SciELO - Scientific Electronic Library Online

 
vol.50 issue4Nutritional state and its connection with food consumption and level of physical activity in times of COVID-19 pandemicDesign and validation of a scale to measure food literacy among university students author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • On index processCited by Google
  • Have no similar articlesSimilars in SciELO
  • On index processSimilars in Google

Share


Revista chilena de nutrición

On-line version ISSN 0717-7518

Rev. chil. nutr. vol.50 no.4 Santiago Aug. 2023

http://dx.doi.org/10.4067/s0717-75182023000400392 

Artículo Original

Dietary patterns associated with physical activity and sedentary behavior in university students in Mexico

Patrones dietéticos asociados con actividad física y comportamiento sedentario en estudiantes universitarios de México

María del Pilar Ramírez-Díaz1  * 
http://orcid.org/0000-0003-0720-9125

Jorge Fernando Luna-Hernández1 
http://orcid.org/0000-0002-6458-4960

Evalinda López-Cruz1 
http://orcid.org/0000-0003-2285-0922

Alberto González-Jiménez2 
http://orcid.org/0000-0003-3535-0687

1Unity of Biological and Health Sciences. University of the Isthmus, Campus Juchitan, Oaxaca, Mexico. Carretera Transistmica Juchitán-La ventosa km 14, 71102, Juchitán, Oaxaca. Mexico.

2Public Health Coordination of the Comprehensive Health Care System, University of Veracruz, Xalapa, Veracruz, Mexico. Ernesto Ortiz Medina S/N, Obrero Campesina, 91020, Xalapa-Enríquez, Veracruz, Mexico.

ABSTRACT

Dietary patterns (DPs) are conditioned by a large number of factors, including physical activity and sedentary lifestyle, however, there is limited information on their interaction. This study aimed to identify DPs and their associations with physical activity and sedentary behavior among university students from Mexico. We conducted a cross-sectional, observational and analytical study. The participants were university students from Southwest Mexico. A total of 419 participants who did not suffer from any disease that affects oral nutrition or that prevented them from performing physical activity were included. DPs were generated from a principal component analysis and associations were analyzed using a logistic regression model. Three DPs were identified: “western”, “prudent” and “traditional”. The traditional pattern was significantly associated with high physical activity (OR: 2.78; 95% CI: 1.34-5.75) and was a protective factor against sedentary lifestyle (OR: 0.35; 95% CI: 0.13-0.93). The results show that a high physical activity and a lower sedentary lifestyle were associated with a healthier dietary pattern in the study population. It is important to implement interventions towards nutrition, physical activity and sedentary behavior for the population being studied.

Keywords: Dietary patterns; Feeding patterns; Physical activity; Sedentary behavior; University students

RESUMEN

Los patrones dietéticos están condicionados por una gran cantidad de factores, entre ellos la actividad física y el sedentarismo, sin embargo, existe poca información sobre su interacción. Este estudio tuvo como objetivo identificar patrones dietéticos y sus asociaciones con la actividad física y el comportamiento sedentario entre estudiantes universitarios del suroeste de México. Se trata de un estudio transversal, observacional y analítico. Los participantes fueron estudiantes universitarios del suroeste de México. Se incluyeron un total de 419 participantes que no padecían alguna enfermedad que condicionara su nutrición oral, ni condición que les impidiera realizar actividad física. Se realizó un análisis de componentes principales para determinar los patrones dietéticos, mientras que se utilizó un modelo de regresión logística para verificar las asociaciones. Se identificaron tres patrones dietéticos: “occidental”, “prudente” y “tradicional”. El patrón tradicional se asoció significativamente con actividad física elevada (OR: 2,78; IC 95%: 1,34-5,75) y fue un factor protector contra el sedentarismo (OR: 0,35; IC 95%: 0,13-0,93). Los resultados muestran que una alta actividad física y un menor sedentarismo se asociaron con un patrón dietético más saludable en la población de estudio. Es importante implementar intervenciones hacia la nutrición, la actividad física y el sedentarismo para la población en estudio.

Palabra clave: Actividad física; Comportamiento sedentario; Estudiantes universitarios; Patrones de alimentación; Patrones dietéticos

INTRODUCTION

Physical inactivity and sedentary behavior are modifiable factors associated with diseases of public health significance, namely diabetes, cardiovascular diseases, cancer, and metabolic syndrome, among others1. Worldwide, 27.5% of adults are physically inactive2, while in Mexico the 2018 National Health and Nutrition Survey (ENSANUT, per its Spanish acronym) reported that 17.3% of adults were insufficiently active that is, adults practiced <150 minutes of moderate or vigorous activity per week and women followed the recommendations to a lesser extent3. Likewise, sedentary behavior refers to all those wakefulness activities such as, sitting, lying down and use of screens for entertainment purposes with minimum energy expenditure (≤1.5 MET). In Mexico, using a cut-off point of > 7 hours per day, sedentary behavior is estimated to be 11.3%4.

