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Data mining for health: staking out the ethical territory of digital phenotyping
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Digital phenotyping uses smartphone and wearable signals to measure cognition, mood, and behavior. This promising new approach has been developed as an objective, passive assessment tool for the diagnosis and treatment of mental illness. Digital phenotyping is currently used with informed consent in research studies but is expected to expand to broader uses in healthcare and direct-to-consumer applications. Digital phenotyping could involve the collection of massive amounts of individual data and potential creation of new categories of health and risk assessment data. Because existing ethical and regulatory frameworks for the provision of mental healthcare do not clearly apply to digital phenotyping, it is critical to consider its possible ethical, legal, and social implications. This paper addresses four major areas where guidelines and best practices will be helpful: transparency, informed consent, privacy, and accountability. It will be important to consider these issues early in the development of this new approach so that its promise is not limited by harmful effects or unintended consequences.
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“Additional advantages include the provision of real-time and longitudinal patient data, potentially leading to a more accurate diagnosis and treatment decisions, moment-to-moment monitoring, and timely interventions [13].”
From The therapeutic alliance in digital mental health interventions for serious mental illnesses: narrative review (2020) · cites this paper as [13]
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Cites
Eckstein, S. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511550089.028 · Cited by 3,514
Privacy and human behavior in the age of information
Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015) · Science · doi:10.1126/science.aaa1465 · Cited by 1,879
Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Char, D. S., Shah, N. H., & Magnus, D. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1714229 · Cited by 1,857
Lenggenhager, J. (1947) · PubMed · Cited by 1,772
Digital Phenotyping: Technology for a New Science of Behavior
Insel, T. R. (2017) · JAMA · doi:10.1001/jama.2017.11295 · Cited by 833
Self-citationAutomatic personality assessment through social media language.
Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., Ungar, L. H., & Seligman, M. E. P. (2015) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000020 · Cited by 802
Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4273 · Cited by 782
Automated analysis of free speech predicts psychosis onset in high-risk youths
Bedi, G., Carrillo, F., Cecchi, G. A., Slezak, D. F., Sigman, M., Mota, N. B., Ribeiro, S., Javitt, D. C., Copelli, M., & Corcoran, C. M. (2015) · npj Schizophrenia · doi:10.1038/npjschz.2015.30 · Cited by 673
Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015) · Nature Biotechnology · doi:10.1038/nbt.3223 · Cited by 425
Characterizing and predicting postpartum depression from shared facebook data
Choudhury, M. D., Counts, S., Horvitz, E., & Hoff, A. (2014) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2531602.2531675 · Cited by 391
Research works printed but not linked: 1
- Torous, J., Luo, J. & Chan, Steven R. Mental health apps: what to tell patients.Curr. Psychiatry 17, 21–25 (2018).
Cited by
Sorted by citations
Developing and adopting safe and effective digital biomarkers to improve patient outcomes
Coravos, A., Khozin, S., & Mandl, K. D. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0090-4 · Cited by 361
Laï, M.-C., Brian, M., & Mamzer, M.-F. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-019-02204-y · Cited by 294
Li, F., Ruijs, N., & Lü, Y. (2022) · AI · doi:10.3390/ai4010003 · Cited by 284
Tremain, H., McEnery, C., Fletcher, K., & Murray, G. (2020) · JMIR Mental Health · doi:10.2196/17204 · Cited by 193
Reliable and Resilient AI and IoT-Based Personalised Healthcare Services: A Survey
Taimoor, N., & Rehman, S. (2021) · IEEE Access · doi:10.1109/access.2021.3137364 · Cited by 196
Notice of Retraction: AI Techniques for COVID-19
Hussain, A. A., Bouachir, O., Al-Turjman, F., & Aloqaily, M. (2020) · IEEE Access · doi:10.1109/access.2020.3007939 · Cited by 154
RetractedDigital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health
Oudin, A., Maatoug, R., Bourla, A., Ferreri, F., Bonnot, O., Millet, B., Schoeller, F., Mouchabac, S., & Adrien, V. (2023) · Journal of Medical Internet Research · doi:10.2196/44502 · Cited by 156
The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges
Sarwar, T., Seifollahi, S., Chan, J., Zhang, X., Aksakallı, V., Hudson, I. L., Verspoor, K. M., & Cavedon, L. (2022) · ACM Computing Surveys · doi:10.1145/3490234 · Cited by 151
Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study
Martinez‐Martin, N., Greely, H. T., & Cho, M. K. (2021) · JMIR mhealth and uhealth · doi:10.2196/27343 · Cited by 128
Self-citationA new agenda for personality psychology in the digital age?
