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Ethical Considerations of Digital Phenotyping from the Perspective of a Healthcare Practitioner

  • Paul Dagum
  • Christian MontagEmail author
Chapter
Part of the Studies in Neuroscience, Psychology and Behavioral Economics book series (SNPBE)

Abstract

In this chapter we introduce digital phenotyping and its applications to healthcare. Despite the promise of this new form of clinical diagnosis in medicine and psychiatry, use of digital phenotyping raises several ethical concerns. We use insights derived from a clinical case study to frame these different ethical questions. We discuss how current healthcare practice and privacy policies address these questions and impose requirements for non-healthcare scientists and practitioners using digital phenotyping. We emphasize that this chapter frames the discussion from the perspective of the healthcare practitioner. We conclude by briefly reviewing more strongly theoretically based discussions of this emerging topic.

Notes

Conflict of Interest

Christian Montag mentions that he currently receives funding from Mindstrong Health for a project on molecular genetics and digital phenotyping. Of importance, his views on ethics as presented in this work have not been influenced by this financial support for his research.

References

  1. Biomarkers Definitions Working Group (2001) Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. CPT Pharmacomet Syst Pharmacol 69(3):89–95.  https://doi.org/10.1067/mcp.2001.113989CrossRefGoogle Scholar
  2. CareerBuilder (2018) More than half of employers have found content on social media that caused them not to hire a candidate, according to recent CareerBuilder survey. PRN Newswire. https://www.prnewswire.com/news-releases/more-than-half-of-employers-have-found-content-on-social-media-that-caused-them-not-to-hire-a-candidate-according-to-recent-careerbuilder-survey-300694437.html. Accessed 8 Jul 2019
  3. Chittaranjan G, Blom J, Gatica-Perez D (2013) Mining large-scale smartphone data for personality studies. Pers Ubiquit Comput 17(3):433–450.  https://doi.org/10.1007/s00779-011-0490-1CrossRefGoogle Scholar
  4. Cobb RW, Coughlin JF (1998) Are elderly drivers a road hazard?: Problem definition and political impact. J Aging Stud 12(4):411–427.  https://doi.org/10.1016/S0890-4065(98)90027-5CrossRefGoogle Scholar
  5. Dagum P (2018) Digital biomarkers of cognitive function. NPJ Digit Med 1(1):10.  https://doi.org/10.1038/s41746-018-0018-4CrossRefPubMedPubMedCentralGoogle Scholar
  6. Dawkins R (1982) The extended phenotype. Oxford University Press, OxfordGoogle Scholar
  7. Dorsey ER, Papapetropoulos S, Xiong M, Kieburtz K (2017) The first frontier: digital biomarkers for neurodegenerative disorders. Digit Biomark.  https://doi.org/10.1159/000477383CrossRefPubMedPubMedCentralGoogle Scholar
  8. Eichstaedt JC, Smith RJ, Merchant RM et al (2018) Facebook language predicts depression in medical records. Proc Natl Acad Sci USA 115(44):11203–11208.  https://doi.org/10.1073/pnas.1802331115CrossRefPubMedGoogle Scholar
  9. Elenko E, Underwood L, Zohar D (2015) Defining digital medicine. Nat Biotechnol 33(5):456–461.  https://doi.org/10.1038/nbt.3222CrossRefPubMedGoogle Scholar
  10. Fuller D, Shareck M, Stanley K (2017) Ethical implications of location and accelerometer measurement in health research studies with mobile sensing devices. Soc Sci Med 191:84–88.  https://doi.org/10.1016/j.socscimed.2017.08.043CrossRefPubMedGoogle Scholar
  11. Hokke S, Hackworth NJ, Quin N et al (2018) Ethical issues in using the internet to engage participants in family and child research: a scoping review. PLoS ONE 13(9):e0204572.  https://doi.org/10.1371/journal.pone.0204572CrossRefPubMedPubMedCentralGoogle Scholar
  12. Insel TR (2017) Digital phenotyping: technology for a new science of behavior. JAMA Netw 318(13):1215–1216.  https://doi.org/10.1001/jama.2017.11295CrossRefGoogle Scholar
  13. ISO—International Organization for Standardization (2019) ISO—International Organization for Standardization. http://www.iso.org/cms/render/live/en/sites/isoorg/home.html. Accessed 8 Jul 2019
  14. Jain SH, Powers BW, Hawkins JB, Brownstein JS (2015) The digital phenotype. Nat Biotechnol 33(5):462–463CrossRefGoogle Scholar
  15. Kerchner GA, Dougherty RF, Dagum P (2015) Unobtrusive neuropsychological monitoring from smart phone use behavior. Alzheimers Dement 11(7):272–273.  https://doi.org/10.1016/j.jalz.2015.07.358CrossRefGoogle Scholar
  16. Kosinski M, Matz SC, Gosling SD et al (2015) Facebook as a research tool for the social sciences: opportunities, challenges, ethical considerations, and practical guidelines. Am Psychol 70(6):543–556.  https://doi.org/10.1037/a0039210CrossRefPubMedGoogle Scholar
  17. Leefeldt E (2019) California bans gender in setting car insurance rates. CBS NEWS. https://www.cbsnews.com/news/car-insurance-california-bans-gender-as-a-factor-in-setting-rates/