On the other hand, it has been observed that in young adult populations the most active people tend to maintain healthier diets5. However, there are some contradictory results that may be influenced by methodological aspects6. In this sense, Mexico is undergoing a nutritional transition that has changed dietary patterns (DPs). Some factors, such as globalization and industrialization, have allowed processed, ultra-processed and energy-dense foods to be incorporated into the traditional diet, which in turn has led to a decrease in the consumption of fresh and minimally processed foods rich in vitamins, minerals and fiber7. These changes are viewed in the high consumption of discretionary foods, namely sugary, non-dairy drinks (86.7%), sugary cereals (35.9%) and snacks, sweets and desserts (29.8%)8. The university phase is a period of interest, since students undergo a complex change process and are exposed to a set of unique factors that determine dietary intake9, namely increased autonomy, high study load, and high levels of stress, anxiety, low physical activity, skipping meals, nibbling, fast foods and extreme diets due to weight concerns, among others10. The association between DPs, physical activity and sedentary lifestyle is much clearer in children and adolescents11, but not in adults12. In addition, in Mexico there is limited evidence on these associations in university students13. In this sense there is evidence where dietary patterns have been identified in university students as “healthy”, “traditional-westernized” and “animal protein and alcohol drinks”, with a healthy pattern being associated with a higher level of physical activity13. On the other hand, in a nationally representative sample of adults, individuals with a traditional pattern reported a higher level of physical activity14.

It is important to mention that the study population is characterized by a high degree of poverty and a higher proportion of the country's indigenous population, which preserves its cultural identity, factors that can influence food and physical activity.

On this basis, this study aimed to identify DPs and their association with physical activity and sedentary behavior among university students from Southwest Mexico.

MATERIALS AND METHODS

Study design and participants

This cross-sectional, observational and analytical study was conducted among a sample of students enrolled at Universidad del Istmo (Tehuantepec, Ixtepec and Juchitán campuses). Before starting the study, potential participants were asked if they had any medical diagnosis that compromised their oral nutrition or if they had any condition that prevented them from doing physical activity during the period from March to July 2019. A sample was determined considering the total number of students enrolled. The sampling was probabilistic, stratified and proportional by campus. The final sample consisted of 419 students (227 women and 192 men) aged 17-30 years old, and the average age was 20.4 years.

Dietary assessment

A Semi-Quantitative Food Frequency Questionnaire (SFFQ) validated for the Mexican population15 was used. The questionnaire was adapted to include frequently consumed regional foods, which were obtained through a pilot test carried out on a population with similar characteristics to that of the study. The frequency of consumption consisted of 50 foods grouped in 13 categories, according to their nutritional characteristics (Table 1). Participants were surveyed in person by other students from the School of Nutrition, Universidad del Istmo who were previously trained.

Table 1 Food groups used for dietary pattern analysis. 

Vegetables Tomato, lettuce, zucchini, carrot, chayote
Fruits Banana, melon, apple, orange, pineapple
Legumes Beans, lentils
Red meat Pork, beef, sausage, ham
White meat Chicken, fish, tuna
Eggs Eggs
Dairy products Cheese, milk, yogurt
Cereals Rice, pasta, potato, tortilla
Traditional foods Tlayuda, garnachas, tostadas, pollo garnachero, totopos
Fast food Pizza, hamburgers, hot dogs
Pastries/cookies Cookies, cakes, packaged cakes
Sweetened beverages Coffee, tea, juice, fruit-infused water, processed fruit nectars or fruit pulps
Table sugar Table sugar, raw sugar and sweetener

Participants reported consumption according to days of the week, times per day, and number of servings (g, ml) consumed in the last seven days. Edible grams of fruits, vegetables and meat were considered, including the density factor for beverages. Then, the daily intake for each food was determined to estimate the total energy consumed per day. Consumption of one or more foods above three standard deviations >3SD were excluded from the analysis according to previously reported methodology16. The food composition tables considered as reference were developed by the National Institute of Health Sciences and Nutrition “Salvador Zubirán”. Energy per food group was summed and converted to a percentage of total energy intake.

Dietary pattern

A principal component analysis (PCA) was conducted to identify DPs for each of the 13 food groups. The factors were rotated by an orthogonal (Varimax) transformation in order to avoid correlation. The Kaiser-Meyer-Olkin (KMO) measure was used to verify sampling adequacy prior to the factor analysis. The KMO value was greater than 0.5. Likewise, the Bartlett's test of sphericity was used, which indicated that the correlation matrix was not an identity matrix and whose value was p<0.001. According to the scree plot test and its interpretation, eigenvalues >1.5 were retained and each factor was defined by at least 3 food groups with a factor loading of >0.30, which has been shown to contribute significantly to the DP17,18. Each participant received a score for each of the identified patterns. The score was calculated by summing the consumption of each food group weighted by its factor loading.

