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. ^

Determining appropriate measurement methods for etiological research of computing-related musculoskeletal symptoms and disorders

Two studies among college students were conducted to evaluate appropriate measurement methods for etiological research on computing-related upper extremity musculoskeletal disorders (UEMSDs). ^ A cross-sectional study among 100 graduate students evaluated the utility of symptoms surveys (a VAS scale and 5-point Likert scale) compared with two UEMSD clinical classification systems (Gerr and Moore protocols). The two symptom measures were highly concordant (Lin's rho = 0.54; Spearman's r = 0.72); the two clinical protocols were moderately concordant (Cohen's kappa = 0.50). Sensitivity and specificity, endorsed by Youden's J statistic, did not reveal much agreement between the symptoms surveys and clinical examinations. It cannot be concluded self-report symptoms surveys can be used as surrogate for clinical examinations. ^ A pilot repeated measures study conducted among 30 undergraduate students evaluated computing exposure measurement methods. Key findings are: temporal variations in symptoms, the odds of experiencing symptoms increased with every hour of computer use (adjOR = 1.1, p < .10) and every stretch break taken (adjOR = 1.3, p < .10). When measuring posture using the Computer Use Checklist, a positive association with symptoms was observed (adjOR = 1.3, p < 0.10), while measuring posture using a modified Rapid Upper Limb Assessment produced unexpected and inconsistent associations. The findings were inconclusive in identifying an appropriate posture assessment or superior conceptualization of computer use exposure. ^ A cross-sectional study of 166 graduate students evaluated the comparability of graduate students to College Computing & Health surveys administered to undergraduate students. Fifty-five percent reported computing-related pain and functional limitations. Years of computer use in graduate school and number of years in school where weekly computer use was ≥ 10 hours were associated with pain within an hour of computing in logistic regression analyses. The findings are consistent with current literature on both undergraduate and graduate students. ^

A survey of the implementation status of environmental management systems in U.S. colleges and universities, and, An environmental management system implementation model for U.S. colleges and universities

A census of 925 U.S. colleges and universities offering masters and doctorate degrees was conducted in order to study the number of elements of an environmental management system as defined by ISO 14001 possessed by small, medium and large institutions. A 30% response rate was received with 273 responses included in the final data analysis. Overall, the number of ISO 14001 elements implemented among the 273 institutions ranged from 0 to 16, with a median of 12. There was no significant association between the number of elements implemented among institutions and the size of the institution (p = 0.18; Kruskal-Wallis test) or among USEPA regions (p = 0.12; Kruskal-Wallis test). The proportion of U.S. colleges and universities that reported having implemented a structured, comprehensive environmental management system, defined by answering yes to all 16 elements, was 10% (95% C.I. 6.6%–14.1%); however 38% (95% C.I. 32.0%–43.8%) reported that they had implemented a structured, comprehensive environmental management system, while 30.0% (95% C.I. 24.7%–35.9%) are planning to implement a comprehensive environmental management system within the next five years. Stratified analyses were performed by institution size, Carnegie Classification and job title. ^ The Osnabruck model, and another under development by the South Carolina Sustainable Universities Initiative, are the only two environmental management system models that have been proposed specifically for colleges and universities, although several guides are now available. The Environmental Management System Implementation Model for U.S. Colleges and Universities developed is an adaptation of the ISO 14001 standard and USEPA recommendations and has been tailored to U.S. colleges and universities for use in streamlining the implementation process. In using this implementation model created for the U.S. research and academic setting, it is hoped that these highly specialized institutions will be provided with a clearer and more cost-effective path towards the implementation of an EMS and greater compliance with local, state and federal environmental legislation. ^

