Instituto Nacional De Salud Pública

INSTITUTO NACIONAL DE SALUD PÚBLICA

Trabajo de tesis:

Efecto de la promoción del consumo de agua y su dotación sobre la calidad de la dieta de mujeres con exceso de peso: Ensayo clínico aleatorizado

Alumna: Sonia Concepción Rodríguez Ramírez e-mail: [email protected]

Doctorado en Ciencias en Nutrición Poblacional 1° Generación

Cuernavaca, Mor.

Diciembre 2013 1

COMITÉ DE TESIS

Directora: Dra. Teresa González de Cossío

Asesores: Dr. Ignacio Méndez Ramírez

Dra. Katherine L. Tucker

Índice

Resumen 4 Introducción 5 Artículo 1 Abstract 8 Introducción 9 Metodología 9 Resultados 13 Discusión 15 Referencias 18 Tablas y figuras 21 Artículo 2 Abstract 31 Introducción 32 Metodología 33 Resultados 37 Discusión 39 Referencias 43 Tablas 46 Conclusiones finales 53 Anexos Lista de alimentos por grupo de alimentos, artículo 1 55 Lista de alimentos por grupo de alimentos, artículo 2 57 Diagrama de flujo de tamaño de muestra 58

Resumen

Objetivos: Evaluar el efecto de una intervención de promoción y dotación de agua sobre la calidad de la dieta un mujeres con exceso de peso. Un objetivo secundario fue estimar la prevalencia de sub-reporte de energía y analizar si era diferencias entre los grupos de estudio y su impacto sobre predictores dietéticos de pérdida de peso. Metodología: Los criterios de inclusión al estudio fueron: Mujeres con Índice de Masa Corporal (IMC) ≥ 25 y < 39, edad entre 18 y 45 años y auto-reporte de energía proveniente de BE ≥ 250 kcal/d. Las mujeres se signaron aleatoriamente a uno de los dos grupos de estudio, grupo de dotación de agua + educación (Agua+Ed) o grupo de educación (Ed), 120 mujeres por grupo. Cada grupo recibió mensualmente consejería de nutrición, y el grupo Agua+Ed recibió agua cada dos semanas. Se recolectaron 3 recordatorios de 24 horas, antropometría e información demográfica en la etapa basal, a los 3, 6 y 9 meses. Por medio de análisis de factores se identificaron patrones dietéticos de alimentos que no eran bebidas. Para las mujeres que completaron el estudio (n = 189), se analizó la ingesta de energía, macronutrimentos, azúcar y BE y patrones dietéticos por grupo de estudio. En un análisis secundario, las participantes fueron clasificadas por su consumo de agua (< 1.2 y ≥ 1.2 l/d). La estimación de sub-reporte de energía se identificó por la disparidad entre ingesta de energía reportada (IER) y el requerimiento de energía predicho (REP), el cual fue calculado utilizando ecuaciones derivadas de análisis de agua doblemente marcada ajustadas por déficit de energía estimado, basado en cambios de peso durante el seguimiento. Se analizó la asociación, a los 6 meses, entre consumo de grupos de alimentos y pérdida de peso definida como ≥ 3% del peso basal. Resultados: Mayor proporción de mujeres en el grupo Agua+Ed que en el grupo Ed reportó disminución de ingesta de BE e incremento de ingesta de agua en cada etapa (P < 0.05). Ambos grupos reportaron ingesta de energía reducida, con más baja ingesta en el grupo Agua+Ed sólo a los 3 meses. Tres patrones dietéticos fueron identificados: Saludable, tradicional y variado. En general, la adherencia al patrón saludable incrementó a los tres meses y continuó alta en el grupo Agua+Ed a los 6 meses. El grupo Agua+Ed reportó menor ingesta de hidratos de carbono y más alta ingesta de proteínas a los 3 y 6 meses, con consistente más alta ingesta de energía derivada de grasas. Alta ingesta de agua fue asociada con reducida ingesta de energía, mayor adherencia al patrón saludable y menor ingesta de hidratos de carbono, sin incremento de ingesta de grasas. Respecto a sub-reporte, éste incrementó de la etapa basal (4%) a > 39% después de la intervención, lo cual fue más bajo que >50% cuando no se tomó en cuenta la pérdida de peso. Las mujeres que sub-reportaron tuvieron mayor IMC que las que tuvieron reporte plausible y no hubo diferencias entre los grupos de estudio. Ajustando por sub-reporte ligeramente fortaleció la asociación entre ingesta de alimentos saludables (frutas, vegetales y lácteos) y la pérdida de peso. Conclusiones: La intervención con agua dirigió a un mejoramiento en algunos factores dietéticos. Este efecto fue más fuerte en el análisis basado en el consumo de agua que en el análisis de intención al tratamiento. Aunque el sub-reporte de energía no fue diferencial entre grupos de estudio, es necesario estimarlo para saber si es necesario considerarlo o no en el análisis.

Introducción

La dieta es uno de los principales determinantes de la obesidad, debido al balance crónico positivo entre ingestión de energía y la energía gastada, así como la mala distribución de macronutrimentos en la dieta (alto porcentaje de la energía derivado de grasas y carbohidratos), independientemente de la cantidad de energía. En México hemos observado que los cambios en los patrones dietéticos han ido en paralelo con los cambios en la prevalencia de obesidad, predominando los patrones caracterizados por alta presencia de alimentos densos en energía y baja presencia de alimentos que contienen fibra y micronutrimentos.

La literatura científica muestra que diferentes intervenciones han sido implementadas con el fin de controlar y corregir el problema de sobrepeso y obesidad en diferentes grupos de población, a través del mejoramiento de la calidad de la dieta. Una de estas intervenciones ha sido la del reemplazo de bebidas endulzadas por agua, la cual tiene como hipótesis que habrá una disminución en el consumo de energía proveniente del grupo de bebidas endulzadas al incrementar el consumo de agua y el posible cambio favorable del consumo de otros grupos de alimentos (disminución de alimentos ricos en energía). Hasta el momento son pocos los estudios que han analizado este tipo de intervenciones en población abierta y es de nuestro conocimiento que no hay estudios de este tipo realizados en población mexicana.

Este documento presenta los resultados finales del trabajo de tesis “Efecto de una intervención de la dotación y promoción del consumo de agua sobre la calidad de la dieta en mujeres con exceso de peso”, el cual tuvo como objetivo general: Evaluar el efecto de la promoción del consumo de agua y su dotación sobre la calidad de la dieta en mujeres adultas con exceso de peso. Las preguntas específicas de este trabajo fueron: ¿La intervención tuvo efecto en la ingestión de energía?, ¿La intervención tuvo efecto en la distribución de macronutrimentos?, ¿La intervención tuvo efecto en el consumo de patrones de consumo de alimentos que no eran bebidas?

Los resultados del análisis están divididos en dos artículos. El primer artículo responde a las preguntas planteadas del efecto de la intervención sobre la calidad de la dieta. Se presentan los resultados por intención al tratamiento y agrupando a la población por intensidad del consumo de agua (medido por cantidad de agua consumida).

El segundo artículo contiene la estimación de reporte implausible en energía. Dado que mujeres con exceso de peso son una población que tiende a sub-reportar su consumo y que este fenómeno incrementa durante el seguimiento en estudios prospectivos, los cuales cuentan con una intervención, se realizó este artículo con el fin de estimar el porcentaje de mujeres que sub-reportaban la ingesta de energía e identificar si existían diferencias entre los grupos de estudio. La estimación de subreporte se hizo por etapa de estudio. Posteriormente se analizó al sub-reporte como posible confusor en la asociación de consumo de diferentes grupos de alimentos y pérdida de peso durante el estudio.

Cabe mencionar que los artículos de esta tesis se encuentran en idioma inglés debido a que será el idioma en que serán enviados a publicación. Al final del documento se presentan las conclusiones derivadas de este trabajo y los anexos (lista de alimentos y diagrama de flujo del tamaño de muestra).

ARTÍCULO #1

Effect of a water intervention on diet quality in Mexican overweight women: Randomized controlled trial

Spanish title: Efecto de la promoción del consumo de agua y su dotación sobre la calidad de la dieta de mujeres con exceso de peso: Ensayo clínico aleatorizado

Authors: Sonia Rodríguez-Ramírez,1 Teresa González-Cossio,1 Michelle A. Mendez, 4 Katherine L. Tucker,2 Ignacio Méndez-Ramírez,3 Sonia Hernández-Cordero, 1 Barry Popkin 4 1Center of Research in Nutrition and Health, National Institute of Public Health, Mexico 2Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging at Tufts University, Boston, MA 3Department of Probability and Statistics, Institute of Research in Applied Mathematics and Systems, National University of Mexico 4Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC

This trial was register at clinicaltrial.gov: NCT0124510

Abbreviations SSBs: sugar-sweetened beverages kg: kilogram CHOICE: Choose Healthy Options Consciously Everyday RCT: randomized controlled trial BMI: body mass index kcal/d: kilocalories per day g: grams MET: total metabolic equivalent SES: socioeconomic status ml/d: milliliters per day l/d: liters per day

ABSTRACT Background: Despite major efforts to replace sugar-sweetened beverage (SSB) consumption with water, minimal information exists on the impact of shifting beverage patterns on food group intake patterns from randomized controlled trials. Objective: We evaluated the effect of a water intake intervention on diet quality in overweight Mexican women. Methods: Women with a body mass index (BMI) ≥ 25 and < 39 (18–45 years old), and with self- reported high intake of SSBs (≥ 250 kilocalories per day [kcal/d]) were randomly allocated to either the water and education provision (WEP) group (n = 120) or the education provision only (EP) group (n = 120). Each group received monthly nutrition counseling, and the WEP grou8p received biweekly water deliveries. Three 24-hour food recalls and anthropometric and demographic information were collected at baseline and three, six, and nine months. Factor analysis identified food intake patterns. For the completers (n = 189), we analyzed energy, macronutrient, sugar, and SSB intakes and food intake patterns by study group, and classified participants based on actual water intake (< 1.2 and ≥ 1.2 liters per day [l/d]). Results: A higher proportion of the WEP than the EP group reported decreased SSB intake and increased water intake at each wave (P < 0.05). Both groups reported reduced energy intake, with lower intake in the WEP group only at three months. Three food patterns were identified: healthy, traditional, and varied. Overall, adherence to the healthy pattern increased at three months, and it remained high in the WEP group at six months before declining. The WEP group reported lower carbohydrate intake and higher energy intake from protein at three and six months, along with consistently higher proportional energy intake from fat (but not higher absolute intake). High water intake was associated with significant and sustained reduced energy intake, increased adherence to the healthy pattern, and reduced carbohydrate intake, without increases in fat intake. Conclusions: The water intervention led to improvement in some dietary factors. This effect was stronger in analyses based on actual water consumption than in intent to treat analyses.

INTRODUCTION Mexico has experienced a major increase in obesity (71.9% of adult women) and diabetes in the past two decades (Rivera, Barquera et al. 2002, Barquera, Tovar-Guzman et al. 2003, Rivera, Barquera et al. 2004, Durazo-Arvizu, Barquera et al. 2006, Barquera, Peterson et al. 2007, Olaiz-Fernández, Rivera- Dommarco et al. 2007, Barquera, Campos et al. 2013) with concurrent shifts toward a diet high in added sugars, sodium, and saturated fat (Rivera, Barquera et al. 2004, Barquera, Hotz et al. 2006, Barquera, Hernández et al. 2008). These dietary shifts are linked with increased risks of diabetes, cardiovascular disease, and many cancers (WCRF 2007, Malik, Popkin et al. 2010, Malik, Popkin et al. 2010).

Worldwide intake of sugar-sweetened beverages (SSBs) has increased dramatically in recent years (Popkin, Adair et al. 2012). In Mexico in 2006, the percentage of energy intake from caloric beverages was 20.1% and 22.3% in adolescents and adults, respectively. This includes whole milk, SSBs, natural and industrialized juices, aguas frescas (a mix of water, fruit, and sugar), and tea and coffee with sugar (Barquera, Hernández et al. 2008).

It has been argued that replacing beverages that provide a substantial portion of daily energy intake with noncaloric beverages may be a useful strategy for weight reduction or weight gain prevention (Dennis, Flack et al. 2009, Hu 2013). Observational evidence suggests that drinking plain water is associated with weight loss and with reduction in energy intake and higher proportion of energy intake derived from protein (Stookey, Constant et al. 2007, Stookey, Constant et al. 2008, Wang, Ludwig et al. 2009, Daniels and Popkin 2010). In a laboratory setting, adults who consumed a water preload before a meal were shown to reduce energy intake from the meal (Walleghen, Orr et al. 2007, Davy, Dennis et al. 2008).

