961 resultados para BODY-MASS


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BACKGROUND: Physical activity (PA) has been consistently implicated in the etiology of obesity, whereas recent evidence on the importance of sedentary time remains inconsistent. Understanding of dose-response associations of PA and sedentary time with overweight and obesity in adults can be improved with large-scale studies using objective measures of PA and sedentary time. The purpose of this study was to examine the strength, direction and shape of dose-response associations of accelerometer-based PA and sedentary time with body mass index (BMI) and weight status in 10 countries, and the moderating effects of study site and gender. METHODS: Data from the International Physical activity and the Environment Network (IPEN) Adult study were used. IPEN Adult is an observational multi-country cross-sectional study, and 12 sites in 10 countries are included. Participants wore an accelerometer for seven consecutive days, completed a socio-demographic questionnaire and reported height and weight. In total, 5712 adults (18-65 years) were included in the analyses. Generalized additive mixed models, conducted in R, were used to estimate the strength and shape of the associations. RESULTS: A curvilinear relationship of accelerometer-based moderate-to-vigorous PA and total counts per minute with BMI and the probability of being overweight/obese was identified. The associations were negative, but weakened at higher levels of moderate-to-vigorous PA (>50 min per day) and higher counts per minute. No associations between sedentary time and weight outcomes were found. Complex site- and gender-specific findings were revealed for BMI, but not for weight status. CONCLUSIONS: On the basis of these results, the current Institute of Medicine recommendation of 60 min per day of moderate-to-vigorous PA to prevent weight gain in normal-weight adults was supported. No relationship between sedentary time and the weight outcomes was present, calling for further examination. If moderator findings are confirmed, the relationship between PA and BMI may be country- and gender-dependent, which could have important implications for country-specific health guidelines. © 2015 Macmillan Publishers Limited All rights reserved.

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To assess the relationships between player characteristics (including age, playing experience, ethnicity, physical fitness) and in-season injury in elite Australian football.

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BackgroundWe have previously demonstrated that between the years 1980 and 2000, the mean body mass index (BMI) of the urban Australian population increased, with greater increases observed with increasing BMI. The current study aimed to quantify trends over time in BMI according to education between 1980 and 2007.MethodsWe compared data from the 1980, 1983 and 1989 National Heart Foundation Risk Factor Prevalence Studies, 1995 National Nutrition Survey, 2000 Australian Diabetes, Obesity and Lifestyle Study and the 2007 National Health Survey. For survey comparability, analyses were restricted to urban Australian residents aged 25-64 years. BMI was calculated from measured height and weight. The education variable was dichotomised at completion of secondary school. Four age-standardised BMI indicators were compared over time by sex and education: mean BMI, mean BMI of the top five percent of the BMI distribution, prevalence of obesity (BMI⩾30 kg/m(2)), prevalence of class II(+) obesity (BMI⩾35 kg/m(2)).ResultsBetween 1980 and 2007, the mean BMI among men increased by 2.5 kg/m(2) and 1.7 kg/m(2) for those with low and high education levels, respectively, corresponding to increases in obesity prevalence of 20(from 12% to 32%) and 11(10% to 21%) %-points. Among women mean BMI increased by 2.9 kg/m(2) and 2.4 kg/m(2) for those with low and high education levels respectively, corresponding to increases in obesity prevalence of 16(12% to 28%) and 12(7% to 19%) %-points. The prevalence of class II(+) obesity among men increased by 9(1% to 10%) and 4(1% to 5%) %-points for those with low and high education levels, and among women increased by 8(4% to 12%) and 4(2% to 6%) %-points. Absolute and relative differences between education groups generally increased over time.ConclusionsEducational differences in BMI have persisted among urban Australian adults since 1980 without improvement. Obesity prevention policies will need to be effective in those with greatest socio-economic disadvantage if we are to equitably and effectively address the population burden of obesity and its corollaries.International Journal of Obesity accepted article preview online, 16 March 2015. doi:10.1038/ijo.2015.27.

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This study was conducted at St James‘s Hospital, Dublin, Ireland, in 2010. It evaluated the dose intensity and toxicities experienced by patients of normal and increased body mass index treated with FOLFOX chemotherapy, and demonstrated that overweight patients may tolerate doses based on actual body weight.

