Despite growing international interest in Baby-Led Weaning (BLW), we know almost nothing about food and nutrient intake in infants following baby-led approaches to infant feeding. The aim of this ...paper was to determine the impact of modified BLW (i.e., Baby-Led Introduction to SolidS; BLISS) on food and nutrient intake at 7⁻24 months of age. Two hundred and six women recruited in late pregnancy were randomized to Control (
= 101) or BLISS (
= 105) groups. All participants received standard well-child care. BLISS participants also received lactation consultant support to six months, and educational sessions about BLISS (5.5, 7, and 9 months). Three-day weighed diet records were collected for the infants (7, 12, and 24 months). Compared to the Control group, BLISS infants consumed more sodium (percent difference, 95% CI: 35%, 19% to 54%) and fat (6%, 1% to 11%) at 7 months, and less saturated fat (-7%, -14% to -0.4%) at 12 months. No differences were apparent at 24 months of age but the majority of infants from both groups had excessive intakes of sodium (68% of children) and added sugars (75% of children). Overall, BLISS appears to result in a diet that is as nutritionally adequate as traditional spoon-feeding, and may address some concerns about the nutritional adequacy of unmodified BLW. However, BLISS and Control infants both had high intakes of sodium and added sugars by 24 months that are concerning.
Actigraphy is often used to measure sleep in pediatric populations, despite little confirmatory evidence of the accuracy of existing sleep/wake algorithms. The aim of this study was to determine the ...performance of 11 sleep algorithms in relation to overnight polysomnography in children and adolescents.
One hundred thirty-seven participants aged 8-16 years wore two Actigraph wGT3X-BT (wrist, waist) and three Axivity AX3 (wrist, back, thigh) accelerometers over 24-h. Gold standard measures of sleep were obtained using polysomnography (PSG; Embletta MPRPG, ST + Proxy and TX Proxy) in the home environment, overnight. Epoch by epoch comparisons of the Sadeh (two algorithms), Cole-Kripke (three algorithms), Tudor-Locke (four algorithms), Count-Scaled (CS), and HDCZA algorithms were undertaken. Mean differences from PSG values were calculated for various sleep outcomes.
Overall, sensitivities were high (mean ± SD: 91.8%, ± 5.6%) and specificities moderate (63.8% ± 13.8%), with the HDCZA algorithm performing the best overall in terms of specificity (87.5% ± 1.3%) and accuracy (86.4% ± 0.9%). Sleep outcome measures were more accurately measured by devices worn at the wrist than the hip, thigh or lower back, with the exception of sleep efficiency where the reverse was true. The CS algorithm provided consistently accurate measures of sleep onset: the mean (95%CI) difference at the wrist with Axivity was 2 min (-6; -14,) and the offset was 10 min (5, -19). Several algorithms provided accurate measures of sleep quantity at the wrist, showing differences with PSG of just 1-18 min a night for sleep period time and 5-22 min for total sleep time. Accuracy was generally higher for sleep efficiency than for frequency of night wakings or wake after sleep onset. The CS algorithm was more accurate at assessing sleep period time, with narrower 95% limits of agreement compared to the HDCZA (CS:-165 to 172 min; HDCZA: -212 to 250 min).
Although the performance of existing count-based sleep algorithms varies markedly, wrist-worn devices provide more accurate measures of most sleep measures compared to other sites. Overall, the HDZCA algorithm showed the greatest accuracy, although the most appropriate algorithm depends on the sleep measure of focus.
Despite the widespread use of actigraphy in pediatric sleep studies, there are currently no age-related normative data.
To systematically review the literature, calculate pooled mean estimates of ...actigraphy-derived pediatric nighttime sleep variables and to examine the magnitude of change with age.
A systematic search was performed across eight databases of studies that included at least one actigraphy sleep variable from healthy children aged 0-18 years. Data suitable for meta-analysis were confined to ages 3-18 years with seven actigraphy variables analyzed using random effects meta-analysis and meta-regression performed using age as a covariate.
In total, 1334 articles did not meet inclusion criteria; 87 had data suitable for review and 79 were suitable for meta-analysis. Pooled mean estimates for overnight sleep duration declined from 9.68 hours (3-5 years age band) to 8.98, 8.85, 8.05, and 7.4 for age bands 6-8, 9-11, 12-14, and 15-18 years, respectively. For continuous data, the best-fit (R2 = 0.74) equation for hours over the 0-18 years age range was 9.02 - 1.04 × (age/10)^2 - 0.83. There was a significant curvilinear association between both sleep onset and offset with age (p < .001). Sleep latency was stable at 19.4 min per night. There were significant differences among the older age groups between weekday and weekend/nonschool days (18 studies). Total sleep time in 15-18 years old was 56 min longer, and sleep onset and offset almost 1 and 2 hours later, respectively, on weekend or nonschool days.
