Personal and Ubiquitous Computing

, Volume 22, Issue 4, pp 867–877 | Cite as

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

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


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.


Salivary cortisol Stress Lateral flow assay Alertness 


HPA axis

Hypothalamic-pituitary-adrenal axis


Cortisol awakening response


Psychomotor vigilance task



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.


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)


  1. 1.
    O’Connor TM, O’Halloran DJ, Shanahan F (2000) The stress response and the hypothalamic-pituitary-adrenal axis : from molecule to melancholia. Q J Med 93:323–333CrossRefGoogle Scholar
  2. 2.
    McEwen BS (2004) Protection and damage from acute and chronic stress: allostastis and allostatic overload and relevance to the pathophysiology of psychiatric disorders. Ann N Y Acad Sci 1032:1–7CrossRefGoogle Scholar
  3. 3.
    Shannon M, King TL, Kennedy HP (2007) Allostasis: a theoretical framework for understanding and evaluating perinatal health outcomes. JOGNN 36:125–134CrossRefGoogle Scholar
  4. 4.
    Gold PW, Loriaux DL, Roy A, Kling MA, Calabrese JR, Kellner CH, Nieman LK, Post RM, Pickar D, Gallucci W, Avgerinos P, Paul S, Oldfield EH, Cutler GB Jr, Chrousos GP (1986) Responses to corticotropin releasing hormone in the hypocortisolism of depression and Cushing’s disease. N Engl J Med 314:1329–1335CrossRefGoogle Scholar
  5. 5.
    Bailey SL, Heitkemper MM (2001) Circadian rhythmicity of cortisol and body temperature: morningness-eveningness effects. Chronobiol Int 18:249–261. CrossRefGoogle Scholar
  6. 6.
    Van Dongen HPA, Dinges DF (2005) Circadian Rhythms in Sleepiness, Alertness, and Performance. In: Kryger MH, Roth T, Dement WC (eds) Princ Pract Sleep Med. 4th ed. pp 435–443Google Scholar
  7. 7.
    Hellhammer DH, Wüst S, Kudielka BM (2009) Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinology 34:163–171. CrossRefGoogle Scholar
  8. 8.
    Ward Thompson C, Roe J, Aspinall P, Mitchell R, Clow A, Miller D (2012) More green space is linked to less stress in deprived communities: evidence from salivary cortisol patterns. Landsc Urban Plan 105:221–229. CrossRefGoogle Scholar
  9. 9.
    Petrowski K, Wintermann G-B, Schaarschmidt M, Bornstein SR, Kirschbaum C (2013) Blunted salivary and plasma cortisol response in patients with panic disorder under psychosocial stress. Int J Psychophysiol 88:35–39. CrossRefGoogle Scholar
  10. 10.
    La Marca-Ghaemmaghami P, La Marca R, Dainese SM et al (2013) The association between perceived emotional support, maternal mood, salivary cortisol, salivary cortisone, and the ratio between the two compounds in response to acute stress in second trimester pregnant women. J Psychosom Res 75:314–320. CrossRefGoogle Scholar
  11. 11.
    Yamaguchi M, Matsuda Y, Sasaki S, Sasaki M, Kadoma Y, Imai Y, Niwa D, Shetty V (2013) Immunosensor with fluid control mechanism for salivary cortisol analysis. Biosens Bioelectron 41:186–191. CrossRefGoogle Scholar
  12. 12.
    Zangheri M, Cevenini L, Anfossi L, Baggiani C, Simoni P, di Nardo F, Roda A (2015) A simple and compact smartphone accessory for quantitative chemiluminescence-based lateral flow immunoassay for salivary cortisol detection. Biosens Bioelectron 64:63–68. CrossRefGoogle Scholar
  13. 13.
    Carneiro D, Novais P, Augusto JC, Payne N (2017) New methods for stress assessment and monitoring at the workplace. IEEE Trans Affect Comput:1–1.
  14. 14.
    Umeda T, Hiramatsu R, Iwaoka T et al (1981) Use of saliva for monitoring unbound free cortisol levels in serum. Clin Chim Acta 110:245–253. CrossRefGoogle Scholar
  15. 15.
    Dinges DF, Powell JW (1985) Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations. Behav Res Methods Instrum Comput 17:652–655CrossRefGoogle Scholar
