ArticleFrontiers in public health2025
A w-ACT model for sarcopenia among community-dwelling older adults based on National Basic Public Health Services: development and validation study.
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 1 paper.
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Who cites it
1 citing paper in PubMed.
- Enhancing sarcopenia screening in primary care: a machine learning approach using simple physical tests vs. SARC-F in 2,788 community-dwelling older adults.Frontiers in public health · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Sarcopenia leads to substantial health and well-being impairments in older adults, underscoring the need for early detection to facilitate intervention. Despite its importance, community settings face challenges with data accessibility, model interpretability, and predictive accuracy. Objective: To develop a local, data-driven, machine learning-based predictive model aimed at identifying high-risk sarcopenia populations among community-dwelling older adults. Methods: The study encompassed 910 participants over 60 years old from the National Basic Public Health Services (NBPHS) program. Sarcopenia was ascertained by the Asian Working Group for Sarcopenia (AWGS) criteria. We leveraged Logistic Regression and seven additional machine learning models for risk prediction, employing the LASSO method for feature selection, employing LASSO regression with 10-fold cross-validation for feature selection. The optimal lambda.1se threshold identified four key predictors forming the w-ACT model (weight, Age, Calf circumference, Triglycerides). A comprehensive set of 10 diagnostic indicators was utilized to assess model performance. Results: The Random Forest-based w-ACT model demonstrated superior performance, with an AUC of 0.872 (95%CI: 0.793,0.950) (validation set) and MCC of 0.566, 0.841 (95%CI: 0.777,0.904) (test set) and MCC of 0.511. Key predictors included weight, age, calf circumference, and triglycerides. SHAP analysis confirmed clinical interpretability. Conclusion: The w-ACT model offers a reliable, interpretable tool for community-based sarcopenia screening, leveraging accessible variables to guide preventive care.
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Registered trials
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