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Topic: Early‐life experiences, the transmission of health and disease within families, and the influence of cumulative risks as well as protective factors throughout life shape the trajectory of health, including mental health. Long‐term health trajectories established early in life are influenced by biologic, social, and environmental factors. Negative trajectories may be more salient if exposures to adversity occur during critical developmental periods. Purpose: The purpose of this brief is to (a) review pediatric health disparities related to depression and the intergenerational transmission of pediatric depression using a Life Course Health Development (LCHD) model and (b) provide recommendations for pediatric mental health research. Sources: Peer‐reviewed papers available for PubMed, CINAL, and Medline. Other sources include published books, papers, and gray materials. Conclusions: The LCHD model is a perspective to guide and foster new scientific inquiry about the development of mental health outcomes over the life course. The model enables synthesis of mental health, nursing, and public health, linking mental health prevention, risk reduction, and treatment in children. [ABSTRACT FROM AUTHOR]

Objective: Only 17% of adolescents with type 1 diabetes (T1D) are currently meeting their glycemic targets despite advances in diabetes technologies. Self‐management behaviors and challenges specific to use of diabetes technologies are insufficiently studied in adolescents. We aimed to describe the experience of diabetes technology self‐management, including facilitators and barriers, among preteens/adolescents with low and high A1C. Research Design and Methods: Youth (10‐18 years of age) with T1D who use insulin pump therapy were recruited from the larger quantitative cohort of a mixed methods study for participation in semi‐structured qualitative interviews. Maximum variability sampling was used to recruit youth with A1C <7.5% (n = 5) and A1C >9% (n = 5). Participants' personal insulin pump and continuous glucose monitoring data were downloaded and served as a visual reference. Interviews were analyzed using a qualitative descriptive approach. Results: Participants were 50% female with a median age of 14.9 years and 80% used CGM. The sample was predominantly white (90.0%). Analysis produced four major themes, Bad Day, Expect the Unexpected, Nighttime Dependence, and Unpredictability, It's Really a Team and interconnecting subthemes. Youth characterized ''Bad Days'' as those requiring increased diabetes focus and self‐management effort. The unpredictability (''Expect the Unexpected'') of glucose outcomes despite attention to self‐management behaviors was considerable frustration. Conclusions: Diabetes devices such as insulin pumps are complex machines that rely heavily on individual proficiency, surveillance, and self‐management behaviors to achieve clinical benefit. Our findings highlight the dynamic nature of self‐management and the multitude of factors that feed youths' self‐management behaviors. [ABSTRACT FROM AUTHOR]

Objective: Despite increased diabetes device use, few adolescents with type 1 diabetes (T1D) meet glycemic targets. We examine associations between utilization of insulin pumps and continuous glucose monitoring (CGM) and glycemic control. Research Design and Methods: This prospective cohort study included 80 youths (10‐18 years of age) with T1D. Multiple linear regression and linear mixed models (LMM) were used to estimate the effects of device self‐management on HbA1c and daily time in range (70‐180 mg/dL), respectively. Results: Every blood glucose (BG) input/day was associated with a 0.2% decrease in HbA1c (95% CI: −0.297, −0.013), each bolus/day was associated with a 0.2% decrease (−0.327, −0.057), and use of CGM was associated with a 0.5% decrease (−1.00, −0.075). Among CGM users (n = 45) every 10% increase in CGM use was associated with a 0.3% decrease in HbA1c (−0.390, −0.180). In LMM accounting for within subject and between subject variability, there was a negative association between BG input/day frequency (coefficient = −1.880, [−2.640, −1.117]) and time in range. Residual random effects for CGM users were large showing time in range varied between youth with a SD of 15.0% (3 hours and 36 minutes) (SE 2.029, [11.484, 19.530]). Time in range varied significantly from day‐to‐day with SD of 18.6% (4 hours and 40 minutes) (SE0.455, [17.690, 19.473]). Conclusions: Device self‐management behaviors among youth are significantly associated with both HbA1c and time in range. Our findings showing an association between reduced time in range and increased self‐management behaviors is novel and deserves further investigation. [ABSTRACT FROM AUTHOR]