ABSTRACT
BACKGROUND: There is a steep increase in the consumer use of complementary alternative medicine (CAM), with many users unaware of the need to inform their health care providers. Various predictors including psychosocial factors such as beliefs and behavior have been accounted for preference toward CAM use, with varying results. METHODS: This study investigates the belief and attitude regarding preference toward CAM use among the Malaysian population by using a questionnaire-based, cross-sectional study. RESULTS: A large majority of the 1,009 respondents admitted to taking at least one type of CAM (n=730, 72.3%). Only 20 (1.9%) respondents were found to have negative beliefs (total score <35), 4 (0.4%) respondents had neutral beliefs (total score =35), and 985 (97.6%) respondents had positive belief toward CAM (total score >36). A total of 507 (50.2%) respondents were categorized as having a negative CAM attitude, while 502 (49.8%) respondents were categorized as having a positive CAM attitude. It was demonstrated that there was a positive correlation between belief and attitude score (ρ=0.409, P<0.001). Therefore, the higher the belief in CAM, the more positive the attitude was toward CAM. Those who were using CAM showed a stronger belief (P=0.002), with a more positive attitude (P<0.001) toward it, than those who were not using CAM. CONCLUSION: Identifying belief regarding preference toward CAM use among the public could potentially reveal those with a higher tendency to use CAM. This is important as not everyone feels the need to reveal the use of CAM to their health care providers, which could lead to serious repercussions such as interactions and adverse effects.
ABSTRACT
Thoracic electrical impedance tomography (EIT) is a noninvasive, radiation-free monitoring technique whose aim is to reconstruct a cross-sectional image of the internal spatial distribution of conductivity from electrical measurements made by injecting small alternating currents via an electrode array placed on the surface of the thorax. The purpose of this paper is to discuss the fundamentals of EIT and demonstrate the principles of mechanical ventilation, lung recruitment, and EIT imaging on a comprehensive physiological model, which combines a model of respiratory mechanics, a model of the human lung absolute resistivity as a function of air content, and a 2-D finite-element mesh of the thorax to simulate EIT image reconstruction during mechanical ventilation. The overall model gives a good understanding of respiratory physiology and EIT monitoring techniques in mechanically ventilated patients. The model proposed here was able to reproduce consistent images of ventilation distribution in simulated acutely injured and collapsed lung conditions. A new advisory system architecture integrating a previously developed data-driven physiological model for continuous and noninvasive predictions of blood gas parameters with the regional lung function data/information generated from absolute EIT (aEIT) is proposed for monitoring and ventilator therapy management of critical care patients.
