Research question: The current study investigates the income elasticities and socio-economic determinants of direct and indirect sports expenditure categories by means of a log normal hurdle regression. Research methods: The data stem from a representative sample of 3005 Flemish families with school-aged children, gathered through a sports-specific survey. A log normal hurdle regression was used to calculate the determining factors and expenditure elasticities of expenditure on sports participation. Results and findings: The results indicate that income, education and the age of the youngest child are positively related to almost all sports expenditure categories, while the number of family members and degree of urbanisation are significant for only a number of the expenditure categories. The elasticity value of the direct sports expenses is smaller than is the case for indirect sports expenditure. Between the expenditure categories large differences exist, as relatively large elasticities are found for sports holidays, transport and sports food and drinks, as opposed to low values of sports events, sports club membership, entrance fees for sports infrastructure, sports camps, clothing, footwear and equipment. Implications: The fact that income significantly influences all expenditure categories demonstrates that further policy intervention is required to make sports consumption more accessible to lower income groups. Sports enterprises and policymakers need to be aware that negative income shifts have a more profound impact on the indirect expenditure categories, and that certain sports activities (e.g. participation events) are relatively more favoured by low-income groups than is the case for sports club membership
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Given the recent economic crisis and the risen poverty rates, sports managers need to get insight in the effect of income and other socio-economic determinants on the household time and money that is spent on sports participation. By means of a Tobit regression, this study analyses the magnitude of the income effect for the thirteen most practiced sports by households in Flanders (the Dutch speaking part of Belgium), which are soccer, swimming, dance, cycling, running, fitness, tennis, horse riding, winter sports, martial arts, volleyball, walking and basketball. The results demonstrate that income has a positive effect on both time and money expenditure on sports participation, although differences are found between the 13 sports activities. For example, the effect of income on time and money expenditure is relatively high for sports activities like running and winter sports, while it is lower for other sports such as fitness, horse riding, walking and swimming. Commercial enterprises can use the results of this study to identify which sports to focus on, and how they will organise their segmentation process. For government, the results demonstrate which barriers prevent people from taking part in specific sports activities, based upon which they should evaluate their policy decisions.
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It has been suggested that physical education (PE) and active transport can make a meaningful contribution to children's physical activity (PA) levels. However, data on the contribution these activities to total PA is scarce, and PE's contribution to total physical activity energy expenditure (PAEE) has to our knowledge never been determined. This is probably explained by the methodological complexity of determining PAEE (Welk, 2002). In this paper, we present the first data of an ongoing study using combined heart rate monitoring and accelerometry, together with activity diaries. Over the six measurement days, PE contributed 5% to total PAEE, and 16% to school-related PAEE, whereas active transportation had a much larger contribution.
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Background: When the resting energy expenditure (REE) of overweight and obese adolescents cannot be measured by indirect calorimetry, it has to be predicted with an equation. Objective: The aim of this study was to examine the validity of published equations for REE compared with indirect calorimetry in overweight and obese adolescents. Design: Predictive equations based on weight, height, sex, age, fatfree mass (FFM), and fat mass were compared with measured REE. REE was measured by indirect calorimetry, and body composition was measured by dual-energy X-ray absorptiometry. The accuracy of the REE equations was evaluated on the basis of the percentage of adolescents predicted within 10% of REE measured, the mean percentage difference between predicted and measured values (bias), and the root mean squared prediction error (RMSE). Results: Forty-three predictive equations (of which 12 were based on FFM) were included. Validation was based on 70 girls and 51 boys with a mean age of 14.5 y and a mean (6SD) body mass index SD score of 2.93 6 0.45. The percentage of adolescents with accurate predictions ranged from 74% to 12% depending on the equation used. The most accurate and precise equation for these adolescents was the Molnar equation (accurate predictions: 74%; bias: –1.2%; RMSE: 174 kcal/d). The often-used Schofield-weight equation for age 10–18 y was not accurate (accurate predictions: 50%; bias: +10.7%; RMSE: 276 kcal/d). Conclusions: Indirect calorimetry remains the method of choice for REE in overweight and obese adolescents. However, the sex-specific Molnar REE prediction equation appears to be the most accurate for overweight and obese adolescents aged 12–18 y. This trial was registered at www.trialregister.nl with the Netherlands Trial Register as ISRCTN27626398.
