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ObjectiveTo identify the prevalence and associated factors of overweight and obesity in Brazilian commercial airline pilots.MethodsAn observational, cross-sectional study involving 1198 Brazilian commercial airline pilots with a sampling power >80% (ß=20%) and confidence level of 95% (α=5%) was carried out. The pilots completed an on-line questionnaire collecting data on sociodemographics, work, health, lifestyle and sleep. Poisson regression, with robust variance (stepwise forward technique), was employed to analyze the factors associated with excess weight (overweight and obesity). The models were adjusted for the variables age, marital status and education. The data were analyzed using the STATA 12.0 program.ResultsThe prevalence of overweight among the pilots was 53.7% and of obesity was 14.6%. The probability of being overweight was highest among pilots working night shifts for 6–10 years and that had difficulty relaxing after work, where perceived morningness was a protective factor. Risk factors for obesity included working night-shifts for 6–10 years, having difficulty relaxing after work, sleeping <6 h on days off, having other diagnosed diseases, and practicing <150 min/week of physical exercise.ConclusionIt was concluded that the prevalence of overweight and obesity among the commercial airline pilots was high and represents a public health problem in this population. Excess weight was associated with time working night-shifts, difficulty relaxing after work, inadequate sleep on days off, having other chronic diseases, and physical inactivity. In this context, nutritional status can be regarded as the result of dynamic and complex interactions promoted by occupational, sleep and health factors.
ObjectiveTo identify the prevalence and associated factors of overweight and obesity in Brazilian commercial airline pilots.MethodsAn observational, cross-sectional study involving 1198 Brazilian commercial airline pilots with a sampling power >80% (ß=20%) and confidence level of 95% (α=5%) was carried out. The pilots completed an on-line questionnaire collecting data on sociodemographics, work, health, lifestyle and sleep. Poisson regression, with robust variance (stepwise forward technique), was employed to analyze the factors associated with excess weight (overweight and obesity). The models were adjusted for the variables age, marital status and education. The data were analyzed using the STATA 12.0 program.ResultsThe prevalence of overweight among the pilots was 53.7% and of obesity was 14.6%. The probability of being overweight was highest among pilots working night shifts for 6–10 years and that had difficulty relaxing after work, where perceived morningness was a protective factor. Risk factors for obesity included working night-shifts for 6–10 years, having difficulty relaxing after work, sleeping <6 h on days off, having other diagnosed diseases, and practicing <150 min/week of physical exercise.ConclusionIt was concluded that the prevalence of overweight and obesity among the commercial airline pilots was high and represents a public health problem in this population. Excess weight was associated with time working night-shifts, difficulty relaxing after work, inadequate sleep on days off, having other chronic diseases, and physical inactivity. In this context, nutritional status can be regarded as the result of dynamic and complex interactions promoted by occupational, sleep and health factors.
RESUMO AMORAS, D. R. Impacto do exercício aeróbico intenso (corrida) na musculatura do sistema estomatognático-avaliação eletromiográfica, espessura muscular, força de mordida e eficiência mastigatória. 2014. 111 p.
Background: Body mass index (BMI) has some limitations for nutritional diagnosis since it does not represent an accurate measure of body fat and it is unable to identify predominant fat distribution. Aim: To develop a BMI based on the ratio of trunk mass and height. Methods: Fifty-seven patients in preoperative evaluation to bariatric surgery were evaluated. The preoperative anthropometric evaluation assessed weight, height and BMI. The body composition was evaluated by bioimpedance, obtaining the trunk fat free mass and fat mass, and trunk height. Trunk BMI (tBMI) was calculated by the sum of the measurements of the trunk fat free mass (tFFM) and trunk fat mass (tFM) in kg, divided by the trunk height squared (m2)). The calculation of the trunk fat BMI (tfBMI) was calculated by tFM, in kg, divided by the trunk height squared (m2)). For the correction and adjustment of the tBMI and tfBMI, it was calculated the relation between trunk extension and height, multiplying by the obtained indexes. Results: The mean data was: weight 125.3±19.5 kg, height 1.63±0.1 m, BMI was 47±5 kg/m2) and trunk height was 0.52±0,1 m, tFFM was 29.05±4,8 kg, tFM was 27.2±3.7 kg, trunk mass index was 66.6±10.3 kg/m², and trunk fat was 32.3±5.8 kg/m². In 93% of the patients there was an increase in obesity class using the tBMI. In patients with grade III obesity the tBMI reclassified to super obesity in 72% of patients and to super-super obesity in 24% of the patients. Conclusion: The trunk BMI is simple and allows a new reference for the evaluation of the body mass distribution, and therefore a new reclassification of the obesity class, evidencing the severity of obesity in a more objectively way.
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