Journal of Nutritional Health & Food Science

Background
Self-reported height and weight measurement is a simple and a non-invasive method of collecting data in population surveys. However, the inaccuracy of self-reported data may bias the population nutritional status evaluation. The aim of this study is to compare the obesity prevalence estimated with self-reported anthropometric data and with measured weight and height using data from the National Health Survey, Brazil, 2013.

Methods
A three-stage cluster sampling (census tracts, households, and individuals) with stratification of the primary sampling units and random selection in all stages was used to select Brazilian adults aged 18 years and over. Excluding pregnant women, measurements of weight and height were taken among all selected adults and were also self-reported during the interview on the same day. Logistic regression models were used to investigate the sociodemographic factors and lifestyles associated with the outcome "reported weight and height during the interview”. Obesity prevalence estimates calculated with self-reported and measured weight and height were compared in all variable categories.

Results From the sample of 59,402 adults, 70.2% self-reported weight and height. Men were most likely to report their weight (OR = 1.14) and white people when compared to non-white (OR = 0.60). People from the upper socioeconomic class are 6.5 times more likely to report their weight and height during the interview. Healthy habits and medical consultation in the past year were significantly associated with the outcome. Among women, obesity prevalence estimated with self-reported measures (20.5%) was significantly lower than those with measured weight and height (24.4%), with larger differences among women of high socioeconomic status. No significant differences were found among men.

Conclusions
Given that the body mass index is used as a guide for identifying health risks, these findings indicate that direct measurement of height and weight should be performed whenever possible in Brazilian surveys to adequately support public health policies.
Keywords: Health surveys; Nutritional status; Obesity; Self-reported anthropometric measures.

Currently, obesity is one of the most important global health problems and is considered a worldwide epidemic due to the progressive increase in the last decades in many developed and developing countries [1]. Because obesity is a risk factor for several non-communicable chronic diseases, it is responsible for a considerable loss both in life expectancy and quality of life [2, 3].

Globally, a one-third increase in years of life lost due to excess weight/obesity has been estimated since 2003 [4]. Increasing trends in obesity-related diseases such as diabetes and cardiovascular diseases are associated with the increasing number of premature deaths and high degree of severe functional limitations and disabilities due to those NCDs [5].

In Brazil, the increase in the obesity prevalence took place together with the decline in undernutrition in children and adults. The antagonism in the temporal trends of undernutrition and obesity was a striking feature of the nutritional shift in Brazil [6]. Comparing the prevalence of overweight and obesity across the years 2000s, there is a relevant increase in prevalence estimates for both men and women [7]. In 2017, the Global burden of disease(GBD) estimates indicated that high Body mass index (BMI) was responsible for 13% of all deaths in Brazil, with cardiovascular disease and diabetes the most prevalent causes of deaths [8].

Monitoring obesity prevalence is essential for public health.Assessing anthropometric measures through health surveys allows us to monitor the overweight/obesity trends in different geographic areas andto identify the main determinants essential to support public health policies for preventing obesity and promoting healthy habits since childhood [9].

There are several different ways of measuring obesity. The Body mass index (BMI) is the most used indicator in clinical practice and research [10]. This indicator is calculated by the ratio between the weight and the square of the height. According to the World Health Organization (WHO) classification, values higher or equal to 25.0 kg/m² indicate weight excess and values higher or equal to30.0 kg/m² characterize obesity [1].

Self-reported height and weight measurement is a simple, inexpensive, and non-invasive method of collecting data inpopulation surveys. However, the inaccuracy of self-reported data may bias the Body max index (BMI) estimates and thus the nutritional status evaluation [11]. The exclusion of participants unaware of their weight or height is an additional source of bias that decreases the sensitivity in measuring the BMI and estimating obesity prevalence [12].