Physical activity

Physical activity was determined using the International Physical Activity Questionnaire (IPAQ) - short form. This questionnaire assesses physical activity in different domains (domestic activities, free time, work and transportation) and considers intensity, frequency and duration of each activity. Physical activity was classified as follows: low, moderate and high. Such classification was made in accordance with indications stated in the guidelines for data processing and analysis of the IPAQ19.

Ethical considerations

The study was carried out in line with the principles stated in the Declaration of Helsinki. Assent and informed consent to participate in the study was collected from participants. Furthermore, the study was supported by the relevant education authorities. This study does not represent any risk for the participants, since only surveys were applied, and the approval of an institutional review board was not necessary.

Statistical analysis

Data were analyzed using the statistical package Stata V.14 (Stata Statistical Software: Release 14. College Station, TX: StataCorp LP). A descriptive analysis of the main characteristics of the study population was performed. The scores of each pattern were categorized into quintiles, with the lowest quintile indicating low adherence, while the upper quintile indicating high adherence. General characteristics were compared across quintiles: for quantitative variables a variance analysis (ANOVA) was carried out and for differences between categorical variables the chi-square test was used.

The association between DPs with physical activity (high vs low-medium) and sedentarism (>6 hrs vs < 6 hrs) was carried out through a logistic regression model, adjusted for DPs, age range, sex, identifying with an indigenous group and area of study. A value of P<0.05 was considered statistically significant.

RESULTS

Of the total sample 54.2% were women, 50.3% were in the 17-20 age range, and 14.3% of the sample was characterized as indigenous. Regarding academics, 42% were from the health sciences, followed by management (34.1%) and engineering (23.9%). With regard to physical activity, 49.9% was classified as moderate, followed by high and low (25.8% and 24.3%, respectively). Regarding sedentarism, 85.7% reported sitting for six or more hours, while the rest indicated less than six hours (14.3 %) (Table 2).

Table 2 Sample demographic characteristics, physical activity and sedentary behavior. 

n= 419 %
Sex
Female 227 54.2
Male 192 45.8
Age
17-20 223 53.2
>20 196 46.8
Indigenous
Yes 60 14.3
No 359 85.7
Academic area
Health 143 42
Engineering 100 23.9
Management 176 34.1
Physical activity
Low 102 24.3
Moderate 209 49.9
High 108 25.8
Sedentary behavior, hours
≥ 6 359 85.7
< 6 60 14.3

The average consumption of the thirteen food groups was used for the pattern analysis considering the previously indicated criteria. Three DPs were identified, which together explained 33% of the total variance. The “traditional” pattern explained 13.19% of variance and was characterized by the consumption of vegetables, fruits, white meat and traditional food. The “western” pattern explained 10.69% of the variance and was identified by the consumption of red meat, dairy products, and fast food. The “prudent” pattern explained 10.03% of the variance and was differentiated by the consumption of vegetables, white meat, eggs and cereals (Table 3).

Table 3 Dietary patterns according to factor loading matrix. 

Food groups Factor loading Traditional Factor loading Western Factor loading Prudent
Q1 Q5 Q1 Q5 Q1 Q5
Vegetables 0.599 0.490 2.588 1.232 1.302 0.343 0.689 1.838
Fruits 0.640 1.866 11.300 4.904 4.840 4.130 8.184
Legumes 2.959 4.818 3.377 5.976 2.591 5.047
Red meat 5.567 4.268 0.520 2.352 8.819 4.253 5.593
White meat 0.409 3.023 7.429 4.759 5.319 0.521 3.085 8.673
Eggs 3.121 3.260 2.619 3.229 0.616 1.456 6.121
Dairy products 8.963 10.318 0.350 7.125 15.549 8.434 10.247
Cereals 1.760 2.710 3.305 2.694 0.390 1.452 4.466
Traditional food 0.366 3.213 8.917 6.015 3.257 -0.623 12.029 1.912
Fast food 3.165 1.810 0.464 0.513 6.752 2.767 1.769
Pastries/cookies -0.549 14.940 3.368 6.303 5.956 4.307 9.903
Sweetened beverages 12.290 8.472 -0.656 13.050 2.728 11.161 10.055
Table sugar -0.341 2.454 0.718 -0.573 3.050 0.501 1.138 1.924
Energy (kcal) 2570.162 2706.082 2459.276
Eigenvalue 1.710 1.390 1.300
Variance explained (%) 13.190 10.690 10.060

Bartlett's test <0.001. KMO= 0.51

Students in the highest quintile of adherence to the traditional pattern were categorized in their highest proportion (p<0.001) in the area of health sciences (30.8%), had higher physical activity (33.3%) and energy consumption was lower than in the other quintiles (p= 0.005 and p= 0.009, respectively) (Table 4). Participants in the highest quintile of adherence to the western pattern were more frequently categorized (24.5%) in the academic area of health sciences (p= 0.031).

