions, patients and providers need support in understanding and managing illness over time. Focusing on endometriosis, an enigmatic chronic condition, we conducted interviews with specialists and focus groups with patients to elicit their work in care specifically pertaining to dealing with an enigmatic disease, both independently and in partnership, and how technology could support these efforts. We found that the work to care for the illness, including reflecting on the illness experience and planning for care, is significantly compounded by the complex nature of the disease: enigmatic condition means uncertainty and frustration in care and management; the multi-factorial and systemic features of endometriosis without any guidance to interpret them overwhelm patients and providers; the different temporal resolutions of this chronic condition confuse both patients and provides; and patients and providers negotiate medical knowledge and expertise in an attempt to align their perspectives. We note how this added complexity demands that patients and providers work together to find common ground and align perspectives, and propose three design opportunities (considerations to construct a holistic picture of the patient, design features to reflect and make sense of the illness, and opportunities and mechanisms to correct misalignments and plan for care) and implications to support patients and providers in their care work. Specifically, the enigmatic nature of endometriosis necessitates complementary approaches from human-centered computing and artificial intelligence, and thus opens a number of future research avenues.

The increasing availability of health data and knowledge about computationally modeling human physiology opens new opportunities for personalized predictions in health. Yet little is known about how individuals interact and reason with personalized predictions. To explore these questions, we developed a smartphone app, GlucOracle, that uses self-tracking data of individuals with type 2 diabetes to generate personalized forecasts for post-meal blood glucose levels. We pilot-tested GlucOracle with two populations: members of an online diabetes community, knowledgeable about diabetes and technologically savvy; and individuals from a low socio-economic status community, characterized by high prevalence of diabetes, low literacy and limited experience with mobile apps. Individuals in both communities engaged with personal glucose forecasts and found them useful for adjusting immediate meal options, and planning future meals. However, the study raised new questions as to appropriate time, form, and focus of forecasts and suggested new research directions for personalized predictions in health.

BACKGROUND: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation that might result in short and long-term health complications and even death if not properly managed. Currently, there is no cure for diabetes. However, self-management of the disease, especially keeping BG in the recommended range, is central to the treatment. This includes actively tracking BG levels and managing physical activity, diet, and insulin intake. The recent advancements in diabetes technologies and self-management applications have made it easier for patients to have more access to relevant data. In this regard, the development of an artificial pancreas (a closed-loop system), personalized decision systems, and BG event alarms are becoming more apparent than ever. Techniques such as predicting BG (modeling of a personalized profile), and modeling BG dynamics are central to the development of these diabetes management technologies. The increased availability of sufficient patient historical data has paved the way for the introduction of machine learning and its application for intelligent and improved systems for diabetes management. The capability of machine learning to solve complex tasks with dynamic environment and knowledge has contributed to its success in diabetes research. MOTIVATION: Recently, machine learning and data mining have become popular, with their expanding application in diabetes research and within BG prediction services in particular. Despite the increasing and expanding popularity of machine learning applications in BG prediction services, updated reviews that map and materialize the current trends in modeling options and strategies are lacking within the context of BG prediction (modeling of personalized profile) in type 1 diabetes. OBJECTIVE: The objective of this review is to develop a compact guide regarding modeling options and strategies of machine learning and a hybrid system focusing on the prediction of BG dynamics in type 1 diabetes. The review covers machine learning approaches pertinent to the controller of an artificial pancreas (closed-loop systems), modeling of personalized profiles, personalized decision support systems, and BG alarm event applications. Generally, the review will identify, assess, analyze, and discuss the current trends of machine learning applications within these contexts. METHOD: A rigorous literature review was conducted between August 2017 and February 2018 through various online databases, including Google Scholar, PubMed, ScienceDirect, and others. Additionally, peer-reviewed journals and articles were considered. Relevant studies were first identified by reviewing the title, keywords, and abstracts as preliminary filters with our selection criteria, and then we reviewed the full texts of the articles that were found relevant. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming among the authors. RESULTS: The initial search was done by analyzing the title, abstract, and keywords. A total of 624 papers were retrieved from DBLP Computer Science (25), Diabetes Technology and Therapeutics (31), Google Scholar (193), IEEE (267), Journal of Diabetes Science and Technology (31), PubMed/Medline (27), and ScienceDirect (50). After removing duplicates from the list, 417 records remained. Then, we independently assessed and screened the articles based on the inclusion and exclusion criteria, which eliminated another 204 papers, leaving 213 relevant papers. After a full-text assessment, 55 articles were left, which were critically analyzed. The inter-rater agreement was measured using a Cohen Kappa test, and disagreements were resolved through discussion. CONCLUSION: Due to the complexity of BG dynamics, it remains difficult to achieve a universal model that produces an accurate prediction in every circumstance (i.e., hypo/eu/hyperglycemia events). Recently, machine learning techniques have received wider attention and increased popularity in diabetes research in general and BG prediction in particular, coupled with the ever-growing availability of a self-collected health data. The state-of-the-art demonstrates that various machine learning techniques have been tested to predict BG, such as recurrent neural networks, feed-forward neural networks, support vector machines, self-organizing maps, the Gaussian process, genetic algorithm and programs, deep neural networks, and others, using various group of input parameters and training algorithms. The main limitation of the current approaches is the lack of a well-defined approach to estimate carbohydrate intake, which is mainly done manually by individual users and is prone to an error that can severely affect the predictive performance. Moreover, a universal approach has not been established to estimate and quantify the approximate effect of physical activities, stress, and infections on the BG level. No researchers have assessed model predictive performance during stress and infection incidences in a free-living condition, which should be considered in future studies. Furthermore, a little has been done regarding model portability that can capture inter- and intra-variability among patients. It seems that the effect of time lags between the CGM readings and the actual BG levels is not well covered. However, in general, we foresee that these developments might foster the advancement of next-generation BG prediction algorithms, which will make a great contribution in the effort to develop the long-awaited, so-called artificial pancreas (a closed-loop system).

