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相关概念视频

Stress Prevention and Stress Management Techniques II01:23

Stress Prevention and Stress Management Techniques II

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Personality types, particularly Type A and Type B, significantly influence how individuals respond to stress. These personality distinctions are marked by varying levels of ambition, competitiveness, and coping styles, all of which shape an individual's resilience to stressors.
Type A Personality: Driven and Easily Stressed
Individuals with Type A personalities are often highly competitive and ambitious and operate with a strong sense of urgency. Commonly labeled as...
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Stress: General Loading Conditions01:15

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To grasp the intricacy of real-world conditions where multiple loads are applied simultaneously to a structure, one might visualize a section passing through a specific point within a body, aligned parallel to the xy plane. This section is subjected to various forces, including original loads, normal forces, and shearing forces.
The shearing force, possessing potential directionality within the plane of the section, is simplified into two component forces running parallel to the x and y axes....
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Introduction to Stress and Lifestyle01:27

Introduction to Stress and Lifestyle

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Stress is a multifaceted response to events perceived as challenging or threatening, highlighting physical, emotional, cognitive, and behavioral reactions. Physically, stress can lead to fatigue, sleep disruptions, and various health issues such as frequent colds, chest pains, and nausea. Emotionally, it can manifest as anxiety, depression, irritability, and anger triggered by both minor and major life events. Cognitively, it may result in difficulty in concentration, memory, and...
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General State of Stress01:21

General State of Stress

179
The general state of stress within a material can be accurately depicted using a stress tensor. This tensor encapsulates the internal forces distributed within a material subjected to external forces or deformations.
Specifically, consider a tetrahedral element where one face, labeled XYZ, is perpendicular to the line OA, and the remaining faces align with the coordinate axes with point O as the origin. At any point, such as point O, the stress tensor can be used to determine the stress...
179
Components of Stress01:23

Components of Stress

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Stress analysis under multiple loading conditions is intricate, necessitating a comprehensive grasp of normal and shearing stresses. Consider a small cube at point O, subjected to stress on all six faces, visible or not. Normal stress components σx, σy, σz act perpendicularly to the x, y, and z axes. Shearing stress components τxy and τxz are exerted on faces perpendicular to these axes.
Interestingly, the hidden cube faces also experience these stresses, equal and...
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Stress Prevention and Stress Management Techniques I01:26

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Stress prevention and management are crucial for maintaining well-being and building resilience. Techniques to manage stress include cultivating qualities like conscientiousness, a sense of personal control, and self-efficacy. Each of these traits significantly reduces stress and promotes healthier lifestyle choices and outcomes.
Conscientiousness
Conscientious individuals tend to be organized, responsible, and disciplined. They prioritize completing tasks and following structured routines,...
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预测工人的压力:使用工作方式特征应用高性能算法

Hiroki Iwamoto1, Saki Nakano1, Ryotaro Tajima1

  • 1Shionogi & Co., Ltd., Osaka, Japan.

JMIR AI
|August 2, 2024
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种新的算法,使用个性化的工作数据来预测员工的压力. 根据远程工作率对员工进行分组,提高了压力预测的准确性,并确定了不同工作风格的独特压力因素.

关键词:
日本 日本 日本 日本 日本.高性能算法 高性能算法问卷调查问卷 问卷调查问卷压力预测模型的模型.远程工作远程工作.穿戴式设备是一种可穿戴的设备.

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科学领域:

  • 职业健康 职业健康 职业健康
  • 数据科学数据科学数据科学
  • 心理健康 心理健康

背景情况:

  • 远程工作率和工作特征与员工的压力有关.
  • 现有的压力预测模型缺乏个性化和高性能.
  • 个人化生活方式数据集成用于压力建模尚未得到充分探索.

研究的目的:

  • 开发一种新的,高性能算法来预测员工的压力.
  • 创建一个个性化的压力预测模型,以适应个人工作风格.
  • 评估远程工作率对压力预测准确性的影响.

主要方法:

  • 未来的观察性研究涉及190名员工超过12周.
  • 收集的数据包括可穿戴设备指标 (睡眠,活动,心率),班次工作细节和每周问卷 (K6,行为,错过的饭菜).
  • 基于类似的工作风格特征和远程工作率,使用邻里集群 (N=20) 开发了一个预测模型.

主要成果:

  • 拟议的方法实现了曲线下的面积 (AUC) 为0.84.
  • 根据远程工作率,特征对压力预测的重要性有很大差异.
  • 高度远程工作群体:与跳过午餐,工作时间偏差,心率变化和睡眠时间相关的压力.
  • 低远程工作群体:与工作到达时间,心率水平,心率变化和卡路里消耗相关的压力.

结论:

  • 根据工作方式 (远程工作率) 将员工分为群组,显著改善了压力预测性能.
  • 社区集群方法通过跨远程工作层次的独特贡献特征证明了其有效性.
  • 结合工作方式特征的个性化压力预测模型是有效的.