Wen Hongyi, Sobolev Michael, Vitale Rachel, Kizer James, Pollak J P, Muench Frederick, Estrin Deborah
Cornell Tech, Cornell University, New York, NY, United States.
Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States.
JMIR Ment Health. 2021 Jan 27;8(1):e25019. doi: 10.2196/25019.
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.
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.
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).
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.
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.
ClinicalTrials.gov NCT03006653; https://clinicaltrials.gov/ct2/show/NCT03006653.
移动健康技术已证明智能手机应用程序和传感器有能力收集与患者活动、行为和认知相关的数据。它还提供了一个机会，通过持续传感来了解诸如电池寿命和屏幕使用时间等日常被动移动指标与心理健康结果之间的关系。冲动性是众多身心健康问题的一个潜在因素。然而，很少有研究旨在帮助我们理解移动传感器和自我报告数据如何能增进我们对冲动行为的理解。
本研究的目的是通过一个跨平台移动传感应用程序，探索使用移动传感器数据被动检测和监测自我报告的状态冲动性及冲动行为的可行性。
我们招募了26名参与者，他们是一项更大的冲动性研究的一部分，在苹果操作系统（iOS）和安卓平台上进行了为期21天的真实世界连续移动传感研究。移动传感系统（mPulse）从通话记录、电池充电和屏幕查看中收集数据。为了验证模型，我们使用移动传感功能来预测常见的自我报告的冲动性特征、客观的移动行为和认知测量，以及状态冲动性和与冲动行为相关的构念（即冒险、注意力和情感）的生态瞬时评估（EMA）。
总体而言，研究结果表明，诸如通话记录、电池充电和屏幕查看等手机使用的被动测量方法可以预测特质和状态冲动性以及冲动行为的不同方面。对于冲动性特质，模型显著解释了寻求刺激、计划和缺乏毅力特质的方差，但未能解释运动、紧迫性、缺乏预谋和注意力特质。通话记录、电池充电和屏幕查看的被动传感特征在解释和预测基于特质的寻求刺激方面特别有用。在每日层面，该模型成功预测了客观行为测量，如延迟折扣任务中的当前偏差、认知注意力任务中的委托和遗漏错误，以及冒险任务中的总收益。我们的模型还预测了关于积极性、压力、生产力、健康和情绪及情感的每日EMA问题。也许最有趣的是，该模型未能预测使用表面有效的问题来测量前一天冲动性的每日EMA。
该研究证明了开发特质和状态冲动性表型以及从日常手机传感器检测冲动行为的潜力。讨论了当前研究的局限性以及构建更精确的被动传感模型的建议。
ClinicalTrials.gov NCT03006653；https://clinicaltrials.gov/ct2/show/NCT03006653。
Zotero 插件