Study population
This was a cross-sectional study of 201 healthy undergraduate female students of Taibah University, Madinah, Saudi Arabia. The sample size of this study was determined based on a standard deviation (SD) of 1.4 [22], a total width of 0.5, a confidence level of 95%, and a standardized width of the confidence interval of 0.36. Data were collected between January and March 2019. Using a random convenience sampling method, students were recruited from different colleges in Madinah (Medicine, Dentistry, Pharmacy, Nursing, Computer Science, Business Administration, and Family Sciences). Students with medical conditions that may affect weight and food intake, pregnant students, and students who were following any special diet were excluded.
Participants were invited to participate in the study after explaining the objectives of the study. Contact information were collected to schedule a face-to-face interview for data collection. On the day of the interview, all participants were asked to sign a consent form and to answer questions asked by the researcher to complete the study questionnaire, which was consisting of 20 items. The questionnaire collected data regarding demographics (age, major, semester level, parents-education, marital-status, number of children and income), medical history, anthropometrics (weight, height, waist circumference (WC)), dietary intake (via 24-hr dietary recall). Also, the questionnaire included questions regarding the knowledge of what is added sugar; if the participants believe they understand what added sugar is; if the participants know what are the negative health consequences resulting from consuming extensive amount of added sugar; if participants are making an effort to reduce added sugar.
Anthropometric and dietary data
Anthropometric measurements were assessed using a standardized procedure. Height was measured in centimeter by a measuring tape on straight wall shoeless, while weight was measured using a digital scale (Beurer). Body mass index (BMI) was calculated as weight in kilograms divided by squared height in meters [23]. Each student’s BMI was classified based on the WHO classification into four categories: underweight (≤ 18.5 kg/m2), healthy weight (18.5 and 24.9 kg/m2), overweight (25.0 and 29.9 kg/m2), and obese (≥ 30 kg/m2) [24]. Waist circumference was measured in centimeters at the midpoint between the last costal margin and the iliac crest by using a flexible measuring tape to assess abdominal obesity. Height and WC measurements were adjusted to the nearest 0.5 cm. The WHO cutoff of 88 cm for female was used to assess WC [25].
Single 24-hour dietary recall of a weekday was collected from the total sample. Additional 24-hour dietary recall of a weekend day was collected from a subsample of 25% (48 participants) to adjust for within person variations in order to estimate the usual intake of the students included in this study. Participants were encouraged to remember the foods that were consumed during the last 24 hours. Real life-size replicas food models were used to assess students to estimate the proper quantities of the food consumed. Dietary data were entered in Nutritics software® (version 5.09), Dublin. When added sugar content of foods were not available in Nutritics, the number was determined from the nutrition fact label and entered into the software. Ethical approval of this study was obtained from the Ethical Committee of the College of Applied Medical Sciences at Taibah University (CLN201811).
Statistical analysis
Descriptive data were presented as means±standard deviations (SDs), and frequency (percent). The added sugar intake categorized into two groups based on the recommendation of <5% or >5% of total energy intake. Fisher’s exact test was performed to test the association between the groups of added sugar intake and all categorical variables included in this study. Independent t-test was performed to test the difference in mean BMI, WC, and nutrient intake across the two added sugar groups. Variables with skewed distribution, including protein, added sugar, saturated fat, trans fat, calcium, iron, zinc, vitamin D, and B12, were normalized by taking the log or square root function then t-test was performed. In cases where distributions were not normalized using the log or square root function, non-parametric tests were used to test for differences in means across the different groups. A multiple linear regression model was performed to determine the association between added sugar intake and all of the macro- (energy, protein, fat, saturated fat, trans fat, carbohydrates and fiber) and micro-nutrients intake (calcium, iron, zinc, vitamin B12, sodium, potassium and vitamin D). All regression models were adjusted for energy intake. Statistical tests were performed using SAS® software version 9.4 (2013, SAS Institute Inc., Cary, NC, USA). All tests were two-tailed with a significant level of 95%.