Few experimental studies have analyzed the effect of longer-term SSB substitution with plain water on energy intake and weight loss. Data from the PREMIER study (a 18-mo multicenter randomized trial designed to test the blood pressure–lowering effects of 2 multicomponent behavioral interventions in adults) showed that a decrease of one serving per day of SSBs was associated with a 0.49 kilogram (kg) decrease in weight at six months (Chen, Appel et al. 2009). Results from the Choose Healthy Options Consciously Everyday (CHOICE) study showed a significant decrease in energy intake after six months in groups with beverage substitution (water or non-caloric sweetened beverages) compared with the control group (Tate, Turner-McGrievy et al. 2012). However, few randomized trials have examined the dietary effects of changing beverage intake patterns over sustained periods (Vartanian, Schwartz et al. 2007, Allison and Mattes 2009, Tate, Turner-McGrievy et al. 2012, Piernas 2013, Te Morenga, Mallard et al. 2013).

Information from randomized controlled trials (RCTs) on the impact of shifting beverage patterns on individual food groups, food patterns, or macronutrient intake is limited. We used data from a Mexican RCT to examine how introduction of plain water affects dietary intake quality. The study was a nine-month RCT in which all participants received dietary counseling while water was provided randomly to half of the sample to evaluate the effect of a water intervention on the diet quality of overweight women.

MATERIALS AND METHODS Study design: This study uses data from a clinical trial titled Randomized Controlled Trial of a Water Beverage Intervention for Reducing Risk Factors of Metabolic Syndrome in Young Mexican Free-Living Women (Hernandez-Cordero 2012).

Participants: Women were recruited, enrolled, and followed between April 2009 and August 2011 in Cuernavaca, Morelos, Mexico. Eligible participants were overweight (body mass index [BMI] 25.0–38.9) adult females aged 18 to 45 years with no chronic diseases, who reported consuming at least 250 kilocalories per day (kcal/d) from caloric beverages (including soft drinks; juices; sports drinks; sweetened tea and coffee; alcoholic beverages; and milk-based beverages, excluding milk).

Exclusion criteria were weight loss greater than 5% of current body weight in the previous 6 months; participation in a diet to reduce weight at the time of recruitment; pregnancy or lactation at the time of recruitment or during the previous 6 months, or planned pregnancy in the following 12 months; treatment for any medical condition that could impact metabolic function, energy intake, or change in body weight (e.g., diabetes, cancer, hypothyroidism); history of myocardial infarction or heart surgery, such as bypass or angioplasty; participation in a regime to increase muscle mass, or use of anabolic compounds; alcohol dependence, defined as 21 or more drinks per week (Marmot M 1995); or plans for moving within the coming year.

Participants’ written informed consent was obtained prior to any research activity. The study was approved by the Ethics Committee of the National Institute of Public Health in Mexico.

Randomization: Eligible women were randomly assigned to either intervention group. To limit seasonal effects on beverage consumption, 24 blocks of 10 women (5 women in the WEP group and 5 women in the EP group) were recruited. A total of 240 women 1 were enrolled.

Intervention: This nine-month RCT assigned participants to one of two groups: the water and education provision (WEP) group or the education provision (EP) group (or control group). Both groups received equal time in all education efforts. The WEP group received water and education activities oriented to reducing the intake of SSBs (carbonated and noncarbonated beverages, coffee and tea with sugar, juice drinks, soy beverages, sugar-sweetened aguas frescas , energy drinks, and smoothies) and substituting increased water consumption. Bottled water (2 to 3 liters of water per person per day) was home delivered during the intervention period and/or was available for pickup every two weeks during the nine-month intervention.

To support the modification of beverage intake in the WEP group, participants took part in monthly face-to-face meetings either individually or with a group. Group meetings (2 to 10 participants) were facilitated by trained dietitians and psychologists. During these meetings, challenges associated with the proposed commitment to behavior change and potential solutions were discussed. Methods of formative research to develop the strategies to promote water consumption are described elsewhere (Hernandez- Cordero 2012).

Participants in the EP (control) group received general nutrition advice, not related to water intake, regarding healthy a diet based on “El Plato del Bien Comer,” which is Mexico’s food consumption guideline (SSA 2006); their energy needs (at the initial meeting); and food selection relative to a healthy diet. They were not provided information on reducing SSB intake or increasing water intake or information related to beverage consumption and health. Participants in this group were contacted following the same schedule

1A total of 1,756 women responded to the study invitation, of whom 1,217 did not fulfill the phone screening criteria. 10

and frequency as were the WEP group participants. Further details regarding the study design and methodology are available elsewhere (Hernandez-Cordero 2012).

Dietary Intake Data: Dietary intake data were collected at baseline and three, six, and nine months using three 24-hour dietary recalls at each time point. Trained interviewers visited the participants’ homes to collect information in the place where food was prepared and to obtain portion sizes as accurately as possible. Recalls were collected on two nonconsecutive weekdays and one weekend day, all three days randomly selected within a 14-day period. Total daily energy intake (kcal) and macronutrient intake (grams [g] and percentage of energy derived from them) were calculated using the National Institute of Public Health food composition table supplemented with information from the US Department of Agriculture food composition table (USDA 2010) and other sources. To calibrate estimates of usual intake, we used PC-SIDE software, version 1.0 (Iowa State University of Science and Technology, USA, 2003) to account for day-to- day variability in intake in participants.

We classified foods into 38 food groups reflecting preparation methods and behaviors as well as food types. These food groups were vegetables, fruits, meats, meat stews, poultry, poultry stews, processed meats, breads and rolls, ready-to-eat unsweetened cereals, ready-to-eat sweetened cereals, sandwiches and filled rolls, pastas and noodles, rice and other grains, rice dishes, eggs, cheese products, yogurt products, legumes, legume dishes, corn-based dishes, corn tortillas, nuts, salty snacks, fried potatoes, cakes and pies, industrialized cakes and cookies, fish dishes, pan dulce (traditional sweet bread made with refined wheat flour, sugar, and fat) and quick breads, caloric sweeteners, desserts, soups and broths, nonstarchy vegetable stews, starchy vegetable stews, fast foods, avocados, salad dressings, and sauces and condiments.Examples of the primary foods classified in these food groups are in appendix 1.

Covariable Assessment: Anthropometric measures: Weight was measured using a Tanita (model BWB-627-A, 100 g precision) digital scale. Height was measured only at baseline using a calibrated, wall- mounted stadiometer (Shorr Productions, Model 17802, 1 millimeter precision). Participants were instructed to remove their shoes and to stand upright with their backs and heels against the wall for this measurement (Shamah, Villalpando et al. 2006). BMI was calculated to classify overweight or obesity according to the cutoff points proposed by the World Health Organization (Consultation and WHO 2000).

Physical activity: We estimated energy expenditure using accelerometers (Actigraph GT3X, For Pensacola, FL, USA), which measure accelerations in the anteroposterior, mediolateral, and vertical directions. Participants were asked to wear the Actigraph for one week at waist level at the right anterior axillary in a nylon pouch attached to a belt. We estimated total metabolic equivalent of task (MET) minutes per day and average MET over 24 hours using an equation proposed by S. E. Crouteret et al., which is a regression in two steps able to distinguish between walking/running and other lifestyle activities (Crouter, Clowers et al. 2006). We used average MET per day to define categories of levels of physical activity with cutoff points proposed by the Institute of Medicine (IOM. 2005).

Socioeconomic status: We constructed an indicator of socioeconomic status (SES) with principal components analysis (Reyment and Jvreskog 1996). This methodology has been validated to describe SES differentiation in a population (Vyas and Kumaranayake 2006) and has been previously used in Mexican populations (Barquera, Campos-Nonato et al. 2009, Bonvecchio, Safdie et al. 2009). It includes variables related to housing conditions (such as flooring and roofing materials), ownership of home appliances (refrigerator, stove, washing machine, television, radio, video player, telephone, and computer), and 11

number of rooms (other than bathroom, kitchen, and corridors). We divided the standardized factor obtained into tertiles to represent three SES categories: low, middle, and high.

Statistical Analysis: The original study was designed to test the benefit of replacing SSBs with water on plasma triglyceride levels and weight reduction. It was estimated that 120 subjects would be necessary to identify meaningful differences between groups, allowing for 75% attrition. With the final sample size (102 and 87 participants in the WEP and EP groups, respectively), we had at least 80% power to detect differences in total energy intake (210 kcal/d) and energy from macronutrients (2% and 5% to protein and fat, respectively) between study groups.

Differences in baseline characteristics (age, BMI, dietary intake, education, marital status, and SES categories) were analyzed using t-tests for continuous variables and Chi-square tests for categorical variables. Analyses for this study focused on exploring differences in the dietary intakes of participants in the WEP versus the EP group and differences in dietary intake associated with self-reported high versus low water consumption regardless of group assignment. The analyzed outcome variables were total energy intake (kcal/d), absolute macronutrient intake (g of proteins, carbohydrates, and fat per day) and percentage of energy intake from macronutrients, percentage of energy intake from SSBs, sugar intake (g/day of added and natural sugar), and food patterns.

We derived food patterns using factor analysis at each time point with the food groups described above (n = 38). Beverages were excluded and analyzed separately as input variables. Because substantial proportions of women were non-consumers of some food groups, we categorized food group consumption into either two or three categories: non- or low consumers versus high consumers or non-consumers, low consumers, and high consumers. We categorized food groups with ≥ 50% of the consumers in all time points into three groups and dichotomized those with lower intakes. We standardized intake variables for use in factor analysis.

In brief, we examined two to six factor solutions and used scree plots to identify those most meaningful in terms of the patterns and previous literature. Based on this, we selected a solution of three factors at each time point. With the exception of baseline, factors were meaningfully very similar over time. Therefore to evaluate changes over time and the effect of the intervention, we used the factors identified at three months as referents and applied these loadings to each food group in the other stages (baseline, six months, and nine months). Using this procedure, we derived the same factors in every stage of the study. We then calculated Z scores for each participant for each factor at each time point, in which the standardized category of intake of each of the food groups (mean = 0, standard deviation = 1) were weighted by their factor loadings and summed.

We examined effects on dietary intake of time, treatment group, and treatment-by-time interactions in mixed-effects models for each outcome using the independent structure of the covariance matrix. Models were adjusted by baseline age, baseline BMI category (overweight or obese), and season of enrollment. In a secondary analysis we analyzed the effective exposure to the intervention, defined according to water consumption independent of the assigned study group. Water consumption was categorized according to the median consumption in the sample as a whole group < 1,200 and ≥ 1,200 milliliters per day (ml/d) during the entire period. We performed all analyses d using a two sided P-value < 0.05 to define statistical significance. Data analysis was performed using STATA, version 12.1 (Stata Corporation, TX, USA). 12

RESULTS Details on screening and follow-up are available elsewhere (Hernández-Cordero S 2012). There were no significant differences in the baseline characteristics of women assigned to the EP or the WEP group, including age, BMI, physical activity, and baseline energy intake (table 1). Women were 33.1 ± 6.8 and 33.8 ± 6.8 years old in the EP and the WEP groups, respectively. Mean BMI was the same in both groups, and mean baseline energy intakes were similar (1,913 vs. 1,952 kcal/d). Energy intakes from SSBs and water consumption (ml) were also similar between study groups. Education level was similar between the EP (12.3 ± 3.4) and the WEP (12.7 ± 2.9) groups, and more than 50% of the women lived with a partner in both groups. The percentage of women with low SES appeared to be higher in the EP group compared with the WEP group, but the difference was not statistically significant. Physical activity did not differ between groups.

table 1 about here

We compared the baseline characteristics of women who continued to participate through the end of the study (n = 189; 87 and 102 in the EP and WEP arms, respectively) and women who dropped out (n = 51, 21.2%) and found no significant differences. For example, total energy intake was 1,977 and 1,952 kcal/d, and BMI was 31.1 and 30.4 in women who completed the study and women who dropped out, respectively (P ≥ 0.05). Effect of the Water Intervention on Dietary Intake : Participants in both the EP and the WEP groups reported significant reductions in total energy intake per day after baseline (table 2). However, total energy intake was significantly lower in the WEP group compared with the EP group only at three months (P = 0.046). The analysis of overall macronutrient intakes (from foods and beverages) showed no differences between arms in absolute protein or fat intakes (except at nine months, when fat intake was higher in the WEP group). Carbohydrate intake was significantly lower in the WEP group than in the EP group at three and six months. The percentage of energy derived from protein increased in both groups from baseline to three, six, and nine months, but this increase was significantly higher in the WEP group than the EP group at three and six months. The WEP group (vs. the EP group) had consistently lower intake of energy derived from carbohydrates, and consistently higher energy intake from fat after baseline and across all time periods. table 2 about here

Both groups decreased consumption of SSBs per day during the follow-up compared to baseline intake (table 2). The intake was significantly lower in the WEP group compared with the EP group at three months (EP = 15.6, CI 95% 14-17%; WEP = 10.3, CI 95% 9-12%), six months (EP = 17.3, CI 95% 16-19%; WEP = 9.6, CI 95% 8-11%), and nine months (EP = 17.8, CI 95% 16-19%; WEP = 10.3,CI 95% 9-12%) (P < 0.001). We observed similar trends for sugar intake per day (including natural and added sugars), which was lower over time (P < 0.001) and lower in the WEP group at every time point during the follow-up: EP = 93.5 g (CI 95% 89-98 g) and WEP = 79.9 g, (CI 95% 76-84 g) at three months, EP = 97.6 g (CI 95% 93-102 g) and WEP = 80.0 g (CI 95% 76-84 g) at six months, and EP = 93.1 g (CI 95% 89-98 g) and WEP = 79.5 g (CI 95% 75-84) at nine months (P < 0.001). The results for water consumption were the reverse, participants in the WEP arm reported significantly higher consumption compared with the EP group at every time point (P < 0.001).

