An introduction to radiation protection in medicine

"Combining facets of health physics with medicine, An Introduction to Radiation Protection in Medicine covers the background of the subject and the medical situations where radiation is the tool to diagnose or treat human disease. Encouraging newcomers to the field to properly and efficiently function in a versatile and evolving work setting, it familiarizes them with the particular problems faced during the application of ionizing radiation in medicine. The text builds a fundamental knowledge base before providing practical descriptions of radiation safety in medicine. It covers basic issues related to radiation protection, including the physical science behind radiation protection and the radiobiological basis of radiation protection. The text also presents operational and managerial tools for organizing radiation safety in a medical workplace. Subsequent chapters form the core of the book, focusing on the practice of radiation protection in different medical disciplines. They explore a range of individual uses of ionizing radiation in various branches of medicine, including radiology, nuclear medicine, external beam radiotherapy, and brachytherapy. With contributions from experienced practicing physicists, this book provides essential information about dealing with radiation safety in the rapidly shifting and diverse environment of medicine."--publisher website

Calibration and validation of wearable monitors

Background Wearable monitors are increasingly being used to objectively monitor physical activity in research studies within the field of exercise science. Calibration and validation of these devices are vital to obtaining accurate data. This article is aimed primarily at the physical activity measurement specialist, although the end user who is conducting studies with these devices also may benefit from knowing about this topic. Best Practices Initially, wearable physical activity monitors should undergo unit calibration to ensure interinstrument reliability. The next step is to simultaneously collect both raw signal data (e.g., acceleration) from the wearable monitors and rates of energy expenditure, so that algorithms can be developed to convert the direct signals into energy expenditure. This process should use multiple wearable monitors and a large and diverse subject group and should include a wide range of physical activities commonly performed in daily life (from sedentary to vigorous). Future Directions New methods of calibration now use "pattern recognition" approaches to train the algorithms on various activities, and they provide estimates of energy expenditure that are much better than those previously available with the single-regression approach. Once a method of predicting energy expenditure has been established, the next step is to examine its predictive accuracy by cross-validating it in other populations. In this article, we attempt to summarize the best practices for calibration and validation of wearable physical activity monitors. Finally, we conclude with some ideas for future research ideas that will move the field of physical activity measurement forward.

Effects of short-duration and long-duration exercise on lipoprotein(a)

Purpose Most studies that use either a single exercise session, exercise training, or a cross-sectional design have failed to find a relationship between exercise and plasma lipoprotein(a) [Lp(a)] concentrations. However, a few studies investigating the effects of longer and/or more strenuous exercise have shown elevated Lp(a) concentrations, possibly as an acute-phase reactant to muscle damage. Based on the assumption that greater muscle damage would occur with exercise of longer duration, the purpose of the present study was to determine whether exercise of longer duration would increase Lp(a) concentration and creatine kinase. (CK) activity more than exercise of shorter duration. Methods Ten endurance-trained men (mean +/- SD: age, 27 +/- 6 yr; maximal oxygen consumption [(V)over dotO(2max)], 57 +/- 7 mL(.)kg(-1) min(-1)) completed two separate exercise sessions at 70% (V)over dotO(2max). One session required 900 kcal of energy expenditure (60 +/- 6 min), and the other required 1500 kcal (112 +/- 12 min). Fasted blood samples were taken immediately before (0-pre), immediately after (0-post), 1 d after (1-post), and 2 d after (2-post) each exercise session. Results CK activity increased after both exercise sessions (mean +/- SE; 800 kcal: 0-pre 55 +/- 11, 1-post 168 +/- 64 U(.)L(-1.)min(-1); 1500 kcal: 0-pre 51 +/- 5, 1-post 187 +/- 30, 2-post 123 +/- 19 U(.)L(-1.)min(-1); P < 0.05). However, median Lp(a) concentrations were not altered by either exercise session (800 kcal: 0-pre 5.0 mg(.)dL(-1), 0-post 3.2 mg(.)dL(-1), 1-post 4.0 mg(.)dL(-1), 2-post 3.4 mg(.)dL(-1); 1500 kcal: 0-pre 5.8 mg(.)dL(-1), 0-post 4.3 mg(.)dL(-1), 1-post 3.2 mg(.)dL(-1), 2-post 5.3 mg(.)dL(-1)). In addition, no relationship existed between exercise-induced changes in CK activity and Lp(a) concentration (800 kcal: r = -0.26; 1500 kcal: r = -0.02). Conclusion These results suggest that plasma Lp(a) concentration will not increase in response to minor exercise-induced muscle damage in endurance-trained runners.