Type 2 Diabetes Mellitus (T2DM) is a common chronic condition that requires management of one's lifestyle, including nutrition. Critically, patients often lack a clear understanding of how everyday meals impact their blood glucose. New predictive analytics approaches can provide personalized mealtime blood glucose forecasts. While communicating forecasts can be challenging, effective strategies for doing so remain little explored. In this study, we conducted focus groups with 13 participants to identify approaches to visualizing personalized blood glucose forecasts that can promote diabetes self-management and understand key styles and visual features that resonate with individuals with diabetes. Focus groups demonstrated that individuals rely on simple heuristics and tend to take a reactive approach to their health and nutrition management. Further, the study highlighted the need for simple and explicit, yet information-rich design. Effective visualizations were found to utilize common metaphors alongside words, numbers, and colors to convey a sense of authority and encourage action and learning.

The design of personal health informatics tools has traditionally been explored in self-monitoring and behavior change. There is an unmet opportunity to leverage self- tracking of individuals and study diseases and health conditions to learn patterns across groups. An open research question, however, is how to design engaging self-tracking tools that also facilitate learning at scale. Furthermore, for conditions that are not well understood, a critical question is how to design such tools when it is unclear which data types are relevant to the disease. We outline the process of identifying design requirements for self-tracking endometriosis, a highly enigmatic and prevalent disease, through interviews (N=3), focus groups (N=27), surveys (N=741), and content analysis of an online endometriosis community (1500 posts, N=153 posters) and show value in triangulating across these methods. Finally, we discuss tensions inherent in designing self-tracking tools for individual use and population analysis, making suggestions for overcoming these tensions.

The ever-growing volume of information within online health communities (OHCs) presents an urgent need for new solutions that improve the efficiency of information organization and retrieval for their members. To meet this need, OHCs may choose to adopt off-the-shelf platforms that provide novel features for information management, but were not specifically designed to meet these communities' needs. The questions remain, however, as to the impact of these new platforms on social dynamics within OHCs and their well-being. To examine these questions, we qualitatively studied a migration of a popular OHC, focusing on diabetes self-management, between two off-the-shelf social computing platforms. Despite improving information management, the migration served as a catalyst to reveal the importance of features for identity management and closed circle communication that were not apparent to either the management or the membership of the community. We describe the study and draw implications for research and design for OHCs.

