Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study

Journal of Medical Internet Research
John ZuluetaAlex Leow

Abstract

Mood disorders are common and associated with significant morbidity and mortality. Better tools are needed for their diagnosis and treatment. Deeper phenotypic understanding of these disorders is integral to the development of such tools. This study is the first effort to use passively collected mobile phone keyboard activity to build deep digital phenotypes of depression and mania. The objective of our study was to investigate the relationship between mobile phone keyboard activity and mood disturbance in subjects with bipolar disorders and to demonstrate the feasibility of using passively collected mobile phone keyboard metadata features to predict manic and depressive signs and symptoms as measured via clinician-administered rating scales. Using a within-subject design of 8 weeks, subjects were provided a mobile phone loaded with a customized keyboard that passively collected keystroke metadata. Subjects were administered the Hamilton Depression Rating Scale (HDRS) and Young Mania Rating Scale (YMRS) weekly. Linear mixed-effects models were created to predict HDRS and YMRS scores. The total number of keystrokes was 626,641, with a weekly average of 9791 (7861), and that of accelerometer readings was 6,660,890, with a weekly ...Continue Reading

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Citations

Jul 4, 2020·JMIR Mental Health·Henning DausMatthias Backenstrass
May 30, 2020·Journal of the American Medical Informatics Association : JAMIA·Claudia VeselAlex Leow
Sep 19, 2019·Scientific Reports·Rafail-Evangelos MastorasLeontios J Hadjileontiadis
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Software Mentioned

Apache
R
AnySoftKeyboard
BiAffect
R package car
R package piecewiseSEM
lme4

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