SciELO - Scientific Electronic Library Online

vol.24 número7Prevalência e fatores associados ao baixo peso em idosos brasileirosBarreiras percebidas para o consumo de frutas e de verduras ou legumes em adultos brasileiros índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados




Links relacionados


Ciência & Saúde Coletiva

versão impressa ISSN 1413-8123versão On-line ISSN 1678-4561

Ciênc. saúde coletiva vol.24 no.7 Rio de Janeiro jul. 2019  Epub 22-Jul-2019 


Weight gain in public hospital workers: a retrospective cohort study

Denise Renani von Brixen Montzel1

Bruna Vieira de Lima Costa2

Flávia Moraes Silva3

1 Nutricionista da Saúde do Trabalhador, Hospital Nossa Senhora da Conceição. Av. Francisco Trein 596, Cristo Redentor. 91350-200 Porto Alegre RS Brasil.

2 Departamento de Nutrição, Escola de Enfermagem, Universidade Federal de Minas Gerais. Belo Horizonte MG Brasil.

3 Departamento de Nutrição, Universidade Federal de Ciências da Saúde de Porto Alegre. Porto Alegre RS Brasil.


This study assessed changes in the nutrition profile of public hospital workers over a period of three decades and the association between nutritional status and occupational factors. A retrospective cohort study was conducted with staff taken on in 1980, 1990, and 2000 still working in the hospital in 2013. The following data was obtained from staff records: sociodemographic characteristics; and body weight and height, recorded during pre-employment and periodic medical examinations. The latter was used to calculate body mass index (BMI).The final sample consisted of 386 workers (76.4% women and 88.1% white) with a mean age of 29.3 ± 7.3 years. Mean body weight and BMI at admission were highest in the 2000 cohort (W = 66.3 ± 12.5kg; BMI = 21.3 ± 2.5 kg/m2), compared to 1980 (W = 56.7 ± 10.2 kg; BMI = 21.3 ± 2.5 kg/m2)and 1990 (W = 62.2 ± 11.5 kg; BMI = 22.9 ± 3.3 kg/m2) cohorts. Variation in weight and BMI between the pre-employment examination and final periodic examination was highest in the 2000 cohort. When stratified by sex, this difference in variation was observed only in men. No association was found between variation in body weight and BMI and work shift and occupation. The increase in weight and BMI reflects the nutritional transition in Brazil, underscoring the need for nutritional surveillance and the implementation of health education programs directed at staff.

Key words: Cohort study; Weight gain; Nutritional status; Occupational health


Obesity and overweight has grown at an alarming rate in recent decades around the world across all population groups, regardless of ethnicity, age, and socioeconomic status, and currently present a major public health problem of epidemic proportions1. Excess weight significantly increases the risk of developing debilitating diseases with high social cost, such as type 2 diabetes2, hypertension, and cardiovascular diseases3.

Global epidemiological data shows that the worldwide prevalence of obesity nearly tripled between 1975 and 2016. More than 1.9 billion adults aged 18 years and older were overweight in 2016. Of these, 650 million were obese4. It is estimated that 38% of the world’s adult population will be overweight and 20% obese by the year 20305.

According to Brazil’s 2008-2009 Family Budget Survey (Pesquisa de Orçamentos Familiares - POF) involving over 188,000 people, around 50% of the adult population are overweight, while 12.5% of men and 16.9% of women were obese. In the last 34 years, the prevalence of overweight among adults had increased from18.5% to 50.1% in men, and from 28.7% to 48% in women6. Data from the 2015 VIGITEL survey showed that the national prevalence of overweight was 53.9% and that it was higher among men (57.6%,compared to 50.8% in women). The findings also showed a positive relationship between age and overweight and an inverse relationship between level of education and overweight among women7.

This picture is no different in the workplace, with one study involving workers from various countries reporting a prevalence of overweight of 28%8. Studies conducted in Brazil have reported prevalence rates ranging from 36% among public employees at a University9 to 53% among industrial workers10, 45.4% in bank workers11, and 44.4% among local government staff12.

