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

中文翻译：

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被动检测冲动行为的mPulse移动传感模型：探索性预测研究

背景：移动健康技术已证明智能手机应用程序和传感器能够收集与患者活动，行为和认知有关的数据。它还提供了通过连续感测来了解日常被动移动指标（如电池寿命和屏幕时间）如何与心理健康结果相关的机会。冲动性是许多身心健康问题的潜在因素。但是，很少有研究可以帮助我们了解移动传感器和自我报告数据如何改善我们对冲动行为的理解。目的：这项研究的目的是探讨使用移动传感器数据通过跨平台移动传感应用程序被动地检测和监视自我报告的状态冲动和冲动行为的可行性。方法：我们招募了26位参与者，这些参与者是冲动性研究的一部分，他们在21天之内参与了在Apple操作系统（iOS）和Android平台上的真实，连续的移动感应研究。移动传感系统（mPulse）从通话记录，电池充电和屏幕检查中收集数据。为了验证该模型，我们使用了移动感知功能来预测常见的自我报告的冲动特征，客观的移动行为和认知测度以及状态冲动的生态瞬时评估（EMA）和与冲动行为有关的构造（即冒险，注意，并影响）。结果：总体而言，研究结果表明，使用手机的被动措施（例如通话记录，电池充电和屏幕检查）可以预测特征，状态冲动和冲动行为的不同方面。对于冲动性状，模型显着解释了感官寻求，计划和缺乏毅力性状的差异，但未能解释运动，紧迫感，缺乏冥想和注意性状。来自通话记录，电池充电和屏幕检查的被动感测功能在解释和预测基于特征的感测中特别有用。在日常水平上，该模型成功地预测了客观行为指标，例如延迟贴现任务中的当前偏差，认知注意任务中的佣金和遗漏错误以及冒险任务中的总收益。我们的模型还预测了有关积极性，压力，生产力，健康状况以及情绪和情感的每日EMA问题。也许最有趣的是，该模型未能预测设计为使用面部有效问题来衡量前一天冲动性的每日EMA。结论：该研究证明了发展特质和状态冲动表型以及检测日常手机传感器冲动行为的潜力。讨论了当前研究的局限性和建立更精确的被动传感模型的建议。试用注册： ClinicalTrials.gov NCT03006653; https://clinicaltrials.gov/ct2/show/NCT03006653