Effect of a single session of exercise on lipoprotein(a)

Lipoprotein(a) (Lp(a)) is bound to apolipoprotein B-100 by disulfide linkage and is associated in the upper density range of low density lipoprotein cholesterol. Persons with elevated concentrations of Lp(a) are regarded as having an increased risk for premature coronary artery disease. Although many studies exist evaluating the effects of a single session of exercise on lipids and lipoproteins, little information is available concerning the effects of exercise on Lp(a). Therefore, the purpose of this study was to determine the effects of a single exercise session on plasma Lp(a). Twelve physically active men completed two 30-min submaximal treadmill exercise sessions: low intensity (LI, 50% VO2max) and high intensity (HI, 80% VO2max). Blood samples were obtained immediately before and after exercise. Total cholesterol (LI: before 4.22 +/- 0.26, after 4.24 +/- 0.28; HI: before 4.24 +/- 0.31, after 4.11 +/- 0.28 mmol . l(-1), mean +/- SE) and triglyceride (LI: before 1.14 +/- 0.16, after 1.06 +/- 0.16; HI: before 1.12 +/- 0.19, after 1.21 +/- 0.19 mmol . l(-1)) concentrations did not differ immediately after either exercise session, nor did Lp(a) concentrations differ immediately after either exercise session (LI: before 4.1 +/- 2.2, after 4.0 +/- 2.1; HI: before 3.9 +/- 2.2, after 3.7 +/- 2.0 mg . dl(-1)). These results suggest that neither a low nor a high intensity exercise session lasting 30 min in duration has an immediate effect on plasma Lp(a).

Using objective physical activity measures with youth : How many days of monitoring are needed?

Purpose The purpose of this study was to establish the minimal number of days of monitoring required for accelerometers to assess usual physical activity in children. Methods A total of 381 students (189 M, 192 F) wore a CSA 7164 uniaxial accelerometer for seven consecutive days. To examine age-related trends students were grouped as follows: Group I: grades 1-3 (N = 92); Group II: grades 4-6 (N = 98); Group III: grades 7-9 (N = 97); Group IV: grades 10-12 (N = 94). Average daily time spent in moderate-to-vigorous physical activity (MVPA) was calculated from minute-by-minute activity counts using the regression equation developed by Freedson et al. (1997). Results Compared with adolescents in grades 7 to 12, children in grades 1 to 6 exhibited less day-to-day variability in MVPA behavior. Spearman-Brown analysts indicated that between 4 and 5 d of monitoring would be necessary to a achieve a reliability of 0.80 in children, and between 8 and 9 d of monitoring would be necessary to achieve a reliability of 0.80 in adolescents. Within all grade levels, the 7-d monitoring protocol produced acceptable estimates of daily participation in MVPA (R = 0.76 (0.71-0.81) to 0.87 (0.84-0.90)). Compared with weekdays, children exhibited significantly higher levels of MVPA on weekends, whereas adolescents exhibited significantly lower levels of MVPA on weekends. Principal components analysis revealed two distinct time components for MVPA during the day for children (early morning, rest of the day), and three distinct time components for MVPA during the day for adolescents (morning, afternoon, early evening). Conclusions These results indicate that a 7-d monitoring protocol provides reliable estimates of usual physical activity behavior in children and adolescents and accounts for potentially important differences in weekend versus weekday activity behavior as well as differences in activity patterns within a given day.

Physical activity levels among children attending after-school programs

Purpose To describe the physical activity (PA) levels of children attending after-school programs, 2) examine PA levels in specific after-school sessions and activity contexts, and 3) evaluate after-school PA differences in groups defined by sex and weight status. Methods One hundred forty-seven students in grades 3-6 (mean age: 10.1 +/- 0.7, 54.4% male, 16.5% overweight (OW), 22.8% at-risk for OW) from seven after-school programs in the midwestern United States wore Actigraph GT1M accelerometers for the duration of their attendance to the program. PA was objectively assessed on six occasions during an academic year (three fall and three spring). Stored activity counts were uploaded to a customized data-reduction program to determine minutes of sedentary (SED), light (LPA), moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA) physical activity. Time spent in each intensity category was calculated for the duration of program attendance, as well as specific after-school sessions (e.g., free play, snack time). Results On average, participants exhibited 42.6 min of SED, 40.8 min of LPA, 13.4 min of MPA, and 5.3 min of VPA. The average accumulation of MVPA was 20.3 min. Boys exhibited higher levels of MPA, VPA, and MVPA, and lower levels of SED and LPA, than girls. OW and at-risk-for-OW students exhibited significantly less VPA than nonoverweight students, but similar levels of LPA, MPA, and MVPA. MVPA levels were significantly higher during free-play activity sessions than during organized or structured activity sessions. Conclusion After-school programs seem to be an important contributor to the PA of attending children. Nevertheless, ample room for improvement exists by making better use of existing time devoted to physical activity.

