Prediction of Mood Instability with Passive Sensing Journal Article uri icon

Overview

abstract

  • Mental health issues, which can be difficult to diagnose, are a growing concern worldwide. For effective care and support, early detection of mood-related health concerns is of paramount importance. Typically, survey based instruments including Ecologically Momentary Assessments (EMA) and Day Reconstruction Method (DRM) are the method of choice for assessing mood related health. While effective, these methods require some effort and thus both compliance rates as well as quality of responses can be limited. As an alternative, We present a study that used passively sensed data from smartphones and wearables and machine learning techniques to predict mood instabilities, an important aspect of mental health. We explored the effectiveness of the proposed method on two large-scale datasets, finding that as little as three weeks of continuous, passive recordings were sufficient to reliably predict mood instabilities.

publication date

  • September 9, 2019

has restriction

  • closed

Date in CU Experts

  • February 1, 2020 10:48 AM

Full Author List

  • Morshed MB; Saha K; Li R; D'Mello SK; De Choudhury M; Abowd GD; Plötz T

author count

  • 7

Other Profiles

Electronic International Standard Serial Number (EISSN)

  • 2474-9567

Additional Document Info

start page

  • 1

end page

  • 21

volume

  • 3

issue

  • 3