SCH: INT: Collaborative Research: Enhancing Context-Awareness and Personalization for Intensively Adaptive Smoking Cessation Messaging Interventions

SCH：INT：合作研究：增强情境意识和个性化，以实现强化适应性戒烟消息干预

• 批准号: 1722792
• 负责人: Benjamin Marlin
• 金额: $ 64.02万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1722792&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Enhancing

According to the Centers for Disease Control, tobacco use remains the leading preventable cause of death in the US, causing approximately 480,000 deaths each year, and incurring over $150 billion in healthcare costs. As a result, scalable, low cost, and effective smoking cessation interventions are clearly needed. This research aims to develop and validate new messaging-based smoking cessation support intervention systems that will leverage recent advances in smart wearable technologies to significantly enhance efficacy. The system will integrate wearable sensors that continuously estimate an individual's level of stress and craving as well as the occurrence of smoking. This information will be used to enhance the context awareness of the intervention system, allowing it to continuously adapt both the content and delivery timing of intervention components for each individual. By developing scalable messaging-based smoking cessation support interventions with improved personal relevance, this research has the potential to lead to direct benefits to society by more effectively helping individuals to quit smoking. To accomplish the goal of providing effective, personalized smoking cessation interventions, this research will develop and evaluate the models, algorithms, and wearable-phone-cloud computational infrastructures required to support the context inferences, personalization, and delivery timing optimizations required. Starting from the team's extensive prior work, this research will contribute to (1) advances in mobile health sensing and context inference with low-cost, low-power sensors; (2) advances in real-time, stream-based active learning for personalizing context inference models; (3) advances in contextualized recommender systems to personalize message selection based on inferred contexts; and (4) advances in robust, real-time wearable-phone-cloud data analytics systems. This work will also make substantial contributions to enhancing research infrastructure through open source software releases that can be leveraged by the research community to yield benefits in other high-profile health areas including heart disease, obesity, and addiction.

根据疾病控制中心的数据，烟草使用仍然是美国主要的可预防死亡原因，每年导致约48万人死亡，并导致超过1500亿美元的医疗费用。因此，显然需要可扩展、低成本和有效的戒烟干预措施。这项研究旨在开发和验证新的基于消息的戒烟支持干预系统，该系统将利用智能可穿戴技术的最新进展来显著提高疗效。该系统将集成可穿戴传感器，不断估计个人的压力和渴望程度以及吸烟的发生。这些信息将用于增强干预系统的背景感知，使其能够针对每个人不断调整干预组件的内容和交付时间。通过开发可扩展的基于消息的戒烟支持干预措施并提高个人相关性，这项研究有可能通过更有效地帮助个人戒烟而直接造福社会。为了实现提供有效的、个性化的戒烟干预措施的目标，本研究将开发和评估支持情景推断、个性化和交付时间优化所需的模型、算法和可穿戴电话云计算基础设施。从该团队广泛的前期工作开始，这项研究将有助于(1)使用低成本、低功耗传感器的移动健康感知和上下文推理的进展；(2)用于个性化情境推理模型的实时、基于流的主动学习的进展；(3)基于推断的情境的个性化消息选择的情境推荐系统的进展；以及(4)健壮、实时的可穿戴电话-云数据分析系统的进展。这项工作还将通过开放源码软件发布，为加强研究基础设施做出实质性贡献，研究社区可以利用这些软件在其他备受瞩目的健康领域产生好处，包括心脏病、肥胖症和成瘾。

项目成果

SleepLess: personalized sleep monitoring using smartphones and semi-supervised learning

• DOI: 10.1007/s40012-023-00389-8
• 发表时间: 2023-11
• 期刊: CSI Transactions on ICT
• 作者: P. Mammen;Camellia Zakaria;Prashant J. Shenoy

Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions.

评估上下文推断错误和部分可观察性对即时自适应干预的 RL 方法的影响。

• 发表时间: 2023
• 期刊: Proceedings of machine learning research
• 作者: Karine,Karine;Klasnja,Predrag;Murphy,SusanA;Marlin,BenjaminM
• 通讯作者: Marlin,BenjaminM

Towards Joint Segmentation and Active Learning for Block-Structured Data Streams

面向块结构数据流的联合分割和主动学习

• 发表时间: 2019
• 期刊: and Labeling for Mining and Learning
• 作者: Conrad Holtsclaw, Meet P.

Hierarchical Active Learning for Model Personalization in the Presence of Label Scarcity

标签稀缺情况下模型个性化的分层主动学习

• DOI: 10.1109/bsn.2019.8771081
• 发表时间: 2019
• 期刊: IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN
• 作者: Natarajan, Annamalai;Ganesan, Deepak;Marlin, Benjamin M.
• 通讯作者: Marlin, Benjamin M.