We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data assimilation can be used to forecast future glucose values, to impute previously missing glucose values, and to infer type 2 diabetes phenotypes. At the heart of data assimilation is the mechanistic model, here an endocrine model. Such models can vary in complexity, contain testable hypotheses about important mechanics that govern the system (eg, nutrition's effect on glucose), and, as such, constrain the model space, allowing for accurate estimation using very little data.

Objective: To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload. Methods: Participatory design was used to develop Glucolyzer, an interactive tool featuring hierarchical clustering and a heatmap visualization to help registered dietitians (RDs) identify associative patterns between blood glucose levels and per-meal macronutrient composition for individuals with type 2 diabetes (T2DM). Ten RDs participated in a within-subjects experiment to compare Glucolyzer to a static logbook format. For each representation, participants had 25 minutes to examine 1 month of diabetes self-monitoring data captured by an individual with T2DM and identify clinically meaningful patterns. We compared the quality and accuracy of the observations generated using each representation. Results: Participants generated 50% more observations when using Glucolyzer (98) than when using the logbook format (64) without any loss in accuracy (69% accuracy vs 62%, respectively, p = .17). Participants identified more observations that included ingredients other than carbohydrates using Glucolyzer (36% vs 16%, p = .027). Fewer RDs reported feelings of information overload using Glucolyzer compared to the logbook format. Study participants displayed variable acceptance of hierarchical clustering. Conclusions: Visual analytics have the potential to mitigate provider concerns about the volume of self-monitoring data. Glucolyzer helped dietitians identify meaningful patterns in self-monitoring data without incurring perceived information overload. Future studies should assess whether similar tools can support clinicians in personalizing behavioral interventions that improve patient outcomes.

The ubiquity of internet-based nutrition information sharing indicates an opportunity to use social computing platforms to promote nutrition literacy and healthy nutritional choices. We conducted a series of experiments with unpaid volunteers using an online Nutrition Knowledge Test. The test asked participants to examine pairs of photographed meals and identify meals higher in a specific macronutrient (e.g., carbohydrate). After each answer, participants received no feedback on the accuracy of their answers, viewed proportions of peers choosing each response, received correctness feedback from an expert dietitian with or without expert-generated explanations, or received correctness feedback with crowd-generated explanations. The results showed that neither viewing peer responses nor correctness feedback alone improved learning. However, correctness feedback with explanations (i.e., modeling) led to significant learning gains, with no significant difference between explanations generated by experts or peers. This suggests the importance of explanations in social computing-based casual learning about nutrition and the potential for scaling this approach via crowdsourcing.

Americans' health has reached a dangerous obesity epidemic from overconsumption and unhealthy food choices. In response, persuasive games for health encourage healthier lifestyles typically by providing positive reinforcement for the desired behaviors. However, positive reinforcement is only one of the many possibly effective approaches. We explore two types of message framing in a nutrition game, Monster Appetite (MA). In MA, players' choices of high or low calorie snacks impact visual appearance of their monster avatar. MA utilizes two types of health messages: subversive, which encourages players to make unhealthy choices and focuses on costs, and inoculation, which encourages players to eventually defend healthy choices and focuses on benefits. We test message framing's effect by tracking users' purchasing behavior in our online snack shop, Snackazon. The study showed that when positive messages were embedded in MA mixed with negative visuals through the monster avatars, participants exhibited better snack choices post-gameplay.

