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  3. Deep Learning To Promote Health Through Sports And Physical Training.
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  3. Deep Learning To Promote Health Through Sports And Physical Training.

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Deep learning to promote health through sports and physical training.

Xinyue Li1

  • 1Department of Sports, Nanjing Forestry University, Nanjing, China.

Frontiers in Public Health
|June 11, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel Health Improvement Score (HIS) prediction model using deep learning. The model accurately forecasts health improvements from diverse data, outperforming existing methods.

Keywords:
artificial intelligencedeep learninghealth improvementphysical trainingsports sciencetime-series analysis

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Area of Science:

  • Biomedical Engineering
  • Data Science
  • Health Informatics

Background:

  • Physical activity is vital for health and chronic disease prevention.
  • Accurately assessing health improvements from sports and training is challenging.
  • Deep learning and time-series analysis offer new possibilities for personalized health prediction.

Purpose of the Study:

  • To develop a Health Improvement Score (HIS) prediction model.
  • To leverage deep learning and time-series analysis for enhanced health trend assessment.
  • To create a personalized and accurate model for evaluating health improvements.

Main Methods:

  • A sequence-to-sequence deep learning architecture with Long Short-Term Memory (LSTM) networks and an attention mechanism was employed.
  • The model integrates heterogeneous time-series data: physiological, activity, sleep, and body measurements.
  • Data from 384 participants over 32 days were used for training and evaluation.

    • Main Results:

    • The proposed HIS prediction model demonstrated superior performance compared to traditional and machine learning models.
    • Achieved 22.8% lower Mean Absolute Error (MAE) and 19.3% lower Root Mean Squared Error (RMSE).
    • Reported 6.5% higher R-squared and 7.9% higher Explained Variance Score (EVS).

    Conclusions:

    • The HIS prediction model effectively captures complex temporal dependencies in health data.
    • The model significantly improves the accuracy of health improvement predictions.
    • This approach offers a more personalized and precise method for monitoring health progress.