Preview

V.M. BEKHTEREV REVIEW OF PSYCHIATRY AND MEDICAL PSYCHOLOGY

Advanced search

Digital phenotyping as a new method of screening for mental disorders

https://doi.org/10.31363/2313-7053-2022-4-96-100

Abstract

   The introduction of information technologies is inextricably linked with improving the quality and accessibility of medical care, as well as reducing the cost of medical services. Digital phenotyping is one of the clinical tools in the field of information technology that allows you to evaluate a person’s phenotype using various personal information devices, such as a smartphone, tablet, smartwatch, various sensors and other computer tools. The advantage of digital phenotyping is the ability to receive information about the patient’s condition in real time, without inpatient and outpatient monitoring and even without the active participation of the patient himself. This fact significantly expands the possibilities of screening and diagnosis of mental disorders, and also helps to track the risks of relapses and take timely measures to prevent an exacerbation of the disease. Information technologies have great prospects for use for scientific purposes — they provide an opportunity to conduct research online that does not require visiting research centers, while at the same time reducing the time and costs of ongoing clinical trials. However, the use of digital phenotyping for scientific and clinical purposes has a number of limitations. For further improvement of digital phenotyping in order to screen psychopathology and subsequent assessment of the condition of patients, it is necessary to develop new psychometric tools used in electronic form and devoid of the shortcomings of questionnaires that are currently being used. This critical review provides data on the current opportunities and problems of digital phenotyping, as well as the prospects for its development.

About the Authors

E. D. Kasyanov
V. M. Bekhterev National Medical Research Center for Psychiatry and Neurology
Russian Federation

Evgeny D. Kasyanov

Saint Petersburg



Ya. V. Kovaleva
V. M. Bekhterev National Medical Research Center for Psychiatry and Neurology
Russian Federation

Yana V. Kovaleva

Saint Petersburg



G. E. Mazo
V. M. Bekhterev National Medical Research Center for Psychiatry and Neurology
Russian Federation

Galina E. Mazo

Saint Petersburg



References

1. Kasyanov E. D., Rukavishnikov G. V., Kibitov Aю A. et al. Modern approaches to the genetics of depression: scopes and limitations. Zhurnal Nevrologii i Psikhiatrii imeni S. S. Korsakova. 2021; 121 (5-2): 61-66. (In Russ.). doi: 10.17116/jnevro202112105261

2. El-Miedany Y. «Telehealth and telemedicine: how the digital era is changing standard health care». Smart Homecare Technology and TeleHealth. 2017; 4: 43–52. doi: 10.2147/SHTT.S116009

3. Faurholt-Jepsen M., Frost M., Vinberg M., Christensen E. M., Bardram J. E., Kessing L. V. Smartphone data as objective measures of bipolar disorder symptoms. Psychiatry Res. 2014; 217 (1-2): 124-7. doi: 10.1016/j.psychres.2014.03.009.

4. Graber M. L., Byrne C., Johnston D. The impact of electronic health records on diagnosis. Diagnosis (Berl). 2017; 4 (4): 211-223. doi: 10.1515/dx-2017-0012.

5. Hieronymus F., Østergaard S. Rating, berating or overrating antidepressant efficacy? The case of the Hamilton depression rating scale. European Neuropsychopharmacology. 2021; 52: 12-14

6. Hsin H., Fromer M., Peterson B., Walter C., Fleck M., Campbell A., Varghese P., Califf R. Transforming Psychiatry into Data-Driven Medicine with Digital Measurement Tools. NPJ Digit Med. 2018; 1: 37. doi: 10.1038/s41746-018-0046-0.

7. Hyde C. L., Nagle M. W., Tian C. et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nat Genet. 2016; 48 (9): 1031–1036. doi: 10.1038/ng.3623.

8. Ian Barnett, John Torous, Patrick Staples, Luis Sandoval, Matcheri Keshavan, Jukka-Pekka Onnela. Relapse prediction in schizophrenia through digital phenotyping: a pilot study. Neuropsychopharmacology. 2018; 43 (8): 1660-1666. doi: 10.1038/s41386-018-0030-z.

9. Inan O. T., Tenaerts P., Prindiville S. A., Reynolds H. R., Dizon D. S., Cooper-Arnold K., Turakhia M., Pletcher M. J., Preston K. L., Krumholz H. M., Marlin B. M., Mandl K. D., Klasnja P., Spring B., Iturriaga E., Campo R., Desvigne-Nickens P., Rosenberg Y., Steinhubl S. R., Califf R. M. Digitizing clinical trials. NPJ Digit Med. 2020; 3: 101. doi: 10.1038/s41746-020-0302-y.

10. Insel T. R. Digital phenotyping: a global tool for psychiatry. World Psychiatry. 2018; 17 (3): 276-277. doi: 10.1002/wps.20550.

11. Insel T. R. Digital Phenotyping: Technology for a New Science of Behavior. JAMA. 2017; 318 (13): 1215-1216. doi: 10.1001/jama.2017.11295.