Montag, C., & Elhai, J. D. (2019) · Personality and Individual Differences · doi:10.1016/j.paid.2019.03.045 · Cited by 125
Deisenhofer, A., Barkham, M., Beierl, E. T., Schwartz, B., Doorn, K. A., Beevers, C. G., Berwian, I. M., Blackwell, S. E., Bockting, C., Brakemeier, E., et al. (2023) · Behaviour Research and Therapy · doi:10.1016/j.brat.2023.104443 · Cited by 123
Gooding, P., & Kariotis, T. (2021) · JMIR Mental Health · doi:10.2196/24668 · Cited by 100
Mapping the rise of digital mental health technologies: Emerging issues for law and society
Gooding, P. (2019) · International Journal of Law and Psychiatry · doi:10.1016/j.ijlp.2019.101498 · Cited by 93
Dimensions of Ethical Direct-to-Consumer Neurotechnologies
Kreitmair, K. (2019) · AJOB Neuroscience · doi:10.1080/21507740.2019.1665120 · Cited by 84
Alzheimer's disease digital biomarkers multidimensional landscape and AI model scoping review
Qi, W., Zhu, X., Wang, B., Shi, Y., Dong, C., Shen, S., Li, J., Zhang, K., He, Y., Zhao, M., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01640-z · Cited by 78
“Additionally, traditional privacy regulations, such as HIPAA, face challenges in non-traditional clinical environments 171 .”
Galderisi, S., Appelbaum, P. S., Gill, N., Gooding, P., Herrman, H., Melillo, A., Myrick, K., Pathare, S., Savage, M., Szmukler, G., et al. (2024) · World Psychiatry · doi:10.1002/wps.21230 · Cited by 76
Ethical dilemmas posed by mobile health and machine learning in psychiatry research
Jacobson, N. C., Bentley, K. H., Walton, A., Wang, S. B., Fortgang, R. G., Millner, A. J., Coombs, G., Rodman, A. M., & Coppersmith, D. D. L. (2020) · Bulletin of the World Health Organization · doi:10.2471/blt.19.237107 · Cited by 76
Rusz, J., Krack, P., & Tripoliti, E. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105922 · Cited by 74
Is There an App for That?: Ethical Issues in the Digital Mental Health Response to COVID-19
Skorburg, J. A., & Yam, J. (2021) · AJOB Neuroscience · doi:10.1080/21507740.2021.1918284 · Cited by 65
Möllmann, N. R. J., Mirbabaie, M., & Stieglitz, S. (2021) · Health Informatics Journal · doi:10.1177/14604582211052391 · Cited by 68
Vilaza, G. N., & McCashin, D. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.689736 · Cited by 60
The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact
Zhang, Y., Wang, J., Zong, H., Singla, R. K., Ullah, A., Liu, X., Wu, R., Ren, S., & Shen, B. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01602-5 · Cited by 59
Wearable Technology in Clinical Practice for Depressive Disorder
Fedor, S., Lewis, R., Pedrelli, P., Mischoulon, D., Curtiss, J. E., & Picard, R. W. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2215898 · Cited by 59
A scoping review of neurodegenerative manifestations in explainable digital phenotyping
Alfalahi, H., Dias, S. B., Khandoker, A. H., Chaudhuri, К. R., & Hadjileontiadis, L. J. (2023) · npj Parkinson s Disease · doi:10.1038/s41531-023-00494-0 · Cited by 58
Design Bioethics: A Theoretical Framework and Argument for Innovation in Bioethics Research
Pavarini, G., McMillan, R., Robinson, A., & Singh, I. (2021) · The American Journal of Bioethics · doi:10.1080/15265161.2020.1863508 · Cited by 55
Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review
Dlima, S. D., Shevade, S., Menezes, S. R., & Ganju, A. (2022) · JMIR Bioinformatics and Biotechnology · doi:10.2196/39618 · Cited by 53
Digital biomarkers for non-motor symptoms in Parkinson’s disease: the state of the art
