Type and timing of ultra-processed foods consumption and its association with dietary intake and physical activity in women with obesity living in poverty.

BACKGROUND: The present study aimed to investigate the type and timing of ultra-processed foods (UPF) consumption and its association with dietary intake (DI) and physical activity (PA) in women with obesity living in poverty. METHODS: A cross-sectional study was employed. Obesity was defined by at least two criteria (body mass index, waist circumference or % fat mass). Poverty was defined as the three lowest classes of the Brazilian Economic Classification Criterion. PA was measured with triaxial accelerometers and DI was assessed with three 24-h dietary recalls. Foods were categorised according to the NOVA classification, with UPF classified into five subgroups, as well as the timing of consumption into six meals. RESULTS: In total, 56 adult women were included. Overall energy intake was 1653.21 (503.22) kcal/day. UPF intake was 21.62% (11.94%) kcal/day, being higher at breakfast (4.91% kcal/day), afternoon snack (5.39% kcal/day) and dinner (5.01% kcal/day). Only UPF subgroup 4 (sandwich biscuits, sweets, or treats) showed a positive association with energy intake (ß = 54.40 [27.6, 81.10] kcal/day) and a negative association with protein intake (ß = -0.31% [-0.48%, -0.14%] kcal/day). UPF consumption in morning (ß = -0.41% [-0.79%, -0.02%] kcal/day) and afternoon (ß = -0.18% [-0.33%, -0.04%] kcal/day) snacks was associated with lower protein intake. Furthermore, lunchtime UPF consumption was positively associated with walking time (ß = 0.16% [0.02%; 0.30%]) and steps/hour (ß = 8.72 [1.50; 15.94] steps/h). CONCLUSIONS: Women with obesity living in poverty consume more UPF during breakfast, afternoon snack and dinner. Physical activity is positively associated with UPF consumption at lunch. UPF, such as sandwich biscuits, sweets or treats, contribute to increasing energy intake and reducing protein intake.

Use of the activPAL® triaxial accelerometer to estimate total energy expenditure in low-income women: differences between body mass index classifications

ABSTRACT Objective: This study aimed to assess the agreement between the total energy expenditure (TEE) estimated by the activPAL® triaxial accelerometers (ACC) and the TEE measured by the doubly labeled water method (DLW), as well as to assess if these values differ between the classifications of body mass index (BMI). Materials and methods: This is a cross-sectional study. Low-income adult women (19-45y) with BMI ≥ 18.5 kg/m2 were included. Accelerometry data (activPAL®) were collected over 7 consecutive days, which were used to calculate TEE-ACC and compared with DLW data. The Bland-Altman method, concordance correlation coefficient and root mean square error were used to assess agreement between methods. Results: The sample consisted of 55 women with a mean age of 31 ± 5 years. The agreement between TEE-ACC and TEE-DLW showed a bias of -142.5 kcal (-7.1%). Among the BMI classifications, participants with normal weight show a bias of -417.1 kcal (-21.0%), participants with overweight, -87.5 kcal (-3.9%) and participants with obesity, 97.5 kcal (4.3%). Furthermore, the bias between the methods showed a significant and positive correlation with the body weight (r = 0.49; p < 0.01). Conclusion: The TEE-ACC estimates from activPAL® were reasonably accurate when compared to the TEE-DLW, especially in women with overweight and obesity, being much less accurate in individuals with normal weight.

Estimates of resting energy expenditure and total energy expenditure using predictive equations in adults with overweight and obesity: a systematic review with meta-analysis.

CONTEXT: Energy expenditure predictive equations can generate inaccurate estimates for overweight or obese individuals. OBJECTIVE: The objective of this review was to determine which predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) have the lowest bias and the highest precision in adults with overweight and obesity. DATA SOURCES: Searches were performed in January 2022 in MEDLINE, Web of Science, Scopus, CENTRAL, and the gray literature databases. DATA EXTRACTION: Meta-analyses were performed with equations included in more than 1 study. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The Egger test was performed to assess potential publication biases, and metaregressions were conducted to explore the heterogeneity. Findings were presented separated by participants' body mass index classification (overweight and obesity). DATA ANALYSIS: Sixty-one studies were included. The FAO/WHO/UNU (1985) equation, which uses only body weight in its formula, showed the lowest bias in estimating REE (mean difference [MD] = 8.97 kcal; 95% CI = -26.99; 44.94). In the subgroup analysis for individuals with obesity, the Lazzer (2007) equation showed the lowest bias (MD = 4.70 kcal; 95% CI = -95.45; 104.86). The Harris-Benedict equation (1919) showed the highest precision values for individuals with overweight (60.65%) and for individuals with obesity (62.54%). Equations with body composition data showed the highest biases. The equation proposed by the Institute of Medicine (2005) showed the lowest bias (MD = -2.52 kcal; 95% CI = -125.94; 120.90) in estimating the TEE. Most analyses showed high heterogeneity (I2 > 90%). There was no evidence of publication bias. CONCLUSION: For individuals with overweight, the FAO/WHO/UNU (1985) and the Harris-Benedict equations (1919) showed the lowest bias and the highest precision in predicting the REE, respectively. For individuals with obesity, the Harris-Benedict equation (1919) showed the highest precision and the Lazzer equation (2007) showed the lowest bias. More studies are needed on predictive equations to estimate the TEE. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42021262969.