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Background & aim The aim of this study was to describe a decrease in resting energy expenditure during weight loss that is larger than expected based on changes in body composition, called adaptive thermogenesis (AT), in overweight and obese older adults. Methods Multiple studies were combined to assess AT in younger and older subjects. Body composition and resting energy expenditure (REE) were measured before and after weight loss. Baseline values were used to predict fat free mass and fat mass adjusted REE after weight loss. AT was defined as the difference between predicted and measured REE after weight loss. The median age of 55 y was used as a cutoff to compare older with younger subjects. The relation between AT and age was investigated using linear regression analysis. Results In this study 254 (M = 88, F = 166) overweight and obese subjects were included (BMI: 31.7 ± 4.4 kg/m2, age: 51 ± 14 y). The AT was only significant for older subjects (64 ± 185 kcal/d, 95% CI [32, 96]), but not for younger subjects (19 ± 152 kcal/d, 95% CI [−9, 46]). The size of the AT was significantly higher for older compared to younger adults (β = 47, p = 0.048), independent of gender and type and duration of the weight loss program. Conclusions We conclude that adaptive thermogenesis is present only in older subjects, which might have implications for weight management in older adults. A reduced energy intake is advised to counteract the adaptive thermogenesis.
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Background & aims: Individual energy requirements of overweight and obese adults can often not be measured by indirect calorimetry, mainly due to the time-consuming procedure and the high costs. To analyze which resting energy expenditure (REE) predictive equation is the best alternative for indirect calorimetry in Belgian normal weight to morbid obese women.Methods: Predictive equations were included when based on weight, height, gender, age, fat free mass and fat mass. REE was measured with indirect calorimetry. Accuracy of equations was evaluated by the percentage of subjects predicted within 10% of REE measured, the root mean squared prediction error (RMSE) and the mean percentage difference (bias) between predicted and measured REE.Results: Twenty-seven predictive equations (of which 9 based on FFM) were included. Validation was based on 536 F (18–71 year). Most accurate and precise for the Belgian women were the Huang, Siervo, Muller (FFM), Harris–Benedict (HB), and the Mifflin equation with 71%, 71%, 70%, 69%, and 68% accurate predictions, respectively; bias −1.7, −0.5, +1.1, +2.2, and −1.8%, RMSE 168, 170, 163, 167, and 173 kcal/d. The equations of HB and Mifflin are most widely used in clinical practice and both provide accurate predictions across a wide range of BMI groups. In an already overweight group the underpredicting Mifflin equation might be preferred. Above BMI 45 kg/m2, the Siervo equation performed best, while the FAO/WHO/UNU or Schofield equation should not be used in this extremely obese group.Conclusions: In Belgian women, the original Harris–Benedict or the Mifflin equation is a reliable tool to predict REE across a wide variety of body weight (BMI 18.5–50). Estimations for the BMI range between 30 and 40 kg/m2, however, should be improved.
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Rationale: It is well established that resting energy expenditure (REE) decreases with age. Data derived from indirect calorimetry (IC) are still limited with respect to the number of high aged individuals, BMI groups and health conditions. Therefore, IC generated REE of the BASAROT sample and those calculated according to the Harris-Benedict (HB) equation were used to re-evaluate the proposed association between REE and age. Methods: The IC-BASAROT sample combines the result of IC performed in 2622 individuals from 10 centers (7 Germany, 2 Italy, 1 Netherlands) done under strictly standardized conditions (e.g. at least 8h of fasting) in free-living, mostly healthy adults aged 18 to 100 years including all BMI ranges. IC was performed by canopy technique (Cosmed Quark RMR/Sensor Medics Vmax29) in 96.5% of cases and by face mask (Cosmed Fitmate) in 3.5%. Weight was measured by calibrated scales and height was determined to the nearest of 1mm. Results: REE in the total sample (BMI: 26.9±9.1 kg/m², 43.7±17.6 y) correlated more positively with body weight than with BMI (r=0.768; p<0.001 vs. r=0.571; p<0.001). Gender+body weight explained 75% of REE variance, gender+BMI 69% and gender+age only 28%. To reduce confounding by body weight we performed age-related analysis in the subgroup of women weighing 50-79 kg (n=780, BMI: 23.4±3.4 kg/m², 41.4±18.5 y) and men weighing 60-89 kg (n=500, BMI: 24.9±3.0 kg/m², 47.5±19.3 y) and compared results with REEHB (tab. 1). IC results from 18 to 100 y showed an approximately 50% lower decrease in REE than HB in women (-129 kcal/d vs. - 257 kcal/d) and in men (-200 kcal/d vs. -406 kcal/d, tab. 1). REEIC (n=1280) did not correlate with age (r=-0.042; p=0.132). In line, we observed a significant overestimation of REE by HB up to 39 y in both sexes and an underestimation in men 60 y of age and older. Conclusion: Age-related decline in REE appears to be lower than expected and might due to changes in body composition both in the younger and older generation. No indication of the often proposed systematic overestimation of HB in women was seen. Overall, findings should be considered in future models for estimating REE.
