Detecting Depression of Cancer Patients with Daily Mental Health Logs from Mobile Applications

Mobile mental health trackers, the mobile applications that gather self-reported mental logs from users, have gained recent attention from clinicians as a tool for detecting patients’ depression. However, critics have raised questions about the validity of the data collected from mental health trackers, which ask only a few simple questions using the face emoticon scale. This is the first study to address this issue, and we provide theoretical discussion that leads to the following hypotheses: (1) simpler but larger datasets collected daily from mobile mental health trackers can serve as good indicators to detect patients’ depression, and (2) higher adherence to mobile mental health trackers enhances screening accuracy. We test our hypotheses using the dataset of 5,792 sets of daily mental health logs collected from 78 breast cancer patients. Our random logistic panel regression and ROC analysis results, as well as k-means clustering analysis, provide strong supports for both hypotheses

Dynamics of Social Influence on New Employees’ Use of Volitional IS: m-EHR Case in Hospital Setting

It is widely recognized that user resistance to Information Systems (IS) is particularly high in hospitals. In this regard, the future of mobile Electronic Health Record (m-EHR) systems is highly in question, mainly because their usage is not mandatory. Aiming to provide insights on how best to promote the use of m-EHR in hospitals, we investigate the effect of social influences on m-EHR usage by new doctors who recently began working at a hospital. Drawing upon the concept of organizational socialization and social influences, we hypothesize that coworkers’ m-EHR usage is positively associated with one by new doctors, and the strength of this association varies by the coworkers’ type of usage, by the hierarchical rankings of coworkers, and by the stage of socialization process in which the new doctors are situated. Our analyses using longitudinal m-EHR usage data (595,914 logs of 737 doctors) generally support our hypotheses