METRIKAMIND - Development of a Digital Mental Health Ecosystem for Workplace Environments

NCT ID: NCT06650176

Last Updated: 2025-03-27

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 400 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-09-30
• Study Completion Date: 2026-04-30

Brief Summary

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.

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.

Conditions

Depression Disorders; Anxiety

Study Design

• Observational Model Type: ECOLOGIC_OR_COMMUNITY
• Study Time Perspective: PROSPECTIVE

Study Groups

Workers on sick leave

This cohort consists of workers currently on sick leave due to mental health issues, specifically anxiety and depression, being managed by a "Mutua Colaboradora de la Seguridad Social" in Spain. The intervention targets this population to assess and improve mental health through the Metrikamind digital platform, aiming to facilitate their recovery and return to work. Participants are adult employees from various sectors, ensuring a diverse and representative sample. The study focuses on employing the Metrikamind platform's advanced psychometric tools, including bifactor models, to accurately assess and manage the participants' mental health. This intervention seeks to validate the effectiveness of digital health tools in reducing the duration of sick leave and improving overall mental well-being, thereby supporting employees in their transition back to regular work activities.

Lifestyle Management

Intervention Type: BEHAVIORAL

The Metrikamind intervention features a unique digital ecosystem designed to manage mental health specifically in workplace contexts, with a focus on aiding recovery and facilitating the return to work for employees on sick leave. This innovative tool employs bifactor models to enhance the accuracy of self-reported data, effectively countering potential faking behaviors. Through continuous, real-time data collection on a user-friendly platform, Metrikamind tracks and assesses various mental health conditions, including stress, anxiety, and depression. The intervention\'s advanced analytics enable personalized feedback and targeted interventions, improving the detection, management, and ultimately, the mitigation of mental health issues. By focusing on individuals currently unable to work due to mental health challenges, Metrikamind aims to support their recovery and accelerate their readiness to return to work, thus addressing both immediate and long-term recovery trajectories.

Interventions

Lifestyle Management

The Metrikamind intervention features a unique digital ecosystem designed to manage mental health specifically in workplace contexts, with a focus on aiding recovery and facilitating the return to work for employees on sick leave. This innovative tool employs bifactor models to enhance the accuracy of self-reported data, effectively countering potential faking behaviors. Through continuous, real-time data collection on a user-friendly platform, Metrikamind tracks and assesses various mental health conditions, including stress, anxiety, and depression. The intervention\'s advanced analytics enable personalized feedback and targeted interventions, improving the detection, management, and ultimately, the mitigation of mental health issues. By focusing on individuals currently unable to work due to mental health challenges, Metrikamind aims to support their recovery and accelerate their readiness to return to work, thus addressing both immediate and long-term recovery trajectories.

Intervention Type: BEHAVIORAL

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 70 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

David Gallardo-Pujol

OTHER

Sponsor Role: lead

Responsible Party

David Gallardo-Pujol

Professor of Psychology

Responsibility Role: SPONSOR_INVESTIGATOR

Locations

Faculty of Psychology

Barcelona, Barcelona, Spain

Countries

Spain

Central Contacts

David Gallardo-Pujol, PhD in Clinical Psychology

Role: CONTACT

david.gallardo@ub.edu

34 93 312 58 65

Facility Contacts

Vicedegà de Recerca, PhD

Role: primary

vd.psicologia.recerca@ub.edu

+34933125010

Other Identifiers

CPP2021-008590

Identifier Type: -

Identifier Source: org_study_id