The Relationship between Clinical, Momentary, and Sensor-based Assessment of Depression
Open accessAbstract
The clinical assessment of severity of depressive symptoms is commonly performed with standardized self-report questionnaires, most notably the patient health questionnaire (PHQ-9), which are usually administered in a clinic. These questionnaires evaluate symptoms that are stable over time. Ecological momentary assessment (EMA) methods, on the other hand, acquire patient ratings of symptoms in the context of their lives. Today's smartphones allow us to also obtain objective contextual information, such as the GPS location, that may also be related to depression. Considering clinical PHQ-9 scores as ground truth, an interesting question is to what extent the EMA ratings and contextual sensor data can be used as potential predictors of depression. To answer this question, we obtained PHQ-9 scores from 18 participants with a variety of depressive symptoms in our lab, and then collected their EMA and GPS sensor data using their smartphones over a period of two weeks. We analyzed the relationship between GPS sensor features, EMA ratings, and the PHQ-9 scores. While we found a strong correlation between a number of sensor features extracted from the two-week period and the PHQ-9 scores, the other relationships remained non-significant. Our results suggest that depression is better evaluated using long-term sensor-based measurements than the momentary ratings of mental state or short-term sensor information.
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The findings in this study are supported by prior research that investigated the relationship between screen interactions and mental health.
“The findings in this study are also supported by prior research that investigated the relationship between screen interactions and mental health [3,27,28,33].”
The number of times a participant interacts with their phone, including screen lock and unlocks, was found to correlate with participants' mental health state.
“Similar findings have been reported previously [3,33], where the number of times a participant interacts with their phone, including screen lock and unlocks, was found to correlate with participants’ mental health state.”
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- Authors
- Saeb, S., Zhang, M., Kwasny, M., Karr, C., Kording, K., & Mohr, D.
- Affiliations
- Northwestern University (US)
- Michigan State University (US)
- Corresponding author
- Saeb, Sohrab
- Venue
- Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare
- Publisher
- ICST
- Published
- 2015
- Volume
- 2015
- ISSN
- 2153-1633 (ISSN-L)
- PMC
- PMC4667797
- OpenAlex
- W2290270843
- Cited by
- 89
- References
- 9
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 15 · 2023: 7 · 2022: 18 · 2021: 12 · 2020: 7 · 2019: 4 · 2018: 8 · 2017: 4 · 2016: 4
- FWCI
- 7.12
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Pervasive Computing Technologies for Healthcare (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (P20MH090318)
- 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
- depression, PHQ-9, ecological momentary assessment, smartphone sensors, depression severity prediction
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