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Year : 2021  |  Volume : 30  |  Issue : 2  |  Page : 191-192  Table of Contents     

Digital phenotyping in psychiatry: When mental health goes binary

1 Department of Psychiatry, Armed Forces Medical College, Pune, Maharashtra, India
2 Department of Psychiatry, Dr. D. Y. Patil Medical College, Pune, Maharashtra, India

Date of Submission25-Oct-2021
Date of Acceptance10-Nov-2021
Date of Web Publication23-Nov-2021

Correspondence Address:
Prof. Jyoti Prakash
Armed Forces Medical College, Pune - 411 040, Maharashtra
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ipj.ipj_223_21

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How to cite this article:
Prakash J, Chaudhury S, Chatterjee K. Digital phenotyping in psychiatry: When mental health goes binary. Ind Psychiatry J 2021;30:191-2

How to cite this URL:
Prakash J, Chaudhury S, Chatterjee K. Digital phenotyping in psychiatry: When mental health goes binary. Ind Psychiatry J [serial online] 2021 [cited 2022 Nov 29];30:191-2. Available from: https://www.industrialpsychiatry.org/text.asp?2021/30/2/191/330938

While the scientific explorations have churned out the role of micro/nano molecules and omics in psychiatry, the art of observation of an individual in its entirety, in an interaction, has gone underemphasized. The microscopic scrutiny of fractions somehow has overshadowed macroscopic study of interactions. While our classificatory systems are concerned with rise of gaming disorders and other Internet-related mental health problems, industries have been closely observing online behaviors of these users, making sense of their need/motivation, likes/dislikes, and utilizing these to promote sale and reap benefits. This concept of observing online proxies of behavior and emotions to understand human psyche has ramified into dimensions of mental health care and is called digital phenotyping.

Digital phenotyping is defined as moment-by-moment quantification of individual-level human phenotype in situ, using data from personal digital devices.[1] Given that, of more than 3 billion people with Internet access around the world, one-third resides in India or China; this concept has enough catchment population to study human behavior, or aberrations thereof.[2] The study has shown that an average smartphone user has around 60–90 apps installed in his phone, uses 30 of these in a month or 9 a day, and spends around 2.25 h on these apps per day.[3] Digital phenotyping, also called personal sensing, can render live proxy of human behavior and emotion, and may change overall realm of psychiatry.[1],[4]

Digital phenotyping considers data from smartphones and other digital wearable. These data may be active (real inputs from user) or passive (input from sensors). Various digital biomarkers which are being utilized in digital phenotyping are geolocation, calls (outgoing/incoming/not answered, duration/timing), messages (SMS/WhatsApp, length/timing), finger taps (speed, number), status of phone (Wi-Fi, Bluetooth, and power on/off state), ambient/preferred light, data on accelerometer, gyroscope, magnetometer or pedometer, sleep, heart rate/variability, screen behavior such as scrolling/clicking/tapping, speech, and voice modulation, frequency of battery charging, call log, navigation path on net, app visit, and update frequency.[5],[6],[7]

Effective assimilation of these data to understand and individualize human behavior may lead us to digital personalized psychiatry, by improving diagnostic process, designing individualized treatment plans, facilitating monitoring of behavior or treatment effects/adverse effects, predicting onset or relapse of illness, preventing mental morbidity, and fostering positive mental health or by risk reduction in psychiatry.[8] Given the advancement in machine learning, the data are going to be better and more real in future and possibility of evaluation and management, more precise.[7]

Use of digital phenotyping is being explored in many behavioral problems and illnesses, such as addiction,[9] autism spectrum disorders,[10] posttraumatic stress disorder,[11] schizophrenia,[12] perinatal psychiatry,[13] mood disorders,[14] sleep disorders,[15] child and adolescent psychiatry,[16] and suicide prevention[17]. Shift of pronoun to first-person singular has been seen in depression.[18] Semantic/phonemic fluency has been found impaired in first-episode depression.[19] Latent period between space and character or intervening time between scrolling and clicking has shown to be a fair surrogate of cognitive/affective trait or state.[20]

However, there are concerns with digital phenotyping. Currently, data are mostly based on convenience sampling, and thus, the characteristics may vary with the target group. Generalization of results and enhanced precision will require well-validated research in this direction and replication of findings. Evidence would be further required for its clinical utility and scalability.[8] Collection of data is riddled with ethical issue of informed consent, privacy, transparency, and accountability.[21] These digital markers currently are being used extensively by third parties for the purpose of digital analysis and marketing. Commodification of health-care data may impinge on privacy/freedom of the users.[22] Standard guidelines of use by professional bodies, robust protocol for data collection, and statutory body for ethical scrutiny may mitigate these problems.

To summarize, though the science of digital phenotyping is still in infancy stage for its use in psychiatry, the conceptual merit and available literature holds adequate shine and promises for its robust application in future.

