Recently, the advent of digital twins (DTs) has sparked significant interest in both business and academics offering new perspectives into our increasingly digitalized society. The aim of DT is to create a digital representation of a physical entity, be it a process, an object, or a system, by using the entity’s characteristics and behavior. This enables real-time prediction, optimization, monitoring, control, and better decision-making. The adoption of digital twins is growing in various industry verticals due to the rapid proliferation of the Internet of Things (IoT), computing power, artificial intelligence, and big data. Furthermore, the incorporation of metaverse and augmented reality technologies with digital twins bridges the gapbetweenthephysicalandvirtualworldsinmanufacturingbyprovidingintuitivewaystovisualizeand interact with DT data. Identifying the main performance metrics of a DT is crucial to develop a high-fidelity replica of the physical entity to ensure better informed decisions. Research and industry have focused on accuracy and synchronization as key performance metrics with varying definitions. To this end, the aim of this study is to survey the synchronization metrics used in the existing related literature and to establish a more representative synchronization measure for digital twins. Among all metrics, the freshness of the information delivered to the DT, coined as Age of Information (AoI), remains the most adopted measure. In this paper, we review the evaluation of AoI in different network scenarios and introduce Age of Digital Twin (AoDT) to reliably measure the synchronization gap between digital and physical twins. We proceed to formulating AoDT expressions in common network scenarios to be used for planning and developing high-fidelity digital twins