  16. 16.
    Van Dongen HPA, Maislin G, Mullington JM, Dinges DF (2003) The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and Total sleep deprivation. Sleep 26:117–126CrossRefGoogle Scholar
  17. 17.
    Kay M, Rector K, Consolvo S et al (2013) PVT-Touch: adapting a reaction time test for touchscreen devices. In: 7th Int. Conf. Pervasive Comput. Technol. Healthc. Work. Venice, Italy, pp 248–251Google Scholar
  18. 18.
    Wright KP Jr, Hull JT, Czeisler CA (2002) Relationship between alertness, performance and body temperature in humans. Am J Phys Regul Integr Comp Phys 283:R1370–R1377. Google Scholar
  19. 19.
    Lamond N, Jay SM, Dorrian J, Ferguson SA, Roach GD, Dawson D (2008) The sensitivity of a palm-based psychomotor vigilance task to severe sleep loss. Behav Res Methods 40:347–352CrossRefGoogle Scholar
  20. 20.
    Basner M, Dinges DF (2011) Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss. Sleep 34:581–591CrossRefGoogle Scholar
  21. 21.
    Lieberman HR, Waldhauser F, Garfield G, Lynch HJ, Wurtman RJ (1984) Effects of melatonin on human mood and performance. Brain Res 323:201–207CrossRefGoogle Scholar
  22. 22.
    Laakso M-L, Porkka-Heiskanen T, Alila A, Stenberg D, Johansson G (1990) Correlation between salivary and serum melatonin: dependence on serum melatonin levels. J Pineal Res 9:39–50CrossRefGoogle Scholar
  23. 23.
    Voultsios A, Kennaway DJ, Dawsont D (1997) Salivary melatonin as a circadian phase marker: validation and comparison to plasma melatonin. J Biol Rhythm 12:457–466CrossRefGoogle Scholar
  24. 24.
    Loh S, Lamond N, Dorrian J, Roach G, Dawson D (2004) The validity of psychomotor vigilance tasks of less than 10-minute duration. Behav Res Methods Instrum Comput 36:339–346CrossRefGoogle Scholar
  25. 25.
    Basner M, Mollicone D, Dinges DF (2011) Validity and sensitivity of a brief psychomotor vigilance test (PVT-B) to total and partial sleep deprivation. Acta Astronaut 69:949–959. CrossRefGoogle Scholar
  26. 26.
    Basner M, Rubinstein J (2011) Fitness for duty: a 3 minute version of the psychomotor vigilance test predicts fatigue related declines in luggage screening performance. J Occup Environ Med 53:1146–1154CrossRefGoogle Scholar
  27. 27.
    Chalder T, Berelowitz G, Pawlikowska T, Watts L, Wessely S, Wright D, Wallace EP (1993) Development of a fatigue scale. J Psychosom Res 37:147–153CrossRefGoogle Scholar
  28. 28.
    Hruschka DJ, Kohrt BA, Worthman CM (2005) Estimating between- and within-individual variation in cortisol levels using multilevel models. Psychoneuroendocrinology 30:698–714. CrossRefGoogle Scholar
  29. 29.
    Lu Z, O’Dell D, Srinivasan B, Rey E, Wang R, Vemulapati S, Mehta S, Erickson D (2017) A rapid diagnostic testing platform for iron and vitamin A deficiency. Proc Natl Acad Sci 114:13513–13518. CrossRefGoogle Scholar
  30. 30.
    Srinivasan B, O’Dell D, Finkelstein JL, Lee S, Erickson D, Mehta S (2018) ironPhone: mobile device-coupled point-of-care diagnostics for assessment of iron status by quantification of serum ferritin. Biosens Bioelectron 99:115–121. CrossRefGoogle Scholar
  31. 31.
    Vemulapati S, Rey E, O’Dell D, Mehta S, Erickson D (2017) A quantitative point-of-need assay for the assessment of vitamin D3 deficiency. Sci Rep 7:14142. CrossRefGoogle Scholar
  32. 32.
    Rey EG, O’Dell D, Mehta S, Erickson D (2017) Mitigating the hook effect in lateral flow sandwich immunoassays using real-time reaction kinetics. Anal Chem 89:5095–5100. CrossRefGoogle Scholar
  33. 33.
    Humphrey SP, Williamson RT (2001) A review of saliva: normal composition, flow, and function. J Prosthet Dent 85:162–169. CrossRefGoogle Scholar

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
    Email author
  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

Personalised recommendations