We identified three factors in the food pattern analysis, labeled as healthy, traditional, and varied patterns (Table 3). Of the total 37 food group, we present only food groups with loadings > 0.35 or <- 0.35. 13

The food groups with high positive loadings in the healthy pattern were cheese, yogurts, breads and rolls, fruits, and vegetables. One food group, pan dulce and quick breads, had a high negative loading in this pattern. In the traditional pattern, the food groups with high positive loadings were meat stews, legumes, corn tortillas, rice and grain dishes, and desserts. The food group with a negative loading in this pattern was sandwiches and filled rolls. In the varied pattern the food groups with high positive loadings were nuts, pasta and noodles, non-starchy vegetable dishes, and sweets and sweeteners. The food groups with negative loadings in this pattern were salad dressings and poultry.

table 3 about here

Changes in dietary pattern over time indicate that adherence to the healthy pattern was increased at three months in both groups, followed by a large decline at six months in the EP group, which was significantly different from the WEP group (P < 0.005) (figure 1). However, the groups converged at nine months, when adherence also fell in the WEP group. The score for adherence to the traditional pattern decreased over time, with no difference between study groups. Adherence to the varied pattern initially declined in both arms and was lower in the more rapidly declining EP group at six months, but was not significant. In contrast to the healthy pattern, adherence to the varied pattern increased in both groups at nine months.

figure 1 about here

Association between Levels of Water Consumption and Dietary Intake: In a secondary analysis, we evaluated the effect of the intensity of the shifts in plain water consumption independently of the assigned intervention group. At baseline, 75.7% of the women (70.1% in the EP group and 80.4% in the WEP group) had low water consumption (< 1.2 l/d). Consumption increased over time, and more than half of the women at three, six, and nine months reported high water consumption (≥ 1.2 l/d), particularly in the WEP group (84.3%, versus 36.8% in the EP group) (table 4). table 4 about here

Total energy intake decreased over time in both the low- and the high-water consumption groups, but energy intake was lower in the high-water consumption group at both three and six months rather than only at three months, as observed for the water intervention arm (table 5). Carbohydrate intake was significantly lower in high water consumers than in low water consumers at three and six months. In contrast to the transient increase in energy from protein observed for the water intervention arm, the percentage of energy derived from protein was consistently and significantly higher and the percentage of energy derived from carbohydrates lower in the high-water consumption versus the low-water consumption group (P < 0.02 at three and six months). Unlike the higher percentage of energy derived from fat observed in the WEP versus the EP group, there was no significant difference between water intake groups. The percentage of energy from SSBs and sugar intake also decreased significantly in the high-water consumption group during all follow-up periods (P < 0.001). High water consumers were consistently more adherent to the healthy pattern during the follow-up (P < 0.050 at six months), whereas low water consumers appeared to follow a more traditional diet and the difference between groups is significant at 3 months (P < 0.050), (figure 2). table 5 and figure 2 about here

DISCUSSION It has been hypothesized that substituting water for SSBs may be associated with improved weight status due to reduced energy intake from both foods and beverages (Te Morenga, Mallard et al. 2013), but few trials have examined how replacing SSBs with water may influence dietary quality. In this nine-month RCT among overweight women with substantial SSB consumption at baseline, participants in the arm that received water plus nutrition education reported significantly greater reductions in carbohydrate and sugar intakes compared to the arm that received nutrition education only. Over time, however, both arms reduced their energy intake, and energy intake was significantly lower in the WEP group only at the three- month follow up. Indeed whereas the WEP group reported transiently higher energy intake from protein through six months, the percentage of energy intake from fat was higher than among the EP women. The WEP arm sustained greater adherence to a healthy pattern than the EP arm, characterized by high fruit and vegetable intakes at six months. However, by nine months adherence to this pattern declined to similarly low levels in both arms, while adherence to a more varied pattern increased.

In contrast to the somewhat ambiguous and more short-lived differences in intake observed based on assigned treatment arms, a secondary analysis of levels of water intake reported by participants found clearer and more sustained associations with higher dietary quality. Elevated water consumption (> 1.2 l/d) was associated with a significant reduction in energy intake sustained through six months accompanied by a significantly reduced energy intake from carbohydrates, SSBs, and sugar and by a significantly increased energy intake from protein but no significant increase in energy intake from fat. Compared to those with low water consumption, high water consumers maintained higher adherence to the healthy food pattern through the end of the trial, although the difference was not always significant. High water consumers also reported lower adherence to the traditional pattern, which included meat dishes and desserts, and to the varied pattern, which included vegetable dishes but also sweets and sweeteners.

In this RCT participants in the WEP group improved their diet by significantly reducing carbohydrate, sugar, and SSB intakes compared with the EP group, and by increasing their water consumption in the period of follow-up. Effects on energy intake were only significant for the WEP versus the EP group at three months. Decreases in carbohydrate intake existed at all three times for the WEP group, and a significant increase in the percentage of energy intake from protein for the WEP group existed at three and six months, but not at nine months. Factor analyses identified three food patterns. The WEP (vs. the EP) group showed significantly higher adherence to the healthy pattern only at six months. In a secondary analysis, high water intake was associated with an increased likelihood of following the healthy food pattern. High water consumers also reduced their total energy and carbohydrate intakes, and percentage of energy derived from carbohydrates, and increased the percentage of energy derived from protein at three and six months (but not at nine) compared to low water consumers. High (vs. low) water consumers did reduce SSB and sugar intakes at all three time periods.

SSB intake decreased significantly in the WEP group compared with the EP group throughout the study. This finding was similar to that in the evaluation of CHOICE, an RCT (Tate, Turner-McGrievy et al. 2012). In that study researchers compared the replacement of caloric beverages with water or diet beverages as a method of weight loss over six months in adults. They found higher decreases in SSB intake in the intervention group (diet beverages and water group) compared with the control group. In our study at baseline SSB intake corresponded to approximately 21.0% of the total energy intake and decreased to 10.2% in our WEP group.

The observed increase in energy derived from protein, after decreasing SSB intake is similar to results in a US study conducted in overweight women, which found that decreases in SSB consumption after 12 months were associated with increases in energy derived from protein (Stookey, Constant et al. 2007). We found both an increase in energy derived from protein and a decrease in energy derived from carbohydrates in the WEP group, which is desirable, as increasing the proportion of protein to carbohydrates in the diets of adult women may have positive effects on body composition, blood lipids, glucose homeostasis, and satiety during weight loss (Layman, Boileau et al. 2003).

Unlike other studies (Vartanian, Schwartz et al. 2007), we did not find a difference in energy intake between groups at every time point. For example, J. D. Stookey et al. found a decrease in total energy intake associated with a decrease in SSB consumption (Stookey, Constant et al. 2007). In our study, despite reducing energy intake from carbohydrates, participants in the WEP group in general did not significantly decrease their total energy intake relative to participants in the EP group. Rather, they replaced their carbohydrate intake with protein and fat.

Our focus on overall food patterns found a trend similar to other studies. A study of Spanish adults found that high SSB consumption was associated positively with a Western dietary pattern (characterized by high consumption of processed meat products, red meats, and fast food) and negatively with a Mediterranean dietary pattern (characterized by consumption of fruits, vegetables, and fish and seafood) (Sanchez-Villegas, Toledo et al. 2009). In our study, there was an overall improvement in dietary pattern in both groups, which was expected, as both received information about a healthy diet.

In our secondary analysis based on water consumption, we found that not all women in the WEP group increased water consumption during the follow-up and, unexpectedly, a proportion of women in the EP group increased their water consumption. At the end of follow-up, more than a third of the EP women were high water consumers. Thus it was useful to examine high versus the low water consumers in the secondary analysis. As noted here, and in other studies on high water intake, high water consumers showed significant improvements in key dietary outcomes (less SSB and sugar consumption, reduced carbohydrate intake, increased energy from protein, and reduced total energy intake) (Stookey, Constant et al. 2008).

Limitations of this study include a differential retention rate between groups with higher loss of EP participants during the follow-up. This phenomenon may be due to the fact that women in the WEP group may have been more motivated to remain in the study because, in contrast to the EP group, these participants received weekly water shipments. However, the baseline variables were not significantly associated with the retention of participants. Another potential study limitation is an important bias in energy reporting, which was non-differential by study group, reported in detail in elsewhere (Rodríguez- Ramírez. Sonia 2013). Briefly, the prevalence of underreporting was about 4% at baseline and between 39% and 54% post-intervention. Although the prevalence of energy underreporting was high among study participants, it was similar between treatment groups, hence the decision not to adjust for this condition. Inferences drawn from the intervention are thus unchanged by underreporting of energy consumption.

The strengths of this study include that, to our knowledge, this is the first study in Mexican overweight women where an intervention with water has been evaluated for its effects on diet quality in a randomized control trial. Another strength of this study is that we completed three 24-hour recalls at each time point, so we were able to adjust the distribution of energy and macronutrient intake for intra-subject

variability, which allowed us to have a better estimate of the intake (Hoffmann, Boeing et al. 2002). Our results provide important insight into the effect of increased water intake, which reduced SSB intake.

In conclusion, although the intervention and promotion of water did not result in large behavior changes or significant effects on total energy intake, a secondary analysis comparing high and low water drinkers showed that those women who really changed drinking behavior did reduce their energy and carbohydrate intake, and were more likely to consume the healthy diet pattern. Further analysis is necessary to understand how women’s participation in various activities, such as education meetings, affected the results.

References

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Table 1 Baseline characteristics of the study population EP WEP group group (n = (n = p Characteristic 87) 102) value 33.1 ± 33.8 ± Age (years) 6.8 6.8 0.436 31.1 ± 31.1 ± BMI (kg/m 2) 4.0 3.8 0.989 1995.8 1962.2 ± ± Energy intake (kcal/d) 256.5 241.2 0.381 Energy from SSBs 407 ± 403 ± (kcal/d) 155 128 0.838 Total water intake 870 ± 735 ± (ml/d) 692 567 0.142 Physical activity category (%) Sedentary 31.0 29.4 0.810 Low 60.9 59.8 Moderate and active 8.1 10.8 12.3 ± 12.7 ± Education (years) 3.4 2.9 0.313 Marital status % (n) Married/living with 66.7 54.9 someone (58) (56) 0.099 33.3 45.1 Not married (29) (46) Socioeconomic status %* Low 39.1 30.4 0.358 Middle 32.2 32.4 High 28.7 37.2 *N = 189. No significant differences between groups.

