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. ^

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.^

Understanding the Influence of Photon Energy on 6MV Non Reference Dosimetry and CT Dosimetry using TLD and OSLD

Measurement of the absorbed dose from ionizing radiation in medical applications is an essential component to providing safe and reproducible patient care. There are a wide variety of tools available for measuring radiation dose; this work focuses on the characterization of two common, solid-state dosimeters in medical applications: thermoluminescent dosimeters (TLD) and optically stimulated luminescent dosimeters (OSLD). There were two main objectives to this work. The first objective was to evaluate the energy dependence of TLD and OSLD for non-reference measurement conditions in a radiotherapy environment. The second objective was to fully characterize the OSLD nanoDot in a CT environment, and to provide validated calibration procedures for CT dose measurement using OSLD. Current protocols for dose measurement using TLD and OSLD generally assume a constant photon energy spectrum within a nominal beam energy regardless of measurement location, tissue composition, or changes in beam parameters. Variations in the energy spectrum of therapeutic photon beams may impact the response of TLD and OSLD and could thereby result in an incorrect measure of dose unless these differences are accounted for. In this work, we used a Monte Carlo based model to simulate variations in the photon energy spectra of a Varian 6MV beam; then evaluated the impact of the perturbations in energy spectra on the response of both TLD and OSLD using Burlin Cavity Theory. Energy response correction factors were determined for a range of conditions and compared to measured correction factors with good agreement. When using OSLD for dose measurement in a diagnostic imaging environment, photon energy spectra are often referenced to a therapy-energy or orthovoltage photon beam – commonly 250kVp, Co-60, or even 6MV, where the spectra are substantially different. Appropriate calibration techniques specifically for the OSLD nanoDot in a CT environment have not been presented in the literature; furthermore the dependence of the energy response of the calibration energy has not been emphasized. The results of this work include detailed calibration procedures for CT dosimetry using OSLD, and a full characterization of this dosimetry system in a low-dose, low-energy setting.