Corrigendum: Association of parent-child interactions with parental psychological distress and resilience during the COVID-19 pandemic.

[This corrects the article DOI: 10.3389/fped.2023.1150216.].

Association of parent-child interactions with parental psychological distress and resilience during the COVID-19 pandemic.

Introduction: The effects of psychological distress/resilience on parent-child engagement (e.g., family dinners, reading) during the COVID-19 pandemic have not been well studied. Among very young children from underrepresented backgrounds enrolled in the ongoing longitudinal Bronx Mother Baby Health Study of healthy term infants, we (1) examined associations between exposures to COVID-19-related events, demographic factors and parental psychological distress and resilience; and (2) correlated these factors with parent-child engagement activities. Methods: Between June 2020-August 2021, parents of 105 Bronx Mother Baby Health Study participants aged birth-25 months completed questionnaires related to exposures to COVID-19-related events, frequency of positive parent-child engagement activities, food and housing insecurity, and parental psychological distress and resilience. Families were also asked open ended questions about the pandemic's impact. Results: 29.8% and 47.6% of parents reported food and housing insecurity, respectively. Greater exposures to COVID-19-related events were associated with increased parental psychological distress. Positive parent-child interactions were associated with demographic factors and higher levels of maternal education, but not with exposures to COVID-19-related events. Discussion: This study adds to a growing body of literature on the negative impacts of COVID-19 exposures and psychosocial stressors on families during the pandemic, supporting the need for enhanced mental health resources and social supports for families.

Fetal Growth and Intrauterine Epigenetic Programming of Obesity and Cardiometabolic Disease.

Epidemiologic studies have shown an association between an adverse intrauterine environment (eg, exposure to malnutrition) and an increased risk of developing cardiometabolic disease in adulthood. These studies laid the foundation for the developmental origins of health and disease hypothesis, which states that limited nutrient supply to the fetus results in physiologic and metabolic adaptations that favor survival but result in unfavorable consequences in the offspring if there is excess nutrition after birth. This discrepancy in the pre- and postnatal milieus, perceived as stress by the offspring, may confer an increased risk of developing cardiometabolic disease later in life. Thus, early life exposures result in programming or changes in cellular memory that have effects on health throughout the life course. One of the mechanisms by which programming occurs is via epigenetic modifications of genes, processes that result in functionally relevant changes in genes (ie, gene expression) without an alteration in the genotype. In this review, we will describe how fetal exposures, including under- and overnutrition, affect neonatal and childhood growth and the future risk for cardiometabolic disease.

Using Data From the Microsoft Kinect 2 to Quantify Upper Limb Behavior: A Feasibility Study.

The objective of this study was to assess whether the novel application of a machine learning approach to data collected from the Microsoft Kinect 2 (MK2) could be used to classify differing levels of upper limb impairment. Twenty-four healthy subjects completed items of the Wolf Motor Function Test (WMFT), which is a clinically validated metric of upper limb function for stroke survivors. Subjects completed the WMFT three times: 1) as a healthy individual; 2) emulating mild impairment; and 3) emulating moderate impairment. A MK2 was positioned in front of participants, and collected kinematic data as they completed the WMFT. A classification framework, based on Riemannian geometry and the use of covariance matrices as feature representation of the MK2 data, was developed for these data, and its ability to successfully classify subjects as either "healthy," "mildly impaired," or "moderately impaired" was assessed. Mean accuracy for our classifier was 91.7%, with a specific accuracy breakdown of 100%, 83.3%, and 91.7% for the "healthy," "mildly impaired," and "moderately impaired" conditions, respectively. We conclude that data from the MK2 is of sufficient quality to perform objective motor behavior classification in individuals with upper limb impairment. The data collection and analysis framework that we have developed has the potential to disrupt the field of clinical assessment. Future studies will focus on validating this protocol on large populations of individuals with actual upper limb impairments in order to create a toolkit that is clinically validated and available to the clinical community.