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Abstract <p>The growing prevalence of diabetes necessitates effective management strategies that enable patients to monitor and control their condition in a convenient and accessible manner. Mobile health (mHealth) applications have emerged as powerful tools in diabetes management, allowing users to track glucose levels, medication adherence, diet, and physical activity. However, the abundance of available mHealth apps makes identifying the most suitable option challenging. This study proposes a comprehensive multi-criteria group decision-making (MCGDM) framework that integrates the Fuzzy Best-Worst Method (FBWM), the Weighted Heronian Mean (WHM), and Fuzzy TOPSIS (FTOPSIS) for evaluating diabetes management mHealth apps. The framework determines criteria weights using FBWM, aggregates expert opinions through WHM, and ranks the alternatives via FTOPSIS. This hybrid approach leverages the strengths of each method to address the uncertainty inherent in expert judgments and offers a robust alternative to traditional Likert-based evaluations. A case study on diabetes app selection demonstrates the method’s applicability. Although the core structure relies on TOPSIS, VIKOR and MARCOS were also employed to benchmark WHM’s effect. Statistical tests confirmed that the WHM enhances both robustness and sensitivity. This study contributes an integrated framework to support systematic decision-making in the mHealth domain.</p> Graphic abstract <p></p>

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A Hybrid Decision-Making Framework for Evaluating mHealth App Quality: Integrating Fuzzy BWM with the Weighted Heronian Mean

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月21日

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  • Melda Kevser Akgün,
  • Servet Soygüder

摘要

Abstract

The growing prevalence of diabetes necessitates effective management strategies that enable patients to monitor and control their condition in a convenient and accessible manner. Mobile health (mHealth) applications have emerged as powerful tools in diabetes management, allowing users to track glucose levels, medication adherence, diet, and physical activity. However, the abundance of available mHealth apps makes identifying the most suitable option challenging. This study proposes a comprehensive multi-criteria group decision-making (MCGDM) framework that integrates the Fuzzy Best-Worst Method (FBWM), the Weighted Heronian Mean (WHM), and Fuzzy TOPSIS (FTOPSIS) for evaluating diabetes management mHealth apps. The framework determines criteria weights using FBWM, aggregates expert opinions through WHM, and ranks the alternatives via FTOPSIS. This hybrid approach leverages the strengths of each method to address the uncertainty inherent in expert judgments and offers a robust alternative to traditional Likert-based evaluations. A case study on diabetes app selection demonstrates the method’s applicability. Although the core structure relies on TOPSIS, VIKOR and MARCOS were also employed to benchmark WHM’s effect. Statistical tests confirmed that the WHM enhances both robustness and sensitivity. This study contributes an integrated framework to support systematic decision-making in the mHealth domain.

Graphic abstract