From Learning to Habit: Microlearning as a Promotive and Secondary Preventive Educational Strategy to Improve Diabetes Mellitus Self-Management in Adolescents

Abstract

Diabetes mellitus in adolescents is a chronic condition that requires effective self-management to prevent long-term complications. Microlearning, an educational approach based on short, interactive, and digital modules, has emerged as a potential strategy to enhance adolescents’ knowledge, skills, and motivation in diabetes self-management. This literature review aims to examine the effectiveness of microlearning as a promotive and secondary preventive intervention and to identify factors influencing its success. Methods: A systematic literature search was conducted using PubMed, Scopus, and ScienceDirect databases, following the PRISMA 2020 framework. Three review stages (identification and screening, eligibility assessment, and inclusion) were applied based on PICOS criteria. Out of 95 initial articles, 4 studies met the inclusion criteria. All included studies employed experimental or pilot intervention designs involving adolescents with type 1 or type 2 diabetes, assessing knowledge, attitudes, and self-management behaviors. Results and Discussion: Microlearning was shown to improve adolescents’ knowledge, motivation, and self-management skills. Key factors for success included adequate intervention duration, content personalization, support from healthcare professionals, access to technology, and family involvement. Digital interventions also facilitated early detection of complications, glycemic control, and preventive behaviors. Limitations of the studies included small sample sizes, short intervention durations, and potential reporting bias, limiting the generalizability of findings. Conclusion: Microlearning demonstrates potential as an effective promotive and secondary preventive educational strategy for enhancing diabetes self-management in adolescents. Further research with larger samples, longer intervention durations, and objective evaluation methods is needed to strengthen empirical evidence and optimize implementation in clinical practice and digital health contexts.