This longitudinal, convergent mixed methods study leverages a real-time approach to develop a theory of self-management among adolescents with T1D who use CGM. We will conduct a grounded theory analysis to describe self-management in this population, which will inform future intervention development. We aim to enroll approximately 30 to 40 adolescents with T1D who already use CGM to complete 30 days of real-time, narrative data collection via SMS text messaging and 30 days of CGM. Additional data will be collected via a baseline survey and one-time follow-up interview to further develop the theory.

When applied to diabetes, real-time data collection (eg, ecological momentary assessment) can allow us to more accurately assess T1D self-management beliefs, behaviors, and event experiences, reduce recall bias, help researchers to understand behaviors in context, and support the development of timely and tailored interventions [29-32]. With the growth of CGM among adolescents, continuous and real-time glucose data have been incorporated into momentary assessments [1,33] While real-time quantitative assessments help to characterize important variations that influence self-management, the broader context of an individual’s experience during those moments remains poorly understood. A mixed methods approach blending real-time and contextual data could address these gaps to provide a more comprehensive understanding of adolescents’ experiences and identify potential intervention targets and strategies. In this study, we will use a qualitative real-time approach to further explore the lived experience of T1D self-management from the perspectives of adolescents using diabetes technology.

The QUALITY study is informed by the capability, opportunity, and motivation model of behavior (COM-B) [34] by assessing several relevant influences on adolescent self-management behaviors and exploring these behaviors through the routine, reactive, and reflective cycles that are performed in real-time by adolescents with T1D using diabetes devices. The COM-B model [34] serves as a helpful framework for describing T1D self-management behaviors in adolescents who use diabetes technology. According to the COM-B model, to perform a particular behavior, an individual must feel that they are capable of doing so (physically and psychologically), have the opportunity to do so (socially and physically), and be motivated to do so (automatically and reflectively) [34]. COM-B has frequently been used as a tool to develop intervention targets to change individual behaviors and can also be used as a framework to explore and describe influences on diabetes self-management [35-37]. For example, in the context of T1D self-management, the behavior of “checking blood glucose levels” would require the knowledge and skills needed to test through finger pricks or look at a CGM display (ie, capability), access to glucometers or CGM and comfort checking blood glucose levels in front of others (ie, opportunity), and the belief that testing blood glucose levels is desirable and impactful on health (ie, motivation).

T1D requires many interconnected behaviors to optimize self-management. Through an expert panel and literature review, Hamilton et al [38] identified 150 distinct self-management behaviors related to T1D that are performed in three self-regulatory behavioral cycles: (1) routine, (2) reactive, and (3) reflective. Each of these cycles is supported by or inhibited by capability, opportunities, and motivation for a given behavior [38]. Re-examining the example above of checking blood glucose levels, an adolescent with T1D may decide to check their glucose using CGM as a part of their daily self-management routine (ie, routine behavior), in response to hypoglycemia or hyperglycemia symptoms or a CGM alarm (ie, reactive behavior), or by reviewing trends in glucose available on their insulin pump, CGM receiver, or CGM mobile app to make later adjustments (ie, reflective behavior). COM-B highlights the complexity of improving self-management behaviors in adolescence, as each individual behavior is influenced by the interplay of capabilities, opportunities, and motivations in routine, reactive, and reflective contexts which occur every day, in real-time, and in diverse settings. Figure 1 presents the adapted COM-B used to inform the QUALITY study.

To ensure a youth-centered design and improve engagement with this study, the study team will make iterative changes to the study design and data collection tools based on feedback from an online patient advisory council. The Type 1 Diabetes Youth Advisory Council (T1DYAC) was established in 2024 to guide T1D research conducted primarily in our laboratory. T1DYAC members are youth (ages 14‐24 y) with T1D who have an interest in improving research about T1D in youth through discussion with research teams. T1DYAC members represent a range of ages, diabetes durations and histories, and diabetes devices used. During monthly virtual meetings, the group is presented with a research study and provides feedback on a specific study element (eg, recruitment flyers and approach, research questions, and interpretation of findings). In addition, members complete surveys between monthly meetings to provide written feedback and pointed insights on study materials.

This study will recruit patients from the University of Michigan (U-M) Pediatric Diabetes Clinic at C.S. Mott Children’s Hospital. As of December 2025, there are approximately 700 current patients aged 12 to 18 years with T1D; among these, 357 (50%) patients are male, 554 (79%) are White, 85 (12%) are Black or African American, 12 (2%) are Asian, 36 (5%) are more than one or another race, and 36 (5%) are Hispanic or Latino. In addition, 417 of these patients (62%) have public insurance, and 687 (98%) are English speaking. Likewise, over 92% currently use CGM. The makeup of the clinical population is similar to that of the US population of people with T1D, where 68% are non-Hispanic White, 16% African American, 13% Hispanic, 3% other races, and nearly 80% use CGM [39]. While disparities persist, differences in CGM by age, race and ethnicity, and insurance type have narrowed significantly in the last 15 years [2,5].

