ArticleDigital health
Exploring the influential factors and improvement strategies for digital information literacy among the elderly: An analysis based on integrated learning algorithms.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The replica dilemma: older patients' views on virtual models made from their health data-a qualitative study.BMC health services research · 2026Article
- A scoping review on the feasibility and efficacy of mobile health applications in the management of breakthrough pain in cancer patients.BMC health services research · 2026Article
- Digital health literacy and associated factors among community-dwelling older adults with multimorbidity: a cross-sectional study.Frontiers in public health · 2026Article
- Exploring the relationship between smartphone use diversity and depressive symptoms among older adults.BMC public health · 2025Article
- A study on the relationship between technological affordance of age-appropriate smart wearable devices and the well-being of older adults.Frontiers in public health · 2025Article
- Digital health literacy among urban community-dwelling older adults in China: Current status and determinants.Digital healthArticle
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Abstract
Objective: Despite previous research identifying factors such as age, education level, income, and interest in technology that influence digital literacy among the elderly, this study attempts to use machine learning algorithms, especially ensemble learning algorithms, to predict and identify the key factors that affect the digital information literacy of the elderly, so as to propose effective strategies to improve the elderly's ability to utilize digital information and better integrate into the digital society. Methods: This study used primary data on older adults from the Digital Divide Survey 2022 conducted by the Korea National Information Society Agency. A predictive model was built, and 15 variables that were highly important in predicting digital information literacy were identified. Prediction accuracy was assessed using an ensemble of algorithms including Random Forest, LGBM, XGBoost, AdaBoost, and CatBoost. Results: The study found that in addition to demographic factors and personal technology use ability factors, relationship support factors and social digital environment factors are also important predictors of digital information literacy for the elderly. Among different predictive models, the CatBoost model, based on boosting ensemble, exhibited the highest predictive accuracy at 86.2%, followed by Random Forest (85.5%), LGBM (85.2%), XGBoost (84.5%), and AdaBoost (83.8%). The predictive accuracies of these models were higher than those of traditional machine learning models, indicating the effectiveness of ensemble learning algorithms in predicting digital information literacy among the elderly. Conclusions: The academic significance of this study lies in the application of artificial intelligence technologies to the social sciences, specifically demonstrating the effectiveness of ensemble learning algorithms in predicting factors influencing the digital literacy levels of the elderly. This approach provides a novel and powerful tool for addressing complex social issues. The practical significance lies in the proposed strategies for improving the digital literacy of the elderly based on the research results, including education and training, social relationship support, social participation, technical support, and policy formulation, aiming to help the elderly better adapt to the digital environment, narrow the digital divide, and enhance the elderly's sense of participation and happiness in the digital society.
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