The goal of this observational study is to develop and validate a digital ecosystem designed to assess and manage mental health in workplace environments. The primary purpose is to understand how digital tools can contribute to better mental health management and to gauge their effectiveness in a typical work setting. The study also aims to enhance the prediction of mental health outcomes and the course of mental health conditions through more accurate assessments. The main questions it aims to answer are:

1. How do digital assessments improve the detection and management of mental health issues like depression and anxiety in the workplace?
2. Can a digital ecosystem effectively reduce the overall cost and impact of mental health issues on productivity and employee well-being?
3. How effective are bifactor models in detecting and mitigating the impact of faking in self-reported mental health assessments in occupational settings?

Participants will:

1. Engage with the Metrikamind platform to complete periodic mental health assessments.
2. Provide feedback on their experience and any changes in their mental health status, with particular attention to the accuracy and honesty of self-reported data facilitated by the implementation of bifactor models.
3. Participate in follow-up surveys to gauge long-term effects of using the digital tools on their mental health and workplace productivity.

This study involves adult participants currently employed in various sectors undergoing a sick leave, who will use the Metrikamind platform over a six-month period. The research aims to collect data on the usability and effectiveness of the platform, analyzing changes in participants' mental health through their interaction with the digital tools provided. By incorporating advanced psychometric techniques like bifactor models, the study seeks to enhance the reliability of data and improve the prediction of mental health outcomes, providing a solid foundation for potential wider application in corporate health strategies.

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.

Inclusion Criteria:

• Adults aged 18 to 70 years.
• Currently on sick leave due to mental health issues.
• Has access to and is capable of using a digital device (smartphone, tablet, or computer) to interact with the Metrikamind platform.
• Proficient in the language of the intervention and assessment tools.

Exclusion Criteria:

• Individuals with severe psychiatric conditions requiring inpatient care or those at high risk of suicide, which might complicate the intervention or pose a danger to the participant.
• Significant cognitive impairments that would interfere with the participant's ability to comprehend or engage with the digital platform or to provide reliable self-reported data.
• Currently participating in other clinical trials that might interfere with the outcomes of this study.
• Lack of regular access to internet services, which are necessary for the digital intervention and data collection.

The study population for this clinical trial will be sourced from employees currently on sick leave due to mental health issues, who are being followed up by a Mutua Colaboradora de la Seguridad Social in Spain. These employees typically represent a range of industries and occupations, highlighting a diverse workforce experiencing mental health challenges that impact their ability to work. This population is characterized by individuals who are in the process of receiving or awaiting benefits from the social security system due to their health-related work absences. Participants will be selected based on specific inclusion and exclusion criteria to ensure that they are suitable for a study assessing the effectiveness of digital interventions aimed at improving mental health and facilitating a return to work. The selection will focus on adults aged 18 to 65 who have a diagnosed mental health condition such as anxiety or depression, ensuring a relevant and representative sample.

The investigators commit to the open sharing of all collected IPD that underlie the results reported in our publications, subject to ensuring privacy and confidentiality. All IPD will be thoroughly anonymized to remove personal identifiers, ensuring compliance with GDPR and other relevant data protection regulations. The anonymized datasets, along with the associated metadata, study protocol, statistical analysis plan, and other relevant study materials, will be made available in an institutional repository and on the Open Science Framework (OSF) following the completion of the study and the publication of primary outcomes. This approach supports the replication of our research and contributes to the broader scientific community by providing access to high-quality data for further analysis. Sharing these data aligns with current best practices in psychological research, promoting transparency, reproducibility, and the ethical use of information for scientific inquiry.

In accordance with current standards in psychology, the anonymized individual participant data (IPD) and supporting information from the Metrikamind project will be made available upon publication of the main results. The data will be accessible with no time limit, ensuring perpetual availability for secondary analysis and replication studies. This approach promotes ongoing scientific inquiry and verification. The data will be hosted on the Open Science Framework (OSF) and a designated institutional repository, providing access to researchers globally.

Access to the anonymized individual participant data (IPD) and supporting information from the Metrikamind project will be unrestricted. Once the data is published, it will be available to any interested researcher, practitioner, or member of the public globally. Access will be facilitated through the Open Science Framework (OSF) and a designated institutional repository, where users can freely download the data without any need for prior approval. This open access approach is intended to encourage a wide range of analyses, fostering innovation and collaboration in the field of mental health research.