Adult Weight Management

AWM: Eating Frequency and Patterns (2013)


Bes-Rastrollo M, Sanchez-Villegas A, Basterra-Gortari FJ, Nunez-Cordoba JM, Toledo E, Serrano-Martinez M. Prospective study of self-reported usual snacking and weight gain in a Mediterranean cohort: The SUN project. Clin Nutr 2010; 29 (3): 323-330.

PubMed ID: 19748710
Study Design:
Prospective Cohort Study
B - Click here for explanation of classification scheme.
Quality Rating:
Positive POSITIVE: See Quality Criteria Checklist below.
Research Purpose:

To assess the relationship between self-reported snacking and weight gain and obesity in a Spanish middle-aged free-living cohort of university graduates.

Inclusion Criteria:
  • Used data from Seguimiento Universidad de Navarra-University of Navarra (SUN) project follow-up.
  • Participants who had completed a baseline assessment by September 2005 were included in this study.
Exclusion Criteria:
Participants excluded:
  • Did not answer any follow-up questionnaires
  • Reported extremely low values (less than 500kcal per day for women, less than 800kcal per day for men) or extremely high values (greater than 3,500kcal per day for women, greater than 4,000kcal per day for men) for total energy intake
  • Followed a special diet
  • Were pregnant at baseline or during follow-up
  • Were missing information about snacking or covariates of interest in questionnaires.
Description of Study Protocol:


  • Recruitment began in December 1999 for SUN project
  • Participants who had completed a baseline assessment by September 2005 were included in this study.


  • Prospective longitudinal cohort study
  • Baseline data collection included FFQ, medical history, health habits, lifestyle and sociodemographic variables (including smoking, physical activity)
  • Self-reported weight was recorded at baseline and every two years during follow-up. 

Blinding used 

Not used

Statistical Analysis

  • Least-squares regression models used to evaluate association between snacking and weight change per year
  • Analyses of covariance used to adjust the average weight change per year for potential confounders
  • Non-conditional logistic regression models assessed relationship between snacking and increment of weight of at least 3kg per year and 5kg per year
  • Odds ratio and 95% CI calculated using non-snacker participants and BMI categories
  • Multivariate Cox's regression analyses used for the association between snacking and risk of incident obesity (BMI ≥30); excluded obese participants at baseline; converted hazard ratios to rate advancement period
  • Multi-collinearity tested using tolerance and its reciprocal the variance inflation factor
  • Adjusted for age, sex, physical activity, smoking, TV viewing, sitting hours, baseline BMI, fast-food consumption, alcohol, fiber, and total energy intake; in additional multivariate models controlled for physical activity, smoking habits, consumption of meats and fruits/vegetables.
  • Sensitivity analyses conducted excluding participants with chronic disease at baseline or during follow-up
  • Analyses performed using SPSS version 15.0; P values significant at P<0.05.


Data Collection Summary:

Timing of Measurements

Data were collected at baseline and every two years after baseline

Dependent Variables

  • Weight (self-report), weight change (calculated), and BMI (calculated)
  • Risk of incident obesity (using multivariate Cox's analyses after excluding obese participants at baseline).

Independent Variables

  • Self-reported usual snacking habits and dietary habits assessed through questionnaire and included the following:
    • Nutrient intake (total energy, carbohydrate, protein, fat, saturated fat, monounsaturated fat, polyunsaturated fat, glycemic load, alcohol intake, fiber intake)
    • Food intake (fruits and vegetables, nuts, biscuits, non-handmade pastries and bakery, chocolates and cakes, sugar-sweetened beverages, fruit juices, alcoholic beverages, fast-food, processed meat, chip potatoes)
  • Usual snackers was defined as those participants who answered yes to the following question: "Do you usually eat between meals (snacking)?"

