955 resultados para Health expenditure
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A long-running issue in appetite research concerns the influence of energy expenditure on energy intake. More than 50 years ago, Otto G. Edholm proposed that "the differences between the intakes of food [of individuals] must originate in differences in the expenditure of energy". However, a relationship between energy expenditure and energy intake within any one day could not be found, although there was a correlation over 2 weeks. This issue was never resolved before interest in integrative biology was replaced by molecular biochemistry. Using a psychobiological approach, we have studied appetite control in an energy balance framework using a multi-level experimental system on a single cohort of overweight and obese human subjects. This has disclosed relationships between variables in the domains of body composition [fat-free mass (FFM), fat mass (FM)], metabolism, gastrointestinal hormones, hunger and energy intake. In this Commentary, we review our own and other data, and discuss a new formulation whereby appetite control and energy intake are regulated by energy expenditure. Specifically, we propose that FFM (the largest contributor to resting metabolic rate), but not body mass index or FM, is closely associated with self-determined meal size and daily energy intake. This formulation has implications for understanding weight regulation and the management of obesity.
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BACKGROUND/OBJECTIVEs A decline in resting energy expenditure (REE) beyond that predicted from changes in body composition has been noted following dietary-induced weight loss. However, it is unknown whether a compensatory downregulation in REE also accompanies exercise (EX)-induced weight loss, or whether this adaptive metabolic response influences energy intake (EI). SUBJECTS/METHODS Thirty overweight and obese women (body mass index (BMI)=30.6±3.6 kg/m2) completed 12 weeks of supervised aerobic EX. Body composition, metabolism, EI and metabolic-related hormones were measured at baseline, week 6 and post intervention. The metabolic adaptation (MA), that is, difference between predicted and measured REE was also calculated post intervention (MApost), with REE predicted using a regression equation generated in an independent sample of 66 overweight and obese women (BMI=31.0±3.9 kg/m2). RESULTS Although mean predicted and measured REE did not differ post intervention, 43% of participants experienced a greater-than-expected decline in REE (−102.9±77.5 kcal per day). MApost was associated with the change in leptin (r=0.47; P=0.04), and the change in resting fat (r=0.52; P=0.01) and carbohydrate oxidation (r=−0.44; P=0.02). Furthermore, MApost was also associated with the change in EI following EX (r=−0.44; P=0.01). CONCLUSIONS Marked variability existed in the adaptive metabolic response to EX. Importantly, those who experienced a downregulation in REE also experienced an upregulation in EI, indicating that the adaptive metabolic response to EX influences both physiological and behavioural components of energy balance.
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There is currently some debate about whether the energy expenditure of domestic tasks is sufficient to confer health benefits. The aim of this study was therefore to measure the energy cost of five activities commonly undertaken by mothers of young children. Seven women with at least one child younger than five years of age spent 15 minutes in each of the following activities: sitting quietly, vacuum cleaning, washing windows, walking at moderate pace (approx 5km/hour), walking with a stroller and grocery shopping in a super-market. Each of the six 'trials' was completed on the same day, in random order. A carefully calibrated portable gas analyser was used to measure oxygen uptake during each activity, and data were converted to units of energy expenditure (METS). Vacuum cleaning, washing windows and walking with and without a stroller were found to be 'moderate intensity activities' (3 to 6 METs), but supermarket shopping did not reach this criterion. The MET values for these activities were similar to those reported in the Compendium of Physical Activities (Ainsworth et al., 2000). However, the energy expenditures of walking, both with and without a stroller, were higher than those reported in the Compendium. The findings suggest that some of the tasks associated with domestic caring duties are conducted at an intensity which is sufficient to confer some health benefit. Such benefits will only accrue however if the daily duration of these activities is sufficient to meet current guidelines.
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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.
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Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.
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This study examined the tracking of selected measures of physical activity, inactivity, and fitness in a cohort of rural youth. Students (N = 181, 54.7% female, 63.5% African American) completed test batteries during their fifth-(age = 10.7 +/- 0.7 years), sixth-, and seventh-grade years. The Previous Day Physical Activity Recall (PDPAR) was used to assess 30-min blocks of vigorous physical activity (VPA), moderate-to-vigorous physical activity (MVPA), TV watching and other sedentary activities, and estimated energy expenditure (EE). Fitness measures included the PWC 170 cycle ergometer test, strength tests, triceps skinfold thickness, and BMI. Intraclass correlation coefficients (ICCs) for VPA, MVPA, and after-school EE ranged from 0.63 to 0.78. ICCs ranged from 0.49 to 0.71 for measures of inactivity and from 0.78 to 0.82 for the fitness measures. These results indicate that measures of physical activity, inactivity, and physical fitness tend to track during the transition from elementary to middle school.
