ArticleScientific reports2025
Machine learning modeling for predicting adherence to physical activity guideline.
Article in Scientific reports, 2025. 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.
- Physical activity level and its determinants in patients with cancer: a secondary analysis using a decision-tree approach.Journal of cancer survivorship : research and practice · 2026Article
- Why they do not move: An explainable machine learning analysis of physical activity barriers in obese adolescents and tool translation.BMC public health · 2026Article
- Machine Learning in Adapted Physical Activity: Clinical Applications, Monitoring, and Implementation Pathways for Personalized Exercise in Chronic Conditions: A Narrative Review.Journal of functional morphology and kinesiology · 2026Review
- Neural Complexity of Implicit Attitudes Predicts Exercise Behavior in Hypertensive Patients: An EEG Entropy Study.Brain sciences · 2026Article
- Analysis of lifestyle factors associated with physical activity participation among university female students using deep belief networks.Frontiers in psychology · 2026Article
- Development and Validation of Predictive Models for Non-Adherence to Antihypertensive Medication.Medicina (Kaunas, Lithuania) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
This study aims to create predictive models for PA guidelines by using ML and examine the critical determinants influencing adherence to the PA guidelines. 11,638 entries from the National Health and Nutrition Examination Survey were analyzed. Variables were categorized into demographic, anthropometric, and lifestyle categories. 18 prediction models were created by 6 ML algorithms and evaluated via accuracy, F1 score, and area under the curve (AUC). Additionally, we employed permutation feature importance (PFI) to assess the variable significance in each model. The decision tree using all variables emerged as the most effective method in the prediction for PA guidelines (accuracy = 0.705, F1 score = 0.819, and AUC = 0.542). Based on the PFI, sedentary behavior, age, gender, and educational status were the most important variables. These results highlight the possibilities of using data-driven methods with ML in PA research. Our analysis also identified crucial variables, providing valuable insights for targeted interventions aimed at enhancing individuals' adherence to PA guidelines.
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Registered trials
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