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

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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.

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Abbreviations

HPA axis:

Hypothalamic-pituitary-adrenal axis

CAR:

Cortisol awakening response

PVT:

Psychomotor vigilance task

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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.

Author information

Author notes

  1. Aadhar Jain

    Present address: Department of Electrical Engineering, University of California Santa Cruz, Santa Cruz, California, USA

  2. Saeed Abdullah

    Present address: College of Information Sciences and Technology, Pennsylvania State University, State College, PA, USA

Authors and Affiliations

  1. Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, USA

    Elizabeth Rey, Aadhar Jain & David Erickson

  2. Department of Information Science, Cornell University, Ithaca, NY, USA

    Saeed Abdullah & Tanzeem Choudhury

  3. Division of Nutritional Sciences, Cornell University, Ithaca, NY, USA

    David Erickson

Authors
  1. Elizabeth Rey
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  2. Aadhar Jain
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  4. Tanzeem Choudhury
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  5. David Erickson
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Corresponding author

Correspondence to David Erickson.

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

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Rey, E., Jain, A., Abdullah, S. et al. Personalized stress monitoring: a smartphone-enabled system for quantification of salivary cortisol. Pers Ubiquit Comput 22, 867–877 (2018). https://doi.org/10.1007/s00779-018-1164-z

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  • DOI: https://doi.org/10.1007/s00779-018-1164-z

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