Over the years online health communities (OHCs) have become an important source of information regarding health management and a place for social interaction and emotional support. Previous research suggested that these two types of social support have intricate and complex relationships. In this paper, we report on the results from a secondary analysis of qualitative interviews conducted during several studies examining how individuals make sense of the information collected within an online forum dedicated to diabetes self-management, TuDiabetes. The analysis suggested that informational and socio-emotional needs can at times complement each other, but can also lead to contradictory priorities and expectations for OHC members. Specifically, the study suggested that there are important tensions between these two positions in regards to appropriate topics and focus of conversations, the desire for homogeneity and diversity in opinions, the perceived importance of identifying authoritative voices, and the importance of personal and health-related information in contextualizing members' posts. We discuss these tensions and draw implications for the design of future OHCs.

OBJECTIVE: The purpose of this systematic review and meta-analysis was to examine the effect of health information technology (HIT) diabetes self-management education (DSME) interventions on glycemic control in medically underserved patients. MATERIALS AND METHODS: Following an a priori protocol, 5 databases were searched. Studies were appraised for quality using the Cochrane Risk of Bias assessment. Studies reporting either hemoglobin A1c pre- and post-intervention or its change at 6 or 12 months were eligible for inclusion in the meta-analysis using random effects models. RESULTS: Thirteen studies met the criteria for the systematic review and 10 for the meta-analysis and represent data from 3257 adults with diabetes (mean age 55 years; 66% female; 74% racial/ethnic minorities). Most studies ( n = 10) reflected an unclear risk of bias. Interventions varied by HIT type: computer software without Internet ( n = 2), cellular/automated telephone ( n = 4), Internet-based ( n = 4), and telemedicine/telehealth ( n = 3). Pooled A1c decreases were found at 6 months (-0.36 (95% CI, -0.53 and -0.19]; I 2 = 35.1%, Q = 5.0), with diminishing effect at 12 months (-0.27 [95% CI, -0.49 and -0.04]; I 2 = 42.4%, Q = 10.4). DISCUSSION: Findings suggest that medically underserved patients with diabetes achieve glycemic benefit following HIT DSME interventions, with dissipating but significant effects at 12 months. Telemedicine/telehealth interventions were the most successful HIT type because they incorporated interaction with educators similar to in-person DSME. CONCLUSION: These results are similar to in-person DSME in medically underserved patients, showing that well-designed HIT DSME has the potential to increase access and improve outcomes for this vulnerable group.

Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric physics and engineering that uses Bayesian modeling to infuse data with human knowledge represented in a mechanistic model, to generate real-time, personalized, adaptable glucose forecasts; (ii) model averaging of data assimilation output; and (iii) dynamical Gaussian process model regression. The proposed data assimilation machine, the primary focus of the paper, uses a modified dual unscented Kalman filter to estimate states and parameters, personalizing the mechanistic models. Model selection is used to make a personalized model selection for the individual and their measurement characteristics. The data assimilation forecasts are empirically evaluated against actual postprandial glucose measurements captured by individuals with type 2 diabetes, and against predictions generated by experienced diabetes educators after reviewing a set of historical nutritional records and glucose measurements for the same individual. The evaluation suggests that the data assimilation forecasts compare well with specific glucose measurements and match or exceed in accuracy expert forecasts. We conclude by examining ways to present predictions as forecast-derived range quantities and evaluate the comparative advantages of these ranges.

Crowd work provides solutions to complex problems effectively, efficiently, and at low cost. Previous research showed that feedback, particularly correctness feedback can help crowd workers improve their performance; yet such feedback, particularly when generated by experts, is costly and difficult to scale. In our research we investigate approaches to facilitating continuous observational learning in crowdsourcing communities. In a study conducted with workers on Amazon Mechanical Turk, we asked workers to complete a set of tasks identifying nutritional composition of different meals. We examined workers' accuracy gains after being exposed to expert-generated feedback and to two types of peer-generated feedback: direct accuracy assessment with explanations of errors, and a comparison with solutions generated by other workers. The study further confirmed that expert-generated feedback is a powerful mechanism for facilitating learning and leads to significant gains in accuracy. However, the study also showed that comparing one's own solutions with a variety of solutions suggested by others and their comparative frequencies leads to significant gains in accuracy. This solution is particularly attractive because of its low cost, minimal impact on time and cost of job completion, and high potential for adoption by a variety of crowdsourcing platforms.