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Purpose The aim of this study was to assess the predictive validity of three accelerometer prediction equations (Freedson et aL, 1997; Trost et aL, 1998; Puyau et al., 2002) for energy expenditure (EE) during overland walking and running in children and adolescents. Methods 45 healthy children and adolescents aged 10-18 completed the following protocol, each task 5-mins in duration, with a 5-min rest period in between; walking normally; walking briskly; running easily and running fast. During each task participants wore MTI (WAM 7164) Actigraphs on the left and right hips. VO2 was monitored breath by breath using the Cosmed K4b2 portable indirect calorimetry system. For each prediction equation, difference scores were calculated as EE measured minus EE predicted. The percentage of 1-min epochs correctly categorized as light (<3 METs), moderate (3-5.9 METs), and vigorous (≥6 METS) was also calculated. Results The Freedson and Trost equations consistently overestimated MET level. The level of overestimation was statistically significant across all tasks for the Freedson equation, and was significant for only the walking tasks for the Trost equation. The Puyau equation consistently underestimated AEE with the exception of the walking normally task. In terms of categorisation, the Freedson equation (72.8% agreement) demonstrated better agreement than the Puyau (60.6%). 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 overland walking and running. However, the cut points generated by these equations maybe useful for classifying activity as either, light, moderate, or vigorous.
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Background Administrative data from the Australian Pharmaceutical Benefits Scheme (PBS) showed rapid growth of esomeprazole dispensing when it was launched. Australia has universal prescription medicine coverage (the PBS), which included esomeprazole from August 2002. Free samples of new medicines are commonly provided to doctors. Objectives To determine if a relationship exists between marketing expenditure on samples and the dispensing rate for esomeprazole in Australia between June 2002 and September 2006. Methods Quarterly sample expenditures at product/brand level for proton pump inhibitors (PPIs) for Australian general practitioners were obtained for July 2002 to September 2006. Corresponding PBS dispensing data were obtained for all PPIs and converted to defined daily dose (DDD)/1000 population/day. Spending on samples was calculated as dollars per dispensed prescription and plotted against time on the Australian market. Results: Total PPI usage increased from 34.2 to 50.8 DDD/1000 population/ day over the study period. Expenditure on samples per dispensed prescription was higher when a PPI was new on the market and diminished over 5-6 years to a relatively constant level. The rapid decline in this ratio was demonstrated by a case study following esomeprazole from launch in Australia for almost 5 years clearly demonstrating the initial investment to drive sales. Conclusion A relationship appears to exist between expenditure on esomeprazole samples and its usage in Australia. A high initial investment was followed by a rapid reduction in cost per prescription dispensed, predominantly due to growth in market share. This trend was consistent with other PPIs
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The growth in demand and expenditure currently being experienced in the Australian health sector is also accompanied by a rise in dysfunctional customer behaviour, such as verbal abuse and physical violence, perpetrated against health service providers. While service failure and poor recovery are known to trigger consumer misbehaviour, this study investigates whether lower than expected perceived service quality generates cognitive and emotional appraisals that trigger two common forms of misbehaviour: refusal to participate and verbal abuse. Data were collected using a 2 × 2 between-subjects experiment administered via online written survey and analysed using path modelling. The findings indicate that perceptions of service encounter quality have an indirect effect on whether consumers refuse to participate in the service and/or verbally abuse the service provider through the mediating effect of anger.
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Although a number of studies have examined the role of gastric emptying (GE) in obesity, the influences of habitual physical activity level, body composition and energy expenditure (EE) on GE have received very little consideration. In this study, we have compared GE in active and inactive males, and we have characterised relationships with body composition (fat and fat free mass) and EE. Forty-four males (Active: n=22, Inactive: n=22; range BMI 21-36kg/m2; range percent fat mass 9-42%) were studied, with GE of a standardised (1676 kJ) pancake meal being assessed by 13C-octanoic acid breath test, body composition by air displacement plethysmography, resting metabolic rate (RMR) by indirect calorimetry and activity EE (AEE) by accelerometry. Results showed that GE was faster in active compared to inactive males (mean ±SD half time (t1/2): Active: 157±18 and Inactive: 179±21 min, p<0.001). When data from both groups were pooled, GE t1/2 was associated with percent fat mass (r=0.39, p<0.01) and AEE (r =-0.46, p<0.01). After controlling for habitual physical activity status, the association between AEE and GE remained, but not that for percent fat mass and GE. BMI and RMR were not associated with GE. In summary, faster GE is considered to be a marker of a habitually active lifestyle in males, and is associated with a higher AEE and lower percent fat mass. The possibility that GE contributes to a gross physiological regulation (or dysregulation) of food intake with physical activity level deserves further investigation.
