JMIR Publications

ABSTRACT

Background:

Smartphones allow for real-time monitoring of patients’ behavioural activities in a naturalistic setting. These data are suggested as markers of mental state in bipolar disorder (BD).

Objective:

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

Methods:

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

Results:

Objective behavioural data collected from smartphones and their relation to BD states were as follows: (1) depressed patients tended to make phone calls less frequently than in euthymia; (2) the number of incoming answered calls was lower in depression as compared to euthymia and, at the same time, missed incoming calls were more frequent and increased as depressive symptoms intensified; (3) the fraction of outgoing calls was higher in manic states; (4) the variability of duration of calls was positively correlated to the severity of symptoms; (5) the number and length of sent text messages was positively correlated to the severity of manic symptoms. We also observed that self-assessment of mood was lower in depressive and higher in manic states.

Conclusions:

Smartphone-based behavioural parameters are valid markers in assessing the severity of affective symptoms and discriminating between mood states. This opens a way toward early detection of worsening of the mental state and thereby increases the patient’s chance of improving the course of the illness.