Prediction of Mood Instability with Passive Sensing

Mehrab bin Morshed, Koustuv Saha, Richard Li, Sidney k. D'mello, Munmun De choudhury, Gregory d. Abowd, Thomas Plötz

Research output: Contribution to journalConference articlepeer-review


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.
Original languageEnglish (US)
Pages (from-to)1-21
JournalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Issue number3
StatePublished - Sep 9 2019
Externally publishedYes


Dive into the research topics of 'Prediction of Mood Instability with Passive Sensing'. Together they form a unique fingerprint.

Cite this