Although the relationship between work characteristics and weight gain is well-known10,12, further research and new approaches are required to identify occupational vulnerabilities in specific groups of workers. Occupational health surveillance should be continuous and capable of describing the health profile of different occupations, encompassing health situation analysis and the characterization of work and of socioeconomic and environmental profiles13. In view of the above, the present study assessed changes over the last three decades in the nutrition profile of workers from a public hospital in Porto Alegre, Brazil and the association between nutritional status and occupation and work shift.


A retrospective cohort study was conducted with workers from a public hospital in Porto Alegre, Brazil with 5,070 staff. The sample consisted of all staff taken on in 1980, 1990, and 2000 (229, 474, and 329, respectively) and still working in the hospital in 2013.At the time of data collection, the number and percentage of staff taken on in 1980, 1990, 2000 and still working in the hospital in 2013was 86 (37.6%), 212 (44.7%), and 164 (49.4%), respectively.

The following data was taken from staff records: sociodemographic characteristics (skin color, gender, age, occupation, and work shift) and body weight and height, measured and recorded by the occupational physician during the pre-employment and periodic medical examinations. Workers whose staff records were incomplete and did not show all body weight and height measurements from the pre-employment medical examination and/or periodic medical examinations were excluded. The final sample consisted of 386 workers (68 from 1980, 175 from 1990, and 143 from 2000).

Skin color was self-reported as either “white” or “non-white” and work shift was classified as “day shift” (those who work in the morning and/or afternoon) or “night shift” (those who work at night and on-call shift staff). Occupation was classified as “care staff”, for staff whose work involved patient contact, and “administrative staff”, for other staff.

Nutritional status was assessed using the body mass index (BMI),based on weight in kilograms divided by height in meters squared and classified according to the following cut-off points proposed by the World Health Organization2: < 18.5 kg/m2, underweight; between 18.5 and 24.9 kg/m2, healthy weight; between 25 and 29.9 kg/m2, overweight; and ≥ 30 Kg/m2, obese. Weight gain per decade was also calculated for each sample member.

The study protocol was approved by the Ethics Committee of the Grupo Hospitalar Conceição.

Statistical analysis

A descriptive analysis of the distribution of the variables was conducted using the Kolmogorov-Smirnov normality test. The comparison of the continuous variables was conducted using ANOVA or the Kruska-Wallis test, depending on the distribution of the variables. Post hoc analyses were carried out using Tukey’s or Dunn’s test. The categorical variables were compared using the Chi-squared test. BMI and body weight at each pre-employment and periodic examination was compared using the general linear models for repeated measures. Post hoc analyses were carried out using Tukey’s test. Levene’s test was used to test for homoscedasticity (the equality of variances).

Student’s t-test and the Mann-Whitney test were used for the comparison of the study variables according to gender, work shift, and occupation, respecting the normality of the continuous variables. The categorical variables were compared using the Chi-squared test.

The results were expressed as mean ± standard deviation for parametric quantitative variables, median and interquartile range (P25-P75) for non-parametric quantitative variables, and absolute and relative frequency for categorical variables. Statistical analysis was carried out using the software package SPSS 18.0®. A significance level of 0.05 was adopted.


General sample characteristics

The results show that 76.4% of the workers were women and 88.1% white. The mean age of the workers when they started working at the hospital was 29.31 ± 7.31 years, while mean weight and BMI were 62.76 ± 12.13 kg 23.08 ± 3.45 kg/m2, respectively.

Table 1 shows the general characteristics of the workers by year of entry. The results show that body weight and BMI upon entry is significantly higher in each successive cohort No statistically significant difference was found between thecohortsin relation to gender, skin color, and occupation no one difference between groups was observed.

Table 1 General characteristics of the workers by year of hospital admission (Porto Alegre, Brazil 2017). 

