Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

By Sohrab Saeb, Mi Zhang, C. Karr, Stephen M. Schueller, Marya E Corden, Konrad Paul Kording, David C. Mohr. Journal of Medical Internet Research, 2015

748 citations recorded by the source corpus.

Background Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms. Objective The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying depressive symptom severity. Methods A total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app (Purple Robot) for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey (PHQ-9). Behavioral features were developed and extracted from GPS location and phone usage data. Results A number of features from GPS data were related to depressive symptom severity, including circadian movement (regularity in 24-hour rhythm; r=-.63, P=.005), normalized entropy (mobility between favorite locations; r=-.58, P=.012), and location variance (GPS mobility independent of location; r=-.58, P=.012). Phone usage features, usage duration, and usage frequency were also correlated (r=.54, P=.011, and r=.52, P=.015, respectively). Using the normalized entropy feature and a classifier that distinguished participants with depressive symptoms (PHQ-9 score ≥5) from those without (PHQ-9 score <5), we achieved an accuracy of 86.5%. Furthermore, a regression model that used the same feature to estimate the participants’ PHQ-9 scores obtained an average error of 23.5%. Conclusions Features extracted from mobile phone sensor data, including GPS and phone usage, provided behavioral markers that were strongly related to depressive symptom severity. While these findings must be replicated in a larger study among participants with confirmed clinical symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach.

Subfields: Applied Psychology, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/11056c51c02584dee59e35c0a83d60065b818e89

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Citations in the corpus

Systematic review of smartphone-based passive sensing for health and wellbeing (2017, methodology): “…also highlighted the objectivity of smartphone sensor measurements [31,34,36,39,41,42,44,45,49,53], the ability to take frequent measurements [29,34,37,38,41,55,57], the possibility of performing just-in-time and adaptive interventions [52,55,61], and reduced burden for patients [29 – 31, 35,”
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The Technology Behind Personal Digital Assistants: An overview of the system architecture and key components (2017, background): “Making it available opens up new research areas like fitness and health applications or opens up new solutions to already existing problems [6].”
Three Problems With Current Digital Mental Health Research … and Three Things We Can Do About Them (2017, background): “New technologies and methods, such as artificial intelligence (12), natural language processing (13), and virtual reality (14), open fundamentally new intervention paradigms.”
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Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges (2016, neutral): “Another application for these more complex algorithms is in computing higher-level mobility patterns, such as variability of time spent in different locations, distance travelled in a given day, or the routineness of a person’s mobility patterns (e.g., Farrahi & Gatica-Perez, 2008; Saeb et al., 2015”
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