Search

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., (© 2021 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

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., (© 2021 Wiley Periodicals LLC.)

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., (© 2020 John Wiley & Sons A/S . Published by John Wiley & Sons Ltd.)

Engaging in positive healthy lifestyle behaviors continues to be a public health challenge, requiring innovative solutions. As the market for voice assistants (Amazon Alexa, Google Assistant, and Apple Siri) grows and people increasingly use them to assist their daily tasks, there is a pressing need to explore how voice assistant (VA) technology may be used in behavioral health interventions. A scoping review of literature was conducted to address a PICO (Population, Intervention, Comparison, and Outcome) question: across populations, how does the use of voice assistants in behavioral health research/interventions influence healthy lifestyle behaviors versus control or comparison interventions? To inform the science, a secondary aim of this review was to explore characteristics of VAs used in behavioral health research. The review was conducted following Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines with scoping review extension (PRISMA-ScR). Ten studies satisfied the inclusion criteria, representing research published through February 2019. Studies spanned pediatric to elderly populations, covering a vast array of self-management and healthy lifestyle behaviors. The majority of interventions were multicomponent, involving more than one of the following behavior change techniques grouped by cluster: shaping knowledge, self-belief, repetition and substitution, feedback and monitoring, goals and planning, antecedents, natural consequences, comparison of behavior, and identification. However, most studies were in early stages of development, with limited efficacy trials. VA technology continues to evolve and support behavioral interventions using various platforms (e.g., Interactive Voice Response [IVR] systems, smartphones, and smart speakers) which are used alone or in conjunction with other platforms. Feasibility, usability, preliminary efficacy, along with high user satisfaction of research adapted VAs, in contrast to standalone commercially available VAs, suggest a role for VAs in behavioral health intervention research., (© Society of Behavioral Medicine 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Diabetes devices, like insulin pumps and continuous glucose monitors (CGMs), capture and store patient adherence and utilization data that can be retrieved or downloaded providing objective information on self-management behaviors; yet, diabetes device data remain underutilized in research. Objective: The aim of the study was to examine the usability and feasibility of personal diabetes device data collected using a clinical download platform retooled for research purposes. Methods: Investigators evaluated the feasibility of raw diabetes device data collection. One hundred eight preteens and adolescents with Type 1 diabetes and their parents provided consent/assent. Results: Data were successfully collected from the diabetes devices (insulin pumps and CGM) of 97 youth using a clinical download software adapted for research, including data from all three commercially available CGM systems and insulin pumps brands, which contained all current and previous models of each insulin pump brand. The time required to download, mode of connection, and process varied significantly between brands. Despite the use of an agnostic download software, some outdated device brands and cloud-based CGM data were unsupported during data collection. Within the download software, dummy clinical accounts were created for each study participant, which were then linked back to a master study account for data retrieval. Raw device data were extracted into seven to eight Excel files per participant, which were then used to develop aggregate daily measures. Discussion: Our analysis is the first of its kind to examine the feasibility of raw diabetes device data using a clinical download software. The investigators highlight issues encountered throughout the research process, along with mitigating strategies to inform future inquiry. Conclusion: This study demonstrates the feasibility of raw data collection, from a wide variety of insulin pump and CGM brands, through the retooling of a clinical download software. Data from these personal devices provide a unique opportunity to study self-management behavior and the glycemic response of individuals in their everyday environments. [ABSTRACT FROM AUTHOR]