Clinical Outcomes of an Intensity Program for Children With Movement Challenges.

PURPOSE: Physical therapists at an outpatient pediatric facility developed and implemented an Intensity Program for children with movement challenges. The program was initiated on the basis of best evidence, parent advocacy, and clinician expertise. The purpose of this investigation is to analyze outcome data gathered from the program since 2012 to determine the effect of the program along with any specific child characteristics that were more likely to lead to positive outcomes. METHODS: A variety of outcome data were analyzed to compare preprogram performance with postprogram performance. RESULTS: Program participants made statistically significant and clinically important improvement in most outcome measures. Parents were highly satisfied with the program, including 98% of respondents who indicated that they would like to repeat their participation in the program. CONCLUSIONS: The results of this investigation suggest that many children with movement challenges are likely to benefit from participation in an Intensity Program.

A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis.

This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender.

Exploring Occupational Health Professionals Understanding and Needs in Regard to Total Worker Health (TWH).

BACKGROUND: Occupational health professionals (OHPs) are in a unique position to impact the health and well-being of employees at work and outside of work. One way of achieving this holistic health goal is to integrate the concept of Total Worker Health® (TWH) into the organization's culture. It is critical for OHPs to develop the ability to incorporate TWH into their practices, yet there are gaps in our understanding of OHP's attitudes toward change and toward TWH, their level of TWH knowledge, and the number of OHPs who have adopted TWH. METHODS: An electronic survey was administered to a national sample of 4,777. This cross-sectional study used Qualtrics to record survey responses measuring knowledge of TWH, attitude toward change, resistance to change, transformational leadership ability, perception of organizational readiness, and leadership commitment. FINDINGS: The total sample size was 253 (5.3%). Most respondents were bachelors prepared nurses (75.1%) with greater than 10 years' experience (71.5%) and employed in manufacturing (42.6%). Approximately 74% (n = 125) of respondents knew about TWH, but did not have a program in place or were unsure of the existence of one. A high percentage (74.0%) were open to implementing TWH, had favorable attitudes toward change (M = 3.9 on a 5-point Likert-type scale), but needed education on how to move forward (56.0%). CONCLUSIONS/APPLICATION TO PRACTICE: Findings suggest that most OHPs know about TWH, but generally have not adopted the TWH concept at their worksites. However, they are open to implementing TWH programs and have favorable attitudes toward change.

An Examination of Grouping and Spatial Organization Tasks for High-Dimensional Data Exploration.

How do analysts think about grouping and spatial operations? This overarching research question incorporates a number of points for investigation, including understanding how analysts begin to explore a dataset, the types of grouping/spatial structures created and the operations performed on them, the relationship between grouping and spatial structures, the decisions analysts make when exploring individual observations, and the role of external information. This work contributes the design and results of such a study, in which a group of participants are asked to organize the data contained within an unfamiliar quantitative dataset. We identify several overarching approaches taken by participants to design their organizational space, discuss the interactions performed by the participants, and propose design recommendations to improve the usability of future high-dimensional data exploration tools that make use of grouping (clustering) and spatial (dimension reduction) operations.

Putting the "I" in Interaction: Interactive Interfaces Personalized to Individuals.

Interactive data exploration and analysis is an inherently personal process. One's background, experience, interests, cognitive style, personality, and other sociotechnical factors often shape such a process, as well as the provenance of exploring, analyzing, and interpreting data. This Viewpoint posits both what personal information and how such personal information could be taken into account to design more effective visual analytic systems, a valuable and under-explored direction.

A Provenance Task Abstraction Framework.