Daalen, J. M. J., van den Bergh, R., Prins, E. M., Moghadam, M. S. C., van den Heuvel, R., Veen, J., Mathur, S., Meijerink, H., Mirelman, A., Darweesh, S. K. L., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01144-2 · Cited by 52
Ogbete, J. C., Aminu-Ibrahim, A., & Ambali, K. B. (2021) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit217561 · Cited by 48
Grosman-Rimon, L., & Wegier, P. (2024) · Medicine · doi:10.1097/md.0000000000039136 · Cited by 46
Neuroethics at 15: The Current and Future Environment for Neuroethics
Emerging Issues Task Force, I. N. S. (2019) · AJOB Neuroscience · doi:10.1080/21507740.2019.1632958 · Cited by 45
Leveraging Data Science for Global Health
Celi, L. A., Majumder, M. S., Ordóñez, P., Osorio, J. S., Paik, K., & Somai, M. M. (2020) · Springer eBooks · doi:10.1007/978-3-030-47994-7 · Cited by 48
An Ethics Checklist for Digital Health Research in Psychiatry: Viewpoint
Shen, F. X., Silverman, B. C., Monette, P., Kimble, S., Rauch, S. L., & Baker, J. T. (2022) · Journal of Medical Internet Research · doi:10.2196/31146 · Cited by 43
“Yet, the road to this innovative future is fraught with ethical dilemmas [7-10], and ethical, legal, and social implications (ELSI) on issues such as informed consent and return of results must be reexamined in light of research employing previously unavailable deep and computational characterization of humans based o…”
BackgroundMulvenna, M. D., Bond, R., Delaney, J., Dawoodbhoy, F. M., Boger, J. N., Potts, C., & Turkington, R. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00445-8 · Cited by 43
Aminu-Ibrahim, A., & Ogbete, J. C. (2023) · Shodhshauryam International Scientific Refereed Research Journal · doi:10.32628/shisrrj23678 · Cited by 47
Kim, M., Yang, J., Ahn, W.-Y., & Choi, H. J. (2021) · Journal of Medical Internet Research · doi:10.2196/27218 · Cited by 41
The ethics of data mining in healthcare: challenges, frameworks, and future directions
Ahmed, M. M., Okesanya, O. J., Oweidat, M. H., Othman, Z. K., Musa, S. S., & Lucero‐Prisno, D. E. (2025) · BioData Mining · doi:10.1186/s13040-025-00461-w · Cited by 41
Assche, E. V., Ramos‐Quiroga, J. A., Pariante, C. M., Sforzini, L., Young, A. H., Flossbach, Y., Gold, S. M., Hoogendijk, W. J. G., Baune, B. T., & Maron, E. (2022) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2022.05.007 · Cited by 40
Illner, V., Novotný, M., Kouba, T., Tykalová, T., Šimek, M., Sovka, P., Švihlík, J., Růžička, E., Šonka, K., Dušek, P., et al. (2024) · Movement Disorders · doi:10.1002/mds.29921 · Cited by 40
Brinberg, M., & Ram, N. (2021) · Journal of Communication · doi:10.1093/joc/jqab012 · Cited by 36
Digital Phenotyping: an Epistemic and Methodological Analysis
Coghlan, S., & D’Alfonso, S. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00492-1 · Cited by 35
Returning Individual Research Results from Digital Phenotyping in Psychiatry
Shen, F. X., Baum, M. L., Martinez‐Martin, N., Miner, A. S., Abraham, M. E., Brownstein, C. A., Cortez, N. G., Evans, B. J., Germine, L. T., Glahn, D. C., et al. (2023) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2180109 · Cited by 34
Self-citationSchultebraucks, K., Yadav, V., & Galatzer‐Levy, I. R. (2020) · Digital Biomarkers · doi:10.1159/000512394 · Cited by 32
Nayak, B., Bhattacharyya, S. S., & Krishnamoorthy, B. (2019) · Business Strategy & Development · doi:10.1002/bsd2.59 · Cited by 31
Ethical Considerations of Digital Phenotyping from the Perspective of a Healthcare Practitioner
Dagum, P., & Montag, C. (2019) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-31620-4_2 · Cited by 30
