Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data
Abstract
Depression is a serious health disorder. In this study, we investigate the feasibility of depression screening using sensor data collected from smartphones. We extract various behavioral features from smartphone sensing data and investigate the efficacy of various machine learning tools to predict clinical diagnoses and PHQ-9 scores (a quantitative tool for aiding depression screening in practice). A notable feature of our study is that we leverage a dataset that includes clinical ground truth. We find that behavioral data from smartphones can predict clinical depression with good accuracy. In addition, combining behavioral data and PHQ-9 scores can provide prediction accuracy significantly exceeding each in isolation, indicating that behavioral data captures relevant features that are not reflected by PHQ-9 scores. Finally, we develop multi-feature regression models for PHQ-9 scores that achieve significantly improved accuracy compared to direct regression models based on single features.
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Saeb et al [29] and Farhan et al [30] used 50% as each day's completeness threshold for passive data.
“Saeb et al [29] and Farhan et al [30] used 50% as each day’s completeness threshold for passive data.”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [30]
No significant relationship was found between the other smartphone features (total distance, location entropy, normalized location entropy and time spent at home) and mental health measures.
“However, contrary to previous findings (19, 67–69), we did not find a significant relationship between the other smartphone features (total distance, location entropy, normalized location entropy and time spent at home) and mental health measures.”
From Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data (2021) · cites this paper as [67]
Participants who move about more through geographic space are less depressed.
“supports previous research demonstrating that participants who move about more through geographic space are less depressed (19, 67).”
From Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data (2021) · cites this paper as [67]
Greater diversity in visited locations predicted lower depression severity.
“The finding that greater diversity in visited locations predicted lower depression severity supports previous research demonstrating that participants who move about more through geographic space are less depressed (19, 67).”
From Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data (2021) · cites this paper as [67]
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- Authors
- Farhan, A. A., Yue, C., Morillo, R., Ware, S., Lu, J., Bi, J., Kamath, J., Russell, A., Bamis, A., & Wang, B.
- Affiliations
- University of Connecticut (US)
- UConn Health (US)
- ORCID
- Venue
- 2016 IEEE Wireless Health (WH)
- Publisher
- IEEE
- Published
- 2016-10
- Pages
- 1-8
- OpenAlex
- W2560951263
- Cited by
- 115
- References
- 28
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 20 · 2023: 8 · 2022: 21 · 2021: 21 · 2020: 12 · 2019: 11 · 2018: 7 · 2017: 5
- FWCI
- 18.01
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Wireless Health (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- major depressive disorder, depression screening, behavioral characteristics, PHQ-9
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