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Digital phenotyping

From Wikipedia, the free encyclopedia

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]

Research platforms and commercialization

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One of the first implementations of digital phenotyping on smart phones was the Funf Open Sensing Framework, developed at the MIT Media Lab and launched on October 5, 2011.[13] Members of the Funf team interested in profiling and predicting human behavior formed a commercial venture called Behavio in 2012.[14] In April 2013, it was announced that the Behavio team had joined Google.[15] The Funf platform has inspired other mobile phone sensor logging platforms for psychology and behavior applications, such as the Purple Robot platform, developed by the CBITS (Center for Behavioral Intervention Technologies) at Northwestern University in 2012,[16] which has since expanded and remains an active GITHUB project.

Among the academic research community, there are now many digital phenotyping platforms. Popular open-source digital phenotyping platforms include Beiwe[17], AWARE[18], EARS,[19] mindLAMP,[20] RADAR-CNS among others and there is currently no metric to determine which is most popular.

In terms of commercialization, in 2017, former head of the National Institutes of Mental Health, Tom Insel, joined Rick Klausner and Paul Dagum to form the founding team of MindStrong Health, which uses digital phenotyping methods combined with machine learning to develop new paradigms for mental health assessment and development of new digital biomarkers for mental health.[21] As of 2021 the company's website does not mention digital phenotyping.

Criticisms

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The widespread adoption of digital phenotyping across diverse research domains necessitates robust methodological guidelines. Passive data collection, a cornerstone of this approach, poses a significant challenges at every stage of the research process.[22][23][24] From the outset, researchers grapple with clearly defining the constructs under investigation, a task complicated by the obscure nature of digital phenomena.[25][26][27] Subsequent decisions about data capture devices, applications, and cleaning protocols further amplify the complexity.[22] The analysis phase introduces another layer of challenges, particularly when employing computationally demanding techniques such as machine learning.[23] Optimizing model performance through careful data partitioning and hyperparameter tuning is essential but requires essential knowledge.[28] Recently published templates aim to address these challenges by providing standardized approaches to digital phenotyping research, potentially facilitating greater consistency and comparability across studies.[29]