Self-citationA process-oriented approach to respecting privacy in the context of mobile phone tracking
Harari, G. M. (2019) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2019.09.007 · Cited by 33
Barnett, S., Huckvale, K., Christensen, H. M., Venkatesh, S., Mouzakis, K., & Vasa, R. (2019) · Journal of Medical Internet Research · doi:10.2196/16399 · Cited by 29
Show me your smartphone… and then I will show you your brain structure and brain function
Montag, C., Elhai, J. D., & Dagum, P. (2021) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.272 · Cited by 27
Self-citationNeuroethics at the interface of machine learning and schizophrenia
McFarlane, J., & Illes, J. (2020) · Schizophrenia · doi:10.1038/s41537-020-0108-6 · Cited by 26
Smart Sensors for Health Research and Improvement
Garatva, P., Terhorst, Y., Meßner, E.-M., Karlen, W., Pryss, R. C., & Baumeister, H. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_23 · Cited by 26
Ethical issues in direct-to-consumer healthcare: A scoping review
Nagappan, A., Kalokairinou, L., & Wexler, A. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000452 · Cited by 27
O’Leary, A., Lahey, T., Lovato, J. L., Loftness, B. C., Douglas, A., Skelton, J. A., Cohen, J. G., Copeland, W., McGinnis, R. S., & McGinnis, E. W. (2024) · Sensors · doi:10.3390/s24103214 · Cited by 24
Faurholt‐Jepsen, M., Busk, J., Rohani, D. A., Frost, M. M., Tønning, M. L., Bardram, J. E., & Kessing, L. V. (2022) · Journal of Affective Disorders · doi:10.1016/j.jad.2022.03.054 · Cited by 28
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Bouchikhi, M. E., Weerts, S., & Clavien, C. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1468646 · Cited by 23
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Potier, R. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01218 · Cited by 24
Landry, V., Matschek, J., Pang, R., Munipalle, M., Tan, K., Boruff, J. T., & Li‐Jessen, N. Y. K. (2025) · European Respiratory Review · doi:10.1183/16000617.0246-2024 · Cited by 23
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Hossain, M. A. (2024) · doi:10.63125/vg522x27 · Cited by 19
Adamo, J. E., Bienvenu, R. V., Dolz, F., Liebman, M., Nilsen, W., & Steele, S. J. (2020) · Digital Biomarkers · doi:10.1159/000505289 · Cited by 19
Caporusso, E., Melillo, A., Perrottelli, A., Giuliani, L., Marzocchi, F. F., Pezzella, P., & Giordano, G. M. (2025) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2025.1543005 · Cited by 19
Vlisides‐Henry, R. D., Gao, M. M., Thomas, L. R., Kaliush, P. R., Conradt, E. D., & Crowell, S. E. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.618442 · Cited by 19
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Schmidt, S., & D’Alfonso, S. (2023) · Acta Psychologica · doi:10.1016/j.actpsy.2023.103886 · Cited by 19
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Stange, J. P., Li, J., Xu, E. P., Ye, Z., Zapetis, S. L., Phanord, C. S., Wu, J., Sellery, P., Keefe, K., Forbes, E. E., et al. (2023) · Journal of Psychopathology and Clinical Science · doi:10.1037/abn0000849 · Cited by 18
Aschbacher, K., Rivera, L. M., Hornstein, S., Nelson, B. W., Forman‐Hoffman, V. L., & Peiper, N. C. (2023) · Psychosomatic Medicine · doi:10.1097/psy.0000000000001230 · Cited by 19
Faurholt‐Jepsen, M., Rohani, D. A., Busk, J., Tønning, M. L., Frost, M. M., Bardram, J. E., & Kessing, L. V. (2024) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2024.01.003 · Cited by 18
Digital phenotyping in psychiatry
Prakash, J., Chaudhury, S., & Chatterjee, K. (2021) · Industrial Psychiatry Journal · doi:10.4103/ipj.ipj_223_21 · Cited by 19
