The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Data mining for health: staking out the ethical territory of digital phenotyping
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
