Behavioral coding of videotaped eating and feeding interactions can provide researchers with rich observational data and unique insights into eating behaviors, food intake, food selection as well as interpersonal and mealtime dynamics of children and their families. Unlike self-report measures of eating and feeding practices, the coding of videotaped eating and feeding behaviors can allow for the quantitative and qualitative examinations of behaviors and practices that participants may not self-report. While this methodology is increasingly more common, behavioral coding protocols and methodology are not widely shared in the literature. This has important implications for validity and reliability of coding schemes across settings. Additional guidance on how to design, implement, code and analyze videotaped eating and feeding behaviors could contribute to advancing the science of behavioral nutrition. The objectives of this narrative review are to review methodology for the design, operationalization, and coding of videotaped behavioral eating and feeding data in children and their families, and to highlight best practices. When capturing eating and feeding behaviors through analysis of videotapes, it is important for the study and coding to be hypothesis driven. Study design considerations include how to best capture the target behaviors through selection of a controlled experimental laboratory environment versus home mealtime, duration of video recording, number of observations to achieve reliability across eating episodes, as well as technical issues in video recording and sound quality. Study design must also take into account plans for coding the target behaviors, which may include behavior frequency, duration, categorization or qualitative descriptors. Coding scheme creation and refinement occur through an iterative process. Reliability between coders can be challenging to achieve but is paramount to the scientific rigor of the methodology. Analysis approach is dependent on the how data were coded and collapsed. Behavioral coding of videotaped eating and feeding behaviors can capture rich data “in-vivo” that is otherwise unobtainable from self-report measures. While data collection and coding are time-intensive the data yielded can be extremely valuable. Additional sharing of methodology and coding schemes around eating and feeding behaviors could advance the science and field.

Congenital cytomegalovirus (cCMV) is the most common congenital infection in the United States, with 1 of 200 live births affected. It is the leading viral cause of intrauterine fetal demise and miscarriage. It is a common cause of neonatal hearing loss, second only to genetic factors. Yet, health care provider awareness remains low. The purpose of this article is to provide a brief overview of the epidemiology, presentation, diagnosis, and treatment of antenatal cytomegalovirus (CMV) infection and cCMV in the neonate. Maternal CMV infection in pregnancy often presents with mild cold‐like symptoms or is asymptomatic. The virus can be vertically transmitted to a growing fetus, the risk of transmission and severity of fetal impact varying by timing of exposure during pregnancy. Most neonates born with cCMV show no signs at birth, yet 15% to 25% will have long‐term adverse neurodevelopmental conditions. Misconceptions that cCMV cannot be prevented or that neonates born without signs of the disease will be unaffected are common. Evidence supporting antenatal education around behavioral change to lower a woman's risk of acquiring CMV during pregnancy is mounting. CMV infection during pregnancy should be co‐managed with a maternal‐fetal medicine specialist. There is early evidence for the use of antiviral medication in reducing risk of vertical transmission. Identification of cCMV during pregnancy may help ensure the neonate receives timely treatment after birth. Midwives can play an important role in providing antenatal education about cCMV risk reduction and in initiating a diagnostic evaluation when there is clinical suspicion.

BACKGROUND AND OBJECTIVES: Picky eating is common, yet little is known about trajectories of picky eating in childhood. Our objectives were to examine trajectories of child picky eating in low-income US children from ages 4 to 9 years and associations of those trajectories with participant characteristics, including child BMI z score (BMIz) and maternal feeding-behavior trajectories. METHODS: Mother-child dyads (N = 317) provided anthropometry and reported on picky eating and maternal feeding behaviors via questionnaires at child ages 4, 5, 6, 8, and 9 years. At baseline, mothers reported on demographics and child emotional regulation. Trajectories of picky eating and maternal feeding behaviors were identified by using latent class analysis. Bivariate analyses examined associations of picky-eating trajectory membership with baseline characteristics and maternal feeding-behavior trajectory memberships. A linear mixed model was used to examine the association of BMIz with picky-eating trajectories. RESULTS: Three trajectories of picky eating emerged: persistently low (n = 92; 29%), persistently medium (n = 181; 57%), and persistently high (n = 44; 14%). Membership in the high picky-eating trajectory was associated with higher child emotional lability and lower child emotional regulation. Picky eating was associated with restriction (P = .01) and demandingness (P < .001) trajectory memberships, such that low picky eating was associated with low restriction and high picky eating was associated with high demandingness. Medium and high picky-eating trajectories were associated with lower BMIz. CONCLUSIONS: Picky eating appears to be traitlike in childhood and may be protective against higher BMIz.

Background With the emergence of newborn congenital cytomegalovirus (cCMV) screening programs, more infants are being diagnosed and require long-term follow-up. The objective of the study was to summarize the literature to date on neurodevelopmental outcomes in children with cCMV with attention to study-specific definitions of disease severity (symptomatic vs. asymptomatic). Methods This systematic scoping review included studies of children with cCMV (≤18 years-old) measuring neurodevelopment in ≥1 domain: global, gross motor, fine motor, speech/language, and intellectual/cognitive. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. PubMed, PsychInfo, and Embase databases were searched. Results 33 studies met inclusion criteria. Global development most frequently measured (n = 21), followed by cognitive/intellectual (n = 16) and speech/language (n = 8). Most (31/33) studies differentiated children by cCMV severity (symptomatic vs. asymptomatic), definitions of which ranged broadly. 15/21 studies described global development categorically (e.g., normal vs. abnormal). Across studies and domains, children with cCMV generally had equivalent or lower scores (vs. controls or normed measures). Conclusions Variation in definitions of cCMV severity and blunt categorical outcomes may limit the generalizability of findings. Future studies should utilize standardized definitions of disease severity and in-depth measurement and reporting of neurodevelopmental outcomes in children with cCMV. Impact Neurodevelopmental delays are common among children with cCMV, although gaps in the literature to have made quantification of such delays challenging. Variation in definitions of asymptomatic and symptomatic cCMV as well as the use of categorical outcomes of neurodevelopment (e.g., normal vs. abnormal) limits the generalizability and clinical utility of findings.

OBJECTIVE: To examine the association between congenital cytomegalovirus (cCMV) and autism spectrum disorder (ASD) administrative diagnoses in US children. METHODS: Cohort study using 2014 to 2020 Medicaid claims data. We used diagnosis codes to identify cCMV (exposure), ASD (outcome), and covariates among children enrolled from birth through ≥4 to <7 years. Covariates include central nervous system (CNS) anomaly or injury diagnosis codes, including brain anomaly, microcephaly within 45 days of birth, cerebral palsy, epilepsy, or chorioretinitis. We used Cox proportional hazards regression models to estimate hazard ratios and 95% confidence intervals, overall and stratified by sex, birth weight and gestational age outcome (low birth weight or preterm birth), and presence of CNS anomaly or injury. RESULTS: Among 2 989 659 children, we identified 1044 (3.5 per 10 000) children with cCMV and 74 872 (25.0 per 1000) children with ASD. Of those with cCMV, 49% also had CNS anomaly or injury diagnosis codes. Children with cCMV were more likely to have ASD diagnoses (hazard ratio: 2.5; 95% confidence interval: 2.0–3.2, adjusting for birth year, sex, and region). This association differed by sex and absence of CNS anomaly or injury but not birth outcome. CONCLUSIONS: Children with (versus without) cCMV diagnoses in Medicaid claims data, most of whom likely had symptomatic cCMV, were more likely to have ASD diagnoses. Future research investigating ASD risk among cohorts identified through universal cCMV screening may help elucidate these observed associations.