There are controversies in the national and international literature on the validity of self-reported anthropometry to assess the nutritional status of the population. Studies in Brazil, United States and Mexico indicated that reporting errors resulted in small biases and self-reported anthropometry was sufficiently valid for epidemiological purposes [13-15]. In contrast, analyses of anthropometric data in Japan, Korea, and Spain [16-18] led to the conclusion that self-reported weight and height were not sufficiently accurate to assert the validity of their use in epidemiological studies on the general adult population. Finally, some articles have indicated the validity of self-reported weight and height to assess the overall population nutritional status but show significant differences in some specific population groups [19,20].

In Brazil, self-reported weight and height have been annually monitored by the VIGITEL survey (“Surveillance of Risk and Protection Factors for Chronic Diseases by Telephone Survey”) since 2006 [21]. The National Health Survey carried out in 2013 brought the opportunity to validate self-reported anthropometric data in Brazil as measured weight and height are also available for all participants [22].

The aim of this study is to compare the obesity prevalence estimated with self-reported anthropometric data and with measured weight and height using data from the National Health Survey-2013. Under the hypothesis that the demographic, socioeconomic and lifestyle factors as well as health care use may influence the self-reported weight and height during interviews [12, 17, 18], these factors were analyzed as potential explanatory variables for missing self-reported anthropometric data and misestimation of obesity prevalence.

Study Design
The National Health Survey is a nationwide household-based survey carried out by the Ministry of Health in partnership with the Brazilian Institute of Geography and Statistics (IBGE) in year 2013 aimed at assessing health conditions and health system performance of Brazilian states and regions. The 2013 PNS was approved by the National Commission of Ethics in Research (CONEP) in June 2013 (No. 328.159).

Sampling
In the PNS, 2013 the surveyed population includes Brazilian residents of private households, except those located in special census tracts (barracks, military bases, halls of residence, settlements, boats, penitentiaries, penal colonies, prisons, jails, asylums, orphanages, convents, and hospitals) and in indigenous lands. A three-stage cluster sampling (census tracts, households, and individuals) was used with stratification of the primary sampling units (PSUs) and random selection in each stage. Census tracts or set of sectors compose the primary sampling units (PSUs), households are the units of the second stage and the residents selected with equiprobability in sampled households define the third-stage units. Details of the sampling process are available in a previous publication [22].

At the end of fieldwork, 69,994 households were occupied, and 64,348 household interviews were held. The non-response rate was 8.1%. The expansion factors were calculated by the inverse of the selection probability product at each stage [23].

Measurement Instruments
The PNS questionnaire is divided into three parts: characteristics of the household (water and sanitation, electricity, household assets); demographic and health information on all household residents (sociodemographic characteristics, access and utilization of health care, private health insurance coverage for all household members); and the individual questionnaire, which includes modules on self-perception of health, lifestyles, and noncommunicable chronic diseases). Only one household member (key informant) answers the first and second parts for all household residents. The individual questionnaire is answered by a resident selected with equal probability in each sampled household [24].

The total sample of PNS-2013 included 60,202 selected adult individuals, who answered the individual questionnaire and had their anthropometric measurements of weight and height measured by a previously trained interviewer. Excluding 800 women who were pregnant at the survey interview, weight and height were measured in a total of 59,402 individuals. Additionally, self-reported weight and height were questioned during the individual interview on the same day as measurements, allowing a comparison of self-reported weight and height measurements with those measured. The following questions were asked during the interview “Do you know your weight?" and "Do you know your height?"with answers (yes; no). In the case of affirmative responses, participants were asked to specify their weight and height.

To measure the weight, portable scales with a maximum weight of 180 kilos were used and calibrated daily to ensure the standardization of measurements throughout the anthropometric data collection in the survey. The participants were weighed barefoot, without accessories and wearing light clothing. To measure the height, a stadiometer with a capacity of 210 centimeters was used. The equipment was positioned on a wall with minimal unevenness at the household. The measurement details are described in the PNS anthropometry manual (www. pns.fiocruz.br).

