Next Article in Journal
Identifying Eating Occasion-Based Opportunities to Improve the Overall Diets of Australian Adolescents
Previous Article in Journal
Adherence to a Vegetarian Diet and Diabetes Risk: A Systematic Review and Meta-Analysis of Observational Studies
 
 
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Lifestyle Patterns and Weight Status in Spanish Adults: The ANIBES Study

1
FIDEC Foundation, University of the Basque Country (UPV/EHU), Gurtubay s/n, 48010 Bilbao, Spain
2
Department of Physiology, Faculty of Medicine and Nursery, University of the Basque Country (UPV/EHU), Bo Sarriena s/n, Leioa, 48940 Bizkaia, Spain
3
Department of Biochemistry and Molecular Biology II, Institute of Nutrition and Food Sciences, Centre of Biomedical Research, University of Granada, Campus de la Salud, Avda. del Conocimiento, Armilla, 18100 Granada, Spain
4
CIBEROBN, Biomedical Research Networking Center for Physiopathology of Obesity and Nutrition, Carlos III Health Institute, Madrid 28029, Spain
5
ImFINE Research Group, Department of Health and Human Performance, Universidad Politécnica de Madrid, C/Martín Fierro 7, 28040 Madrid, Spain
6
Department of Nutrition, Faculty of Pharmacy, Complutense University of Madrid, Plaza Ramón y Cajal s/n, 28040 Madrid, Spain
7
Research Institute of Biomedical and Health Sciences, Faculty of Health Sciences, University of Las Palmas de Gran Canaria, Paseo Blas Cabrera Felipe “Físico” s/n, 35016 Las Palmas de Gran Canaria, Spain
8
Department of Pharmaceutical and Health Sciences, Faculty of Pharmacy, CEU San Pablo University, Urb. Montepríncipe, Crta. Boadilla Km. 5.3, Boadilla del Monte, 28668 Madrid, Spain
9
Spanish Nutrition Foundation (FEN), C/General Álvarez de Castro 20. 1a pta, 28010 Madrid, Spain
10
Department of Food Sciences and Physiology, University of Navarra, C/Irunlarrea 1, 31008 Pamplona, Spain
*
Author to whom correspondence should be addressed.
Nutrients 2017, 9(6), 606; https://doi.org/10.3390/nu9060606
Received: 10 April 2017 / Revised: 8 June 2017 / Accepted: 8 June 2017 / Published: 14 June 2017

Abstract

:
Limited knowledge is available on lifestyle patterns in Spanish adults. We investigated dietary patterns and possible meaningful clustering of physical activity, sedentary behavior, sleep time, and smoking in Spanish adults aged 18–64 years and their association with obesity. Analysis was based on a subsample (n = 1617) of the cross-sectional ANIBES study in Spain. We performed exploratory factor analysis and subsequent cluster analysis of dietary patterns, physical activity, sedentary behaviors, sleep time, and smoking. Logistic regression analysis was used to explore the association between the cluster solutions and obesity. Factor analysis identified four dietary patterns, “Traditional DP”, “Mediterranean DP”, “Snack DP” and “Dairy-sweet DP”. Dietary patterns, physical activity behaviors, sedentary behaviors, sleep time, and smoking in Spanish adults aggregated into three different clusters of lifestyle patterns: “Mixed diet-physically active-low sedentary lifestyle pattern”, “Not poor diet-low physical activity-low sedentary lifestyle pattern” and “Poor diet-low physical activity-sedentary lifestyle pattern”. A higher proportion of people aged 18–30 years was classified into the “Poor diet-low physical activity-sedentary lifestyle pattern”. The prevalence odds ratio for obesity in men in the “Mixed diet-physically active-low sedentary lifestyle pattern” was significantly lower compared to those in the “Poor diet-low physical activity-sedentary lifestyle pattern”. Those behavior patterns are helpful to identify specific issues in population subgroups and inform intervention strategies. The findings in this study underline the importance of designing and implementing interventions that address multiple health risk practices, considering lifestyle patterns and associated determinants.

1. Introduction

Overweight and obesity have progressively increased during the last decades and have become a major issue in public health, both in developed and developing economies across the five continents [1,2]. In Spain, recent data report that more than half of adults aged 18–64 years are classified as overweight or obese [3,4]. This is particularly worrying for the negative impact of this condition on health and quality of life [2,5,6]. In fact, the Global Burden of Disease project highlights that high body mass index (BMI) values were among the main risk factors driving most death and disability combined in the country in 2015 [7].
Overweight and obesity result from an imbalance between energy intake and expenditure. The role of diet in obesity is complex. Most research in this area has focused on specific foods and nutrients [8,9]. Nevertheless, the analysis of food patterns is particularly interesting, since foods are usually consumed in combinations and those may have synergistic, antagonistic, or moderating effects [10].
In addition, to date, it is acknowledged that joint interactions of multiple variables acting at different levels influence weight gain, such as lifestyles [11,12,13], sleep, including rhythm, duration or quality of sleep [14,15], eating behaviors [16], socioeconomic level, education, and other factors [4,11]. Data-driven methods explore the similarities between different food options in specific population groups [8]. Specifically, cluster analyses include several techniques aimed at grouping together individuals sharing a number of features or similar lifestyles. This classification allows for a better understanding of the influences of behaviors and lifestyles, as well as the potential cumulative effects of an unhealthy combination of those factors on the development of overweight and obesity [16]. Research of dietary patterns and the potential combination of those with other lifestyles can contribute to identifying effective strategies for the prevention of overweight and obesity among adults, as well as its social and health consequences [8].
Considering the above, the objectives of this paper are (a) to identify food patterns in the Spanish adult population; (b) to investigate if energy balance-related behaviors tend to assemble into meaningful patterns in Spanish adults; (c) to describe existing relationships between socio-demographical factors and different lifestyle patterns; and, finally; (d) to analyze the potential association of those correlates with excess body weight.

2. Materials and Methods

The data for this analysis were obtained from the ANIBES study, an observational cross-sectional study conducted on a random sample of the Spanish population aged 9–75 years. The aims and procedures used in ANIBES have been previously reported [17,18].
Briefly, the sample design of the ANIBES study was based on the Spanish population census 2012 according to sex, age, residence, and regional population size. A stratified multistep sampling procedure was used, with random selection of addresses in the municipalities and age and sex quotas for individuals within households. Interlocked quotas were established for age in the regions and size of habitat within the region. The sample selection procedure was based on random paths; 128 sampling points were selected.
The final sample of the study consisted of 2009 individuals (1013 men, 50.4%, 996 women, 49.6%). In addition, a boost sample was recruited for the younger groups (9–12 years; 13–17 years, and 18–24 years) in order to ensure at least 200 individuals in each age group. Data for individuals aged 18–64 years were used for this analysis. That age range is meaningful from a risk prevention point of view, since it includes adults in the active working life span, thus, relevant when designing targeted preventive and health promotion strategies, considering different settings, such as workplaces, primary health care, and community-based interventions. In addition, the focus on the 18–64 year-old range was also relevant for comparison with previous studies conducted in Spain [19]. Data were collected between mid-September and mid-November 2013.
The final protocol of the study was approved by the ethical research committee of the Community of Madrid, Spain.

