Q3147160 (Q3147160): Difference between revisions
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(Created claim: summary (P836): Objective: Design, develop and evaluate a personalised intervention to prevent depression in the workplace, based on information and communication technologies, predictive risk algorithms and decision support systems (DSS) for employees. Methods: We will conduct a double-blind, randomised controlled trial with two parallel branches and a year of follow-up. The trial will be conducted in 7 provinces of 6 autonomous communities. 3,160 depressi...) |
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Objective: Design, develop and evaluate a personalised intervention to prevent depression in the workplace, based on information and communication technologies, predictive risk algorithms and decision support systems (DSS) for employees. Methods: We will conduct a double-blind, randomised controlled trial with two parallel branches and a year of follow-up. The trial will be conducted in 7 provinces of 6 autonomous communities. 3,160 depression-free workers will be recruited and randomly assigned to the intervention group (e-predictD-Work) or active control. The e-predictD-Work intervention is self-guided, has a biopsychosocial approach and is multi-component (9 modules: physical exercise, improving sleep, expanding relationships, problem solving, improving communication, assertiveness, decision-making, managing thoughts and reducing work stress). The e-predictD-Work intervention pivots on an already validated risk algorithm and a DSS that helps workers develop their own personalised depression prevention plans, which the patient will implement and the system will monitor by offering feedback. It will be implemented on the worker’s smartphone by means of an APP. The main result will be the cumulative incidence of depression most measured by CIDI and as secondary results the reduction of depressive (PHQ-9) and anxious symptoms (GAD-7), the risk of depression (predictive risk algorithm), quality of life (SF-12 and EuroQol) and cost-effectiveness and cost-utility will be evaluated. (English) | |||||||||||||||
| Property / summary: Objective: Design, develop and evaluate a personalised intervention to prevent depression in the workplace, based on information and communication technologies, predictive risk algorithms and decision support systems (DSS) for employees. Methods: We will conduct a double-blind, randomised controlled trial with two parallel branches and a year of follow-up. The trial will be conducted in 7 provinces of 6 autonomous communities. 3,160 depression-free workers will be recruited and randomly assigned to the intervention group (e-predictD-Work) or active control. The e-predictD-Work intervention is self-guided, has a biopsychosocial approach and is multi-component (9 modules: physical exercise, improving sleep, expanding relationships, problem solving, improving communication, assertiveness, decision-making, managing thoughts and reducing work stress). The e-predictD-Work intervention pivots on an already validated risk algorithm and a DSS that helps workers develop their own personalised depression prevention plans, which the patient will implement and the system will monitor by offering feedback. It will be implemented on the worker’s smartphone by means of an APP. The main result will be the cumulative incidence of depression most measured by CIDI and as secondary results the reduction of depressive (PHQ-9) and anxious symptoms (GAD-7), the risk of depression (predictive risk algorithm), quality of life (SF-12 and EuroQol) and cost-effectiveness and cost-utility will be evaluated. (English) / rank | |||||||||||||||
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| Property / summary: Objective: Design, develop and evaluate a personalised intervention to prevent depression in the workplace, based on information and communication technologies, predictive risk algorithms and decision support systems (DSS) for employees. Methods: We will conduct a double-blind, randomised controlled trial with two parallel branches and a year of follow-up. The trial will be conducted in 7 provinces of 6 autonomous communities. 3,160 depression-free workers will be recruited and randomly assigned to the intervention group (e-predictD-Work) or active control. The e-predictD-Work intervention is self-guided, has a biopsychosocial approach and is multi-component (9 modules: physical exercise, improving sleep, expanding relationships, problem solving, improving communication, assertiveness, decision-making, managing thoughts and reducing work stress). The e-predictD-Work intervention pivots on an already validated risk algorithm and a DSS that helps workers develop their own personalised depression prevention plans, which the patient will implement and the system will monitor by offering feedback. It will be implemented on the worker’s smartphone by means of an APP. The main result will be the cumulative incidence of depression most measured by CIDI and as secondary results the reduction of depressive (PHQ-9) and anxious symptoms (GAD-7), the risk of depression (predictive risk algorithm), quality of life (SF-12 and EuroQol) and cost-effectiveness and cost-utility will be evaluated. (English) / qualifier | |||||||||||||||
point in time: 12 October 2021
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Revision as of 15:06, 12 October 2021
Project Q3147160 in Spain
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | No label defined |
Project Q3147160 in Spain |
Statements
10,600.0 Euro
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21,200.0 Euro
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50.0 percent
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1 January 2019
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31 March 2022
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INSTITUTO DE INVESTIGACION SANITARIA ARAGON
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41°39'7.67"N, 0°52'51.38"W
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50297
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Objetivo: Diseñar, desarrollar y evaluar una intervención personalizada para prevenir la depresión en el ámbito laboral, basada en tecnologías de la información y comunicación, algoritmos de riesgo predictivos y sistemas de apoyo a las decisiones (DSS) para los trabajadores empleados. Métodos: Llevaremos a cabo un ensayo aleatorio controlado, doble ciego, con dos ramas paralelas y un año de seguimiento. El ensayo será conducido en 7 provincias de 6 comunidades autónomas. Se reclutarán 3.160 trabajadores libres de depresión que serán asignados aleatoriamente al grupo de intervención (e-predictD-Work) o al control activo. La intervención e-predictD-Work es auto-guiada, tiene un enfoque biopsicosocial y es multi-componente (9 módulos: ejercicio físico, mejorar el sueño, ampliar relaciones, resolución de problemas, mejorar la comunicación, asertividad, toma de decisiones, manejar pensamientos y reducir el estrés laboral). La intervención e-predictD-Work pivota sobre un algoritmo de riesgo ya validado y un DSS que ayuda a los trabajadores a elaborar sus propios planes personalizados de prevención de la depresión, que el paciente implementará y el sistema monitorizará ofreciendo feedback. Se implementará en el Smartphone del trabajador mediante una APP. El resultado principal será la incidencia acumulada de depresión mayor medida por el CIDI y como resultados secundarios se evaluarán la reducción de los síntomas depresivos (PHQ-9) y ansiosos (GAD-7), del riesgo de depresión (algoritmo de riesgo predictD), calidad de vida (SF-12 y EuroQol) y el coste-efectividad y coste-utilidad. (Spanish)
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Objective: Design, develop and evaluate a personalised intervention to prevent depression in the workplace, based on information and communication technologies, predictive risk algorithms and decision support systems (DSS) for employees. Methods: We will conduct a double-blind, randomised controlled trial with two parallel branches and a year of follow-up. The trial will be conducted in 7 provinces of 6 autonomous communities. 3,160 depression-free workers will be recruited and randomly assigned to the intervention group (e-predictD-Work) or active control. The e-predictD-Work intervention is self-guided, has a biopsychosocial approach and is multi-component (9 modules: physical exercise, improving sleep, expanding relationships, problem solving, improving communication, assertiveness, decision-making, managing thoughts and reducing work stress). The e-predictD-Work intervention pivots on an already validated risk algorithm and a DSS that helps workers develop their own personalised depression prevention plans, which the patient will implement and the system will monitor by offering feedback. It will be implemented on the worker’s smartphone by means of an APP. The main result will be the cumulative incidence of depression most measured by CIDI and as secondary results the reduction of depressive (PHQ-9) and anxious symptoms (GAD-7), the risk of depression (predictive risk algorithm), quality of life (SF-12 and EuroQol) and cost-effectiveness and cost-utility will be evaluated. (English)
12 October 2021
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Zaragoza
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Identifiers
PI18_01653
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