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 .
The sleep of remitted bipolar outpatients: a controlled naturalistic study using actigraphy.
BACKGROUND Several sleep laboratory studies suggest sleep abnormalities in bipolar disorder . However , this is the first study to compare remitted bipolar subjects with controls on actigraphic and subjective sleep parameters in a naturalistic setting over 5 nights . METHODS Nineteen subjects with Bipolar I Disorder and 19 age- and gender-matched healthy controls were included . Objective  sleep parameters  were estimated using wrist actigraphs .  Subject-rated sleep diaries and mood ratings were also completed . Sleep data were averaged for each subject across nights , and raw score st and ard deviations were calculated as a measure of within-subject variability . RESULTS Multivariate analyses of variance found significant group differences for both actigraphic ( F(4,33)=3.80 , P=0.012 ) and subjective measures ( F(4,31)=3.18 , P=0.027 ) . Univariate analyses identified reliable differences in sleep onset latency ( subjective ) , sleep duration ( subjective ) , and variability of sleep duration and night wake time ( actigraphic ) . Binary backward stepwise logistic regression demonstrated that a combination of three sleep measures correctly predicted disorder status in 84 % of cases . LIMITATIONS Failure to match on sociodemographic and employment status is a limitation that may provide an alternative explanation for some findings . Furthermore , in the bipolar group 18 of 19 subjects were in receipt of psychotropic medication , compared to none of the healthy control group . Also , no information was recorded about family history of mental disorders in the control group . CONCLUSIONS The study suggests that the sleep of remitted bipolar out patients measured in naturalistic setting s is characteristically different from controls : bipolar subjects sleep longer , report longer onset latencies , and display greater variability across nights
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 .