Data Analysis
In this study, the following sociodemographic variables were considered: gender (male, female); age range (18-19, 30-39, 40- 49, 50-59, 60-69, 70 years or older); skin color (white, black, brown); and degree of education (incomplete elementary school, complete elementary and incomplete high school, complete high school or more).

To establish the socioeconomic status (SES), we calculated an index based on the number of household goods (attributing higher scores to those with higher value), degree of education of the household head, and presence of monthly paid housekeeper, using a standard Brazilian classification scale for socioeconomic class: (A/B) upper class; C (middle class); and lower class (D/E), adapting the Brazilian Economic Classification Criteria [25].

Regarding lifestyles, the following health behaviors were considered: regular intake of fruits and vegetables; regular practice of leisure-time physical activity; meal replacement with snacks, pizza, sandwiches on 3 days or more per week; consumption of sweets or chocolates on 3 days or more per week. Finally, to investigate the influence of health care utilization on the self-reported anthropometric measures, the following indicators were used: medical consultation and health examinations (checked blood pressure, blood glucose, cholesterol) in the last 12 months prior to the survey; a year or more prior to the survey; and never.

In the statical data analysis, logistic regression models were used to identify the factors associated to missing self-reported weight, height and both measurements during the interview. The crude and adjusted odds ratio (OR), controlled by degree of education, gender and age range were the measures of association.

The obesity prevalence was estimated with the measured weight and height and with the self-reported anthropometric measures. Prevalence estimates were compared according to the categories of the variables considered in the study and the Mc Nemar test was used for testing differences at the 5% level.

As the PNS design used stratification of census tracts and multiple stage cluster selection, the complex sample design was considered in the statistical analysis.

A total of 59,402 people had their weight and height measured at the PNS. Among these participants, 85.6% reported their weight and 74.9% reported their height during the interview.

Results presented in table 1 show men were most likely to report their weight when compared to women (OR = 1.14) and white individuals when compared to non-white ones (OR = 0.60). Regarding age, higher odds of reporting weight were found among adults aged 30-39 and 40-49 years while the lower oddsratio was found among the oldest (OR = 0.59). Socioeconomic inequalities were found: the higher the degree of education and the better the SES, the higher theodds of self-reporting weight.

Associations of self-reporting weight with health behaviors were also found (Table 1). People who have healthy behaviors, such as regular fruit and vegetable intake and regular practice of leisure physical activity, are more likely to report their own weight (OR = 1.22 and OR = 1.84, respectively). However, among individuals who have unhealthy behaviors, such as those who regularly replace meals and consume sweets, direct significant associations with the outcome were also found.

Regarding self-reporting height, the sociodemographic and lifestyle variables were the same that showed a significant association with self-reporting weight, but the odds ratios were higher, and the gradients by educational level and SES were more pronounced (Table 1).

As to reporting both anthropometric measures during the interview, only 70.2% reported both weight and height, resulting in a final loss of 29.8% for the body mass index estimation. The results from the logistic regressions showed higher odds of reporting weight and height (ORcrude and adjusted) among males, people aged 30-59 years, and white people. Large socioeconomic gradients were found. People with better educational levelare 4 timesmore likely to inform both anthropometric measures, andpeople from the upper socioeconomic class are 6.5 times more likely of reporting their weight and height during the interview (Table 2).

Regarding health behaviors, people who regularly intake fruits and vegetables (FLV) and practice recommended physical activity are more likely to report their anthropometric measures (OR = 1.3 and OR = 1.8, respectively). However, people who have unhealthy nutritional behaviors, such as those who regularly replace meals and consume sweets and chocolates, are also more likely to report their weight and height (Table 2).

Analysis of the associations between heath care utilization and self-reporting weight and height showed significant odds-ratios with medical consultation, blood pressure measurement and cholesterol and glucose tests in the last 12 months prior to the survey. The highest significant adjusted oddratio was found among individuals who measured their blood pressure in the past 12 months, even after controlling for sex, age groups and degree of education (OR adjusted = 2.18) (Table 3).