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BACKGROUND: Ecological models of health behaviour are an important conceptual framework to address the multiple correlates of obesity. Several single-country studies previously examined the relationship between the built environment and obesity in adults, but results are very diverse. An important reason for these mixed results is the limited variability in built environments in these single-country studies. Therefore, the aim of this study was to examine associations between perceived neighbourhood built environmental attributes and BMI/weight status in a multi-country study including 12 environmentally and culturally diverse countries. METHODS: A multi-site cross-sectional study was conducted in 17 cities (study sites) across 12 countries (Australia, Belgium, Brazil, China, Colombia, Czech Republic, Denmark, Mexico, New Zealand, Spain, the UK and USA). Participants (n = 14222, 18-66 years) self-reported perceived neighbourhood environmental attributes. Height and weight were self-reported in eight countries, and measured in person in four countries. RESULTS: Three environmental attributes were associated with BMI or weight status in pooled data from 12 countries. Safety from traffic was the most robust correlate, suggesting that creating safe routes for walking/cycling by reducing the speed and volume of traffic might have a positive impact upon weight status/BMI across various geographical locations. Close proximity to several local destinations was associated with BMI across all countries, suggesting compact neighbourhoods with more places to walk related to lower BMI. Safety from crime showed a curvilinear relationship with BMI, with especially poor crime safety being related to higher BMI. CONCLUSIONS: Environmental interventions involving these three attributes appear to have international relevance and focusing on these might have implications for tackling overweight/obesity.

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We address the question of whether physiological flexibility in relation to climate is a general feature of the metabolic properties of birds. We tested this hypothesis in hand-raised Garden Warblers (Sylvia borin), long-distance migrants, which normally do not experience great temperature differences between summer and winter. We maintained two groups of birds under cold and warm conditions for 5 months, during which their body mass and food intake were monitored. When relatedness (siblings vs. non-siblings) of the experimental birds was taken into account, body mass in cold-acclimated birds was higher than in warm-acclimated birds. BMR, measured at the end of the 5-month temperature treatment, was also higher in the cold- than the warm-acclimated group. Migrant birds thus seem to be capable of the same metabolic cold-acclimation response as has been reported in resident birds. The data support the hypothesis that physiological flexibility is a basic trait of the metabolic properties of birds.

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The energy content of the deposited reserve tissue depended on the condition of the birds, since the energy required for body mass gain was low in lean birds and high in fat birds. Maintenance metabolism was relatively low compared to wader species wintering in temperate regions, suggested to be an adaptation towards reduced endogenous heat production, which may help in avoiding heat stress under tropical conditions. -from Authors

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Purpose We analyzed the changes in the body mass index (BMI) distribution for urban Australian adults between 1980 and 2007.

Methods We used data from participants of six consecutive Australian nation-wide surveys with measured weight and height between 1980 and 2007. We used quantile regression to estimate mean BMI (for percentiles of BMI) and prevalence of severe obesity, modeled by natural splines in age, date of birth, and survey date.

Results Since 1980, the right skew in the BMI distribution for Australian adults has increased greatly for men and women, driven by increases in skew associated with age and birth cohort/period. Between 1980 and 2007, the average 5-year increase in BMI was 1 kg/m2 (0.8) for the 95th percentile of BMI in women (men). The increase in the median was about a third of this, and for the 10th percentile, a fifth of this. We estimated that for the cohort born in 1960 around 31% of men and women were obese by age 50 years compared with 11% of the 1930 birth cohort.

Conclusions There have been large increases in the right skew of the BMI distribution for urban Australian adults between 1980 and 2007, and birth cohort effects suggests similar increases are likely to continue.

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BACKGROUND: Recent evidence suggests that a substantial subgroup of the population who have a high-risk waist circumference (WC) do not have an obese body mass index (BMI). This study aimed to explore whether including those with a non-obese BMI but high risk WC as 'obese' improves prediction of adiposity-related metabolic outcomes.

METHODS: Eleven thousand, two hundred forty-seven participants were recruited. Height, weight and WC were measured. Ten thousand, six hundred fifty-nine participants with complete data were included. Adiposity categories were defined as: BMI(N)/WC(N), BMI(N)/WC(O), BMI(O)/WC(N), and BMI(O)/WC(O) (N = non-obese and O = obese). Population attributable fraction, area under the receiver operating characteristic curve (AUC), and odds ratios (OR) were calculated.

RESULTS: Participants were on average 48 years old and 50 % were men. The proportions of BMI(N)/WC(N), BMI(N)/WC(O), BMI(O)/WC(N) and BMI(O)/WC(O) were 68, 12, 2 and 18 %, respectively. A lower proportion of diabetes was attributable to obesity defined using BMI alone compared to BMI and WC combined (32 % vs 47 %). AUC for diabetes was also lower when obesity was defined using BMI alone (0.62 vs 0.66). Similar results were observed for all outcomes. The odds for hypertension, dyslipidaemia, diabetes and CVD were increased for those with BMI(N)/WC(O) (OR range 1.8-2.7) and BMI(O)/WC(O) (OR 1.9-4.9) compared to those with BMI(N)/WC(N).

CONCLUSIONS: Current population monitoring, assessing obesity by BMI only, misses a proportion of the population who are at increased health risk through excess adiposity. Improved identification of those at increased health risk needs to be considered for better prioritisation of policy and resources.