Control Variables

  • Age
  • Sex
  • Physical activity
  • Smoking
  • TV viewing
  • Sitting hours
  • Baseline BMI
  • Fast-food consumption
  • Alcohol, fiber, total energy intake, consumption of meats and fruits/vegetables.
Description of Actual Data Sample:
  • Initial N: 20,095 in SUN cohort
  • Attrition (final N): 10,162 university graduates included in the analysis
  • Age: Mean age 39 years (SD: 12)
  • Ethnicity: Not reported exactly but predominantly Spanish
  • Other Relevant Demographics and Anthropometrics:
    • Baseline for overall sample:
      • 54% female
      • Average age 39 years
      • Average weight 69kg
      • Average BMI 23.8
      • Smokers 23%
      • Ex-smokers 29%
    • Baseline for non-snackers vs. usual snackers:
  Non-snackers  Usual snackers  P value
N 6,704  3,458   
 Women (percent) 50  62  <0.001  
 Age (years) 40 (0.14) 37  <0.001  
 Weight (kg) 68 (0.03) 71  <0.001  
 BMI (kg/m2) 23.5 (0.04) 24.3  <0.001  
 TV viewing (hours per day) 5.4 (0.04) 5.2  0.003 
 Sitting (hours per day) 6 (0.02) 0.191 
 Physical activity (METs-hours per week) 25 (0.26) 24  0.111 
Smoking     <0.001  
 Smokers 23%  23%   
 Ex-smokers 31%  23%   
  • Location: Spain.
Summary of Results:

Key Findings

  • Usual snackers had a higher total energy intake, total fat intake, higher glycemic load, and were more likely to consume nuts, non-handmade bakery foods, chocolates, sugar-sweetened beverages, fast-food and processed meat
  • Usual snackers had a statistically significant higher mean weight gain per year during follow-up in multivariate analyses and were at higher risk of gaining substantial weight during follow-up
  • After adjusting for potential confounders, self-reported between-meal snacking was significantly associated with a higher risk of substantial weight gain (more than 3kg per year; P<0.001; more than 5kg per year, P<0.001, >10% of baseline weight, P<0.001)
  • The habit of self-reported between-meal snacking was significantly associated with a higher risk of becoming obese during follow-up
  • Among participants with a baseline BMI lower than 30kg/m2, there were 258 new cases of obesity
  • Usual snackers presented an adjusted 69% higher risk of becoming obese during follow-up (hazard ratio=1.69, 95% confidence interval: 1.30-2.20)
  • Controlling for chronic disease did not change results significantly.

Baseline nutrients and food composition by snacking category

  Non-snackers (n=6,704) Usual snackers (n=3,458) P value
Total energy intake (kcal per day) 2,364 (7) 2,455 (11) <0.001
Carbohydrate intake
(percent of energy)
43.7 (0.09) 43.1 (0.13) 0.001
Protein intake (percent of energy 17.9 (0.04) 17.7 (0.06) 0.001
Fat intake (percent of energy) 36.2 (0.08) 37.0 (0.11) <0.001
Glycemic load 182 (0.82) 185 (1.17) <0.001
Nuts (grams per day) 6.9 (0.1) 7.6 (0.2) 0.003
Pastries and bakery (non-handmade (grams per day) 14.4 (0.3) 16.6 (0.4) <0.001
Chocolates/cakes (grams per day) 16.4 (0.2) 22.2 (0.4) <0.001
Sugar-sweetened beverages (grams per day) 56.4 (1.4) 69.2 (2.2) <0.001
Fast-food* (grams per day) 20.3 (0.2) 21.8 (0.3) <0.001
Processed meat (grams per day) 42.5 (0.4) 47.3 (0.5) <0.001

Alcohol intake, fiber intake, fruits and vegetables, biscuits, fruit juices, alcoholic beverages, and chip potatoes were not statistically significant.

*Fast-food was the sum of hamburgers, sausages, and pizza consumption.


Average (95% confidence interval) weight change (grams per year) during follow-up according to snacking
  Non-snackers Usual snackers P value
Crude weight change
(grams per year)
119 (94-142) 184 (147-222) 0.003
Age- and sex-adjusted weight change (grams per year) 126
169 (133-204) 0.06
Multivariatea adjusted weight change (grams per year) 131 (94-168) 188 (143-233) 0.01
Multivariateb adjusted weight change (grams per year) 136 (62-208) 196 (121-271) 0.006

Odds Ratios (Ors) and 95% confidence intervals (CI) of substantial weight gain (≥3kg per year) during follow-up according to snacking.


