Hence , continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms .
BACKGROUND Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders ( unipolar and bipolar disorders ) .
However , individual studies have reported on different and sometimes contradicting results , and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published .
OBJECTIVE The objectives of this systematic review were to ( 1 ) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and ( 2 ) investigate the strength and statistical significance of these correlations across studies .
The answers to these questions could potentially help identify which objective features have shown most promising results across studies .
Voice analysis as an objective state marker in bipolar disorder
Changes in speech have been suggested as sensitive and valid measures of depression and mania in bipolar disorder . The present study aim ed at investigating ( 1 ) voice features collected during phone calls as objective markers of affective states in bipolar disorder and ( 2 ) if combining voice features with automatically generated objective smartphone data on behavioral activities ( for example , number of text messages and phone calls per day ) and  electronic self-monitored data ( mood ) on illness activity would increase the accuracy as a marker of affective states . Using smartphones , voice features , automatically generated objective smartphone data on behavioral activities  and  electronic self-monitored data  were collected from 28 out patients with bipolar disorder in naturalistic setting s on a daily basis during a period of 12 weeks . Depressive and manic symptoms were assessed using the Hamilton Depression Rating Scale 17-item and the Young Mania Rating Scale , respectively , by a research er blinded to smartphone data . Data were analyzed using r and om forest algorithms . Affective states were classified using voice features extracted during everyday life phone calls . Voice features were found to be more accurate , sensitive and specific in the classification of manic or mixed states with an area under the curve (AUC)=0.89 compared with an AUC=0.78 for the classification of depressive states . Combining voice features with automatically generated objective smartphone data on behavioral activities  and electronic self-monitored data  increased the accuracy , sensitivity and specificity of classification of affective states slightly . Voice features collected in naturalistic setting s using smartphones may be used as objective state markers in patients with bipolar disorder
Hence , continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms .
BACKGROUND Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders ( unipolar and bipolar disorders ) .
However , individual studies have reported on different and sometimes contradicting results , and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published .
OBJECTIVE The objectives of this systematic review were to ( 1 ) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and ( 2 ) investigate the strength and statistical significance of these correlations across studies .
The answers to these questions could potentially help identify which objective features have shown most promising results across studies .