Interventions to promote medication adherence among children with epilepsy: An integrative review

Problem: Many children with epilepsy face challenges in adhering to their medication, leading to inadequate seizure control. However, the most effective intervention is still unclear. This integrative review's main goal was to examine and synthesize the existing literature on interventions for promoting medication adherence in children with epilepsy. Eligibility criteria: This integrative review followed Whittemore and Knafl's five-stage framework. Four electronic databases (PubMed, ScienceDirect, Scopus, and CINAHL Complete) were systematically searched from 2013 until 2024 to identify eligible studies published in the English language. The key search terms included "Children with epilepsy" AND "medication adherence" AND "intervention." Studies reporting on the implementation and evaluation of medication adherence interventions in children with epilepsy were eligible. Quality assessment and narrative synthesis were subsequently undertaken. Sample: A total of 17 studies were included in the review. Results: Five interventions were found, including educational, behavioral, and mixed intervention types, using technology and family involvement. Promoting medication adherence is crucial, but tailored interventions for different age groups and sustained support are needed. Conclusions: Promoting medication adherence is of utmost importance to enhance the knowledge of children who have epilepsy and their families, and to increase medication adherence. However, there is still a need to develop interventions that are appropriate for children of different ages and their families, which should be suitable and sustainable during treatment. Implications: Pediatric nurses should consider socioeconomic factors, ethnicity, family functioning, and parental distress. Strategies include monitoring adherence, continuous communication, and technology support for children with epilepsy during treatment. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.