## 7 Conclusions and Future Work  
This paper presented a comprehensive, multi-layer security audit of the PHGL-COVID platform as a real-world mHealth system composed of wearable, mobile, and containerized backend components. By applying a diverse set of static and dynamic analysis techniques, ranging from BLE sniffing and reverse engineering to container vulnerability scanning, we identified systemic vulnerabilities across all architectural layers and illustrated how they could be exploited in sequence to construct full-chain attack scenarios. Unlike prior research that addresses mHealth components in isolation (e.g., wearables or mobile apps), our study introduced a cross-domain threat modelling approach that uncovered interdependencies between wearable data leaks, insecure token handling, and exposed backend services. The discovery of 28 CVEs (12 critical) in the containerized images, insecure BLE characteristics on the Galaxy Watch 7, and persistent overprivileged Android apps highlighted a risk chain wherein vulnerabilities could propagate and amplify across the system. For example, we demonstrated that biometric data intercepted via BLE could be injected into a mobile application with excessive permissions and ultimately exploited to manipulate backend APIs, without requiring root access or firmware modifications. From a deployment perspective, these findings call for a shift in how we secure healthcare systems, not only at the horizontal layer of each device, but vertically across their interaction boundaries. Future mHealth architectures must adopt mutual authentication between components, enforce real-time telemetry for threat detection, and harden enforcement layers (e.g., SELinux, verified boot, and API access control) to reduce the implicit trust traditionally granted within healthcare device chains. Security policies should become operational rules, not optional guidelines, particularly in clinical and low-resource care environments. Equally important, this study raises regulatory questions. The act of taking consumer-grade hardware and using it for some medical purpose identifies care platforms with unique exposures to liberties that never have been contemplated by the vendor. Without meaningful institutional and legal guidance, there are significant challenges in attempting to align an assumption that commercial compliance equates to cybersecurity strength. Beyond the vulnerabilities identified, this section also serves to reflect critically on the limitations of this study and discuss future research directions. The audit was performed on a single device model (Galaxy Watch 7, Galaxy A55) in each category, and outcomes may not be reproducible with different hardware or firmware in the future. The audit was limited by what can be discovered under the commonly configured and observed behaviors from the selected deployment. We could not complete extensive fuzzing or kernel-level testing, as well. Because the experiments were undertaken in a lab environment, there were no sensitive patient data. As part of our ongoing work, we intend to extend the current methodology to incorporate federated learning mechanisms for adaptive intrusions detections, simulate attack scenarios across different network topologies, and to implement context-centered policy agent to adaptively respond to threat signals. We are also exploring the possibility of developing open-source toolkits so that other mHealth deployments can recreate a multi-layer audit framework similar to this one. This work ultimately suggests that mHealth security cannot work just by patching layers and needs a shift towards reengineering trust relations across all components. Medical data integrity, device behaviors, and user privacy are a shared commitment and risk model which needs to be unified under a resilient, adaptive and context aware security framework that can withstand the ever-evolving threat landscape and successfully safeguard human dignity in the digital health ecosystem. Conceptualization, E.M.T. and M.D.