1 Table 2 Diet indicators throughout the study, by study group P value Group x time Assessment period Time interaction Outcome Baseline Baseline Baseline variable 3 mo. vs. 6 mo. vs. 9 mo. vs. to to to and group Baseline 3 mo. 6 mo. 9 mo. Group baseline baseline baseline 3 mo. 6 mo. 9 mo. Total energy intake (kcal/d) 1,997 1,587 1,599 1,461 EP group (1941, 2053) (1531, 1643) (1543, 1655) (1405, 1517) 0.367 < 0.001 < 0.001 < 0.001 0.046 0.061 0.992 1,961 1,469 1,487 1,425 WEP group (1910, 2013) (1418, 1521) (1435, 1538) (1374, 1477) Energy from protein (%/d) 13.3 15.9 14.8 15.1 EP group (12.9, 13.6) (15.5, 16.2) (14.5, 15.2) (14.8, 15.5) 0.543 < 0.001 < 0.001 < 0.001 0.004 0.001 0.303 13.1 16.6 15.6 15.3 WEP group (12.8, 13.4) (16.3, 16.9) (15.3, 16.0) (15.0, 15.6) Energy from carbohydrates (%/d) 56.3 55.8 56.2 56.1 EP group (55.6, 57.1) (55.1, 56.5) (55.5, 56.9) (55.4, 56.8) 0.342 0.198 0.722 0.606 < 0.001 < 0.001 < 0.001 56.8 53.9 53.5 54.0 WEP group (56.2, 57.5) (53.2, 54.6) (52.8, 54.1) (53.4, 54.7) Energy from fat (%/d) 30.4 28.3 29.0 28.7 EP group (29.7, 31.0) (27.7, 28.9) (28.3, 29.6) (28.1, 29.3) 0.448 < 0.001 < 0.001 < 0.001 0.004 < 0.001 < 0.001 30.0 29.5 30.9 30.7 WEP group (29.5, 30.6) (28.9, 30.1) (30.3, 31.4) (30.1, 31.2) Protein (g/d) 67.4 63.4 59.6 55.7 EP group (65.7, 69.1) (61.7, 65.0) (58.0, 61.3) (54.0, 57.4) 0.114 < 0.001 < 0.001 < 0.001 0.741 0.974 0.406 65.6 61.1 57.7 55.0 WEP group (64.0, 67.1) (59.6, 62.6) (56.2, 59.3) (53.4, 56.5)

Carbohydrates (g/d) 287 227 229 208 EP group (278, 295) (218, 235) (220, 237) (199, 216) 0.562 < 0.001 < 0.001 < 0.001 0.003 < 0.001 0.161 283 204 202 195 WEP group (275, 291) (196, 212) (195, 210) (188, 203) Fat (g/d) 69.1 51.0 52.3 47.4 EP group (66.8, 71.3) (48.7, 53.3) (50, 54.6) (45.1, 49.6) 0.228 < 0.001 < 0.001 < 0.001 0.893 0.486 0.020 67.2 49.3 51.6 49.5 WEP group (65.0, 69.3) (47.2, 51.4) (49.5, 53.7) (47.4, 51.6) Energy from SSBs (%/d) 20.1 15.6 17.3 17.8 EP group (18.5, 21.8) (14.0, 17.2) (15.7, 18.9) (16.1, 19.4) 0.496 < 0.001 0.004 0.017 < 0.001 < 0.001 < 0.001 20.9 10.3 9.6 10.3 WEP group (19.4, 22.4) (8.8, 11.9) (8.1, 11.2) (8.8, 11.8) Sugar (g/d) 121.0 93.5 97.6 93.1 EP group (116, 125) (89.0, 98.0) (93.1, 102) (88.6, 97.6) 0.480 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 123 79.9 80.0 79.5 WEP group (119, 127) (75.7, 84.0) (75.8, 84.1) (75.4, 83.7) Water consumption (ml/d) 871 1,035 1,044 1,088 EP group (735, 1007) (899, 1172) (908, 1180) (952, 1225) 0.150 0.015 0.010 0.001 < 0.001 < 0.001 < 0.001 734 1,727 1,823 1,880 WEP group (608, 860) (1601, 1853) (1697, 1949) (1754, 2006) 1Models were adjusted by baseline variables: age, BMI category (overweight or obese), and season of the year. N = 189 participants with information in every time point. Significant differences in bold type.

Table 3 Food group loadings by food pattern

Food group* Healthy Traditional Varied Salad dressings, sauces (emulsions), and dips -0.481 Cheese products 0.487 Yogurt products 0.407 Meat dishes with starchy vegetables 0.474 Poultry -0.492 Legumes 0.540 Nuts, nut butters, seeds, and coconut 0.474 Breads and rolls, unsweetened, and flour tortillas 0.352 Pan dulce and quick breads -0.351 Sandwiches and filled rolls -0.596 Corn tortillas 0.520 Pasta, noodles, and pasta or noodle dishes 0.404 Rice and other grain dishes 0.530 Fruits, fresh, frozen, or dried 0.555 Vegetables, fresh or frozen 0.524 Nonstarchy vegetable dishes 0.437 Sweets and sweeteners 0.446 Desserts 0.363 *Only food groups with loadings > 0.35 or <- 0.35 are presented.

Figure 1 Adherence to dietary patterns by study group

Health pattern Traditional pattern .6 .4 .4 .2 * .2 0 0 -.2 LinearPrediction, Fixed Portion LinearPrediction, FixedPortion -.2 -.4 1 2 3 4 1 2 3 4 Stage Stage

EP WEP EP WEP

Varied pattern .6 .4 .2 0 -.2 LinearPrediction, Fixed Portion -.4 1 2 3 4 Stage

EP WEP

*P < 0.05. N = 189 (EP = 87, WEP = 102) participants.

Table 4 Distribution of water consumption by study arm

Category of water 3 6 9 consumption Baseline mo. mo. mo. Low water consumption < 1.2 l/d, % (n) 70.1 66.7 59.8 63.2 EP (61) (58) (52) (55) 80.4 17.6 17.6 15.7 WEP (82) (18) (18) (16) High water consumption ≥ 1.2 l/d, % (n) 29.9 33.3 40.2 36.8 EP (26) (29) (35) (32) 19.6 82.3 82.3 84.3 WEP (20) (84) (35) (86)

Table 5 Diet indicators by water-consumption group 1, 2 P value Assessment period Time Group x time interaction

Outcome Baseline Baseline Baseline variable 3 mo. vs. 6 mo. vs. 9 mo. vs. to to to and group Baseline 3 mo. 6 mo. 9 mo. Group baseline baseline baseline 3 mo. 6 mo. 9 mo. Total energy (kcal/d) 1,975 1581 1,621 1,473 Low water (1933, 2017) (1527, 1636) (1565, 1678) (1417, 1529) 0.786 < 0.001 < 0.001 < 0.001 0.030 0.004 0.224 1,985 1,484 1,489 1,423 High water (1918, 2053) (1438, 1530) (1444, 1534) (1378, 1468) Energy from protein (%/d) 13.2 15.7 14.6 14.9 Low water (12.9, 13.5) (15.3, 16.0) (14.2, 14.9) (14.6, 15.3) 0.785 < 0.001 < 0.001 < 0.001 0.002 0.001 0.103 13.1 16.7 15.7 15.4 High water (12.7, 13.6) (16.3, 16.9) (15.4, 16.0) (15.1, 15.7) Energy from carbohydrate s (%/d) 56.6 55.9 55.9 55.8 Low water (56.1, 57.2) (55.2, 56.7) (55.1, 56.7) (55.0, 56.6) 0.912 0.108 0.111 0.070 0.009 0.013 0.082 56.6 54.0 54.0 54.5 High water (55.6, 57.5) (53.4, 54.6) (53.4, 54.6) (53.9, 55.1) Energy from fat (%/d) 30.2 28.4 29.5 29.2 Low water (29.7, 30.6) (27.7, 29.0) (28.8, 30.2) (28.6, 30.2) 0.786 < 0.001 0.100 0.020 0.175 0.315 0.261 30.3 29.3 30.3 30.1 High water (29.5, 31.1) (28.8, 29.9) (29.7, 30.8) (29.5, 30.6) Protein (g/d) 66.4 62.3 59.3 55.5 Low water (65.2, 67.7) (60.6, 63.9) (57.5, 61.0) (53.7, 57.2) 0.923 < 0.001 < 0.001 < 0.001 0.962 0.550 0.919 27

66.3 62.1 58.2 55.2 High water (64.2, 68.4) (60.7, 63.5) (56.8, 59.6) (53.8, 56.6) Carbohydrate s (g/d) 284.8 227.6 231.1 208.6 Low water (278, 291) (219, 236) (222, 240) (200, 217) 0.852 < 0.001 < 0.001 < 0.001 0.003 < 0.001 0.094 285.9 205.7 204.9 196.9 High water (275, 296) (199, 213) (198, 212) (190, 204) Fat (g/d) 67.8 50.9 53.9 48.5 Low water (66.1, 69.5) (48.7, 53.2) (51.5, 56.2) (46.2, 50.8) 0.548 < 0.001 < 0.001 < 0.001 0.271 0.052 0.663 68.8 49.6 50.7 48.5 High water (65.9, 71.6) (47.7, 51.5) (48.9, 52.6) (46.7, 50.4) Energy from SSBs (%/d) 20.5 16.2 17.4 17.3 Low water (19.2, 21.7) (14.6, 17.9) (15.6, 19.1) (15.5, 19.0) 0.869 < 0.001 0.002 0.001 < 0.001 < 0.001 < 0.001 20.7 10.4 10.7 11.6 High water (18.6, 22.8) (9.0, 11.8) (9.3, 12.1) (10.2, 13.0) Sugar (g/d) 122.0 93.4 97.1 92.3 Low water (118, 125) (88.9, 97.9) (92.5, 102) (87.6, 96.9) 0.901 < 0.001 < 0.001 < 0.001 0.004 0.001 0.016 121.6 81.2 82.8 81.9 High water (116, 127) (77.4, 85.0) (79.0, 86.5) (78.2, 85.6) 1Low water consumption if < 1.2 l/d, high water consumption if ≥ 1.2 l/d. 2Models were adjusted by baseline variables: age, BMI category (overweight or obese), and season of the year (n = 189). Significant differences in bold type.

Figure 2 Adherence to dietary patterns by level of water consumption

Healthy pattern Traditional pattern .4 .6 * .2 * .4 0 .2 0 -.2 LinearPrediction, Fixed Portion LinearPrediction, Fixed Portion -.2 -.4

1 2 3 4 1 2 3 4 Stage Stage

Low water High water Low water High water

Varied pattern .6 .4 .2 0 LinearPrediction, Portion Fixed -.2

1 2 3 4 Stage

Low water High water

*P < 0.05 N = 189 participants.

ARTÍCULO #2

Food Group Intakes Predict Weight Loss Despite Nondifferential Energy Underreporting among Overweight Women in a Dietary Counseling Intervention Trial

Spanish title: La ingesta de grupos de alimentos predice la pérdida de peso a pesar del subreporte de energía no diferencial en mujeres con sobrepeso, en un ensayo clínico con una intervención de consejería dietética.

Sonia Rodríguez-Ramírez,1 Teresa González-Cossio,1 Sonia Hernández-Cordero, 1 Barry Popkin,2 Michelle A. Mendez 2 1Center of Research in Nutrition and Health, National Institute of Public Health, Mexico 2Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC

Corresponding author: Sonia Rodríguez-Ramírez Instituto Nacional de Salud Pública Av. Universidad 655 Col. Santa Maria Ahuacatitlan Cuernavaca, Mor. CP 62100 Mexico e-mail: [email protected]

ABSTRACT Background: Little is known about the impact of dietary underreporting in weight loss trials. Objective: We used a randomized controlled trial (RCT) to estimate the prevalence of misreporting and its impact on dietary predictors of weight loss. Design: Women (n = 240) in Cuernavaca, Mexico, enrolled in a randomized weight loss trial with two arms: dietary counseling only and dietary counseling plus provision of bottled drinking water. Participants were 18 to 45 years old with body mass indexes (BMIs) ≥ 25 kilograms per square meter, no chronic diseases, and baseline intakes of sugar-sweetened beverages ≥ 250 kilocalories per day. Measures were obtained at baseline and three, six, and nine months. Underreporters were identified by the disparity between reported energy intakes and predicted energy requirements from doubly labeled water equations adjusted for energy deficits estimated based on weight changes. Associations between six-month changes in food group intake and meaningful weight loss, defined as ≥ 3% of baseline weight, were estimated. Results: Underreporting increased from 4% at baseline to > 39% postintervention, which was lower than estimates of > 50% that did not account for weight loss. Underreporters had higher baseline BMIs and higher % of being married than plausible reporters, but these were nondifferential by treatment arm. Adjustment for misreporting slightly strengthened associations between increases in healthy food group consumption (fruits, vegetables, and dairy products) and weight loss but did not change the overall results. Conclusions: Underreporting increased substantially after baseline in this weight loss trial but was nondifferential in an RCT where both arms received dietary counseling. Future trials should examine whether energy underreporting is highly prevalent, is differential across arms, and may attenuate associations between dietary change and weight loss.