1980 (n = 68) 1990 (n = 175) 2000 (n = 143) Valor de P
Age (years) 24.76±4.36a 28.26±6.21b 32.81±8.07c <0.0011
Gender (Female) 58 (85.3%) 125 (71.5%) 109 (76.2%) 0.1662
Skin color (White) 61 (89.7%) 153 (87.42%) 126 (87.5%) 0.1672
Shift (Day) 37 (54.4%) 123 (70.3%) 105 (73.4%) 0.0171
Care staff 51 (75%) 106 (60.6%) 92 (64.3%) 0.1872
Administrative staff 17 (25%) 69 (39.4%) 51 (35.7%)
Weight (Kg) 56.68±10.25a 62.22±11.48b 66.31±12.55c <0.0011
BMI (Kg/m2) 21.30±2.52a 22.86±3.28b 24.19±3.64c <0.0011

BMI = body mass index. 1 ANOVA (post hoc analysis carried out using Tukey’s test with significant differences represented by superscript letters) 2 Chi-squared test (residual analysis with significant differences represented by superscript letters). Data presented as mean ± standard deviation or number and percentage of workers with the characteristic.

Variation in body weight and BMI between the pre-employment medical examination and last periodic medical examination of 2010

The 1980,1990, and 2000 cohorts underwent six, four, and two periodic medical examinations, respectively, besides the pre-employment examination. Table 2 shows that there was significant difference in variation in body weight and BMI between the pre-employment examination and last periodic examination between the three cohorts.

Table 2 Variation of body weight and BMI of the workers from decade to decade by year of entry (Porto Alegre, Brazil 2017). 

1980 n = 68 1990 n = 173 2000 n = 139 Valor de p1
∆ weight(kg)
Total 4.17 (2.08 – 6.17)a 3.50 (-2.95 – 9.50)a,b 7.00 (2.50 – 12.00)c < 0.001
Women 3.83 (2.00 – 5.83) 4.23 (-1.00 – 11.13) 5.00 (2.00 – 10.00) 0.199
Men 5.50 (2.25 – 6.33)a -0.25 (-6.00 – 7.25)a,b 9.00 (5.00 – 14.00)a,c 0.012
∆ BMI (kg/m2)
Total 1.54 (0.82 – 2.30)a 1.21 (.0.94 – 4.15)a,b 2.60 (0.92 – 4.44)a,c < 0.001
Women 1.47 (0.80– 2.34)a 1.01 (-0.74 – 4.12)a 2.08 (0.77 – 4.05)a 0.112
Men 1.64 (0.79 – 2.10)a 1.41 (-1.69 – 4.45)a,b 3.09 (1.68 – 5.00)a,c 0.019

BMI = body mass index. 1 The Kruska-Wallis test. Post-hoc analysis conducted using Dunn’s test; p < 0.05: superscript letters indicate significant differences between groups. Data presented as median and interquartile range (P25 – P75).

The 1980 cohort gained a mean of 12.5 (6.3 - 18.5) kg between the pre-employment examination and the last periodic examination (equivalent to 4.2 kg/10 years), while the 1990 cohort gained 7.0 (-5.9 - 19.0) kg (equivalent to 3.5 kg/10 years), and the2000 cohort gained 7.0 (2.5 - 12.0) kg (p < 0.001). BMI varied 4.63 (2.47 - 6.89) kg/m2between the pre-employment examination and the last periodic examination inthe1980 cohort, 2.41 (-1.89 - 8.29) kg/m2 in 1990 cohort, and 2.59 (0.92 - 4.44) kg/m2in the 2000 cohort (p < 0.001).

Prevalence of underweight at entry was significantly higher in the 1980 cohort (14.7%), compared to in the 1990 (5.1%) and 2000 (1.4%) cohorts (p < 0.001). There was no significant difference in prevalence of healthy weight at entry between the 1980 (73.5%) and 1990 (70.9%) cohorts. However, prevalence was significantly higher in the 2000 cohort (58.7%) (p < 0.001). Prevalence of obesity at entry was zero in the 1980 cohort, compared to 4% and 5.6% in the 1990 and 2000 cohorts, respectively (p = 0.143). Prevalence of overweight at entry was significantly higher in the 2000 cohort (33.6%), compared to 1980 (11.8%) and 1990 (18.9%) cohorts(p < 0.001).