Tabla 4 General characteristics and differences by quintiles of dietary patterns. 

Tradicional Occidental Prudente
Total n(419) Q1 n= 84 Q3 n= 84 Q5 n= 83 pa Q1 n = 84 Q3 n= 84 Q5 n= 83 pa Q1 n= 84 Q3 n= 84 Q5 n= 83 pa
Energy(kcal) 2976.75 3276.04 2762.93 2570.16 0.009 2722.34 3012.6 2706.08 0.068 3536.2 3012.9 2459.27 <0.001
Age
17-20(%) 53.2 21.52 15.7 20.2 0.198 21.52 19.28 19.28 0.945 21.08 20.18 18.39 0.889
>20(%) 46.8 18.37 25.0 19.4 18.37 20.92 20.41 0.889 18.88 19.9 21.43
Sex
Mujeres(%) 54.2 19.4 19.4 22.9 0.553 19.4 21.6 17.6 0.342 23.8 17.6 20.3 0.251
Hombres (%) 45.8 20.8 20.8 16.1 20.8 18.2 22.4 15.6 22.9 19.3
Academic areas
Health Sciences(%) 34.1 14.0 16.8 30.8 <0.001 13.3 24.5 24.5 0.031 21.7 17.5 23.1 0.235
Engineering(%) 23.9 30.0 14.0 14.0 17.0 23.0 18.0 20.0 21.0 23.0
Management(%) 42.0 19.3 26.1 14.2 27.3 17.8 17.1 18.8 21.6 15.3
Indigenism
Si(%) 14.3 13.3 26.6 23.3 0.414 18.3 26.6 20.0 0.509 23.3 15.0 23.3 0.562
No(%) 85.7 21.2 18.9 19.2 20.3 18.9 19.8 19.5 20.9 19.2
Physical activity
Baja(%) 24.3 23.5 17.7 12.7 0.005 23.5 16.7 19.6 0.910 18.6 13.7 20.6 0.092
Moderada(%) 49.9 20.1 22.5 16.3 20.6 21.1 18.2 21.1 21.5 16.3
Alta(%) 25.8 16.7 17.6 33.3 15.7 21.3 23.1 19.4 23.2 25.9
Sedentary behavior
≥6 hours 85.7 21.5 19.2 18.7 0.251 20.3 19.8 19.5 0.937 19.2 20.6 19.2 0.629
<6hours 14.3 11.7 25.0 26.7 18.3 21.7 21.7 25.0 16.7 23.3

aANOVA for continuous variables or chi-square for categorical variables.

Regarding energy consumption, the highest quintile of adherence to the prudent pattern had a lower energy consumption (p<0.001) (Table 4).

After adjustment by, DPs, age, sex, indigenous identify and academic area, greater adherence to the traditional pattern was positively associated with high physical activity (OR:2.78; 95% CI: 1.34-5.75); while for sedentarism a negative association was found (OR:0.35; 95% CI: 0.13-0.93) (Table 5).

Table 5 Association of dietary patterns with physical activity and sedentary behavior (Adjusted Odds Ratios with 95% Confidence Intervals). 

Unadjusted Adjusted
High activity Sedentary behavior High activity Sedentary behavior
n= 419(%) OR 95% Cl P OR 95% Cl P ORa 95% Cl P ORa 95% Cl P
Traditional
Q1 84(20) 1.00 1.00 1.00 1.00
Q2 84(20) 1.03 0.48-2.22 0.920 0.51 0.18-1.38 0.189 0.99 0.45-2.16 0.986 0.47 0.17-1.30 0.149
Q3 84(20) 1.08 0.51-2.30 0.831 0.42 0.16-1.12 0.085 1.25 0.57-2.73 0.578 0.47 0.17-1.27 0.138
Q4 84(20) 1.05 0.49-2.24 0.886 0.69 0.24-1.93 0.481 1.06 0.49-2-29 0.879 0.67 0.24-1.89 0.453
Q5 83(19.8) 2.99 1.48-6.02 0.002 0.40 0.15-1.05 0.066 2.78 1.34-5.75 0.006 0.35 0.13-0.93 0.037
Western
Q1 84(20) 1.00 1.00 1.00 1.00
Q2 84(20) 1.49 0.69-3.19 0.306 0.79 0.32-1.93 0.613 1.49 0.69-3-19 0.306 0.74 0.30-1.83 0.527
Q3 84(20) 1.6 0.73-3.49 0.237 0.8 0.33-1.96 0.641 1.60 0.73-3.49 0.237 0.65 0.26-1.62 0.361
Q4 84(20) 1.37 0.63-2.96 0.424 1.1 0.43-2.78 0.838 1.37 0.63-2.96 0.424 1.02 0.39-2.61 0.965
Q5 83(19.8) 1.51 0.70-3.24 0.285 0.8 0.33-1.94 0.627 1.51 0.70-3.24 0.285 0.65 0.26-1.62 0.362
Prudent
Q1 84(20) 1.00 1.00 1.00 1.00
Q2 84(20) 0.55 0.24-1.27 0.167 1.7 0.68-4.27 0.251 0.55 0.24-1.27 0.167 1.77 0.69-4.52 0.227
Q3 84(20) 1.53 0.73-3.18 0.25 1.45 0.59-3.52 0.409 1.53 0.73-3.18 0.250 1.39 0.56-3.44 0.472
Q4 84(20) 1.07 0.51-2.25 0.849 1.17 0.50-2.75 0.703 1.07 0.51-2.25 0.849 1.2 0.50-2.87 0.673
Q5 83(19.8) 1.56 0.75-3.23 0.225 0.96 0.41-2.20 0.927 1.56 0.75-3.23 0.225 0.9 0.38-2.1 0.817

