The Arabic Version of the Adult Eating Behavior Questionnaire among Saudi Population: Translation and Validation.

Inherited individual differences in eating behaviors known as "appetitive traits" can be measured using the Adult Eating Behavior Questionnaire (AEBQ). The AEBQ can be used to assess individuals that require intervention regarding their weight, eating habits, and for the identification of eating disorders. Arabic eating behavior assessment tools are few. This study, therefore, aimed to translate and validate the AEBQ in Arabic language (AEBQ-Ar) and to confirm the factor structure while assessing the internal consistency of all subscales. Participants completed the AEBQ-Ar and reported their sociodemographic data online. Exploratory factor analysis (EFA) was used and internal reliability was assessed using Cronbach's α. Correlations between AEBQ-Ar subscales and body mass index (BMI) were done using Pearson's correlation. A sample of 596 adults, mean age of 35.61 ± 12.85 years, was recruited from Saudi Arabia. The 6-factor structure was the best model, excluding emotional under- eating subscale and merging enjoyment of food and food responsiveness subscales. Internal consistency was acceptable for all subscales (Cronbach's α = 0.89-0.66). Emotional over- eating was positively associated with BMI, and slowness in eating was negatively associated with BMI. The AEBQ-Ar with 6-subscales appears to be a valid and reliable psychometric questionnaire to assess appetitive traits in Arabic speakers.

Nutritional Biomarkers and Factors Correlated with Poor Sleep Status among Young Females: A Case-Control Study.

Poor sleep status is associated with several health problems. Nutritional biomarkers and factors related to poor sleep are understudied. This study aimed to identify nutrition biomarkers and factors related to sleep status in healthy young Saudi females. The study included 92 normal-weight and obese Saudi females aged 19−25. Fasting blood glucose, insulin, and lipid profiles were measured. Insulin resistance was calculated on the basis of the homeostasis model assessment of insulin resistance (HOMA-IR) method. Anthropometric, stress, physical activity, and dietary data were collected. Data on the polyphenol content in foods were retrieved from the Phenol-Explorer database. The sleep status was assessed using the Pittsburgh sleep quality index (PSQI). Associations between variables were assessed using the multiple logistic regression model. Around 76% of the participants had poor sleep status (PSQI > 5). Multiple logistic regression reported high polyphenol intake as a protective factor against poor sleep (OR 0.24; 95% CI 0.07−0.83; p = 0.03) and HOMA-IR as an independent risk for poor sleep (OR 4.97; 95% CI 1.11−22.31; p = 0.04). Other nutritional biomarkers and factors, such as BMI, lipid profile, and vitamins, revealed a trend but were not significant. In conclusion, poor sleep status is associated with insulin resistance and low polyphenol intake among women of reproductive age.