背景:人工智能(AI)是各种算法和快速兴起的技术的总称,在工作场所健康促进和预防(WHPP)方面具有巨大潜力。WHPP干预措施旨在通过行为和组织措施或通过减少工作场所相关疾病和相关危险因素的负担来改善人们的健康和福祉。虽然人工智能一直是其他健康相关领域的研究重点,如公共卫生或生物医学,人工智能向WHPP研究的转变还有待系统研究。
目的:系统范围审查旨在全面评估AI在WHPP中的当前使用概况。研究结果将用于指出未来的研究方向。得出以下研究问题:(1)WHPP背景下人工智能算法和技术的研究特点是什么,(2)哪些具体的WHPP字段(预防、行为,和组织方法)由人工智能算法和技术解决,和(3)什么样的干预措施会导致哪些结果?
方法:在三个学术数据库PubMed中进行了系统的范围界定文献综述(PRISMA-ScR),IEEE,和ACM在2023年7月,搜索2000年1月至2023年12月发表的文章。研究需要1)同行评审,2)用英语写,3)专注于(4)在WHPP或(5)相关领域中进行的任何基于AI的算法或技术。关于研究设计的信息,人工智能算法和技术,WHPP字段,用Rayyan对PICO框架进行了盲目提取和总结。
结果:共纳入10项研究。风险预防和建模是最确定的WHPP字段(n=6),其次是行为健康促进(n=4)和组织健康促进(n=1)。四项研究侧重于心理健康。大多数人工智能算法都是基于机器学习的,三项研究使用了组合的深度学习算法。人工智能算法和技术主要在智能手机应用程序中实现(例如,以Chatbot的形式)或将智能手机用作数据源(例如,GPS)。行为方法的范围为8至12周,并与对照组进行了比较。三项研究评估了AI模型或框架的鲁棒性和准确性。
结论:尽管AI在与健康相关的研究中引起了越来越多的关注,审查显示,WHPP中的人工智能研究很少。我们的结果表明,人工智能在WHPP的个性化和风险预测方面是有前途的,但目前的研究并未涵盖WHPP的范围。除此之外,未来的研究将受益于WHPP所有领域的广泛研究,纵向数据,和报告指南。
背景:于2023年7月5日在开放科学框架[1]注册。
BACKGROUND: Artificial intelligence (AI) is an umbrella term for various algorithms and rapidly emerging technologies with huge potential for workplace health promotion and prevention (WHPP). WHPP interventions aim to improve people\'s health and well-being through behavioral and organizational measures or by minimizing the burden of workplace-related diseases and associated risk factors. While AI has been the focus of research in other health-related fields, such as public health or
biomedicine, the transition of AI into WHPP research has yet to be systematically investigated.
OBJECTIVE: The systematic scoping
review aims to comprehensively assess an overview of the current use of AI in WHPP. The results will be then used to point to future research directions. The following research questions were derived: (1) What are the study characteristics of studies on AI algorithms and technologies in the context of WHPP? (2) What specific WHPP fields (prevention, behavioral, and organizational approaches) were addressed by the AI algorithms and technologies? (3) What kind of interventions lead to which outcomes?
METHODS: A systematic scoping literature
review (PRISMA-ScR [Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews]) was conducted in the 3 academic databases PubMed, Institute of Electrical and Electronics Engineers, and Association for Computing Machinery in July 2023, searching for papers published between January 2000 and December 2023. Studies needed to be (1) peer-reviewed, (2) written in English, and (3) focused on any AI-based algorithm or technology that (4) were conducted in the context of WHPP or (5) an associated field. Information on study design, AI algorithms and technologies, WHPP fields, and the patient or population, intervention, comparison, and outcomes framework were extracted blindly with Rayyan and summarized.
RESULTS: A total of 10 studies were included. Risk prevention and modeling were the most identified WHPP fields (n=6), followed by behavioral health promotion (n=4) and organizational health promotion (n=1). Further, 4 studies focused on mental health. Most AI algorithms were machine learning-based, and 3 studies used combined deep learning algorithms. AI algorithms and technologies were primarily implemented in smartphone apps (eg, in the form of a chatbot) or used the smartphone as a data source (eg, Global Positioning System). Behavioral approaches ranged from 8 to 12 weeks and were compared to control groups. Additionally, 3 studies evaluated the robustness and accuracy of an AI model or framework.
CONCLUSIONS: Although AI has caught increasing attention in health-related research, the
review reveals that AI in WHPP is marginally investigated. Our results indicate that AI is promising for individualization and risk prediction in WHPP, but current research does not cover the scope of WHPP. Beyond that, future research will profit from an extended range of research in all fields of WHPP, longitudinal data, and reporting guidelines.
BACKGROUND: OSF Registries osf.io/bfswp; https://osf.io/bfswp.