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 .
State-related differences in heart rate variability in bipolar disorder.
Heart rate variability ( HRV ) is a vali date d measure of sympato-vagal balance in the autonomic nervous system . HRV appears decreased in patients with bipolar disorder ( BD ) compared with healthy individuals , but the extent of state-related alterations has been sparingly investigated . The present study examined differences in HRV between affective states in BD . A heart rate and movement sensor weighing 8 g collected average acceleration , heart rate and the two slowest and fastest heart beats ( of the most recent 16 beats ) every 30 s over a period of at least three consecutive weekdays and nights in a prospect i ve longitudinal design from a total of 31 different affective states in 16 out patients with BD . A proxy measure of HRV was calculated as the difference between the second-shortest and the second-longest inter-beat-interval collected during each of the epochs . Analyses were based on over 100.000 HRV data -points . In unadjusted analyses and in analyses adjusted for age , gender and heart rate , during a manic state HRV was increased by 18 % compared with a depressed state ( eB = 1.18 , 95 % CI : 1.16 - 1.20 , p < 0.001 ) and increased by 17 % compared with a euthymic state ( eB = 1.17 , 95 % CI : 1.15 - 1.19 , p < 0.001 ) , whereas there was no difference between a depressive state and a euthymic state ( eB = 0.98 , 95 % CI : 0.96 - 1.00 , p = 0.12 ) . Further inclusion of BMI as a covariate did not alter any of the associations . HRV appears to be altered in a state-dependent manner in bipolar disorder and could represent a c and i date state marker . Further studies with larger sample sizes are warranted
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 .