  18. Madrid A, Smith D, Alvarez-Horine S et al (2017) Assessing anhedonia with quantitative tasks and digital and patient reported measures in a multi-center double-blind trial with BTRX-246040 for the treatment of major depressive disorder. Neuropsychopharmacology 43:372–372Google Scholar
  19. Markowetz A, Błaszkiewicz K, Montag C et al (2014) Psycho-Informatics: big data shaping modern psychometrics. Med Hypotheses 82(4):405–411.  https://doi.org/10.1016/j.mehy.2013.11.030CrossRefPubMedGoogle Scholar
  20. Martinez-Martin N, Insel TR, Dagum P et al (2018) Data mining for health: staking out the ethical territory of digital phenotyping. npj Digital Med 1(1):68.  https://doi.org/10.1038/s41746-018-0075-8
  21. Matz SC, Kosinski M, Nave G, Stillwell DJ (2017) Psychological targeting as an effective approach to digital mass persuasion. Proc Natl Acad Sci USA 114(48):12714–12719.  https://doi.org/10.1073/pnas.1710966114CrossRefPubMedGoogle Scholar
  22. Mindstrong (2019). Mindstrong Health. https://mindstronghealth.com/
  23. Montag C, Baumeister H, Kannen C et al (2019) Concept, possibilities and pilot-testing of a new smartphone application for the social and life sciences to study human behavior including validation data from personality psychology. J 2(2):102–115.  https://doi.org/10.3390/j2020008
  24. Montag C, Błaszkiewicz K, Lachmann B et al (2014) Correlating personality and actual phone usage: evidence from psychoinformatics. J Individ Differ 35(3):158–165.  https://doi.org/10.1027/1614-0001/a000139CrossRefGoogle Scholar
  25. National Association of Insurance Commissioners (2012) Credit-based insurance scores: how an insurance company can use your credit to determine your premium. https://www.naic.org/documents/consumer_alert_credit_based_insurance_scores.htm. Accessed 8 Jul 2019
  26. Nebeker C, Lagare T, Takemoto M et al (2016) Engaging research participants to inform the ethical conduct of mobile imaging, pervasive sensing, and location tracking research. Behav Med Pract Policy Res 6(4):577–586.  https://doi.org/10.1007/s13142-016-0426-4CrossRefGoogle Scholar
  27. Reardon S (2017) AI algorithms to prevent suicide gain traction. Nature NewsGoogle Scholar
  28. Rodarte C (2017) Pharmaceutical perspective: how digital biomarkers and contextual data will enable therapeutic environments. Digit Biomark.  https://doi.org/10.1159/000479951CrossRefGoogle Scholar
  29. Rubeis G, Steger F (2019) Internet- und mobilgestützte Interventionen bei psychischen Störungen. Nervenarzt 90(5):497–502.  https://doi.org/10.1007/s00115-018-0663-5CrossRefPubMedGoogle Scholar
  30. Saeb S, Zhang M, Karr CJ et al (2015) Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J Med Internet Res 17(7):e175.  https://doi.org/10.2196/jmir.4273CrossRefPubMedPubMedCentralGoogle Scholar
  31. Scism L (2017) New York car insurers could soon be banned from asking about your education. The Wall Street Journal. https://www.wsj.com/articles/new-york-car-insurers-could-soon-be-banned-from-asking-about-your-education-1494952287
  32. Settanni M, Azucar D, Marengo D (2018) Predicting individual characteristics from digital traces on social media: a meta-analysis. Cyberpsychology Behav Soc Netw 21(4):217–228.  https://doi.org/10.1089/cyber.2017.0384CrossRefGoogle Scholar
  33. Smith DG, Saljooqi K, Alvarez-Horine S et al (2018) Exploring novel behavioral tasks and digital phenotyping technologies as adjuncts to a clinical trial of BTRX-246040. International Society of CNS Clinical Trials and MethodologyGoogle Scholar
  34. Stachl C, Bühner M (2015) Show me how you drive and I’ll tell you who you are recognizing gender using automotive driving parameters. Procedia Manuf 3:5587–5594.  https://doi.org/10.1016/j.promfg.2015.07.743CrossRefGoogle Scholar
  35. Stachl C, Hilbert S, Au J-Q et al (2017) Personality traits predict smartphone usage: personality traits predict smartphone usage. Eur J Pers 31(6):701–722.  https://doi.org/10.1002/per.2113CrossRefGoogle Scholar
  36. Torous J, Onnela J-P, Keshavan M (2017) New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Transl Psychiatry 7(3):e1053.  https://doi.org/10.1038/tp.2017.25CrossRefPubMedPubMedCentralGoogle Scholar
  37. Vanderhoff H, Jeglic EL, Donovick PJ (2011) Neuropsychological assessment in prisons: ethical and practical challenges. J Correct Health Care 17(1):51–60.  https://doi.org/10.1177/1078345810385914CrossRefPubMedGoogle Scholar
  38. Wang Y, Kosinski M (2018) Deep neural networks are more accurate than humans at detecting sexual orientation from facial images. J Pers Soc Psychol 114(2):217–228CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Mindstrong HealthMountain ViewUSA
  2. 2.Institute of Psychology and EducationUlm UniversityUlmGermany

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