Complexity analysis of physiological signals in the wearable era: New insights into chronic diseases and aging
- 1Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai 200233, China
- 2Department of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Sixth People’ s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
Abstract
The human body is a complex and irregular system that generates diverse physiological signals. In the era of wearable technology, an increasing number of physiological signals can be continuously monitored, generating rich time series data that capture the dynamic physiological fluctuations and may reveal subtle signs of dysregulation. For example, continuous glucose monitoring (CGM) systems generate glucose time series data. As such, new metrics harnessing the time series nature of physiological signals may serve as sensitive markers of health. Of them, complexity focuses more on the dynamical characteristics of physiological signals and can reveal hidden information that linear methods may miss, allowing for the handling of nonstationary data and providing a more precise capture of individual differences. Importantly, the complexity of physiological signals may reflect the regulatory capacity, adaptability, and functional reserves of the body, which are often impaired in chronic diseases and during aging. Using CGM-derived glucose complexity as a representative example, this review discusses the clinical significance and applications of complexity in chronic diseases and aging. Finally, we outline future directions for complexity-related research, particularly regarding the roles of artificial intelligence (AI), wearable devices, and clinical translation.
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BibTeX
@article{yao2026complexity,
title = {{Complexity analysis of physiological signals in the wearable era: New insights into chronic diseases and aging}},
author = {Yuan Yao and Jinghao Cai and Yaxin Wang and Jiaying Ni and Li Cao and Jingyi Lu and Jian Zhou},
journal = {Chinese Medical Journal},
year = {2026},
month = sep,
doi = {10.1097/cm9.0000000000004295},
url = {https://doi.org/10.1097/cm9.0000000000004295},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Complexity analysis of physiological signals in the wearable era: New insights into chronic diseases and aging
AU - Yuan Yao
AU - Jinghao Cai
AU - Yaxin Wang
AU - Jiaying Ni
AU - Li Cao
AU - Jingyi Lu
AU - Jian Zhou
PY - 2026
DA - 2026/09/08
JO - Chinese Medical Journal
DO - 10.1097/cm9.0000000000004295
UR - https://doi.org/10.1097/cm9.0000000000004295
AB - The human body is a complex and irregular system that generates diverse physiological signals. In the era of wearable technology, an increasing number of physiological signals can be continuously monitored, generating rich time series data that capture the dynamic physiological fluctuations and may reveal subtle signs of dysregulation. For example, continuous glucose monitoring (CGM) systems generate glucose time series data. As such, new metrics harnessing the time series nature of physiological signals may serve as sensitive markers of health. Of them, complexity focuses more on the dynamical characteristics of physiological signals and can reveal hidden information that linear methods may miss, allowing for the handling of nonstationary data and providing a more precise capture of individual differences. Importantly, the complexity of physiological signals may reflect the regulatory capacity, adaptability, and functional reserves of the body, which are often impaired in chronic diseases and during aging. Using CGM-derived glucose complexity as a representative example, this review discusses the clinical significance and applications of complexity in chronic diseases and aging. Finally, we outline future directions for complexity-related research, particularly regarding the roles of artificial intelligence (AI), wearable devices, and clinical translation.
SN - 0366-6999
SN - 2542-5641
ER -