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 .
Beyond the Randomized Controlled Trial: A Review of Alternatives in mHealth Clinical Trial Methods
Background R and omized controlled trials ( RCTs ) have long been considered the primary research study design capable of eliciting causal relationships between health interventions and consequent outcomes . However , with a prolonged duration from recruitment to publication , high-cost trial implementation , and a rigid trial protocol , RCTs are perceived as an impractical evaluation methodology for most mHealth apps . Objective Given the recent development of alternative evaluation method ologies and tools to automate mHealth research , we sought to determine the breadth of these methods and the extent that they were being used in clinical trials . Methods We conducted a review of the Clinical Trials.gov registry to identify and examine current clinical trials involving mHealth apps and retrieved relevant trials registered between November 2014 and November 2015 . Results Of the 137 trials identified , 71 were found to meet inclusion criteria . The majority used a r and omized controlled trial design ( 80 % , 57/71 ) . Study design s included 36 two-group pretest-posttest control group comparisons ( 51 % , 36/71 ) , 16 posttest-only control group comparisons ( 23 % , 16/71 ) , 7 one-group pretest-posttest design s ( 10 % , 7/71 ) , 2 one-shot case study design s ( 3 % , 2/71 ) , and 2 static-group comparisons ( 3 % , 2/71 ) . A total of 17 trials included a qualitative component to their methodology ( 24 % , 17/71 ) . Complete trial data collection required 20 months on average to complete ( mean 21 , SD 12 ) . For trials with a total duration of 2 years or more ( 31 % , 22/71 ) , the average time from recruitment to complete data collection ( mean 35 months , SD 10 ) was 2 years longer than the average time required to collect primary data ( mean 11 , SD 8) . Trials had a moderate sample size of 112 participants . Two trials were conducted online ( 3 % , 2/71 ) and 7 trials collected data continuously ( 10 % , 7/68 ) . Onsite study implementation was heavily favored ( 97 % , 69/71 ) . Trials with four data collection points had a longer study duration than trials with two data collection points : F4,56=3.2 , P=.021 , η2=0.18 . Single-blinded trials had a longer data collection period compared to open trials : F2,58=3.8 , P=.028 , η2=0.12 . Academic sponsorship was the most common form of trial funding ( 73 % , 52/71 ) . Trials with academic sponsorship had a longer study duration compared to industry sponsorship : F2,61=3.7 , P=.030 , η2=0.11 . Combined , data collection frequency , study masking , sample size , and study sponsorship accounted for 32.6 % of the variance in study duration : F4,55=6.6 , P<.01 , adjusted r2=.33 . Only 7 trials had been completed at the time this retrospective review was conducted ( 10 % , 7/71 ) . Conclusions mHealth evaluation methodology has not deviated from common methods , despite the need for more relevant and timely evaluations . There is a need for clinical evaluation to keep pace with the level of innovation of mHealth if it is to have meaningful impact in informing payers , providers , policy makers , and patients
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 .