12. Kidron C. A., Kirmayer L. J. Global Mental Health and Idioms of Distress: The Paradox of Culture-Sensitive Pathologization of Distress in Cambodia. Cult Med Psychiatry. 2019; 43( 2): 211-235. doi: 10.1007/s11013-018-9612-9.

13. Kleiman E. M., Nock M. K. Real-time assessment of suicidal thoughts and behaviors. Curr Opin Psychol. 2018; 22: 33-37. doi: 10.1016/j.copsyc.2017.07.026.

14. Lydon-Staley D. M., Barnett I., Satterthwaite T. D., Bassett D. S. Digital phenotyping for psychiatry: Accommodating data and theory with network science methodologies. Curr Opin Biomed Eng. 2019; 9: 8-13. doi: 10.1016/j.cobme.2018.12.003.

15. Melcher J., Hays R., Torous J. Digital phenotyping for mental health of college students: a clinical review. Evid Based Ment Health. 2020; 23 (4): 161-166. doi: 10.1136/ebmental-2020-300180.

16. Morcillo Serra C., González Romero J. L. New digital healthcare technologies. Med Clin (Barc). 2020; 154 (7): 257-259. English, Spanish. doi: 10.1016/j.medcli.2019.07.004.

17. Onnela J. P. Opportunities and challenges in the collection and analysis of digital phenotyping data. Neuropsychopharmacology. 2021; 46 (1): 45-54. doi: 10.1038/s41386-020-0771-3.

18. Onnela J. P., Rauch S. L. Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health. Neuropsychopharmacology. 2016; 41 (7): 1691-6. doi: 10.1038/npp.2016.7.

19. Orsolini L., Fiorani M., Volpe U. Digital Phenotyping in Bipolar Disorder: Which Integration with Clinical Endophenotypes and Biomarkers? Int J Mol Sci. 2020; 21 (20): 7684. doi: 10.3390/ijms21207684.

20. Radhakrishnan K., Kim M. T., Burgermaster M., Brown R. A., Xie B., Bray M. S., Fournier C. A. The potential of digital phenotyping to advance the contributions of mobile health to self-management science. Nurs Outlook. 2020; 68 (5): 548-559. doi: 10.1016/j.outlook.2020.03.007.

21. Rosa C., Marsch L. A., Winstanley E. L., Brunner M., Campbell A. N. C. Using digital technologies in clinical trials: Current and future applications. Contemp Clin Trials. 2021; 100: 106219. doi: 10.1016/j.cct.2020.106219.

22. Sequeira L., Battaglia M., Perrotta S., Merikangas K., Strauss J. Digital Phenotyping With Mobile and Wearable Devices: Advanced Symptom Measurement in Child and Adolescent Depression. J Am Acad Child Adolesc Psychiatry. 2019; 58 (9): 841-845. doi: 10.1016/j.jaac.2019.04.011. Erratum in: J Am Acad Child Adolesc Psychiatry. 2020; 59 (12): 1408.

23. Shenoy A., Appel J. M. Safeguarding Confidentiality in Electronic Health Records. Camb Q Healthc Ethics. 2017; 26 (2): 337-341. doi: 10.1017/S0963180116000931. PMID: 28361730

24. Stanghellini G., Leoni F. Digital Phenotyping: Ethical Issues, Opportunities, and Threats. Front Psychiatry. 2020; 11: 473. doi: 10.3389/fpsyt.2020.00473.

25. Stanghellini G., Leoni F. Digital Phenotyping: Ethical Issues, Opportunities, and Threats. Front Psychiatry. 2020; 11: 473. doi: 10.3389/fpsyt.2020.00473

26. Tachakra S., Wang X. H., Istepanian R. S., Song Y. H. Mobile e-health: the unwired evolution of telemedicine. Telemed J E Health. 2003; 9 (3): 247-57. doi: 10.1089/153056203322502632.

27. Torous J., Onnela J. P., Keshavan M. New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Transl Psychiatry. 2017; 7 (3): e1053. doi: 10.1038/tp.2017.25.

28. Wang Y. P., Gorenstein C. Psychometric properties of the Beck Depression Inventory-II: a comprehensive review. Braz J Psychiatry. 2013; 35 (4): 416-31. doi: 10.1590/1516-4446-2012-1048.

29. Zulueta J., Piscitello A., Rasic M., Easter R., Babu P., Langenecker S. A., McInnis M., Ajilore O., Nelson P. C., Ryan K., Leow A. Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. J Med Internet Res. 2018; 20 (7): e241. doi: 10.2196/jmir.9775.


Review

For citations:


Kasyanov E.D., Kovaleva Ya.V., Mazo G.E. Digital phenotyping as a new method of screening for mental disorders. V.M. BEKHTEREV REVIEW OF PSYCHIATRY AND MEDICAL PSYCHOLOGY. 2022;56(4):96-100. (In Russ.) https://doi.org/10.31363/2313-7053-2022-4-96-100

Views: 975


ISSN 2313-7053 (Print)
ISSN 2713-055X (Online)