2.1. Measurements

2.1.1. Lifestyle Factors

Diet

Dietary intake was assessed by means of a face-to-face 24-h diet recall interview assisted by a food picture atlas to estimate portion sizes. In addition participants completed a three-day food record aided by a tablet device (Samsung Galaxy Tab 2 7.0, Samsung Electronics, Suwon, South Korea), two consecutive weekdays and one weekend day, recording all foods and beverages consumed at home and away from home. Processing of food record inputs, coding and data cleaning has been extensively reported elsewhere [17,18]. Energy and nutrient intakes were calculated using new specifically-developed software, (VD-FEN 2.1 software -Dietary Evaluation Programme, Spanish Nutrition Foundation, Madrid, Spain) for the ANIBES study [20]. Consumption of all food and beverages was arranged into 16 food groups, 45 subgroups, and 754 food items, for in-depth analysis, based on the structure of the food composition database considering similarities in nutrient profile (Supplementary Materials Table S1).

Physical Activity

Physical activity data were collected by face-to-face interview using the validated International Physical Activity Questionnaire (IPAQ) [21]. Total minutes per week were computed for moderate to vigorous physical activity based on the IPAQ guidelines for data processing and analyses [22]. Data were cleaned and truncated based on IPAQ guidelines and previous research [23]. Additionally, the total minutes per week of commuting-related physical activity (walking, biking) were computed. IPAQ data was used for this analysis. Z-scores of minutes per week for each type of activity were calculated.

Sleep Duration

Sleep habits included the number of hours slept per night, on average, as reported by each individual.

Smoking

Smoking habits were assessed by different items in the protocol, including “During the past year, how many cigarettes per day did you smoke on average?”

2.1.2. Body Measurements

Anthropometric measurements were taken individually by trained interviewers, following international standard procedures previously tested in two pilot studies [24], reported in detail elsewhere [17,18]. Body mass index (BMI) was calculated as body weight in kilograms divided by the square of body height in meters. Overweight status was defined as BMI ≥ 25; obesity as BMI ≥ 30.

2.1.3. Covariates

Education

The education levels were established in accordance with the Spanish educational system.
After preliminary analysis of the distribution of the variable, categories were collapsed and recoded into a three-point scale, as follows: (1) low (less than seven years of education; primary school or less); (2) medium (7–12 years of education; lower to higher secondary education); and (3) high (13 years or more of education; higher vocational, college, and university studies).

Geographical Area

Geographical area in the country was collapsed into four different categories: north-northwest region; eastern region; central region and southern region.

2.2. Data Cleaning

Detailed data cleaning procedures have been previously described [17,18]. Participants were considered fully eligible after a verified quality check of the input from the tablet device of adequately completed three-day food records. After data cleaning stages, individuals remained in the database if they had successfully completed both face-to-face interviews during fieldwork and had measured weight, height, and waist circumference data. Of the initial sample of 1655 recruited individuals aged 18–64 years, 1617 individuals satisfied the inclusion criteria and had complete data for all of the variables included in this analysis.

2.3. Data Analysis

All statistical tests were performed using IBM SPSS Statistics for Windows, Version 22.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were computed for each variable.

2.3.1. Dietary Patterns

Exploratory factor analysis was performed to identify underlying dietary patterns, using the average food weight (g/day) consumed by each individual (three-day food record plus one-day 24-h recall) from 38 food groups as input variables. Food groups were used to further collapse dietary intake data in order to avoid missing data from non-consumers of episodically consumed foods. Z-scores for each food group were calculated to prevent the components being dominated by the foods that provide the highest amounts. Bartlett’s test of sphericity and the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy were used to verify the appropriateness of factor analysis. Factors were also orthogonally rotated (the varimax option) to enhance the difference between loadings, which allowed for easier interpretability.
Factors were retained based on the following criteria: factor eigenvalue > 1.20, identification of a break point in the scree plot, the proportion of variance explained, and factor interpretability [25].
The strength and direction of the associations between patterns and food groups were described through a rotated factor loading matrix. Food groups with factor loadings > 0.30 and communality > 0.20 were retained in the patterns identified. The factor score for each pattern was constructed by summing the observed intakes of the component food items weighted by the factor loading. A high factor score for a given pattern indicated high intake of the foods constituting that food factor, and a low score indicated low intake of those foods.

2.3.2. Lifestyle Patterns

To identify clusters with similar dietary patterns, physical activities, sedentary activities, sleeping habits and smoking a combination of hierarchical and non-hierarchical clustering analysis was used [26]. The variables used had different arithmetic scales; thus, Z-scores were calculated to standardize the dataset before clustering, to avoid a greater contribution to the distance of variables having larger ranges than variables with smaller ranges. Univariate and multivariate outliers (>3 SD) were removed. First, hierarchical cluster analysis was performed using Ward’s method, based on squared Euclidian distances. Several possible cluster solutions were identified and compared to inform the next step, considering the coefficients and fusion level. A non-hierarchical k-means clustering procedure was used, specifying the number of clusters identified in the first step, using a random initial seed and 10 iterations in order to further refine the preliminary solution by optimizing the classification. The final cluster solution was selected based on interpretability and the percent of the study population in each cluster. Reliability and stability of the final cluster solution was tested by randomly taking a subsample (50%) of the total sample and repeating the analyses on this subsample. To check agreement, a kappa statistic was calculated between the cluster solutions of the subsample and that of the total sample.
Pearson’s chi-square tests were used to investigate the differences in cluster distribution by gender, age group, education level, geographical area, and BMI status. One-way ANOVA was used to compare physical activities, sedentary behaviors and sleep time across clusters stratified by gender and age group. General linear models were used to estimate multivariate means for food consumption and dietary pattern scores across clusters adjusted for age and energy intake. Binary logistic regression analysis was used to explore the prevalence odds ratios for obesity and overweight among lifestyle patterns. The models were adjusted for energy intake, sex, age, educational level, and geographical area. Statistical tests were two-tailed with a 5% level of significance.

3. Results

3.1. Sample Characteristics

After exclusion of outliers and participants with incomplete data, 1617 subjects aged 18–64 years, 781 men and 836 women, were included in the analyses (Table S2). Some 38% of the sample was classified in the BMI range 25–29.9 and 21.6% had BMI values ≥ 30. There was no significant difference between men and women in age distribution, level of education, or geographical area. However, overweight and obesity rates were significantly higher in men than women.

3.2. Dietary Patterns

Dietary patterns were computed for the entire sample. Bartlett’s test of sphericity and KMO = 0.591 supported the appropriateness of factor analysis. Four major factors were extracted, which explained 33.1% of the variance in the model. The first dietary pattern (DP) was labeled “Traditional DP” which had the highest loading on olive oil and vegetables, high scores on fish, meat, and fruit, and negative scores on pasta and so-called “pre-cooked” foods, which include food items such as croquettes and other processed foods usually prepared deep-fried for consumption. A DP labeled “Mediterranean DP” had high scores on water, fruit, yoghourt, fish, vegetables, cheese, and olive oil, and negative scores on meat and sugar sweetened beverages. A DP labeled “Snack DP” had high scores on bread, processed and cold meats, alcoholic beverages, salted snacks, cheese, and juices. Finally, “Dairy-sweet DP” had high scores on milk, sugar and sweets, cakes, pastry, and juices, and negative scores on alcoholic beverages. Overall, these patterns explained 33.07% of the variance (Figure 1).
Mean factor scores for “Traditional DP” and “Mediterranean DP” were significantly higher in the oldest age group (50–64 years), while “Dairy-sweet DP” factor scores were significantly higher in the younger ones (18–30 years). Men had significantly higher scores than women for “Snack DP” adjusted for age and energy intake; “Mediterranean DP” and “Dairy-sweet DP” had significantly higher scores among women. “Mediterranean DP” and “Traditional DP” factor scores were significantly higher in people with a higher educational level.
Factor scores for the “Traditional DP” and “Dairy-sweet DP”, adjusted for energy intake, age, and gender, were significantly higher in the north-northwest region, while “Mediterranean DP” and “Snack DP” factor scores were significantly higher in the Eastern Mediterranean region.

3.3. Lifestyle Patterns

Based on the four identified DPs, minutes per week of vigorous, moderate physical activity, walking, biking, sedentary time, sleep duration on weekdays, and smoking habits, the three-cluster solution was found to be adequate and meaningful regarding the different patterns. The kappa statistic (κ = 0.94) suggested good agreement.
Differential characteristics of each cluster are identified by high (above 0) or low Z-scores (below 0) comparing cluster centers in Z-scores (Figure S1). Cluster 1, labeled “Mixed diet-physically active-low sedentary lifestyle pattern”, drew a lifestyle pattern with high scores on the “Snack DP” (Z-score = 0.42), the “Mediterranean DP” (Z-score = 0.27), walking (Z-score = 0.58), vigorous physical activity (Z-score = 2.09) and moderate physical activity (Z-score=0.33), combined with low scores on sedentary time (Z-score = −0.40) and sleeping (Z-score = −0.25). The second cluster, labeled “Not poor diet-low physical activity-low sedentary lifestyle pattern”, had low scores on “Dairy-sweet DP” (Z-score = −0.40), “Snack DP” (Z-score = −0.16), vigorous physical activity (Z-score = −0.34) and sedentary time (Z-score = −0.19). Finally, a third cluster labeled “Poor diet-low physical activity-sedentary lifestyle pattern” had high scores on “Dairy-sweet DP” (Z-score = 1.09), “Snack DP” (Z-score = 0.19) and sedentary time (Z-score = 0.73) and scored negatively on moderate (Z-score = −0.42) and vigorous physical activity (Z-score = −0.26), as well as smoking (Z-score = −0.21).
Characteristics of the subjects classified in the lifestyle patterns identified are described in Table 1. The “Mixed diet-physically active-low sedentary lifestyle pattern” included 13% of the sample and a significantly higher proportion of men. The “Not poor diet-low physical activity-low sedentary lifestyle pattern” included 63.3% of the sample, a significantly higher proportion of women. The “Poor diet-low physical activity-sedentary lifestyle pattern” included 23.6% of the sample.
A higher percentage of people across age groups were classified into the “Not poor diet-low physical activity-low sedentary lifestyle pattern”. However, a significantly lower proportion of individuals in the older age group (50–64 years) was classified in the “Mixed diet-physically active-low sedentary lifestyle pattern” and a higher proportion of people aged 18–30 years were classified into the “Poor diet-low physical activity-sedentary lifestyle pattern”. The highest proportion of people with a lower educational level was classified into the “Not poor diet-low physical activity-low sedentary lifestyle pattern”. There was no difference in the clusters by geographical area.
Prevalence rates of obesity were significantly higher among people allocated in the “Not poor diet-low physical activity-low sedentary lifestyle pattern” and overweight in the “Mixed diet-physically active-low sedentary lifestyle pattern”, however, those differences were not significant in the stratified analysis by age and gender.
Table 2 describes physical activity behaviors, sedentary, sleep time on weekdays, smoking behavior, and dietary pattern Z-scores in the lifestyle patterns. Vigorous physical activity, moderate physical activity, walking time, as well as the Z-scores of the Mediterranean DP were significantly higher in men and women classified in the “Mixed diet-physically active-low sedentary lifestyle pattern”. Z-scores for “Snack DP” were also higher in men in that lifestyle pattern. Men and women in the “Poor diet-low physical activity-sedentary lifestyle pattern” had significantly higher scores on the “Dairy-sweet DP” and sedentary time.
Consumption of selected food groups and beverages by lifestyle pattern in men and women is described in Table 3. Consumption of fruit, pasta, olive oil, water and alcoholic beverages, particularly wine and beer, was significantly higher in men and women included in the “Mixed diet-physically active-low sedentary lifestyle pattern”. Consumption of milk, cakes and pastry, sugar and sweets was significantly higher in men and women classified in the “Poor diet-low physical activity-sedentary lifestyle pattern”. Men in this lifestyle pattern showed significantly higher consumption of pre-cooked deep fried foods and high alcoholic content beverages. Women in this pattern had significantly higher consumption of savory snacks, juices and sugar sweetened soft drinks beverages.
Prevalence of obesity was compared between lifestyle patterns, adjusting for gender, age, educational level, geographical area and energy intake (Table 4). The prevalence odds ratio (POR) for obesity in men, 0.52 (IC 95% 0.29–0.92) allocated in the “Mixed diet-physically active-low sedentary lifestyle pattern” was significantly lower compared to those in the “Poor diet-low physical activity-sedentary lifestyle pattern”.

4. Discussion

In this cross-sectional study conducted in a random sample of Spanish adult population aged 18–64 years, four dietary patterns were characterized by factor analysis and subsequently used to identify three different lifestyle patterns based on the DPs, physical activities, sedentary time, and sleeping and smoking habits, using cluster analyses: a “Mixed diet-physically active-low sedentary lifestyle pattern”, a “Not poor diet-low physical activity-low sedentary lifestyle pattern” which included 63.3% of the sample, and a “Poor diet-low physical activity-sedentary lifestyle pattern”. A higher proportion of people aged 18–30 years was classified into the “Poor diet-low physical activity-sedentary lifestyle pattern”. The prevalence odds ratio for obesity in men allocated in the “Mixed diet-physically active-low sedentary lifestyle pattern” was significantly lower compared to those in the “Poor diet-low physical activity-sedentary lifestyle pattern”.
Analysis of dietary patterns has become an important approach in food consumption studies and nutritional epidemiology [11,27,28] Researchers report different methodological approaches and procedures to identify diet and lifestyle patterns, such as a priori defined patterns or data-driven, identified using principal components (PCA) [29], factor analysis [11,30], cluster analysis [31,32,33] and, more recently, hybrid methods [2]. Some pieces of research have identified lifestyle patterns associated to specific phenotypes and obesity in several population groups [34,35] and contributed to unravel the consequences that different diets may have on health [2,11,35,36,37,38].
In this study factor analysis was used to identify dietary patterns that were later used in cluster analysis. In the ANIBES study four DPs were singled out in adults. Similar DPs have been reported [37]. In Spain, in the context of the DORICA study, a pooled analysis of population-based cross-sectional surveys conducted between 1990 and 2000, using cluster analysis of three different DPs were characterized. A “Meat rich DP” with higher consumption of meat, cereals, and potatoes, an “Unbalanced DP” with higher consumption of milk and low consumption of vegetables and cereals, and a “Mediterranean DP” with higher consumption of vegetables, fruit, fish, and olive oil. The “Unbalanced DP” was associated with higher alcohol consumption, inadequate fruit and vegetable consumption and poor physical activity. This pattern was associated to higher prevalence odds ratio for metabolic syndrome. Conversely, the “Mediterranean DP” was associated with better fat intake quality profile and fiber intake [19].
More recently other authors have identified two DPs using factor analysis in Spanish adults in the context of Food4Me Project. A pattern with higher consumption of fast and processed foods, potatoes, red and white meats, processed cereals, and low consumption of fruit and vegetables, directly associated with BMI. A second DP showed higher consumption of eggs, fish, legumes, nuts, low-calorie beverages, oil, vegetables, and white meat [8].
Despite cross-sectional studies not allowing for causal inference, food patterns with higher energy density and low fiber content have been associated with higher obesity prevalence rates [2]. In longitudinal studies, such as the Baltimore Study, dietary patterns high in reduced-fat dairy, high fiber grains and cereals, vegetables and fruits, and low in meats, sugar-sweetened soft drinks, refined grains, and high fat dairy products, were significantly associated with lower weight gain, prospectively [39]. An energy-dense, high-saturated fat and low-fiber dietary pattern with high loadings of fast foods and snacks and low loadings of fruits and vegetables was shown to increase body weight, waist circumference, blood pressure, serum insulin, and lipid profile during a 10-year follow-up in severely obese Swedish adults [40]. In line with this, in adult Canadians a strong and consistent relation between an energy-dense, high-fat, low-fiber density dietary pattern and the risk of obesity was observed, significant in different population subgroups [2].
In the ANIBES study, 45.6% of male and 27.5% of female Spanish adults did not meet the recommendation of 150 min/week of moderate PA. In addition, 56.2% of male and 74% of female adults did not meet the recommendations for vigorous PA [13]. In this study we analyzed if these physical activity behaviors combined in a meaningful way with other energy balance-related behaviors, DPs, and lifestyles. We also used this procedure to identify dietary and lifestyle patterns in Spanish children and adolescents and identified two patterns: an “Unhealthier lifestyle pattern” a combination of low physical activity and poorer diet, and a “Healthier lifestyle pattern” which combined high physical activity, low sedentary behavior, longer sleep duration, and healthier diet.
Studies analyzing individual behaviors have found healthier eating habits, such as adequate fruit and vegetable consumption, to be more prevalent among women than in men [41], but adherence to physical activity recommendations were more prevalent in men than in women [42]. In line with the above, in this study “Snack DP” mean scores adjusted for age and energy intake were significantly higher in men than in women. Conversely, those of the “Mediterranean DP” were significantly higher in women. Men and women classified in the “Mixed diet-physically active-low sedentary lifestyle pattern” showed the highest mean scores for the “Mediterranean DP”. However, men in that lifestyle pattern showed the highest mean scores for the “Snack DP”, as well. The highest mean scores for the “Dairy sweet DP” were observed in men and women classified into the “Poor diet-low physical activity-sedentary lifestyle pattern”.
In this study a significantly higher proportion of men was classified into the “Mixed diet-physically active-low sedentary lifestyle pattern” (71.9%), while significantly more women were classified into the “Not poor diet-low physical activity-low sedentary lifestyle pattern” (58.5%). Men classified in the “Mixed diet-physically active-low sedentary lifestyle pattern” were less likely to be obese (POR 0.52; 95% IC 0.29–0.93), compared to those in the “Poor diet-low physical activity-sedentary lifestyle pattern”. Cassidy et al. reported obese adults to be two to five times more likely to report an “unhealthy phenotype”, a combination of low physical activity, high TV viewing, and poor sleep duration compared to normal weight adults in the UK Biobank [43].
A significantly higher proportion of women classified into the “Mixed diet-physically active-low sedentary lifestyle pattern” had a high educational level (35.6%). Women with a low educational level were more likely to be obese compared to those highly educated. Clustering and co-occurrence of multiple risk behaviors has been reported to be more likely in men and women with lower education or intermediate educational level compared with those with higher education [44].
As shown in this study, individuals may follow a variety of unhealthy lifestyle behaviors which combine to favor weight gain, or mixtures of healthy and unhealthy practices. The findings in this study underline the importance of designing and implementing interventions that address multiple health risk habits, considering lifestyle patterns, clustering of risk behaviors, and associated determinants. It is recognized that interventions which target more than one risk behavior have the potential for greater health benefits, enhance health promotion opportunities and can contribute to reduce health care costs. Such an approach allows for more adequate tailoring of interventions to participants’ profiles and helps to address health inequalities, reinforcing policies to ensure interventions accessible to socially-disadvantaged groups.
The ANIBES study has several strengths which include the careful design, protocol, and methodology used for dietary data collection and validated questionnaires used to collect information on physical activity which have shown good reliability and reproducibility. The study was conducted on a random population sample of the Spanish population aged 9–75 years. A limitation of this study is its cross-sectional design, which provides evidence for associations but not causal relationships. Measures of physical activity relied on self-reports and could be biased, although a careful multistep quality control procedure was implemented to minimize bias. In addition, we used factor analysis to identify DPs based on food consumption information collected by three-day food records and one 24-h recall, considering food groups to further collapse dietary intake data and avoid missing data from non-consumers of episodically consumed foods. Finally, both factor analysis and cluster analysis are procedures commonly used to identify DPs and analyze clustering of lifestyles. However, those procedures rely on several subjective decisions that may influence outcomes regarding the number and type of patterns and clusters identified.

5. Conclusions

Four dietary patterns were characterized in Spanish adults aged 18–64 years, one of them closer to the Mediterranean DP. These patterns were subsequently used to identify three different lifestyle patterns: a “Mixed diet-physically active-low sedentary lifestyle pattern”, a “Not poor diet-low physical activity-low sedentary lifestyle pattern” which included 63.3% of the sample, and a “Poor diet-low physical activity-sedentary lifestyle pattern”. A higher proportion of people aged 18–30 years was classified into the “Poor diet-low physical activity-sedentary lifestyle pattern”. The prevalence odds ratio for obesity in men allocated in the “Mixed diet-physically active-low sedentary lifestyle pattern” was significantly lower compared to those in the “Poor diet-low physical activity-sedentary lifestyle pattern”. Although prospective research on large population samples would be desirable to further analyze and track over time the lifestyle patterns and how they influence the development of overweight and obesity, these behavior patterns are helpful to identify specific issues in population subgroups and inform intervention strategies. Further research is needed on factors associated with lifestyle patterns to gain insight in the population subgroups at higher risk. The findings in this study underline the importance of designing and implementing interventions that address multiple health risk practices, considering lifestyle patterns and associated determinants.

Supplementary Materials

The following are available online at www.mdpi.com/2072-6643/9/6/606/s1, Table S1: Food groups and subgroups in the ANIBES Study, Table S2: Characteristics of the sample analyzed, Figure S1: Final cluster center scores for lifestyle patterns identified in Spanish adults aged 18–64 years in the ANIBES study.

Acknowledgments

The authors would like to thank Coca-Cola Iberia and IPSOS for its support and technical advice, particularly Rafael Urrialde and Javier Ruiz.

Author Contributions

C.P.R. analyzed the data and drafted the manuscript. M.G. contributed to draft the manuscript. J.A. contributed to the analysis and wrote the manuscript. This author is a member of the Scientific Advisory Board of the ANIBES study and, together with the other members, was responsible for careful review of the protocol, design, and methodology. This author provided continuous scientific advice for the study and for the interpretation of results. A.G., M.G.G., R.M.O. and L.S.M. are members of the Scientific Advisory Board of the ANIBES study, and were responsible for careful review of the protocol, design, and methodology. These authors provided continuous scientific advice for the study and for the interpretation of results. These authors also critically reviewed the manuscript. G.V.M., Principal Investigator of the ANIBES study, was responsible for the design, protocol, methodology, and follow-up checks of the study. All authors approved the final version of the manuscript.

Conflicts of Interest

The ANIBES study was financially supported by a grant from Coca-Cola Iberia through an agreement with the Spanish Nutrition Foundation (FEN). The funding sponsors had no role in the design of the study, in the collection, analyses, or interpretation of the data; in the writing of the manuscript, and in the decision to publish the results. The authors declare no conflict of interest.

References

  1. Margetts, B. Feedback on WHO/FAO global report on diet, nutrition and prevention of chronic diseases (NCD). Public Health Nutr. 2003, 6, 423–424. [Google Scholar] [CrossRef] [PubMed]
  2. Jessri, M.; Wolfinger, R.D.; Lou, W.Y.; L’Abbé, M.R. Identification of dietary patterns associated with obesity in a nationally representative survey of Canadian adults: Application of a priori, hybrid, and simplified dietary pattern techniques. Am. J. Clin. Nutr. 2017, 105, 669–684. [Google Scholar] [CrossRef] [PubMed]
  3. López-Sobaler, A.M.; Aparicio, A.; Aranceta-Bartrina, J.; Gil, A.; González-Gross, M.; Serra-Majem, L.; Varela-Moreiras, G.; Ortega, R.M. Overweight and General and Abdominal Obesity in a Representative Sample of Spanish Adults: Findings from the ANIBES Study. Biomed Res. Int. 2016, 2016, 8341487. [Google Scholar] [CrossRef] [PubMed]
  4. Basterra-Gortari, F.J.; Bes-Rastrollo, M.; Ruiz-Canela, M.; Gea, A.; Martinez-Gonzalez, M.A. Prevalencia de obesidad y diabetes en adultos españoles, 1987–2012. Med. Clin. 2017, 148, 250–256. [Google Scholar] [CrossRef] [PubMed]
  5. Eguaras, S.; Toledo, E.; Hernández-Hernández, A.; Cervantes, S.; Martínez-González, M.A. Better Adherence to the Mediterranean Diet Could Mitigate the Adverse Consequences of Obesity on Cardiovascular Disease: The SUN Prospective Cohort. Nutrients 2015, 7, 9154–9162. [Google Scholar] [CrossRef] [PubMed]
  6. Lacoppidan, S.A.; Kyrø, C.; Loft, S.; Helnæs, A.; Christensen, J.; Hansen, C.P.; Dahm, C.C.; Overvad, K.; Tjønneland, A.; Olsen, A. Adherence to a Healthy Nordic Food Index Is Associated with a Lower Risk of Type-2 Diabetes—The Danish Diet, Cancer and Health Cohort Study. Nutrients 2015, 7, 8633–8644. [Google Scholar] [CrossRef] [PubMed]
  7. GBD 2015 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioral, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016, 388, 1659–1724. [Google Scholar]
  8. San-Cristobal, R.; Navas-Carretero, S.; Celis-Morales, C.; Brennan, L.; Walsh, M.; Lovegrove, J.A.; Daniel, H.; Saris, W.H.M.; Traczyk, I.; Manios, Y.; et al. Analysis of Dietary Pattern Impact on Weight Status for Personalised Nutrition through On-Line Advice: The Food4Me Spanish Cohort. Nutrients 2015, 7, 9523–9537. [Google Scholar] [CrossRef] [PubMed]
  9. Brown, R.C.; Tey, S.L.; Gray, A.R.; Chisholm, A.; Smith, C.; Fleming, E.; Parnell, W. Association of Nut Consumption with Cardiometabolic Risk Factors in the 2008/2009 New Zealand Adult Nutrition Survey. Nutrients 2015, 7, 7523–7542. [Google Scholar] [CrossRef] [PubMed]
  10. Kremers, S.P. Theory and practice in the study of influences on energy balance-related behaviors. Patient Educ. Couns. 2010, 79, 291–298. [Google Scholar] [CrossRef] [PubMed]
  11. Mikkilä, V.; Räsänen, L.; Raitakari, O.T.; Pietinen, P.; Viikari, J. Consistent dietary patterns identified from childhood to adulthood: The Cardiovascular Risk in Young Finns Study. Br. J. Nutr. 2005, 93, 923–931. [Google Scholar] [CrossRef] [PubMed]
  12. Pronk, N.P.; Anderson, L.H.; Crain, A.L.; Martinson, B.C.; O’Connor, P.J.; Sherwood, N.E.; Whitebird, R.R. Meeting recommendations for multiple healthy lifestyle factors. Prevalence, clustering, and predictors among adolescent, adult, and senior health plan members. Am. J. Prev. Med. 2004, 27, 25–33. [Google Scholar] [CrossRef] [PubMed]
  13. Mielgo-Ayuso, J.; Aparicio-Ugarriza, R.; Castillo, A.; Ruiz, E.; Ávila, J.M.; Aranceta-Batrina, J.; Gil, Á.; Ortega, R.M.; Serra-Majem, L.; Varela-Moreiras, G.; et al. Physical Activity Patterns of the Spanish Population Are Mostly Determined by Sex and Age: Findings in the ANIBES Study. PLoS ONE 2016, 11, e0149969. [Google Scholar] [CrossRef] [PubMed]
  14. Hunsberger, M.; Mehlig, K.; Börnhorst, C.; Hebestreit, A.; Moreno, L.; Veidebaum, T.; Kourides, Y.; Siani, A.; Molnar, D.; Sioen, I.; et al. Dietary Carbohydrate and Nocturnal Sleep Duration in Relation to Children’s BMI: Findings from the IDEFICS Study in Eight European Countries. Nutrients 2015, 7, 10223–10236. [Google Scholar] [CrossRef] [PubMed]
  15. Cao, Y.; Wittert, G.; Taylor, A.W.; Adams, R.; Shi, Z. Associations between Macronutrient Intake and Obstructive Sleep Apnoea as Well as Self-Reported Sleep Symptoms: Results from a Cohort of Community Dwelling Australian Men. Nutrients 2016, 8, 207. [Google Scholar] [CrossRef] [PubMed]
  16. El Ansari, W.; Suominen, S.; Berg-Beckhoff, G. Is Healthier Nutrition Behaviour Associated with Better Self-Reported Health and Less Health Complaints? Evidence from Turku, Finland. Nutrients 2015, 7, 8478–8490. [Google Scholar] [CrossRef] [PubMed]
  17. Ruiz, E.; Ávila, J.M.; Castillo, A.; Valero, T.; del Pozo, S.; Rodriguez, P.; Aranceta Bartrina, J.; Gil, A.; González-Gross, M.; Ortega, R.M.; et al. The ANIBES Study on Energy Balance in Spain: Design, protocol and methodology. Nutrients 2015, 7, 970–998. [Google Scholar] [CrossRef] [PubMed]
  18. Ruiz, E.; Ávila, J.M.; Valero, T.; del Pozo, S.; Rodriguez, P.; Aranceta-Bartrina, J.; Gil, A.; González-Gross, M.; Ortega, R.M.; Serra-Majem, L.; et al. Energy Intake, Profile, and Dietary Sources in the Spanish Population: Findings of the ANIBES Study. Nutrients 2015, 7, 4739–4762. [Google Scholar] [CrossRef] [PubMed]
  19. Aranceta Bartrina, J.; Pérez-Rodrigo, C.; Serra-Majem, L.; Tur Marí, J.A.; Mataix Verdú, J.; Tojo Sierra, R.; Ribas Barba, L.; Martinez de Vitoria, E.; Millán Núñez-Cortés, J. Dieta y riesgo cardiovascular en España: Estudio DORICA (III). In Dieta y Riesgo Cardiovascular. Estudio DORICA II; Aranceta, J., Foz, M., Gil, B., Jover, E., Mantilla, T., Millán, J., Monereo, S., Moreno, B., Eds.; Médica Panamericana: Madrid, Spain, 2007; pp. 121–162. [Google Scholar]
  20. Moreiras, O.; Carbajal, A.; Cabrera, L.; Cuadrado, C. Tablas de Composición de Alimentos, 15th ed.; Pirámide: Madrid, Spain, 2011. [Google Scholar]
  21. Roman-Viñas, B.; Serra-Majem, L.; Hagströmer, M.; Ribas-Barba, L.; Sjöström, M.; Segura-Cardona, R. International physical activity questionnaire: Reliability and validity in a Spanish population. Eur. J. Sport Sci. 2010, 10, 297–304. [Google Scholar] [CrossRef]
  22. IPAQ. Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ)—Short and Long Forms. November 2005. Available online: https://sites.google.com/site/theipaq/scoring-protocol (accessed on 27 October 2015).
  23. Bauman, A.; Ainsworth, B.E.; Bull, F.; Craig, C.L.; Hagströmer, M.; Sallis, J.F.; Pratt, M.; Sjöström, M. Progress and Pitfalls in the Use of the International Physical Activity Questionnaire (IPAQ) for Adult Physical Activity Surveillance. J. Phys. Act. Health 2009, 6, S5–S8. [Google Scholar] [CrossRef] [PubMed]
  24. Marfell-Jones, M.; Olds, T.; Stewart, A.; Carter, L. International Standards for Anthropometric Assessment; International Society for the Advancement of Kinanthropometry: Potchefstroom, South Africa, 2006; pp. 1–137. [Google Scholar]
  25. Newby, P.K.; Tucker, K.L. Empirically derived eating patterns using factor or cluster analysis: A review. Nutr. Rev. 2004, 62, 177–203. [Google Scholar] [CrossRef] [PubMed]
  26. Everitt, B.S.; Landau, S.; Leese, M.; Stahl, D. Cluster Analysis, 5th ed.; JohnWiley & Sons, Ltd.: West Sussex, UK, 2011. [Google Scholar]
  27. Hu, F.B. Dietary pattern analysis: A new direction in nutritional epidemiology. Curr. Opin. Lipidol. 2002, 13, 3–9. [Google Scholar] [CrossRef] [PubMed]
  28. Hodge, A.; Bassett, J. What can we learn from dietary pattern analysis? Public Health Nutr. 2016, 19, 191–194. [Google Scholar] [CrossRef] [PubMed]
  29. Varraso, R.; Garcia-Aymerich, J.; Monier, F.; Le Moual, N.; De Batlle, J.; Miranda, G.; Pison, C.; Romieu, I.; Kauffmann, F.; Maccario, J. Assessment of dietary patterns in nutritional epidemiology: Principal component analysis compared with confirmatory factor analysis. Am. J. Clin. Nutr. 2012, 96, 1079–1092. [Google Scholar] [CrossRef] [PubMed]
  30. Wadolowska, L.; Kowalkowska, J.; Czarnocinska, J.; Jezewska-Zychowicz, M.; Babicz-Zielinska, E. Comparing dietary patterns derived by two methods and their associations with obesity in Polish girls aged 13–21 years: The cross-sectional GEBaHealth study. Perspect. Public Health 2017, 137, 182–189. [Google Scholar] [CrossRef] [PubMed]
  31. Wirfalt, A.K.; Jeffery, R.W. Using cluster analysis to examine dietary patterns: Nutrient intakes, gender, and weight status differ across food pattern clusters. J. Am. Diet. Assoc. 1997, 97, 272–279. [Google Scholar] [CrossRef]
  32. Leech, R.M.; McNaughton, S.A.; Timperio, A. The clustering of diet, physical activity and sedentary behavior in children and adolescents: A review. Int. J. Behav. Nutr. Phys. Act. 2014, 11, 4. [Google Scholar] [CrossRef] [PubMed]
  33. Pérez-Rodrigo, C.; Gil, A.; González-Gross, M.; Ortega, R.M.; Serra-Majem, L.; Varela-Moreiras, G.; Aranceta-Bartrina, J. Clustering of Dietary Patterns, Lifestyles, and Overweight among Spanish Children and Adolescents in the ANIBES Study. Nutrients 2016, 8, 11. [Google Scholar] [CrossRef] [PubMed]
  34. Allès, B.; Samieri, C.; Lorrain, S.; Jutand, M.A.; Carmichael, P.H.; Shatenstein, B.; Gaudreau, P.; Payette, H.; Laurin, D.; Barberger-Gateau, P. Nutrient Patterns and Their Food Sources in Older Persons from France and Quebec: Dietary and Lifestyle Characteristics. Nutrients 2016, 8, 225. [Google Scholar] [CrossRef] [PubMed]
  35. Blondin, S.A.; Mueller, M.P.; Bakun, P.J.; Choumenkovitch, S.F.; Tucker, K.L.; Economos, C.D. Cross-Sectional Associations between Empirically-Derived Dietary Patterns and Indicators of Disease Risk among University Students. Nutrients 2016, 8, 3. [Google Scholar] [CrossRef] [PubMed]
  36. Appannah, G.; Pot, G.K.; Huang, R.C.; Oddy, W.H.; Beilin, L.J.; Mori, T.A.; Jebb, S.A.; Ambrosini, G.L. Identification of a dietary pattern associated with greater cardiometabolic risk in adolescence. Nutr. Metab. Cardiovasc. Dis. 2015, 25, 643–650. [Google Scholar] [CrossRef] [PubMed]
  37. Bell, L.K.; Edwards, S.; Grieger, J.A. The Relationship between Dietary Patterns and Metabolic Health in a Representative Sample of Adult Australians. Nutrients 2015, 7, 6491–6505. [Google Scholar] [CrossRef] [PubMed]
  38. Shu, L.; Zheng, P.F.; Zhang, X.Y.; Si, C.J.; Yu, X.L.; Gao, W.; Zhang, L.; Liao, D. Association between Dietary Patterns and the Indicators of Obesity among Chinese: A Cross-Sectional Study. Nutrients 2015, 7, 7995–8009. [Google Scholar] [CrossRef] [PubMed]
  39. Newby, P.K.; Muller, D.; Hallfrisch, J.; Andres, R.; Tucker, K.L. Food patterns measured by factor analysis and anthropometric changes in adults. Am. J. Clin. Nutr. 2004, 80, 504–513. [Google Scholar] [PubMed]
  40. Johns, D.J.; Lindroos, A.K.; Jebb, S.A.; Sjostrom, L.; Carlsson, L.M.; Ambrosini, G.L. Dietary patterns, cardiometabolic risk factors, and the incidence of cardiovascular disease in severe obesity. Obesity 2015, 23, 1063–1070. [Google Scholar] [CrossRef] [PubMed]
  41. Mackenbach, J.D.; Brage, S.; Forouhi, N.G.; Griffin, S.J.; Wareham, N.J.; Monsivais, P. Does the importance of dietary costs for fruit and vegetable intake vary by socioeconomic position? Br. J. Nutr. 2015, 114, 1464–1470. [Google Scholar] [CrossRef] [PubMed]
  42. de Azevedo Barros, M.B.; Guimarães Lima, M.; Barbosa Medina, L.P.; Landman Szwarcwald, C.; Carvalho Malta, D. Social inequalities in health behaviors among Brazilian adults: National Health Survey, 2013. Int. J. Equity Health 2016, 15, 148. [Google Scholar] [CrossRef] [PubMed]
  43. Cassidy, S.; Chau, J.Y.; Catt, M.; Bauman, A.; Trenell, M.I. Low physical activity, high television viewing and poor sleep duration cluster in overweight and obese adults; a cross-sectional study of 398,984 participants from the UK Biobank. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 57. [Google Scholar] [CrossRef] [PubMed]
  44. Meader, N.; King, K.; Moe-Byrne, T.; Wright, K.; Graham, H.; Petticrew, M.; Power, C.; White, M.; Sowden, A.J. A systematic review on the clustering and co-occurrence of multiple risk behaviours. BMC Public Health 2016, 16, 657. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Factor loadings after varimax rotation on identified dietary patterns of food groups retained. Eigenvalues: Traditional DP = 2.15; Mediterranean DP = 1.64; Snack DP = 1.55; Dairy-sweet DP = 1.28. Percent of variance explained: Traditional DP: 8.74%; Mediterranean DP: 8.39%; Snack DP: 8.19%; Dairy sweet DP: 7.74%. Total variance explained 33.07%. Absolute values less than 0.20 are not shown.
Figure 1. Factor loadings after varimax rotation on identified dietary patterns of food groups retained. Eigenvalues: Traditional DP = 2.15; Mediterranean DP = 1.64; Snack DP = 1.55; Dairy-sweet DP = 1.28. Percent of variance explained: Traditional DP: 8.74%; Mediterranean DP: 8.39%; Snack DP: 8.19%; Dairy sweet DP: 7.74%. Total variance explained 33.07%. Absolute values less than 0.20 are not shown.
Nutrients 09 00606 g001
Table 1. Gender, age group, educational level, geographical area, and BMI status by lifestyle pattern.
Table 1. Gender, age group, educational level, geographical area, and BMI status by lifestyle pattern.
CharacteristicsMixed Diet-Physically Active-Low Sedentary Lifestyle PatternNot Poor Diet-Low Physical Activity-Low Sedentary Lifestyle PatternPoor Diet-Low Physical Activity-Sedentary Lifestyle PatternAllχ2p
n%n%n%n
All21013.0102063.338123.61611
Gender
 Men15171.942341.520453.577870.10.000
 Women5928.159758.517746.5833
Age group
 18–30 years6129.021320.913936.541360.30.000
 31–49 years11052.448747.718348.0780
 50–64 years3918.632031.45915.5418
Educational level 27.80.000
 Primary or less5526.230429.87319.2432
 Secondary9545.250749.718949.6791
 Higher6028.620920.511931.2388
Geographical area
 North-northwest3617.116616.37419.42764.70.577
 Eastern-Mediterranean7033.335034.312733.3547
 Center4822.923322.89625.2377
 South5626.727126.68422.0411
BMI status
 Normal weight8841.938537.717746.565017.30.002
 Overweight9042.938637.813635.7612
 Obese3215.224924.46817.8349
Pearson’s chi-square tests were used to investigate the differences in cluster distribution by gender, age group, education level, geographical area, and BMI status.
Table 2. Physical activity behaviors, sedentary, sleep time on weekdays, smoking behavior, and dietary pattern scores in the lifestyle patterns by gender *.
Table 2. Physical activity behaviors, sedentary, sleep time on weekdays, smoking behavior, and dietary pattern scores in the lifestyle patterns by gender *.
Mixed Diet-Physically Active-Low Sedentary Lifestyle PatternNot Poor Diet-Low Physical Activity-Low Sedentary Lifestyle PatternPoor Diet-Low Physical Activity-Sedentary Lifestyle PatternFp
MeanSDMedianMeanSDMedianMeanSDMedian
Menn = 151n = 423n = 204
Traditional DPscore0.041.030.000.051.040.00−0.030.950.001.20.314
Mediterranean DPscore0.251.350.06−0.171.00−0.23−0.120.96−0.098.00.000
Snack DPscore0.661.290.520.140.950.070.461.140.319.90.000
Dairy-sweet DPscore−0.050.99−0.11−0.490.61−0.511.031.210.88126.90.000
Walking (min/week)447.8434.4240.0284.7291.7210.0240.7274.9150.020.30.000
Moderate PA (min/week)478.4426.9360.0321.1360.1180.0174.1234.8112.532.80.000
Vigorous PA (min/week)706.4291.7720.081.8127.30.0102.3155.40.0734.10.000
Sedentary time (h/day)3.62.03.04.42.34.07.13.76.496.80.000
Sleeping (h/day)6.62.37.07.11.97.57.02.07.03.90.021
Smoking (cig/day)4.36.90.06.48.70.03.16.00.010.20.000
Womenn = 59n = 597n = 177
Traditional DPscore−0.301.02−0.200.020.96−0.02−0.121.00−0.107.20.001
Mediterranean DPscore0.331.060.450.030.890.050.090.920.097.20.001
Snack DPscore−0.200.94−0.37−0.380.75−0.49−0.120.82−0.297.20.001
Dairy-sweet DP’ score0.030.74−0.02−0.330.60−0.361.160.891.09203.00.000
Walking (min/week)528.6364.1420.0267.0270.2180.0247.5263.0180.025.60.000
Moderate PA (min/week)740.3399.6750.0545.8442.9420.0316.9332.2210.025.20.000
Vigorous PA (min/week)692.0325.3630.045.992.20.057.8120.20.0685.40.000
Sedentary time (h/day)3.62.13.04.02.24.06.64.16.060.50.000
Sleeping (h/day)6.22.57.07.11.97.57.02.27.56.30.002
Smoking (cig/day)2.55.60.03.97.00.02.35.20.04.50.011
* Physical activities, sedentary time, sleep, and smoking compared by ANOVA considering age group. General linear models adjusted for age and energy intake to compare DP scores.
Table 3. Consumption of selected food groups and beverages by lifestyle pattern in men and women.
Table 3. Consumption of selected food groups and beverages by lifestyle pattern in men and women.
Mixed Diet-Physically Active-Low Sedentary Lifestyle PatternNot Poor Diet-Low Physical Activity-Low Sedentary Lifestyle PatternPoor Diet-Low Physical Activity-Sedentary Lifestyle PatternFp
MeanSDMedianMeanSDMedianMeanSDMedian
Men (n = 781)(n = 151)(n = 423)(n = 204)
Vegetables (g/day)184.9110.9165.4185.1112.4162.5178.297.2165.00.980.374
Fruit (g/day)183.1231.0136.7145.0172.797.5139.5144.0103.93.240.040
Legumes (g/day)16.723.17.516.219.110.513.618.27.12.190.113
Meat (g/day)127.292.3111.7109.875.395.8124.977.4116.30.560.573
Processed and cold meats (g/day)55.846.644.342.536.034.250.339.145.92.460.086
Fish (g/day)73.690.847.762.566.939.355.857.135.43.010.050
Eggs (g/day)40.846.431.332.533.421.328.330.420.09.580.000
Milk (mL/day)155.5122.9139.7125.6100.7115.0267.5178.5249.460.420.000
Cheese (g/day)25.441.415.215.820.010.019.222.212.94.390.013
Yoghourt (g/day)62.374.541.742.364.20.046.262.020.83.620.027
Pasta (g/day)22.627.512.516.220.011.717.720.211.73.080.047
Bread (g/day)94.457.483.383.644.680.097.558.285.01.150.318
Cakes and pastry (g/day)30.336.116.721.125.711.757.846.350.244.800.000
Sugar and sweets (g/day)15.015.410.010.09.87.524.718.521.843.230.000
Pre-cooked foods (g/day)73.083.050.076.386.945.880.091.346.33.450.032
Savory snacks (g/day)6.112.10.04.710.10.07.514.30.00.510.603
Olive oil (mL/day)20.08.920.218.08.816.717.37.518.06.400.002
Juices (mL/day)71.0123.90.040.079.80.088.6175.40.01.510.221
Sugar sweetened soft drinks (mL/day)104.2151.541.797.7186.70.0127.2192.247.52.440.088
Water (mL/day)843.4647.7695.8638.0537.2513.3757.4582.2685.03.620.027
Alcoholic beverages (mL/day)186.1259.471.7176.4241.258.3102.7181.90.014.330.000
Low alcohol content bevs (mL/day)1.65.70.02.811.60.03.419.40.02.950.05
High alcohol content bevs (mL/day)184.5257.671.7173.6238.855.899.3177.30.014.310.00
Mixed Diet-Physically Active-Low Sedentary Lifestyle PatternNot Poor Diet-Low Physical Activity-Low Sedentary Lifestyle PatternPoor Diet-Low Physical Activity-Sedentary Lifestyle PatternFp
MeanSDMedianMeanSDMedianMeanSDMedian
Women (n = 833)n = 59n = 597n = 177
Vegetables (g/day)168.590.6156.2195.3115.3168.8174.4110.4162.65.1970.006
Fruit (g/day)186.5175.6152.5162.4173.0111.5143.8147.0110.00.9290.395
Legumes (g/day)10.913.85.014.718.710.013.624.23.31.4330.239
Meat (g/day)78.863.166.790.963.482.5101.670.189.22.0540.129
Processed and cold meats (g/day)37.131.529.733.530.126.737.532.229.21.5820.206
Fish (g/day)55.450.445.059.862.940.058.165.938.30.1420.868
Eggs (g/day)25.926.321.325.024.720.724.524.219.51.4970.224
Milk (mL/day)175.0104.7175.0148.2103.1141.7271.2138.3255.072.0010.000
Cheese (g/day)20.120.512.515.117.110.018.721.313.31.2890.276
Yoghourt (g/day)53.956.730.843.757.220.851.458.722.52.5500.079
Pasta (g/day)18.720.511.714.519.18.315.419.710.81.7680.171
Bread (g/day)64.838.456.765.739.660.066.733.764.211.9890.000
Cakes and pastry (g/day)31.637.120.720.922.615.056.340.950.054.3450.000
Sugar and sweets (g/day)15.612.413.711.311.38.230.225.025.352.8160.000
Pre-cooked foods (g/day)63.177.641.761.171.641.758.061.541.74.5610.011
Savory snacks (g/day)5.99.60.03.47.40.09.014.82.07.0450.001
Olive oil (mL/day)17.28.215.817.97.817.317.38.517.06.5800.001
Juices (mL/day)51.080.50.032.560.00.061.386.813.33.3480.036
Sugar sweetened soft drinks (mL/day)60.5130.20.077.9149.70.096.4159.733.33.5430.029
Water (mL/day)764.2563.1658.3649.0474.2550.0753.6560.2641.74.6890.009
Alcoholic beverages (mL/day)84.4132.530.068.5141.60.047.0100.40.08.8420.000
Low alcohol content bevs (mL/day)1.14.10.01.410.50.01.67.30.01.5180.220
High alcohol content bevs (mL/day)83.3131.230.067.2140.50.045.597.80.08.5160.000
General linear models were used to estimate multivariate means for food consumption across lifestyle patterns adjusted for age and energy intake.
Table 4. Prevalence odds ratios (POR) of obesity in men and women, according to age group, level of education, geographical area, and lifestyle pattern.
Table 4. Prevalence odds ratios (POR) of obesity in men and women, according to age group, level of education, geographical area, and lifestyle pattern.
MenWomen
POR95% C.I.PORpPOR95% C.I.PORp
LowerUpperLowerUpper
Age group
 50–64 years 0.000 0.000
 18–30 years0.290.170.480.0000.290.160.520.000
 31–49 years0.610.420.890.0110.520.350.780.002
Level of education
 High 0.293 0.000
 Primary or less1.410.872.280.1623.111.745.580.000
 Secondary1.080.691.690.7301.851.063.240.031
Geographical area
 South 0.017 0.704
 North-northwest1.130.651.990.6580.830.451.500.532
 Eastern-Mediterranean1.991.253.160.0031.110.701.760.672
 Center1.450.882.400.1410.880.511.520.649
Lifestyle pattern
 Poor diet-low physical activity-sedentary lifestyle pattern 0.058 0.648
 Mixed diet-physically active-low sedentary lifestyle pattern0.520.290.930.0271.160.482.800.738
 Not poor diet-low physical activity-low sedentary lifestyle pattern0.920.591.440.7261.300.752.250.358
Note: Binary logistic regression models adjusted for age group, educational level, geographical area, and energy intake.

Share and Cite

MDPI and ACS Style

Pérez-Rodrigo, C.; Gianzo-Citores, M.; Gil, Á.; González-Gross, M.; Ortega, R.M.; Serra-Majem, L.; Varela-Moreiras, G.; Aranceta-Bartrina, J. Lifestyle Patterns and Weight Status in Spanish Adults: The ANIBES Study. Nutrients 2017, 9, 606. https://doi.org/10.3390/nu9060606

AMA Style

Pérez-Rodrigo C, Gianzo-Citores M, Gil Á, González-Gross M, Ortega RM, Serra-Majem L, Varela-Moreiras G, Aranceta-Bartrina J. Lifestyle Patterns and Weight Status in Spanish Adults: The ANIBES Study. Nutrients. 2017; 9(6):606. https://doi.org/10.3390/nu9060606

Chicago/Turabian Style

Pérez-Rodrigo, Carmen, Marta Gianzo-Citores, Ángel Gil, Marcela González-Gross, Rosa M. Ortega, Lluis Serra-Majem, Gregorio Varela-Moreiras, and Javier Aranceta-Bartrina. 2017. "Lifestyle Patterns and Weight Status in Spanish Adults: The ANIBES Study" Nutrients 9, no. 6: 606. https://doi.org/10.3390/nu9060606

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop