The GuideView System for Interactive, Structured, Multi-modal Delivery of Clinical Guidelines

GuideView is a computerized clinical guideline system which delivers clinical guidelines in an easy-to-understand and easy-to-use package. It may potentially enhance the quality of medical care or allow non-medical personnel to provide acceptable levels of care in situations where physicians or nurses may not be available. Such a system can be very valuable during space flight missions when a physician is not readily available, or perhaps the designated medical personnel is unable to provide care. Complex clinical guidelines are broken into simple steps. At each step clinical information is presented in multiple modes, including voice,audio, text, pictures, and video. Users can respond via mouse clicks or via voice navigation. GuideView can also interact with medical sensors using wireless or wired connections. The system's interface is illustrated and the results of a usability study are presented

Formative Evaluation to Determine Facilitators and Barriers to Nurse-driven Implementation: Designing an Inpatient mHealth Intervention to Support Smoking Cessation

The inpatient setting is often a missed opportunity for the introduction of technology to promote health using behavioral techniques. Nurses are stakeholders in the implementation of technology for patients in the inpatient setting and are essential for the determination of feasibility and relevance. The objective of this study was to identify facilitators and barriers for introduction of health-related patient technology, and specifically the appropriateness of mobile health (mHealth) technology in the hospital setting as identified by nurse leaders and staff. Methods of formative evaluation included nurse leader and staff semi-structured interviews and qualitative analysis. Nurses are comfortable with patients using mHealth technology in the inpatient setting. Facilitators for the introduction of technology to hospitalized patients were identified. Based on the formative evaluation findings, we developed an Implementation Program for mHealth technology introduction in the inpatient setting

The Intersection of Persuasive System Design and Personalization in Mobile Health: Statistical Evaluation

Background: Persuasive technology is an umbrella term that encompasses software (eg, mobile apps) or hardware (eg, smartwatches) designed to influence users to perform preferable behavior once or on a long-term basis. Considering the ubiquitous nature of mobile devices across all socioeconomic groups, user behavior modification thrives under the personalized care that persuasive technology can offer. However, there is no guidance for developing personalized persuasive technologies based on the psychological characteristics of users. Objective: This study examined the role that psychological characteristics play in interpreted mobile health (mHealth) screen perceived persuasiveness. In addition, this study aims to explore how users’ psychological characteristics drive the perceived persuasiveness of digital health technologies in an effort to assist developers and researchers of digital health technologies by creating more engaging solutions. Methods: An experiment was designed to evaluate how psychological characteristics (self-efficacy, health consciousness, health motivation, and the Big Five personality traits) affect the perceived persuasiveness of digital health technologies, using the persuasive system design framework. Participants (n=262) were recruited by Qualtrics International, Inc, using the web-based survey system of the XM Research Service. This experiment involved a survey-based design with a series of 25 mHealth app screens that featured the use of persuasive principles, with a focus on physical activity. Exploratory factor analysis and linear regression were used to evaluate the multifaceted needs of digital health users based on their psychological characteristics. Results: The results imply that an individual user’s psychological characteristics (self-efficacy, health consciousness, health motivation, and extraversion) affect interpreted mHealth screen perceived persuasiveness, and combinations of persuasive principles and psychological characteristics lead to greater perceived persuasiveness. The F test (ie, ANOVA) for model 1 was significant (F9,6540=191.806; PR2 of 0.208, indicating that the demographic variables explained 20.8% of the variance in perceived persuasiveness. Gender was a significant predictor, with women having higher perceived persuasiveness (P=.008) relative to men. Age was a significant predictor of perceived persuasiveness with individuals aged 40 to 59 years (PPF13,6536=341.035; PR2 of 0.403, indicating that the demographic variables self-efficacy, health consciousness, health motivation, and extraversion together explained 40.3% of the variance in perceived persuasiveness. Conclusions: This study evaluates the role that psychological characteristics play in interpreted mHealth screen perceived persuasiveness. Findings indicate that self-efficacy, health consciousness, health motivation, extraversion, gender, age, and education significantly influence the perceived persuasiveness of digital health technologies. Moreover, this study showed that varying combinations of psychological characteristics and demographic variables affected the perceived persuasiveness of the primary persuasive technology category