Quintile 1 (Q1) as reference

aLogistic regression; values were adjusted for: age, indigenous identity, academic areas (health sciences, engineering, management).

DISCUSSION

This study aimed to analyze DPs among university students from the Isthmus of Tehuantepec, located in Oaxaca, Mexico, and their associations with physical activity and sedentary behavior. Three DPs were identified: “western”, “prudent” and “traditional”. Regarding physical activity, women were less active compared to men. Furthermore, a positive association of “traditional” pattern with high physical activity and less sedentary behavior was found.

The western pattern, characterized by the consumption of red meat, dairy products, and fast food (hamburgers, pizza, hot-dogs), has been described in another study in Mexico20. This type of pattern has been associated with developing overweight and obesity, insulin resistance, high levels of LDL cholesterol, coronary heart disease, type 2 diabetes mellitus, and hypertension6,20,21. Therefore, the presence of this pattern in the population university is a concern because of the possible metabolic implications in the future.

In this study, the prudent pattern, which was characterized by the consumption of vegetables, white meat, eggs and cereals and less consumption of typical foods, is similar to that reported by Denova et al. in Mexican adults, which is characterized by the consumption of vegetables, fruit and cereals [18], and by another study in Mexico22. This pattern has been reported as healthy23 and negatively associated with metabolic problems and lower cardiovascular risk18. This is expected, since including food groups, such as fruits, vegetables and whole grains, increases the consumption of vitamins, minerals, phytochemicals and fiber24, which benefit health.

The traditional pattern characterized by the consumption of fruits, vegetables, dairy products and traditional food (garnachas, tlayuda, pollo garnachero, tostadas and totopos), has been described in other investigations in Mexico. Flores et al. reported a traditional pattern mainly characterized by the consumption of tortillas and legumes14. Corn tortillas are a food usually present in the different traditional DPs reported in Mexico. However, in the study population, the corn tortilla is usually replaced by the consumption of other corn-based foods typical of the Isthmus of Tehuantepec, namely totopos, which may be baked or fried. While it is true that naming pattern derives from a series of subjective decisions25 and may vary due to the availability of certain typical foods in each region, it has been reported that the different variations describing the traditional Mexican pattern have been considered healthy. This information has been confirmed by a recent systematic review26. Although the basis of typical or traditional foods are considered healthy, some of these foods are fried, which could reduce their nutritional quality. However, the factor loadings of fruits, vegetables and white meat were higher than those of traditional food together with low consumption of sugar and pastries, which is why the traditional pattern in this study can still be considered healthy.

Some authors have reported a traditional-Westernized dietary pattern, as traditional foods and foods typically included in the Western diet, such as fast food27, merge into one dietary pattern. Betancourt et al. reported a traditional-Westernized dietary pattern characterized by the consumption of traditional Mexican foods, namely tubers, legumes, native cereals, animal fats and milk, as well as Western foods such as pastries, sugars, refined cereals, fast foods and sugar-sweetened beverages13. In other studies, this DP is reported as transitional23. This type of DP has been attributed to the nutritional transition exacerbated by the inclusion of processed foods into the traditional diets of various countries. In Mexico, the international agreements, like the United States-Mexico-Canada Agreement (USMCA) that substituted the previous free-trade agreement of North America (NAFTA) have facilitated the entry of several food industries and have increased the availability of ultra-processed foods7. High energy food and foods rich in sugar, sodium, fat and very low in fiber have been added to the traditional diet. It is noteworthy that Oaxaca is one of the states located in the less developed regions of the country, preserves a greater cultural identity and has a larger indigenous population, which could explain the presence of the traditional pattern. On the other hand, the extent of poverty, urbanization and level of schooling may explain the tendency to consume high-energy foods, as they are cheaper in a transition towards a Western pattern, as previously reported28. However, there are other sociodemographic and cultural variables that must be explored in future studies to show how these factors may influence DPs.