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Background: Paediatric onset inflammatory bowel disease (IBD) may cause alterations in energy requirements and invalidate the use of standard prediction equations. Our aim was to evaluate four commonly used prediction equations for resting energy expenditure (REE) in children with IBD. Methods: Sixty-three children had repeated measurements of REE as part of a longitudinal research study yielding a total of 243 measurements. These were compared with predicted REE from Schofield, Oxford, FAO/WHO/UNU, and Harris-Benedict equations using the Bland-Altman method. Results: Mean (±SD) age of the patients was 14.2 (2.4) years. Mean measured REE was 1566 (336) kcal per day compared with 1491 (236), 1441 (255), 1481 (232), and 1435 (212) kcal per day calculated from Schofield, Oxford, FAO/WHO/UNU, and Harris-Benedict, respectively. While the Schofield equation demonstrated the least difference between measured and predicted REE, it, along with the other equations tested, did not perform uniformly across all subjects, indicating greater errors at either end of the spectrum of energy expenditure. Smaller differences were found for all prediction equations for Crohn's disease compared with ulcerative colitis. Conclusions: Of the commonly used equations, the equation of Schofield should be used in pediatric patients with IBD when measured values are not able to be obtained. (Inflamm Bowel Dis 2010;) Copyright © 2010 Crohn's & Colitis Foundation of America, Inc.
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OBJECTIVES: There is controversy in the literature regarding the effect of inflammatory bowel disease (IBD) on resting energy expenditure (REE). In many cases this may have resulted from inappropriate adjustment of REE measurements to account for differences in body composition. This article considers how to appropriately adjust measurements of REE for differences in body composition between individuals with IBD. PATIENTS AND METHODS: Body composition, assessed via total body potassium to yield a measure of body cell mass (BCM), and REE measurements were performed in 41 children with Crohn disease and ulcerative colitis in the Royal Children's Hospital, Brisbane, Australia. Log-log regression was used to determine the power function to which BCM should be raised to appropriately adjust REE to account for differences in body composition between children. RESULTS: The appropriate value to "adjust" BCM was found to be 0.49, with a standard error of 0.10. CONCLUSIONS: Clearly, there is a need to adjust for differences in body composition, or at the very least body weight, in metabolic studies in children with IBD. We suggest that raising BCM to the power of 0.5 is both a numerically convenient and a statistically valid way of achieving this aim. Under circumstances in which the measurement of BCM is not available, raising body weight to the power of 0.5 remains appropriate. The important issue of whether REE is changed in cases of IBD can then be appropriately addressed. © 2007 Lippincott Williams & Wilkins, Inc.
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Background: A knowledge of energy expenditure in infancy is required for the estimation of recommended daily amounts of food energy, for designing artificial infant feeds, and as a reference standard for studies of energy metabolism in disease states. Objectives: The objectives of this study were to construct centile reference charts for total energy expenditure (TEE) in infants across the first year of life. Methods: Repeated measures of TEE using the doubly labeled water technique were made in 162 infants at 1.5, 3, 6, 9 and 12 months. In total, 322 TEE measurements were obtained. The LMS method with maximum penalized likelihood was used to construct the centile reference charts. Centiles were constructed for TEE expressed as MJ/day and also expressed relative to body weight (BW) and fat-free mass (FFM). Results: TEE increased with age and was 1.40,1.86, 2.64, 3.07 and 3.65 MJ/day at 1.5, 3, 6, 9 and 12 months, respectively. The standard deviations were 0.43, 0.47, 0.52,0.66 and 0.88, respectively. TEE in MJ/kg increased from 0.29 to 0.36 and in MJ/day/kg FFM from 0.36 to 0.48. Conclusions: We have presented centile reference charts for TEE expressed as MJ/day and expressed relative to BW and FFM in infants across the first year of life. There was a wide variation or biological scatter in TEE values seen at all ages. We suggest that these centile charts may be used to assess and possibly quantify abnormal energy metabolism in disease states in infants.
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Background: Better understanding of body composition and energy metabolism in pediatric liver disease may provide a scientific basis for improved medical therapy aimed at achieving optimal nutrition, slowing progression to end-stage liver disease (ESLD), and improving the outcome of liver transplantation. Methods: Twenty-one children less than 2 years of age with ESLD awaiting liver transplantation and 15 healthy, aged-matched controls had body compartment analysis using a four compartment model (body cell mass, fat mass, extracellular water, and extracellular solids). Subjects also had measurements of resting energy expenditure (REE) and respiratory quotient (RQ) by indirect calorimetry. Nine patients and 15 control subjects also had measurements of total energy expenditure (TEE) using doubly labelled water. Results: Mean weights and heights were similar in the two groups. Compared with control subjects, children with ESLD had higher relative mean body cell mass (33 ± 2% vs 29 ± 1% of body weight, P < 0.05), but had similar fat mass, extracellular water, and extracellular solid compartments (18% vs 20%, 41% vs 38%, and 7% vs 13% of body weight respectively). Compared with control subjects, children with ESLD had 27% higher mean REE/body weight (0.285 ± 0.013 vs 0.218. ± 0.013 mJ/kg/24h, P < 0.001), 16% higher REE/unit cell mass (P < 0.05); and lower mean RQ (P < 0.05). Mean TEE of patients was 4.70 ± 0.49 mJ/24h vs 3.19 ± 0.76 in controls, (P < 0.01). Conclusions: In children, ESLD is a hypermetabolic state adversely affecting the relationship between metabolic and non-metabolic body compartments. There is increased metabolic activity within the body cell mass with excess lipid oxidation during fasting and at rest. These findings have implications for the design of appropriate nutritional therapy.