With the global aging trend, the management of geriatric risks faced by older adults and the effectiveness of health education for this population are gaining increasing importance. Literature indicates that health education for older adults, when delivered through methods that support individual engagement and the use of digital tools, yields more effective results in terms of learning and behavior change. This doctoral study aims to evaluate the effect of a digital education model, structured based on the Health Belief Model and developed using branching scenario animation technique, on geriatric risk knowledge level and frailty status among older adults residing in nursing homes.
The study will be conducted as a randomized controlled trial in three nursing homes in Istanbul, with measurements at baseline (T0), immediately post-intervention (T1), 1-month follow-up (T2), and 3-month follow-up (T3). Based on power analysis, 66 participants will be randomly assigned to intervention and control groups (33 per group) using block randomization. The intervention group will receive scenario-based digital animation education developed on the Vyond platform and structured according to the theoretical components of the Health Belief Model. The control group will receive standard care during the study period and will be offered the same digital animation education after completion of all assessments (post 3-month follow-up).
The primary outcome is geriatric risk knowledge level, assessed using an interactive digital game called "Knowledge Wheel." Secondary outcomes include frailty status, measured by the Edmonton Frail Scale, and technology acceptance. Changes in knowledge and frailty levels measured through assessment tools will reveal the short- and medium-term effects of the education. Thus, the model's impact on health-related behavior change in older adults will be comprehensively evaluated.
The unique aspect of this study is that it will be one of the first experimental studies to implement an interactive scenario-based digital education model in older adults. The findings are expected to contribute to the development of age-friendly health education models.