   References Top

Torous J, Kiang MV, Lorme J, Onnela JP. New tools for new research in psychiatry: A scalable and customizable platform to empower data driven smartphone research. JMIR Ment Health 2016;3:e16.  Back to cited text no. 1
International Telecommunication Union. Available from: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/treemap.aspx. [Last accessed on 2021 Oct 24].  Back to cited text no. 2
Digital Phenotyping: A Revolution or a Privacy Breach? – MedCity News. Available from: https://medcitynews.com/2019/01/digital-phenotyping-a-revolution-or-a-privacy-breach/. [Last accessed on2021 Oct 24].  Back to cited text no. 3
Mohr DC, Zhang M, Schueller SM. Personal sensing: Understanding mental health using ubiquitous sensors and machine learning. Annu Rev Clin Psychol 2017;13:23-47.  Back to cited text no. 4
Torous J, Staples P, Onnela JP. Realizing the potential of mobile mental health: New methods for new data in psychiatry. Curr Psychiatry Rep 2015;17:602.  Back to cited text no. 5
Wang X, Vouk N, Heaukulani C, Buddhika T, Martanto W, Lee J, et al. HOPES: An integrative digital phenotyping platform for data collection, monitoring, and machine learning. J Med Internet Res 2021;23:e23984.  Back to cited text no. 6
Potier R. The digital phenotyping project: A psychoanalytical and network theory perspective. Front Psychol 2020;11:1218. doi: 10.3389/fpsyg.2020.01218.  Back to cited text no. 7
Huckvale K, Venkatesh S, Christensen H. Toward clinical digital phenotyping: A timely opportunity to consider purpose, quality, and safety. NPJ Digit Med 2019;2:88.  Back to cited text no. 8
Ferreri F, Bourla A, Mouchabac S, Karila L. e-Addictology: An overview of new technologies for assessing and intervening in addictive behaviors. Front Psychol 2018;9:51.  Back to cited text no. 9
Hswen Y, Gopaluni A, Brownstein JS, Hawkins JB. Using twitter to detect psychological characteristics of self-identified persons with autism spectrum disorder: A feasibility study. JMIR Mhealth Uhealth 2019;7:e12264.  Back to cited text no. 10
Bourla A, Mouchabac S, El Hage W, Ferreri F. e-PTSD: An overview on how new technologies can improve prediction and assessment of posttraumatic stress disorder (PTSD). Eur J Psychotraumatol 2018;9:1424448.  Back to cited text no. 11
Wisniewski H, Henson P, Torous J. Using a smartphone app to identify clinically relevant behavior trends via symptom report, cognition scores, and exercise levels: A case series. Front Psychol 2019;10:652.  Back to cited text no. 12
Fealy S, Chan S, Wynne O, Dowse E, Ebert L, Ho R, et al. The support for new mums project: A protocol for a pilot randomized controlled trial designed to test a postnatal psychoeducation smartphone application. J Adv Nurs 2019;75:1347-59.  Back to cited text no. 13
Brietzke E, Hawken ER, Idzikowski M, Pong J, Kennedy SH, Soares CN. Integrating digital phenotyping in clinical characterization of individuals with mood disorders. Neurosci Biobehav Rev 2019;104:223-30.  Back to cited text no. 14
Teo JX, Davila S, Yang C, Hii AA, Pua CJ, Yap J, et al. Digital phenotyping by consumer wearables identifies sleep-associated markers of cardiovascular disease risk and biological aging. Commun Biol 2019;2:361.  Back to cited text no. 15
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;58:841-5.  Back to cited text no. 16
Kleiman EM, Turner BJ, Fedor S, Beale EE, Picard RW, Huffman JC, et al. Digital phenotyping of suicidal thoughts. Depress Anxiety 2018;35:601-8.  Back to cited text no. 17
Pennybaker JW, Mehl MR, Niederhoffer KG. Psychological aspects of natural language. Use: Our words, our selves. Annu Rev Psychol 2003;54:547-77.  Back to cited text no. 18
Vicent-Gil M, Keymer-Gausset A, Serra-Blasco M, Carceller-Sindreu M, de Diego-Adeliño J, Trujols J, et al. Cognitive predictors of illness course at 12 months after first-episode of depression. Eur Neuropsychopharmacol 2018;28:529-37.  Back to cited text no. 19
Dagum P. Digital Phenotyping in Psychiatry. NPJ Digit Med 2018;1:10.  Back to cited text no. 20
Martinez-Martin N, Insel TR, Dagum P, Greely HT, Cho MK. Data mining for health: Staking out the ethical territory of digital phenotyping. NPJ Digit Med 2018;1:68.  Back to cited text no. 21
Cosgrove L, Karter JM, Morrill Z, McGinley M. Psychology and surveillance capitalism: The risk of pushing mental health apps during the COVID-19 pandemic. J Humanist Psychol 2020;60:611-25.  Back to cited text no. 22


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