Body mass index of Vietnamese children ages 6--15 years old

Childhood overweight can increase the risk of chronic diseases later in life. To determine the prevalence, trends and determinants of overweight among children ages 6-15 years old in Vietnam, we assessed data on body mass index (BMI) and demographic and socio-economic characteristics obtained from the 1992 Vietnam Living Standard Survey (1992 VLSS), the 1997 Vietnam Living Standard Survey (1997 VLSS), and the 2000 General Nutrition Survey (2000 GNS). These surveys used multi-stage cluster sample designs to produce nationally representative samples of Vietnamese children ages 6-15 years in 1992-1993, 1997-1998 and 2000. BMI classification was determined using cut-off values set by the International Obesity Task Force (IOTF). The mean prevalence of at risk of overweight and overweight among Vietnamese children rapidly increased from 0.4% in 1992 to 2.0% in 2000, along with a high prevalence of underweight (33.4% in 2000). Increases in weight, height and BMI varied according to gender, area of residence and socioeconomic status. Age, areas of residence and education of the household head are statistically significant predictors of at risk of overweight and overweight. This study identified the prevalence and trends of weight among children crucial to understanding the prevention of child overweight in Vietnam. ^

Relationship between dietary patterns and body mass index in Hispanic children ages 4--19 years of age

Background. In over 30 years, the prevalence of overweight for children and adolescents has increased across the United States (Barlow et al., 2007; Ogden, Flegal, Carroll, & Johnson, 2002). Childhood obesity is linked with adverse physiological and psychological issues in youth and affects ethnic/minority populations in disproportionate rates (Barlow et al., 2007; Butte et al., 2006; Butte, Cai, Cole, Wilson, Fisher, Zakeri, Ellis, & Comuzzie, 2007). More importantly, overweight in children and youth tends to track into adulthood (McNaughton, Ball, Mishra, & Crawford, 2008; Ogden et al., 2002). Childhood obesity affects body functions such as the cardiovascular, respiratory, gastrointestinal, and endocrine systems, including emotional health (Barlow et al., 2007, Ogden et al., 2002). Several dietary factors have been associated with the development of obesity in children; however, these factors have not been fully elucidated, especially in ethnic/minority children. In particular, few studies have been done to determine the effects of different meal patterns on the development of obesity in children. Purpose. The purpose of the study is to examine the relationships between daily proportions of energy consumed and energy derived from fat across breakfast, lunch, dinner, and snack, and obesity among Hispanic children and adolescents. Methods. A cross-sectional design was used to evaluate the relationship between dietary patterns and overweight status in Hispanic children and adolescents 4-19 years of age who participated in the Viva La Familia Study. The goal of the Viva La Familia Study was to evaluate genetic and environmental factors affecting childhood obesity and its co-morbidities in the Hispanic population (Butte et al., 2006, 2007). The study enrolled 1030 Hispanic children and adolescents from 319 families and examined factors related to increased body weight by focusing on a multilevel analysis of extensive sociodemographic, genetic, metabolic, and behavioral data. Baseline dietary intakes of the children were collected using 24-hour recalls, and body mass index was calculated from measured height and weight, and classified using the CDC standards. Dietary data were analyzed using a GEE population-averaged panel-data model with a cluster variable family identifier to include possible correlations within related data sets. A linear regression model was used to analyze associations of dietary patterns using possible covariates, and to examine the percentage of daily energy coming from breakfast, lunch, dinner, and snack while adjusting for age, sex, and BMI z-score. Random-effects logistic regression models were used to determine the relationship of the dietary variables with obesity status and to understand if the percent energy intake (%EI) derived from fat from all meals (breakfast, lunch, dinner, and snacks) affected obesity. Results. Older children (age 4-19 years) consumed a higher percent of energy at lunch and dinner and less percent energy from snacks compared to younger children. Age was significantly associated with percentage of total energy intake (%TEI) for lunch, as well as dinner, while no association was found by gender. Percent of energy consumed from dinner significantly differed by obesity status, with obese children consuming more energy at dinner (p = 0.03), but no associations were found between percent energy from fat and obesity across all meals. Conclusions. Information from this study can be used to develop interventions that target dietary intake patterns in obesity prevention programs for Hispanic children and adolescents. In particular, intervention programs for children should target dietary patterns with energy intake that is spread throughout the day and earlier in the day. These results indicate that a longitudinal study should be used to further explore the relationship of dietary patterns and BMI in this and other populations (Dubois et al., 2008; Rodriquez & Moreno, 2006; Thompson et al., 2005; Wilson et al., in review, 2008). ^