โก Quick Summary
This study introduces a machine learning framework for generating personalized fitness recommendations that align with national health goals. Utilizing the NHANES dataset, the framework aims to optimize physical activity planning while ensuring fairness across demographic subgroups.
๐ Key Details
- ๐ Dataset: National Health and Nutrition Examination Survey (NHANES)
- ๐งฉ Features used: Biometric, behavioral, and demographic data
- โ๏ธ Technology: XGBoost, Decision Trees, Artificial Neural Networks
- ๐ Performance: XGBoost achieved a MeanIoU of 0.789 and F1 scores exceeding 0.79
๐ Key Takeaways
- ๐ Personalized fitness interventions are crucial for addressing individual health variability.
- ๐ก Machine learning can effectively tailor fitness recommendations to align with national health strategies.
- ๐ฉโ๐ฌ Data integration from NHANES and BRFSS enhances the relevance of predictions.
- ๐ XGBoost outperformed other models in terms of accuracy and fairness.
- ๐ Fairness gaps were maintained below 0.05 across demographic subgroups.
- ๐ค Model consistency was observed across age, gender, and ethnicity.
- ๐ High reliability and low variance were confirmed through residual error analysis.
- ๐ Potential for integration of AI in public health strategies is significant.
๐ Background
The increasing prevalence of chronic diseases and public health concerns has highlighted the need for data-driven fitness interventions. While national health programs provide general guidelines, they often fail to account for the individual variability in health status, lifestyle, and demographics. This study aims to bridge that gap by leveraging machine learning to create personalized fitness recommendations.
๐๏ธ Study
The research utilized the NHANES dataset, which includes comprehensive biometric, behavioral, and demographic data. To enhance the behavioral relevance of the predictions, supplemental variables from the Behavioral Risk Factor Surveillance System (BRFSS) were integrated, capturing psychological, motivational, and environmental factors that influence physical activity adherence.
๐ Results
The XGBoost model demonstrated superior performance, achieving a MeanIoU of 0.789 and F1 scores exceeding 0.79 across all risk categories. The model showed consistency across various demographic groups, with fairness gaps maintained below 0.05. This indicates a high level of reliability and low variance in the predictions.
๐ Impact and Implications
The findings of this study suggest a promising pathway for integrating AI-driven personalized fitness plans into national health strategies. By addressing individual variability and ensuring fairness, this approach could significantly enhance public health outcomes and promote equitable access to fitness interventions.
๐ฎ Conclusion
This research highlights the transformative potential of machine learning in personalizing fitness recommendations. By aligning these recommendations with national health goals, we can support more effective and equitable public health strategies. The future of fitness interventions looks promising with the integration of AI technologies!
๐ฌ Your comments
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Personalized fitness recommendations using machine learning for optimized national health strategy.
Abstract
Rising concerns over public health and chronic disease prevalence have intensified the demand for data-driven, personalized fitness interventions. While national health programs offer general guidelines, they often lack the granularity required to address individual variability in health status, lifestyle, and demographic context. This paper presents a machine learning framework to generate personalized fitness recommendations aligned with national health goals. Leveraging population-scale data, the aim is to optimize physical activity planning while maintaining fairness and clinical relevance across demographic subgroups. The study utilizes the National Health and Nutrition Examination Survey (NHANES) dataset, integrating biometric, behavioral, and demographic features. To enhance the behavioral relevance of our predictions, we integrated supplemental variables from the Behavioral Risk Factor Surveillance System (BRFSS), capturing psychological, motivational, and environmental factors that influence physical activity adherence. After preprocessing, models were developed using XGBoost, Decision Trees, and Artificial Neural Networks. Both regression (to estimate weekly activity minutes) and classification (to assign risk groups) tasks were addressed. Performance was evaluated through MeanIoU, Dice Score, sensitivity, and specificity. Demographic fairness was assessed via subgroup residuals and fairness gap analysis. XGBoost achieved superior performance, with a MeanIoU of 0.789 and F1 scores exceeding 0.79 across all risk categories. Model consistency was observed across age, gender, and ethnicity, with fairness gaps below 0.05. Residual error analysis and risk classification confirmed high reliability and low variance. The proposed system demonstrates the feasibility of using AI to personalize fitness plans at scale. It offers a pathway to integrate precision fitness with national policy, supporting equitable and effective public health strategies.
Author: [‘Chen J’, ‘Wang Y’]
Journal: Sci Rep
Citation: Chen J and Wang Y. Personalized fitness recommendations using machine learning for optimized national health strategy. Personalized fitness recommendations using machine learning for optimized national health strategy. 2025; 15:41652. doi: 10.1038/s41598-025-25566-4