Designing ubiquitous computing for reflection and learning in diabetes management

This dissertation proposes principles for the design of ubiquitous health monitoring applications that support reflection and learning in context of diabetes management. Due to the high individual differences between diabetes cases, each affected individual must find the optimal combination of lifestyle alterations and medication through reflective analysis of personal diseases history. This dissertation advocates using technology to enable individuals' proactive engagement in monitoring of their health. In particular, it proposes promoting individuals' engagement in reflection by exploiting breakdowns in individuals' routines or understanding; supporting continuity in thinking that leads to a systematic refinement of ideas; and supporting articulation of thoughts and understanding that helps to transform insights into knowledge. The empirical evidence for these principles was gathered thought the deployment studies of three ubiquitous computing applications that help individuals with diabetes in management of their diseases. These deployment studies demonstrated that technology for reflection helps individuals achieve their personal disease management goals, such as diet goals. In addition, they showed that using technology helps individuals embrace a proactive attitude towards their health indicated by their adoption of the internal locus of control.Ph.D.Committee Chair: Elizabeth D. Mynatt; Committee Member: Abowd, Gregory; Committee Member: Bruckman, Amy; Committee Member: Dourish, Paul; Committee Member: Nersessian, Nanc

Enabling Personalized Decision Support with Patient-Generated Data and Attributable Components

Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition falls short, but not all ML-generated information is of equal utility for making health-related decisions. We develop and apply attributable components analysis (ACA), a method inspired by optimal transport theory, to type 2 diabetes self-monitoring data to identify patterns of association between nutrition and blood glucose control. In comparison with linear regression, we found that ACA offers a number of characteristics that make it promising for use in decision support applications. For example, ACA was able to identify non-linear relationships, was more robust to outliers, and offered broader and more expressive uncertainty estimates. In addition, our results highlight a tradeoff between model accuracy and interpretability, and we discuss implications for ML-driven decision support systems

Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype

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

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

Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype

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

Interacting with eHealth - Towards grand challenges for HCI

While health records are increasingly stored electronically, we, as citizens, have little access to this data about ourselves. We are not used to thinking of these official records either as ours or as useful to us. We increasingly turn to the Web, however, to query any ache, pain or health goal we may have before consulting with health care professionals. Likewise, for proactive health care such as nutrition or fitness, or to find fellow-sufferers for post diagnosis support, we turn to online resources. There is a potential disconnect between points at which professional and lay eHealth data and resources intersect for preventative or proactive health care. Such gaps in information sharing may have direct impact on practices we decide to take up, the care we seek, or the support professionals offer. In this panel, we consider several places within proactive, preventative health care in particular HCI has a role towards enhancing health knowledge discovery and health support interaction. Our goal is to demonstrate how now is the time for eHealth to come to the forefront of the HCI research agenda