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

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://clinicaltrials.gov/ct2/show/NCT03006653

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

“…The majority of articles (59%) used smartphone sensing to infer mental health conditions. While six articles examined overall mental health [ 13 , 24 , 25 , 26 , 27 , 68 ], other studies examined specific factors such as mood [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ] and stress [ 76 , 77 , 78 ]. Additionally, studies also examined specific mental health conditions such as depression [ 7 , 8 , 67 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ], schizophrenia [ 90 , 91 , 92 , 93 ], and bipolar disorder [ 94 , 95 ].…”

“…Perhaps one of the more striking findings was the variety of different approaches employed across the literature in terms of sensing (e.g., active/passive) and the applications that were deployed for data collection. Although a small number of studies used existing frameworks and datasets [ 9 , 27 , 75 , 76 , 81 ], the majority of them developed custom applications from the ground up for their research. As such, significant time and resources would have been expended on the bespoke development of data-collection applications and analysis pipelines, unique to each individual study.…”

Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study

“…This setting of large, unlabeled data sets with sparse supervision appears frequently in the field of digital health care. Notable examples include passive mobile sensing studies for mental health and well-being [11][12][13][14][15][16][17][18][19][20], digital therapeutics for children with autism spectrum disorder that record videos of the child [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], and passive brain sensors for brain-computer interfaces [38][39][40][41][42][43][44][45][46]. As such, this study protocol can be considered as one of the first tests of a broader emerging paradigm in precision health.…”

Microtemporal Dynamics of Dietary Intake, Physical Activity, and Impulsivity in Adult Attention-Deficit/Hyperactivity Disorder: Ecological Momentary Assessment Study Within Nutritional Psychiatry

“…However, participants might be less likely to report foods and drinks and respond to prompts when being (more) impulsive, which might have caused some bias (ie, systematic noncompliance). Therefore, more objective assessments of dietary intake (eg, passive detection of eating events [82] and automatized photo-based dietary assessment) and impulsivity (eg, passive detection of impulsive behavior [83]) are desirable. However, although self-reports are generally prone to bias, particularly self-reports of food intake [84], assessing food intake in real time or near real time, as done in this study, minimizes recall biases compared with typically used retrospective dietary assessments (eg, FFQs).…”