Cortisol detection methods for stress monitoring in connected health

Talha Iqbal*, Adnan Elahi, William Wijns, Atif Shahzad

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Everyday responsibilities and lifestyle issues are the main cause of physical and psychological stress, which deteriorates the individual's health. Prolonged exposure to stress triggers the adrenocorticotrophic hormonal (ACTH) system and causes the release of cortisol hormones from the adrenal cortex. Many other biomarkers are affected by stress, but cortisol is considered the most vital and potentially clinically useful biomarker for stress estimation and monitoring. Accurate and timely detection of increased cortisol levels might improve the diagnosis, treatment, and prevention of stress-related diseases such as anxiety disorders, metabolic dysregulation, and cardiovascular diseases. Unfortunately, most of the cortisol assessments are currently performed only in laboratories and there is no point-of-care solution for ambulatory/real-time cortisol assessment. This review aims to provide an overview of the most promising techniques, currently used for cortisol detection and the challenges associated with them. The review also provides a feasibility report about measuring cortisol levels in different bio-fluids (for example, urine), a correlation of perceived stress with cortisol levels, and methods/devices used in the laboratory as well as in the ambulatory environment for cortisol detection. The overall conclusion suggests that significant research efforts and investments are required for the development of an accurate, rapid, and repeatable cortisol measuring device that can be used for connected health applications.

Original languageEnglish
Article number100079
Number of pages14
JournalHealth Sciences Review
Volume6
Early online date6 Feb 2023
DOIs
Publication statusPublished - Mar 2023

Keywords

  • Cortisol
  • Stress monitoring
  • Invasive/non-invasive methods
  • Saliva
  • Hair
  • Sweat
  • Blood
  • Wearable sensors
  • Connected health

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