## 4 Results and Discussions
### The Decision-Making Trial and Evaluation Laboratory-Based Analytic Network Process (DNAP) Analysis  
This study adopted the Decision-Making Trial and Evaluation Laboratory-based Analytic Network Process (DNAP) method to calculate the limit supermatrix, which served as the weight value for \"DANP importance calculation\" and final ranking. According to the analysis results in Table 4, the weight rankings of the seven design indicators--\"Compatibility\", \"Foresight\", \"Integration\", \"Comfort\", \"Appearance\", \"Customization\", and \"Intelligence\"--were determined. These indicators were identified as the most critical factors in wearable device design based on expert evaluations. Each indicator and its implications for wearable device design are discussed below:  
1. Compatibility  
The analysis identifies \"Compatibility\" as a critical factor in wearable device design, emphasizing its role in ensuring the smooth integration with existing technologies. High compatibility has been shown to positively influence user adoption and retention [67]. [PERSON] et al. (2020) further highlighted that compatibility is the primary determinant for the continued use of wearable health devices [52]. These findings confirm that software and hardware compatibility must be prioritized during the design and development process to meet user expectations and technological standards. 2. Foresight  
\"Foresight\" reflects the need to anticipate technological evolution and the integration of wearable devices into daily life and healthcare. While current references on wearable device foresight remain limited, interdisciplinary collaboration is crucial for future advancements [76]. This study confirms the importance of envisioning wearable devices not only as tools for data collection but also as platforms for the interaction among individuals and groups, aligning with predictions about embedded wearable technology [77]. 3. Integration  
\"Integration\" is vital for ensuring that wearable devices can seamlessly expand their functionalities and interact with IoT and artificial intelligence systems. [PERSON] et al. (2017) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Criterion** & **Criterion** & **Index** & **Index Weight** & **Goal Weight** & **Importance** \\ \cline{3-5}  & **Weight Value** & & **Value** & **Value** & **Ranking** \\ \hline \multirow{2}{*}{1-1. Quality Appearance} & \multirow{2}{*}{0.1431} & 1-1-1. Appearance & 0.4811 & 0.0689 & 5 \\ \cline{3-5}  & & 1-1-2. Comfort & 0.5189 & 0.0743 & 4 \\ \hline \multirow{3}{*}{1-2. Bio-Friendliness} & \multirow{3}{*}{0.1574} & 1-2-1. Safety & 0.3371 & 0.0531 & 10 \\ \cline{3-5}  & & 1-2-2. Sustainability & 0.3041 & 0.0479 & 11 \\ \cline{3-5}  & & 1-2-3. Convenience & 0.3587 & 0.0565 & 9 \\ \hline \multirow{3}{*}{2-1. Personalized Design} & \multirow{3}{*}{0.1866} & 2-1-1. Privacy & 0.3097 & 0.0578 & 8 \\ \cline{3-5}  & & 2-1-2. Customization & 0.3481 & 0.0650 & 6 \\ \cline{3-5}  & & 2-1-3. Intelligence & 0.3422 & 0.0638 & 7 \\ \hline \multirow{3}{*}{2-2. Function Enhancement} & \multirow{3}{*}{0.1728} & 2-2-1. Energy Consumption & 0.1807 & 0.0312 & 16 \\ \cline{3-5}  & & 2-2-2. Connectivity & 0.1978 & 0.0342 & 15 \\ \cline{1-1} \cline{3-5}  & & 2-2-3. Interactivity & 0.2054 & 0.0355 & 13 \\ \cline{1-1} \cline{3-5}  & & 2-2-4. Accuracy & 0.2042 & 0.0353 & 14 \\ \cline{1-1} \cline{3-5}  & & 2-2-5. Reliability & 0.2118 & 0.0366 & 12 \\ \hline
3-1. Device Extension & 0.1710 & 3-1-1. Compatibility & 1.0000 & 0.1710 & 1 \\ \hline
3-2. Future Outlook & \multirow{3}{*}{0.1691} & 3-2-1. Foresight & 0.5069 & 0.0857 & 2 \\ \cline{3-5}  & & 3-1-2. Integration & 0.4930 & 0.0834 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Decision-Making Trial and Evaluation Laboratory-based Analytic Network Process analysis. demonstrated the risks of data inconsistencies across devices, underscoring the need for robust integration mechanisms [53].