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periods (1st to 9th semesters) were considered, an appropriate frequency of consumption of fruits and vegetables among students estimated at 24.9 % (6), with a margin of error of 5 % and a power of 90 %. Thus, to represent the course it would be necessary to interview 118 students. To compensate for possible losses, the final sample consisted of 125 individuals. 40 Data collection Students were asked to answer a structured questionnaire, previously validated for the group under study, divided by the following items: socioeconomic, family information, dietary intake and health status and questionnaire of food intake frequency. This questionnaire was applied individually, in order to better understand the eating habits of the students of the nutrition course. The food frequency questionnaire (FFQ), according to Willett (7), was used. It was simplified, and developed from the food pyramid which was adapted to the Brazilian population by Sichieri and Everhart (8) to investigate the food intake of students of nutrition. The FFQ was composed of 70 food items and the frequency of consumption was represented by 7 categories, namely: daily (once a day, twice or more times a day), weekly (once a week, two to four times a week), monthly (less than once a month, one to three times a month) and never. The changes in the volume and fractioning of the diet during the academic period were evaluated and compared with the intake over the weekends (3). Prevalence was adopted to describe the categorical variables and for the continuous variables, mean and standard deviation were adopted. To analyze the income variable, the minimum wage in the period of data collection was considered, R$545.00, and the anthropometric classification was performed by the calculation of the body mass index (BMI), calculated by weight (kg)/height² (m), using the height and weight values provided by the participants. BMI was classified according to the cut-off point established by the World Health Organization (9). In regards to the identification of dietary patterns, initially the transformation of frequencies of consumption to daily frequency was performed, as proposed by Coelho et al (2011) (10). Thus, the food value of 1 (one) was attributed when the food was consumed once a day. For consumption of more than once a day, the value of 1 was multiplied by the reported daily frequency. The information which contemplated the weekly and monthly intervals used the average interval of the frequency, divided by the period: weekly (7), and monthly (30). Food groups according to the nutritional characteristics were defined. In this regard, 13 food groups were established (1) snacks; (2) sweets and sugars; (3) fat; (4) breads and cereals; (5) roots and tubers; (6) legumes; (7) dairy products; (8) meat and eggs; (9) sausages; (10) artificial beverages; (11) coffee and tea (12) fruit and natural juice (13) vegetables. Statistical analysis After the construction of the food groups, factor analysis (FA) was adopted for key components, in order to identify the dietary patterns of the students of Nutrition. Prior to implementation of the factorial method, the adequacy of the applicability of this method to the data set was evaluated. For this, we used the Kaiser-Meyer-Olkin (KMO) test of adequacy and Bartlett’s sphericity test. The value of KMO (0.77) and the Bartlett’s test of sphericity (p <0.001) suggest that the data is suitably applied to factorial analysis. We used the percentage of commonality to evaluate Pereira-Santos M. y cols. the permanence of the food groups in the factor analysis. The commonality of the database ranged from 0.49 to 0.75. These values were above the recommended minimum commonality 0.311. After the extraction of the dietary patterns, they were labeled according to the food groups present in each factor and the academic context of the student. With respect to the sample size, it showed a ratio of 9.5, considered appropriate due to the fact that the recommended ratio to compose the study sample should be between five to ten times the number of food items in the food frequency questionnaire (11). The dietary patterns were extracted by a principal component analysis (PCA). After the extraction of the factors (dietary patterns), they were rotated in accordance with varimax rotation, in order to enhance the interpretability of each factor. The number of extracted factors that best represent the data set was defined by the graphic test Cattell or Scree plot, with accepted factors having eigenvalues above 1. The selected food groups to compose each dietary pattern were those with factor loadings ≥0.4. The internal consistency of each factor was assessed using Cronbach’s Alpha test and to evaluate the sample size, the relative number of individuals/food items was used. The Cronbach Alpha index showed an acceptable value of 0.74, indicating homogeneity of the eating pattern. Finally, the labeled standards were based on the food groups which comprised each factor. After the extraction of the dietary patterns, and after analyzing the factor loadings, they were labeled: Traditional Pattern, test day pattern, end of semester pattern and anxiety pattern. These labels took into account the composition of the food groups of each pattern and the moments of stress experienced by students in different periods of the semester. Thus, the traditional pattern includes foods that are part of the local food culture of the region, and are present in the food habits of the population, using socio-cultural and traditional characteristics (12). The test day pattern was named as such due to it containing processed foods and high glycemic index foods, which are easy to be prepared, saving time to study for the exams of the semester. The end of semester pattern was characterized by foods like sweets and snacks, foods associated with the reduced availability of time the student has for the preparation of food due to the workload of the end of the semester, as well as the absence of some main meals. The anxiety pattern, characterized by coffee, teas and fats, reveal a pattern found in eager students who adopt a greater consumption of foods that contain caffeine to keep the body alert for performing academic activities. The program Statistical Package for Social Sciences, version 17, was used for data analysis. Ethical issues With regard to the ethical aspects of the research that originated this study, they were evaluated and approved by the Research Ethics Committee, and all students signed the consent form free and willingly, with anonymity and information confidentiality guaranteed. RESULTS The main descriptive characteristics of the 125 graduates are presented in table 1. The average age of students was 22.3


Rev Nutr 43-1
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