Wearable health technologies support continuous monitoring and personalized healthcare. However, their sustained use depends on key attributes such as functionality, usability, safety in design, customization, openness, and interoperability, which collectively shape trust and trustworthiness. Selecting an optimal device is a complex Multi-Criteria Decision-Making (MCDM) problem, as it requires evaluating alternatives across interrelated and sometimes conflicting attributes. Existing approaches often rely on subjective criteria weighting or fuzzy models with limited expressive capacity. This study proposes a novel hybrid group decision-making framework that integrates the Opinion Weight Criteria Method (OWCM) with the Additive Ratio Assessment (ARAS) method, extended under the Interval-Valued Fermatean Fuzzy Set (IVFFS) environment. The framework models higher‑order uncertainty, derives balanced criteria weights, and offers a transparent ranking of wearable devices. A case study evaluating eight devices across six trust‑related criteria demonstrates the usefulness of the approach. Sensitivity analysis confirms the stability and robustness of the final ranking. The results contribute a mathematically grounded and interpretable decision-support model for healthcare technology assessment. Findings show that customization and safety in design were the most influential criteria, while interoperability ranked lowest. The proposed framework offers a transparent, robust, and adaptable tool to support end-users, healthcare professionals, and policymakers in selecting trustworthy wearable healthcare devices.
Bilquise et al. (Mon,) studied this question.