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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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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.
What counts as a claim ›How we measure confidence ›How we check this ›
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