OBJECTIVE: To investigate subjective experiences and patterns of engagement with a novel electronic tool for facilitating reflection and problem solving for individuals with type 2 diabetes, Mobile Diabetes Detective (MoDD). METHODS: In this qualitative study, researchers conducted semi-structured interviews with individuals from economically disadvantaged communities and ethnic minorities who are participating in a randomized controlled trial of MoDD. The transcripts of the interviews were analyzed using inductive thematic analysis; usage logs were analyzed to determine how actively the study participants used MoDD. RESULTS: Fifteen participants in the MoDD randomized controlled trial were recruited for the qualitative interviews. Usage log analysis showed that, on average, during the 4 weeks of the study, the study participants logged into MoDD twice per week, reported 120 blood glucose readings, and set two behavioral goals. The qualitative interviews suggested that individuals used MoDD to follow the steps of the problem-solving process, from identifying problematic blood glucose patterns, to exploring behavioral triggers contributing to these patterns, to selecting alternative behaviors, to implementing these behaviors while monitoring for improvements in glycemic control. DISCUSSION: This qualitative study suggested that informatics interventions for reflection and problem solving can provide structured scaffolding for facilitating these processes by guiding users through the different steps of the problem-solving process and by providing them with context-sensitive evidence and practice-based knowledge related to diabetes self-management on each of those steps. CONCLUSION: This qualitative study suggested that MoDD was perceived as a useful tool in engaging individuals in self-monitoring, reflection, and problem solving.

OBJECTIVE: To investigate how individuals with diabetes and diabetes educators reason about data collected through self-monitoring and to draw implications for the design of data-driven self-management technologies. MATERIALS AND METHODS: Ten individuals with diabetes (six type 1 and four type 2) and 2 experienced diabetes educators were presented with a set of self-monitoring data captured by an individual with type 2 diabetes. The set included digital images of meals and their textual descriptions, and blood glucose (BG) readings captured before and after these meals. The participants were asked to review a set of meals and associated BG readings, explain differences in postprandial BG levels for these meals, and predict postprandial BG levels for the same individual for a different set of meals. Researchers compared conclusions and predictions reached by the participants with those arrived at by quantitative analysis of the collected data. RESULTS: The participants used both macronutrient composition of meals, most notably the inclusion of carbohydrates, and names of dishes and ingredients to reason about changes in postprandial BG levels. Both individuals with diabetes and diabetes educators reported difficulties in generating predictions of postprandial BG; their predictions varied in their correlations with the actual captured readings from r = 0.008 to r = 0.75. CONCLUSION: Overall, the study showed that identifying trends in the data collected with self-monitoring is a complex process, and that conclusions reached by both individuals with diabetes and diabetes educators are not always reliable. This suggests the need for new ways to facilitate individuals' reasoning with informatics interventions.

Online health communities collect vast amounts of information and opinions in regards to health and wellness management. However, these opinions are usually stored within lengthy and loosely structured discussion threads; synthesizing information in these threads can be challenging. In this mixed-methods study, grounded in the theoretical perspective of collective sensemaking, we examined patterns of communication within an online diabetes community TuDiabetes. The results of the study suggest that members of TuDiabetes often construct shared meaning through deep discussions, back and forth negotiation of perspectives, and resolution of conflicts in opinions. However, unlike participants of other sensemaking communities, members of TuDiabetes often value multiplicity of opinions rather than consensus. We use study results to draw implications for the design of computing platforms for facilitating collective sensemaking that promote construction of shared knowledge yet embrace diversity of opinions.

Personal health-tracking technologies have become a part of mainstream culture. Their growing popularity and widespread adoption present an opportunity for the design of new interventions to improve wellness and health. However, there is an increasing concern that these technologies are failing to inspire long-term adoption. In order to understand why users abandon personal health-tracking technologies, we analyzed advertisements of secondary sales of such technologies on Craigslist. We conducted iterative inductive and deductive analyses of approximately 1600 advertisements of personal health-tracking technologies posted over the course of one month across the US. We identify health motivations and rationales for abandonment and present a set of design implications. We call for improved theories that help translate between existing theories designed to explain psychological effects of health behavior change and the technologies that help people make those changes.

