Abstract
BackgroundMobile 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.ObjectiveThe 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.MethodsWe 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).ResultsOverall, 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.ConclusionsThe 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 RegistrationClinicalTrials.gov NCT03006653; https://clinicaltrials.gov/ct2/show/NCT03006653
Highlights
Mobile health technology has demonstrated the ability of smartphone apps and sensors to collect high-fidelity and high-frequency data pertaining to patient activity, behavior, symptoms, cognition, and context [1]
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
We found significant correlations between passive data and five of the components of trait impulsivity: (1) motor positively correlated with the entropy features extracted from screen checking (r=0.39, P=.05), suggesting that the temporal distribution of phone usage was associated with the trait of acting without thinking; (2) nonplanning correlated with several passive features, including the usage mean (r=0.46, P=.02) and usage deviations (r=0.55, P=.004) of screen-checking duration; (3) sensation seeking positively correlated with battery charging entropy (r=0.48, P=.01) and the screen-checking frequency (r=0.43, P=.03); (4) urgency negatively correlated with call entropy (r=–0.39, P=.04); and (5) perseverance positively correlated with the standard deviation of screen checking (r=0.50, P=.01)
Summary
Mobile health (mHealth) technology has demonstrated the ability of smartphone apps and sensors to collect high-fidelity and high-frequency data pertaining to patient activity, behavior, symptoms, cognition, and context [1]. In behavioral and mental health, digital phenotyping [2,3,4] or personal sensing [5] has been proposed as an approach to quantify the “moment-by-moment and continuous individual-level human phenotype” using data from sensors on smartphones. Building on this potential, prior research using mobile sensing technology focused on specific psychological disorders [6,7,8,9,10,11] or general mental and physical well-being [12,13,14]. Few studies have been designed to help us understand how mobile sensors and self-report data can improve our understanding of impulsive behavior
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