Developmental trajectories of late preterm infants and predictors of academic performance.

BACKGROUND: The trajectories of late preterm development from infancy to kindergarten reading and math, and predictors of academic resilience and risk are unknown. METHODS: Sample included 1200 late preterm infants (LPIs) from the Early Childhood Longitudinal Study, Birth Cohort. Objective measurements of development at 9 and 24 months (Bayley-SFR) and reading and math academic achievement at preschool and kindergarten were standardized; trajectories of late preterm development from 9 months to kindergarten reading and math were identified using latent class growth analysis. Multinomial logistic regression [aOR, 95% CI] identified predictors of academic resilience and risk. RESULTS: Four trajectory groups were observed for reading and three for math. More optimal trajectories (in reading and math) and academic resilience were associated with experiencing sensitive parenting and preschool attendance. Suboptimal (at-risk) trajectories (in reading or math) and an increased odds of academic risk were associated with

Identification of phenotypes in paediatric patients with acute respiratory distress syndrome: a latent class analysis.

BACKGROUND: Previous latent class analysis of adults with acute respiratory distress syndrome (ARDS) identified two phenotypes, distinguished by the degree of inflammation. We aimed to identify phenotypes in children with ARDS in whom developmental differences might be important, using a latent class analysis approach similar to that used in adults. METHODS: This study was a secondary analysis of data aggregated from the Randomized Evaluation of Sedation Titration for Respiratory Failure (RESTORE) clinical trial and the Genetic Variation and Biomarkers in Children with Acute Lung Injury (BALI) ancillary study. We used latent class analysis, which included demographic, clinical, and plasma biomarker variables, to identify paediatric ARDS (PARDS) phenotypes within a cohort of children included in the RESTORE and BALI studies. The association of phenotypes with clinically relevant outcomes and the performance of paediatric data in adult ARDS classification algorithms were also assessed. FINDINGS: 304 children with PARDS were included in this secondary analysis. Using latent class analysis, a two-class model was a better fit for the cohort than a one-class model (p<0·001). Latent class analysis identified two classes: class 1 (181 [60%] of 304 patients with PARDS) and class 2 (123 [40%] of 304 patients with PARDS), referred to as phenotype 1 and 2 hereafter. Phenotype 2 was characterised by higher concentrations of inflammatory biomarkers, a higher incidence of vasopressor use, and more frequent diagnosis of sepsis, consistent with the adult hyperinflammatory phenotype. All levels of severity of PARDS were observed across both phenotypes. Children with the hyperinflammatory phenotype (phenotype 2) had worse clinical outcomes than those with the hypoinflammatory phenotype (phenotype 1), with a longer duration of mechanical ventilation (median 10·0 days [IQR 6·3-21·0] for phenotype 2 vs 6·6 days [4·1-10·8] for phenotype 1, p<0·0001), and higher incidence of mortality (17 [13·8%] of 123 patients vs four [2·2%] of 181 patients, p=0·0001). When using adult phenotype classification algorithms in children, the soluble tumour necrosis factor receptor-1 (sTNFr1), vasopressor use, and interleukin (IL)-6 variables gave an area under the curve (AUC) of 0·956, and the sTNFr1, vasopressor use, and IL-8 variables gave an AUC of 0·954, compared with the gold standard of latent class analysis. INTERPRETATION: Latent class analysis identified two phenotypes in children with ARDS with characteristics similar to those in adults, including worse outcomes among patients with the hyperinflammatory phenotype. PARDS phenotypes should be considered in design and analysis of future clinical trials in children. FUNDING: US National Institutes of Health.

Introducing Dietary Self-Monitoring to Undergraduate Women via a Calorie Counting App Has No Effect on Mental Health or Health Behaviors: Results From a Randomized Controlled Trial.

BACKGROUND: Strong positive relationships between dietary self-monitoring and eating disorder risk are seen in population-based, observational studies. However, current evidence cannot establish causality. Furthermore, little is known about other mental and behavioral health consequences of dietary self-monitoring among college women, a population vulnerable to eating disorders. OBJECTIVE: To determine if introducing dietary self-monitoring via a popular smartphone app to undergraduate women impacts eating disorder risk, other aspects of mental health, or health behaviors including dietary intake and physical activity. DESIGN: Randomized controlled trial. PARTICIPANTS/SETTING: Undergraduate women who had not engaged in dietary self-monitoring in the past year and who were at low-risk for an eating disorder participated between May and October 2019 (n = 200). INTERVENTION: Participants were randomly assigned to engage in dietary self-monitoring via MyFitnessPal for approximately 1 month or to receive no intervention. MAIN OUTCOME MEASURES: Self-report data on eating disorder risk, other mental health outcomes, and health behaviors were collected at baseline and post-intervention. STATISTICAL ANALYSES PERFORMED: Linear and logistic regressions were utilized to test hypotheses. RESULTS: Adherence to the intervention was high, with participants recording their dietary intake via MyFitnessPal on average 89.1% of days between baseline and post-intervention. Assignment to the intervention was not associated with changes in eating disorder risk, anxiety, depressive symptoms, body satisfaction, quality of life, nutritional intake, physical activity, screen time, or other forms of weight-related self-monitoring (all P > .05). CONCLUSIONS: Among dietary self-monitoring naive undergraduate women with low-risk of an eating disorder, dietary self-monitoring via MyFitnessPal for 1 month did not increase eating disorder risk, impact other aspects of mental health, or alter health behaviors including dietary intake. The null results in our study may be due to the selection of a low-risk sample; future research should explore whether there are populations for whom dietary self-monitoring is contraindicated.