Key words: Randomized controlled trial, energy underreporting, weight loss

INTRODUCTION Weight loss in randomized controlled trials focused on diet often requires substantial changes in intakes of some food groups. Recent studies suggest that shifts in intakes of key food groups are universally predictive of successful weight loss regardless of the type of dietary strategy adopted, such as promoting adherence to low-fat, low-carbohydrate, or Mediterranean diet patterns (Schulz, Kroke et al. 2002, Raynor, Jeffery et al. 2004, Nakade, Lee et al. 2009, Canfi, Gepner et al. 2011). However, the evidence supporting that intakes of these food groups are linked to reduced risk of excess weight gain is surprisingly limited and ambiguous (Tohill, Seymour et al. 2004, Rolls, Ello-Martin et al. 2008). Implausible dietary reporting has been identified as a challenge in analyses of self-reported dietary intake data and may undermine the ability to identify dietary factors associated with successful weight loss. Underreporting may be especially problematic in weight management or dietary interventions because of social desirability (the tendency to respond in such a way as to avoid criticism or negative evaluation) and social approval (the tendency to seek praise) biases and the high prevalence of overweight and obesity, all of which are associated with misreporting (Novotny, Rumpler et al. 2003, Miller, Abdel-Maksoud et al. 2008). Although implausible reporting has negative implications for interpretation of the relationship between diet and health outcomes, few intervention studies account for this bias beyond excluding subjects with extreme energy intakes. Few intervention studies have examined the prevalence of low energy reporting at baseline, and even fewer have examined whether there are substantial increases in energy intake underreporting after baseline (Caan, Ballard-Barbash et al. 2004, Johnson, Friedman et al. 2005). The ideal way to identify implausible reporters would be to compare reported energy intake with an objective estimate of energy intake, such as doubly labeled water (DLW) combined with the change in the body energy stores measured by dual-energy X-ray absorptiometry (De Jonge, DeLany et al. 2007, Hall and Chow 2011). However, such methods are not feasible in large-scale studies due to cost, DLW availability to researchers or clinicians, and participant burden (Thomas, Schoeller et al. 2010). Other methods that have been suggested to identify underreporters are based on the extent of the disparity between reported energy intake (rEI) and predicted energy requirement (pER). One of these is the Goldberg method, which suggests determining the validity of the rEI by comparing it with the estimated total energy expenditure (TEE), expressed as a multiple of the resting metabolic rate (Goldberg, Black et al. 1991, Black 2000). A more recent alternative to identify implausible reporters is similarly based on the extent of the disparity between rEI and pER, but unlike the Goldberg method, this alternative uses the dietary reference intake (DRI) prediction equations estimated based on DLW experiments. There is evidence that these DRI equations correlate well with measured TEE (Huang, Roberts et al. 2005, IOM. 2005, Mendez, Popkin et al. 2011). M. A. Mendez and colleagues (2011) found that the Goldberg method, when revised to use alternative equations to calculate a basal metabolic rate more suitable for overweight populations, performed very similarly to the pER method based on DLW equations. Estimating implausibly low energy intakes in weight loss intervention trials is more complex, as the prevalence of underreporting may be inflated if energy deficits linked to weight loss achieved after baseline are ignored. Several weight loss studies with detailed biomarker data on energy balance and measured weight changes have estimated the reduction in energy intake necessary to achieve 32

the measured weight loss, taking into account changes in metabolism as well as weight reduction (Thomas, Schoeller et al. 2010, Hall and Chow 2011, Pieper, Redman et al. 2011). These estimates can be used to take into account measured weight loss in longitudinal studies to better estimate implausible energy reporting. The objectives in this study were (1) to estimate the prevalence of misreporting at baseline and during the course of an intervention trial, taking into account reduced energy intake linked to achieved weight loss; (2) to evaluate whether underreporting is related to sociodemographic factors and initial weight status; and (3) to assess whether accounting for underreporting confounds the associations between changes in food group consumption and weight loss in a dietary intervention cohort.

MATERIALS AND METHODS Study design This study is derived from the original Randomized Controlled Trial of a Water Beverage Intervention Trial for Reducing Risk Factors of Metabolic Syndrome in Young Mexican Free-living Women (Clinical Trials Gov Identifier: NCT0124510), whose objective was to determine whether replacement of sugar-sweetened beverages (SSBs) with water could reduce plasma triglyceride concentration and a set of other cardiometabolic factors as secondary outcomes.

Participants Women were recruited, enrolled, and followed between April 2009 and August 2011 in Cuernavaca, Morelos, Mexico. Eligible participants were overweight (body mass index [BMI] 25.0– 38.9) adult females ages 18 to 45 without chronic diseases who reported consuming at least 250 kilocalories per day (kcals/d) of caloric beverages (including carbonated drinks; natural juices; juice and sport drinks; sweetened tea and coffee; alcoholic beverages; and milk-based beverages, excluding milk). The exclusion criteria are detailed elsewhere (Hernandez-Cordero 2012). We randomly assigned participants into one of two groups: the water and education provision (WEP) group or the education provision (EP) group. Both groups received education about a healthy diet for nine months. The major differences were water provision and education activities oriented specifically to reducing the intake of SSBs (carbonated and noncarbonated beverages, coffee and tea with sugar, juice drinks, soy beverages, sugar-sweetened aguas frescas , energy drinks, and smoothies) and substituting increased water consumption for the WEP group. Bottled water (2 to 3 liters of water per person per day) was home delivered to the WEP group during the intervention period. We recruited 240 women between April 2009 and November 2010 and followed them for nine months. Further details regarding the study design and methodology can be found elsewhere (Hernandez-Cordero 2012). We obtained participants’ written informed consent prior to any research activity. The study was approved by the Ethics Committee of the National Institute of Public Health.

Dietary intake data Dietary intake data were collected at baseline and three, six, and nine months using three 24-hour dietary recalls on each occasion. Dietary information included two nonconsecutive weekdays and one weekend day, all three days randomly selected within a 14-day period. Trained interviewers 33

visited the participants’ homes to collect the information in the place where food was prepared and obtained portion sizes as accurately as possible.

Food groups We classified foods and beverages into 13 food and 2 beverage groups. The food groups were fruits, vegetables (excluding vegetable stews), vegetable-based stews (dishes in which the principal ingredients are vegetables and that include other ingredients, such as oil, eggs, meats, dairy products), potato stews (dishes in which the principal ingredient is potato and that include other ingredients, such as sauces, vegetables, and eggs, but excluding french fries and chips, meats (poultry, beef, and pork), fish and seafood, cereals (excluding corn-based dishes), corn-based dishes (Mexican dishes with several ingredients, such as tortillas or corn dough with meats, rice, beans, or eggs), legumes (beans and lentils), dairy products (cheese and yogurt), dairy-based desserts (milk custard and rice with sugar and milk), other desserts (cookies, cakes, and non-dairy- based desserts), and salty snacks (chips and corn-based snacks, such as popcorn and nachos). Beverage groups were caloric beverages, including SSBs and aguas frescas (water, sugar, and fruit), commercial fruit and vegetable drinks, atoles (water or milk, cereal, and sugar), commercial flavored waters, sweetened coffee and tea, sodas, 100% fruit or vegetable juices, soy beverages, sport/energy beverages, alcoholic beverages, and milk- and fruit-based beverages; and non-sugar- sweetened or noncaloric beverages, including plain water, diet sodas, coffee and tea, and non- sugar-sweetened milk and yogurt drinks.

Energy and macronutrient Intake To estimate energy intake we used a food composition table compiled by the Mexican National Institute of Public Health with links and consistency checks with the US Department of Agriculture (USDA) National Nutrient Database for Standard Reference (USDA 2010). We calculated total daily energy (kcals) and macronutrient intake at each wave by calibrating the estimate of the usual intake, using PC-SIDE software version 1.0 (Iowa State University of Science and Technology, Ames, Iowa, USA, 2003), to account for day-to-day variability in intake for each participant.

Covariable assessment

Anthropometric measures We used a Tanita (model BWB-627-A, 100 gram [g] precision) digital scale to assess weight. We measured height at baseline only using a calibrated, wall-mounted stadiometer (Shorr Productions, model 17802, 1 millimeter [mm] precision). Participants removed their shoes and stood upright with their backs and heels against the wall (Shamah, Villalpando et al. 2006). We used height and weight to calculate BMI and to classify participants as overweight or obese according to the cutoffs proposed by the World Health Organization (WHO, 2008).

Socioeconomic status We constructed an indicator of socioeconomic status (SES) using principal components analysis (Reyment and Jvreskog 1996). The SES index included variables related to housing conditions (such as flooring and roofing materials), ownership of home appliances (refrigerator, stove, washing 34

machine, TV, radio, video player, telephone, and computer), and number of rooms (other than bathroom, kitchen, and corridors). We divided the standardized factor obtained into tertiles to present three SES categories: low, middle, and high. This methodology, which allowed us to classify participants’ households into SES groups, has been validated to describe SES differentiation within a population and has been used previously with the Mexican population (Vyas and Kumaranayake 2006). We obtained information about education level, marital status, and employment status through self-reports. Categories of education level included elementary school or less, secondary or high school, and university. We categorized marital status as single, divorced, or widowed and married or living with a partner. The employment status categories we used were office/skilled worker, manual laborer (in factory, services), and unemployed (housewife, retired, or student).

Physical activity We used accelerometry (Actigraph GT3X, for Pensacola, Fl, USA) to assess physical activity levels (PALs). We asked the participants to wear the accelerometers during one week at each wave within the period when diet information was collected and did not counsel them to modify their activities. They wore the accelerometers at waist level at the right anterior axillary position in a nylon pouch attached to a belt. We considered as valid measures those based on at least eight hours per day and four days of monitor wear. We used total metabolic equivalent of task (MET) minutes per day to define PALs according to the cutoffs proposed by the Institute of Medicine (IOM) (IOM. 2005). As 41 women did not have accelerometry data at baseline, we predicted PALs for them using a linear regression model including METs for these women measured during another period, age, BMI, and socioeconomic status as determinants. We conducted a sensitivity analysis excluding these women, and it did not change the results.

Statistical analysis

Implausible reporting estimate We identified subjects with implausibly low and high rEIs according to the magnitude of the discrepancies between rEIs and pERs using previously published methods based on DLW equations for estimating energy needs (Huang, Roberts et al. 2005). To predict energy requirements, we used the IOM’s most recent DRI equations for overweight women (IOM. 2005), which were developed from a meta-analysis of studies using DLW as the criterion measure of TEE. We calculated standard deviations around pERs using published estimates of variability in components of the energy balance equations (Coefficient of variation of rEI, pER, and TEE), taking into account each participant’s age, gender, PAL, weight status, and number of days of dietary reporting (Huang, Roberts et al. 2005, IOM. 2005, Mendez, Popkin et al. 2011). Predictors of pER in these equations included age, weight, height, and PAL, which was categorized as sedentary, low active, active We classified subjects whose reported intakes fell outside a 1.5 standard deviation (SD) of the pERas likely under- or overreporters. We selected this cutoff because previous research using this method to identify misreporters suggested that it yielded a sample that maximized the concordance between rEI and pER (Huang, Roberts et al. 2005). Furthermore, we conducted a post 35

hoc analysis of a different cutoff to evaluate whether a more stringent cutoff (± 1.0 SD) would yield the best estimates of associations between dietary variables and weight loss. Results of the same analysis using a less restrictive cutoff (± 2.0 SD) showed consistent associations between food group consumption and weight loss. The cutoff of ± 1.5 SD corresponded to 27.4% of the rEI/pER ratio, that is, defining reported intakes as plausible if they fell within 27.4% of pERs. Prediction equations for pERs assume energy balance and no weight changes. Given that our study population was part of a weight loss trial and some achieved substantial weight loss, we adjusted our pERs by incorporating estimates of the energy intake reductions necessary to achieve the measured weight changes observed at three, six, and nine months of the intervention. We based our adjustments on estimates from pooled analyses of weight loss trials, in which an average reduction of 390 kcals/d was required to lose 0.22 kilograms (kg) per week in 12 weeks (Schoeller and Buchholz 2005). A more recent study that used data from multiple intervention trials with detailed longitudinal measures of body weight to estimate changes in energy intake associated with weight loss (Hall and Chow 2011) obtained similar estimates.

