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

Journal: ACS nano
Published Date:

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.

Authors

  • Zhihao Li
    Heilongjiang University of CM, Harbin 150040, China.
  • Bangshun He
    Department of Laboratory Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China.
  • Yiwei Li
    New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
  • Bi-Feng Liu
    The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics and Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Guojun Zhang
    The department of Respiratory Medicine of The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China. Electronic address: zlgj-001@126.com.
  • Songlin Liu
    Hubei Shizhen Laboratory, 16 Huangjia Lake West Road, Wuhan 430065, China.
  • Tony Ye Hu
    Center for Cellular and Molecular Diagnostics, Tulane University School of Medicine, New Orleans, LA, USA. tonyhu@tulane.edu.
  • Ying Li
    School of Information Engineering, Chang'an University, Xi'an 710010, China.