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Background <p>With global populations ageing rapidly, promoting healthy ageing has become increasingly important, particularly in Hong Kong, which has one of the world’s highest life expectancies.</p> Objectives <p>To identify distinct profiles in terms of healthy ageing among Hong Kong adults and to examine their associated factors.</p> Methods <p>We conducted a cross-sectional survey of adults aged 18 years and older in Hong Kong, using a culturally adapted 15-item Healthy Ageing Questionnaire (HAQ). K-modes clustering was applied to identify latent subgroups, with the optimal solution determined by internal validity indices, model-based comparisons, and clinical interpretability based on medoids. Cluster stability was evaluated through bootstrap resampling with Jaccard similarity. Between-cluster differences were examined using ANOVA or chi-square tests, followed by item-level analyses and regression models to identify factors associated with HAQ scores.</p> Results <p>A total of 2,024 completed HAQ questionnaires were included in the analysis. Based on both statistical metrics and clinical considerations, three clusters were identified, reflecting low, moderate, and high healthy ageing profiles. Cluster 1 (lowest scores) was associated with lower education, smoking, and multimorbidity, while Cluster 3 (highest scores) showed higher education levels, non-smoking status, and fewer chronic conditions. HAQ items related to mental health, emotional well-being, and physical activity were most discriminative across clusters. Regression analyses revealed that older age, non-smoking status, and higher educational attainment were consistently associated with higher healthy ageing scores across clusters. Conversely, the presence of multiple chronic conditions, particularly three or more, was linked to lower scores. Gender and living arrangement showed no significant associations.</p> Conclusion <p>Healthy ageing is shaped by multiple interrelated factors. Cluster-based profiling highlights education, smoking, and chronic conditions as key targets for developing tailored public health strategies across different life stages.</p>

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Cluster-based profiling of healthy ageing to inform precision interventions in Hong Kong

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月30日

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  • Jed Montayre,
  • Fen Liu,
  • Juliet Chigozie Donatus Ezulike,
  • Chun Hei Glen Cheng,
  • Moses Ch Dye,
  • Ka Man Carman Leung,
  • Wenjing Ning,
  • Chi On Stanley Shiu,
  • Kay Kuo

摘要

Background

With global populations ageing rapidly, promoting healthy ageing has become increasingly important, particularly in Hong Kong, which has one of the world’s highest life expectancies.

Objectives

To identify distinct profiles in terms of healthy ageing among Hong Kong adults and to examine their associated factors.

Methods

We conducted a cross-sectional survey of adults aged 18 years and older in Hong Kong, using a culturally adapted 15-item Healthy Ageing Questionnaire (HAQ). K-modes clustering was applied to identify latent subgroups, with the optimal solution determined by internal validity indices, model-based comparisons, and clinical interpretability based on medoids. Cluster stability was evaluated through bootstrap resampling with Jaccard similarity. Between-cluster differences were examined using ANOVA or chi-square tests, followed by item-level analyses and regression models to identify factors associated with HAQ scores.

Results

A total of 2,024 completed HAQ questionnaires were included in the analysis. Based on both statistical metrics and clinical considerations, three clusters were identified, reflecting low, moderate, and high healthy ageing profiles. Cluster 1 (lowest scores) was associated with lower education, smoking, and multimorbidity, while Cluster 3 (highest scores) showed higher education levels, non-smoking status, and fewer chronic conditions. HAQ items related to mental health, emotional well-being, and physical activity were most discriminative across clusters. Regression analyses revealed that older age, non-smoking status, and higher educational attainment were consistently associated with higher healthy ageing scores across clusters. Conversely, the presence of multiple chronic conditions, particularly three or more, was linked to lower scores. Gender and living arrangement showed no significant associations.

Conclusion

Healthy ageing is shaped by multiple interrelated factors. Cluster-based profiling highlights education, smoking, and chronic conditions as key targets for developing tailored public health strategies across different life stages.