Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study

Abstract

Background The assessment of behaviors related to mental health typically relies on self-report data. Networked sensors embedded in smartphones can measure some behaviors objectively and continuously, with no ongoing effort. Objective This study aims to evaluate whether changes in phone sensor–derived behavioral features were associated with subsequent changes in mental health symptoms. Methods This longitudinal cohort study examined continuously collected phone sensor data and symptom severity data, collected every 3 weeks, over 16 weeks. The participants were recruited through national research registries. Primary outcomes included depression (8-item Patient Health Questionnaire), generalized anxiety (Generalized Anxiety Disorder 7-item scale), and social anxiety (Social Phobia Inventory) severity. Participants were adults who owned Android smartphones. Participants clustered into 4 groups: multiple comorbidities, depression and generalized anxiety, depression and social anxiety, and minimal symptoms. Results A total of 282 participants were aged 19-69 years (mean 38.9, SD 11.9 years), and the majority were female (223/282, 79.1%) and White participants (226/282, 80.1%). Among the multiple comorbidities group, depression changes were preceded by changes in GPS features (Time: r=−0.23, P=.02; Locations: r=−0.36, P<.001), exercise duration (r=0.39; P=.03) and use of active apps (r=−0.31; P<.001). Among the depression and anxiety groups, changes in depression were preceded by changes in GPS features for Locations (r=−0.20; P=.03) and Transitions (r=−0.21; P=.03). Depression changes were not related to subsequent sensor-derived features. The minimal symptoms group showed no significant relationships. There were no associations between sensor-based features and anxiety and minimal associations between sensor-based features and social anxiety. Conclusions Changes in sensor-derived behavioral features are associated with subsequent depression changes, but not vice versa, suggesting a directional relationship in which changes in sensed behaviors are associated with subsequent changes in symptoms.

Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study

“…The differences in populations and applied methods could be potential reasons for the slightly inconsistent results. Both our study and that study [ 22 ] have shown that the changes in mobility prior to changes in depressive symptom severity can be captured by mobile phones. An interesting finding is that the number of residential locations was positively correlated (φ4=0.05, P =.02) with the subsequent PHQ-8 score ( Table 3 ), which is opposite to their negative correlation (ρ=−0.09, P =.001) at the within-individual level ( Table 2 ).…”

“…In recent years, there have been several studies [ 12 - 22 ] exploring the associations between depressive symptom severity and mobility features extracted from phone-collected location data that have shown that mobility measured by phones is negatively associated with the severity of depressive symptoms which is consistent with past survey-based studies; however, not many have explored the direction of the relationships between depression and mobility over time. Meyerhoff et al [ 22 ] recently found that phone-derived mobility features were correlated with subsequent changes in depression, but not vice versa. However, the autoregressive nature of depressive states and mobility levels [ 23 - 25 ] and the influence of individual differences may affect the results.…”

“…Conversely, change in PHQ-8 score was significantly and negatively correlated (φ3=−0.07, P <.001) with the subsequent circadian rhythm measured by location data. The findings of a recent study [ 22 ] showed changes in several mobility features were associated with subsequent depression changes, but not vice versa. The differences in populations and applied methods could be potential reasons for the slightly inconsistent results.…”

Digital phenotyping correlations in larger mental health samples: analysis and replication

“…Using an elastic net model, which is a penalised regression model that uses both L1 and L2 loss, and 34 features from the data, the authors obtained an area under the curve (AUC) of 0.656 for predicting mood. In another large study of 288 participants studying mood and anxiety, Meyerhoff et al 6 employed a different approach, looking at correlations between changes in weekly survey scores and changes in passive data features. Focusing on GPS, call, text and app usage features, this study also reported low correlations similar to Nickels et al 5 Meyerhoff et al also separated participants into groups, using k -means clustering on the participants’ clinical scores, and found that some correlations were higher in groups exhibiting symptoms.…”

“…Focusing on GPS, call, text and app usage features, this study also reported low correlations similar to Nickels et al 5 Meyerhoff et al also separated participants into groups, using k -means clustering on the participants’ clinical scores, and found that some correlations were higher in groups exhibiting symptoms. 6 In this work, we aim to explore correlations in a large data-set collected with the mindLAMP app from college student participants, to assess if we observe correlations of a similar magnitude to Nickels et al 5 and Meyerhoff et al 6 In addition, we explore whether changing the group of participants that we use for analysis (such as by setting data-quality thresholds or by splitting into clinical groups) will allow us to identify more clinically meaningful correlations. Finally, we aim to test a classifier for predicting survey scores with passive and survey data, to assess whether passive data signals alone are enough to build predictive models or if survey data is necessary to provide a stronger signal.…”