Synergizing Nanosensor-Enhanced Wearable Devices with Machine Learning for Precision Health Management Benefiting Older Adult Populations.

Abstract

Population aging presents significant health challenges and socioeconomic burdens globally, driving an increased demand for precision health management. In the era of big data, the exponential growth of health information is accelerating advances in precision health strategies for older adults. For this population, effective strategies can be achieved by the integration of wearable devices, nanosensors, and machine learning. Wearable devices enable continuous monitoring of diverse, real-time health metrics, serving as vital tools for collecting comprehensive health data. Nanosensors can be loaded into wearable devices to enhance their performance by significantly improving detection sensitivity and specificity, thereby increasing the accuracy and reliability of the data collected. Meanwhile, machine learning provides powerful methods for rapid and efficient analysis of large-scale health data, driving the optimization of nanosensors as well as wearable devices. This review examines the synergistic roles of wearable devices, nanosensors, and machine learning in the precision health management field, focusing on the value of big health data (i.e., big data in health care). We begin by exploring wearable devices as critical tools for gathering extensive health information, followed by an in-depth discussion of how nanosensors enhance data quality. Subsequently, we highlight the contributions of machine learning algorithms to the precise analysis of big health data and propose several proactive health management strategies from the perspective of "diagnosis-analysis-prevention". Finally, we present perspectives on the future integration of these technologies to advance comprehensive health management, precision diagnostics, and personalized medicine for older individuals.