Velarie Yaa Ansu Baidoo

Background: Chrononutrition emphasizes alignment of meal timing with circadian rhythms, as misalignment of eating and sleep behaviors may adversely affect health. Aim: We examined the relationship between timing of dietary macronutrients and sleep quality. Methods: This post hoc analysis used data from the Sistas Inspiring Sistas through Activity and Support randomized trial. Participants completed the Pittsburgh Sleep Quality Index and a 24-h dietary recall at baseline ( n = 251), 3 months ( n = 159), and 12 months ( n = 127). Meal-timing metrics included clock times for meals and the percentage of daily macronutrient intake consumed after 5 p.m. Linear mixed-effects models adjusted for relevant covariates. Summary: Later breakfast timing was associated with poorer sleep quality at 12 months ( β = 0.64, p = 0.02). No significant associations were observed for lunch or dinner timing, or macronutrient intake after 5 p.m. Later breakfast timing may be associated with lower sleep quality. Further studies should explore meal timing and composition in diverse populations.

Study Objective: The objective of this study was to examine the association between the timing of dietary macronutrients and sodium intake and sleep quantity and quality. Methods: This was a cross-sectional study that included 34 adults between 21 and 50 years of age. The main outcome measures were objective sleep measures assessed from three nights of wrist actigraphy including sleep duration, fragmentation, and wake after sleep onset (WASO), and one night of polysomnography (PSG), including rapid eye movement (REM) sleep, non-REM stage 2 (N2), stage 3 (N3), and WASO. Multiple linear regression models and linear mixed models were used to estimate the associations between sleep measures and dietary measures (carbohydrates, fats, saturated fats, proteins, and sodium). Dietary timing was examined in two ways: (1) the average amount of each nutrient consumed within 3 hours of sleep start, and (2) the interval between the final intake of each nutrient and sleep. Results: Average fat intake within 3 hours of sleep was associated with greater WASO from PSG (β = 4.48, p = 0.01). No other associations were found between the macronutrients or sodium intake (p > 0.05) within 3 hours of sleep and the sleep parameters from PSG or actigraphy. Similarly, no associations were found between any of the PSG or actigraphy sleep measures and the interval between final nutrient intakes and sleep with sleep duration. Conclusions: The study suggests that greater fat but not carbohydrate, protein, saturated fat, or sodium intake close to sleep may be associated with greater sleep disruption; however, no other associations were observed.

The FIT Game is a multicomponent school-based incentive program aimed at increasing children’s fruit and vegetable (FV) intake. There has been no previous report on how playing the game at school influences FV intake away from school. To examine children’s (n=37) FV intake away from school while participating in the FIT Game program at school. FV intake away from school was assessed using the ASA24-Kids-2014 Dietary Assessment Tool. Paired samples t-tests and the generalized linear model repeated measures analysis of variance were used to examine the difference in children’s mean FV intake away from school. During the final three days of the FIT Game intervention, we observed no change in FV consumption away from school (p=0.30). Similarly, no differences were observed between FV intake away from school before the implementation of the FIT Game and during the final three days (p=0.81). The FIT Game modestly decreased the children’s FV intake in a nonstatistically significant manner away from school. Our previous report showed an increase in children’s FV intake at school; thus, the net effect of the game was a significant increase in total daily FV intake.

Oral supplementation may improve the dietary intake of magnesium, which has been identified as a shortfall nutrient. We conducted a pilot study to evaluate appropriate methods for assessing responses to the ingestion of oral magnesium supplements, including ionized magnesium in whole blood (iMg²⁺) concentration, serum total magnesium concentration, and total urinary magnesium content. In a single-blinded crossover study, 17 healthy adults were randomly assigned to consume 300 mg of magnesium from MgCl₂ (ReMag^®, a picosized magnesium formulation) or placebo, while having a low-magnesium breakfast. Blood and urine samples were obtained for the measurement of iMg²⁺, serum total magnesium, and total urine magnesium, during 24 h following the magnesium supplement or placebo dosing. Bioavailability was assessed using area-under-the-curve (AUC) as well as maximum (C_max) and time-to-maximum (T_max) concentration. Depending on normality, data were expressed as the mean ± standard deviation or median (range), and differences between responses to MgCl₂ or placebo were measured using the paired t-test or Wilcoxon signed-rank test. Following MgCl₂ administration versus placebo administration, we observed significantly greater increases in iMg²⁺ concentrations (AUC = 1.51 ± 0.96 vs. 0.84 ± 0.82 mg/dL•24h; C_max = 1.38 ± 0.13 vs. 1.32 ± 0.07 mg/dL, respectively; both p < 0.05) but not in serum total magnesium (AUC = 27.00 [0, 172.93] vs. 14.55 [0, 91.18] mg/dL•24h; C_max = 2.38 [1.97, 4.01] vs. 2.24 [1.98, 4.31] mg/dL) or in urinary magnesium (AUC = 201.74 ± 161.63 vs. 139.30 ± 92.84 mg•24h; C_max = 26.12 [12.91, 88.63] vs. 24.38 [13.51, 81.51] mg/dL; p > 0.05). Whole blood iMg²⁺ may be a more sensitive measure of acute oral intake of magnesium compared to serum and urinary magnesium and may be preferred for assessing supplement bioavailability.