Suchergebnisse

Purpose. To determine the barriers and facilitators associated with willingness to use personal health information management (PHIM) systems to support an existing worksite wellness program (WWP). Design. The study design involved a Web-based survey. Setting. The study setting was a regional hospital. Subjects. Hospital employees comprised the study subjects. Measures. Willingness, barriers, and facilitators associated with PHIM were measured. Analysis. Bivariate logit models were used to model two binary dependent variables. One model predicted the likelihood of believing PHIM systems would positively affect overall health and willingness to use. Another predicted the likelihood of worrying about online security and not believing PHIM systems would benefit health goals. Results. Based on 333 responses, believing PHIM systems would positively affect health was highly associated with willingness to use PHIM systems (p < .01). Those comfortable online were 7.22 times more willing to use PHIM systems. Participants in exercise-based components of WWPs were 3.03 times more likely to be willing to use PHIM systems. Those who worried about online security were 5.03 times more likely to believe PHIM systems would not help obtain health goals. Conclusions. Comfort with personal health information online and exercise-based WWP experience was associated with willingness to use PHIM systems. However, nutrition-based WWPs did not have similar effects. Implementation barriers relate to technology anxiety and trust in security, as well as experience with specific WWP activities. Identifying differences between WWP components and addressing technology concerns before implementation of PHIM systems into WWPs may facilitate improved adoption and usage.

Objective: The aim of this study was to evaluate the effects of secondary tasks on the driving performance of individuals with mild traumatic brain injuries (TBIs). Background: Studies suggest detrimental impacts of driving with TBI or while distracted but the impact of driver distraction on TBI drivers is not well documented. Method: Bayesian regression models were used to estimate the effect of relatively simple secondary tasks on driving performance of TBI and healthy control (HC) drivers. A driving simulator was used to develop prior distribution of task effects on driving performance for HCs. An on-road study was conducted with TBI and HC drivers to generate effect estimates for the posterior distributions. The Bayesian models were also compared to frequentist models. Results: During a coin-sorting task, all drivers exhibited larger maximum lateral acceleration and larger standard deviation of speed than in a baseline driving segment. There were no significant driving performance differences between the TBI and the HC drivers during the tasks. Across all tasks, TBI drivers spent more time looking at the tasks and made more frequent glances toward the tasks. Conclusions: The findings show that even drivers with mild TBI have significantly longer and more glances toward the tasks compared to the HCs. Application: This study demonstrates a Bayesian approach and how the results differ from frequentist statistics. Using prior distributions in a Bayesian model helps account for the probabilities associated with otherwise unknown parameters. This method strengthens the Bayesian parameter estimates compared to that of a frequentist model.

Objective: The purpose of this research was to compare gesture-function mappings for experts and novices using a 3D, vision-based, gestural input system when exposed to the same context of anesthesia tasks in the operating room (OR). Background: 3D, vision-based, gestural input systems can serve as a natural way to interact with computers and are potentially useful in sterile environments (e.g., ORs) to limit the spread of bacteria. Anesthesia providers' hands have been linked to bacterial transfer in the OR, but a gestural input system for anesthetic tasks has not been investigated. Methods: A repeated-measures study was conducted with two cohorts: anesthesia providers (i.e., experts) ( N = 16) and students (i.e., novices) ( N = 30). Participants chose gestures for 10 anesthetic functions across three blocks to determine intuitive gesture-function mappings. Reaction time was collected as a complementary measure for understanding the mappings. Results: The two gesture-function mapping sets showed some similarities and differences. The gesture mappings of the anesthesia providers showed a relationship to physical components in the anesthesia environment that were not seen in the students' gestures. The students also exhibited evidence related to longer reaction times compared to the anesthesia providers. Conclusion: Domain expertise is influential when creating gesture-function mappings. However, both experts and novices should be able to use a gesture system intuitively, so development methods need to be refined for considering the needs of different user groups. Application: The development of a touchless interface for perioperative anesthesia may reduce bacterial contamination and eventually offer a reduced risk of infection to patients.