Lena Mamykina

Design lessons from the fastest q&a site in the west

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.

Investigating health management practices of individuals with diabetes

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.

No longer wearing: investigating the abandonment of personal health-tracking technologies on craigslist

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.

The future state of clinical data capture and documentation: a report from AMIA's 2011 Policy Meeting

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. AMIAs 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.

Adopting the sensemaking perspective for chronic disease self-management

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.

Time Aura: interfaces for pacing

Historically one of the visions for human-computer symbiosis has been to augment human intelligence and extend peoples cognitive abilities. In this paper, we present two visually-based systems to enhance a persons 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.

Collective Sensemaking in Online Health Forums

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.

How Do Residents Spend Their Shift Time? A Time and Motion Study With a Particular Focus on the Use of Computers.

Purpose To understand how much time residents spend using computers compared with other activities, and what residents use computers for. Method This time and motion study was conducted in June and July 2010 at NewYork-Presbyterian/Columbia University Medical Center with seven residents (first-, second-, and third-year) on the general medicine service. An experienced observer shadowed residents during a single day shift, captured all their activities using an iPad application, and took field notes. The activities were captured using a validated taxonomy of clinical activities, expanded to describe computer-based activities with a greater level of detail. Results Residents spent 364.5 minutes (50.6%) of their shift time using computers, compared with 67.8 minutes (9.4%) interacting with patients. In addition, they spent 292.3 minutes (40.6%) talking with others in person, 186.0 minutes (25.8%) handling paper notes, 79.7 minutes (11.1%) in rounds, 80.0 minutes (11.1%) walking or waiting, and 54.0 minutes (7.5%) talking on the phone. Residents spent 685 minutes (59.6%) multitasking. Computer-based documentation activities amounted to 189.9 minutes (52.1%) of all computer-based activities time, with 128.7 minutes (35.3%) spent writing notes and 27.3 minutes (7.5%) reading notes composed by others. Conclusions The study showed that residents spent considerably more time interacting with computers (over 50% of their shift time) than in direct contact with patients (less than 10% of their shift time). Some of this may be due to an increasing reliance on computing systems for access to patient data, further exacerbated by inefficiencies in the design of the electronic health record.

Structured scaffolding for reflection and problem solving in diabetes self-management: qualitative study of mobile diabetes detective

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.

Personalized glucose forecasting for type 2 diabetes using data assimilation

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.