Validity of the computer science and applications (CSA) activity monitor in children

Purpose The purpose of this study was to evaluate the validity of the CSA activity monitor as a measure of children's physical activity using energy expenditure (EE) as a criterion measure. Methods Thirty subjects aged 10 to 14 performed three 5-min treadmill bouts at 3, 4, and 6 mph, respectively. While on the treadmill, subjects wore CSA (WAM 7164) activity monitors on the right and left hips. (V) over dot O-2 was monitored continuously by an automated system. EE was determined by multiplying the average (V) over dot O-2 by the caloric equivalent of the mean respiratory exchange ratio. Results Repeated measures ANOVA indicated that both CSA monitors were sensitive to changes in treadmill speed. Mean activity counts from each CSA unit were not significantly different and the intraclass reliability coefficient for the two CSA units across all speeds was 0.87. Activity counts from both CSA units were strongly correlated with EE (r = 0.86 and 0.87, P < 0.001). An EE prediction equation was developed from 20 randomly selected subjects and cross-validated on the remaining 10. The equation predicted mean EE within 0.01 kcal.min(-1). The correlation between actual and predicted values was 0.93 (P < 0.01) and the SEE was 0.93 kcal.min(-1). Conclusion These data indicate that the CSA monitor is a valid and reliable tool for quantifying treadmill walking and running in children.

Effects of child care policy and environment on physical activity

Child care centers differ systematically with respect to the quality and quantity of physical activity they provide, suggesting that center-level policies and practices, as well as the center's physical environment, are important influences on children's physical activity behavior. Purpose To summarize and critically evaluate the extant peer-reviewed literature on the influence of child care policy and environment on physical activity in preschool-aged children. Methods A computer database search identified seven relevant studies that were categorized into three broad areas: cross-sectional studies investigating the impact of selected center-level policies and practices on moderate-to-vigorous physical activity (MVPA), studies correlating specific attributes of the outdoor play environment with the level and intensity of MVPA, and studies in which a specific center-level policy or environmental attribute was experimentally manipulated and evaluated for changes in MVPA. Results Staff education and training, as well as staff behavior on the playground, seem to be salient influences on MVPA in preschoolers. Lower playground density (less children per square meter) and the presence of vegetation and open play areas also seem to be positive influences on MVPA. However, not all studies found these attributes to be significant. The availability and quality of portable play equipment, not the amount or type of fixed play equipment, significantly influenced MVPA levels. Conclusions Emerging evidence suggests that several policy and environmental factors contribute to the marked between-center variability in physical activity and sedentary behavior. Intervention studies targeting these factors are thus warranted.

Predictive validity of three ActiGraph energy expenditure equations for children

Purpose This Study evaluated the predictive validity of three previously published ActiGraph energy expenditure (EE) prediction equations developed for children and adolescents. Methods A total of 45 healthy children and adolescents (mean age: 13.7 +/- 2.6 yr) completed four 5-min activity trials (normal walking. brisk walking, easy running, and fast running) in ail indoor exercise facility. During each trial, participants were all ActiGraph accelerometer oil the right hip. EE was monitored breath by breath using the Cosmed K4b(2) portable indirect calorimetry system. Differences and associations between measured and predicted EE were assessed using dependent t-tests and Pearson correlations, respectively. Classification accuracy was assessed using percent agreement, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. Results None of the equations accurately predicted mean energy expenditure during each of the four activity trials. Each equation, however, accurately predicted mean EE in at least one activity trial. The Puyau equation accurately predicted EE during slow walking. The Trost equation accurately predicted EE during slow running. The Freedson equation accurately predicted EE during fast running. None of the three equations accurately predicted EE during brisk walking. The equations exhibited fair to excellent classification accuracy with respect to activity intensity. with the Trost equation exhibiting the highest classification accuracy and the Puyau equation exhibiting the lowest. Conclusions These data suggest that the three accelerometer prediction equations do not accurately predict EE on a minute-by-minute basis in children and adolescents during overground walking and running. The equations maybe, however, for estimating participation in moderate and vigorous activity.