Non-snackers (n=6,704)

Usual snackers 

Weight gain (≥3kg per year) 

Incident cases (percent)  70 (1%)   70 (2%) 
Crude OR (95% CI)  1 (Ref.)   1.96
Age- and sex-adjusted OR (95% CI)  1 (Ref.)    1.88
Multivariatea adjusted OR 
(95% CI) 
1 (Ref.)    1.66
Multivariateb adjusted OR
(95% CI) 
1 (Ref.)    1.50 

Hazard Ratios (HRs) and 95% CI of becoming obese during follow-up in participants not obese at baseline (n=9,709)

  Non-snackers  Usual snackers  RAP (years) 
Person-years  30,197  15,009   
Incident cases (percent)  145 (2.2%)  113 (3.5%)  
Crude HR (95% CI)  1 (Ref.)  1.58 
Age- and sex-adjusted HR
(95% CI) 
1 (Ref.)   2.01 
Multivariatea adjusted HR
(95% CI) 
1 (Ref.)    1.69 
Multivariateb adjusted HR (95% CI)  1 (Ref.)    1.63 

a Adjusted for age (years), sex, physical activity (METs-hours per week, continuous), smoking (non-smokers, smokers, ex-smokers, missing), TV viewing (hours per day, continuous), sitting (hours per day, continuous), total energy intake (kcal per day, continuous), baseline body mass index (tertiles), fast-food consumption (tertiles), sugar-sweetened soft drinks consumption (tertiles), alcohol intake (tertiles), total fiber intake (tertiles).

bAdjusted for the variables of multivariate model 1 plus changes in physical activity (no change, increase, decrease, not known), in smoking habits (non-smokers, ex-smokers, smokers, quitters, starters, missing), and in the consumption of meat and fruits/vegetables (no change, increase, decrease, not known) during follow-up.


Author Conclusion:

In conclusion, between-meal snacking has been associated with a subsequent higher body weight and with an increase in the risk of becoming obese in a free-living cohort of healthy middle-aged Spanish university graduates. Thus, this dietary trend of unstructured eating episodes with food of low nutritional value is a potential risk factor to be discouraged in the promotion of healthy lifestyles to tackle the obesity epidemic. At least, the promotion of alternative nutritious snacks (e.g. nuts, fresh fruit and vegetables) should be considered from a Public Health perspective.

Funding Source:
Government: Spanish Ministry of Health, Navarra Regional Government
University/Hospital: University of Navarra
Reviewer Comments:

Authors note the following limitations:

