Factors Predicting Nurse Intent and Status Regarding Pap Smear Examination in Taiwan: a Cross-sectional Survey.

BACKGROUND: Nurses are the most visible, frontline personnel providing health education to patients. In particular, nurse experience with Pap examinations have the potential to influence women's attitudes toward screening for cervical cancer. However, nurses in Taiwan have lower rates of Pap testing than the general population. Understanding the factors predicting nurse intent to have a Pap exam and Pap exam status would inform interventions and policies to increase their Pap exam uptake. Therefore, the present study was undertaken. MATERIALS AND METHODS: Data were collected by questionnaire from a convenient sample of 504 nurses at a regional hospital in central Taiwan between August and October 2011 and analyzed by descriptive statistics, confirmatory factor analysis, and logistic regression. RESULTS: Nurse intention to have a Pap exam was predicted by younger age, less negative attitudes toward Pap exams, and greater influence of others recommendations. However, nurses were more likely to actually have had a Pap exam if they were older, married, had sexual experience, and had a high intention to have a Pap exam. CONCLUSIONS: Nurses who are younger than 34 years old, unmarried, sexually inexperienced, and with low intention to have a Pap exam should be targeted with interventions to educate them not only about the importance of Pap exams in detecting cervical cancer, but also about strategies to decrease pain and embarrassment during exams. Nurses with less negative attitudes and experiences related to Pap exams would serve as role models to persuade women to have Pap exams, thus increasing the uptake rate of Pap exams in Taiwan.

Wireless and wearable EEG system for evaluating driver vigilance.

Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver's vigilance in real time.

EEG-based learning system for online motion sickness level estimation in a dynamic vehicle environment.

Motion sickness is a common experience for many people. Several previous researches indicated that motion sickness has a negative effect on driving performance and sometimes leads to serious traffic accidents because of a decline in a person's ability to maintain self-control. This safety issue has motivated us to find a way to prevent vehicle accidents. Our target was to determine a set of valid motion sickness indicators that would predict the occurrence of a person's motion sickness as soon as possible. A successful method for the early detection of motion sickness will help us to construct a cognitive monitoring system. Such a monitoring system can alert people before they become sick and prevent them from being distracted by various motion sickness symptoms while driving or riding in a car. In our past researches, we investigated the physiological changes that occur during the transition of a passenger's cognitive state using electroencephalography (EEG) power spectrum analysis, and we found that the EEG power responses in the left and right motors, parietal, lateral occipital, and occipital midline brain areas were more highly correlated to subjective sickness levels than other brain areas. In this paper, we propose the use of a self-organizing neural fuzzy inference network (SONFIN) to estimate a driver's/passenger's sickness level based on EEG features that have been extracted online from five motion sickness-related brain areas, while either in real or virtual vehicle environments. The results show that our proposed learning system is capable of extracting a set of valid motion sickness indicators that originated from EEG dynamics, and through SONFIN, a neuro-fuzzy prediction model, we successfully translated the set of motion sickness indicators into motion sickness levels. The overall performance of this proposed EEG-based learning system can achieve an average prediction accuracy of ~82%.