The Digital Marshmallow Test (DMT) Diagnostic and Monitoring Mobile Health App for Impulsive Behavior: Development and Validation Study (Preprint)

Sobolev, Michael; Vitale, Rachel; Wen, Hongyi; Kizer, James; Leeman, Robert F.; Pollak, John P.; Baumel, Amit; Vadhan, Nehal P.; Estrin, Deborah; Muench, Frederick

doi:10.2196/preprints.25018

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“…The DMT study by Sobolev et al [ 30 ] was designed to develop and test remote assessment of impulsivity using both iOS and Android applications for widespread dissemination to researchers, clinicians, and the general public. The DMT study included a baseline laboratory assessment and a 21-day study using the DMT mobile app [ 30 , 31 ]. Additional details can be found in the paper describing validation of the DMT app [ 30 ] and on the Open Science Framework [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

“…The DMT study included a baseline laboratory assessment and a 21-day study using the DMT mobile app [ 30 , 31 ]. Additional details can be found in the paper describing validation of the DMT app [ 30 ] and on the Open Science Framework [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

“…The DMT app included an adaptation of three exploratory, lab-based behavioral and cognitive measures related to impulse control to mobile devices, called “active tasks”: (1) a mobile Balloon Analogue Risk Task (mBART [ 34 ]), (2) a mobile go/no-go (mGNG [ 35 ]) task, and (3) a mobile delay discounting (mDD [ 36 ]) task. The mobile versions are exploratory and were partially validated as part of the DMT study (see the DMT study [ 30 ] for more details on each of these measures).…”

Section: Methodsmentioning

confidence: 99%

“…Overall, our results corresponded to previous research on impulsivity by demonstrating high correlations between different self-reports but low correlations between behavioral measures and self-reports [ 22 ]. A full description of these results can be found in the paper describing the DMT study [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

“…We selected call logs, battery charging, and screen checking as the mobile sensor data sources. We conducted a 3-week exploratory study with 26 participants as part of a larger mHealth study of impulsive behavior called the Digital Marshmallow Test (DMT) [ 30 ]. To validate the mobile sensing model, we used mobile sensing features to predict common self-reported impulsivity traits, objective behavioral and cognitive measures, and ecological momentary assessment (EMA) of impulsivity and constructs related to impulsive behavior (ie, risk-taking, attention, and affect).…”

Section: Introductionmentioning

confidence: 99%

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

Wen¹,

Sobolev²,

Vitale³

et al. 2021

JMIR Ment Health

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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

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%