BACKGROUND: Self-monitoring is an integral component of many chronic diseases; however few theoretical frameworks address how individuals understand self-monitoring data and use it to guide self-management. PURPOSE: To articulate a theoretical framework of sensemaking in diabetes self-management that integrates existing scholarship with empirical data. METHODS: The proposed framework is grounded in theories of sensemaking adopted from organizational behavior, education, and human-computer interaction. To empirically validate the framework the researchers reviewed and analyzed reports on qualitative studies of diabetes self-management practices published in peer-reviewed journals from 2000 to 2015. RESULTS: The proposed framework distinguishes between sensemaking and habitual modes of self-management and identifies three essential sensemaking activities: perception of new information related to health and wellness, development of inferences that inform selection of actions, and carrying out daily activities in response to new information. The analysis of qualitative findings from 50 published reports provided ample empirical evidence for the proposed framework; however, it also identified a number of barriers to engaging in sensemaking in diabetes self-management. CONCLUSIONS: The proposed framework suggests new directions for research in diabetes self-management and for design of new informatics interventions for data-driven self-management.

Much of what is currently documented in the electronic health record is in response toincreasingly complex and prescriptive medicolegal, reimbursement, and regulatory requirements. These requirements often result in redundant data capture and cumbersome documentation processes. AMIA's 2011 Health Policy Meeting examined key issues in this arena and envisioned changes to help move toward an ideal future state of clinical data capture and documentation. The consensus of the meeting was that, in the move to a technology-enabled healthcare environment, the main purpose of documentation should be to support patient care and improved outcomes for individuals and populations and that documentation for other purposes should be generated as a byproduct of care delivery. This paper summarizes meeting deliberations, and highlights policy recommendations and research priorities. The authors recommend development of a national strategy to review and amend public policies to better support technology-enabled data capture and documentation practices.

OBJECTIVE: To understand the nature of emerging electronic documentation practices, disconnects between documentation workflows and computing systems designed to support them, and ways to improve the design of electronic documentation systems. MATERIALS AND METHODS: Time-and-motion study of resident physicians' note-writing practices using a commercial electronic health record system that includes an electronic documentation module. The study was conducted in the general medicine unit of a large academic hospital. RESULTS: During the study, 96 note-writing sessions by 11 resident physicians, resulting in close to 100 h of observations were seen. Seven of the 10 most common transitions between activities during note composition were between documenting, and gathering and reviewing patient data, and updating the plan of care. DISCUSSION: The high frequency of transitions seen in the study suggested that clinical documentation is fundamentally a synthesis activity, in which clinicians review available patient data and summarize their impressions and judgments. At the same time, most electronic health record systems are optimized to support documentation as uninterrupted composition. This mismatch leads to fragmentation in clinical work, and results in inefficiencies and workarounds. In contrast, we propose that documentation can be best supported with tools that facilitate data exploration and search for relevant information, selective reading and annotation, and composition of a note as a temporal structure. CONCLUSIONS: Time-and-motion study of clinicians' electronic documentation practices revealed a high level of fragmentation of documentation activities and frequent task transitions. Treating documentation as synthesis rather than composition suggests new possibilities for supporting it more effectively with electronic systems.

Collaborative tagging mechanisms are integral to social computing applications in a variety of domains. Their expected benefits include simplified retrieval of digital content, as well as enhanced ability of a community to makes sense of the shared content. We examine the impact of collaborative tagging in context of nutrition management. In a controlled experiment we asked individuals to assess the nutritional value of meals based on photographic images and observed the impact of different types of tags and tagging mechanisms on individuals nutritional sensemaking. The results of the study show that tags enhance individuals' ability to remember the viewed meals. However, we found that some types of tags can be detrimental to sensemaking, rather than supporting it. These findings stress the importance of tagging vocabularies and suggest a need for expert moderation of community sensemaking.

