Objective: To identify dietary factors related to the risk of gaining weight outside recommendations for pregnancy weight gain and birth outcome. Design: An observational study with free-living conditions. Subjects: Four hundred and ninety five healthy pregnant Icelandic women. Methods: The dietary intake of the women was estimated with a semiquantitative food frequency questionnaire covering food intake together with lifestyle factors for the previous 3 months. Questionnaires were filled out at between 11 and 15 weeks and between 34 and 37 weeks gestation. Comparison of birth outcome between the three weight gain groups was made with ANOVA and Bonferroni post hoc tests. Dietary factors related to at least optimal and excessive weight gain during pregnancy were represented with logistic regression controlling for potential confounding. Results: Of the women, 26% gained suboptimal and 34% excessive weight during pregnancy. Women in late pregnancy with at least optimal, compared with women with suboptimal, weight gain were eating more (OR ¼ 3.32, confidence interval (CI) ¼ 1.81-6.09, Po0.001) and drinking more milk (OR ¼ 3.10, CI ¼ 1.57-6.13, P ¼ 0.001). The same dietary factors were related to excessive, compared with optimal, weight gain. Furthermore, eating more sweets early in pregnancy increased the risk of gaining excessive weight (OR ¼ 2.52, CI ¼ 1.10-5.77, P ¼ 0.029). Women with a body mass index of 25.0-29.9 kg/m 2 before pregnancy were most likely to gain excessive weight (OR ¼ 7.37, CI 4.13-13.14, Po0.001). Women gaining suboptimal weight gave birth to lighter children (Po0.001) and had shorter gestation (P ¼ 0.008) than women gaining optimal or excessive weight. Conclusion: Women who are overweight before pregnancy should get special attention regarding lifestyle modifications affecting consequent weight gain during pregnancy. They are most likely to gain excessive weight and therefore most likely to suffer pregnancy and delivery complications and struggle with increasing overweight and obesity after giving birth.

This study investigated the role of parental motivation (importance, confidence and readiness) for predicting dropout and outcome from family‐based behavioral treatment for childhood obesity. Parent and child demographics, adherence to treatment, and weight loss parameters were also explored as potential predictors. Eighty‐four obese children (BMI‐standard deviation scores (SDS) >2.14) and a participating parent with each child started treatment consisting of 12 weeks of group and individual treatment sessions (24 sessions total) delivered over a period of 18 weeks. Sixty‐one families (73%) completed treatment and attended follow‐up at 1 year after treatment. Child session attendance and completion of self‐monitoring records served as measures of adherence. In regression analyses, parent reports (pretreatment) of confidence for doing well in treatment was the strongest predictor of treatment completion (P = 0.003) as well as early treatment response (weight loss at week 5) (P = 0.003). This variable remained a significant predictor of child weight loss at post‐treatment (P = 0.014), but was not associated with child outcome at 1‐year follow‐up (P > 0.05). The only significant predictor of child weight loss at that point was child baseline weight (P = 0.001). However, pretreatment parent ratings of importance of and readiness for treatment did not predict dropout or weight loss at any point. The results underscore the importance of addressing parental motivation, specifically parental confidence for changing lifestyle related behaviors, early in the treatment process. Doing so may reduce treatment dropout and enhance treatment outcome.

Background/Aims: Women’s diet can be especially difficult to assess, as women tend to underreport their intakes more often than men and are more likely to do so if they think they are overweight or obese. The aim was to compare two methods to assess women’s diet and how well they associate with biomarkers. The influence and frequency of underreporting was also investigated. Methods: Diet of 53 women was assessed by two 24-hour recalls and a food frequency questionnaire (FFQ). Blood was analyzed for retinol, β-carotene, vitamin C and serum ferritin, and 24-hour urine for nitrogen, potassium and sodium. Underreporting was evaluated with nitrogen excretion vs. intake, and energy intake vs. basal metabolic rate. Results: Energy percent (E%) from macronutrients was similar from FFQ and 24-hour recalls, but total intake was higher from 24-hour recalls (9,516 ± 2,080 vs. 8,183 ± 2,893 kJ, p < 0.01). Intakes of vitamin C and potassium from both methods correlated with their respective biomarkers (r = 0.316–0.393). Underreporters had higher body mass index (BMI) than others (27.7 ± 5.5 vs. 23.8 ± 3.7 kg/m², p < 0.05). They reported lower E% total fat (32 ± 5 vs. 38 ± 6 E%, p < 0.01) and higher E% carbohydrate (49 ± 4 vs. 45 ± 7 E%, p < 0.05). Correlation between intake and biomarkers increased after exclusion of underreporters. Conclusion: For women, FFQ and 24-hour recalls give similar E% and most nutrients correlate, but FFQ gives lower intake. Underreporters have higher BMI and diminish the correlation between calculated intake and biomarkers. This has to be considered when intake data are associated with weight management, disease and lifestyle factors.