The accuracy of passive phone sensors in predicting daily mood
Open accessAbstract
BACKGROUND: Smartphones provide a low-cost and efficient means to collect population level data. Several small studies have shown promise in predicting mood variability from smartphone-based sensor and usage data, but have not been generalized to nationally recruited samples. This study used passive smartphone data, demographic characteristics, and baseline depressive symptoms to predict prospective daily mood. METHOD: Daily phone usage data were collected passively from 271 Android phone users participating in a fully remote randomized controlled trial of depression treatment (BRIGHTEN). Participants completed daily Patient Health Questionnaire-2. A machine learning approach was used to predict daily mood for the entire sample and individual participants. RESULTS: ∼ 0). Focusing on individuals, 13.9% of participants showed significant association (FDR < 0.10) between a passive feature and daily mood. Personalized models combining features provided better prediction performance (median area under the curve [AUC] > 0.50) for 80.6% of participants and very strong prediction in a subset (median AUC > 0.80) for 11.8% of participants. CONCLUSIONS: Passive smartphone data with current features may not be suited for predicting daily mood at a population level because of the high degree of intra- and interindividual variation in phone usage patterns and daily mood ratings. Personalized models show encouraging early signs for predicting an individual's mood state changes, with GPS-derived mobility being the top most important feature in the present sample.
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Passive sensor data can be used to predict various symptoms of mental disorders or mental health problems.
“This type of research has established that passive sensor data can be used to predict various symptoms of mental disorders or mental health problems (23, 33–38), as well as their future development [e.g., (39)].”
From Personalised depression forecasting using mobile sensor data and ecological momentary assessment (2022) · cites this paper as [35]
Missing values leading to subset inclusion is a common finding in real-world data studies, particularly with mobile sensor features.
“This is a common finding in studies based on real-world data, particularly if mobile sensor features are included [see, e.g., (34, 35)].”
From Personalised depression forecasting using mobile sensor data and ecological momentary assessment (2022) · cites this paper as [35]
Preliminary studies suggest that aggregates of smartphone-based passive features are useful in predicting daily mood and presence of depressive symptoms.
“Preliminary studies suggest that aggregates of smartphone-based passive features are useful in predicting daily mood (23) and presence of depressive symptoms (24).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [23]
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 [23]
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 [23]
The finding that activity level was among the most important features is consistent with previous reports.
“is consistent with previous reports (23, 24).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [23]
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- Authors
- Pratap, A., Atkins, D. C., Renn, B. N., Tanana, M. J., Mooney, S. D., Anguera, J. A., & Areán, P. A.
- Affiliations
- Sage Bionetworks (US)
- University of Washington (US)
- University of Utah (US)
- University of California, San Francisco (US)
- Corresponding author
- Areán, Patricia A.
- ORCID
- Venue
- Depression and Anxiety
- Publisher
- Wiley
- Published
- 2018-08-21
- Volume
- 36
- Issue
- 1
- Pages
- 72-81
- ISSN
- 1091-4269 (ISSN-L)
- PMC
- PMC8491547
- OpenAlex
- W2888288032
- Cited by
- 119
- References
- 38
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 19 · 2023: 12 · 2022: 20 · 2021: 21 · 2020: 16 · 2019: 6 · 2018: 2
- FWCI
- 14.73
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- Locations
- Depression and Anxiety (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute of Mental Health (R34MH100466, T32MH073553)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- smartphone usage data, personalized prediction models, mood variability, depression, depressive symptoms, machine learning
- MeSH terms
- Smartphone, Adult, Affect, Depression, Female, Humans, Male, Prospective Studies, Reproducibility of Results, Self Report
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