Visual analytics tools integrate provenance recording to externalize analytic processes or user insights. Provenance can be captured on varying levels of detail, and in turn activities can be characterized from different granularities. However, current approaches do not support inferring activities that can only be characterized across multiple levels of provenance. We propose a task abstraction framework that consists of a three stage approach, composed of 1) initializing a provenance task hierarchy, 2) parsing the provenance hierarchy by using an abstraction mapping mechanism, and 3) leveraging the task hierarchy in an analytical tool. Furthermore, we identify implications to accommodate iterative refinement, context, variability, and uncertainty during all stages of the framework. We describe a use case which exemplifies our abstraction framework, demonstrating how context can influence the provenance hierarchy to support analysis. The article concludes with an agenda, raising and discussing challenges that need to be considered for successfully implementing such a framework.

SIRIUS: Dual, Symmetric, Interactive Dimension Reductions.

Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.

Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics.

Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms.

MOSBIE: a tool for comparison and analysis of rule-based biochemical models.

BACKGROUND: Mechanistic models that describe the dynamical behaviors of biochemical systems are common in computational systems biology, especially in the realm of cellular signaling. The development of families of such models, either by a single research group or by different groups working within the same area, presents significant challenges that range from identifying structural similarities and differences between models to understanding how these differences affect system dynamics. RESULTS: We present the development and features of an interactive model exploration system, MOSBIE, which provides utilities for identifying similarities and differences between models within a family. Models are clustered using a custom similarity metric, and a visual interface is provided that allows a researcher to interactively compare the structures of pairs of models as well as view simulation results. CONCLUSIONS: We illustrate the usefulness of MOSBIE via two case studies in the cell signaling domain. We also present feedback provided by domain experts and discuss the benefits, as well as the limitations, of the approach.

Tdap: the need to educate and immunize.

The resurgence of pertussis is puzzling, especially given the requirement that children complete recommended immunizations prior to school entry. Are adult carriers unknowingly infecting children? What do adult caregivers know about pertussis and the tetanus, diphtheria, and acellular pertussis (Tdap) booster vaccine? How can the incidence of pertussis in children younger than 2 years, a group most at risk for complications of the disease, be reduced? This article examines the incidence of pertussis and strategies to reduce pertussis incidence in the United States. If the need for Tdap vaccine is identified and favorably received by adults, immunization programs can be arranged at worksites to better protect infants who are most at risk for pertussis.

FixingTIM: interactive exploration of sequence and structural data to identify functional mutations in protein families.

BACKGROUND: Knowledge of the 3D structure and functionality of proteins can lead to insight into the associated cellular processes, speed up the creation of pharmaceutical products, and develop drugs that are more effective in combating disease. METHODS: We present the design and implementation of a visual mining and analysis tool to help identify protein mutations across a family of structural models and to help discover the effect of these mutations on protein function. We integrate 3D structure and sequence information in a common visual interface; multiple linked views and a computational backbone allow comparison at the molecular and atomic levels, while a novel trend-image visual abstraction allows for the sorting and mining of large collections of sequences and of their residues. RESULTS: We evaluate our approach on the triosephosphate isomerase (TIM) family structural models and sequence data and show that our tool provides an effective, scalable way to navigate a family of proteins, as well as a means to inspect the structure and sequence of individual proteins. CONCLUSIONS: The TIM application shows that our tool can assist in the navigation of families of proteins, as well as in the exploration of individual protein structures. In conjunction with domain expert knowledge, this interactive tool can help provide biophysical insight into why specific mutations affect function and potentially suggest additional modifications to the protein that could be used to rescue functionality.

Promoting health and safety virtually: key recommendations for occupational health nurses.

Nurses' use of the Internet and social media has surfaced as a critical concern requiring further exploration and consideration by all health care organizations and nursing associations. In an attempt to support this need, the American Nurses Association (2011) published six principles of social networking that offered guidance and direction for nurses. In addition, the National Council of State Boards of Nursing (2011) published a nurse's guide to using social media. Surfing the Internet and using social media for professional and personal needs is extremely common among nurses. What is concerning is when nurses do not separate their professional and personal presence in the virtual world. This article presents an Institutional Review Board-approved pilot survey that explored nurses' use of social media personally and professionally and offers recommendations specifically directed to the occupational health nurse.