RESUMO
A systematic review and meta-analysis was conducted to evaluate the relative effectiveness of different dietary macronutrient patterns on changes in resting energy expenditure (REE) in relation to weight loss, categorized as minimal (<5%) and moderate to high (>5%). Changes in REE were assessed using a DerSimonian and Laird random-effects meta-analysis. A diet lower in carbohydrates (CHO) or higher in fat and protein was associated with smaller reductions in REE, with these trends being more pronounced among participants who experienced moderate to high weight loss. Adjusted meta-regression analysis indicated that, within the participants who experienced moderate to high weight loss, each 1% increase in CHO intake was associated with a reduction of 2.30 kcal/day in REE (95% CI: -4.11 to -0.47, p = 0.013). In contrast, a 1% increase in protein and fat intake was correlated with an increase in REE by 3.00 (95% confidence interval [CI] [1.02, 5.07], p = 0.003) and 0.5 (95% CI [-2.43, 3.41], p = 0.740) kcal/day, respectively. No significant associations were found among participants who experienced minimal weight loss. These findings indicate that, under a caloric deficit, the impact of dietary macronutrient composition on REE may vary depending on the degree of weight loss and individual metabolic responses.
RESUMO
OBJECTIVE: Mobile nutrition applications (apps) provide a simple way for individuals to record their diet, but the validity and inherent errors need to be carefully evaluated. The aim of this study was to assess the validity and clarify the sources of measurement errors of image-assisted mobile nutrition apps. METHODS: This was a cross-sectional study with 98 students recruited from School of Nutrition and Health Sciences, Taipei Medical University. A 3-d nutrient intake record by Formosa Food and Nutrient Recording App (FoodApp) was compared with a 24-h dietary recall (24-HDR). A two-stage data modification process, manual data cleaning, and reanalyzing of prepackaged foods were employed to address inherent errors. Nutrient intake levels obtained by the two methods were compared with the recommended daily intake (DRI), Taiwan. Paired t test, Spearman's correlation coefficients, and Bland-Altman plots were used to assess agreement between the FoodApp and 24-HDR. RESULTS: Manual data cleaning identified 166 food coding errors (12%; stage 1), and 426 food codes with missing micronutrients (32%) were reanalyzed (stage 2). Positive linear trends were observed for total energy and micronutrient intake (all Ptrend < 0.05) after the two stages of data modification, but not for dietary fat, carbohydrates, or vitamin D. There were no statistical differences in mean energy and macronutrient intake between the FoodApp and 24-HDR, and this agreement was confirmed by Bland-Altman plots. Spearman's correlation analyses showed strong to moderate correlations (r = 0.834 â¼ 0.386) between the two methods. Participants' nutrient intake tended to be lower than the DRI, but no differences in proportions of adequacy/inadequacy for DRI values were observed between the two methods. CONCLUSIONS: Mitigating errors significantly improved the accuracy of the Formosa FoodApp, indicating its validity and reliability as a self-reporting mobile-based dietary assessment tool. Dietitians and health professionals should be mindful of potential errors associated with self-reporting nutrition apps, and manual data cleaning is vital to obtain reliable nutrient intake data.