The prevalence of healthy weight (1980 = 38.2%, 1990 = 41.7%, and 2000 = 33.6%; p = 0.411) and obesity (1980 = 20.6%, 1990 = 24.6%, and 2000 = 23.8%; p = 0.751) were not different between groups at the last periodic examination. There was little difference in the frequency of overweight at the last periodic examination between the 1980 and 2000 cohorts (39.7% compared to 37.8%). However, there was a difference between these groups and the 1990 cohort, where frequency was 24.6% (p = 0.012). Finally, the frequency of underweight in the 1980, 1990, and2000 cohorts was 1.5%, 7.4%,and 0.7%, respectively (p = 0.005).

Body weight and BMI by work shift and occupation

Table 3 shows that no statistically significant difference in body weight and BMI was found between the day shift and night shift groups and gender, skin color, and age groups.

Table 3 Characteristics of workers by work shift (Porto Alegre, Brazil 2017). 

Day shift (n = 265) Night shift (n = 121) P-value
Age (years) 29.43 ± 7.57 29.04 ± 6.73 0,6201
Gender (Female) 76.2% 73.6% 0,3562
Skin color (White) 72.1% 66.9% 0,1832
Pre-employment examination 63.11 ± 12.07 61.96 ± 12.27 0,3881
Final periodic examination 71.76 ± 14.99 71.41 ± 14.95 0,8351
BMI (kg/m2)
Pre-employment examination 23.17 ± 3.55 22.86 ± 3.2 0,4111
Final periodic examination 26.5 ± 5.46 26.49 ± 5.56 0,9851
Classification of BMI
Pre-employment examination 0,4882
Underweight 5.3% 4.1%
Healthy weight 66.4% 69.4%
Overweight 22.6% 24%
Obese 5.7% 2.5%
Final periodic examination 0,9262
Underweight 3.8% 4.1%
Healthy weight 37.4% 39.7%
Overweight 31.7% 33.1%
Obese 24.5% 21.5%

BMI = body mass index. 1 Student›s t-test; 2 Chi-squared test.

Table 4 shows that the proportion of male administrative staff is greater than the proportion of male care staff. Furthermore, the body weight of administrative staff is greater than that of care staff in the pre-employment medical examination. However, this difference is not maintained in the last periodic examination.

Table 4 Characteristics of workers by occupation (Porto Alegre, Brazil 2017). 

Care staff n = 248 Administrative staff n = 137 P-value
Age (years) 29.6 ± 6.44 28.8 ± 8.7 0.3491
Gender (Female) 17.7% 35% <0.0012
Skin color (White) 73.8% 65% 0.0752
Pre-employment examination 61.79 ± 11.98 64.44 ± 12.28 0.0421
Final periodic examination 71.15 ± 15.22 72.44 ± 14.52 0.4231
BMI (kg/m2)
Pre-employment examination 22.94 ± 3.48 23.31 ± 3.39 0.3031
Final periodic examination 26.37 ± 5.63 26.71 ± 5.25 0.5671
Classification ofBMI
Pre-employment examination
Underweight 5.2% 4.4% 0.7342
Healthy weight 69.8% 62.8%
Overweight 20.6% 27.7%
Obese 4.4% 5.1%
Final periodic examination
Underweight 4% 3.6% 0.8282
Healthy weight 39.9% 35%
Overweight 30.6% 34.3%
Obese 22.6% 25.5%

BMI = body mass index. 1 Student›s t-test; 2 Chi-squared test.


The findings of this retrospective cohort study show that body weight and BMI at entry was lower in the1980 cohort in comparison to the 1990 and 2000 cohorts. Weight increased at a crescent rate and variation in weight and BMI between successive decades was greatest in the 2000 cohort. Work shift and occupation were not determining factors that account for differences in weight and BMI among workers. The increase in weight and BMI over the last three decades reflects the general increase in overweight and obesity in Brazil6,7in step with global trends14.

Studies in Brazil have reported different prevalence rates of overweight and obesity among hospital workers, ranging from 65.6% in a pre-hospital care service in Salvador15, to 63.9% in a university hospital in Londrina16and 53.3% in a private general hospital in São Paulo17. Another study with nursing staff working in intensive and emergency care units reported prevalence rates of overweight and obesity of 37.8% and 31.1%, respectively18.