These normative values have potential application to assist the interpretation of actigraphy measures from nighttime recordings across the pediatric age range, and aid future research.
Summary
Background
Data on body mass index (BMI) in infants and toddlers worldwide are lacking, relative to older age groups.
Objectives
To describe the growth (weight, length/height, head ...circumference, and BMI z‐score) of New Zealand children under the age of 3 years, and examine differences by sociodemographic characteristics (sex, ethnicity, and deprivation).
Methods
Electronic health data were collected by Whānau Āwhina Plunket, who provide free ‘Well Child’ services for approximately 85% of newborn babies in New Zealand. Data from children under the age of 3, who had their weight and length/height measured between 2017 and 2019, were included. The prevalence of BMI (WHO child growth standards) ≤2nd, ≥85th, and ≥95th percentiles were determined.
Results
Between 12 weeks and 27 months of age, the percentage of infants ≥85th BMI percentile increased from 10.8% (95% CI, 10.4%–11.2%) to 35.0% (34.2%–35.9%). The percentage of infants with high BMI (≥95th percentile) also increased, particularly between 6 months (6.4%; 95% CI, 6.0%–6.7%) and 27 months (16.4%; 15.8%–17.1%). By contrast, the percentage of infants with low BMI (≤2nd percentile) appeared steady between 6 weeks and 6 months, and declined at older ages. The prevalence of infants with a high BMI appears to increase substantially from 6 months across sociodemographic characteristics, with a widening prevalence gap by ethnicity occurring from 6 months, mirroring that of infants with a low BMI.
Conclusions
The number of children with high BMI increases rapidly between 6 months and 27 months of age, suggesting this is an important timeframe for monitoring and preventive action. Future work should investigate the longitudinal growth trajectories of these children to determine if any particular patterns predict later obesity and what strategies could effectively change them.
Whether variation in sleep and physical activity explain marked ethnic and socioeconomic disparities in childhood obesity is unclear. As time spent in one behaviour influences time spent in other ...behaviours across the 24-hour day, compositional analyses are essential. The aims of this study were to determine how ethnicity and socioeconomic status influence compositional time use in children, and whether differences in compositional time use explain variation in body mass index (BMI) z-score and obesity prevalence across ethnic groups.
In all, 690 children (58% European, 20% Māori, 13% Pacific, 9% Asian; 66% low-medium deprivation and 34% high deprivation) aged 6-10 years wore an ActiGraph accelerometer 24-hours a day for 5 days yielding data on sedentary time, sleep, light physical activity (LPA) and moderate-to-vigorous physical activity (MVPA). Height and weight were measured using standard techniques and BMI z-scores calculated. Twenty-four hour movement data were transformed into isometric log-ratio co-ordinates for multivariable regression analysis and effect sizes were back-transformed.
European children spent more time asleep (predicted difference in minutes, 95% CI: 16.1, 7.4-24.9) and in MVPA (6.6 min, 2.4-10.4), and less time sedentary (-10.2 min, -19.8 to -0.6) and in LPA (-12.2 min, -21.0 to -3.5) than non-European children. Overall, 10% more sleep was associated with a larger difference in BMI z-score (adjusted difference, 95% CI: -0.13, -0.25 to -0.01) than 10% more MVPA (-0.06, -0.09 to -0.03). Compositional time use explained 35% of the increased risk of obesity in Pacific compared with European children after adjustment for age, sex, deprivation and diet, but only 9% in Māori and 24% in Asian children.
Ethnic differences in compositional time use explain a relatively small proportion of the ethnic differences in obesity prevalence that exist in children.
Controlling postprandial glycaemia helps to prevent and manage non-communicable diseases. One strategy in controlling glycaemia may be to consume meals in two parts; a preload, followed by the ...remainder of the meal. Our aim was to test preloading a rice meal given for breakfast and lunch on different days, either by splitting the meal (rice preload followed by rice meal) or by using kiwifruit as a preload compared with consuming the rice meal in one sitting. Primary outcomes were glycaemic and insulinaemic responses with secondary outcomes of other hormonal responses, subjective satiety, and subsequent energy intake. Following breakfast, postprandial glycaemic peak concentration was 0.9 (95% CI: 0.2, 1.6) mmol/L lower for the kiwifruit preload compared with the rice meal eaten in one sitting. Following lunch, glycaemic peak concentrations were 1.0 (0.7, 1.4) and 1.1 (0.5, 1.7) mmol/L lower for the rice-split and kiwifruit preload compared with the rice meal alone, respectively. Postprandial insulinaemia area-under-the-curve was 1385 (87, 2684) mU/L·min less for the kiwifruit preload compared with the rice-split. There were no differences among treatments for subsequent energy intake. Meal splitting is useful for lowering postprandial glycaemia, and replacing part of a meal with kiwifruit may help with insulin efficiency without detriment to subsequent energy intake.