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With the obesity epidemic, and the effects of aging populations, human phenotypes have changed over two generations, possibly more dramatically than in other species previously. As obesity is an important and growing hazard for population health, we recommend a systematic evaluation of the optimal measure(s) for population-level excess body fat. Ideal measure(s) for monitoring body composition and obesity should be simple, as accurate and sensitive as possible, and provide good categorization of related health risks. Combinations of anthropometric markers or predictive equations may facilitate better use of anthropometric data than single measures to estimate body composition for populations. Here, we provide new evidence that increasing proportions of aging populations are at high health-risk according to waist circumference, but not body mass index (BMI), so continued use of BMI as the principal population-level measure substantially underestimates the health-burden from excess adiposity.

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Objectives

To examine relationships between body mass index (BMI), prevalence of physician-recorded cardiovascular disease (CVD) risk factors in primary care, and changes in risk with 10% weight change.

Methods

The Counterweight Project conducted a baseline cross-sectional survey of medical records of 6150 obese (BMI ≥ 30 kg/m2), 1150 age- and sex-matched overweight (BMI 25 to <30 kg/m2), and 1150 age- and sex-matched normal weight (BMI 18.5 to <25 kg/m2) controls, in primary care. Data were collected for the previous 18 months to examine BMI and disease prevalence, and then modelled to show the potential effect of 10% weight loss or gain on risk.

Results

Obese patients develop more CVD risk factors than normal weight controls. BMI ≥ 40 kg/m2 exhibits increased prevalence of type 2 diabetes mellitus (DM), odds ratio (OR) men: 6.16 (p < 0.001); women: 7.82 (p < 0.001) and hypertension OR men: 5.51 (p < 0.001); women: 4.16 (p < 0.001). Dyslipidaemia peaked around BMI 35 to <37.5 kg/m2, OR men: 3.26 (p < 0.001); women 3.76 (p < 0.001) and CVD at BMI 37.5 to <40 kg/m2 in men, OR 4.48 (p < 0.001) and BMI ≥ 40 kg/m2 in women, OR 3.98 (p < 0.001).

A 10% weight loss from the sample mean of 32.5 kg/m2 reduced the OR for type 2 DM by 30% and CVD by 20%, while 10% weight gain increased type 2 DM risk by more than 35% and CVD by 20%.

Conclusion

Obesity plays a fundamental role in CVD risk, which is reduced with weight loss. Weight management intervention strategies should be a public health priority to reduce the burden of disease in the population.

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Objectives Prescribed medications represent a high and increasing proportion of UK health care funds. Our aim was to quantify the influence of body mass index (BMI) on prescribing costs, and then the potential savings attached to implementing a weight management intervention.

Methods Paper and computer-based medical records were reviewed for all drug prescriptions over an 18-month period for 3400 randomly selected adult patients (18–75 years) stratified by BMI, from 23 primary care practices in seven UK regions. Drug costs from the British National Formulary at the time of the review were used. Multivariate regression analysis was applied to estimate the cost for all drugs and the ‘top ten’ drugs at each BMI point. This allowed the total and attributable prescribing costs to be estimated at any BMI. Weight loss outcomes achieved in a weight management programme (Counterweight) were used to model potential effects of weight change on drug costs. Anticipated savings were then compared with the cost programme delivery. Analysis was carried out on patients with follow-up data at 12 and 24 months as well as on an intention-to-treat basis. Outcomes from Counterweight were based on the observed lost to follow-up rate of 50%, and the assumption that those patients would continue a generally observed weight gain of 1 kg per year from baseline.

Results The minimum annual cost of all drug prescriptions at BMI 20 kg/m2 was £50.71 for men and £62.59 for women. Costs were greater by £5.27 (men) and £4.20 (women) for each unit increase in BMI, to a BMI of 25 (men £77.04, women £78.91), then by £7.78 and £5.53, respectively, to BMI 30 (men £115.93 women £111.23), then by £8.27 and £4.95 to BMI 40 (men £198.66, women £160.73). The relationship between increasing BMI and costs for the top ten drugs was more pronounced. Minimum costs were at a BMI of 20 (men £8.45, women £7.80), substantially greater at BMI 30 (men £23.98, women £16.72) and highest at BMI 40 (men £63.59, women £27.16). Attributable cost of overweight and obesity accounted for 23% of spending on all drugs with 16% attributable to obesity. The cost of the programme was estimated to be approximately £60 per patient entered. Modelling weight reductions achieved by the Counterweight weight management programme would potentially reduce prescribing costs by £6.35 (men) and £3.75 (women) or around 8% of programme costs at one year, and by £12.58 and £8.70, respectively, or 18% of programme costs after two years of intervention. Potential savings would be increased to around 22% of the cost of the programme at year one with full patient retention and follow-up.

Conclusion Drug prescriptions rise from a minimum at BMI of 20 kg/m2 and steeply above BMI 30 kg/m2. An effective weight management programme in primary care could potentially reduce prescription costs and lead to substantial cost avoidance, such that at least 8% of the programme delivery cost would be recouped from prescribing savings alone in the first year.