Taken together , the literature presented in this dissertation illustrates that 1 ) smartphone-based electronic self-monitoring of mood  seems to reflect clinical ly assessed depressive and manic symptoms and enables the long-term characterization of mood instability in bipolar disorder ; 2 ) preliminary results suggest that smartphone-based automatically generated data ( e.g. the number of text messages sent/day ; the number of incoming and outgoing calls/day ; the number of changes in cell tower IDs/day ; and voice features ) seem to reflect clinical ly assessed depressive and manic symptoms in bipolar disorder ; 3 ) smartphone-based electronic self-monitoring had no effects on the severity of depressive and manic symptoms in bipolar disorder , according to a r and omized controlled trial ; and 4 ) electronic  monitoring of psychomotor activity and heart rate variability seems to reflect illness activity in bipolar disorder and differentiate between patients with bipolar disorder and healthy control individuals .
These findings point toward the usefulness of  electronic monitoring  as a marker of illness in bipolar disorder .
Major reasons for the insufficient effects of current treatment options in bipolar disorder include delayed intervention for prodromal depressive and manic symptoms and decreased adherence to psychopharmacological treatment .
The reliance on subjective information and clinical evaluations when diagnosing and assessing the severity of depressive and manic symptoms calls for less biased and more objective markers .
By using electronic devices , fine-grained data on complex psychopathological aspects of bipolar disorder can be evaluated unobtrusively over the long term .
Using electronic monitoring as a treatment intervention could provide innovative and novel interventions on-dem and with a potential global reach , filling the gap between availability and the need for treatment .
Randomised controlled trial of efficacy of teaching patients with bipolar disorder to identify early symptoms of relapse and obtain treatment
Abstract Objective : To determine the efficacy of teaching patients with bipolar disorder ( manic-depressive psychosis ) to identify early symptoms of relapse and seek prompt treatment from health services . Design : Single blind r and omised controlled trial with matching on four baseline variables using a minimisation algorithm . Setting : Mental health services in four NHS trusts ( one teaching , three non-teaching ) . Subjects : 69 patients with bipolar disorder who had had a relapse in the previous 12 months . Interventions : Seven to 12individual treatment sessions from a research psychologist plus routine care or routine care alone . Main outcome measures : Time to first manic or depressive relapse , number of manic or depressive relapses , and social functioning examined by st and ardised interviews every six months for 18 months . Results : 25th centile time to first manic relapse in experimental group was 65weeks compared with 17weeks in the control group . Event curves of time to first manic relapse significantly differed between experimental and control groups ( log rank 7.04 , df=1 , P=0.008 ) , with significant reductions in the number of manic relapses over 18months ( median difference 30 % ( 95 % confidence interval 8 % to 52 % ) , P=0.013 ) . The experimental treatment had no effect on time to first relapse or number of relapses with depression , but it significantly improved overall social functioning ( mean difference 2.0(0.7to 3.2 ) , P=0.003 ) and employment ( mean difference 0.7(0.1to 1.3 ) , P=0.030 ) by 18months . Conclusion : Teaching patients to recognise early symptoms of manic relapse and seek early treatment is associated with important clinical improvements in time to first manic relapse , social functioning , and employment
Taken together , the literature presented in this dissertation illustrates that 1 ) smartphone-based electronic self-monitoring of mood  seems to reflect clinical ly assessed depressive and manic symptoms and enables the long-term characterization of mood instability in bipolar disorder ; 2 ) preliminary results suggest that smartphone-based automatically generated data ( e.g. the number of text messages sent/day ; the number of incoming and outgoing calls/day ; the number of changes in cell tower IDs/day ; and voice features ) seem to reflect clinical ly assessed depressive and manic symptoms in bipolar disorder ; 3 ) smartphone-based electronic self-monitoring had no effects on the severity of depressive and manic symptoms in bipolar disorder , according to a r and omized controlled trial ; and 4 ) electronic  monitoring of psychomotor activity and heart rate variability seems to reflect illness activity in bipolar disorder and differentiate between patients with bipolar disorder and healthy control individuals .
These findings point toward the usefulness of  electronic monitoring  as a marker of illness in bipolar disorder .
Major reasons for the insufficient effects of current treatment options in bipolar disorder include delayed intervention for prodromal depressive and manic symptoms and decreased adherence to psychopharmacological treatment .
The reliance on subjective information and clinical evaluations when diagnosing and assessing the severity of depressive and manic symptoms calls for less biased and more objective markers .
By using electronic devices , fine-grained data on complex psychopathological aspects of bipolar disorder can be evaluated unobtrusively over the long term .
Using electronic monitoring as a treatment intervention could provide innovative and novel interventions on-dem and with a potential global reach , filling the gap between availability and the need for treatment .