ArticleFrontiers in public health2025
Individual cardiorespiratory fitness exercise prescription for older adults based on a back-propagation neural network.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Article
- Toward Precision Cardiac Rehabilitation: Current Limitations and Future Opportunities of Omics and Artificial Intelligence.Sports medicine (Auckland, N.Z.) · 2026Review
- The Role of Artificial Intelligence in Exercise-Based Cardiovascular Health Interventions: A Scoping Review.Journal of functional morphology and kinesiology · 2025Review
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Authors and funding
4 authors.
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Abstract
Introduction: To explore and develop a backpropagation neural network-based model for predicting and generating exercise prescriptions for improving cardiorespiratory fitness in older adults. Methods: The model is based on data from 68 screened studies. In addition, the model was validated with 64 older adults aged 60-79 years. The root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R Results: The results showed that (1) The mean error ratios for predicting exercise intensity, time and period were 7% ± 12, -5% ± 9% and - 7% ± 14%, respectively, indicating that the estimates were in good agreement with the expected results. (2) Of the 61 subjects who completed the assigned program, cardiorespiratory fitness improved significantly compared with pre-exercise. Improvements ranged from 9.2-10% and 8.9-15.8% for female and male subjects. (3) In addition, 71 and 94% of subjects (43/61) showed cardiorespiratory improvement within plus or minus one standard deviation and plus or minus 1.96 times standard deviation. Discussion: A neural network-based model for exercise prescription for cardiorespiratory fitness improvement in older adults is feasible and effective.
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