RESUMO
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, and (3) what kind of interventions lead to which outcomes? METHODS: A systematic scoping literature review (PRISMA-ScR) was conducted in the three academic databases PubMed, IEEE, and ACM in July 2023, searching for articles 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 PICO framework were extracted blindly with Rayyan and summarized. RESULTS: A total of ten 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). Four studies focused on mental health. Most AI algorithms were machine learning-based, and three studies used combined deep learning algorithms. AI algorithms and technologies were primarily implemented in smartphone applications (eg, in the form of a Chatbot) or used the smartphone as a data source (eg, GPS). Behavioral approaches ranged from 8 to 12 weeks and were compared to control groups. Three 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. CLINICALTRIAL: Registered on 5th July 2023 at Open Science Framework [1].
RESUMO
BACKGROUND: The emerging adulthood is traditionally viewed as a time of optimal health, but also as a critical life span, characterized by changing life circumstances and the establishment of an individual lifestyle. Especially university life seems to hold several challenges impeding the manifestation of a health supporting manner, as many students tend to show a poorer health behavior and a higher amount of health-related problems than comparable age groups. This, along with a steady growth of the higher education sector, brings increased attention to the university setting in the context of prevention. To date, there are few empirical longitudinal and coherent cross-sectional data on the status of students' health literacy, health status, and health behaviors, and on the impact of the study format on students' health. The aim of this prospective cohort study is to reduce this research gap. METHODS: Starting during winter semester 2020/21, the prospective cohort study collects data on health literacy, health status and health behavior on a semester-by-semester basis. All enrolled students of the IST University of Applied Sciences, regardless of study format and discipline, can participate in the study at the beginning of their first semester. The data are collected digitally via a specifically programmed app. A total of 103 items assess the subjectively perceived health status, life and study satisfaction, sleep quality, perceived stress, physical activity, diet, smoking, alcohol consumption, drug addiction and health literacy. Statistical analysis uses (1) multivariate methods to look at changes within the three health dimensions over time and (2) the association between the three health dimensions using multiple regression methods and correlations. DISCUSSION: This cohort study collects comprehensive health data from students on the course of study. It is assumed that gathered data will provide information on how the state of health develops over the study period. Also, different degrees of correlations of health behavior and health literacy will reveal different impacts on the state of students' health. Furthermore, this study will contribute to empirically justified development of target group-specific interventions. TRIAL REGISTRATION: German Clinical Trials Register: DRKS00023397 (registered on October 26, 2020).
