Promoting cognitive support technology use and employment success among postsecondary students with traumatic brain injuries

OBJECTIVE: This article applies positive psychology principles to the purpose and objectives of a five-year, federally-funded initiative to provide cognitive support technology (CST) training and career preparatory services for undergraduate college students with mild and moderate traumatic brain injuries (TBI). METHODS: A total of 48 students with TBI have participated in the project during its first 18 months of operation - 14 of whom are military veterans with disabilities who were in the Iraq and/or Afghanistan theaters. CONCLUSION: Positive psychology interventions such as Best Possible Self, Intensely Positive Experiences, and Asset-based Assessments provide a framework for examining the activities of this multi-site development project

Test Population Selection from Weibull-Based, Monte Carlo Simulations of Fatigue Life

Fatigue life is probabilistic and not deterministic. Experimentally establishing the fatigue life of materials, components, and systems is both time consuming and costly. As a result, conclusions regarding fatigue life are often inferred from a statistically insufficient number of physical tests. A proposed methodology for comparing life results as a function of variability due to Weibull parameters, variability between successive trials, and variability due to size of the experimental population is presented. Using Monte Carlo simulation of randomly selected lives from a large Weibull distribution, the variation in the L10 fatigue life of aluminum alloy AL6061 rotating rod fatigue tests was determined as a function of population size. These results were compared to the L10 fatigue lives of small (10 each) populations from AL2024, AL7075 and AL6061. For aluminum alloy AL6061, a simple algebraic relationship was established for the upper and lower L10 fatigue life limits as a function of the number of specimens failed. For most engineering applications where less than 30 percent variability can be tolerated in the maximum and minimum values, at least 30 to 35 test samples are necessary. The variability of test results based on small sample sizes can be greater than actual differences, if any, that exists between materials and can result in erroneous conclusions. The fatigue life of AL2024 is statistically longer than AL6061 and AL7075. However, there is no statistical difference between the fatigue lives of AL6061 and AL7075 even though AL7075 had a fatigue life 30 percent greater than AL6061

Probabilistic Analysis for Comparing Fatigue Data Based on Johnson-Weibull Parameters

Leonard Johnson published a methodology for establishing the confidence that two populations of data are different. Johnson's methodology is dependent on limited combinations of test parameters (Weibull slope, mean life ratio, and degrees of freedom) and a set of complex mathematical equations. In this report, a simplified algebraic equation for confidence numbers is derived based on the original work of Johnson. The confidence numbers calculated with this equation are compared to those obtained graphically by Johnson. Using the ratios of mean life, the resultant values of confidence numbers at the 99 percent level deviate less than 1 percent from those of Johnson. At a 90 percent confidence level, the calculated values differ between +2 and 4 percent. The simplified equation is used to rank the experimental lives of three aluminum alloys (AL 2024, AL 6061, and AL 7075), each tested at three stress levels in rotating beam fatigue, analyzed using the Johnson- Weibull method, and compared to the ASTM Standard (E739 91) method of comparison. The ASTM Standard did not statistically distinguish between AL 6061 and AL 7075. However, it is possible to rank the fatigue lives of different materials with a reasonable degree of statistical certainty based on combined confidence numbers using the Johnson- Weibull analysis. AL 2024 was found to have the longest fatigue life, followed by AL 7075, and then AL 6061. The ASTM Standard and the Johnson-Weibull analysis result in the same stress-life exponent p for each of the three aluminum alloys at the median, or L(sub 50), live

Geographic Biases in Human Lyme disease Surveillance and Utility of Companion Animal Sentinel Programs: Exploratory Spatial Analysis

