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 .
Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study
Background Relapse prevention in bipolar disorder can be improved by monitoring symptoms in patients ' daily life .  Smartphone apps are easy-to-use , low-cost tools that can be used to assess this information . To date , few studies have examined the usefulness of smartphone data for monitoring symptoms in bipolar disorder . Objective We present results from a pilot test of a smartphone-based monitoring system , Social Information Monitoring for Patients with Bipolar Affective Disorder ( SIMBA ) , that tracked daily mood , physical activity , and social communication in 13 patients . The objective of this study was to investigate whether smartphone measurements predicted clinical symptoms levels and clinical symptom change . The hypotheses that smartphone measurements are ( 1 ) negatively related to clinical depressive symptoms and ( 2 ) positively related to clinical manic symptoms were tested . Methods Clinical rating scales were administered to assess clinical depressive and manic symptoms . Patients used a smartphone with the monitoring app for up to 12 months . R and om-coefficient multilevel models were computed to analyze the relationship between smartphone data and externally rated manic and depressive symptoms . Overall clinical symptom levels and clinical symptom changes were predicted by separating between-patient and within-patient effects . Using established clinical thresholds from the literature , marginal effect plots displayed clinical relevance of smartphone data . Results Overall symptom levels and change in clinical symptoms were related to smartphone measures . Higher overall levels of clinical depressive symptoms were predicted by lower self-reported mood measured by the smartphone ( beta=-.56 , P<.001 ) . An increase in clinical depressive symptoms was predicted by a decline in social communication ( ie , outgoing text messages : beta=-.28 , P<.001 ) and a decline in physical activity as measured by the smartphone ( ie , cell tower movements : beta=-.11 , P=.03 ) . Higher overall levels of clinical manic symptoms were predicted by lower physical activity on the smartphone ( ie , distance travelled : beta=-.37 , P<.001 ) , and higher social communication ( beta=.48 , P=.03 ) . An increase in clinical manic symptoms was predicted by a decrease in physical activity on the smartphone ( beta=-.17 , P<.001 ) . Conclusions Clinical symptoms were related to some objective and subjective smartphone measurements , but not all smartphone measures predicted the occurrence of bipolar symptoms above clinical thresholds . Thus , smartphones have the potential to monitor bipolar disorder symptoms in patients ’ daily life . Further validation of monitoring tools in a larger sample is needed . Conclusions are limited by the low prevalence of manic and depressive symptoms in the study sample . Trial Registration International St and ard R and omized Controlled Trial Number ( IS RCT N ) : 05663421 ; http://www.controlled-trials.com/IS RCT N05663421 ( Archived by WebCite at http://www.webcitation.org/6d9wsibJB
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 .