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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.

项目摘要移动的技术在提供高度创新的动态戒烟方面具有巨大的潜力干预措施。手机传感器、可穿戴技术以及生态等真实的时间数据采集方式瞬时评估（EMA）使收集大量的环境和生理信息成为可能。例如位置、心率和情绪的数据。环境和情境线索，如渴望和接近对其他人来说，吸烟是那些试图戒烟的人的失误的高度预测，这表明失误风险是以直接的、动态的影响为特点。新兴战略，如及时适应通过移动的技术提供量身定制的支持，在最需要的时候。虽然研究已经确定了吸烟失效的前因，从EMA数据的观察，研究一直无法利用全方位的上下文和现有技术的环境数据。考虑到动态影响对失效的重要性，风险，迫切需要准确识别最高失效风险时刻的策略，以改善停止干预。最近的研究已经证明了机器学习在预测个人行为方面的实用性。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从大型数据集建立模型，并能自动适应真实的新数据。本报告的总体目标应用是使用监督机器学习方法来开发自动算法来量化在个人层面上吸烟的风险。具体来说，我们的目标是：1）应用监督机器学习方法来量化吸烟失效的个性化风险，和2）评估的可行性和初步提供由机器学习驱动的个性化、实时自适应干预的有效性真实的时间内预测吸烟失效风险。拟议的研究和培训计划将在俄克拉荷马州大学健康科学中心（OUHSC）和斯蒂芬森癌症中心（SCC）。培训将专注于增加机器学习方法的知识，以及JITAI的执行和分析，这将有助于完成拟议的项目。拟议研究的结果有可能减少参与者和传感器所需数据的数量和频率，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，一种自动化的动态干预，充分利用移动的技术的优势，个人行为和环境背景。