In table 4, the obesity prevalence was estimated with measured and self-reported weight and height. Among women, the estimated obesity prevalence was 24.4%, nearly 4 percentage points higher than the prevalence calculated with self-reported weight and height (20.5%). The highest differences were foundamong women aged 50-59 years (6.4%), women of high socioeconomic status (4.9%), and among white women (4.6%). In relation to nutritional habits, the obesity prevalence estimated with measured weight and height was always higher than the obesity prevalence calculated with self-reported anthropometric measures, whether being a healthy or an unhealthy behavior. As to medical consultation in the past 12 months, a difference of 3.8 percentage points was found. Among men, no significant differences in the prevalence estimates were found.

This study showed that a considerable fraction of the Brazilian population did not report anthropometry during the interview, with only 70% informing both weight and height. Among people most likely to report their anthropometric measurements, there is a predominance of men, young adults, people with higher educational level and socioeconomic status, and adequate health care utilization.

The findings indicated that height was the least self-reported measure corroborating results from previous national studies [13,26]. One possible explanation for reporting weight more frequently is that routine self-weighing is a usual practice, which has a positive influence on weight loss or weight gain prevention [27].

In this study, anthropometry missing information were more often found among people of low educational level and worse socioeconomic status. Evidence of the effects of socioeconomic status on unawareness of nutritional problems have been documented before, with results invariably unfavourable to the disadvantaged groups [28-30], probably due to difficulties in accessing preventive health measures and lack of information on the importance of monitoring weight [31]. Since awareness of weight problems is essential for prevention and treatment, new health care approaches should be implemented in different social contexts focused on raising anthropometry awareness.

As to gender differences in self-reporting anthropometry, the losses were greater among women. The fact that women informed their weight and height less frequently may be associated with the dissatisfaction with their body image, mainly influenced by social and cultural factors [32]. In a study in the United States, women underestimated more than men on weight and men overestimated more than women on height [33]. In a review of studies that examined the accuracy of self-reported weight, all studies reported that women underestimated weight and a significant percentage of women in specific groups had large errors [10].

Our findings also show that the elderly report anthropometric measurements less frequently, corroborating previously documented result [34, 35]. In addition to the lower educational level among old Brazilian people, there is a natural aging process that leads to changes in body composition influencing the anthropometry awareness [36].

Regarding adequate health care utilization, all indicators considered in this study were associated with self-reported weight and height. The results of a recent study in Japan indicated that regular health examination results are positively associated with attitudes toward improving health habits and to acknowledge the obesity-related health risks [37]. In Brazil, population aging has come with the growth of the chronic non-communicable diseases (NCDs) and most public health efforts have focused on promoting health behaviors and reducing risk factorsat primary health care. Routine height and weight measurements in primary health care services [38] have certainly increased the awareness of anthropometric measuresand the rate of self-reporting anthropometry among those who use health care.

In the same context, our findings showed a positive association of healthy behavior adoption with self-reporting anthropometry during the interview. Paradoxically, however, population groups with unhealthy nutritional behaviors were also more likely to report their own weight and height. A recent Brazilian study shows a higher prevalence of some foods considered unhealthy, such as sweets, sandwiches, snacks, and pizzas, among the most favored social segments expressing the concomitance of healthy and unhealthy eating habits [39]. Thus, the more frequent report of the anthropometric measures among people with unhealthy nutrition habits is an example of reverse causation in crosssectional studies, where the association between exposure and outcome is not due to direct causation [40].

