ArticleFrontiers in public health2026
Understanding the role of cybersecurity in the internet of things within the health sector: an empirical study.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
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Abstract
Introduction: The digital healthcare environment is rapidly evolving, driven by the Internet of Things (IoT), which offers benefits like early diagnosis, monitoring, and cost reductions. However, this growth heightens the risk of cyberattacks, raising crucial concerns about patient safety, data breaches, and the necessity of trust for technology adoption. This study explores patient acceptance of digital health technologies for chronic disease management. Methods: A research framework was developed combining Protection Motivation Theory (PMT) and the Task-Technology Fit (TTF) model. This framework was augmented by incorporating comprehensive user-centric, behavioral, and technological dimensions. Data from 603 participants were analyzed using a hybrid methodology of Structural Equation Modeling (SEM) and Artificial Neural Networks (ANNs). The ANN was deployed to capture complex, non-linear relationships among variables, overcoming the linear limitations of traditional SEM to rank predictor importance with higher accuracy. Results: Findings reveal that while cybersecurity concerns exist, they do not actively deter technology adoption; rather, they are statistically secondary to immediate health benefits and system usability, especially for patients with chronic diseases. Factors including functionality, information accuracy, trust, privacy, and training did not significantly influence IoT adoption, while awareness, perceived vulnerability, perceived severity, innovation, risk, and compliance exerted minor effects. Conclusion: This study advances digital health literature by providing a novel, dual-theoretic framework (PMT-TTF) validated through machine learning, demonstrating that utilitarian health value overrides security anxieties in chronic care contexts. The findings offer practical insights for healthcare providers and developers to prioritize user-centric design alongside robust security protocols.
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