Background and Objectives: Mapping and exploratory spatial data analyses are ideal tools for characterizing spread and occurrence of human Lyme disease infection. Unfortunately, many mapped displays utilizing Lyme disease surveillance data are prone to bias due to a lack of consideration for geographical confounders. The objectives of our study were to 1) characterize the geographic effects that boundary and travel related biases have on visualization of human Lyme disease occurrence and 2) apply these findings to develop a more precise methodology for evaluating efficacy of animal sentinel surveillance programs in predicting incidence of human Lyme disease infection.;Methods: County-level human Lyme disease and companion animal tick surveillance data were obtained from relevant state health departments. Data were organized within Microsoft Excel spreadsheets, and sorted by relevant reporting year and county. In Study 1, boundary effects were evaluated for the region containing Kentucky, Maryland, Ohio, Pennsylvania, Virginia, and West Virginia 2010-2014, utilizing a combination of rate smoothing and local indicators of spatial autocorrelation. Trends in disease clustering over time within our multistate region were evaluated utilizing logistic generalized estimating equations. In Study 2, travel associated biases were evaluated only for West Virginia confirmed Lyme disease cases 2011-2015, utilizing a combination of paired t-test, Wilcoxon Rank Signed test, and local indicators of spatial autocorrelation. In Study 3, the efficacy of the companion animal (dog and cat) sentinel surveillance program in West Virginia 2014-2016, was evaluated utilizing a combination of ordinary least squares and spatial regression techniques as well as local indicators of spatial autocorrelation on regression residuals.;Results: Study 1. Analyses indicated statistically significant ( P = 0.05) clustering of human Lyme disease incidence over time. High-high clusters aggregated near counties bordering high incidence states, while low-low clusters aggregated near shared county borders in non-high incidence states. Study 2. Analyses indicated statistical non-equivalency using paired t-test (t = 3.99, df = 54, P = 0.0002) and the non-parametric Wilcoxon Signed Rank test (S=264, P \u3c 0.001) between total overall cases and those obtained within patient\u27s home county, suggesting significant travel-associated bias. Additionally, local indicators of spatial autocorrelation detected statistically significant ( P = 0.05) patterns of clustering in the county level proportion of cases attributable to travel. Study 3. Regression analyses identified significant associations between confirmed cases of human Lyme disease and average number of Ixodes scapularis removed from dogs (ordinary least squares (beta=0.20 P \u3c 0.001) and spatial lag (beta = 0.12, P = 0.002) models) but not cats for the period 2014-2016. Local indicators of spatial autocorrelation produced for spatial lag regression residuals indicated a decrease in model over and underestimation, but identified a higher number of statistically significant outliers than ordinary least squares regression.;Conclusions: Results of spatial and regression analyses 1) indicate significant differential clustering of incident human Lyme disease within WV and surrounding states over time; 2) suggest substantial travel-associated bias in Lyme disease case visualization within WV; and 3) strongly support the use of companion animal, and specially dog sentinel surveillance programs for estimation of human Lyme disease risk within WV. These findings suggest that geographic biases significantly affect visualization of human Lyme disease incidence and support the effectiveness of utilizing dogs as sentinel populations to estimate human risk. Findings of these three studies highlight the importance of using statistical methodologies that can accommodate the spatial structure imbedded within public health surveillance data

Identification and Characterization of Peak Activity, Environmental Variables, and Bacterial Pathogens in A. americanum L. at Ames Plantation, West Tennessee

The status of tick-borne diseases (TBD) in the southeastern United States is uncertain due to a number of factors including, but not limited to emerging pathogens, misdiagnoses, and modifications to landscapes. Ehrlichiosis and rickettiosis are two of the most common TBDs; these are caused by Ehrlichia and Rickettsia bacteria that can be transmitted by a number of different tick species. The objectives of this study were to identify Amblyomma americanum (the Lone Star tick) peak activity and habitat preferences and characterize the potential role of A. americanum in tick-borne disease cycles in southwestern Tennessee. Using vegetation drags and CO2-baited traps, ticks were collected monthly from May to September 2012 from 100 sites on the Ames Plantation Research and Education Center (Ames). Using a one-way analysis of variance, we identified the peak activity of A. americanum for adults as being in May or June and of nymphs as being bimodal with a peak in June and again in August. Trapping data were analyzed in a contingency table; results indicated significant trapping differences in the number of nymphs and adults collected by the two trapping methods. Environmental and trapping data were correlated using an ANCOVA to evaluate trapping efficacy under different environmental stressors and to identify landscapes in which A. americanum adults and nymphs are notably more abundant. Of 925 adult A. americanum screened for Ehrlichia and Rickettsia bacteria, 1.8% (n = 17) and 38% (n = 353) were PCR positive, of which 8 ticks (0.8%) were positive with both pathogens. Using ArcGIS we displayed pathogen positive A. americanum locations; calculating Moran’s I for each pathogen indicated there was no significant clustering among pathogen positive locations. The identification of pathogens and co-infections within A. americanum from western Tennessee warrants further investigations to understand the role ticks and their environment have in the distribution of TBD

A People-Centered Approach to Improving Interprofessional Communication in Health Care

poster abstractAs part of the objectives stated under the Interprofessional Collaborative Practices (IPCP) Model funded through a grant with the Health Resources and Services Administration and Indiana University School of Nursing, it was necessary to better understand the challenges around interprofessional communication across a hospital unit. To carry out this objective, research consultants from Collabo Creative, a design research company, partnered with the Renal Metabolic (B5C5) unit at IU Health Methodist. The main purpose for connecting design researchers with B5C5 was to assist the unit in utilizing a people-centered design approach in order to: 1) understand the current context of interprofessional collaboration and communication, 2) frame pertinent communication design challenges; and 3) develop solutions to improve interprofessional collaboration and communication across the B5C5 unit. Resulting from the 8-month research engagement, Collabo Creative and B5C5 identified four core challenges to interprofessional communication that appear to be relevant to other hospital units in addition to B5C5. These challenges include: 1) patient handoff of information; 2) doctor and patient two-way communication; 3) employee tensions as a result of PCA training; and 4) night-shift inclusion in plan of care. This poster will describe the people-centered design approach and methods that were used to engage B5C5, along with key findings and newly developed interprofessional communication tools resulting from the research project