The comparison of the obesity prevalence calculated with measured weight and height and self-reported anthropometric measures, among women, shows a large and significant difference of about 4 percentage points. The underlying determinants of these biases have been discussed before, placing special emphasis on the role played by social norms [18]. Our findings suggest that women of higher educational level and SES are less prone to report their real weight andthe lower self-reported weight may be reflecting the dissatisfaction with their body image [33]. The most pronounced difference, greater than 6 percentage points, was found among women aged 50-59 years old. This age-group is characterized by the menopause onset, reduction in metabolism, and increasing weight. The abrupt change in body mass is a possible explanatory factor for the underestimation ofselfreported BMI due to discomfort with weight gain and physical changes related to menopause [41].

Different kind of problems were identified in the self-reported anthropometric measures in the Brazilian population. Firstly, BMI missing data are associated with unawareness of weight or height among people of low educational level and inadequate health care utilization. Secondly, underestimation of self-reported isrelated to dissatisfaction with their own body image. Finally, considering the progressive changes in the human body during life, missing anthropometric measures or incorrect self-reported information were found due to the lack of routine measurement of weight and height. As recommended by the Ministry of Health, food and nutrition surveillance should be part of the routine of health care services, especially in primary health care [38]. These routine health consultations are opportune not only for measuring weight and height, but also for promoting healthy behaviors [42, 43].

In conclusion, this study identified the main factors affecting the self-reporting of weight and height during the interview. The findings showed that missing anthropometryself-reported information is socially selective and unequal in the Brazilian population. Moreover, among people who reported weight and height during the interview, inaccurate measurements were found mainly among females, and caused significant inaccuracies in calculation of body mass index and obesity prevalence estimates [11]. Given that BMI is used as a guide for identifying persons at risk for diseases and for monitoring time-spatial trendsin the population nutritional status, these findings indicate that direct measurement of height and weight should be performed whenever possible in Brazilian surveys to adequately support public health policies.

One of the limitations of this studyis related to the quality of measured weight and height during fieldwork due to unevenness of the floor or walls of some sampled households. Other limitations stem from the cross-sectional design of the PNS, and bias in causal associations cannot be disregarded as the presence of risk factors and outcomes are measured simultaneously.

CEP Identification / Approval Number
PNS-2013 was approved in June 2013 by the National Ethics Commission for Research Involving Human Beings (Process No. 328.159).

Individual Collaboration of Each Author in the Preparation of the Manuscript
All authors contributed to the conception, analysis, interpretation of data and writing of the article.
APSF, CLS, GND contributed to the conception and design of the study, analysis and interpretation of data, preparation of preliminary versions of the article and critical review, participation in the final writing and critical review of the article.

CSB, PRBSJ contributed to the analysis and interpretation of the data, participation in the final writing and review of the article.

