Advancing Models and Theories for Digital Behavior Change Interventions

To be suitable for informing digital behavior change interventions, theories and models of behavior change need to capture individual variation and changes over time. The aim of this paper is to provide recommendations for development of models and theories that are informed by, and can inform, digital behavior change interventions based on discussions by international experts, including behavioral, computer, and health scientists and engineers. The proposed framework stipulates the use of a state-space representation to define when, where, for whom, and in what state for that person, an intervention will produce a targeted effect. The "state" is that of the individual based on multiple variables that define the "space" when a mechanism of action may produce the effect. A state-space representation can be used to help guide theorizing and identify crossdisciplinary methodologic strategies for improving measurement, experimental design, and analysis that can feasibly match the complexity of real-world behavior change via digital behavior change interventions

Persuasive Health:Back to the Future

With individual behaviour and lifestyle determining 30-50% of people’s health, research and supportive technology for affecting behaviour alteration remain urgently needed. Most existing persuasive systems are designed to persuade a user to change a finite set of behaviours to achieve a specific goal. However, if the user’s situation or goal changes, such systems cannot adapt to the changes. A much more robust type of persuasive systems is needed today to enable adequate health navigation and to empower people to face and change their own realities in terms of a large variety of health behaviours and lifestyles. In this paper, we provide a perspective on the impressive body of work contributed over the past 15 years, to better look into the future of persuasive health and to the opportunities a broader theoretical framework and practical methodologies may bring about. We present a taxonomy that attempts to explain the contributions in this field including health behaviour theory, cybernetic action behaviour models, social cognitive theory, and control theory. We identify potentially promising approaches to advance persuasive health’s efficacy in empowering individuals to improve their own health outcomes

Pharmacological and non-pharmacological treatments for the Neonatal Abstinence Syndrome (NAS).

Neonatal abstinence syndrome is defined by signs and symptoms of withdrawal that infants develop after intrauterine maternal drug exposure. All infants with documented in utero opioid exposure, or a high pre-test probability of exposure should have monitoring with a standard assessment instrument such as a Finnegan Score. A Finnegan score of \u3e8 is suggestive of opioid exposure, even in the absence of declared use during pregnancy. At least half of infants in most locales can be treated without the use of pharmacologic means. For this reason, symptom scores will drive the decision for pharmacologic therapy. Nevertheless, all infants, regardless of initial manifestations, should be first be managed with non-pharmacologic approaches which in turn, should not be considered as the sole alternative to drug therapy, but rather, as the base upon which all patients are treated. Those who continue to have symptoms despite supportive care should be pharmacologically treated, which in the most severe cases, is life-saving

Data Informed Health Simulation Modeling

Combining reliable data with dynamic models can enhance the understanding of health-related phenomena. Smartphone sensor data characterizing discrete states is often suitable for analysis with machine learning classifiers. For dynamic models with continuous states, high-velocity data also serves an important role in model parameterization and calibration. Particle filtering (PF), combined with dynamic models, can support accurate recurrent estimation of continuous system state. This thesis explored these and related ideas with several case studies. The first employed multivariate Hidden Markov models (HMMs) to identify smoking intervals, using time-series of smartphone-based sensor data. Findings demonstrated that multivariate HMMs can achieve notable accuracy in classifying smoking state, with performance being strongly elevated by appropriate data conditioning. Reflecting the advantages of dynamic simulation models, this thesis has contributed two applications of articulated dynamic models: An agent-based model (ABM) of smoking and E-Cigarette use and a hybrid multi-scale model of diabetes in pregnancy (DIP). The ABM of smoking and E-Cigarette use, informed by cross-sectional data, supports investigations of smoking behavior change in light of the influence of social networks and E-Cigarette use. The DIP model was evidenced by both longitudinal and cross-sectional data, and is notable for its use of interwoven ABM, system dynamics (SD), and discrete event simulation elements to explore the interaction of risk factors, coupled dynamics of glycemia regulation, and intervention tradeoffs to address the growing incidence of DIP in the Australia Capital Territory. The final study applied PF with an SD model of mosquito development to estimate the underlying Culex mosquito population using various direct observations, including time series of weather-related factors and mosquito trap counts. The results demonstrate the effectiveness of PF in regrounding the states and evolving model parameters based on incoming observations. Using PF in the context of automated model calibration allows optimization of the values of parameters to markedly reduce model discrepancy. Collectively, the thesis demonstrates how characteristics and availability of data can influence model structure and scope, how dynamic model structure directly affects the ways that data can be used, and how advanced analysis methods for calibration and filtering can enhance model accuracy and versatility

Optimizing postprandial glucose management in adults with insulin-requiring diabetes: Report and recommendations

Faster-acting insulins, new noninsulin drug classes, more flexible insulin-delivery systems, and improved continuous glucose monitoring devices offer unprecedented opportunities to improve postprandial glucose (PPG) management and overall care for adults with insulin-treated diabetes. These developments led the Endocrine Society to convene a working panel of diabetes experts in December 2018 to assess the current state of PPG management, identify innovative ways to improve self-management and quality of life, and align best practices to current and emerging treatment and monitoring options. Drawing on current research and collective clinical experience, we considered the following issues for the ∼200 million adults worldwide with type 1 and insulin-requiring type 2 diabetes: (i) the role of PPG management in reducing the risk of diabetes complications; (ii) barriers preventing effective PPG management; (iii) strategies to reduce PPG excursions and improve patient quality of life; and (iv) education and clinical tools to support endocrinologists in improving PPG management. We concluded that managing PPG to minimize or prevent diabetes-related complications will require elucidating fundamental questions about optimal ways to quantify and clinically assess the metabolic dysregulation and consequences of the abnormal postprandial state in diabetes and recommend research strategies to address these questions. We also identified practical strategies and tools that are already available to reduce barriers to effective PPG management, optimize use of new and emerging clinical tools, and improve patient self-management and quality of life

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations