Relation Between Sugar-Sweetened Beverage Consumption, Nutrition, and Lifestyle in a Military Population

MILITARY MEDICINE, 181, 10:1335, 2016 Relation Between Sugar-Sweetened Beverage Consumption, Nutrition, and Lifestyle in a Military Population Patrick Mullie, PhD* ; Tom Deliens, MSc*; Peter Clarys, PhD* ABSTRACT Objective to describe the demographic, socioeconomic, and nutritional behaviors associated with of sugar-sweetened beverage (SSB) consumption. Design: cross-sectional. Setting: in January 2014, 26,566 military personnel, representing 84.6% of the 31,412 men and women in active service were invited to participate in an online survey. Included questions were about consumption of fruits and vegetables, meat, SSB, number of breakfasts a week, and military rank. Subjects: 7,252 military subjects. Results: mean (standard deviation) age of the participants was 45.4 (7.9) years for 6,529 males and 41.9 (8.9) years for 723 females. Mean (standard deviation) body mass index was 26.6 (3.6) kg/m 2 for males and 24.5 (3.9) kg/m 2 for females. The probability of consuming daily SSB decreased with age, and with increasing body mass index, being female, and being a noncommissioned officer or officer. Consumption of fruits and vegetables decreased for daily SSB consumption, but meat consumption increased. The odds ratio (95% confidence interval) for daily SSB consumption was 0.65 (0.58 0.74) for daily breakfast and 1.49 (1.30 1.71) for smoking. There was no relation between physical activity and SSB consumption. Conclusions: SSB consumption was associated with attributes of a lower quality diet. INTRODUCTION It was estimated that the proportion of adults with a body mass index (BMI) of 25 kg/m 2 or greater increased between 1980 and 2013 from 29% to 37% in men, and from 30% to 38% in women. 1 Bray et al 2 support the role of sugarsweetened beverages (SSBs) consumption as a contributor to the obesity epidemic, and suggest a reversal may occur with SSB consumption reduction. According to Bray et al, 2 the relation of SSBs to obesity can be attributed to two different effects: the increased caloric intake, and the fact that beverages do not suppress the intake of other food calories to an appropriate degree to prevent weight gain. The recommended quantity of added sugars and SSB in a dietary pattern is the subject of a worldwide debate. Indeed, the recently issued guidelines of the World Health Organization proposed recently a reduction of added sugar from 10 to 5 energy percent. 3 An increasing trend in adiposity has been observed in the past in Belgian army. 4 It is clear that, to answer the physical demands of a military career, an individual has to avoid excess body weight. 5 The workplace can be considered as an important channel to provide health promoting messages, 6 but a thorough knowledge of behaviors associated with increasing adiposity is mandatory in providing tailored, population-based health advice. However, a recent authoritative meta-analysis between sugar consumption and weight gain performed by Te * 1 Faculty of Physical Education and Physiotherapy, Department of Human Biometrics and Biomechanics, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussels, Belgium. Unit of Epidemiology and Biostatistics, Queen Astrid Military Hospital, Bruynstraat 1, B-1120 Brussels, Belgium. Erasmus University College, Laarbeeklaan 121, B-1120 Brussels, Belgium. AMSUS The Society of Federal Health Professionals, 2016 doi: 10.7205/MILMED-D-15-00453 Morenga et al 7 found conflicting results between intervention studies and prospective cohort studies. Although prospective cohort studies observed an increase in adiposity comparing highest versus lowest intake of added sugar, intervention studies aiming at reducing sugar consumption did not lead to drastic weight losses. The discrepancy between observational and interventional research could be the result of the presence of confounding elements in observational research. If unhealthy lifestyle and/or nutritional elements are closely related to SSB consumption, it will be extremely difficult to isolate the single effect of SSB consumption on adiposity in observational studies. In contrast, and by definition, the aim of interventional research is to distribute possible confounders equally between intervention and control groups. There is limited existing data on SSB consumption and weight status in a military population. The aim of this study was to describe the demographic, socioeconomic, and lifestyle behaviors associated with SSB consumption in a military population. METHODS In January 2014, 26,566 Belgium Army soldiers, 84.6% of active service men and women, were invited through e-mail recruitment to complete an anonymous online survey. Participants were given 2 weeks to complete the cross-sectional online survey. In total 8,234 military men and women participated and 982 were excluded, with missing values resulting in a complete data set of 7,252 participants. The online survey included the following questions: what is your age; what are your height and weight (without shoes and clothes); how many portions of fruits and vegetables do you consume during a day (fruit juice and potatoes not included); what is your usual portion of meat at lunch (in grams); what is your military rank (officer, noncommissioned officer, or soldier); how many breakfasts do you take a week; MILITARY MEDICINE, Vol. 181, October 2016 1335

how often do you consume SSBs (never, 1 3 timesamonth, once a week, 2 4 times a week, 5 6 times a week, once a day, and more than once a day); and do you smoke (yes or no). A portion of fruit was defined as a banana, an orange, an apple or a pear, two tangerines, or ten grapes; a portion of vegetables was defined as one tomato, one-quarter of a cucumber, three tablespoons of carrots or broccoli or cauliflower, ten mushrooms, five asparagus, or one ladle of soup. A meat portion was defined as 100 g for a hamburger as reference to help participants to evaluate their portion. Breakfast was defined as solid foods consumed maximum 2 hours after wake up. A portion of SSB was defined as a glass of 200 ml. BMI was classified according to the World Health Organization 8 in normal weight, 18.5 BMI < 25.0 kg/m 2, overweight, 25.0 BMI < 30.0 kg/m 2, and obesity, BMI 30.0 kg/m 2. The International Physical Activity Questionnaire (IPAQ) short form was used to report frequency and duration of walking, as well as moderately and vigorously intensive activities performed for at least 10 minutes duration per session. 9 The IPAQ also collected information on total sitting time. Weekly minutes of walking and participation in moderately intensive and vigorous activities were calculated separately by multiplying the number of days per week by the duration on an average day. Reported minutes per week in each category were weighted by a metabolic equivalent (i.e., multiples of resting energy expenditure) resulting in a physical activity estimate independent of body weight, expressed in metabolic equivalent tasks (minutes per week) and computed by multiplying metabolic equivalent tasks by minutes per week. All physical activity variables in metabolic equivalent tasks by minutes per week (walking, vigorous and moderate physical activity) were categorized into tertiles so that the relative difference in the two outcome variables between the least and the most physically active groups could be observed. STATISTICS All descriptive data are presented as mean with standard deviation or frequencies in percent. Univariate analysis, chi square, and t tests were used to investigate the differences between males and females and between daily and less-thandaily SSB consumption. Binary logistic regression (enter method) was conducted with SSB as the dependent variable, categorized as daily consumption or less-than-daily consumption to make a distinction between occasional and regular consumers. Independent variables in the models were age (years), BMI (continuous in kg/m 2 ), fruits (portions per day), vegetables (portions per day), meat (grams per day), gender (female versus male), military rank as dummy variable (with soldiers as reference), number of breakfasts per week (5 or more versus fewer), smoking (yes versus no), and physical activity as dummy variable (with low physical activity as reference). Results of the binary logistic regression are presented as odds ratios with 95% confidence intervals. All p values are two-sided, and a 5% level of statistical significance was used. The data were analyzed by using IBM SPSS statistics for Windows Version 21.0 (IBM Corp, Armonk, New York). RESULTS Table I presents the general characteristics of the participants. Mean (standard deviation) age was 45.4 (7.9) years for 6,529 males and 41.9 (8.9) years for 723 females. BMI was 26.6 (3.6) kg/m 2 for males and 24.5 (3.9) kg/m 2 for females. Females consumed more fruit and vegetables but less meat than males (p < 0.05). For males, 20.0% of the participants were officers, 57.2% noncommissioned officers, and 22.8% soldiers; for females this was 25.7%, 42.0%, and 32.2%. Males consumed fewer breakfasts a week (69.1% versus 79.9%) and more daily SSBs (22.2% versus 12.4%) than females. The occurrence of smoking and obesity was higher in males compared with females. Finally, males were more vigorously physical active, namely 36.7% versus 28.4% (p < 0.05). TABLE I. Characteristics of the Participants Males Females Characteristic Mean SD Mean SD Body and Diet Age (Years) 45.4* 7.9 41.9* 8.9 BMI (kg/m 2 ) 26.6* 3.6 24.5* 3.9 Fruit (Portions per Day) 1.5* 1.1 1.6* 1.1 Vegetables (Portions per Day) 1.7* 1.1 1.8* 1.1 Meat (g per Day) 170* 66 126* 61 n % n % Rank Officers 1,307* 20.0 186* 25.7 NCO 3,733* 57.2 304* 42.0 Soldiers 1,489* 22.8 233* 32.2 Total 6,529 100.0 723 100.0 Breakfasts per Week Fewer Than 5 2,017* 30.9 145* 20.1 5 or More 4,512* 69.1 578* 79.9 SSB Consumption Less-Than-Daily 5,080* 77.8 633* 87.6 Daily 1,449* 22.2 90* 12.4 Smoking Status Nonsmoking 5,039 77.2 586 81.1 Actual Smoking 1,490 22.8 137 18.9 Weight Status Normal a 2,327* 35.6 445* 61.5 Overweight a 3,209* 49.1 205* 28.4 Obese a 993* 15.2 73* 10.1 Physical Activity Low b 1,799* 27.6 277* 38.3 Moderate b 2,336* 35.8 241* 33.3 Vigorous b 2,394* 36.7 205* 28.4 BMI, body mass index; NCO, noncommissioned officers; SD, standard deviation; SSB, sugar-sweetened beverage. *p < 0.05 for males compared to females. a Normal weight (BMI below 24.9 kg/m 2 ) overweight (BMI between 25.0 and 29.9 kg/m 2 ) obesity (BMI of 30.0 kg/m 2 and more). b Physical activity was stratified in low, moderate, and vigorous according to the IPAQ. 1336 MILITARY MEDICINE, Vol. 181, October 2016

TABLE II. Univariate Analysis of Nutritional and Lifestyle Behaviors Associated With SSB Consumption Less Than Daily SSB Consumption Daily SSB Consumption Characteristic Mean SD Mean SD Body and Diet Age (Years) 45.3* 7.9 44.0* 8.7 BMI (kg/m 2 ) 26.5* 3.6 25.8* 3.7 Fruit (Portions per Day) 1.6* 1.1 1.3* 1.1 Vegetables (Portions per 1.7* 1.1 1.6* 1.1 Day) Meat (g per Day) 163* 66 179* 67 n % n % Gender Males 5,080* 88.9 1,449* 94.2 Females 633* 11.1 90* 5.8 Total 5,713 100.0 1,539 100.0 Rank Officers 1,257* 22.0 236* 15.3 NCO 3,201* 56.0 836* 54.3 Soldiers 1,255* 22.0 467* 30.3 Breakfasts per Week Fewer than 5 1,517* 26.6 645* 41.9 5 or More 4,196* 73.4 894* 58.1 Smoking Status Nonsmoking 4,589* 80.3 1036* 67.3 Actual Smoking 1,124* 19.7 503* 32.7 Weight Status Normal a 2,083* 36.5 689* 44.8 Overweight a 2,754* 48.2 660* 42.9 Obese a 876* 15.3 190* 12.3 Physical Activity Low b 1,637* 28.7 439* 28.5 Moderate b 2,067* 36.2 510* 33.1 Vigorous b 2,009* 35.2 590* 38.3 BMI, body mass index; NCO, noncommissioned officers; SD, standard deviation. *p < 0.05. a Normal weight (BMI below 24.9 kg/m 2 ) overweight (BMI between 25.0 and 29.9 kg/m 2 ) obesity (BMI of 30.0 kg/m 2 and more). b Physical activity was stratified in low, moderate, and vigorous according to the IPAQ. Table II compares the nutritional and lifestyle behaviors between daily SSB consumption versus less-than-daily consumption. In univariate analysis, daily SSB consumers had a lower BMI, as well as lower fruit and vegetables consumption. Mean (standard deviation) meat consumption was higher, with 179 (67) grams per day for daily SSB consumption compared with 163 (66) grams per day for nondaily SSB. Having almost daily a breakfast was more prevalent in nondaily SSB consumption compared with daily SSB (73.4% versus 58.1%; p < 0.05). Daily SSB was associated with higher occurrence of smoking (32.7% versus 19.7%), but with lower prevalence of obesity (p < 0.05). Table III presents the results of a binary logistic regression with SSB consumption as dependent variable. The probability of consuming daily SSB decreased with age, with increasing BMI, being female, and being noncommissioned officer or officer. Consumption of fruits and vegetables decreased with daily SSB consumption, whereas meat consumption increased. The odds ratio (95% confidence interval) for daily SSB consumption was 0.65 (0.58 0.74) for 5 or more breakfasts a week, and 1.49 (1.30 1.71) for smoking. There was no relation between physical activity and SSB consumption. DISCUSSION In the present study, daily SSB consumption is associated with lower consumption of fruits and vegetables, less daily breakfast, and with increased consumption of meat and higher prevalence of smoking. The results of this study, i.e., the relation between SSB consumption and a less healthy lifestyle, confirm previous publications. Balcells et al 10 related SSB consumption to nutrition and lifestyle in 3,910 men and 4,285 women. Less than half of the population consumed soft drinks, with a mean consumption of 36.2 ml/d. The prevalence of sedentary lifestyle increased with the frequency of soft drinks consumption. Daily soft drinks consumption was associated with a low adherence to a Mediterranean dietary pattern, with lower educational level, and with a higher prevalence of smoking. 10 Cullen et al 11 found for the highest SSB consumption a lower daily intake of fruits and vegetables in 207 boys and 297 girls. The lowest SSB tertile was associated with 0.54 portions of fruit and 0.83 portions of vegetables, this was for the highest SSB tertile 0.23 and 0.64. Kvaavik et al 12 and Bes-Rastrollo et al 13 compared a high with low daily intake of SSB. Both study groups reported a higher prevalence of smoking associated with a higher SSB consumption. Several studies associated a higher SSB consumption with more frequent fast-food consumption. 14 16 In conclusion, studies done in different populations confirm our results and the hypothesis that there is a specific behavior associated with regular SSB consumption. This specific behavior characterized by a lower consumption of fruit, vegetables, and a higher consumption of meat and fast food can be described as an obesogenic behavior with increasing risk of adiposity. The close relationship between SSB consumption and unhealthy lifestyle and nutritional behaviors is elusive because of associated statistical limitation. The contribution to the risk of obesity of specific foods or lifestyles is difficult to quantify. For example, relating excessive SSB to adiposity in observational research can be confounded by smoking or ex-smoking if the multivariate models are not properly adjusted for this factor. Second, even after adjustment, residual confounding may remain as a result of inaccurate measurement or of behaviors associated with such consumption. Finally, even if measurements of all factors were perfect, multicollinearity is likely to threaten the correct interpretation of multivariate models. 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TABLE III Multivariate Analysis of Nutritional and Lifestyle Behaviors Associated With SSB Consumption as Dependent Variable Independent Variable B SE Wald p value OR 95% CI Low 95% CI High Age (Years) 0.014 0.004 14.506 <0.001 0.986 0.978 0.993 BMI (kg/m 2 ) 0.064 0.009 49.622 <0.001 0.938 0.921 0.955 Fruit (Portions per Day) 0.176 0.030 34.905 <0.001 0.838 0.791 0.889 Vegetables (Portions per Day) 0.074 0.028 6.954 0.008 0.928 0.878 0.981 Meat (g per Day) 0.002 0.000 30.224 <0.001 1.002 1.002 1.003 Gender (Female Versus Male) 0.716 0.123 33.809 <0.001 0.489 0.384 0.622 NCOs Versus Soldiers 0.344 0.070 24.054 <0.001 0.709 0.618 0.813 Officers Versus Soldiers 0.704 0.097 52.635 <0.001 0.495 0.409 0.598 5 and More Breakfasts a Week Versus Fewer 0.424 0.064 43.544 <0.001 0.654 0.577 0.742 Actual Smoking Versus Nonsmoking 0.399 0.068 33.991 <0.001 1.491 1.304 1.705 Physical Activity Moderate Versus Low a 0.103 0.076 1.831 0.176 0.902 0.777 1.047 Physical Activity High Versus Low a 0.049 0.077 0.410 0.522 0.952 0.818 1.107 Constant 1.583 0.303 27.375 <0.001 4.871 Model χ 2 = 441.436 (df = 12). Pseudo R 2 = 0.092. N = 7,252. The dependent variable in this analysis is SSB consumption coded so that 0 = less than daily consumption and 1 = daily consumption. B, unstandardized regression coefficient; 95% CI, 95% confidence interval; NCOs, noncommissioned officers; OR, odds ratio; SE, standard error; SSB, sugar-sweetened beverage; Wald, Wald statistic. a Physical activity was stratified in low, moderate, and high according to the IPAQ. declare the discrepancy in results between intervention and observational designs. In contrast with other studies, 10 this study found a decrease of BMI with increasing SSB consumption. In previous research, we found that increasing BMI was associated with lower consumption of SSB and increasing consumption of artificially sweetened beverages. 17 An implication of the present study is that focusing obesity prevention on SSB alone could be too restricted. For example, taxation of SSB as economical barrier for consumption of SSB has been proposed as preventive action against obesity. In a systematic review, Green et al 18 found a price elasticity of 0.74, 0.68, and 0.56 for sweets and sweetened beverages according to tertile of income. This means that consumption will decrease with 0.74%, 0.68%, and 0.56% for a 1% increase in price. However, adiposity is not the consequence of SSB consumption alone, but of an imbalance in energy intake and expenditure. If excessive SSB consumption forms part of general unhealthy behavior, it will be extremely difficult to dissociate the specific health effects, i.e., an increase in energetic intake, associated with excessive intake of SSB from other possible energy increasing related behaviors. The close relationship between SSB consumption and unhealthy lifestyle would have clear consequences for public health recommendations, that is to say that trying to act on a specific unhealthy behavior compound for the prevention and treatment of obesity is likely to fail since other compounds are left unchanged. Individuals can increase energy intake from other related behaviors that at the end will limit the benefits to be expected at the start. It remains doubtful if taxation of a single component clustered in an obesogenic behavior will reduce total energy intake and adiposity. The consequence for public health action is that a more global approach influencing energy balance positively may be more appropriate. A limitation of the present study is the cross-sectional design, which does not allow conclusions about causality. A second limitation is the low response rate of 25%. However, the main purpose of this study was not to provide exact estimations of prevalence, but to detect differences in nutritional and lifestyle behaviors associated with daily SSB consumption. In their publication, Lorant et al 19 found that lower educated subjects were less likely to participate in a survey when they had a poor health status compared with better-off groups. This may have led to an underestimation of the relation between nutrition, lifestyle, and SSB in the present study. A military population was selected for this study. Such a population has a strong socioeconomic stratification in officer, noncommissioned officers, and soldiers, which reflects the level of education, with master level, bachelor level, and lower level. Because of the many different manual and nonmanual tasks and occupations present in an army, this sample can be seen as a representative sample for men with an occupation. In conclusion, in this military population, SSB consumption is associated with a specific behavior that can be described as less healthy. Therefore, prevention of adiposity in a military population should accentuate on a more general approach and reward a healthy lifestyle and pose barriers to unhealthy behaviors. However, such multicomponent approach should be validated in clinical trials in preventing adiposity. ACKNOWLEDGMENT The authors are indebted to the participants of this study. REFERENCES 1. Ng M, Fleming T, Robinson M, et al: Global, regional, and national prevalence of overweight and obesity in children and adults during 1980 2013: a systematic analysis for the global burden of disease study 2013. Lancet 2014; 384: 766 81. 2. Bray GA, Popkin BM: Dietary sugar and body weight: have we reached a crisis in the epidemic of obesity and diabetes?: health be damned! Pour on the sugar. Diabetes Care 2014; 37: 950 6. 3. 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