Detecting Bipolar Depression From Geographic Location Data
Open accessAbstract
OBJECTIVE: This paper aims to identify periods of depression using geolocation movements recorded from mobile phones in a prospective community study of individuals with bipolar disorder (BD). METHODS: ). Recorded location data were preprocessed by detecting and removing imprecise data points and features were extracted to assess the level and regularity of geographic movements of the participant. A subset of features were selected using a wrapper feature selection method and presented to 1) a linear regression model and a quadratic generalized linear model with a logistic link function for questionnaire score estimation; and 2) a quadratic discriminant analysis classifier for depression detection in BD participants based on their questionnaire responses. R esults: HC participants did not report depressive symptoms and their features showed similar distributions to nondepressed BD participants. Questionnaire score estimation using geolocation-derived features from BD participants demonstrated an optimal mean absolute error rate of 3.73, while depression detection demonstrated an optimal (median ± IQR) [Formula: see text] score of 0.857 ± 0.022 using five features (classification accuracy: 0.849 ± 0.016; sensitivity: 0.839 ± 0.014; specificity: 0.872 ± 0.047). CONCLUSION: These results demonstrate a strong link between geographic movements and depression in bipolar disorder. S ignificance: To our knowledge, this is the first community study of passively recorded objective markers of depression in bipolar disorder of this scale. The techniques could help individuals monitor their depression and enable healthcare providers to detect those in need of care or treatment.
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Others focused on detecting depression by tracking medication, sleep patterns and actions, location or keypress acceleration.
“Others focused on detecting depression by tracking medication, sleep patterns and actions [20], location [11] or keypress acceleration [165].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [11]
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- Authors
- Palmius, N., Tsanas, A., Saunders, K. E. A., Bilderbeck, A. C., Geddes, J. R., Goodwin, G. M., & De Vos, M.
- Affiliations
- University of Oxford (GB)
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2017-08
- Volume
- 64
- Issue
- 8
- Pages
- 1761-1771
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- PMC
- PMC5947818
- OpenAlex
- W2538898996
- Cited by
- 180
- References
- 40
- Citations by year
- 2026: 8 · 2025: 15 · 2024: 16 · 2023: 24 · 2022: 22 · 2021: 16 · 2020: 39 · 2019: 14 · 2018: 16 · 2017: 10
- FWCI
- 21.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion, cc-by)
- PubMed Central (acceptedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Funders
- EPSRC
- Wellcome Trust Centre (098461/Z/12/Z)
- Sleep, Circadian Rhythms & Neuroscience Institute (102616/Z)
- RCUK Digital Economy Programme (EP/G036861/1)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Keywords
- bipolar depression, bipolar disorder, geolocation data, depression detection, passive monitoring, quadratic discriminant analysis, community-based research
- MeSH terms
- Adult, Bipolar Disorder, Female, Humans, Male, Middle Aged, Patient Identification Systems, Sensitivity and Specificity, Reproducibility of Results, Geographic Information Systems, Cell Phone, Actigraphy, Remote Sensing Technology
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