Digital phenotyping is a multidisciplinary field of science,[1][2][3] first defined in a May 2016 paper in JMIR Mental Health authored by John Torous, Mathew V Kiang, Jeanette Lorme, and Jukka-Pekka Onnela as the "moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices."[2] The data can be divided into two subgroups, called active data and passive data, where the former refers to data that requires active input from the users to be generated, whereas passive data, such as sensor data and phone usage patterns, are collected without requiring any active participation from the user.
Smartphones are well suited for digital phenotyping given their widespread adoption and ownership, the extent to which users engage with the devices, and richness of data that may be collected from them. Smartphone data can be used to study behavioral patterns, social interactions, physical mobility, gross motor activity, and speech production, among others. Smartphone ownership has been in steady rise globally over the past few years. For example, in the U.S., smartphone ownership among adults increased from 35% in 2011 to 64% in 2015,[4] and in 2017 an estimated 95% of Americans own a cellphone of some kind and 77% own a smartphone.[5]
The use of passive data collection from smartphone devices can provide granular information relevant to psychiatric, aging, frailty,[6] and other illness phenotypes.[7] Types of relevant passive data include GPS data to monitor spatial location, accelerometer data to record movement and gross motor activity, and call and messaging logs to document social engagement with others.[8] Passively collected data may also support clinical differentiation between diagnostic groups [9] and monitoring mental health symptoms. [10][11]
The related term 'digital phenotype' was introduced in Nature Biotechnology by Sachin H. Jain and John Brownstein in 2015.[12]
^Onnela, Jukka-Pekka; Rauch, Scott L. (June 2016). "Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health". Neuropsychopharmacology. 41 (7): 1691–1696. doi:10.1038/npp.2016.7. ISSN 0893-133X. PMC 4869063. PMID 26818126.
^ abTorous, John; Kiang, Mathew V; Lorme, Jeanette; Onnela, Jukka-Pekka (2016-05-05). "New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research". JMIR Mental Health. 3 (2): e16. doi:10.2196/mental.5165. ISSN 2368-7959. PMC 4873624. PMID 27150677.
^Brown, Karen (2016-07-19). "Your phone knows how you feel". Harvard Public Health Magazine. Retrieved 2019-11-13.
^Smith, Aaron (2015-04-01). "U.S. Smartphone Use in 2015". Pew Research Center: Internet, Science & Tech. Retrieved 2017-06-27.
^Pyrkov, Timothy V.; Getmantsev, Evgeny; Zhurov, Boris; Avchaciov, Konstantin; Pyatnitskiy, Mikhail; Menshikov, Leonid; Khodova, Kristina; Gudkov, Andrei V.; Fedichev, Peter O. (2018-10-26). "Quantitative characterization of biological age and frailty based on locomotor activity records". Aging. 10 (10): 2973–2990. doi:10.18632/aging.101603. ISSN 1945-4589. PMC 6224248. PMID 30362959.
^Gillett, George (2020). "A day in the life of a psychiatrist in 2050: where will the algorithm take us?". BJPsych Bulletin. 44 (3): 121–123. doi:10.1192/bjb.2020.22. PMC 8170007. PMID 33861188.
^Torous, John; Staples, Patrick; Onnela, Jukka-Pekka (2015-08-01). "Realizing the Potential of Mobile Mental Health: New Methods for New Data in Psychiatry". Current Psychiatry Reports. 17 (8): 61. doi:10.1007/s11920-015-0602-0. ISSN 1523-3812. PMC 4608747. PMID 26073363.
^Gillett, George; McGowan, Niall; Palmius, Niclas; Bilderbeck, Amy; Goodwin, Guy; Saunders, Kate (2021). "Digital Communication Biomarkers of Mood and Diagnosis in Borderline Personality Disorder, Bipolar Disorder, and Healthy Control Populations". Frontiers in Psychiatry. 12 (610457): 610457. doi:10.3389/fpsyt.2021.610457. ISSN 1664-0640. PMC 8060643. PMID 33897487.
^Braund, Taylor A.; Zin, May The; Boonstra, Tjeerd W.; Wong, Quincy J. J.; Larsen, Mark E.; Christensen, Helen; Tillman, Gabriel; O’Dea, Bridianne (2022-05-04). "Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study". JMIR Mental Health. 9 (5): e35549. doi:10.2196/35549. PMC 9118091. PMID 35507385. S2CID 247962553.
^Braund, Taylor A.; O’Dea, Bridianne; Bal, Debopriyo; Maston, Kate; Larsen, Mark E.; Werner-Seidler, Aliza; Tillman, Gabriel; Christensen, Helen (2023-05-15). "Associations Between Smartphone Keystroke Metadata and Mental Health Symptoms in Adolescents: Findings From the Future Proofing Study". JMIR Mental Health. 10: e44986. doi:10.2196/44986. PMC 10227695. PMID 37184904.
^Jain, Sachin H; Powers, Brian W; Hawkins, Jared B; Brownstein, John S (2015). "The digital phenotype". Nature Biotechnology. 33 (5): 462–463. doi:10.1038/nbt.3223. ISSN 1087-0156. PMID 25965751. S2CID 2318642.
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