An Adaptive Cybersecurity Model for Protecting Elderly Patient Data in Wearable Healthcare Systems
The rapid adoption of wearable healthcare technologies has transformed healthcare delivery by enabling continuous patient monitoring, remote diagnostics, and real-time clinical decision-making. Despite these benefits, wearable healthcare systems expose sensitive patient information to numerous cybersecurity threats, including unauthorised access, data tampering, ransomware attacks, denial-of-service attacks, and privacy breaches. Elderly patients are particularly vulnerable because wearable devices continuously collect physiological and behavioural information that must be securely transmitted, stored, and processed. Existing cybersecurity mechanisms often operate independently and fail to provide comprehensive protection across the entire Internet of Medical Things (IoMT) ecosystem. This study presents an adaptive cybersecurity model designed to protect elderly patient data within wearable healthcare systems. The model integrates Multi-Factor Authentication (MFA), Role-Based Access Control (RBAC), AES-256 encryption, blockchain technology, and Multi-Layer Neural Network (MLNN)-based anomaly detection within a unified security architecture. The model adopts a five-layer design consisting of the Data Acquisition Layer, Communication Layer, Intelligent Security Layer, Blockchain Security Layer, and Application and Access Layer to secure data throughout acquisition, transmission, processing, storage, and access stages. A Design Science Research (DSR) methodology was employed to identify cybersecurity challenges, gather stakeholder requirements, design the security architecture, develop a prototype, and evaluate its effectiveness. Stakeholder feedback was collected from 238 respondents, including healthcare professionals, IT personnel, cybersecurity experts, and wearable healthcare users, and informed the development and refinement of the model. The model addresses key limitations of existing security approaches by combining proactive threat detection, decentralised data integrity management, secure access control, and continuous monitoring capabilities. The resulting model enhances confidentiality, integrity, availability, accountability, and adaptability within wearable healthcare environments. The study contributes a comprehensive and scalable cybersecurity model capable of mitigating evolving cyber threats while supporting secure remote patient monitoring, regulatory compliance, and trustworthy healthcare service delivery for elderly patients. Among 156 technical stakeholders, 87.8% rated the model as Effective, Very Effective, or Moderately Effective, indicating strong acceptance for securing elderly patient data. These findings indicate that the model supports secure remote healthcare delivery and regulatory compliance.