A call for open data to develop mental health digital biomarkers
Open accessAbstract
Digital biomarkers of mental health, created using data extracted from everyday technologies including smartphones, wearable devices, social media and computer interactions, have the opportunity to revolutionise mental health diagnosis and treatment by providing near-continuous unobtrusive and remote measures of behaviours associated with mental health symptoms. Machine learning models process data traces from these technologies to identify digital biomarkers. In this editorial, we caution clinicians against using digital biomarkers in practice until models are assessed for equitable predictions ('model equity') across demographically diverse patients at scale, behaviours over time, and data types extracted from different devices and platforms. We posit that it will be difficult for any individual clinic or large-scale study to assess and ensure model equity and alternatively call for the creation of a repository of open de-identified data for digital biomarker development.
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Machine learning models analyzing personal and occupational data can predict mental health risks before they become apparent.
“Furthermore, the use of machine learning models to analyze personal and occupational data can revolutionize preventative strategies and predict mental health risks before they become apparent.”
From Mental Health in Healthcare Workers Post-COVID-19: A Latin American Review and Insights into Personalized Management Strategies (2024) · cites this paper as [45]
The Abstract describes machine learning models that use data from everyday technologies to identify biomarkers associated with mental health symptoms, and gives an example of predicting changes in schizophrenia symptoms. It does not describe analysis of occupational data or establish that models can predict risks before symptoms become apparent. The Abstract also cautions against clinical use until prediction equity is assessed. (LLM, conf=0.88)
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References 14 · Cited by 23
- Authors
- Adler, D. A., Wang, F., Mohr, D. C., Estrin, D., Livesey, C., & Choudhury, T.
- Affiliations
- Cornell Tech (US)
- Weill Cornell Medicine (US)
- Northwestern University (US)
- University of Pennsylvania (US)
- Corresponding author
- Adler, Daniel A.
- Venue
- BJPsych Open
- Publisher
- Royal College of Psychiatrists
- Published
- 2022-03
- Volume
- 8
- Issue
- 2
- Pages
- e58-e58
- ISSN
- 2056-4724 (ISSN-L)
- PMC
- PMC8935940
- OpenAlex
- W4214825345
- Cited by
- 23
- References
- 14
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 9 · 2023: 5 · 2022: 2
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BJPsych Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health
- National Science Foundation (1750326, DGE-1650441)
- National Institute on Aging (RF1AG072449)
- National Institute of Mental Health (R01MH111610)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- digital biomarkers, mental health, open data, algorithmic fairness, smartphone data, wearable devices, social media data, machine learning prediction
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