Accepted for/Published in: JMIR Formative Research
Date Submitted: Apr 7, 2025
Date Accepted: Oct 30, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Developing eHealth Interventions to Improve Diabetes Management in Emerging Adulthood: A Qualitative Formative Study
ABSTRACT
Background:
Emerging adulthood is a high-risk period during which many with type 1 diabetes (T1D) demonstrate suboptimal diabetes management and glycemic control. There is a need for effective and scalable interventions designed specifically for this population. Technology-based approaches are readily accessed by this age group. Further, interventions that are consistent with self-determination theory (SDT) – which posits the fulfillment of psychological needs for autonomy, self-efficacy, and relatedness promote intrinsic motivation for change – may resonate well with emerging adults’ (EAs) developmental needs for establishing independence, autonomy, and growing their social network.
Objective:
To gather patient feedback on three SDT-informed mHealth interventions for EAs with T1D: a Motivational Interviewing-based counseling intervention, one-way text message reminders to complete diabetes care, and a question prompt tool to empower EAs to actively participate during medical visits.
Methods:
In this qualitative formative study, 20 EAs reviewed and provided feedback on the newly developed interventions via individual interviews. Interviews were analyzed using Framework Matrix Analysis, an efficient approach to inductive thematic analysis.
Results:
EAs provided high ratings for intervention acceptability and helpfulness. EAs appreciated the technology-based approach and the tailoring to their demographic characteristics, illness experiences, and personal preferences. They also highlighted SDT-related intervention elements that aligned with SDT. Recommendations for intervention improvement included additional tailoring to personal preferences including the frequency and duration of intervention, intervention content, and personalizing reminders with the recipient’s name.
Conclusions:
EA feedback supports the acceptability and utility of this intervention and will be used to refine the interventions. The unique contribution of each intervention to improvements in glycemic control will be tested in a randomized controlled trial using the multiphase optimization strategy (MOST) to build the most efficacious multicomponent intervention. Clinical Trial: N/A
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