The Role of a Smart Health Ecosystem in Transforming the Management of Chronic Health Conditions
Nourse, R. A., Dingler, T., Kelly, J. T., Kwaśnicka, D., & Maddison, R. (2023) · Journal of Medical Internet Research · doi:10.2196/44265 · Cited by 17
Wearable IoT Devices in Rehabilitation
Aziz, R., Jawed, F., Khan, S. A., & Sundus, H. (2024) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/979-8-3693-2105-8.ch018 · Cited by 17
Rudroff, T. (2025) · Brain Sciences · doi:10.3390/brainsci15050533 · Cited by 17
Ortiz, A., & Mulsant, B. H. (2024) · Journal of Medical Internet Research · doi:10.2196/59826 · Cited by 18
van Heerden, A. C., Wassenaar, D. R., Essack, Z., Vilakazi, K., & Kohrt, B. A. (2019) · Journal of Empirical Research on Human Research Ethics · doi:10.1177/1556264619881334 · Cited by 16
Minding the AI: Ethical Challenges and Practice for AI Mental Health Care Tools
Martinez‐Martin, N. (2021) · Advances in neuroethics · doi:10.1007/978-3-030-74188-4_8 · Cited by 19
Self-citationFirst-person disavowals of digital phenotyping and epistemic injustice in psychiatry
Slack, S. K., & Barclay, L. (2023) · Medicine Health Care and Philosophy · doi:10.1007/s11019-023-10174-8 · Cited by 15
Miller, M., Raugh, I. M., Strauss, G. P., & Harvey, P. D. (2022) · Biomarkers in Neuropsychiatry · doi:10.1016/j.bionps.2022.100047 · Cited by 16
Schweiger, G. (2025) · AI and Ethics · doi:10.1007/s43681-025-00815-4 · Cited by 14
Epistemic inequality in the digital era: Unpacking biases in digital mental health
Tomičić, A., & Gjorgjioska, M. A. (2024) · Theory & Psychology · doi:10.1177/09593543241279131 · Cited by 19
The risks of risk. Regulating the use of machine learning for psychosis prediction
Corsico, P. (2019) · International Journal of Law and Psychiatry · doi:10.1016/j.ijlp.2019.101479 · Cited by 15
Trusting the bot: Addressing the ethical challenges of consumer digital mental health therapy
Martinez‐Martin, N. (2020) · Developments in neuroethics and bioethics · doi:10.1016/bs.dnb.2020.03.003 · Cited by 14
Self-citationPlay the Pain: A Digital Strategy for Play-Oriented Research and Action
Khalili‐Mahani, N., Holowka, E., Woods, S., Khaled, R., Roy, M., Lashley, M., Tristan Glatard, B., Timm‐Bottos, J., Dahan, A., Niesters, M., et al. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.746477 · Cited by 15
Bouchikhi, M. E., Weerts, S., & Clavien, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0315671 · Cited by 13
Neuroethics: Fostering Collaborations to Enable Neuroscientific Discovery
Farahany, N. A., & Ramos, K. M. (2020) · AJOB Neuroscience · doi:10.1080/21507740.2020.1778117 · Cited by 15
Jahanshad, N., Lenzini, P., & Bijsterbosch, J. (2025) · Neuropsychopharmacology · doi:10.1038/s41386-024-01938-8 · Cited by 12
Digitale Technologien zur Verbesserung der psychischen Gesundheit
Fuhr, D. C., Wolf‐Ostermann, K., Hoel, V., & Zeeb, H. (2024) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-024-03842-4 · Cited by 12
Purohit, R., Saineni, S., Chalise, S., Mathai, R., Sambandam, R., Medina-Perez, R., & Bhanusali, N. (2024) · Rheumatology International · doi:10.1007/s00296-024-05737-8 · Cited by 12
Haines, S., Savic, M., Nielsen, S., & Carter, A. (2021) · International Journal of Drug Policy · doi:10.1016/j.drugpo.2021.103549 · Cited by 12
Baumeister, H., & Montag, C. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_1 · Cited by 12
Moore, B., Herington, J., & Tekin, Ş. (2025) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2025.114509 · Cited by 11
