Behavioral and Self-reported Data Collected From Smartphones for the Assessment of Depressive and Manic Symptoms in Patients With Bipolar Disorder: Prospective Observational Study

Abstract

Background: Smartphones allow for real-time monitoring of patients' behavioral activities in a naturalistic setting. These data are suggested as markers for the mental state of patients with bipolar disorder (BD).

Objective: We assessed the relations between data collected from smartphones and the clinically rated depressive and manic symptoms together with the corresponding affective states in patients with BD.

Methods: BDmon, a dedicated mobile app, was developed and installed on patients' smartphones to automatically collect the statistics about their phone calls and text messages as well as their self-assessments of sleep and mood. The final sample for the numerical analyses consisted of 51 eligible patients who participated in at least two psychiatric assessments and used the BDmon app (mean participation time, 208 [SD 132] days). In total, 196 psychiatric assessments were performed using the Hamilton Depression Rating Scale and the Young Mania Rating Scale. Generalized linear mixed-effects models were applied to quantify the strength of the relation between the daily statistics on the behavioral data collected automatically from smartphones and the affective symptoms and mood states in patients with BD.

Results: Objective behavioral data collected from smartphones were found to be related with the BD states as follows: (1) depressed patients tended to make phone calls less frequently than euthymic patients (β=-.064, P=.01); (2) the number of incoming answered calls during depression was lower than that during euthymia (β=-.15, P=.01) and, concurrently, missed incoming calls were more frequent and increased as depressive symptoms intensified (β=4.431, P<.001; β=4.861, P<.001, respectively); (3) the fraction of outgoing calls was higher in manic states (β=2.73, P=.03); (4) the fraction of missed calls was higher in manic/mixed states as compared to that in the euthymic state (β=3.53, P=.01) and positively correlated to the severity of symptoms (β=2.991, P=.02); (5) the variability of the duration of the outgoing calls was higher in manic/mixed states (β=.0012, P=.045) and positively correlated to the severity of symptoms (β=.0017, P=.02); and (6) the number and length of the sent text messages was higher in manic/mixed states as compared to that in the euthymic state (β=.031, P=.01; β=.015, P=.01; respectively) and positively correlated to the severity of manic symptoms (β=.116, P<.001; β=.022, P<.001; respectively). We also observed that self-assessment of mood was lower in depressive (β=-1.452, P<.001) and higher in manic states (β=.509, P<.001).

Conclusions: Smartphone-based behavioral parameters are valid markers for assessing the severity of affective symptoms and discriminating between mood states in patients with BD. This technology opens a way toward early detection of worsening of the mental state and thereby increases the patient's chance of improving in the course of the illness.

Keywords: behavioral markers; bipolar disorder; classification; depressive episodes; generalized linear model; mHealth; manic episodes; mixed-effects regression; remote monitoring; smartphone.