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Digital Phenotypes of Mobile Keyboard Backspace Rates and Their Associations With Symptoms of Mood Disorder:

Qimin Liu^1,2, Emma Ning³, Mindy K Ross⁴

¹Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States.

Journal of Medical Internet Research

|October 29, 2024

Summary

Smartphone keyboard backspace use can identify mood disorder symptoms. This passive sensing method offers a low-burden approach for monitoring mental health and aiding clinical decisions.

Keywords:

clinical decision-making depression digital phenotyping keyboard data keyboard typing mania, mobile phone mixture model mobile keyboard monitoring mood disorder passive sensing phenotypes smartphone

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Area of Science:

Digital phenotyping
Computational psychiatry
Mobile health

Background:

Passive sensing via smartphone keyboard data can identify and monitor mood disorder symptoms with minimal participant burden.
Behavioral phenotyping using mobile keystroke data can assist clinical decision-making and offer insights into individual mood disorder symptoms.

Purpose of the Study:

To derive digital phenotypes from smartphone keyboard backspace usage in 128 community adults.
To analyze 2948 observations using a Bayesian mixture model to understand mood disorder indicators.

Main Methods:

Participants used a custom-built BiAffect smartphone keyboard for up to 4 weeks to capture keystroke dynamics.
Compliance was monitored through follow-up visits, and participants underwent psychiatric evaluations.
A Bayesian mixture model was employed to analyze backspace rates and derive digital phenotypes.

Main Results:

Backspace rates did not significantly differ between healthy controls and mood disorder groups on a continuous scale (P=.11).
A 3-class model identified distinct backspace rate groups (Low, Medium, High), with unipolar mood disorder individuals predominantly in the Medium group.
The High backspace rate group showed significantly higher odds of mania symptoms, including elevated mood, motor activity, and irritability, compared to the Low group.

Conclusions:

Mobile typing kinematics, specifically backspace rates, show promise for mood disorder research and clinical practice.
Passive sensing of typing kinematics offers a low-burden method for monitoring mental health.
Derived digital phenotypes from backspace rates can help distinguish individuals with and without mood disorder symptoms.