## 1 Introduction  
In modern healthcare settings, devices are integrated with various smart applications, effectively linking these devices to the Internet and integrating them into the Internet of Things (IoT) [1, 2]. Interestingly, the multi-layer feature of IoT architectures [3] enables highly distributed solutions, promotes productivity, usability and simplifies the use of IoT systems. However, the increasing dependence on data generated by a large number of heterogeneous IoT devices also creates multiple security issues [4, 5, 6, 7, 8]. Hence, the successful use of these IoT applications in healthcare depends on accurate decision-making, comprehensive risk assessment, real-time security, and performance evaluations [9]. Among these, the importance of identifying risks involved in a multi-layer IoT architecture is crucial and an emerging research area [10]. In an IoT health system, security attacks can have serious consequences for human life or cause significant financial damage [11]. It is a significant concern that end-users (for example, general users, practitioners, and/or automated systems in an IoT-Health architecture) may not be able to perceive any potential risks or degraded security as a result of attacks [12]. End-users need to be equipped with cybersecurity situational awareness and understand the security status at each layer in IoT environments. To improve this awareness, end-users need to have access to security information in real-time. Cybersecurity situational awareness [13; 14] provides IoT-Health end-users with valuable insights to build trust in secure systems and respond accurately and promptly to cybersecurity risks arising from vulnerabilities. Remarkably, in contrast to many other scenarios, security in a digital health context is not necessarily a binary secure/insecure decision. Data from less trustworthy systems transferred over insecure channels can still add valuable context for diagnosing a patient and determining treatment. However, diagnosing and monitoring a patient's health status should never solely rely on such insecure data. Therefore, to facilitate proper decision-making from an end user's view, it is crucial to extend IoT applications with precise situational awareness with respect to the status of the overall system, including all data sources and processing steps. One possible approach to address this gap was developed by [PERSON] et al., that suggests adding security metadata to provenance information [15]. Security metadata are the relevant information to represent security evidence at any layer of an IoT architecture. Such metadata can include evidence on active security controls, security protocols used for communication, evidence of system configuration status and update, information on software status running on a device, etc. [15]. The proposed security-aware IoT provenance model, named Prov-IoT, determines the information needed to continuously estimate the risk of IoT data being compromised at different steps of data propagation. The Prov-IoT model establishes the theoretical foundations for adding security-relevant features in IoT data propagation. Further, the authors proposed a proof-of-concept-based experimental analysis [16] with designed security metadata to demonstrate the feasibility of their proposed model. However, the potential impact of integrating user experience and security metadata into real-world case studies to elevate system security awareness through risk analysis remains largely untapped. This presents a significant opportunity for exploration and innovation in the field of security management. Therefore, it is pivotal to inspect systems to identify available and suitable security metadata and whether the system can, in practice, provide/report those. It is also essential to understand different types of security metadata from various viewpoints to further explore how they are helpful in empowering users' cybersecurity situational awareness and designing future trustworthy systems.