See also

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References

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  1. ^ 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.
  2. ^ a b Torous, 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.
  3. ^ Brown, Karen (2016-07-19). "Your phone knows how you feel". Harvard Public Health Magazine. Retrieved 2019-11-13.
  4. ^ Smith, Aaron (2015-04-01). "U.S. Smartphone Use in 2015". Pew Research Center: Internet, Science & Tech. Retrieved 2017-06-27.
  5. ^ "Mobile Fact Sheet". Pew Research Center: Internet, Science & Tech. 2017-01-12. Retrieved 2017-06-27.
  6. ^ 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.
  7. ^ 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.
  8. ^ 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.
  9. ^ 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.
  10. ^ 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.
  11. ^ 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.
  12. ^ 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.
  13. ^ "Funf Blog". funf-blog.blogspot.com. Retrieved 2021-01-22.
  14. ^ "Knight Foundation Bets Mobile Sensor Startup, Behav.io, Is The Future of Journalism". TechCrunch. 18 June 2012. Retrieved 2021-01-22.
  15. ^ D'Orazio, Dante (2013-04-12). "Google gains team behind Behavio, a startup that uses smartphone data to make predictions". The Verge. Retrieved 2021-01-22.
  16. ^ "Your smartphone knows when you're depressed". The Daily Dot. 2015-07-16. Retrieved 2021-01-22.
  17. ^ "Digital Phenotyping and Beiwe Research Platform". Onnela Lab. 21 July 2017. Retrieved 2021-01-22.
  18. ^ "AWARE-Light – Digital phenotyping and experience sampling on smartphones". Retrieved 2024-12-06.
  19. ^ Lind, Monika N.; Byrne, Michelle L.; Wicks, Geordie; Smidt, Alec M.; Allen, Nicholas B. (July 2018). "The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing". JMIR Mental Health. 5 (3): e10334. doi:10.2196/10334. PMC 6134227. PMID 30154072.
  20. ^ Torous, John; Wisniewski, Hannah; Bird, Bruce; Carpenter, Elizabeth; David, Gary; Elejalde, Eduardo; Fulford, Dan; Guimond, Synthia; Hays, Ryan; Henson, Philip; Hoffman, Liza (2019-06-01). "Creating a Digital Health Smartphone App and Digital Phenotyping Platform for Mental Health and Diverse Healthcare Needs: an Interdisciplinary and Collaborative Approach". Journal of Technology in Behavioral Science. 4 (2): 73–85. doi:10.1007/s41347-019-00095-w. ISSN 2366-5963. S2CID 150589575.
  21. ^ "Former Director of the National Institute of Mental Health, Dr. Thomas Insel, Joins Mindstrong Health as President and Co-Founder". Mindstrong Health. 2017-05-11. Retrieved 2021-01-22.
  22. ^ a b Davidson, Brittany I. (2022-01-01). "The crossroads of digital phenotyping". General Hospital Psychiatry. 74: 126–132. doi:10.1016/j.genhosppsych.2020.11.009. ISSN 0163-8343. PMID 33653612.
  23. ^ a b Hicks, Jennifer L.; Althoff, Tim; Sosic, Rok; Kuhar, Peter; Bostjancic, Bojan; King, Abby C.; Leskovec, Jure; Delp, Scott L. (2019-06-03). "Best practices for analyzing large-scale health data from wearables and smartphone apps". npj Digital Medicine. 2 (1): 45. doi:10.1038/s41746-019-0121-1. ISSN 2398-6352. PMC 6550237. PMID 31304391.
  24. ^ Velozo, Joana De Calheiros; Habets, Jeroen; George, Sandip V.; Niemeijer, Koen; Minaeva, Olga; Hagemann, Noëmi; Herff, Christian; Kuppens, Peter; Rintala, Aki; Vaessen, Thomas; Riese, Harriëtte; Delespaul, Philippe (January 2024). "Designing daily-life research combining experience sampling method with parallel data". Psychological Medicine. 54 (1): 98–107. doi:10.1017/S0033291722002367. ISSN 0033-2917. PMID 36039768.
  25. ^ Huckvale, Kit; Venkatesh, Svetha; Christensen, Helen (2019-09-06). "Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety". npj Digital Medicine. 2 (1): 88. doi:10.1038/s41746-019-0166-1. ISSN 2398-6352. PMC 6731256. PMID 31508498.
  26. ^ Mohr, David C.; Zhang, Mi; Schueller, Stephen M. (2017-05-08). "Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning". Annual Review of Clinical Psychology. 13 (1): 23–47. doi:10.1146/annurev-clinpsy-032816-044949. ISSN 1548-5943. PMC 6902121. PMID 28375728.
  27. ^ Langener, Anna M.; Stulp, Gert; Kas, Martien J.; Bringmann, Laura F. (2023-03-17). "Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review". JMIR Mental Health. 10 (1): e42646. doi:10.2196/42646. PMC 10132048. PMID 36930210.
  28. ^ Yang, Li; Shami, Abdallah (2020-11-20). "On hyperparameter optimization of machine learning algorithms: Theory and practice". Neurocomputing. 415: 295–316. arXiv:2007.15745. doi:10.1016/j.neucom.2020.07.061. ISSN 0925-2312.
  29. ^ Langener, Anna M.; Siepe, Björn S.; Elsherif, Mahmoud; Niemeijer, Koen; Andresen, Pia K.; Akre, Samir; Bringmann, Laura F.; Cohen, Zachary D.; Choukas, Nathaniel R.; Drexl, Konstantin; Fassi, Luisa; Green, James; Hoffmann, Tabea; Jagesar, Raj R.; Kas, Martien J. H. (2024-08-07). "A template and tutorial for preregistering studies using passive smartphone measures". Behavior Research Methods. doi:10.3758/s13428-024-02474-5. ISSN 1554-3528. PMC 11525430. PMID 39112740.

Further reading

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