Energy expenditure, body-part discomfort and mental work load among nurses

The purpose of this prospective observational field study was to present a model for measuring energy expenditure among nurses and to determine if there was a difference between the energy expenditure of nurses providing direct care to adult patients on general medical-surgical units in two major metropolitan hospitals and a recommended energy expenditure of 3.0 kcal/minute over 8 hours. One-third of the predicted cycle ergometer VO2max for the study population was used to calculate the recommended energy expenditure.^ Two methods were used to measure energy expenditure among participants during an 8 hour day shift. First, the Energy Expenditure Prediction Program (EEPP) developed by the University of Michigan Center for Ergonomics was used to calculate energy expenditure using activity recordings from observation (OEE; n = 39). The second method used ambulatory electrocardiography and the heart rate-oxygen consumption relationship (HREE; n = 20) to measure energy expenditure. It was concluded that energy expenditure among nurses can be estimated using the EEPP. Using classification systems from previous research, work load among the study population was categorized as "moderate" but was significantly less than (p = 0.021) 3.0 kcal/minute over 8 hours or 1/3 of the predicted VO2max.^ In addition, the relationships between OEE, body-part discomfort (BPCDS) and mental work load (MWI) were evaluated. The relationships between OEE/BPCDS and OEE/MWI were not significant (p = 0.062 and 0.091, respectively). Among the study population, body-part discomfort significantly increased for upper arms, mid-back, lower-back, legs and feet by mid-shift and by the end of the shift, the increase was also significant for neck and thighs.^ The study also provided documentation of a comprehensive list of nursing activities. Among the most important findings were the facts that the study population spent 23% of the workday in a bent posture, walked an average of 3.14 miles, and spent two-thirds of the shift doing activities other than direct patient care, such as paperwork and communicating with other departments. A discussion is provided regarding the ergonomic implications of these findings. ^

Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^

Genetic variability and the hypothalamic control of energy balance

Background. Obesity is a major health problem throughout the industrialized world. Despite numerous attempts to curtail the rapid growth of obesity, its incidence continues to rise. Therefore, it is crucial to better understand the etiology of obesity beyond the concept of energy balance.^ Aims. The first aim of this study was to first investigate the relationship between eating behaviors and body size. The second goal was to identify genetic variation associated with eating behaviors. Thirdly, this study aimed to examine the joint relationships between eating behavior, body size and genetic variation.^ Methods. This study utilized baseline data ascertained in young adults from the Training Interventions and Genetics of Exercise (TIGER) Study. Variables assessed included eating behavior (Emotional Eating Scale, Eating Attitudes Test-26, and the Block98 Food Frequency Questionnaire), body size (body mass index, waist and hip circumference, waist/hip ratio, and percent body fat), genetic variation in genes implicated related to the hypothalamic control of energy balance, and appropriate covariates (age, gender, race/ethnicity, smoking status, and physical activity. For the genetic association analyses, genotypes were collapsed by minor allele frequency, and haplotypes were estimated for each gene. Additionally, Bayesian networks were constructed in order to determine the relationships between genetic variation, eating behavior and body size.^ Results. We report that the EAT-26 score, Caloric intake, percent fat, fiber intake, HEAT index, and daily servings of vegetables, meats, grains, and fats were significantly associated with at least one body size measure. Multiple SNPs in 17 genes and haplotypes from 12 genes were tested for their association with body size. Variation within both DRD4 and HTR2A was found to be associated with EAT-26 score. In addition, variation in the ghrelin gene (GHRL) was significantly associated with daily Caloric intake. A significant interaction between daily servings of grains and the HEAT index and variation within the leptin receptor gene (LEPR) was shown to influence body size.^ Conclusion. This study has shown that there is a substantial genetic component to eating behavior and that genetic variation interacts with eating behavior to influence body size.^