A list of patients with T1D, aged 12 to 18 years, who (1) use CGM, (2) received care in the U-M Pediatric Diabetes Clinic in the past 12 months, and (3) have at least 1 HbA_1c laboratory result from the past 12 months will be generated using DataDirect (University of Michigan Medical School) and Electronic Medical Record Search Engine (EMERSE) [40]—university-developed tools for performing systematic searches of structured and unstructured patient medical records [41]. The list will include basic demographic, clinical, and contact information for identified patients.

Consistent with a grounded theory approach, we will use theoretical sampling to help ensure that adequate data are collected to address our research aims. For our initial sampling strategy (a priori) [42], we seek to enroll participants with diverse diabetes outcomes and experiences. We will intentionally recruit and enroll participants with HbA_1c of 9% or less and more than 9% to ensure adolescents with low and high glycemic levels are represented. We aim to recruit and enroll approximately equal groups with high and low glycemic levels. A purposive sampling approach will also be leveraged using demographic and clinical factors including age, gender, years since diagnosis, and other social determinants of health (eg, family income and food insecurity measures) to create a sample with varied experiences and backgrounds. Sociodemographic backgrounds that are under-represented within the clinical population (ie, Hispanic or Latino) in the sample will be intentionally oversampled. As data collection and analysis proceed, recruitment and sampling strategies may shift to explore variation in participant responses, develop relationships between codes and categories, and add details to the descriptions and characteristics of categories in the analysis [42].

Recruitment will be conducted in waves to allow for flexibility and needed changes to meet theoretical sampling goals. During each wave, approximately 100 to 200 identified patients will be contacted via electronic and/or postal mail up to 5 times with study information and a link to an online screening survey to confirm interest and eligibility. Study information will also be shared through (1) flyers and patient newsletters posted by the U-M Pediatric Diabetes Clinic, (2) UMHealthResearch.org, a secure platform at U-M for sharing study information with potential participants, and (3) emails and newsletters shared with patient diabetes councils. If recruitment goals are not reached through these methods, patients may also be approached in person at the U-M Pediatric Diabetes Clinic by nonclinician members of the study team.

Study eligibility criteria include: (1) current patient at Michigan Medicine with at least 1 visit and HbA_1c measurement in the last 6 months, (2) aged 12 to 18 years upon enrollment, (3) diagnosed with T1D for at least 6 months, (4) used CGM for at least 6 months, (5) used CGM for at least 20 days per month, (6) able to communicate in English, and (7) have access to a phone with SMS text messaging capabilities. Eligibility will be assessed via the screening survey and confirmed via medical record review.

A sample size estimate (N=30‐40) was determined a priori based on estimates of the amount of qualitative data needed to reach data sufficiency across participants with varied glycemic levels and diabetes experiences [43,44]. Due to the longitudinal nature of data collection and multiple data sources in this study, we anticipate having sufficient data to inform our grounded theory. As is common in qualitative research and grounded theory, additional data may be collected as needed to further develop findings and reach theoretical saturation [45]. The same sample will be used for quantitative data collection, including baseline surveys of diabetes characteristics and psychosocial measures, and 30 days of CGM data. Using identical samples in a convergent mixed methods design reduces threats to validity (eg, sample size legitimation) and ensures that appropriate conclusions can be drawn through integration [46,47].

All participants (and a parent or guardian for minors) will complete an enrollment call to review the informed consent document with a member of the study team and provide electronic or written consent or assent to enroll in the study. Throughout the study, survey invitations will include the study team’s contact information in the event of any questions or concerns. Participants will also receive contact information for the pediatric diabetes clinic and reminders to reach out to their diabetes care team or the provider on call with diabetes-related questions, concerns, and emergencies. In addition, participants will be instructed not to share any identifiable information through SMS text messaging (ie, name and school) as it is not a secure method of communication. All incoming survey responses will be stored securely, and the exported data will be deidentified for analysis. Participants can earn up to US $150 for completing all study activities, and final payments will be sent as a gift card or a check in the mail, depending on the participant’s preference. This study was reviewed and approved by the U-M Institutional Review Board (HUM00230299) prior to beginning recruitment.

Overall study participation will last approximately 6 weeks. Participants will complete (1) a one-time baseline survey to collect participant demographics, diabetes history, and psychosocial influences on T1D self-management; (2) 30 days of daily SMS text messaging surveys and CGM to evaluate real-time psychosocial influences on T1D self-management and glycemic levels; and (3) an optional semistructured interview to gain additional insights on T1D self-management approaches. Following enrollment and prior to data collection, participants will receive basic training on how to complete the daily surveys using a practice survey that includes sample questions with various formats (open-ended and closed-ended) and provides examples of survey prompts and outro messages. All survey data will be collected and managed using a secure REDCap (Research Electronic Data Capture; Vanderbilt University) database [48,49]. A study timeline and overview of data collection procedures for each participant are presented in Figure 2.

Participant demographic information and clinical characteristics will be collected upon enrollment using an online survey. Demographic measures include age, race and ethnicity, gender, and student and/or employment status. Clinical characteristics include age at diabetes diagnosis, recent (within the last 12 mo) hospitalization for diabetes-related complications, typical medical care for diabetes, and social support for diabetes management. Finally, questions on diabetes technology use include which type and brand of CGM and insulin pump are used, for how long, and typical use (how many d in the mo and use of automated insulin delivery). The participant demographics and clinical characteristics items are included in Multimedia Appendix 1.

