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Abstract:
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Smartphones provide an interactive interface that can passively measure various aspects of the user’s behavior from device sensors, as well as actively collect self-ratings (e.g. mood, stress etc.) obtained via daily ecological momentary assessment. Taken together with traditional clinical assessments, these measures have the potential to provide unique insight into the outcome trajectory of patients with major depressive disorder undergoing treatment. The potential to predict patient adherence to treatment in clinical trials of psychotherapy is a necessary step to modify future sessions in order to improve compliance and therefore efficacy. There are unique challenges of such predictions due to the noisy nature (missing or under-reporting) of passive and actively mHealth data. The nature of missing passive data is unique in the sense that the missed labels are not observed. In this talk, I will introduce these and other challenges of mHealth data analysis and propose solutions to address such challenges. Finally, I will demonstrate an application of our methods with mHealth data collected on older adults undergoing treatment for major depressive disorder.
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