Although the effect of physical activity and dietary habits on the presence or absence of diseases has been established, the capability of these two behaviors to indirectly modify each other has been poorly documented29. Some studies have reported an association of high physical activity with healthier DPs that include fruits, vegetables, whole grains, and other foods that benefit health30. On the other hand, low physical activity has been associated with less healthy patterns related to adverse health effects31. It has been proposed that physical activity may act as a reward ‘buffer’ against liking and craving for high-fat foods, while low physical activity levels may lead people to adhere to hedonic hunger32. The impact of physical activity on food reward is difficult to assess due to the lack of randomized controlled trials and differences between study designs33. However, some studies have reported that the most active people, either men or women, tend to consume less fat compared to those who are sedentary or inactive, and they also consume a greater amount of fiber and tend to consume healthier foods34,35. One of the few prospective studies reported that people who increased their physical activity also reported an improvement in diet quality36. On this basis, an association between high physical activity with the prudent pattern was expected to be found, as documented37. However, such association was not observed in our study. Instead, it was noted that the most active students adhered to the consumption of a traditional pattern, which has been considered healthy26. Thus, the association between the traditional pattern and high physical activity in the study population would not be a coincidence, as it is in line with another study carried out in Mexico24. Another recent study concluded that a 12-week physical exercise intervention reduced consumption of high-reward foods38, which might strengthen the hypothesis that the consumption of the healthiest food, might be motivated by high physical activity or that behavioral changes can go hand-in-hand.

Furthermore, our results revealed a high proportion of sedentary students, which is a concern, as sedentary behavior has been reported to be associated with both a lower consumption of healthy foods39 and, generally, unhealthy dietary habits. Therefore, the occurrence of chronic non-communicable diseases may be conditioned by these aspects. In this sense, an association of the western pattern with low physical activity and sedentary behavior was expected to be found, as reported in Mexico20. However, there was no such association. This could be explained because in our study sedentary behavior was generally assessed by considering the total hours per day that participants spent in a sitting position. Nonetheless, there are no defined cut-off points for sedentary behavior, since the variability of waking hours is influenced by the context of each country. Also, different sedentary behaviors were not studied in depth, which may be associated with a less healthy pattern. However, it was observed that, according to the cut-off point considered for our study, the traditional pattern was associated as a protective factor against sedentary behavior, which is in line with other studies reporting that healthier patterns are inversely associated with sedentary behaviors40.

This study has some limitations. One of them is the study design, since causality cannot be inferred. Thus, it is not completely clear whether physical activity induces a change in diet or vice versa. However, as mentioned above, studies in this field tend to suggest the influence of physical activity on food consumption. Another limitation was the relatively low frequency of foods, which led to a reduced number of food groups. Nonetheless, it is important to point out that it is our understanding that this is the first study dealing with analysis of DPs through principal components and their associations with physical activity and sedentary behavior in the study region, which lay the foundations for further research.

CONCLUSIONS

Among university students we identified the following eating patterns: “traditional”, “western” and “prudent”, considering the first and the third as healthy. In adjusted analysis, we observed that greater adherence to the traditional pattern was associated with higher physical activity and less sedentarism.

The results are inconclusive and more evidence is needed to demonstrate the relationship between high physical activity, sedentarism and eating. Future studies may consider designing interventions focused on improving diet quality, increasing physical activity and decreasing sedentary behavior, through multidisciplinary programs to encourage healthy behaviors in the university population.

Acknowledgements.

We thank the institutional authorities for allowing the development of this study and the students who participated.

REFERENCES

1 Guo C, Zhou Q, Zhang D, Qin P, Li Q, Tian G, et al. Association of Total Sedentary Behaviour and Television Viewing with Risk of Overweight/Obesity, Type 2 Diabetes and Hypertension: A Dose-Response Meta-Analysis. Diabetes Obes. Metab. 2020; 22: 79-80. [ Links ]

2 Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants. Lancet Glob Health. 2018; 6: e1077–e1086. [ Links ]

3 Shamah-Levy T, Vielma-Orozco E, Heredia-Hernández O, Romero-Martínez M, Mojica-Cuevas J, Cuevas-Nasu L, et al. National Health and Nutrition Survey 2018-19: National results. Cuernavaca, Mexico: National Institute of Public Health, 2020. [ Links ]

4 Medina C, Jáuregui A, Hernández C, Shamah T, Barquera S. Physical inactivity and sitting time prevalence and trends in Mexican adults. Results from three national surveys. PLoS ONE. 2021; 16: e0253137. [ Links ]

5 Loprinzi PD, Smit E, Mahoney S. Physical activity and dietary behavior in US adults and their combined influence on health. Mayo Clin Proc. 2014; 89: 190-198. [ Links ]

6 Ottevaere C, Huybrechts I, Benser J, De Bourdeaudhuij I, Cuenca-Garcia M, Dallongeville J, HELENA Study Group et al. Clustering patterns of physical activity, sedentary and dietary behavior among European adolescents: The HELENA study. BMC Public Health. 2011; 11: 328. [ Links ]

7 Tello J, Garcillán PP, Ezcurra E. How dietary transition changed land use in Mexico. Ambio. 2020; 49:1676-1684. [ Links ]