This paper analyzes a Question & Answer site for programmers, Stack Overflow, that dramatically improves on the utility and performance of Q&A systems for technical domains. Over 92% of Stack Overflow questions about expert topics are answered - in a median time of 11 minutes. Using a mixed methods approach that combines statistical data analysis with user interviews, we seek to understand this success. We argue that it is not primarily due to an a priori superior technical design, but also to the high visibility and daily involvement of the design team within the community they serve. This model of continued community leadership presents challenges to both CSCW systems research as well as to attempts to apply the Stack Overflow model to other specialized knowledge domains.

The continuing epidemics of diabetes and obesity create much need for information technologies that can help individuals engage in proactive health management. Yet many of these technologies focus on such pragmatic issues as collecting and presenting health information and modifying individuals' behavior. At the same time, researchers in clinical community argue that individuals' perception of their identity has dramatic consequences for their health behaviors. In this paper we discuss results of a deployment study of a mobile health monitoring application. We show how individuals with considerable diabetes experience found a unique way to adopt this health-monitoring application to construct and negotiate their identities as persons with a chronic disease. We argue that viewing health management from identity construction perspective opens new opportunities for research and design in technologies for health.

In the recent years, the number of individuals engaged in self-care of chronic diseases has grown exponentially. Advances in computing technologies help individuals with chronic diseases collect unprecedented volumes of health-related data. However, engaging in reflective analysis of the collected data may be challenging for the untrained individuals. We present MAHI, a health monitoring application that assists newly diagnosed individuals with diabetes in acquiring and developing reflective thinking skills through social interaction with diabetes educators. The deployment study with twenty five newly diagnosed individuals with diabetes demonstrated that MAHI significantly contributed to individuals' achievement of their diabetes management goals (changing diet). More importantly, MAHI inspired individuals to adopt Internal Locus of Control, which often leads to persistent engagement in self-care and positive health outcomes.

Chronic diseases, endemic in the rapidly aging population, are stretching the capacity of healthcare resources. Increasingly, individuals need to adopt proactive health attitudes and contribute to the management of their own health. We investigate existing diabetes self-management practices and ways in which reflection on prior actions impacts future lifestyle choices. The findings suggest that individuals generate and evaluate hypotheses regarding health implications of their actions. Thus, health-monitoring applications can assist individuals in making educated choices by facilitating discovery of correlations between their past actions and health states. Deployment of an early prototype of a health-monitoring application demonstrated the need for careful presentation techniques to promote more robust understanding and to avoid reinforcement of biases.

Historically one of the visions for human-computer symbiosis has been to augment human intelligence and extend people's cognitive abilities. In this paper, we present two visually-based systems to enhance a person's ability to flexibly control their pace while engaged in a cognitively demanding activity. In these investigations, we explore pacing interfaces that minimize the cognitive demands for assessing a current pace, provide ambient cues that can be quickly interpreted without incurring significant interruption from the current task, and place knowledge in the world to flexibly support different pacing strategies. Evaluation of our pacing interfaces shows that technology can successfully support pacing.

This paper describes the evolution of a concept, Contact Point, the research process through which it evolved, and the work context and practices which drove its evolution. Contact Point is a web-based application that helps a business manage its relationships with its customers. It can also be used within a business as a means for managing the relationship between parts of the business. In this paper we describe a study of the applicability of Contact Point to the technical services organization and field personnel of a medical device manufacturer. We found that there were opportunities to potentially reduce call volume through Contact Point. We discovered, however, that the technical service representatives sometimes filled roles other than providing information in their telephone conversations with field personnel. These functions included reassuring callers that the callers' answers to questions were correct, providing a rationale for information, and redirecting calls to other departments. The ability to share a document and collaborate in real time was viewed as very valuable. We also discovered that the field personnel need information from a variety of other people in order to do their jobs. These observations were used to enhance the next iteration of Contact Point and to develop strategies for the introduction of Contact Point to users.