Cytoview: development of a cell modelling framework

The biological cell, a natural self-contained unit of prime biological importance, is an enormously complex machine that can be understood at many levels. A higher-level perspective of the entire cell requires integration of various features into coherent, biologically meaningful descriptions. There are some efforts to model cells based on their genome, proteome or metabolome descriptions. However, there are no established methods as yet to describe cell morphologies, capture similarities and differences between different cells or between healthy and disease states. Here we report a framework to model various aspects of a cell and integrate knowledge encoded at different levels of abstraction, with cell morphologies at one end to atomic structures at the other. The different issues that have been addressed are ontologies, feature description and model building. The framework describes dotted representations and tree data structures to integrate diverse pieces of data and parametric models enabling size, shape and location descriptions. The framework serves as a first step in integrating different levels of data available for a biological cell and has the potential to lead to development of computational models in our pursuit to model cell structure and function, from which several applications can flow out

Crossing the Chasm between Clinical Evidence and Clinical Practice: Persuasive Technology and Translational Science

Abstract. Translational Science is receiving increasing attention in order to ac- celerate the process of developing useful clinical interventions starting with basic biological discoveries. The stages in translation are denoted T0, T1, T2, T3, T4. The four transitions between stages can pose formidable difficulties and are called the four ‘Valleys of Death’. In this paper we suggest that the method- ologies of Persuasive Technology and Behavior Change Support Systems can provide a conceptual and theoretical framework for crossing the valley between T2 (Clinical Research) and T3 (Clinical Implementation). We present several studies that provide intriguing evidence for this suggestion. Keywords: Translational Science, Persuasive Technology, Behavior Change Support System

Translational Research in Space Exploration

This viewgraph presentation reviews NASA's role in medical translational research, and the importance in research for space exploration. The application of medical research for space exploration translates to health care in space medicine, and on earth

The Integrated Medical Model: A Risk Assessment and Decision Support Tool for Space Flight Medical Systems

The Integrated Medical Model (IMM) is a decision support tool that is useful to mission planners and medical system designers in assessing risks and designing medical systems for space flight missions. The IMM provides an evidence based approach for optimizing medical resources and minimizing risks within space flight operational constraints. The mathematical relationships among mission and crew profiles, medical condition incidence data, in-flight medical resources, potential crew functional impairments, and clinical end-states are established to determine probable mission outcomes. Stochastic computational methods are used to forecast probability distributions of crew health and medical resource utilization, as well as estimates of medical evacuation and loss of crew life. The IMM has been used in support of the International Space Station (ISS) medical kit redesign, the medical component of the ISS Probabilistic Risk Assessment, and the development of the Constellation Medical Conditions List. The IMM also will be used to refine medical requirements for the Constellation program. The IMM outputs for ISS and Constellation design reference missions will be presented to demonstrate the potential of the IMM in assessing risks, planning missions, and designing medical systems. The implementation of the IMM verification and validation plan will be reviewed. Additional planned capabilities of the IMM, including optimization techniques and the inclusion of a mission timeline, will be discussed. Given the space flight constraints of mass, volume, and crew medical training, the IMM is a valuable risk assessment and decision support tool for medical system design and mission planning

The Integrated Medical Model

The goals of the Integrated Medical Model (IMM) are to develop an integrated, quantified, evidence-based decision support tool useful to crew health and mission planners and to help align science, technology, and operational activities intended to optimize crew health, safety, and mission success. Presentation slides address scope and approach, beneficiaries of IMM capabilities, history, risk components, conceptual models, development steps, and the evidence base. Space adaptation syndrome is used to demonstrate the model's capabilities