Differences in the variation in body weight were also found by a study involving 1,341 men and women in Florianópolis, which reported mean weight gain of 10.4 ± 9.3 kg in women and 11.1 ± 9.1 kg in men over a period of 12 years19. Research involving staff from an energy company in São Paulo reported an increase in overweight equivalent to a variation in BMI of over 4kg/m2over the course of 20 years20, while a study with doctors showed a 17% increase in prevalence of overweight over a 15 year period21.

People who are overweight are more likely to be absent from work due to illness. Being overweight means that daily work activities can become more taxing, as extra weight puts additional strain and pressure on the back and influences posture. It can also influence work performance and productivity, negatively affecting quality of life at work22. Furthermore, overweight and obesity can lead to the increased use of health services, resulting in higher costs to businesses and society23.

The fact that the 2000 cohort had a higher body weight at entry and greater weight gain per decade in comparison to the other cohorts may be associated with the nutrition transition and the impact of increasingly sedentary lifestyles, coupled with high intake of energy-dense foods24. Eating outside of the home, increased consumption of ultra-processed foods, and the replacement of meals with snacks with a high concentration of energy, sugar and salt, contribute to increased energy intake25. However, the higher body weight in the 2000 cohort may also be related to age (young adults aged 30 years and over). In this respect, a population-based study conducted with 2,436 adults in Denmark between 1982 and 1994 reported mean weight gain of between 0.9 and 1.2 kg/year in individuals aged between 30 and 40 years26. In addition, a longitudinal analysis of seven cohort studies with German adults aged between18 and 83 years initially assessed between 1994 and 2007 and followed up for a period of four to 11.9 years showed mean weight gain of 0.25 kg/year. The results showed that weight gain was associated with age at the beginning of the period and significantly greater in people aged under 45 years, corroborating the findings of the present study, which showed pronounced weight gain in young adults27. Another study comparing seven population-based prospective cohorts in Finland spanning different times periods (1972 – 2007) reported weight gain of 0.3kg/year in both sexes, showing that it was more pronounced in cohorts where follow-up was started later (1980 and 1990 x 1970) and in younger individuals, as in the present study28.

The manifestation of the nutrition transition in these workers is worrying, given that there is a direct association between the increase in prevalence and incidence of obesity and the escalating prevalence and incidence of diabetes, hypertension, and cardiovascular diseases, which are chronic diseases that have high impact on morbidity and mortality and elevated social cost11,29. A cohort study with 114,281 American nurses reported that the risk of developing type 2 diabetes was 1.9 times greater in those who gained between 5 and 8 kg throughout the study period (1976 – 1990), compared to nurses whose weight remained stable4. Evidence shows that people who are obese have a two-fold increased risk of hypertension, compared with non-obese people16. Furthermore, studies have shown that the risk of cardiovascular disease is greater in obese women30 and people who work night shifts31. The latter have a 40% increased risk of developing cardiovascular diseases, in comparison to people who work during the day31. A study conducted in Brazil revealed a 67% increased risk of cardiovascular disease among people who work night shifts32, while no association was found between working during the day and increase in weight and BMI.

The fact that the present study was retrospective and that data was collected from staff records is a potential limitation, give that this methodology makes it impossible to standardize data collection. In this respect, incomplete staff records meant that a considerable proportion of workers could not be included in the study. Furthermore, convenience sampling was used, where the proportion of workers included in the sample was less than 50% of the total number of staff taken on each period, meaning that the sample may not be representative. This may be justified by the length of time between employment start date and data collection (minimum of10 years and maximum of 30 years), meaning that data for staff who no longer work at the hospital was not available. Finally, the lack of more detailed information on the staff records prevented the assessment of complementary variables, such as presence of comorbidities associated with overweight and obesity like diabetes, hypertension, and dyslipidemia.

On the other hand, this study offers valuable insights into the health profile of workers and provides important inputs to inform the development of health strategies for specific groups of workers. The increase in overweight observed by this study is alarming given that excessive weight gain increases the risk of morbidity, disability, and mortality, leading to increased social costs. Furthermore, the present study provides data on the nutrient profile of hospital workers from the last 30 years and is original in so far as it compares workers who joined the hospital in different decades, different occupations, and day and night shift workers.

