Automatic question generation system is getting popularity for generating objective test items (Goto, Kojiri, Watanabe, Iwata, & Yamada, 2009, 2010; Liu, Rus, & Liu, 2016).
Our results showed that 3D GIS helped to generate objective measures on view related features.
This article presents a GIS approach to generate objective information of population exposure to specific pesticide ingredients for large areas.
Based on these studies, it seems that automatically generated objective smartphone data may represent an objective marker in bipolar disorder.
To overcome this issue, research groups around the world using smartphones for objective monitoring in bipolar disorder are trying to communicate with IT companies regarding getting access to uniform detailed automatically generated objective smartphone data in future studies.
Of special interest are studies that strengthen the scientific foundation of injury prevention and control and generate objective and practical knowledge to reduce injury morbidity and mortality on a population level.
Overall, replication studies including larger sample sizes of patients monitoring symptoms and automatically generated objective smartphone data for prolonged periods of time are needed to validate previous study findings.
Along this line, the validity of automatically generated objective smartphone data compared with self-monitored depressive and manic symptoms may be affected by a decreased illness insight during affective episodes.
In bipolar disorder research, future studies using automatically generated objective smartphone data, or combination of these, for illness monitoring may lead to detection of new trait and state markers (Torous et al. 2017; McIntyre et al. 2014).
Regarding detection bias, most of the studies did not state whether the clinicians collecting outcome data or patients conducting self-monitoring were blinded to the automatically generated objective smartphone data, and information on the clinicians' rater experience was lacking.
