mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study

Hongyi Wen, Michael Sobolev, Rachel Vitale, James Kizer, J P Pollak, Frederick Muench, Deborah Estrin, Hongyi Wen, Michael Sobolev, Rachel Vitale, James Kizer, J P Pollak, Frederick Muench, Deborah Estrin

Abstract

Background: Mobile health technology has demonstrated the ability of smartphone apps and sensors to collect data pertaining to patient activity, behavior, and cognition. It also offers the opportunity to understand how everyday passive mobile metrics such as battery life and screen time relate to mental health outcomes through continuous sensing. Impulsivity is an underlying factor in numerous physical and mental health problems. However, few studies have been designed to help us understand how mobile sensors and self-report data can improve our understanding of impulsive behavior.

Objective: The objective of this study was to explore the feasibility of using mobile sensor data to detect and monitor self-reported state impulsivity and impulsive behavior passively via a cross-platform mobile sensing application.

Methods: We enrolled 26 participants who were part of a larger study of impulsivity to take part in a real-world, continuous mobile sensing study over 21 days on both Apple operating system (iOS) and Android platforms. The mobile sensing system (mPulse) collected data from call logs, battery charging, and screen checking. To validate the model, we used mobile sensing features to predict common self-reported impulsivity traits, objective mobile behavioral and cognitive measures, and ecological momentary assessment (EMA) of state impulsivity and constructs related to impulsive behavior (ie, risk-taking, attention, and affect).

Results: Overall, the findings suggested that passive measures of mobile phone use such as call logs, battery charging, and screen checking can predict different facets of trait and state impulsivity and impulsive behavior. For impulsivity traits, the models significantly explained variance in sensation seeking, planning, and lack of perseverance traits but failed to explain motor, urgency, lack of premeditation, and attention traits. Passive sensing features from call logs, battery charging, and screen checking were particularly useful in explaining and predicting trait-based sensation seeking. On a daily level, the model successfully predicted objective behavioral measures such as present bias in delay discounting tasks, commission and omission errors in a cognitive attention task, and total gains in a risk-taking task. Our models also predicted daily EMA questions on positivity, stress, productivity, healthiness, and emotion and affect. Perhaps most intriguingly, the model failed to predict daily EMA designed to measure previous-day impulsivity using face-valid questions.

Conclusions: The study demonstrated the potential for developing trait and state impulsivity phenotypes and detecting impulsive behavior from everyday mobile phone sensors. Limitations of the current research and suggestions for building more precise passive sensing models are discussed.

Trial registration: ClinicalTrials.gov NCT03006653; https://ichgcp.net/clinical-trials-registry/NCT03006653.

Keywords: digital phenotyping; impulse control; impulsivity; mHealth; mobile health; mobile sensing; self-control; self-regulation.

Conflict of interest statement

Conflicts of Interest: None declared.

©Hongyi Wen, Michael Sobolev, Rachel Vitale, James Kizer, JP Pollak, Frederick Muench, Deborah Estrin. Originally published in JMIR Mental Health (http://mental.jmir.org), 27.01.2021.

Figures

Figure 1
Figure 1
Conceptual framework of passive sensor data and inferred behavior.
Figure 2
Figure 2
Correlation between the 15 features of mobile sensor data and trait impulsivity scales (15-item short form of the Barratt Impulsiveness Scale [BIS-15] and UPPS) and subscales. Ent: entropy; Freq: frequency per hour; Mean: use mean; SD: use deviations; Use: use duration per hour.

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