Using Machine Learning to Develop Just-in-Time Adaptive Interventions for Smoking Cessation

使用机器学习开发及时的自适应戒烟干预措施

基本信息

批准号：
10524021
负责人：
Emily Taylor Hebert
金额：
$ 24.9万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-01 至 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10524021
关键词：
Accelerometer Address Affect Algorithms Behavior Cancer Center Cellular Phone Classification Control Groups Cues Data Data Analytics Data Collection Data Set Demographic Factors Development Devices Diet Ecological momentary assessment Effectiveness Frequencies Funding Health Sciences Health behavior Heart Rate Individual Intervention Knowledge Location Machine Learning Measures Methodology Methods Modeling Monitor Moods Oklahoma Participant Patient Self-Report Pattern Physical activity Physiological Positioning Attribute Randomized Reporting Research Risk Sensitivity and Specificity Smoking Smoking Behavior Smoking Cessation Intervention Social Environment Substance Use Disorder System Technology Telephone Time Training United States National Institutes of Health Universities Wrist adaptive intervention automated algorithm battery life contextual factors craving experience improved innovation large datasets machine learning algorithm machine learning method machine learning prediction mobile application mobile computing personalized intervention predictive modeling prevent psychologic risk prediction secondary analysis sensor socioenvironmental factor standard care supervised learning trend wearable device

项目摘要

PROJECT SUMMARY Mobile technology has enormous potential for delivering highly innovative, dynamic smoking cessation interventions. Phone sensors, wearable technology, and real time data collection methods such as ecological momentary assessment (EMA) have made it possible to collect a wealth of environmental and physiological data such as location, heart rate, and mood. Environmental and situational cues such as craving and proximity to others smoking are highly predictive of lapse among those trying to quit, suggesting that lapse risk is characterized by immediate, dynamic influences. Emerging strategies such as just-in-time adaptive interventions (JITAI), aim to prevent smoking lapse using tailored support delivered via mobile technology in the moments when it is most needed. Although research has identified antecedents of smoking lapse based on observations from EMA data, studies have been unable to utilize the full spectrum of contextual and environmental data available with current technology. Given the importance of dynamic influences on lapse risk, there is a critical need for strategies that accurately identify moments of highest lapse risk to improve cessation interventions. Recent research has demonstrated the utility of machine learning to predict individual behavior. Machine learning is a robust data analytic strategy that can produce highly accurate predictive models from large datasets and can automatically adapt to new data in real time. The overall objective of this application is to use supervised machine learning methods to develop an automated algorithm to quantify smoking lapse risk at the individual level. Specifically, we aim: 1) to apply supervised machine learning methods to quantify personalized risk of smoking lapse, and 2) to evaluate the feasibility and preliminary effectiveness of delivering a personalized, just-in-time adaptive intervention driven by machine learning prediction of smoking lapse risk in real time. The proposed research and training plan will take place at The University of Oklahoma Health Sciences Center (OUHSC) and the Stephenson Cancer Center (SCC). Training will focus on increasing knowledge of machine learning methodology, and the conduct and analysis of JITAIs, which will facilitate completion of the proposed project. Results of the proposed research have the potential to reduce the amount and frequency of data needed from participants and sensors, enabling the development of less burdensome interventions. It is expected that completion of these aims will yield preliminary data to inform an automated, dynamic intervention that fully utilizes the strengths of mobile technology for measuring individual behavior and environmental context in real time.

项目概要移动技术在实现高度创新、动态戒烟方面具有巨大潜力干预措施。手机传感器、可穿戴技术以及生态等实时数据收集方法瞬时评估（EMA）使得收集大量的环境和生理数据成为可能位置、心率和情绪等数据。环境和情境线索，例如渴望和接近对其他人来说，吸烟对于那些试图戒烟的人来说是戒烟的高度预测因素，这表明戒烟风险是其特点是直接的、动态的影响。新兴策略，例如即时自适应干预措施 (JITAI)，旨在通过移动技术提供的定制支持来预防戒烟最需要它的时刻。尽管研究已经根据以下因素确定了戒烟的前因：根据 EMA 数据的观察结果，研究无法充分利用上下文和利用现有技术可获得环境数据。鉴于动态影响对失效的重要性风险，迫切需要能够准确识别最高失误风险时刻的策略来改进戒烟干预措施。最近的研究证明了机器学习在预测个体方面的效用行为。机器学习是一种强大的数据分析策略，可以产生高度准确的预测来自大型数据集的模型，并且可以实时自动适应新数据。本次活动的总体目标应用程序是使用监督机器学习方法来开发自动化算法来量化个人层面的戒烟风险。具体来说，我们的目标是：1）应用监督机器学习量化个性化戒烟风险的方法，以及 2) 评估可行性和初步由机器学习驱动提供个性化、及时的适应性干预的有效性实时预测戒烟风险。拟议的研究和培训计划将在俄克拉荷马大学健康科学中心 (OUHSC) 和斯蒂芬森癌症中心 (SCC)。训练将专注于增加机器学习方法的知识以及 JITAI 的实施和分析，这将有助于完成拟议项目。拟议研究的结果有可能减少参与者和传感器所需的数据量和频率，从而能够开发减少繁琐的干预措施。预计这些目标的完成将产生初步数据以供参考自动化的动态干预，充分利用移动技术的优势进行测量实时的个人行为和环境背景。