A Functional Description of Adult Picky Eating Using Latent Profile Analysis

Abstract Objective Research has indicated that adult picky eating (PE) is associated with elevated psychosocial impairment and limited dietary variety and fruit and vegetable intake; however, research operationalizing PE behaviors is limited. Previous research identified a PE profile in children, marked by high food avoidance (satiety responsiveness, fussiness, and slow eating) and low food approach (food enjoyment and responsiveness) appetitive traits. The present study aimed to replicate a similar latent eating behavior profile in an adult sample. Methods A sample of 1339 US adults recruited through Amazon’s MTurk completed an online survey that included a modified self-report version of the Child Eating Behavior Questionnaire (CEBQ-A). Latent profile analysis was employed to identify eating profiles using the CEBQ-A subscales, ANCOVAs were employed to examine profile differences on various self-report measures, and eating profiles were compared across BMI classifications. Results Analyses converged on a four-profile solution, and a picky eater profile that closely resembled the past child profile emerged. Participants in the picky eater profile (18.1%) scored higher on measures of adult PE and social eating anxiety compared to all other profiles, scored higher on eating-related impairment and depression than moderate eating profiles, and were more likely to be of normal weight. Discussion A distinct adult PE profile was observed, indicating childhood PE and appetitive behaviors may carry over into adulthood. Research identifying meaningful groups of picky eaters will help to shed light on the conditions under which picky eating is a risk factor for significant psychosocial impairment or distress, or weight-related problems

Do differences in compositional time use explain ethnic variation in the prevalence of obesity in children? Analyses using 24-hour accelerometry

© 2019, Springer Nature Limited. Background/Objectives: 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. Methods: 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. Results: 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. Conclusions: Ethnic differences in compositional time use explain a relatively small proportion of the ethnic differences in obesity prevalence that exist in children

Establishing normal values for pediatric nighttime sleep measured by actigraphy: A systematic review and meta-analysis

Background: Despite the widespread use of actigraphy in pediatric sleep studies, there are currently no age-related normative data. Objectives: 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. Methods: 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. Results: 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. Conclusion: 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

Parental experiences of short term supported use of a do‐it‐yourself continuous glucose monitor (DIYrtCGM): A qualitative study

Aims To investigate the experiences of parents caring for young children with type 1 diabetes type 1 diabetes using a do-it-yourself continuous glucose monitor (DIYrtCGM) in a supported setting. Methods Exit interviews were conducted with parents from 11 families at the end of the MiaoMiao study: a randomised cross-over trial focusing on parental fear of hypoglycaemia. Technical support was provided to participants while using DIYrtCGM during the trial. A convenience sampling approach was used to recruit parents. An in-depth, semi-structured interview approach was used. Thematic analysis was used to identify key themes and subthemes. Results Parents identified that remote monitoring enabled proactive management and that overall alarms/glucose alerts were useful. Some parents reported reductions in anxiety, increased independence for their child, and improvements in the child–parent relationship. However, parents also reported regular signal loss with DIYrtCGM, along with complicated apps and challenges troubleshooting technical problems. Despite this, nine of the 11 families continued to use the system after the end of the trial. Conclusions Do-it-yourself continuous glucose monitoring (CGM) was on balance beneficial for the parents interviewed. However, while access to CGM shifted the burden of care experienced by parents, burden did not significantly reduce for all parents, as the improved glycaemic control that they achieved was accompanied with the responsibility for continually monitoring their child’s data. Supported use of do-it-yourself CGM may be an achievable, cost-effective option for parents caring for children with type 1 diabetes in countries without funded access to CGM

Compositional principal component analysis generates gut microbiota profiles that associate with children\u27s diet and body composition

