Using digital phenotyping to accurately detect depression severity
Abstract
Development of digital biomarkers holds promise for enabling scalable, time-sensitive, and cost-effective strategies to monitor symptom severity among those with major depressive disorder (MDD). The current study examined the use of passive movement and light data from wearable devices to assess depression severity in 15 patients with MDD. Using over 1 week of movement data, we were able to significantly assess depression severity with high precision for self-reported (r = 0.855; 95% confidence interval [CI], 0.610-0.950; p = 4.95 × 10) and clinician-rated (r = 0.604; 95% CI, 0.133-0.894; p = 0.017) symptom severity. Pending replication, the present data suggest that the use of passive wearable sensors to inform healthcare decisions holds considerable promise.
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Jacobson et al. demonstrated that passive movement and light exposure data could effectively gauge depression severity in 15 medicated outpatients with MDD over one week, even in cases of high severity.
“in the study conducted by Jacobson et al. (24), passive movement and light exposure data were analyzed in 15 medicated outpatients with MDD over a week. The study demonstrated that passive movement and light data could effectively gauge depression severity, even in cases of high severity.”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [24]
Several digital biomarkers have been investigated for MDD characterization and diagnosis, including physical activity patterns, voice features, light exposure measurements, mobile phone GPS, and normal smartphone usage duration and frequency.
“In the recent years, several digital biomarkers have been investigated for MDD characterization and diagnosis such as measure patterns of physical activity (24, 25) features from voice samples (26, 27), light exposure measurements (25), mobile phone global positioning systems (GPS) and normal usage of smartphones such as usage duration and frequency (22).”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [24]
Approximately 1 in 5 adults in the US will experience major depressive disorder (MDD) at some point in their lifetime.
“approximately 1 in 5 adults in the US will experience major depressive disorder (MDD) at some point in their lifetime.2”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [2]
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Digital Health Tools for the Passive Monitoring of Depression: A Systematic Review of Methods
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Digital health technologies and major depressive disorder
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- Authors
- Jacobson, N. C., Weingarden, H., & Wilhelm, S.
- Affiliations
- Harvard University (US)
- Venue
- Journal of Nervous & Mental Disease
- Published
- 2019-10
- Volume
- 207
- Issue
- 10
- Pages
- 893-896
- ISSN
- 0022-3018 (ISSN-L)
- 1539-736X
- OpenAlex
- W2980148744
- Cited by
- 94
- References
- 16
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 9 · 2023: 15 · 2022: 22 · 2021: 15 · 2020: 11
- FWCI
- 14.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Nervous and Mental Disease (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- digital phenotyping, major depressive disorder, depression severity, digital biomarkers, wearable devices, smartphone sensors, symptom monitoring
- MeSH terms
- Wearable Electronic Devices, Adult, Major Depressive Disorder, Depressive Disorder, Major, Female, Humans, Male, Middle Aged, Phenotype, Psychiatric Status Rating Scales, Severity of Illness Index
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