Mental Health and Mobile Communication Profiles of Crowdsourced Participants

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) are highly prevalent and burdensome. To increase mental health screening rates, the digital health research community has been exploring the ability to augment self reporting instruments with digital logs. Crowdsourced workers are being increasingly recruited for behavioral health research studies as demographically representative samples are desired for later translational applications. Overshadowed by predictive modeling, descriptive modeling has the ability to expand knowledge and understanding of the clinical generalizability of models trained on data from crowdsourced participants. In this study, we identify mobile communication profiles of a crowdsourced sample. To achieve this, we cluster features derived from time series of call and text logs. The psychiatric, behavioral, and demographic characteristics were notably different across the four identified mobile communication profiles. For example, the profile that had the lowest average depression and anxiety screening scores only shared incoming text logs. This cluster had statistically significantly different depression and anxiety screening scores in comparison to the cluster that shared the most outgoing text logs. These profiles expose important insights regarding the generalizability of crowdsourced samples to more general clinical populations and increase understanding regarding the limitations of crowdsourced samples for translational mental health research.