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SCH: Detecting and mapping stress patterns across space and time: Multimodal modeling of individuals in real-world physical and social work environments

SCH：检测和映射跨空间和时间的压力模式：现实世界物理和社会工作环境中个体的多模态建模

基本信息

批准号：
2204942
负责人：
Shawn Roll
金额：
$ 110万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204942&HistoricalAwards=false
关键词：
SCH Detecting mapping stress patterns

项目摘要

Stress has been identified as the health epidemic of the 21st century, and office-related work is a significant driver of stress among Americans due to long hours, rapid deadlines, heavy workload, and job insecurity. Yet, office workers are often entirely unaware of the impact of stress until they notice symptoms of declining physical or mental health or well-being, such as musculoskeletal discomfort, headaches, poor sleep, or lack of motivation. Even more problematic, most individuals do not know how their work activities and the physical and social work environments are related to stress and other health outcomes. While stress is almost always treated as unfavorable, stress can be positive. Opportunities exist to better understand how to promote eustress that is energizing and essential for productivity, and minimize distress that leads to negative emotions, disturbed bodily states, strain, and burnout. Thus, this project aims to generate new analytic models to uncover and map the patterns and pathways that influence work-related stress to understand the primary contributing factors to stress across space and time. The project will develop methods for integrating different types of data from the environment, the person, and other existing technologies to identify patterns that inform personalized solutions for improving self-awareness and management of work-related health and well-being. By developing a deeper individualized understanding and detection of eustress and distress, this project will impact and advance workplace health and wellness. The project will serve as a foundation for the development of sensing systems embedded within smart workplaces to automate environmental supports or provide behavioral feedback. These impacts will not only lead to improved worker health and well-being but can support decreased worker absenteeism and improved productivity. Thus, the project has the potential to change the way health and well-being are promoted and achieved in the office by engaging the worker in their health and wellness and ultimately reducing social and financial losses due to stress. The work will also have broader impacts regarding several criteria of NSF interest. It will promote awareness of the effects of the built, social, and work environments on health and well-being to encourage K-12 students to pursue careers in science and engineering. It will enhance the infrastructure for research and education by incorporating findings into the curriculum across multiple disciplines and disseminating findings via publications, presentations, and other media. The project will use a stakeholder-engaged, transactional approach to describe individualized experiences of stress and develop multimodal models using a wide range of bio-behavioral, environmental, and activity engagement sensing technologies to identify the most valuable combinations of data that inform personalized, automated, or technology-supported intervention approaches to stress management as workers engage in their daily work. To build an individually contextualized understanding of stress among office workers, machine learning methods that can operate with heterogeneous and noisy multimodal data streams at multiple temporal resolutions, including enabling unsupervised discovery of behavioral routines will be developed. Individual interviews and ecological momentary assessment (EMA) surveys will be used to characterize each participant, their work, and how they understand the concepts of stress (i.e., distress and eustress), particularly related to their work. Mobile and wearable technologies will be evaluated to understand stress experiences as workers engage in different workspaces (e.g., home, formal, public) across time. Sensing methods that could be embedded within the formal workspace to obtain alternative, complementary, or additional data useful in determining experiences of worker stress will be evaluated for differentiating worker distress from eustress. Specifically, the contribution of the physical environment, task engagement, posture, and worker emotive states to the understanding of stress will be examined. Additionally, through focus groups that will elicit user insights, feedback, and preferences, the work will advance our knowledge about acceptance of technology for health in work settings, and how that interacts with stress/health self-management including privacy, trustworthiness, acceptance, preferred/appropriate methods for feedback or automation. Novel machine learning methods will be developed and employed to predict positive and negative stress from multimodal data that include reference assessments of behavioral traits and baseline states–including those related to stress, affect, and the job–that serve as constructs for modeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

压力已被确定为21世纪的健康流行病，而与办公室相关的工作由于工作时间长、截止日期快、工作量大和工作不安全而成为美国人压力的重要驱动因素。然而，办公室工作人员通常完全没有意识到压力的影响，直到他们注意到身体或心理健康或幸福感下降的症状，如肌肉骨骼不适，头痛，睡眠不佳或缺乏动力。更有问题的是，大多数人不知道他们的工作活动以及物理和社会工作环境如何与压力和其他健康结果相关。虽然压力几乎总是被认为是不利的，但压力也可以是积极的。我们有机会更好地了解如何促进对生产力至关重要的积极压力，并尽量减少导致负面情绪、身体状态紊乱、紧张和倦怠的痛苦。因此，该项目旨在生成新的分析模型，以揭示和映射影响工作相关压力的模式和途径，以了解跨空间和时间的压力的主要影响因素。该项目将开发整合来自环境，人和其他现有技术的不同类型数据的方法，以确定为个性化解决方案提供信息的模式，以提高自我意识和管理与工作相关的健康和福祉。通过发展更深入的个性化理解和检测的压力和痛苦，这个项目将影响和促进工作场所的健康和福祉。该项目将作为开发嵌入智能工作场所的传感系统的基础，以自动化环境支持或提供行为反馈。这些影响不仅会改善工人的健康和福祉，而且可以支持减少工人缺勤和提高生产力。因此，该项目有可能改变促进和实现办公室健康和福祉的方式，让工作人员参与其健康和福祉，并最终减少因压力造成的社会和经济损失。这项工作还将对NSF感兴趣的几个标准产生更广泛的影响。它将促进对建筑，社会和工作环境对健康和福祉的影响的认识，以鼓励K-12学生追求科学和工程职业。它将通过将研究结果纳入多个学科的课程并通过出版物，演讲和其他媒体传播研究结果来加强研究和教育的基础设施。该项目将使用一个企业主参与的交易方法来描述个性化的压力体验，并使用广泛的生物行为，环境和活动参与感测技术开发多模式模型，以确定最有价值的数据组合，这些数据组合可以为工人参与日常工作时的压力管理提供个性化，自动化或技术支持的干预方法。为了建立对办公室工作人员压力的个体情境化理解，将开发可以在多个时间分辨率下使用异构和嘈杂的多模态数据流的机器学习方法，包括实现行为例程的无监督发现。个人访谈和生态瞬时评估（EMA）调查将用于描述每个参与者，他们的工作，以及他们如何理解压力的概念（即，（二）与工作有关的，特别是与工作有关的。将对移动的和可穿戴技术进行评估，以了解工人从事不同工作时的压力体验（例如，家庭、正式场合、公共场合）。传感方法，可以嵌入在正式的工作场所，以获得替代，补充，或额外的数据，在确定工人的压力经验有用的将进行评估，区分工人的痛苦从eustress。具体而言，物理环境，任务参与，姿势和工人的情绪状态的压力的理解的贡献将被检查。此外，通过焦点小组将引出用户的见解，反馈和偏好，这项工作将推进我们对工作环境中健康技术接受程度的了解，以及如何与压力/健康自我管理相互作用，包括隐私，可信度，接受度，首选/适当的反馈或自动化方法。将开发并采用新型机器学习方法来根据多模态数据预测积极和消极压力，这些数据包括行为特征和基线状态的参考评估，包括与压力、影响、还有工作该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查进行评估来支持的搜索.