Study participants will complete a one-time baseline survey. Measures were selected to align with components of the COM-B model and the literature on psychosocial influences on self-management and diabetes device use. The survey includes measures of diabetes self-management behaviors, depression, diabetes distress, attitudes towards diabetes technology, problem-solving, and self-efficacy with diabetes self-management. A summary of survey instruments, sample items, and their alignment with the COM-B model is included in Table 1. Additional details can be found in Multimedia Appendix 2.

SMS text messaging survey data will be collected using the “survey as a text conversation” feature in REDCap [48,49], which mimics a typical SMS text messaging conversation where questions are sent one-at-a-time and each question is sent only after a response is received. Surveys will automatically close after completion or after 3 hours if no response is received, and the next survey will automatically be sent at the prescheduled time. SMS text messaging was selected for daily surveys based on discussions with T1DYAC members, the high prevalence of smartphone use among US teens (95%) [58], and high rates of smartphone initiation by the age of 12 years (75%) [59]. In addition, the daily and end-of-day surveys were designed to have no more than 10 questions at a time to reduce the overall participant burden.

Beginning after the baseline survey is completed, participants will receive SMS text messaging survey invitations 4 times per day on a fixed schedule in the morning (10 AM), afternoon (2 PM), early evening (6 PM), and end-of-day (10 PM) [3] The morning, afternoon, and early evening surveys focus on interactions with their diabetes technology, challenges in managing diabetes, and other events that have occurred in the last few hours. For example, 1 question asks, “What have you been doing in the last few hours (ie, exercising, eating, at school, etc)?” In the evenings (10 PM), participants will complete a guided reflection using open-ended questions. During the reflection, they will be asked to open their diabetes device app and review the graph of their glucose levels throughout the day. Qualitative and quantitative data will be integrated during data collection as participants provide written reflections on their CGM data in daily SMS text messaging surveys. In addition, they will share information about what activities they did that day and how they feel events and activities impacted their diabetes. For example, the first question asks, “Overall, how did you feel about your diabetes today?” Sample daily and end-of-day survey question items and their alignment with the COM-B model can be found in Table 1. Complete daily and end-of-day SMS text messaging surveys are included in Multimedia Appendix 3. Participant response rates will be tracked automatically by REDCap throughout the study. In addition, participants will complete an open-ended text messaging survey after the 30 days of daily surveys are completed to qualitatively share their study experiences, burden, and study acceptability.

During the same 30 days, participants will be asked to continue CGM as prescribed to collect glucose levels. Depending on the device manufacturer, glucose levels are measured automatically every 1 to 15 minutes. By not introducing a new device, reactivity is reduced, and we are able to capture typical self-management behaviors and glycemic levels in this population. Likewise, participants do not have to learn to use a different device solely for the purpose of this study. Glycemic data are not blinded from participants to maintain normalcy with their daily routine and to maintain their safety in managing potentially dangerous high and low glucose levels during the study period. After completion of the daily survey portion of the study, participants will receive a secure REDCap survey link to share their CGM data from the previous 30 days downloaded as a .csv file from their CGM platform (eg, Dexcom Clarity). In addition, participants will be asked to reflect on the feasibility and acceptability of the study through 7 survey questions. Questions include study barriers, facilitators, and reflections on the length and timing of data collection (see Multimedia Appendix 4 for full closeout survey).

A subsample of participants (n=20‐30) will be invited to complete a 1-hour follow-up interview with a member of the study team trained in qualitative data collection and analysis. A maximum variation sampling approach will be leveraged and rely on recent HbA_1c levels, baseline survey scores, age, gender, race or ethnicity, diabetes duration, and diabetes device use as well as responses to the SMS text messaging surveys. Interviews are intended to further explore their approach to self-management using their diabetes devices. Interview prompts will focus on the participants’ daily approach to managing their diabetes, including decision-making, problem-solving, and interactions with technology. Alignment of interview prompts with the COM-B model is described in Table 1. Likewise, a portion of the interview will be dedicated to exploring study feasibility and acceptability, including perceived study burden and experiences of behavior change during the study period. Interviews will be conducted using Health Insurance Portability and Accountability Act (HIPAA)-compliant web conference tools (eg, Zoom; Zoom Communication, Inc), audio-video recorded, and transcribed for analysis. In line with a traditional qualitative approach, analysis and data collection will be completed simultaneously, and the final sample size will be determined by theoretical saturation.

Qualitative (open-ended survey responses and interview transcripts) and quantitative (baseline survey and CGM) data will be analyzed simultaneously and integrated to address the study aims. For aim 1, we will use a constructivist grounded theory approach, integrating findings from the daily survey and interview data with CGM and psychosocial measures. For aim 2, we will use a descriptive approach, integrating findings from the closeout survey and follow-up interview to assess feasibility and acceptability. Figure 3 summarizes the analysis and integration plan across data sources.