Joint modeling of heterogeneous sensing data for depression assessment via multi-task learning
Open accessAbstract
Depression is a common mood disorder that causes severe medical problems and interferes negatively with daily life. Identifying human behavior patterns that are predictive or indicative of depressive disorder is important. Clinical diagnosis of depression relies on costly clinician assessment using survey instruments which may not objectively reflect the fluctuation of daily behavior. Self-administered surveys, such as the Quick Inventory of Depressive Symptomatology (QIDS) commonly used to monitor depression, may show disparities from clinical decision. Smartphones provide easy access to many behavioral parameters, and Fitbit wrist bands are becoming another important tool to assess variables such as heart rates and sleep efficiency that are complementary to smartphone sensors. However, data used to identify depression indicators have been limited to a single platform either iPhone, or Android, or Fitbit alone due to the variation in their methods of data collection. The present work represents a large-scale effort to collect and integrate data from mobile phones, wearable devices, and self reports in depression analysis by designing a new machine learning approach. This approach constructs sparse mappings from sensing variables collected by various tools to two separate targets: self-reported QIDS scores and clinical assessment of depression severity. We propose a so-called heterogeneous multi-task feature learning method that jointly builds inference models for related tasks but of different types including classification and regression tasks. The proposed method was evaluated using data collected from 103 college students and could predict the QIDS score with an R2 reaching 0.44 and depression severity with an F1-score as high as 0.77. By imposing appropriate regularizers, our approach identified strong depression indicators such as time staying at home and total time asleep.
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Only two studies have combined physiological and smartphone data streams in models to monitor changing depressive symptoms.
“only two studies have combined these data streams in models to monitor changing depressive symptoms (26, 27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [27]
Only one of the two studies that combined physiological and smartphone data streams relied on exclusive passive data collection.
“Only one relied on exclusive passive data collection (27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [27]
Prior investigations have primarily focused on detecting presence or absence of depression rather than on assessing its severity, and they have shown overall low accuracy.
“This has resulted in possible reliability problems, since these investigations have primarily focused on detecting presence or absence of depression rather than on assessing its severity, and they have shown overall low accuracy (14–18, 23–27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [27]
Studies conducted with patients with depression have mostly relied on self-report questionnaires to assign diagnoses and/or measure variation in symptoms and train the models.
“Those studies conducted with patients with depression have mostly relied on self-report questionnaires to assign diagnoses and/or measure variation in symptoms and train the models.”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [27]
Previous evaluations of models to estimate depression passively have primarily relied on examining correlations between estimated and observed symptoms.
“Previous evaluations of models to estimate depression passively have primarily relied on examining correlations between estimated and observed symptoms (18, 26, 27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [27]
Previous models estimating mood states with moderate accuracy have included measures of heart rate (HR) and HRV.
“Previous models estimating mood states with moderate accuracy have included measures of heart rate (HR) (27, 44) and HRV (26).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [27]
The time-split mobile phone model's correlation (r = 0.7) was higher than a previous model combining features from the fitbit and from smartphones (best r2 = 0.44 or r = 0.66) and a model aggregating mobile-based and physiological features (r = 0.58).
“was higher than the one of a previous model combining features from the fitbit and from smartphones (the best model yielded an r2 = 0.44 or r = 0.66) (27) and the one of a model aggregating mobile-based and physiological features (r = 0.58) (26).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [27]
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- Authors
- Lu, J., Shang, C., Yue, C., Morillo, R., Ware, S., Kamath, J., Bamis, A., Russell, A., Wang, B., & Bi, J.
- Affiliations
- University of Connecticut, Department of Computer Science and Engineering, Storrs, CT, USA
- University of Connecticut (US)
- UConn Health (US)
- Amazon (United States) (US)
- Venue
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
- Published
- 2018-03-26
- Volume
- 2
- Issue
- 1
- Pages
- 1-21
- ISSN
- 2474-9567 (ISSN-L)
- OpenAlex
- W2794506852
- Cited by
- 85
- References
- 54
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 14 · 2023: 14 · 2022: 13 · 2021: 8 · 2020: 11 · 2019: 6 · 2018: 2
- FWCI
- 8.84
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
- Funders
- NSF (IIS-1407205, IIS-1320586, DBI-1356655)
- NIH (R01DA037349, K02DA043063)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- depression assessment, multi-task learning, smartphone sensors, wearable devices, Fitbit, self-reported depression, depression severity prediction, sparse feature learning, sleep duration, time at home, university students, human behavior patterns
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