Is integrating video into tech-based patient education effective for improving medication adherence? – A review

Abstract

Abstract Patient education is a crucial part of the efforts to improve the level of medication adherence. Successful diabetes self-management will be achieved if health practitioners (doctors, pharmacists, nurses, and other health-related professionals) provide education to their patients. One of the multimedia platforms that are now popular in patient education and health promotion is video. This study primarily aimed to review published research examining integration of video-based digital interventions into tech-based patient education methods to promote medication adherence in diabetic patients. All of the articles presented in this review were collected from the PubMed, Science Direct, and Sage Publication databases. This review compared study samples, intervention characteristics (length of intervention and learning media), and adherence outcomes, as well as additional outcomes across the studies. Of the 304 articles reviewed, seven studies that met the criteria were found and included in the review. Overall, 57.14% of the integrated video-based interventions demonstrated improved medication adherence and 42.86% demonstrated improvement in other related health outcomes. The interventions were 7 weeks to 6 months in length and were in the form of storytelling with videos, smartphone apps, educational programs, or web-based education. It can be concluded that video-based digital interventions aimed to improve medication adherence in diabetic patients are promising. For optimum outcomes, this study highly suggests that a combination of video (synchronous and asynchronous) and a good patient education program is an ideal form of intervention for patients with type 2 diabetes. Regarding the education platform, the intervention could be humanoid robots, storytelling, mobile health (mHealth) apps, websites, robot-enhanced diabetes care, and other related tech-based programs or even conventional models.