NCT06650176 | NOT YET RECRUITING | Depression Disorders

Detailed Description:

Background and Rationale: Mental health in the workplace is a growing concern globally, with significant impacts on productivity, employee engagement, and overall organizational health. Traditional methods of addressing mental health at work often fall short due to lack of early detection, stigma associated with mental health issues, and inadequate monitoring of interventions, but also faking good or bad due to absenteeism or presentism. The Metrikamind project aims to address these challenges by developing a robust digital ecosystem that facilitates early detection, continuous monitoring, and effective management of mental health issues within the workplace, while mitigating faking. This system leverages the latest advancements in psychometrics and digital health technologies to create a user-friendly, scalable, and scientifically valid tool that can be integrated into everyday work environments. Objective: The primary objective of the Metrikamind project is to validate the effectiveness of a digital mental health assessment and management system in improving the detection, monitoring, and management of mental health issues in workplace settings. The project will evaluate how such digital tools can contribute to reducing the economic and human costs associated with workplace mental health issues, such as depression and anxiety. Study Design: This observational study will utilize a longitudinal design, where participants will interact with the Metrikamind platform over a period of six months. The study will recruit adult employees from various sectors to ensure a diverse participant pool that reflects a wide range of work environments. Participants will use the platform to complete regular mental health assessments, which include both quantitative and qualitative measures. Methods: Participants will be asked to engage with a series of assessments developed based on bifactor models, which are designed to detect and mitigate the effects of faking and social desirability bias in self-report assessments. These models improve the accuracy and reliability of mental health assessments by allowing for the separation of specific factors from general mental health constructs, thereby enhancing the precision of diagnosis and intervention. Each participant will complete a baseline assessment upon enrollment, followed by monthly follow-up assessments through the platform. The assessments will cover various aspects of mental health, including stress, anxiety, depression, and overall emotional well-being. Additional data on work performance and engagement will be collected through integrations with workplace productivity tools where available. Data Analysis: Data collected will be analyzed using advanced statistical techniques to determine the effectiveness of the digital platform in improving mental health outcomes. Analysis will include the use of longitudinal data analysis methods to track changes in mental health over time and to identify predictors of improvement or deterioration. Correlational analysis will also be employed to explore the relationships between mental health data and workplace productivity metrics. Innovative Components: The Metrikamind project incorporates several innovative aspects: Use of Bifactor Models: By applying bifactor models, the project stands at the forefront of psychometric innovation, offering more nuanced insights into mental health assessments. Real-Time Data Analytics: The platform provides real-time analytics, allowing both participants and supervisors (with consent) to monitor mental health status and intervene when necessary promptly. Integration with Work Tools: Integration with existing workplace tools and platforms ensures that the mental health management system fits seamlessly into the daily routines of employees, thereby enhancing usability and compliance. Ethical Considerations: All participants will provide informed consent before participating in the study. The study will adhere to the ethical guidelines laid out by the EU Regulation 2016/679 (GDPR) and the Organic Data Protection Law 3/2018, ensuring the highest standards of data privacy and security. All data will be anonymized, and personal identifiers will be removed before analysis to maintain confidentiality. Expected Outcomes: The Metrikamind project is expected to demonstrate the efficacy of digital tools in managing workplace mental health. It aims to provide empirical evidence supporting the integration of such tools into standard workplace practices, potentially leading to a paradigm shift in how mental health is managed at work. Conclusion: By advancing our understanding of digital health applications in the workplace, the Metrikamind project promises to make a significant impact on the field of occupational health psychology, offering new avenues for promoting mental health at work.