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Abstract: INTRODUCTION: Resting energy expenditure (REE) is expected to be higher in athletes because of their relatively high fat free mass (FFM). Therefore, REE predictive equation for recreational athletes may be required. The aim of this study was to validate existing REE predictive equations and to develop a new recreational athlete specific equation.
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Rationale: Obesity is a risk factor for type 2 diabetes (DM2), however not all obese people develop DM2. We explored differences in energy intake and expenditure between obese older adults with and without DM2. Methods: Baseline data from 2 lifestyle interventions with a total of 202 obese older adults were included in the analyses. Obesity was defined as BMI > 30.0, or >27.0 with waist circumference >88 (women) or >102 cm (men). DM2 was confirmed by use of diabetes medication. Subjects were between 55 and 85 years old and 45% was female. Energy intake (EI) was measured by 3-day food diary and physical activity level (PAL) by 3-day movement diary. Resting energy expenditure (REE) was measured using indirect calorimetry and total energy expenditure (TEE) was calculated as REE x PAL. Between group differences were analysed with independent samples T-tests. Results: The obese group with DM2 (n = 117) had more males (67.5% vs 37.6% p < 0.001) and similar BMI (33.3 vs 33.0 kg/m2) compared to the group without DM2 (n = 85). Analyses of males and females separately showed lower PAL in males with DM2 (vs without DM2; 1.37 vs 1.45, p = 0.015), without differences in EI (2055 vs 1953 kcal/d), REE (1970 vs 1929 kcal/d), and TEE (2699 vs 2830 kcal/d). In females with DM2, both PAL (1.38 vs 1.47, p = 0.014) and EI (1543 vs 1839 kcal/d, p = 0.008) were significantly lower, whereas REE (1592 vs 1598 kcal/d) and TEE (2220 vs 2318 kcal/d) did not differ significantly from obese females without DM2. Conclusion: In both males and females, obese older adults with type 2 diabetes showed similar resting and total energy expenditure but lower physical activity level compared to those without DM2. Females with DM2 showed lower energy intake. On average, subjects seem to have a negative energy balance, which is probably due to a combination of underreporting of intake and over-reporting of activity.
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OBJECTIVE: To evaluate if using surface neuromuscular electrical stimulation (NMES) for paralyzed lower-limb muscles results in an increase in energy expenditure and if the number of activated muscles and duty cycle affect the potential increase.DESIGN: Cross-sectional study.RESULTS: Energy expenditure during all NMES protocols was significantly higher than the condition without NMES (1.2 ± 0.2 kcal/min), with the highest increase (+ 51%; +0.7 kcal/min, 95% CI: 0.3 - 1.2) for the protocol with more muscles activated and the duty cycle with a shorter rest period. A significant decrease in muscle contraction size during NMES was found with a longer stimulation time, more muscles activated or the duty cycle with a shorter rest period.CONCLUSION: Using NMES for paralyzed lower-limb muscles can significantly increase the energy expenditure compared to sitting without NMES with the highest increase for the protocol with more muscles activated and the duty cycle with a shorter rest period. Muscle fatigue occurred significantly with the more intense NMES protocols which might cause a lower energy expenditure in a longer protocol. Future studies should further optimize the NMES parameters and investigate the long-term effects of NMES on weight management in people with SCI.
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