Khati, A., Wickersham, J. A., Rosen, A. O., Luces, J. R., Copenhaver, N., Jeri‐Wahrhaftig, A., Halim, M. A. A., Azwa, I., Gautam, K., Ooi, K. H., et al. (2022) · JMIR Formative Research · doi:10.2196/42939 · Cited by 13
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Jaiswal, A., Shah, A., Harjadi, C., Windgassen, E., & Washington, P. Y. (2024) · JMIR Formative Research · doi:10.2196/59794 · Cited by 11
Using passive surveillance technology for overdose prevention: Key ethical and implementation issues
van Draanen, J., Satti, S., Morgan, J., Gaudette, L., Knight, R., & Ti, L. (2021) · Drug and Alcohol Review · doi:10.1111/dar.13373 · Cited by 11
Mayer, G., Zafar, A., Hummel, S., Landau, F., & Schultz, J. (2023) · BMJ Mental Health · doi:10.1136/bmjment-2023-300831 · Cited by 10
Harnessing Digital Phenotyping for Early Self-Detection of Psychological Distress
Zakai, J. G., & Alharthi, S. A. (2025) · Healthcare · doi:10.3390/healthcare13162008 · Cited by 10
On the Need for Digital Phenotyping to Obtain Insights into Mental States in the COVID-19 Pandemic
Montag, C., Dagum, P., & Elhai, J. D. (2020) · Digital Psychology · doi:10.24989/dp.v1i2.1857 · Cited by 11
Self-citationBuilding the Thermometer for Mental Health.
Chauvin, J. J., & Insel, T. R. (2018) · Europe PMC (PubMed Central) · Cited by 10
Self-citationBond, R., Moorhead, A., Mulvenna, M. D., O’Neill, S. M., Potts, C., & Murphy, N. (2019) · Behaviour and Information Technology · doi:10.1080/0144929x.2019.1648553 · Cited by 10
Butorac, I., McNaney, R., Seguin, J. P., Olivier, P. L., Northam, J. C., Tully, L. A., Carl, T., & Carter, A. (2025) · JMIR Pediatrics and Parenting · doi:10.2196/65163 · Cited by 9
Bodenstein, K., Paquin, V., Sekhon, K., Lesage, M., Cinalioglu, K., Rej, S., Vahia, I. V., & Sekhon, H. (2023) · Springer eBooks · doi:10.1007/978-3-031-43356-6_7 · Cited by 10
Horstkötter, D., Kanne, M., Karbouniaris, S., Lazrak, N., Bulgheroni, M., Sheltawy, E., Giani, L., Gamba, M. L., Pujadas, E. R., Camacho, M., et al. (2025) · Journal of Responsible Technology · doi:10.1016/j.jrt.2025.100119 · Cited by 8
Digital speech biomarkers can measure acute effects of levodopa in Parkinson’s disease
Sousa, M., Krýže, P., Amstutz, D., Petermann, K., Averna, A., Castelli, M., Magalhães, A. D., Jorge, A., Švihlík, J., Tykalová, T., et al. (2025) · npj Parkinson s Disease · doi:10.1038/s41531-025-01045-5 · Cited by 8
Narkbunnum, W., & Wisaeng, K. (2022) · Applied System Innovation · doi:10.3390/asi5060120 · Cited by 8
Bilal, A.-M., Pagoni, K., Iliadis, S. I., Papadopoulos, F. C., Skalkidou, A., & Öster, C. (2024) · JMIR Formative Research · doi:10.2196/53508 · Cited by 10
Metadata
- Authors
- Martinez-Martin, N., Insel, T. R., Dagum, P., Greely, H. T., & Cho, M. K.
- Affiliations
- Stanford University (US)
- Venue
- npj Digital Medicine
- Published
- 2018-12-19
- Volume
- 1
- Issue
- 1
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC6550156
- OpenAlex
- W2905393963
- Cited by
- 218
- References
- 35
- Citations by year
- 2026: 35 · 2025: 32 · 2024: 36 · 2023: 29 · 2022: 19 · 2021: 32 · 2020: 21 · 2019: 13 · 2018: 1
- FWCI
- 21.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Human Genome Research Institute (T32HG008953)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- digital phenotyping, mental health, informed consent, privacy, transparency, accountability, wearable devices, smartphone data, mental illness diagnosis, data protection, moral norms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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