1. World Health Organization (WHO). Global status report on noncommunicable diseases 2014. 1st ed. Geneva: World Health Organization; 2015. 298p.
2. Paola Caroline L Leocadio, Synara C Lopes, Ronaldo P Dias, Jacqueline I AlvarezLeite, Richard L Guerrant, Joao O Malv, et al. The transition from undernutrition to overnutrition under adverse environments and poverty: the risk for chronic diseases. Front Nutr. 2021;8:676044.doi: 10.3389/fnut.2021.676044
3. Stella Lartey, Lei Si, Thomas Lung, Costan G Magnussen, Godfred O Boateng, Nadia Minicuci, et al. Impact of overweight and obesity on life expectancy, quality-adjusted life years and lifetime costs in the adult population of Ghana. BMJ Glob Health. 2020;5(9):e003332.doi: 10.1136/bmjgh-2020-003332
4. Kevin R Fontaine, David T Redden, Chenxi Wang, Andrew O Westfall, David B Allison. Years of life lost due to obesity. JAMA 2003;289:187-193.doi: 10.1001/jama.289.2.187
5. Daphne P Guh, Wei Zhang, Nick Bansback, Zubin Amarsi, C Laird Birmingham, Aslam H Anis. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health. 2009;25:9-88.doi: 10.1186/1471-2458-9-88
6. WolneyLisboa Conde, Carlos Augusto Monteiro. Nutrition transition and double burden of undernutrition and excess of weight in Brazil. Am J ClinNutr. 2014;100(6):1617-1622.doi: 10.3945/ajcn.114.084764
7. Arthur Pate de Souza Ferreira, Celia Landmann Szwarcwald, Giseli Nogueira Damacena. Prevalence of obesity and associated factors in the Brazilian population: a study of data from the 2013 National Health Survey. Rev bras Epidemiol2019; 22:e190024.doi: 10.1590/1980-549720190024
8. FelisbinoMendes MS, Cousin E, Malta DC, Machado ÍE, Ribeiro ALP, Duncan BB, et al. The burden of non-communicable diseases attributable to high BMI in Brazil, 1990-2017: findings from the Global Burden of Disease Study. Popul Health Metr. 2020;18(Suppl 1):18.
9. World Health Organization (WHO). Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organization Technical. Report Series 854. Geneva: World Health Organization, 1995. 463p.
10. Engstrom JL, Paterson SA, Doherty A, Trabulsi M, Speer KL. Accuracy of self -reported height and weight in women: an integrative review of the literature. J Midwifery Women Health. 2003;48(5):338-345.
11. S Connor Gorber, M Tremblay, D Moher, B Gorber. A comparison of direct vs. self-report measures for assessing height, weight and body mass index: a systematic review. Obes Ver. 2007;8(4):307-326.doi: 10.1111/j.1467-789X.2007.00347.x
12. E Ramos, C Lopes, A Oliveira, H Barros. Unawareness of weight and height-the effect on self-reported prevalence of overweight in a population-based study. J Nutr Health Aging. 2009;13(4):310-314.
13. Naiara Ferraz Moreira, Veronica Gronau Luz, Caroline Camila Moreira, Rosangela Alves Pereira, Rosely Sichieri, Marcia Goncalves Ferreira. Self-reported weight and height are valid measures to determine weight status: results from the Brazilian National Health Survey (PNS 2013). Cad SaudePublica.2018;34(5):e00063917.doi: 10.1590/0102-311X00063917
14. Benjamin M Craig, Alexandra K Adam. Accuracy of body mass index categories based on self-reported height and weight among women in the United States. Matern Child Health J. 2009;13:489-496.doi: 10.1007/s10995-008-0384-7
15. Eduardo Ortiz Panozo, Elsa Yunes Diaz, Martin Lajous, Isabelle Romieu, Adriana Monge, Ruy Lopez Ridaura. Validity of self-reported anthropometry in adult Mexican women. SaludPublica Mex. 2017;59(3):266-275.doi: 10.21149/7860
16. Nayu Ikeda. Validity of self-reports of height and weight among the general adult population in Japan: Findings from National Household Surveys, 1986. PLoS One. 2016;11(2):e0148297.doi: 10.1371/journal.pone.0148297
