Acoustic features from speech as markers of depressive and manic symptoms in bipolar disorder: A prospective study.

INTRODUCTION: Voice features could be a sensitive marker of affective state in bipolar disorder (BD). Smartphone apps offer an excellent opportunity to collect voice data in the natural setting and become a useful tool in phase prediction in BD. AIMS OF THE STUDY: We investigate the relations between the symptoms of BD, evaluated by psychiatrists, and patients' voice characteristics. A smartphone app extracted acoustic parameters from the daily phone calls of n = 51 patients. We show how the prosodic, spectral, and voice quality features correlate with clinically assessed affective states and explore their usefulness in predicting the BD phase. METHODS: A smartphone app (BDmon) was developed to collect the voice signal and extract its physical features. BD patients used the application on average for 208 days. Psychiatrists assessed the severity of BD symptoms using the Hamilton depression rating scale -17 and the Young Mania rating scale. We analyze the relations between acoustic features of speech and patients' mental states using linear generalized mixed-effect models. RESULTS: The prosodic, spectral, and voice quality parameters, are valid markers in assessing the severity of manic and depressive symptoms. The accuracy of the predictive generalized mixed-effect model is 70.9%-71.4%. Significant differences in the effect sizes and directions are observed between female and male subgroups. The greater the severity of mania in males, the louder (ß = 1.6) and higher the tone of voice (ß = 0.71), more clearly (ß = 1.35), and more sharply they speak (ß = 0.95), and their conversations are longer (ß = 1.64). For females, the observations are either exactly the opposite-the greater the severity of mania, the quieter (ß = -0.27) and lower the tone of voice (ß = -0.21) and less clearly (ß = -0.25) they speak - or no correlations are found (length of speech). On the other hand, the greater the severity of bipolar depression in males, the quieter (ß = -1.07) and less clearly they speak (ß = -1.00). In females, no distinct correlations between the severity of depressive symptoms and the change in voice parameters are found. CONCLUSIONS: Speech analysis provides physiological markers of affective symptoms in BD and acoustic features extracted from speech are effective in predicting BD phases. This could personalize monitoring and care for BD patients, helping to decide whether a specialist should be consulted.

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

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 (ß=-.064, P=.01); (2) the number of incoming answered calls during depression was lower than that during euthymia (ß=-.15, P=.01) and, concurrently, missed incoming calls were more frequent and increased as depressive symptoms intensified (ß=4.431, P<.001; ß=4.861, P<.001, respectively); (3) the fraction of outgoing calls was higher in manic states (ß=2.73, P=.03); (4) the fraction of missed calls was higher in manic/mixed states as compared to that in the euthymic state (ß=3.53, P=.01) and positively correlated to the severity of symptoms (ß=2.991, P=.02); (5) the variability of the duration of the outgoing calls was higher in manic/mixed states (ß=.0012, P=.045) and positively correlated to the severity of symptoms (ß=.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 (ß=.031, P=.01; ß=.015, P=.01; respectively) and positively correlated to the severity of manic symptoms (ß=.116, P<.001; ß=.022, P<.001; respectively). We also observed that self-assessment of mood was lower in depressive (ß=-1.452, P<.001) and higher in manic states (ß=.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.

Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling.

BACKGROUND: Bipolar disorder (BD) is a chronic illness with a high recurrence rate. Smartphones can be a useful tool for detecting prodromal symptoms of episode recurrence (through real-time monitoring) and providing options for early intervention between outpatient visits. AIMS: The aim of this systematic review is to overview and discuss the studies on the smartphone-based systems that monitor or detect the phase change in BD. We also discuss the challenges concerning predictive modelling. METHODS: Published studies were identified through searching the electronic databases. Predictive attributes reflecting illness activity were evaluated including data from patients' self-assessment ratings and objectively measured data collected via smartphone. Articles were reviewed according to PRISMA guidelines. RESULTS: Objective data automatically collected using smartphones (voice data from phone calls and smartphone-usage data reflecting social and physical activities) are valid markers of a mood state. The articles surveyed reported accuracies in the range of 67% to 97% in predicting mood status. Various machine learning approaches have been analyzed, however, there is no clear evidence about the superiority of any of the approach. CONCLUSIONS: The management of BD could be significantly improved by monitoring of illness activity via smartphone.