Final considerations

The results reveal that the increase in weight gain and BMI observed in all cohorts was more pronounced in the group of workers who joined the hospital in 2000. These findings underscore the need for nutritional surveillance, through the monitoring of the nutritional status of workers and implementation of health education programs aimed at encouraging healthy eating practices and regular physical activity, improving quality of life, and reducing preventable health problems.


1. Organização Pan-Americana de Saúde (OPAS), Organização Mundial de Saúde (OMS). Obesidade. [acessado 2017 Jan 6]. Disponível em: ]

2. Chan JM, Rimm EB, Colditz GA, Stampfer MJ, Willett WC. Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. Diabetes Care 1994; 17(9):961-969. [ Links ]

3. Nguyen T, Lau DC. The obesity epidemic and its impact on hypertension. Can J Cardiol 2012; 28(3):326-333. [ Links ]

4. World Health Organization (WHO). Obesity and overweight. [acessado 2016 Fev 22]. Disponível em: ]

5. Smith KB, Smith MS. Obesity Statistics. Prim Care 2016; 43(1):121-135. [ Links ]

6. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa de orçamento familiar (POF) 2008-2009: Antropometria e Estado Nutricional de Crianças, Adolescentes e adultos no Brasil. [acessado 2016 Set 5]. Disponível em: ]

7. Brasil. Ministério da Saúde (MS). Vigilância de Fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico: VIGITEL Brasil 2014. [acessado 2016 Fev 22]. Disponível em: ]

8. Chandola T, Brunner E, Marmot M. Chronic stress at work and the metabolic syndrome: prospective study. BMJ 2006; 332(7540):521-525. [ Links ]

9. Fonseca MJM, Faerstein E, Chor D, Lopes CS, Andreozzi VL. Associações entre escolaridade, renda e Índice de Massa Corporal em funcionários de uma universidade no Rio de Janeiro, Brasil: Estudo Pró-Saúde. Cad Saude Publica 2006; 22(11):2359-2367. [ Links ]

10. Höfelmann DA, Blank N. Excesso de peso entre trabalhadores de uma indústria: prevalência e fatores associados. Rev Bras Epidemiol 2009; 12(4):657-670. [ Links ]

11. Petarli GB, Salaroli LB, Bissoli NS, Zandonade E. Autoavaliação do estado de saúde e fatores associados: um estudo em trabalhadores bancários. Cad Saude Publica 2015; 31(4):787-799. [ Links ]

12. Freitas PP, Assunção AA, Bassi IB, Lopes ACS. Excesso de peso e ambiente de trabalho no setor público municipal. Rev. Nutr. 2016; 29(4):519-527. [ Links ]

13. Brasil. Ministério da Saúde (MS). Diretrizes de implantação da Vigilância em Saúde do Trabalhador no SUS. Brasília: MS; 2012. [acessado 2016 Set 5]. Disponível em: ]

14. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of Body Mass Index among US adults, 1999 – 2010. JAMA 2012; 307(5):491-497. [ Links ]

15. Cavagioni LC, Pierin AMG. Hipertensão arterial em profissionais que atuam em serviços de atendimento pré-hospitalar. Texto Contexto Enferm 2011; 20(3):235-244. [ Links ]

16. Porto BD, Arruda GA, Altimari LR, Cardoso Júnior CG. Autopercepção de saúde em trabalhadores de um Hospital Universitário e sua associação com indicadores de adiposidade, pressão arterial e prática de atividade física. Cien Saude Colet 2016; 21(4):1113-1122. [ Links ]

17. Sarno F, Monteiro CA. Importância relativa do Índice de Massa Corporal e da circunferência abdominal na predição da hipertensão arterial. Rev Saude Publica 2007; 41(5):788-796. [ Links ]

18. Silveira CDS, Urbanetto JS, Silva PC, Magnago TS, Figueiredo CEP. Perfil de sobrepeso e obesidade em trabalhadores de enfermagem em unidades de cuidado intensivo e emergência. Cien Saude Colet 2013; 6(3):157-162. [ Links ]