17. Kyuhyun Yoon, Soong Nang Jang, Heeran Chun, Sung-Il Cho. Self-reported anthropometric information cannot vouch for the accurate assessment of obesity prevalence in populations of middle-aged and older Korean individuals. Arch GerontolGeriatr. 2014;59(3):584-592.doi: 10.1016/j.archger.2014.08.008       
18. Joan Gil, Toni Mora. The determinants of misreporting weight and height: The role of social norms. Econ Hum Biol. 2011;9(1):78-91.doi: 10.1016/j.ehb.2010.05.016
19. J Y Park, P N Mitrou, R H Keogh, R N Luben, N J Wareham, K T Khaw. Effects of body size and sociodemographic characteristics on differences between self-reported and measured anthropometric data in middle-aged men and women: the EPIC-Norfolk study. Eur J ClinNutr. 2011;65(3):357-367.doi: 10.1038/ejcn.2010.259
20. Maria Dolores Marrodan, JesusRomanMartinezAlvarez, Antonio Villarino, Irene AlferezGarcia, Marisa Gonzalez Montero de Espinosa, NoemiLopezEjeda. Utility of self-reported anthropometric data for evaluation of obesity in the Spanish population; study EPINUT-ARKOPHARMA. Nutr Hosp. 2013;28(3):676-682.doi: 10.3305/nh.2013.28.3.6197
21. Karine Maria de MeloBrebal, Jonas Augusto Cardoso da Silveira, Risia Cristina Egito de Menezes, Silvany Barros OlimpioEpifanio, Patricia de Menezes Marinho, Giovana Longo Silva. Weight gain and changes in nutritional status of Brazilian adults after 20 years of age: a time-trend analysis (2006-2012). Rev Bras Epidemiol. 2020;23:e200045. doi: 10.1590/1980-549720200045
22. Paulo Roberto Borges de Souza Junior, Marcos Paulo Soares de Freitas, Giuseppe de Abreu Antonaci, Celia Landmann Szwarcwald. Sampling design of the National Health Survey 2013. EpidemiolServSaude. 2015;24(2):207-216.doi.org/10.5123/S1679-49742015000200003
23. Brazilian Institute of Geography and Statistics - IBGE. Research Directorate. Coordination of Work and Income. National Health Survey, 2013. Perceptions of health status, lifestyles, chronic diseases. Brazil, Major Regions and Federated Units. Rio de Janeiro: IBGE; 2014. 181p.
24. Celia Landmann Szwarcwald, Deborah Carvalho Malta, Cimar Azeredo Pereira, Maria Lucia Franca Pontes Vieira, Wolney Lisboa Conde, Paulo Roberto Borges de Souza Junior, et al. National Health Survey in Brazil: design and methodology of application. CienSaude Colet. 2014;19(2):333-342.doi: 10.1590/1413-81232014192.14072012
25. ABEP – Brazilian Association of Survey Firms. Brazilian Economic Classification Criteria / 2013.
26. Martins PC, de Carvalho MB, Machado CJ. Use of self-reported measures of height, weight and body mass index in a rural population of Northeast Brazil. Rev Bras Epidemiol. 2015;18(1):137-48.doi.org/10.1590/1980-5497201500010011
27. Jennifer A Linde, Robert W Jeffery, Simone A French, Nicolaas P Pronk, Raymond G Boyle. Self-weighing in weight gain prevention and weight loss trials. Ann Behav Med. 2005;30:210-216.doi: 10.1207/s15324796abm3003_5
28. Johanna C Dekkers, Marieke F van Wier, Ingrid J M Hendriksen, Jos W R Twisk, Willem van Mechelen. Accuracy of self-reported body weight, height and waist circumference in a Dutch overweight working population. BMC Med Res Methodol. 2008;8:69.doi: 10.1186/1471-2288-8-6
29. Patricia Maria Perico Perez, Ines Rugani Ribeiro de Castro, Amanda da Silva Franco, Daniel Henrique Bandoni, Daisy BlumenbergWolkoff. Dietary practices of quota and non-quota students at a Brazilian public university. Cienc. saudecoletiva. 2016;21(2):531-542.doi: 10.1590/1413-81232015212.01732015
30. PranatiPanuganti, T S Mehreen, Ranjit Mohan Anjana, Viswanathan Mohan, E Mayer Davis, Harish Ranjani. Influence of socioeconomic status on knowledge of obesity and diabetes among adolescents in Chennai, South India. Children (Basel). 2017; 4(8):61.doi: 10.3390/children4080061
