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Background <p>Community residents with clustered cardiovascular risk factors may exhibit marked heterogeneity in health behavior patterns, posing challenges for community-based cardiovascular prevention and health behavior support. This study aimed to identify subgroups with different levels of health behavior and examine factors associated with subgroup membership using explainable machine learning.</p> Methods <p>This secondary analysis used cross-sectional questionnaire data from 475 community residents with clustered cardiovascular risk factors. Health behaviors were assessed using the six dimensions of the Health-Promoting Lifestyle Profile II (HPLP-II). Latent profile analysis (LPA) was used to identify latent health behavior profiles that may reflect different behavioral support needs. The sample was split into training and internal validation sets at an 8:2 ratio. Variable selection was performed using Least Absolute Shrinkage and Selection Operator regression and Random Forest. Five classification models were developed and compared, and the model retained for exploratory analysis was subsequently interpreted using Shapley Additive Explanations (SHAP).</p> Results <p>Four health behavior subgroups were identified: low health-promoting behavior profile (9.7%), low-to-moderate health-promoting behavior profile (45.5%), moderate-to-high health-promoting behavior profile (34.3%), and high health-promoting behavior profile (10.5%). In the internal validation set, the Random Forest (RF) model showed a comparatively balanced performance across multiple evaluation metrics, with an accuracy of 0.777, Kappa of 0.640, macro-averaged precision of 0.852, macro-averaged F1 score of 0.714, and area under the curve of 0.892; however, its minority-class identification requires further validation. SHAP analysis indicated that health behavior capability, social support, self-efficacy, and age were key contributors to subgroup classification.</p> Conclusions <p>Health behaviors among community residents with clustered cardiovascular risk factors exhibit marked heterogeneity. These findings provide descriptive evidence of heterogeneity in health behaviors and may support stratified approaches to health behavior support and community-based cardiovascular prevention strategies.</p>

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Health behavior profiles and potential support needs among community residents with clustered cardiovascular risk factors: a latent profile and explainable machine learning analysis for community-based prevention

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Kaihe Li,
  • Xinyi Yan,
  • Ying Zhou,
  • Xuefen Lan,
  • Xiaofen Li,
  • Juanjuan Mai,
  • Minqi Xia,
  • Ruiming Chen

摘要

Background

Community residents with clustered cardiovascular risk factors may exhibit marked heterogeneity in health behavior patterns, posing challenges for community-based cardiovascular prevention and health behavior support. This study aimed to identify subgroups with different levels of health behavior and examine factors associated with subgroup membership using explainable machine learning.

Methods

This secondary analysis used cross-sectional questionnaire data from 475 community residents with clustered cardiovascular risk factors. Health behaviors were assessed using the six dimensions of the Health-Promoting Lifestyle Profile II (HPLP-II). Latent profile analysis (LPA) was used to identify latent health behavior profiles that may reflect different behavioral support needs. The sample was split into training and internal validation sets at an 8:2 ratio. Variable selection was performed using Least Absolute Shrinkage and Selection Operator regression and Random Forest. Five classification models were developed and compared, and the model retained for exploratory analysis was subsequently interpreted using Shapley Additive Explanations (SHAP).

Results

Four health behavior subgroups were identified: low health-promoting behavior profile (9.7%), low-to-moderate health-promoting behavior profile (45.5%), moderate-to-high health-promoting behavior profile (34.3%), and high health-promoting behavior profile (10.5%). In the internal validation set, the Random Forest (RF) model showed a comparatively balanced performance across multiple evaluation metrics, with an accuracy of 0.777, Kappa of 0.640, macro-averaged precision of 0.852, macro-averaged F1 score of 0.714, and area under the curve of 0.892; however, its minority-class identification requires further validation. SHAP analysis indicated that health behavior capability, social support, self-efficacy, and age were key contributors to subgroup classification.

Conclusions

Health behaviors among community residents with clustered cardiovascular risk factors exhibit marked heterogeneity. These findings provide descriptive evidence of heterogeneity in health behaviors and may support stratified approaches to health behavior support and community-based cardiovascular prevention strategies.