Evidence map›Paper›PMID 37614410›Full record

ArticlePediatric diabetes2023

Situational Awareness and Proactive Engagement Predict Higher Time in Range in Adolescents and Young Adults Using Hybrid Closed-Loop.

Laurel H Messer, Paul F Cook, Stephen Voida, Casey Fiesler, Emily Fivekiller, Chinmay Agrawal, Tian Xu, Gregory P Forlenza, Sriram Sankaranarayanan

Open access · hybridAbstract read
In one paragraph

Article in Pediatric diabetes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. Situational awareness predicts self-management of type I diabetes in adolescents and young adults.Health psychology : official journal of the Division of Health Psychology, American Psychological Association · 2026
    Article
  2. Review
  3. "Obviously, Nothing's Gonna Happen in Five Minutes": How Adolescents and Young Adults Infrastructure Resources to Learn Type 1 Diabetes Management.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Laurel H MesserBarbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.ORCID 0000-0001-7493-0989
Paul F CookCollege of Nursing, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.ORCID 0000-0001-7791-9072
Stephen VoidaDepartment of Information Science, University of Colorado Boulder, Boulder, CO, USA.ORCID 0000-0003-3275-407X
Casey FieslerDepartment of Information Science, University of Colorado Boulder, Boulder, CO, USA.ORCID 0000-0002-8743-4201
Emily FivekillerBarbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.ORCID 0000-0002-1808-5597
Chinmay AgrawalDepartment of Computer Science, University of Colorado Boulder, Boulder, CO, USA.ORCID 0009-0000-3181-307X
Tian XuDepartment of Information Science, University of Colorado Boulder, Boulder, CO, USA.ORCID 0000-0003-1835-2760
Gregory P ForlenzaBarbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.ORCID 0000-0003-3607-9788
Sriram SankaranarayananDepartment of Computer Science, University of Colorado Boulder, Boulder, CO, USA.ORCID 0000-0001-7315-4340
University of Colorado Boulder · USUniversity of Colorado Anschutz Medical Campus · US

Funding

University of Colorado Anschutz Medical Campus DRCP30DK116073 · NIDDK · UNIVERSITY OF COLORADO DENVER · PI Maki Nakayama · 2020 to 2026
$10.8M
SCH: Striking a Balance: Trust and Privacy in Using Adolescents' Data for Diabetes Self-ManagementR01AT012288 · NCCIH · UNIVERSITY OF COLORADO · PI VOIDA, STEPHEN · 2022 to 2025
$1.1M
NCCIH NIH HHS R01 AT012288NIDDK NIH HHS P30 DK116073
6 · The paper itself

Abstract

Background: Adolescents and young adults with type 1 diabetes have high HbA1c levels and often struggle with self-management behaviors and attention to diabetes care. Hybrid closed-loop systems (HCL) like the t:slim X2 with Control-IQ technology (Control-IQ) can help improve glycemic control. The purpose of this study is to assess adolescents' situational awareness of their glucose control and engagement with the Control-IQ system to determine significant factors in daily glycemic control. Methods: Adolescents (15-25 years) using Control-IQ participated in a 2-week prospective study, gathering detailed information about Control-IQ system engagements (boluses, alerts, and so on) and asking the participants' age and gender about their awareness of glucose levels 2-3 times/day without checking. Mixed models assessed which behaviors and awareness items correlated with time in range (TIR, 70-180 mg/dl, 3.9-10.0 mmol/L). Results: Eighteen adolescents/young adults (mean age 18 ± 1.86 years and 86% White non-Hispanic) completed the study. Situational awareness of glucose levels did not correlate with time since the last glucose check ( Conclusion: Situational awareness is an independent predictor of TIR and may provide insight into patterns of attention and focus that could positively influence glycemic outcomes in adolescents. Proactive engagements predict better TIR, whereas reactive engagement predicted lower TIR. Future interventions could be designed to train users to develop awareness and expertise in effective diabetes self-management.

Indexed as

AwarenessDiabetes Mellitus, Type 1AdolescentAdultGlucoseGlycemic ControlHumansProspective StudiesYoung AdultGlucose

Identifiers

PMID37614410
PMCPMC10445779
OpenAlexW4377046990

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.