31. Bispo S, Correia MI, Proietti FA, Xavier CC, Caiaffa WT. Nutritional status of urban adolescents: individual, household and neighborhood factors based on data from The BH Health Study. Cad SaudePublica. 2015;31(Suppl 1):232-245. doi.org/10.1590/0102-311X00100514
32. Cria O Gregory, Heidi M Blanck, Cathleen Gillespie, L Michele Maynard, Mary K Serdula. Health perceptions and demographic characteristics associated with underassessment of body weight. Obesity 2008;16(5):979-986.doi: 10.1038/oby.2008.22
33. Marie Claude Paquette, Kim Raine. Sociocultural context of women's body image. Soc Sci Med 2004;59(5):1047-1058. doi: 10.1016/j.socscimed.2003.12.016
34. Hongwei Hsiao, Darlene Weaver, James Hsiao, Jennifer Whitestone, Tsui Ying Kau, Richard Whisler, et al. Comparison of measured and self-reported anthropometric information among firefighters: implications and applications. Ergonomics. 2014;57(12):1886-1897.doi: 10.1080/00140139.2014.952351
35. M F Kuczmarski, R J Kuczmarski, M Najjar. Effects of age on validity of self-reported height, weight, and body mass index: findings from the Third National Health and Nutrition Examination Survey, 1988-1994. J Am Diet Assoc 2001; 101(1):28-34.doi: 10.1016/S0002-8223(01)00008-6
36. Giovani Firpo Del Duca, David Alejandro Gonzalez Chica, Janaina Vieira dos Santos, Alan GoularteKnuth, Maria Beatriz Junqueira de Camargo, Cora Luiza Araujo. Self-reported weight and height for determining nutritional status of adults and elderly: validity and implications for data analysis. Cad Saude Publica2012;28(1):75-87. doi: 10.1590/s0102-311x2012000100008
37. Yukinori Nagakura, Hideaki Kato, Satoshi Asano, Yasuhiro Jinno, ShigeharuTanei.The significant association between health examination results and population health: A cross-sectional ecological study using a Nation-Wide Health Checkup Database in Japan. Int J Environ Res Public Health. 2021;18(2):836.doi: 10.3390/ijerph1802083
38. Ministry of Health. Secretariat of Health Care. Division of Primary Care. Benchmark of food and nutrition surveillance in primary care. Brasília, Brazil, Ministry of Health. 2015. 59 p.
39. Lhais de Paula Barbosa Medina, Marilisa Berti de Azevedo Barros, Neuciani Ferreira da Silva Sousa, Tassia Fraga Bastos, Margareth Guimarães Lima, Celia Landmann Szwarcwald. Social inequalities in the food consumption profile of the Brazilian population: National Health Survey, 2013. Rev Bras Epidemiol. 2019;22(Suppl 02):e190011.doi: 10.1590/1980-549720190011.supl.2
40. Katherine M Flegal, Barry I Graubard, David F Williamson, Richard S Cooper. Reverse causation and illness-related weight loss in observational studies of body weight and mortality. Am J Epidemiol. 2011;173(1):1-9.doi: 10.1093/aje/kwq341
41. Rafaela Martinez Copes, Fabio VasconcellosComim, Felipe Welter Langer, Antonio Aurelio da SilveiraCodevilla, Giovani RuviaroSartori, Cristina de Oliveira, et al. Obesity and fractures in postmenopausal women: A primary-care cross-sectional study at Santa Maria, Brazil. J ClinDensitom. 2015;18(2):165-171.doi: 10.1016/j.jocd.2014.09.005
42. Daniela SaesSartorelli, Livia Castro Crivellenti, Marina Garcia Manochio-Pina, Naiara Franco Baroni, Mariana Rinaldi Carvalho, Rosa Wanda Diez-Garcia, et al. Study Protocol effectiveness of a nutritional intervention based on encouraging the consumption of unprocessed and minimally processed foods and the practice of physical activities for appropriate weight gain in overweight, adult, pregnant women: a randomized controlled trial. BMC Pregnancy Childbirth. 2020;20(1):24.doi: 10.1186/s12884-019-2672-1
43. Vivian C Honorato dos Santos de Carvalho, Sinara L Rossato, Flavio D Fuchs, Erno Harzheim, Sandra C Fuchs. Assessment of primary health care received by the elderly and health related quality of life: a cross-sectional study. BMC Public Health. 2013;13:605.doi: 10.1186/1471-2458-13-605