The Paradigmatic Shift in Chronic Disease Management: A Longitudinal Analysis of Sensor-Based Monitoring, IoMT Architecture, and Clinical Outcomes

Abstract

The global healthcare infrastructure is currently undergoing a transformative redirection, transitioning
from a reactive, episodic model of care toward a proactive, continuous, and data-driven paradigm. This
evolution is necessitated by the burgeoning epidemiological burden of chronic conditions—specifically
diabetes mellitus, congestive heart failure, and chronic obstructive pulmonary disease (COPD)—which
historically have been managed through infrequent clinical consultations and retrospective patient self-
reporting.1 Such traditional methodologies often fail to capture the transient physiological fluctuations
that herald acute exacerbations, leading to suboptimal therapeutic adjustments and increased hospital
readmission rates. The emergence of sensor-based monitoring, underpinned by the Internet of Medical
Things (IoMT), provides a resolution to these information gaps by enabling the real-time acquisition and
analysis of high-fidelity physiological data.1