# Real-Time Type 1 Diabetes Self-Management Decision-Making in Adolescents: Protocol for a Longitudinal Mixed Methods Study Using Text Messaging and Continuous Glucose Monitoring

## TL;DR

This study explores how adolescents with type 1 diabetes make real-time self-management decisions using text messaging and glucose monitoring to improve their diabetes care.

## Contribution

The study introduces a novel real-time mixed methods approach to understand adolescent diabetes self-management behaviors and device engagement.

## Key findings

- Adolescents with T1D struggle to meet glycemic goals despite using continuous glucose monitoring.
- Real-time data collection methods like SMS surveys can reveal psychosocial and contextual influences on diabetes self-management.
- Findings will inform future interventions to improve diabetes outcomes in adolescents.

## Abstract

Type 1 diabetes (T1D) requires repeated self-management behaviors and ongoing problem-solving to maintain optimal glucose levels and prevent complications. Despite increasing adoption of continuous glucose monitoring (CGM), which can alleviate some of the constant self-management burden, adolescents struggle to achieve glycemic recommendations and report low engagement with diabetes device data. Previous studies have used retrospective or quantitative approaches to describe adolescent self-management; however, it is unclear how psychosocial influences (eg, mood and distress) and contexts impact adolescent self-management behaviors and engagement with their diabetes devices in everyday life. Exploration of real-time experiences will help to identify potential targets and strategies for future interventions to improve glycemic outcomes in adolescents with T1D using advanced diabetes technologies.

This study has two aims: (1) to develop a grounded theory of self-management decision-making using diabetes devices among adolescents with T1D and (2) to assess the acceptability and feasibility of longitudinal and real-time qualitative data collection methods in this population.

We will conduct a mixed methods study informed by the capability, opportunities, and motivation of behavior model. Adolescents (aged 12‐18 y) with T1D who regularly use CGMs will be recruited from a Midwest pediatric diabetes clinic. Purposive sampling strategy will ensure participants with varied glycemic levels (hemoglobin A1c [HbA1c] ≤9% and HbA1c >9%) and diabetes experiences (eg, diabetes duration, devices used) are included. Using a longitudinal convergent mixed methods design, enrolled participants (n=30‐40) will complete data collection over 6 weeks including: (1) a baseline survey to capture demographic, clinical, and behavioral characteristics; (2) 30 days of SMS text messaging surveys to describe real-time self-management behaviors, technology use, and decision-making; (3) 30 days of CGM data; and (4) an interview focused on self-management behaviors and technology use. Recruitment will continue until appropriate data completeness and/or theoretical saturation is achieved. Analysis of text responses and interview transcripts will follow a grounded theory approach. Summarized glycemic metrics (eg, time in range) and visuals (ie, ambulatory glucose profile) will be integrated with qualitative findings through participant profiles and joint displays. Integrated findings will be used to refine a grounded theory of daily self-management decision-making using diabetes devices among adolescents with T1D.

As of December 2025, 25 participants have enrolled in this study. We expect SMS text messaging survey completion rates and CGM use near 70% throughout the study period. We anticipate findings to become available in the following several years through conference presentations and peer-reviewed publications.

While routine diabetes self-management behaviors and use of diabetes technologies are important for achieving glycemic goals, adolescents report low adherence to diabetes devices. This real-time mixed methods study will improve our understanding of daily decision-making and influences on diabetes self-management. Findings from this study will identify facilitators and barriers to optimal T1D self-management. In addition, results will inform future studies using real-time qualitative and mixed methods approaches.