AbstractGut microbiota data obtained by DNA sequencing are not only complex because of the number of taxa that may be detected within human cohorts, but also compositional because characteristics of the microbiota are described in relative terms (e.g., “relative abundance” of particular bacterial taxa expressed as a proportion of the total abundance of taxa). Nutrition researchers often use standard principal component analysis (PCA) to derive dietary patterns from complex food data, enabling each participant\u27s diet to be described in terms of the extent to which it fits their cohort\u27s dietary patterns. However, compositional PCA methods are not commonly used to describe patterns of microbiota in the way that dietary patterns are used to describe diets. This approach would be useful for identifying microbiota patterns that are associated with diet and body composition. The aim of this study is to use compositional PCA to describe gut microbiota profiles in 5 year old children and explore associations between microbiota profiles, diet, body mass index (BMI) z-score, and fat mass index (FMI) z-score. This study uses a cross-sectional data for 319 children who provided a faecal sample at 5 year of age. Their primary caregiver completed a 123-item quantitative food frequency questionnaire validated for foods of relevance to the gut microbiota. Body composition was determined using dual-energy x-ray absorptiometry, and BMI and FMI z-scores calculated. Compositional PCA identified and described gut microbiota profiles at the genus level, and profiles were examined in relation to diet and body size. Three gut microbiota profiles were found. Profile 1 (positive loadings on Blautia and Bifidobacterium; negative loadings on Bacteroides) was not related to diet or body size. Profile 2 (positive loadings on Bacteroides; negative loadings on uncultured Christensenellaceae and Ruminococcaceae) was associated with a lower BMI z-score (r = -0.16, P = 0.003). Profile 3 (positive loadings on Faecalibacterium, Eubacterium and Roseburia) was associated with higher intakes of fibre (r = 0.15, P = 0.007); total (r = 0.15, P = 0.009), and insoluble (r = 0.13, P = 0.021) non-starch polysaccharides; protein (r = 0.12, P = 0.036); meat (r = 0.15, P = 0.010); and nuts, seeds and legumes (r = 0.11, P = 0.047). Further regression analyses found that profile 2 and profile 3 were independently associated with BMI z-score and diet respectively. We encourage fellow researchers to use compositional PCA as a method for identifying further links between the gut, diet and obesity, and for developing the next generation of research in which the impact on body composition of dietary interventions that modify the gut microbiota is determined.</jats:p

Impact of a modified version of baby-led weaning on infant food and nutrient intakes: The BLISS randomized controlled trial

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. 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 (n = 101) or BLISS (n = 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

The effect of mild sleep deprivation on diet and eating behaviour in children: Protocol for the Daily Rest, Eating, and Activity Monitoring (DREAM) randomized cross-over trial

© 2019 The Author(s). Background: Although insufficient sleep has emerged as a strong, independent risk factor for obesity in children, the mechanisms by which insufficient sleep leads to weight gain are uncertain. Observational research suggests that being tired influences what children eat more than how active they are, but only experimental research can determine causality. Few experimental studies have been undertaken to determine how reductions in sleep duration might affect indices of energy balance in children including food choice, appetite regulation, and sedentary time. The primary aim of this study is to objectively determine whether mild sleep deprivation increases energy intake in the absence of hunger. Methods: The Daily, Rest, Eating, and Activity Monitoring (DREAM) study is a randomized controlled trial investigating how mild sleep deprivation influences eating behaviour and activity patterns in children using a counterbalanced, cross-over design. One hundred and ten children aged 8-12 years, with normal reported sleep duration of 8-11 h per night will undergo 2 weeks of sleep manipulation; seven nights of sleep restriction by going to bed 1 hr later than usual, and seven nights of sleep extension going to bed 1 hr earlier than usual, separated by a washout week. During each experimental week, 24-h movement behaviours (sleep, physical activity, sedentary behaviour) will be measured via actigraphy; dietary intake and context of eating by multiple 24-h recalls and wearable camera images; and eating behaviours via objective and subjective methods. At the end of each experimental week a feeding experiment will determine energy intake from eating in the absence of hunger. Differences between sleep conditions will be determined to estimate the effects of reducing sleep duration by 1-2 h per night. Discussion: Determining how insufficient sleep predisposes children to weight gain should provide much-needed information for improving interventions for the effective prevention of obesity, thereby decreasing long-term morbidity and healthcare burden. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN12618001671257. Registered 10 October 2018