Digital biomarkers of mood disorders and symptom change
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Abstract Current approaches to psychiatric assessment are resource-intensive, requiring time-consuming evaluation by a trained clinician. Development of digital biomarkers holds promise for enabling scalable, time-sensitive, and cost-effective assessment of both psychiatric diagnosis and symptom change. The present study aimed to identify robust digital biomarkers of diagnostic status and changes in symptom severity over ~2 weeks, through re-analysis of public-use actigraphy data collected in patients with major depressive or bipolar disorder and healthy controls. Results suggest that participants’ diagnostic group status (i.e., mood disorder, control) can be predicted with a high degree of accuracy (predicted correctly 89% of the time, kappa = 0.773), using features extracted from actigraphy data alone. Results also suggest that actigraphy data can be used to predict symptom change across ~2 weeks ( r = 0.782, p = 1.04e-05). Through inclusion of digital biomarkers in our statistical model, which are generalizable to new samples, the results may be replicated by other research groups in order to validate and extend this work.
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Movement and light data are relevant for detecting mood disorders and correlate with energy levels, psychomotor activity, and sleep disturbances during mood episodes.
“Movement and light data were found relevant for detecting mood disorders and correlated with behaviors like energy levels, psychomotor activity, and sleep disturbances during mood episodes.”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [28]
Jacobson et al. used extreme gradient boosting on movement patterns to achieve 89% accuracy in predicting diagnostic groups and monitoring symptom changes over 2 weeks.
“Jacobson et al. (28) conducted a study to identify digital biomarkers for MDD and bipolar disorder (BD) and track symptom changes over 2 weeks. By analyzing movement patterns, they used extreme gradient boosting to achieve an 89% accuracy in predicting diagnostic groups and monitoring symptom changes.”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [28]
Combining MDD and BD data revealed potential transdiagnostic traits.
“Combining MDD and BD data revealed potential transdiagnostic traits.”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [28]
Several studies have demonstrated the potential of using digital biomarkers (based on sensor data from wearable devices or mobile apps) for the prediction and detection of depression.
“Several studies have demonstrated the potential of using digital biomarkers (based on sensor data from wearable devices or mobile apps) for the prediction and detection of depression.[71][72][73]”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [71]
Extreme gradient boosting algorithm (DART) was the most efficient method in similar studies.
“Extreme gradient boosting algorithm (DART [Dropouts meet Multiple Additive Regression Trees]) was used for model training, as it was the most efficient method in similar studies [33,37,58].”
From Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling (2021) · cites this paper as [37]
Actigraphy-based biomarkers that quantify time sequences of rest and active behaviors with accelerometer sensors are known to be useful in predicting mood disorders such as depression and bipolar disorder.
“Actigraphy-based biomarkers that quantify time sequences of rest and active behaviors with accelerometer sensors are known to be useful in predicting mood disorders such as depression and bipolar disorder [34,35].”
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References 11 · Cited by 160
Research works printed but not linked: 2
- [9]Nielsen, D. Why Does XGBoost Win “Every” Machine Learning Competition? Master of Science thesis, Norwegian University of Science and Techology (2016).
- [11]Schapire, R. E. in Proceedings of the 16th international joint conference on Artificial intelligence, Dean, T. (ed.). Vol. 2, 1401–1406 (Morgan Kaufmann Publishers Inc., Stockholm, Sweden, 1999).
- Authors
- Jacobson, N. C., Weingarden, H., & Wilhelm, S.
- Affiliations
- Pennsylvania State University (US)
- Harvard University (US)
- Corresponding author
- Jacobson, Nicholas C.
- Venue
- npj Digital Medicine
- Published
- 2019-02-01
- Volume
- 2
- Issue
- 1
- Pages
- 3-3
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC6550284
- OpenAlex
- W2915095068
- Cited by
- 160
- References
- 11
- Citations by year
- 2026: 34 · 2025: 28 · 2024: 22 · 2023: 21 · 2022: 20 · 2021: 16 · 2020: 15 · 2019: 4
- FWCI
- 16.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Massachusetts General Hospital
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- digital biomarkers, mood disorders, major depressive disorder, bipolar disorder, actigraphy, sleep-wake patterns, psychiatric diagnosis, wearable sensor data
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