  • Cultural differences between countries with regards to the definition of what constitutes a usual snack compared with a main meal can contribute to the results
  • Snacking based on participant self-report
  • Potential selection bias with difference in eligible population (n=15,742) and participants included in the analysis (n=10,162), however, the cohort is large and homogeneous in terms of demographic characteristics and baseline exposure.
Quality Criteria Checklist: Primary Research
Relevance Questions
  1. Would implementing the studied intervention or procedure (if found successful) result in improved outcomes for the patients/clients/population group? (Not Applicable for some epidemiological studies) N/A
  2. Did the authors study an outcome (dependent variable) or topic that the patients/clients/population group would care about? Yes
  3. Is the focus of the intervention or procedure (independent variable) or topic of study a common issue of concern to dieteticspractice? Yes
  4. Is the intervention or procedure feasible? (NA for some epidemiological studies) N/A
Validity Questions
1. Was the research question clearly stated? Yes
  1.1. Was (were) the specific intervention(s) or procedure(s) [independent variable(s)] identified? Yes
  1.2. Was (were) the outcome(s) [dependent variable(s)] clearly indicated? Yes
  1.3. Were the target population and setting specified? Yes
2. Was the selection of study subjects/patients free from bias? Yes
  2.1. Were inclusion/exclusion criteria specified (e.g., risk, point in disease progression, diagnostic or prognosis criteria), and with sufficient detail and without omitting criteria critical to the study? Yes
  2.2. Were criteria applied equally to all study groups? Yes
  2.3. Were health, demographics, and other characteristics of subjects described? Yes
  2.4. Were the subjects/patients a representative sample of the relevant population? Yes
3. Were study groups comparable? Yes
  3.1. Was the method of assigning subjects/patients to groups described and unbiased? (Method of randomization identified if RCT) N/A
  3.2. Were distribution of disease status, prognostic factors, and other factors (e.g., demographics) similar across study groups at baseline? Yes
  3.3. Were concurrent controls or comparisons used? (Concurrent preferred over historical control or comparison groups.) Yes
  3.4. If cohort study or cross-sectional study, were groups comparable on important confounding factors and/or were preexisting differences accounted for by using appropriate adjustments in statistical analysis? Yes
  3.5. If case control study, were potential confounding factors comparable for cases and controls? (If case series or trial with subjects serving as own control, this criterion is not applicable.) N/A
  3.6. If diagnostic test, was there an independent blind comparison with an appropriate reference standard (e.g., "gold standard")? N/A
4. Was method of handling withdrawals described? Yes
  4.1. Were follow-up methods described and the same for all groups? Yes
  4.2. Was the number, characteristics of withdrawals (i.e., dropouts, lost to follow up, attrition rate) and/or response rate (cross-sectional studies) described for each group? (Follow up goal for a strong study is 80%.) Yes
  4.3. Were all enrolled subjects/patients (in the original sample) accounted for? Yes
  4.4. Were reasons for withdrawals similar across groups? Yes
  4.5. If diagnostic test, was decision to perform reference test not dependent on results of test under study? Yes
5. Was blinding used to prevent introduction of bias? No
  5.1. In intervention study, were subjects, clinicians/practitioners, and investigators blinded to treatment group, as appropriate? N/A
  5.2. Were data collectors blinded for outcomes assessment? (If outcome is measured using an objective test, such as a lab value, this criterion is assumed to be met.) No
  5.3. In cohort study or cross-sectional study, were measurements of outcomes and risk factors blinded? No
  5.4. In case control study, was case definition explicit and case ascertainment not influenced by exposure status? N/A
  5.5. In diagnostic study, were test results blinded to patient history and other test results? N/A
6. Were intervention/therapeutic regimens/exposure factor or procedure and any comparison(s) described in detail? Were interveningfactors described? Yes
  6.1. In RCT or other intervention trial, were protocols described for all regimens studied? N/A
  6.2. In observational study, were interventions, study settings, and clinicians/provider described? Yes
  6.3. Was the intensity and duration of the intervention or exposure factor sufficient to produce a meaningful effect? N/A
  6.4. Was the amount of exposure and, if relevant, subject/patient compliance measured? N/A
  6.5. Were co-interventions (e.g., ancillary treatments, other therapies) described? N/A
  6.6. Were extra or unplanned treatments described? N/A
  6.7. Was the information for 6.4, 6.5, and 6.6 assessed the same way for all groups? N/A
  6.8. In diagnostic study, were details of test administration and replication sufficient? N/A
7. Were outcomes clearly defined and the measurements valid and reliable? Yes
  7.1. Were primary and secondary endpoints described and relevant to the question? Yes
  7.2. Were nutrition measures appropriate to question and outcomes of concern? Yes
  7.3. Was the period of follow-up long enough for important outcome(s) to occur? Yes
  7.4. Were the observations and measurements based on standard, valid, and reliable data collection instruments/tests/procedures? ???
  7.5. Was the measurement of effect at an appropriate level of precision? Yes
  7.6. Were other factors accounted for (measured) that could affect outcomes? Yes
  7.7. Were the measurements conducted consistently across groups? Yes
8. Was the statistical analysis appropriate for the study design and type of outcome indicators? Yes
  8.1. Were statistical analyses adequately described and the results reported appropriately? Yes
  8.2. Were correct statistical tests used and assumptions of test not violated? Yes
  8.3. Were statistics reported with levels of significance and/or confidence intervals? Yes
  8.4. Was "intent to treat" analysis of outcomes done (and as appropriate, was there an analysis of outcomes for those maximally exposed or a dose-response analysis)? N/A
  8.5. Were adequate adjustments made for effects of confounding factors that might have affected the outcomes (e.g., multivariate analyses)? Yes
  8.6. Was clinical significance as well as statistical significance reported? Yes
  8.7. If negative findings, was a power calculation reported to address type 2 error? N/A
9. Are conclusions supported by results with biases and limitations taken into consideration? Yes
  9.1. Is there a discussion of findings? Yes
  9.2. Are biases and study limitations identified and discussed? Yes
10. Is bias due to study's funding or sponsorship unlikely? Yes
  10.1. Were sources of funding and investigators' affiliations described? Yes
  10.2. Was the study free from apparent conflict of interest? Yes