8 Shamah-Levy T, Romero-Martínez M, Barrientos-Gutiérrez T, Cuevas-Nasu L, Bautista-Arredondo S, Colchero MA, et al. National Health and Nutrition Survey Covid-19 2020: National results. Cuernavaca, México: National Institute of Public Health, 2021. [ Links ]

9 Sprake EF, Russell JM, Cecil JE, Cooper RJ, Grabowski P, Pourshahidi LK, Barker ME. Dietary patterns of university students in the UK: A cross-sectional study. Nutr J. 2018; 17: 90. [ Links ]

10 Navarro-Prado S, Gonzáles-Jiménez E, Montero-Alonso MA, López-Bueno M, Schmidt-Rio Valle J. Lifestyle and monitoring of dietary intake in students of University of Granada Campus in Melilla. Nutr Hosp. 2015; 31: 2651-2659. [ Links ]

11 Ottevaere C, Huybrechs I, Benser J, De Bourdeaudhuij I, Cuenca-Garcia M, Dallongeville J, et al. Clustering patterns of physical activity, sedentary and dietary behavior among European adolescents: The HELENA study. BMC Public Health. 2011; 11: 328. [ Links ]

12 Charreire H, Kesse-Guyot E, Bertrai S, Simon C, Chaix B, Weber C, et al. Associations between dietary patterns, physical activity (leisure-time and occupational) and television viewing in middle-aged French adults. Br J Nutr. 2011; 105: 902-910. [ Links ]

13 Betancourt-Nuñez A, Márquez-Sandoval F, González-Zapata LI, Babio N, Vizmanos B. Unhealthy dietary patterns among healthcare professionals and students in Mexico. BMC Public Health. 2018; 18: 1246. [ Links ]

14 Mario Flores, Nayeli Macias, Marta Rivera, Ana Lozada, Simón Barquera, Juan Rivera-Dommarco, Katherine L. Tucker, Dietary Patterns in Mexican Adults Are Associated with Risk of Being Overweight or Obese, The Journal of Nutrition. 2010; 140: 1689-1873. [ Links ]

15 Denova-Gutiérrez E, Ramírez-Silva I, Rodríguez-Ramírez S, Jiménez-Aguilar A, Shamah-Levy T, Rivera-Dommarco JA. Validity of a food frequency questionnaire to assess food intake in Mexican adolescent and adult population. Salud Publica Mex. 2016; 58(6): 617-628. [ Links ]

16 Ramírez-Silva I, Jiménez-Aguilar A, Valenzuela-Bravo D, Martinez-Tapia B, Rodríguez-Ramírez S, Gaona-Pineda EB, et al. Methodology for estimating dietary data from the semi-quantitative food frequency questionnaire of the Mexican National Health and Nutrition Survey 2012. Salud Publica Mex. 2016; 58: 629-638. [ Links ]

17 Denova-Gutiérrez E, Castañón S, Talavera J, Flores M, Macías N, Rodríguez-Ramírez S, et al. Dietary Patterns Are Associated with Different Indexes of Adiposity and Obesity in an Urban Mexican Population. J Nutr. 2011; 141(5): 921-927. [ Links ]

18 Denova-Gutiérrez E, Tucker KL, Flores M, Barquera S, Salmerón J. Dietary Patterns Are Associated with Predicted Cardiovascular Disease Risk in an Urban Mexican Adult Population. J Nutr. 2016; 146: 90-97. [ Links ]

19 Guidelines for Data Processing and Analysis of the International Physiscal Activity Questionnaire (IPAQ). Short and Long Forms. Available at: https://sites.google.com/site/theipaq/scoring-protocol [Accessed January 10, 2022]. [ Links ]

20 Romero-Polvo A, Denova-Gutiérrez E, Rivera-Paredez B, Castañón S, Gallegos-Carrillo K, Halley-Castillo E, et al. Association between Dietary Patterns and Insulin Resistance in Mexican Children and Adolescents. Annals of Nutrition and Metabolism. 2012; 61: 142-150. [ Links ]

21 Denova-Gutiérrez E, Castañón S, Talavera JO, Gallegos-Carrillo K, Flores M, Dosamantes-Carrasco D, Willett WC, et al. Dietary Patterns Are Associated with Metabolic Syndrome in an Urban Mexican Population. J Nutr. 2010; 140: 1855-1863. [ Links ]

22 Perng W, Fernandez C, Peterson KE, Zhang Z, Cantoral A, Sanchez BN, et al. Dietary Patterns Exhibit Sex-Specific Associations with Adiposity and Metabolic Risk in a Cross-Sectional Study in Urban Mexican Adolescents. J Nutr. 2017; 147: 1977-1985. [ Links ]

