Towards early detection of depression through smartphone sensing
Abstract
Major depressive disorder is a complex and common mental health disorder that is heterogeneous and varies between individuals. Predictive measures have previously been used to predict depression in individuals. Given the complexity, heterogeneity of major depressive disorder in individuals, and the scarcity of labelled objective depressive behavioural data, predictive measures have shown limited applicability in detecting the early onset of depression. We present a developed system that collects similar smartphone sensor data like in previous predictive analysis studies. We discuss that anomaly detection and entropy analysis methods are best suited for developing new metrics for the early detection of the onset and progression of major depressive disorder.
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The ability to detect early warning signs of depression, continuously and as effortlessly as possible, by extending current assessment methods could have a significant impact in mitigating or addressing depression and its related negative consequences.
“The ability to detect early warning signs of depression, continuously and as effortlessly as possible, by extending current assessment methods could have a significant impact in mitigating or addressing depression and its related negative consequences [3,10,11,14].”
Inherent in these data sets are behavioral patterns: routines, rhythms, activities, and interactions that are useful in complementing traditional depression assessment methods, in studying the mental health of individuals, and in developing timely mental health interventions.
“Inherent in these data sets are behavioral patterns: routines, rhythms, activities, and interactions that are useful in complementing traditional depression assessment methods, in studying the mental health of individuals, and in developing timely mental health interventions [14,22,25-28].”
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Opoku Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., & Ferreira, D. (2021) · JMIR mHealth and uHealth · doi:10.2196/26540 · Cited by 145 · see citing works
Self-citation
- Authors
- Opoku Asare, K., Visuri, A., & Ferreira, D. S. T.
- Affiliations
- University of Oulu, Oulu, Oulu
- University of Oulu (FI)
- Venue
- Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
- Publisher
- ACM
- Published
- 2019-09-09
- Pages
- 1158-1161
- OpenAlex
- W2972933107
- Cited by
- 24
- References
- 16
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 2 · 2023: 3 · 2022: 7 · 2021: 5 · 2020: 2
- FWCI
- 4.41
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- Funders
- Academy of Finland (313224,316253,318927,320089)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- major depressive disorder, early detection of depression, smartphone sensing, smartphone sensor data, anomaly detection, entropy analysis, behavioral data, mental health monitoring
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