I-Corps: Digital Phenotyping to Predict and Prevent Burnout in the Workplace

这个i-Corps项目的更广泛的影响/商业潜力是开发预测软件，以防止年轻人在工作场所精疲力竭。目前，数字生活在心理健康方面发挥着重要作用。该项目旨在开发技术来分析在线行为，以预测和解决工作场所的职业倦怠问题。职业倦怠被认为是工作场所残疾的主要来源。通过人工智能实现的职业倦怠预测和预防可能会使公司提高工作效率，并可能使员工获得更好的心理健康。这项拟议的技术主要针对年轻劳动力，因为移动技术对他们的心理健康影响更大。这项拟议的技术基于分析手机用户的互动和行为，并将它们与心理健康相关联。这项技术的最初应用是游戏开发行业，由于开发团队对制作视频游戏和完成最后期限的高认知需求，职业倦怠很常见。这个i-Corps项目基于一个软件平台的开发，使用数字表型和人工智能来测量行为、认知和情绪，为在高压力环境中工作的年轻专业人员设计有效的临床心理健康干预措施。数字表型识别是一个涉及从现成智能手机收集传感器和社交参数，以及键盘、语音和语音数据的过程。焦虑和抑郁复发的预测指标包括睡眠中断、社交能力和体力活动减少以及情绪变化。语言中的语调和认知功能可以通过数字表型来测量和分析。拟议的技术可能会提供预测可能的倦怠和其他心理健康问题的指标，并提出基于人工智能的解决方案来预防年轻劳动力的倦怠。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of prediction software to prevent young adult burnout in the workplace. Currently, digital lives play a major role in mental health. This project aims to develop technology to analyze online behavior to predict and address burnout in the workplace. Burnout is cited as a primary source of disability in the workplace. Burnout prediction and prevention enabled with artificial intelligence may allow companies to be more productive and may allow better employee mental wellness. The proposed technology is focused on the young workforce due to the higher impact of mobile technology on their mental health. The proposed technology is based on analyzing mobile phone user interactions and behaviors and correlating them to mental health. The initial application of the technology is the game development industry where burnout is common due to the high cognitive demand on development teams to produce video games and to meet deadlines.This I-Corps project is based on the development of a software platform using digital phenotyping and artificial intelligence to measure behavior, cognition, and mood to design effective clinical mental health interventions for young professionals working in highly stressful environments. Digital phenotyping is a process that involves collecting sensor and sociability parameters, as well as keyboard, voice and speech data from off-the-shelf smartphones. Predictors of anxiety and depressive relapses include disrupted sleep, reduced sociability and physical activity, and changes in mood. Intonation in language and cognitive function may be measured via digital phenotyping and analyzed. The proposed technology may deliver metrics to predict possible burnout and other mental health problems, and propose artificial intelligence-based solutions for prevention of burnout in the young workforce.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.