Analysis of factors associated with misreporting and with weight loss We evaluated differences in anthropometry, socioeconomic status, and PAL between categories of energy misreporting at baseline and six months using logistic regression models. Analyses focused on under- rather than overreporters, because few or no women were overreporters after baseline. We conducted the postintervention analysis at six months, because the follow-up rate at nine months was lower than that at six months (n = 189 vs. 198 women, respectively) and lacked statistical power. For analyses of dietary predictors of weight change, we transformed some food group variables using either square roots or logarithms when the distribution was not normal. We used logistic regression models to analyze the association between changes in consumption of each food and beverage group and achieving a meaningful weight loss at six months. We categorized changes in food consumption as low or high according to the median intake. We considered weight loss to be meaningful when a woman had lost ≥ 3.0% (approximately ≥ 2 kg) at six months compared to her weight at baseline (achieved by 30.0% of the sample). We tried to use a cutoff of observed weight loss linked with a meaningful health impact (≥ 5.0% weight loss) (Blackburn 1995). Nevertheless, relatively few women (11.0%, n = 22) lost ≥ 5.0% of their body weight in the short time frame of this study (six months). Our models assessed associations between meaningful weight loss and changes in consumption of each food group adjusting for age, baseline BMI, SES, and baseline intake of the food group in grams. Although our data were derived from a weight loss trial, we carried out our primary analyses on the complete cohort, not taking into account the intervention group. Our goals were primarily methodological, namely, to learn if and how correction for misreporting in a weight loss trial would affect the diet-weight relationship. Most important, in this study all subjects received dietary counseling to promote improved cardiometabolic health and potential weight loss (with the intervention arm additionally receiving bottled water), thus the entire cohort was susceptible to increased energy intake misreporting according to the evidence on the relationship between interventions and misreporting probability (Johnson, Friedman et al. 2005). We performed all analyses with a two-sided p-value < 0.05 set for statistical significance. We used STATA, version 12.1 (Stata Corporation, College station, TX, USA) for data cleaning and analyses. 36

RESULTS We obtained dietary information from 198 women with data at baseline and six months. We evaluated differences in baseline characteristics between the complete sample of women and the women who remained in the study at six months. Forty-two women (17.5%) with information at baseline did not have information at six months. There were no significant differences in implausible reporting at baseline between underreporting and overreporting (underreporting 3.7% and 4.5% and overreporting 0.8% and 0.5% in the complete sample and the sample at six months, respectively). Anthropometric, dietary, and socioeconomic characteristics at baseline were also similar between the two groups (data not shown). The estimated prevalence of underreporting of energy intake was significantly higher in the follow- up stages than at baseline (p < 0.05) using -1.5 SD cutoffs and ignoring changes in weight (between 50.0% and 63.0% at each follow-up, compared with 3.7% at baseline) (table 1). Overreporting estimates were similar during follow-up and at baseline (0.8% at baseline, no overreporters during follow-up). When adjustments for achieved weight loss were incorporated, the prevalence of underreporting was attenuated: the underreporting estimate was reduced to 40.0–54.0%; the estimated overreporting was < 1.0% at all follow-up stages.

Table 1 about here

The prevalence of underreporting at baseline was nondifferential by study arm, with estimates of 5.0% and 2.5% in the bottled water intervention group and the diet counseling–only control group, respectively (p > 0.10). At six months the prevalence of underreporting was 38.9% in both arms (data not shown).

Characteristics of implausible reporters Table 2 presents the dietary, anthropometric, and physical activity characteristics of the participants by category of energy reporting. Due to the low prevalence of overreporting, analyses focused on comparisons of underreporters and plausible reporters. Mean age was very similar between categories of energy reporting (30.2 ± 7.9 in underreporters and 33.4 ± 6.7 in plausible reporters). At baseline the mean BMI was higher in women in the underreporting category than in the plausible category (34.60 ± 4.80 vs. 31.00 ± 3.70, p < 0.05). Similar results were observed at six months, when BMI was higher among underreporters compared to plausible reporters (32.20 ± 3.90 vs. 30.00 ± 3.80, p < 0.05). Participants lost an average of 0.95 ± 2.70 kg from baseline to six months. Weight and BMI changes were higher in the plausible reporters (-1.59 ± 2.70 in weight and -0.59 ± 1.00 in BMI) than in the underreporters (-0.13 ± 2.70 in weight and -0.05 ± 1.00 in BMI) (p < 0.05). The percentage of women with a weight loss ≥ 3.0% of their initial weight was 30.1%, with a higher prevalence among plausible reporters.

Table 2 about here

With respect to dietary variables, underreporters reported lower energy intakes than did plausible reporters at both baseline and six months (1,631 ± 153 vs. 1,990 ± 235 kcal/d, respectively, at 37

baseline and 1,308 ± 225 vs. 1,685 ± 220 kcal/d, respectively, at six months, p < 0.05 for both) . Underreporters also reported a larger decrease in energy intake after baseline than plausible reporters (-821 ± 524 kcal/d and -354 ± 526 kcal/d, respectively, p < 0.05). Physical activity was similar between categories of energy reporting, and the predicted total energy expenditure was higher in underreporters (2,357 ± 212 kcal/d at baseline and 2,148 ± 236 kcal/d at six months) compared with plausible reporters (2,128 ± 192 at baseline and 1,930 ± 219 kcal/d at six months) (p < 0.05). The percentage of energy intake from protein was similar between categories of energy reporting at baseline but was higher in underreporters (16.4 ± 2.0%) than plausible reporters (14.6 ± 1.5%) at six months. The percentage of energy intake from fat was similar at baseline and six months between the underreporting and the plausible reporting categories. In socioeconomic characteristics, more than 50.0% of the women had an education level of secondary or high school, around 30.0% had studied at a university, and less than 10.0% had attended only elementary school. The percentage of women with a low socioeconomic status was similar between the underreporting and the plausible reporting categories (about 35.0%), and the percentage of women with a high socioeconomic status was lower in the underreporting (31.4%) than the plausible reporting category (34.7%), but the difference was not significant. More than 50.0% of women in every category of energy reporting lived with a partner (legally married or common-law). Most of the women in the three categories of energy reporting worked in manual labor jobs, and less than 15.0% worked in office or skilled jobs. With the exception of marital status, there were no differences in socioeconomic characteristics between the categories of energy reporting. Table 3 shows the associations between sociodemographic factors, initial weight status, and underreporting. Initial BMI was positively associated with underreporting in the bivariate and multivariate models (p < 0.05). Age and marital status were negatively and positively associated, respectively, with underreporting in the multivariate model (p < 0.05). The variables of socioeconomic status and education were not associated with underreporting.

Table 3 about here

Food and beverage consumption Table 4 presents food and beverage group consumption at baseline and six months by energy reporting categories and the changes in consumption between reporting categories. At baseline consumption of food groups was similar. For some food groups, the reporting was lower in underreporters. For example, fruit consumption in underreporters was 68.8 ± 71.3 g/d compared with plausible reporters at 70.6 ± 97.5 g/d, and vegetable consumption was 30.3 ± 52.2 g/d vs. 39.8 ± 50.8 g/d. At six months underreporters reported lower consumption in overall consumptionthan plausible reporters, and differences are significant for potato stews, legumes, salty snacks, cereals, and corn-based dishes.

Table 4 about here

Beverage consumption at baseline was very similar between the underreporting and the plausible reporting categories. However, at six months underreporters reported significantly lower consumption of SSBs and higher consumption of plain water than plausible reporters (p < 0.05). With regard to changes in food and beverage consumption, between baseline and six months both groups reported an increase in healthy food groups (fruits, vegetables, and dairy products) except fish, in which underreporters and plausible reporters presented a negative change. Reported intakes of food groups such as dairy desserts, potato stews, legumes, meats, salty snacks, other desserts, cereals, and corn dishes decreased. In beverages there was a decrease in the consumption of SSBs (especially in underreporters, p < 0.05) and an increase in non-sugar- sweetened beverages (including plain water) among underreporters (p < 0.05). Table 5 presents the analysis of associations between food and beverage consumption and meaningful weight loss (≥ 3% of initial weight), accounting for underreporting. This table presents three logistic models: ignoring the energy misreporting, adjusting by category of energy reporting, and limiting the sample to only plausible reporters. We found that in all three models greater than median increases in intakes of fruits, vegetables, and dairy products were associated with a more than twofold increase in the odds of a ≥ 3% weight loss, with somewhat stronger associations (> threefold odds for both fruits and vegetables) in models restricted to plausible reporters. Total consumption of non-sugar-sweetened beverages, including plain water, was also positively associated with weight loss but was not significant in the model of plausible reporters. Increases in other food groups had positive associations with weight loss, but associations were not consistently significant across all three models (vegetable-based stews, potato stews, and legumes were significant only in the model ignoring misreporting and fish and seafood only in the model limited to plausible reporters). An increased intake of corn-based dishes was consistently negatively associated with weight loss (odds ratio [OR] < 0.40 in all models, p < 0.05).

Table 5 about here

A small set of 12 women consumed medications of varied sorts (not for treatment of the study’s major outcomes) at six months. We did not adjust for these random medication regimes, as they were not weight loss or diet related.

DISCUSSION In this study we estimated diet misreporting in overweight women participating in a weight loss randomized controlled trial (RCT), taking into account measured weight changes during the trial. We found evidence that energy intake underreporting increased during the course of the trial. The estimated prevalence of underreporting at baseline was 4% and increased substantially to 39% at six months. The energy underreporting estimate was slightly lower than estimates that overlooked weight loss (50%). Underreporting was positively associated with initial BMI and being married and negatively with age. In general, accounting for underreporting slightly increased the associations between consumption of food and beverage groups and weight loss. Both in the sample as a whole and in the group with plausible energy intakes, we found that the food groups positively associated with

weight loss were fruits, vegetables, dairy products, fish and seafood, and non-sugar-sweetened beverages and that corn-based dishes were negatively associated. Even after adjusting for weight loss during follow-up, we found a high prevalence of underreporting among participants in this weight loss trial. This result is similar to those in a few other intervention studies. R. K. Johnson et al. (2005) reported an increase in the underreporting prevalence from 39.7% at baseline to 60.3% at six months in overweight women in a behavioral weight loss program. Some researchers found that underreporting was more prevalent in obese people who wanted to lose weight (Johansson, Solvoll et al. 1998), which may explain the increase in the prevalence of underreporting in this study. In addition, social desirability has been related to a decrease in reporting accuracy. As a result, participants in a weight loss intervention may feel increased social pressure to keep their weight off or may feel pressure not to report eating foods they believe are inappropriate (Miller, Abdel-Maksoud et al. 2008, Scagliusi, Ferriolli et al. 2009). Furthermore, it has been suggested that underreporters may not understand portion sizes or simply may not recall all foods and beverages (Blundell 2000). However, in our study interviewers were trained to help all participants recall and calculate portion sizes, and underreporting prevalence at baseline was low. Although biomarker evidence has illustrated higher levels of energy misreporting among participants in the treatment arm of dietary interventions than in the usual care group (Natarajan, Pu et al. 2010), in the context of this trial, which did not include a pure control group, underreporting was nondifferential in the intervention arm. This result was probably because of careful attention to ensure that the actual time of interaction with the intervention and the control groups and the counseling about healthy diet was identical, and attendance at the meetings to receive the counseling was similar (5.66 ± 0.87 vs. 5.54 ± 0.91 meetings in the intervention and the control arms, respectively, at six months) )(Pagoto, McDermott et al. 2013). In this study underreporters had higher BMI, lower weight loss, and lower rEI compared with plausible reporters, and a higher percentage was married. Our findings support previous studies with respect to marital status. A study of the quality of energy reporting in pregnant women showed that being married was associated with underreporting (Nowicki, Siega-Riz et al. 2011). Although not statistically significant, in this study underreporters included a higher percentage of women with a low socioeconomic status than the other two categories. A study conducted in Brazilian women found a positive correlation between socioeconomic level (measured by income) and reporting accuracy (Scagliusi, Ferriolli et al. 2008). We found that underreporters reported at baseline and at follow-up reduced consumption of most food and beverage groups. Regarding the change in the consumption of food and beverage groups at baseline and at six months, underreporters reported a healthier diet than plausible reporters; higher positive changes in consumption of healthy food groups, including non-sugar- sweetened beverages (especially water); and higher decreases in consumption of less healthy food groups, such SSBs. As we mentioned above, these women possibly felt pressure to report more socially accepted foods (Miller, Abdel-Maksoud et al. 2008), and although they reported a unrealistically healthy diet, they achieved a lower weight loss compared with plausible reporters. The association between plain fruits and vegetables and weight loss found in this study corroborates the evidence found in other studies. A study conducted in adults in Quebec, Canada, documented an association between a self-reported increase in the consumption of fruits and a 40

