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 .
An online randomised controlled trial to assess the feasibility, acceptability and potential effectiveness of 'Living with Bipolar': a web-based self-management intervention for bipolar disorder: trial design and protocol.
BACKGROUND Bipolar Disorder ( BD ) is a common and severe form of mental illness . Pharmacotherapy is the main treatment offered , but has limited effectiveness , and there is increasing evidence that people with BD respond well to psychological interventions . Inequalities in access to face-to-face psychological interventions mean many people seek support outside of routine health services . AIM To assess a recovery informed web-based self-management intervention for BD to increase access to psychological support . METHOD A small online r and omised controlled trial ( RCT ) will evaluate the feasibility , acceptability and potential effectiveness of the intervention compared to treatment as usual ( TAU ) .  Feasibility and acceptability will be assessed by recruitment , adherence and dropout rates , website usage statistics , user satisfaction scales and a series of qualitative interviews . Effectiveness will be assessed on a range of outcome measures including quality of life , mood symptoms , coping , recovery , and illness beliefs . ANALYSIS Rates of adherence and dropout will be analysed using logistic regression models with health , socioeconomic and treatment factors as explanatory variables . Changes in quality of life mean scores , contrasting between arms , will be assessed using r and om effects models . An exploratory analysis will be performed on the secondary outcomes . Thematic analysis ( Braun & Clarke , 2006 ) will be used to analyse the qualitative interviews . DISCUSSION The benefits , challenges and method ological challenges of developing a web-based intervention and conducting an RCT online are discussed . CONCLUSION The results of this trial will inform a definitive trial ; and the implementation phase will aim to assess the potential for use within the NHS
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 .