Objective
This study aimed to describe how mild sleep deprivation in children changes time spent physically active and sedentary.
Methods
In 2018 through 2020, children (n = 105) with normal sleep ...were randomized to go to bed 1 hour earlier (extension) or 1 hour later (restriction) than their usual bedtime for 1 week, each separated by a 1‐week washout. Twenty‐four‐hour movement behaviors were measured with waist‐worn actigraphy and expressed in minutes and proportions (percentages). Mixed‐effects regression models determined mean differences in time use (95% CI) between conditions. Time gained from sleep lost that was reallocated to other movement behaviors in the 24‐hour day was modeled using regression.
Results
Children (n = 96) gained ~49 minutes of awake time when sleep was restricted compared with extended. This time was mostly reallocated to sedentary behavior (28 minutes; 95% CI: 19‐37), followed by physical activity (22 minutes; 95% CI: 14‐30). When time was expressed as a percentage, the overall composition of movement behavior remained similar across both sleep conditions.
Conclusions
Children were not less physically active when mildly sleep deprived. Time gained from sleeping less was proportionally, rather than preferentially, reallocated to sedentary time and physical activity. These findings suggest that decreased physical activity seems unlikely to explain the association between short sleep and obesity in children.
Stunting and underweight among under-five children in Indonesia are common, raising public health concerns. Whether inappropriate complementary feeding (CF) practices compromise optimal growth during ...late infancy in Indonesia is uncertain. Therefore we characterized and evaluated CF practices in Indonesian infants and investigated their relationship with subsequent growth. We enrolled breastfed infants at 6 months of age (n = 230); and followed them at 9 (n = 202) and 12 months of age (n = 190). We collected socio-demographic and anthropometric data and two-day in-home weighed food records. Relations between WHO CF indicators, sentinel foods, and energy and micronutrient intakes at 9 months and growth at 12 months were explored using multiple linear regression. Stunting and underweight increased from 15.8% and 4.4% at 6 months to 22.6% and 10.5% at 12 months, respectively. Median intakes of calcium, iron, zinc, and riboflavin were below WHO recommendations. Infants consuming fortified infant foods (FIFs) at 9 months had diets with a lower dietary diversity (DD) score (2.3 vs.3.0), energy density, median energy (250 vs. 310 kcal/d) and protein (6.5 vs. 9.1 g/d) intake than non-consumers (p<0.01), despite higher intakes of calcium, iron, and vitamins A and C (p<0.001). Positive relations existed for 9-month consumption of iron-rich/iron fortified infant foods with length-for-age Z-score (LAZ) at 12 months (β = 0.22; 95% CI: 0.01, 0.44; P = 0.04), and for fortified infant foods alone with both LAZ (β = 0.29; 95% CI: 0.09, 0.48; P = 0.04) and weight-for-age Z-score (β = 0.14; 95% CI: 0.02, 0.26; P = 0.02) at 12 months. The positive association of FIFs with subsequent growth may be attributed to their content of both powdered cow's milk and multi-micronutrient fortificants. Nonetheless, mothers should not be encouraged to over-rely on FIFs as they reduce DD.
Anemia has been identified as a severe public health concern among young children in India, however, information on the prevalence of anemia attributed to micronutrient deficiencies is lacking. We ...aimed to assess multiple micronutrient status (iron, zinc, selenium, vitamin A, vitamin D, folate and vitamin B12) in young Indian children and to investigate the role of these seven micronutrients and other non-nutritional factors on hemoglobin concentrations and anemia. One-hundred and twenty children aged 12 to 23 months were included in a cross-sectional nutritional assessment survey, of which 77 children provided a blood sample. Hemoglobin (Hb), serum ferritin, soluble transferrin receptor (sTfR), total body iron, zinc, selenium, retinol binding protein (RBP), folate, vitamin B12 and 25-hydroxyvitamin D (25(OH)D) were measured, and adjusted for inflammation using C-reactive protein (CRP) and α-1-acid glycoprotein (AGP), where appropriate. Predictors for hemoglobin and anemia were identified in multiple regression models. Most of the children were classified as anemic, of which 86 to 93% was associated with iron deficiency depending on the indicator applied. Deficiencies of folate (37%), and notably vitamin D (74%) were also common; fewer children were classified with deficiencies of vitamin B12 (29%), zinc (25%), and vitamin A (17%) and selenium deficiency was nearly absent. Multiple micronutrient deficiencies were common with over half (57%) deficient in three or more micronutrients, and less than 10% of children were classified with adequate status for all the micronutrients measured. Iron status was found to be the only nutritional factor statistically significantly inversely associated with anemia (P = 0.003) in multivariate analysis after controlling for sex. A coordinated multi-micronutrient program is urgently needed to combat the co-existing micronutrient deficiencies in these young children to improve micronutrient status and reduce the high burden of childhood anemia.
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