19. Coelho MSPH, Assis MAA, Moura EC. Aumento do índice de massa corporal após os 20 anos de idade e associação com indicadores de risco ou de proteção para doenças crônicas não transmissíveis. Arq. Bras. Endocrinol Metab 2009; 53(9):1146-1156. [ Links ]

20. Ciorla LAS, Godoy MF. Fatores de risco cardiovascular e mortalidade. Seguimento em longo prazo (até 20 anos) em programa preventivo realizado pela medicina ocupacional. Arq. Bras. Cardiol. 2005; 85(1):20-25. [ Links ]

21. Jardim TSV, Jardim PCVB, Araújo WEC, Jardim LMSSV, Salgado CM. Fatores de risco cardiovascular em coorte de profissionais da área médica – 15 anos de evolução. Arq Bras Cardiol 2010; 95(3):332-338. [ Links ]

22. Colares LGT, Freitas CM. Processo de trabalho e saúde de trabalhadores de uma unidade de alimentação e nutrição: entre a prescrição e o real do trabalho. Cad Saude Publica 2007; 23(12):3011-3020. [ Links ]

23. Thompson D. The costs of obesity: what occupational health nurses need to know. AAOHN J 2007; 55(7):265-270. [ Links ]

24. Jaime PC, Silva ACF, Gentil PC, Claro RM, Monteiro AC. Brazilian obesity prevention and control initiatives obesity reviews. Obes. Rev. 2013; 14(Supl. 2):88-95. [ Links ]

25. Monteiro CA, Levy RB, Claro RM, Castro IR, Cannon G. Increasing consumption of ultra-processed foods and likely impact on human health: evidence from Brazil. Public Health Nutr 2011; 14(1):5-13 [ Links ]

26. Heitmann BL, Garby L. Patterns of long-term weight changes in overweight developing Danish men and women aged between 30 and 60 years. Int J Obes Relat Metab Disord 1999; 23(10):1074-1078. [ Links ]

27. Haftenberger M, Mensink GB, Herzog B, Kluttig A, Greiser KH, Merz B, Nöthlings U, Schlesinger S, Vogt S, Thorand B, Peters A, Ittermann T, Völzke H, Schipf S, Neamat-Allah J, Kühn T, Kaaks R, Boeing H, Bachlechner U, Scheidt-Nave C, Schienkiewitz A. Changes in body weight and obesity status in German adults: results of seven population-based prospective studies. Eur J Clin Nutr 2015 70(3):300-305. [ Links ]

28. Pajunen P, Vartiainen E, Männistö S, Jousilahti P, Laatikainen T, Peltonen M. Intra-individual changes in body weight in population-based cohorts during four decades: the Finnish FINRISK study. Eur J Public Health 2010; 22(1):107-112. [ Links ]

29. Pinheiro ARO, Freitas SFT, Corso ACT. Uma abordagem epidemiológica da obesidade. Rev Nutr 2004; 17(4):523-533. [ Links ]

30. Souza RMRP, Sobral DP, Paz SMRS, Martins MCC. Prevalência de sobrepeso e obesidade entre funcionários plantonistas de unidades de saúde de Teresina, Piauí. Rev Nutr 2007; 20(5):473-482. [ Links ]

31. Chen JD, Lin YC, Hsiao ST. Obesity and high blood pressure of 12-hour night shift female clean-room workers. Chronobiol Int 2010; 27(2):334-344. [ Links ]

32. Pimenta AM, Kac G, Souza RRC, Ferreira LMBA, Siqueira SMF. Trabalho noturno e risco cardiovascular em funcionários de universidade pública. Rev Assoc. Med Bras 2012; 58(2):168-177. [ Links ]


DRB Montzel participated in study design, data analysis and interpretation, and in drafting this article; BVL Costa participated in data interpretation and in the revision of this article; FM Silva participated in study conception and design, data analysis and interpretation, and in drafting this article.

Received: January 11, 2017; Revised: October 04, 2017; Accepted: October 06, 2017

Creative Commons License  This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.