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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]

Purpose: Adolescent diabetes outcomes remain poor despite increased use of diabetes technologies such as insulin pump therapy. Meaningful research examining adolescent insulin pump self‐management has been done, however, a summary of these self‐management findings has not been published. The aim of this literature review is to map, evaluate, and summarize existing adolescent insulin pump self‐management research. Method: A scoping review of three databases was conducted to comprehensively report and synthesize relevant literature published before September 2019. Results: Of the 1295 titles identified, 18 articles met the inclusion criteria and were included in this scoping review. Key insulin pump self‐management behaviors were featured in the literature, most notably self‐monitoring of blood glucose (SMBG) and bolus frequency. Several factors were found to influence pump self‐management including psychological factors, parental support and self‐management transition, insulin pump education and knowledge acquisition, and environmental factors. We uncovered five gaps in the literature including: an unclear delineation of adolescent age; limited minority representation; variability in the definitions and reporting of self‐management behaviors; the role of data sharing and remote monitoring was not addressed; and there remains limited inquiry into diabetes burden and distress associated with insulin pump self‐management. Practice Implications: There exists a consensus that well‐established behaviors, such as bolus and SMBG frequency, influence glycemic outcomes for adolescent insulin pump users, however, full insulin pump utilization and self‐management is poorly understood. Diabetes clinicians should work to support adolescent insulin pump self‐management practices by reinforcing bedrock behaviors while fostering supportive factors found to influence pump self‐management. [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]

Background Adolescents are not meeting current recommendations for daily physical activity, nutrition, and screentime which has been associated with overweight and obesity. Understanding the mediators that facilitate teens in improving their healthy lifestyle behaviors may be helpful in halting this crisis. Purpose The purpose of this systematic review was to assess published findings regarding mediators of adolescent energy balance behaviors. Method We followed the Institute of Medicine guidelines for completing a systematic review. Discussion Fourteen analyses from 12 studies were included with mediating variables tested for nutrition, physical activity, and screen time. Mediators were identified for all three behaviors and were primarily on the individual level of the social ecological model. Conclusions Combining findings from this and other reviews of mediators can help guide researchers in choosing mediating factors for specific target behaviors. [ABSTRACT FROM AUTHOR]