23 Clark P, Mendoza-Gutiérrez CF, Montiel-Ojeda D, Denova-Gutiérrez E, López-González D, Moreno-Altamirano L, Reyes A. A Healthy Diet Is Not More Expensive than Less Healthy Options: Cost-Analysis of Different Dietary Patterns in Mexican Children and Adolescents. Nutrients. 2021; 13: 3871. [ Links ]

24 Perng W, Fernandez C, Peterson KE, Zhang Z, Cantoral A, Sanchez BN, Solano-González M, Téllez-Rojo MM, Baylin A. Dietary Patterns Exhibit Sex-Specific Associations with Adiposity and Metabolic Risk in a Cross-Sectional Study in Urban Mexican Adolescents. J Nutr. 2017;147: 1977-1985. [ Links ]

25 Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: A review. Nutr Rev. 2004;62(5):177–203. [ Links ]

26 Valerino-Perea S, Lara-Castor L, Armstrong MEG, Papadaki A. Definition of the Traditional Mexican Diet and Its Role in Health: A Systematic Review. Nutrients. 2019; 11: 2803. [ Links ]

27 Bojorquez I, Unikel C, Cortez I, Cerecero D. The social distribution of dietary patterns. Traditional, modern and healthy eating among women in a Latin American city. Appetite. 2015; 92: 43-50. [ Links ]

28 Manyanga T, Tremblay MS, Chaput JP. et al. Socioeconomic status and dietary patterns in children from around the world: Different associations by levels of country human development?. BMC Public Health. 2017; 17: 457. [ Links ]

29 Joseph RJ, Alonso-Alonso M, Bond DS, Pascual-Leone A, Blackburn GL. The neurocognitive connection between physical activity and eating behaviour. Obes Rev. 2011; 12: 800-812. [ Links ]

30 Gajda R, Bronkowska M. Dietary patterns of health sciences students in regarding to physical activity levels and somatic indicators of nutritional status. Rocz Panstw Zakl Hig. 2020; 71: 271-278. [ Links ]

31 Wadolowska L, Hamulka J, Kowalkowska J, Kostecka M, Wadolowska K, Biezanowska-Kopec R, et al. Prudent-Active and Fast-Food-Sedentary Dietary-Lifestyle Patterns: The Association with Adiposity, Nutrition Knowledge and Sociodemographic Factors in Polish Teenagers-The ABC of Healthy Eating Project. Nutrients. 2018; 10: 1988. [ Links ]

32 Annesi JJ, Porter KJ. Behavioural support of a proposed neurocognitive connection between physical activity and improved eating behaviour in obese women. Obes Res Clin Pract. 2014; 8: e325-e330. [ Links ]

33 Beaulieu K, Oustric P, Finlayson G. The Impact of Physical Activity on Food Reward: Review and Conceptual Synthesis of Evidence from Observational, Acute, and Chronic Exercise Training Studies. Curr Obes Rep. 2020; 9: 63-80. [ Links ]

34 Beaulieu K, Hopkins M, Gibbons C, Oustric P, Caudwell P, Blundell J, et al. Exercise training reduces reward for high-fat food in adults with overweight/obesity. Med Sci Sports Exerc. 2020; 52: 900-908. [ Links ]

35 Riou ME, Jomphe-Tremblay S, Lamothe G, Finlayson GS, Blundell JE, Decarie-Spain L, et al. Energy compensation following a supervised exercise intervention in women living with overweight/obesity is accompanied by an early and sustained decrease in non-structured physical activity. Front Physiol. 2019; 10: 1048. [ Links ]

36 Parsons TJ, Power C, Manor O. Longitudinal physical activity and diet patterns in the 1958 British birth cohort. Med Sci Sports Exer. 2006; 38: 547-554. [ Links ]

37 Slagter SN, Corpeleijn E, van der Klauw MM. et al. Dietary patterns and physical activity in the metabolically unhealthy obese: The Dutch Lifelines cohort study. Nutr J. 2018; 17: 18. [ Links ]

38 Beaulieu K, Hopkins M, Gibbons C, Oustric P, Caudwell P, Blundell J, et al. Exercise Training Reduces Reward for High-Fat Food in Adults with Overweight/Obesity. Med Sci Sports Exerc. 2020; 52: 900-908. [ Links ]

39 Gillman MW, Pinto BM, Tennstedt S, Glanz K, Marcus B, Friedman RH. Relationships of physical activity with dietary behaviors among adults. Prev Med. 2001; 32: 295-301. [ Links ]

40 Jezewska-Zychowicz M, Gębski J, Guzek D, Świątkowska M, Stangierska D, Plichta M, et al. The Associations between Dietary Patterns and Sedentary Behaviors in Polish Adults (LifeStyle Study). Nutrients. 2018; 10: 1004. [ Links ]

Received: January 20, 2023; Revised: June 12, 2023; Accepted: August 18, 2023

*Corresponding author: María del Pilar Ramírez-Díaz, E-mail: pilar.ramirezdiaz@gmail.com

Conflict of interest. none declared.

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.