Prevention of Depression in the Workplace Through a Personalized Intervention Based on Risk Algorithms, ICTs and Decision Support Systems: Randomized Controlled Trial

The main goal is to design, develop and evaluate a personalized intervention to prevent depression in the workplace, based on Information and Communication Technologies (ICTs), predictive risk algorithms and decision support systems (DSS) for employed workers. The specific goals are: 1) to design and develop a DSS, called e-predictD-Work-DSS to elaborate personalized plans to prevent depression and its monitoring in the employed working population; 2) to design and develop an ICT solution that integrates the DSS on the web, a mobile application (App), the predictD risk algorithm, different intervention modules (including a work stress management module) and a monitoring-feedback system; 3) to evaluate the usability, adherence, acceptability and satisfaction of employed working population with the e-pD-Work intervention; 4) to evaluate the effectiveness of the e-pD-Work intervention to reduce the incidence of major depression, depression and anxiety symptoms, the probability of major depression next year and to improve quality of life; 5) to evaluate the cost-effectiveness and cost-utility of the e-pD-Work intervention to prevent depression.

Methods: This a randomized, double-blind, controlled trial with two parallel arms (e-pD-Work vs active m-Health control) and 12 months follow-up. A total of 3,160 depression-free workers, aged between 18 and 55 years old will be recruited in Spain and randomly assigned to one of the two groups in a 1:1 ratio considering a stratification of age (18-29, 30-39, 40-49, 50-55 years) and sex similar to the Spanish population. Participants, interviewers and statisticians will be blinded to participants' allocation. The e-pD-Work intervention is self-guided, has a biopsychosocial approach and is multi-component (9 modules: physical exercise, improve sleep, expand relationships, solve problems, improve communication, assertiveness, decision making, manage thoughts and reduce work stress). The e-pD-Work intervention will be implemented in the smartphone of the workers and pivot on an already validated risk predictive algorithm and a DSS that helps workers to develop their own personalized depression prevention plans. Primary outcome will be the rate of major depression measured by CIDI. As secondary outcomes: depressive and anxiety symptomatology measured by PHQ-9 and GAD-7 respectively, the risk probability of depression measured by the predictD risk algorithm, quality of life measured by SF-12 and EuroQol, and cost-effectiveness and cost-utility.