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Explainable Machine Learning for Assessing Digital Health Literacy in Older Adults: Validation and Development of a

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Summary

This study developed a machine learning model to predict digital health literacy (DHL) in older adults by combining performance and self-assessments. The model identified factors like digital engagement and health management as key predictors of DHL.

Keywords:
digital health caredigital health literacyeHealth literacymHealthmachine learning

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Area of Science:

  • Gerontology
  • Health Informatics
  • Machine Learning

Background:

  • Digital health literacy (DHL) is crucial for older adults to navigate digital health care.
  • Current assessments often rely on self-reports, lacking objective performance measures.
  • A data-driven approach integrating performance and self-assessment is needed to accurately gauge DHL in older adults.

Purpose of the Study:

  • To develop and validate a machine learning (ML) approach for predicting DHL levels in older adults.
  • To integrate both performance-based and self-assessed evaluations for a comprehensive DHL assessment.
  • To identify key determinants influencing DHL in the older adult population.

Main Methods:

  • A two-stage framework using two independent datasets.
  • Stage 1: Assessed digital comprehension in 30 older adults and collected self-reported DHL from 1000 older adults via surveys (Digital Health Literacy Scale, KeHEALS).
  • Stage 2: Trained and validated ML models (including categorical boosting) to predict DHL using identified features, with SHAP analysis for interpretation.

Main Results:

  • Performance assessment linked device use and education to comprehension; alcohol intake showed negative association.
  • Self-assessed DHL correlated with interest in health apps, self-care confidence, age, lifestyle factors, device use, and exercise.
  • Categorical boosting achieved 0.785 accuracy; SHAP analysis highlighted self-care confidence, health information search, app interest, device use, and exercise as positive predictors, while older age and lifestyle factors were negative.

Conclusions:

  • DHL in older adults is influenced by digital engagement and health management behaviors.
  • An explainable ML framework integrating performance and self-assessment provides a structured approach to evaluate DHL.
  • Findings can inform personalized digital health interventions for older adults in clinical and community settings.