lower increase in body weight and adiposity after a follow-up of almost six years (Drapeau, Després et al. 2004). Increases in the consumption of fruits and vegetables could displace consumption of energy-dense foods, leading to a lower energy intake and ultimately resulting in weight loss. It is important to note that when we analyzed consumption of plain vegetables, we found a positive association with weight loss, but this association was not statistically significant for the vegetable-based stews group. This finding most likely relates to the high energy density of some vegetable dishes with extra kcals from oil, cheese, dressing, and other animal-source foods. Furthermore, other longitudinal studies (cohorts and RCT) have not found an association between these food groups and weight loss (Smith-Warner, Elmer et al. 2000, Drapeau, Després et al. 2004). RCTs have yielded convincing evidence that a high intake of energy-dense foods, including salty snacks and desserts, leads to excess weight gain (Swinburn, Caterson et al. 2004). Unexpectedly, our analysis found a nonsignificant and weak association between higher intakes of salty snacks and weight loss. We speculate that this finding may reflect in part systematic underreporting of this food group in both underreporters and plausible reporters. Another food group positively associated with weight loss was dairy products, such as cheese and yogurt. While there is some documentation that dairy products contribute to weight loss due to their high calcium content, which plays an important role in attenuating adipocyte lipid accretion and weight gain, increasing lipolysis, and preserving thermogenesis during caloric restriction (Zemel 2004), there is no consensus on this relationship, and many studies report conflicting results (Mourao, Bressan et al. 2007, USDA 2010). Our results corroborate this potential relationship. Consumption of corn-based dishes was negatively associated with weight loss. These are highly energy-dense mixed dishes with extensive amounts of fat. In Mexico the consumption of this food group is very common due to the dishes’ high acceptability, availability, variety, and low cost. Our study has some limitations. First, we did not have information about physical activity for 41 women at baseline. A second key limitation was the need to schedule the diet interviews to ensure that the participants were at home. This could bias the report, because women could change their reports or their intakes only for the day of the interview. However, this approach was needed for the total sample, and we found the bias was nondifferential. The lack of direct estimates of TEE to use as a pER is another limitation. However, we used the most recent prediction equations from the IOM, which served as the best proxy of TEE (Huang, Roberts et al. 2005). This study has several strengths. During the follow-up period, only 42 women (17.5%) had dropped out of the study at six months, a small attrition rate. We conducted a sensitivity analysis assuming two extreme scenarios of weight loss by these women and two different scenerios of change of food group consumption (low and high). In this analysis the association between food group consumption and weight loss would not change meaningfully if the women lost to follow-up did not have elevated (> median) increases in intakes of recommended foods, such as fruits and vegetables, also unlikely based on the modest evidence of such dietary changes seen at three months. In addition, three nonconsecutive 24-hour dietary recalls were available at every measurement stage, which allowed us to capture more true variability of the diet. Also we were able to adjust the distribution of energy and macronutrient intake for the intrasubject variability, 41

which allowed a better estimate of the intake (Hoffmann, Boeing et al. 2002). To our knowledge this is one of the few studies that takes into account weight loss between measurement stages in order to estimate implausible reporting in a RCT. In conclusion, in this study the original estimated prevalence of dietary energy intake underreporting was reduced after accounting for weight loss. These data suggest that underreporting is an important problem in studies involving overweight and obese subjects. Underreporting increased sharply after the beginning of the intervention, a trend that persisted after accounting for weight changes, and it was not different between arm groups. In intervention trials, if the underreporting prevalence is different between arms of the study, it is recommended to adjust the analysis for underreporting when the relationship between diet and health outcomes is being evaluated. Future studies should consider examinations of the prevalence of energy underreporting and should consider the adjustment for underreporting if it confounds the association between diet and weight loss.

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Table 1. Classification of energy reporting, considering and not considering weight loss, at baseline and follow-up 1

Method with current weight 2 Method considering weight loss 2

Plausible Plausible Underreporters reporters Overreporters Underreporters reporters Overreporters Baseline, % (n) (n = 240) 3.7 (9)* 95.4 (229) 0.8 (2) — — — 3 months, % (n) (n = 219) 53.2 (116) 46.8 (102) 0.0 (0) 40.2 (88) 59.4 (130) 0.5 (1) 6 months, % (n) (n = 219) 50.0 (99) 50.0 (99) 0.0 (0) 38.9 (77) 61.1 (121) 0.0 (0) 9 months, % (n) (n = 219) 63.0 (119) 37.0 (70) 0.0 (0) 54.5 (90) 45.0 (85) 0.5 (1) 1Using ± 1.5 SD (rEI/pER ≤ 72.6 or ≥ 127.5%) to identify misreporting. 2Adjustment by weight loss. *Significant difference between baseline and follow-up at three, six, and nine months in percentage of underreporting.

Table 2. Anthropometric, dietary, and socioeconomic characteristics of underreporters, plausible reporters, and overreporters 1 Baseline 6 months (n = 198) 2 (n = 198) 3 Underreporter Plausible Plausible s reporters Overreporters Underreporters reporters Overreporters (n = 9) (n = 188) (n = 1) (n = 77) (n = 121) (n = 0)

Sample, % 4.5 94.9 0.5 38.9 61.1 0.0 Age, years 30.2 ± 7.9 33.4 ± 6.7 33.0 — — — Anthropometric variables BMI, kg/m 2 34.60 ± 4.80a 31.00 ± 3.70 29.90 32.20 ± 3.90a 30.00 ± 3.80 — Δ BMI, kg/m 2 Baseline–6 months — — — -0.05 ± 1.00 a -0.59 ± 1.00 — Δ BMI, kg/m 2 3 months–6 months — — — 0.11 ± 0.60 a -0.27 ± 0.70 — Δ Weight baseline –6 months, kg — — — -0.13 ± 2.70 a -1.59 ± 2.70 — Δ Weight 3 months –6 months, kg — — — 0.29 ± 1.50 a -0.69 ± 1.60 — Weight loss ≥ 3%, (%) 3 22.10 35.30 - Dietary variables Reported dietary intake, kcal/d 1,631 ± 153 a 1,990 ± 235 2,785 1,308 ± 225 a 1,685 ± 220 — Δ reported energy intake, kcal/d Baseline–6 months — — — -821 ± 524a -354 ± 526 — Δ reported energy intake, kcal/d 3 months–6 months — — — -184 ± 367 a 51 ± 405 — PAL, %

Sedentary 11.1 30.8 100.0 18.2 28.1 — Low active 55.6 60.6 0.0 71.4 67.8 — Active 33.3 8.5 0.0 10.4 4.1 — Average METs/24 hours 1.5 ± 0.1 1.4 ± 0.1 1.3 1.5 ± 0.1 1.4 ± 0.1 — Predicted total energy expenditure, kcal/d 2,357 ± 212 a 2,128 ± 192 2,100 2,148 ± 236 a 1,930 ± 219 — Ratio rEI/pER, % 69.2 ± 3.3 a 94.0 ± 11.7 132.6 61.1 ± 8.9 a 87.7 ± 9.9 — Energy from protein (%/d) 12.6 ± 0.7 13.2 ± 1.0 11.5 16.4 ± 2.0 a 14.6 ± 1.5 — Energy from fat (%/d) 28.4 ± 2.9 30.2 ± 2.5 28.6 30.1 ± 3.7 30.0 ± 3.4 — Education, % (n) Elementary school or less — — — 7.8 (6) 3.3 (4) — Secondary or high school — — — 61.0 (47) 57.0 (69) — University — — — 31.2 (24) 39.7 (48) — Socioeconomic level, % (n) Low — — — 35.0 (27) 34.7 (42) — Middle — — — 33.8 (26) 30.6 (37) — High — — — 31.2 (24) 34.7 (42) — Marital status, % (n) Single/divorced/widow — — — 29.9 (23) a 43.8 (53) — Married or living with a partner — — — 70.1 (54) a 56.2 (68) — Employment status, % (n) Office/skilled worker (professional, executive) — — — 11.7 (9) 14.0 (17) — Manual labor er (factory, service) — — — 44.2 (34) 50.4 (61) — Unemployed (housewife, retired, or student) — — — 44.2 (34) 35.5 (43) —

1Using ± 1.5 SD (rEI/pER ≤ 72.6 or ≥ 127.5%) to identify misreporting. 2Adjustment by weight loss. 3Percentage of weight loss with respect to baseline weight. aSignificantly different from plausible reporters (p < 0.05).

Table 3. Association between sociodemographic factors, initial weight status, and underreporting at six months Bivariate model Multivariate model

Variable OR 95% conf idence OR 95% conf idence interval interval Initial BMI ( kg/m 2) 1.13* (1.04 –1.22) 1.13* (1.05 –1.23 )

Age (years) 0.95* (0.91–0.99) 0.93* (0.89 –0.98 )

Marital status (1 = married) 1.83 (0.99 –3.35) 2.53* (1.29 –4.98)

Education (years) 0.93 (0.85 –1.02) Middle socioeconomic status 1 1.09 (0.54 –2.19) High socioeconomic status 1 0.89 (0.44 –1.78) 1Socioeconomic status derived from a principal component analysis including housing conditions, ownership of home appliances, and number of rooms in the house. *p < 0.05.

Table 4. Food group consumption (g) at baseline and six months of follow-up and changes in consumption by reporting category

Underreporters (n=77) Plausible reporters (n=121)

Intake at baseline Intake at 6 mo nths Change 1 Intake at baseline Intake at 6 mo nths Change 1 Foods (g) Mean 95% CI Mean 95% CI Mean 95% CI Mean 95% CI Mean 95%CI Mean 95% CI Fruit s2 (21.3, 43.1 (28.2, 58.1) 70.8 (49.7, 91.9) 33.1 44.9) 42.3 (29.9, 54.7) 92.7 (70.0, 115.0) 65.7 (48.6, 82.8) Vegetables 2, 3 (6.3, 13.1 (6.3, 19.8) 22.3 (12.4, 32.3) 13.9 21.5) 21.8 (14.7, 28.9) 34.5 (22.6, 46.4) 27.0 (17.4, 36.6) Vegetable -based (-17.4, stews 30.6 (22.6, 38.7) 23.0 (18.6, 27.5) -7.6 2.2) 26.7 (18.6, 34.7) 23.0 (18.6, 27.5) 4.8 (-2.3, 11.9) Dairy des serts (-15.5, - 20.9 (15.2, 26.6) 10.5 (7.7, 13.3) -10.4 5.3) 18.7 (15.4, 22.1) 14.5 (11.8, 17.3) -4.2 (-8.2, -0.2) Dairy products (0.9, 3.9 (1.0, 6.8) 5.5 (1.2, 9.8) 7.3 13.7) 4.1 (1.4, 6.8) 9.3 (5.0, 13.7) 7.5 (3.3, 11.7) Potato stews (-10.3, - 8.2 (4.7, 11.6) 1.4 (0.8, 2.1) -6.7 3.1) 10.0 (7.7, 12.3) 6.8 (4.7, 8.9) -3.2 (-6.4, 0.0) Legumes (-22.9, - 34.4 (29.4, 39.5) 18.0 a (14.0, 22.0) -16.4 9.9) 39.9 (35.6, 44.2) 32.8 (28.8, 36.8) -7.1 (-13.0, -1.2) Fish & seafood (-8.9, 4.3 (0.7, 7.9) 3.3 (0.2, 6.3) -1.7 5.5) 24.3 (17.3, 31.3) 2.3 (0.6, 4.0) -17.1 (-22.6, -11.6) Meat s (68.3, (-33.8, - 87.2 106.2) 69.8 (54.8, 84.7) -21.9 10.0) 94.2 (78.2, 110.2) 72.0 (58.8, 85.1) -24.8 (-35.1, -14.5) Salt y snacks (corn (-2.9, - based) 3.5 (2.2, 4.8) 1.5 (0.8, 2.1) -2.0 1.1) 5.8 (4.6, 6.9) 4.4 (3.5, 5.3) -1.4 (-2.8, 0.0) Desserts (other (-33.7, - than dairy) 56.1 (44.5, 67.7) 27.8 (19.1, 36.5) -26.7 19.7) 63.7 (53.8, 73.7) 39.1 (30.8, 47.4) -22.9 (-28.6, -17.2) Cereals ( other (-23.1, - than corn) 69.7 (54.6, 84.7) 59.0 a (46.0, 72.0) -12.5 1.9) 82.4 (70.7, 94.1) 82.9 (71.3, 94.4) -0.1 (-8.5, 8.3) Corn -based (130.1, (-128.6, - (244.0, (203.0, dishes 266.3 (232, 300) 156.2 a 182.4) -110.0 a 90.6) 271.2 298.0) 226.8 251.0) -45.9 (-63.7, -28.1) Beverages (milliliters) SSB s 1,100.0 (1,020, 317.1 (234.0, -705.0 a (-758.0, - 1,075.0 (996.0, 635.6 (550.0, - (-435.0, - 50

