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Personalized stress monitoring: a smartphone-enabled system for quantification of salivary cortisol

  • Elizabeth Rey
  • Aadhar Jain
  • Saeed Abdullah
  • Tanzeem Choudhury
  • David Erickson
Original Article

Abstract

Collection of salivary cortisol has been widely used as a method of investigating an array of health parameters. Monitoring of cortisol levels can help us to understand stress levels and the body’s response to stressors. Traditional methods of measuring cortisol in saliva, however, require costly equipment, trained personnel, and transportation of samples to a centralized laboratory. This creates a barrier to personal monitoring of cortisol. It also adds a level of cost and difficulty to large-scale studies which require participants to store and ship their saliva samples. Here, we present a novel system in which an individual with minimal training may collect their own saliva sample and measure it at home. Our system utilizes a lateral flow assay, a portable imaging device, and a smartphone to give salivary cortisol results in less than 15 min. We also demonstrate the use of our system on samples from a human study and give results from that study, which analyzes the relationship between cortisol levels and alertness across multiple days.

Keywords

Salivary cortisol Stress Lateral flow assay Alertness 

Abbreviations

HPA axis

Hypothalamic-pituitary-adrenal axis

CAR

Cortisol awakening response

PVT

Psychomotor vigilance task

Notes

Acknowledgements

A large portion of the work done for this paper was done in the Nanobiotechnology Center at Cornell University. Some of the equipment used in this work was located in the Kotlikoff Lab in the Cornell University College of Veterinary Medicine. Support for statistical analysis was provided by Lynn Johnson at the Cornell Statistical Consulting Unit.

Funding

This work was supported by the National Science Foundation [grant number CBET-1343058], the Robert Wood Johnson Health Data Exploration Agile Research Grant, and the Intel Science and Technology Center for Pervasive Computing.

Compliance with ethical standards

The study was approved by Cornell University’s Institutional Review Board.

Supplementary material

779_2018_1164_MOESM1_ESM.pdf (581 kb)
ESM 1 (PDF 580 kb)

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Elizabeth Rey
    • 1
  • Aadhar Jain
    • 1
    • 2
  • Saeed Abdullah
    • 3
    • 4
  • Tanzeem Choudhury
    • 3
  • David Erickson
    • 1
    • 5
  1. 1.Sibley School of Mechanical and Aerospace EngineeringCornell UniversityIthacaUSA
  2. 2.Department of Electrical EngineeringUniversity of California Santa CruzSanta CruzUSA
  3. 3.Department of Information ScienceCornell UniversityIthacaUSA
  4. 4.College of Information Sciences and TechnologyPennsylvania State UniversityState CollegeUSA
  5. 5.Division of Nutritional SciencesCornell UniversityIthacaUSA

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