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 .
Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study.
OBJECTIVES Recent research indicates that Heart Rate Variability ( HRV ) is affected in Bipolar Disorders ( BD ) patients . To determine whether such alterations are a mere expression of the current mood state or rather contain longitudinal information on BD course , we examined the potential influence of states adjacent in time upon HRV features measured in a target mood state . METHODS Longitudinal evaluation of HRV was obtained in eight BD patients by using a wearable monitoring system developed within the PSYCHE project . We extracted time-domain , frequency-domain and non-linear HRV-features and trained a Support Vector Machine ( SVM ) to classify HRV-features according to mood state . To evaluate the influence of adjacent mood states , we trained SVM with different HRV-feature sets : 1 ) belonging to each mood state considered alone ; 2 ) belonging to each mood state and normalized using information from the preceding mood state ; 3 ) belonging to each mood state and normalized using information from the preceding and subsequent mood states ; 4 ) belonging to each mood state and normalized using information from two r and omly chosen states . RESULTS SVM classification accuracy within a target state was significantly greater when HRV-features from the previous and subsequent mood states were considered . CONCLUSIONS Although preliminary and in need of replications our results suggest for the first time that psychophysiological states in BD contain information related to the subsequent ones . Such characteristic may be used to improve clinical management and to develop algorithms to predict clinical course and mood switches in individual patients
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 .