1,180) 400 .0 ) 652.0) 1,154 .0 ) 721 .0 ) 384 .0 333 .0 ) Non -sugar - sweetened (1,529.0, (742.0, (680.0, (1,275.0, beverages 835.9 (683, 988) 1,714.0 1,898.0) 838.0 934.0) 810.8 941.0) 1,433.0 1,591.0) 594.0 (514.0, 674.0) Milk (-17.0, 62.8 (52.6, 73.1) 53.0 (44.2, 61.8) -7.3 2.4) 58.9 (50.1, 67.7) 77.2 (66.5, 88.0) 21.2 (9.8, 32.6) Plain water (512 .0 , (1 ,296, (715.0, (521 .0 , (1 ,064 .0 , 652.0 791.0) 1,487.0 1,678) 808.0 a 901.0) 635.6 750.0) 1,215.0 1,365.0) 547.0 (470.0, 624.0)

Table 5. Association between consumption of food groups (total g) and weight loss at six months 1 Food group 2 All women Adjusted for misreporting Only plausible OR (95% CI) OR (95% CI) reporters (n = 198) (n = 198) OR (95% CI) (n = 121) Foods Fruit s 2.29 (1.20 –4.38)* 2.30 (1.20 –4.42)* 3.02 (1.32 –6.88)* Vegetables, fresh, frozen, or 2.13 (1.13 –4.02)* 2.15 (1.13 –4.08)* 3.29 (1.44 –7.52)* processed Vegetable -based stews 1.90 (1.01 –3.55)* 1.85 (0.98 –3.48) 2.14 (0.98 –4.67) Dairy des serts 0.67 (0.33 –1.37) 0.62 (0.30 –1.28) 0.80 (0.35 –1.84) Dairy products 2.44 (1.29 –4.64)* 2.33 (1.22 –4.44)* 2.66 (1.19 –5.94)* Potato stew s3 2.30 (1.01 –5.23)* 2.11 (0.92 –4.87) 2.18 (0.84 –5.69) Legumes 1.91 (1.01 –3.58)* 1.71 (0.90 –3.27) 1.60 (0.72 –3.56) Fish & seafood 1.79 (0.85 –3.79) 1.82 (0.85 –3.89) 2.65 (1.03 –6.80)* Meat s 0.56 (0. 30 –1.06 ) 0.54 (0.29 –1.03) 0.68 (0.31 –1.48) Salt y snacks (corn based) 0.94 (0. 45 –1.96 ) 0.82 (0.39 –1.73) 0.84 (0.36 –1.93) Desserts (other than diary) 1.24 (0. 65 –2.38 ) 1.15 (0.59 –2.22) 1.02 (0.46 –2.25) Cereals ( other than corn) 0. 66 (0. 35 –1.22 ) 0.61 (0.32 –1.15) 0.60 (0.28 –1.29) Corn -based dishes 0.39 (0.20 –0.74)* 0.31 (0.15 –0.61)* 0.28 (0.12 –0.62)* Beverage s SSB s 0.76 (0.25 –2.33) 0.74 (0.23 –2.31) 0.73 (0.20 –2.66) Non -sugar -sweetened 1.97 (1.03 –3.75)* 2.10 (1.09 –4.06)* 1.80 (0.82 –3.95) beverages Milk 1.14 (0.58 –2.22) 1.13 (0.57 –2.23) 1.79 (0.76 –4.20) Plain water 1.90 (1.01 –3.60)* 2.05 (1.07 –3.93)* 1.72 (0.79 –3.75) 1Model adjusted by initial BMI, age, SES, and initial consumption of the food group. It was considered as weight loss if the loss was ≥ 3% of the participant’s initial weight. 2Variable in two categories, low and high consumption, according to the median. Reference category is low consumption. 3Potato stews are foods in which the principal ingredient is potato and that include other ingredients, such as sauces, vegetables, and eggs. The group does not include french fries and chips. *Significantly different (p < 0.05).

Conclusiones finales

En este ensayo clínico aleatorizado encontramos que, la intervención de promoción y dotación de agua tuvo efecto en algunas factores dietéticos como ingesta de energía a los tres meses distribución y consumo absoluto de macronutrimentos, incrementando la energía proveniente de proteínas y disminuyendo la de hidratos de carbono, especialmente los simples, ya que la cantidad de azúcar consumida también disminuyó. En lo referente a los patrones dietéticos el efecto se encontró en algunas etapas y para algunos patrones, por ejemplo el grupo de Agua+Ed tuvo mayor adherencia al patrón saludable a los 3 meses. Además, encontramos que los efectos detectados de la intervención, fueron más fuertes cuando en un análisis secundario se analizó la intervención efectiva a través de la categorización del consumo de agua. Respecto a la estimación de subreporte, encontramos una alta prevalencia de subreporte de la ingesta de energía, la cual incrementó durante el seguimiento de las participantes en el estudio. Sin embargo, el subreporte no fue diferencial entre los grupos de estudio, lo que permitió hacer comparaciones relativas entre los mismos en las diferentes variables de la calidad de la dieta. También se encontró que si bien el subreporte fue importante, el ajuste de esta condición en el análisis de la asociación entre consumo de grupos de alimentos y pérdida de peso a los 6 meses de la intervención, sólo mejoró ligeramente.

Los resultados del efecto de esta intervención son similares a los encontrados en estudios del mismo tipo, donde el reemplazo de BE por agua tiene efectos en el mejoramiento de la calidad de la dieta. Esto se ha documentado tanto en ensayos clínicos de corto plazo, como en los de largo plazo, similares en duración a este estudio.

Cabe mencionar las limitaciones de este estudio. Una de ellas es la tasa diferencias de retención entre grupos de intervención, presentando mayores porcentaje de pérdidas durant eel seguimiento en el grupo Ed. Dicho fenómeno podría deberse a que el grupo de Agua+Ed estuvo más motivado para continuar en el estudio, sin embargo, no hubo diferencias estadísticas en las características basales de la muestra total y la muestra al final del estudio. Otra limitación potencial limitación podría ser la alta prevalencia de subreporte de energía, sin embargo, no fue diferencial entre los grupos de estudio.

Entre las fortalezas del estudio destaca el hecho de que este es el primer estudio en México donde el efecto de una intervención con agua es evaluada sobre la calidad de la dieta a largo plazo. La metodología de obtención de información deitética utilizada es de alta calidad al contar con 3 recordatorios de 24 horas por etapa de estudio y participante, lo cual permite tener una mejor estimación de la dieta habitual.

La aportación que ofrece este trabajo es la documentación detallada de los cambios que se presentan en la dieta de mujeres obesas dada una intervención que promueve el consumo de agua en sustitución de bebidas azucaradas; y además que dota de agua al grupo inervenido. como la que aquí se evaluó, de promoción y dotación de agua.

Consideramos que esta documentación permite identificar los alcances de este tipo intervención en la realización de la misma en condiciones reales. El siguiente paso será evaluar algunos otras variables del estudio (por ejemplo, número de pláticas y talleres a las que acudieron las participantes), para documentar si tienen influencia o no sobre los resultados.

Estos resultados serán útiles para el planteamiento de intervenciones futuras considerando las limitaciones y aciertos que se encontraron en este estudio.

ANEXOS

APPENDIX 1. CLASSIFICATION OF FOOD GROUPS AND EXAMPLES OF INCLUDED FOODS, PAPER 1 Food group Included foods 1 Vegetables, fresh, or frozen Carrot, broccoli, chayote, celery, tomato 2 Fruit (fresh, frozen, or dried) Papaya, melon, watermelon, pineapple, apple, banana 3 Meat (pork, beef, goat) Grill Beef and pork meat Dishes with as a principal ingredient Beef and pork meat: 4 Meat stews (with vegetables) meat with sauce, fried, with vegetables 5 Poultry Grill chicken and other poultry Poultry stews (with vegetables or Dishes with as a principal ingredient poultry: chicken with 6 potatoes) sauce, fried, with vegetables 7 Processed meats, poultry, & dishes Ham, sausage, salami, dishes with ham, sausage, salami Breads and rolls, unsweetened and 8 fluor tortilla Bolillo, telera, chapata, baguette, flour tortilla 9 Ready-to-eat cereals, no sugar All brain, raisin bran, corn flakes Sandwiches, chapatas with ham, turkey, cheese, Mexican 10 Sandwiches & filled rolls "tortas" Spaguetti and every kind of pasta, noodle with vegetables Pasta, noodles, and pasta or noodle or 11 dishes fried 12 Rice and other grains Rice and other grainds 13 Rice and other grains dishes Rice and other grains dishes 14 Ready-to-eat cereals,with sugar Zucaritas, zucosos, cherios 15 Egg and egg dishes Egg and egg dishes 16 Cheese & cheese products Cheese (every kind) 17 Yogurt products Yogurt (every kind) 18 Legumes Beans, lentils Dishes with beans or lentils: fried beans, with vegetables, 19 Legume dishes chili, meat, lentils soup, lime beans soup 20 Corn-based dishes Sopes, corn quesadillas, tlacoyos, pozole, tamales 21 Corn tortilla Corn tortilla 22 Nuts, nut butters, seeds, & coconut Nut, peanut, coconut 23 vegetal oil olive oil Salty snacks from grain or starchy vegetables: chips, Salty snacks from grain or starchy doritos, 24 vegetables nachos Fries, fried plantain, and stews where the principal ingredient 25 Fried potatoes is potato 26 Cakes, pies and fried breads Cakes, pies and fried breads (buñuelos, donas, churros)

Processed cakes )gansito, chocorrol, submarinos), Processed cakes, cookies, and cereal cookies, and 27 bars cereal bars Fish & seafood, processed fish, dishes based fich & 28 Fish & seafood seafood 29 Pan dulce and quick breads Pan dulce and quick breads Sweet snack and other sweets, sweeteners, syrups, jellies, 30 Sweet snack and other sweets and toppings Dairy based dessert, calabaza en dulce, capirotada, canned 31 Dessert (with milk or no milk) fruit with sugar 32 Soups, broths and creams Sopas de pasta, verdura, caldos Stews based in vegetables: fried mixed vegetables, 33 Non-starchy vegetable stews vegetables with beans, egg, meat, sauce Stews based in tender corncob: corn with mayo, chili, 34 Starchy vegetables stews "esquites" Burritos, flour quesadilla, "sincronizada", "gringa", burguer and 35 Fast food hot dogs, Pizza & calzone 36 Avocado Guacamole, avocado Salad dressings, sauces (emulsions) and 37 dips Dressing (all style), "mole", peanut sauce, cream "Salsa verde" (green tomato, chile, onion, garlic), "salsa 38 Sauces and condiments roja" (tomato, garlic, chile, onion)

APPENDIX2. DESCRIPTION OF OOD GROUPS, PAPER 2 Food group Description s and examples Foods Fruit s Fresh and dried fruits: pineapples, melons, watermelons, papayas, strawberries Vegetables, fresh, frozen, or Vegetables (excluding mixed dishes that include nonvegetable ingredients): processed salads, carrots, broccoli, celery, onions, tomatoes Vegetable -based stews Dishes in which the principal ingredients are vegetables and that include other ingredients: vegetables with eggs, beans, meats, and fried vegetables Dairy des serts Flan and natillas (milk candy), arroz con leche (rice, sugar, and milk) Dairy products Cheese: requeson , fresco , Oaxaca, cheddar, goat Yogurt (excluding beverages): natural and with fruit Potato stews Stews in which the principal ingredient is potatoes and that include other ingredients, such as sauces, vegetables, and eggs (french fries and chips excluded): potatoes with eggs, onions, and chilies; Spanish tortillas Legumes Beans and lentils: boiled and fried beans, lentil soup Fish & seafood Fish and seafood cooked in all methods, stews Meat s Beef , pork , and poultry Salt y snacks (corn based) Chips and corn -based snacks: nachos, Doritos Desserts (other than dairy) Cakes, cookies, and non -milk -based desserts: carrot cake, chocolate cake, chocolate fondue, calazaba en tacha (pumpkins with sugar), plantains with condensed milk Cereals ( ot her than corn) Breads, sandwiches, rice, cereals ready to eat, pastas Corn -based dishes Mexican dishes with several ingredients, such as tortillas with meats, rice, beans, or eggs; fried tortillas with vegetables; corn dough dishes with sauces, meats, and animal fat or oil: tamales, corn quesadillas, tlacoyos , sopes Beverage s SSB s Sugar -sweetened beverages: aguas frescas (water, sugar, and fruit), commercial fruit/vegetable drinks, atoles (water or milk, cereal, and sugar), commercial flavored waters, sweetened coffee/tea, sodas, 100% fruit and vegetable juices, soy beverages, sport/energy beverages, alcoholic beverages, milk- and fruit-based beverages Non -sugar -sweetened Low - or noncaloric beverages, including plain water, diet sodas, coffee, tea, beverages milk Milk Non -sugar -sweetened milk and yogurt drinks: lactose free, li ght, whole, evaporated Plain water Plain water

Diagrama de flujo del tamaño de muestra