Economic evaluations of eHealth interventions targeting mental health problems in the workplace: a systematic review.

Background: Work-related mental health problems impose significant economic and personal burdens. eHealth interventions may offer low-cost, practical solutions, but guidance on their cost-effectiveness in workplace mental health is limited.

Objective: The objective of this study was to systematically review economic evaluations of workplace eHealth interventions for mental health, offering insights into methodologies and cost-effectiveness outcomes.

Design and development of a digital intervention for workplace stress and mental health (EMPOWER).

Purpose: We describe the design and development of the European Platform to Promote health and wellbeing in the workplace (EMPOWER) digital intervention that provides an integrative user programme meeting the needs of employees and employers in addressing work stress.

Results: A user-centred design process was followed from January 2020 until November 2021. A tailored algorithm was developed to provide support at the individual employee level and the company level.

Study protocol of EMPOWER: A cluster randomized trial of a multimodal eHealth intervention for promoting mental health in the workplace following a stepped wedge trial design.

This article describes the EMPOWER study, a controlled trial aiming to determine the effectiveness and cost-effectiveness of an eHealth intervention to prevent common health problems and reduce presenteeism and absenteeism in the workplace. The EMPOWER intervention spans universal, secondary and tertiary prevention and consists of an eHealth platform delivered via a website and a smartphone app designed to guide employees throughout different modules according to their specific profiles. A stepped-wedge cluster randomized trial will be implemented in four countries (Finland, Poland, Spain and UK) with employees from small and medium enterprises (SMEs) and public agencies.

Psychometric evaluation of the Mental Health Quality of Life (MHQoL) instrument in seven European countries.

Introduction: To make efficient use of available resources, decision-makers in healthcare may assess the costs and (health) benefits of health interventions. For interventions aimed at improving mental health capturing the full health benefits is an important challenge. The Mental Health Quality of Life (MHQoL) instrument was recently developed to meet this challenge.

Estimating an anchored utility tariff for the well-being of older people measure (WOOP) for the Netherlands.

Objective: Health economic evaluations using common health-related quality of life measures may fall short in adequately incorporating all relevant benefits of health and social care interventions targeted at older people. The Well-being of Older People measure (WOOP) is a broader well-being measure that comprises nine well-being domains. The objective of this study was to estimate a utility tariff for the WOOP, to facilitate its application in cost-utility analyses.

Development and psychometric evaluation of the Decision Tool Anxiety Disorders, OCD and PTSD (DTAOP): Facilitating the early detection of patients in need of highly specialized care.

Background: Early identification of patients with an anxiety disorder, obsessive-compulsive disorder (OCD), or post-traumatic stress disorder (PTSD) in need of highly specialized care could facilitate the selection of the optimal initial treatment in these patients. This paper describes the development and psychometric evaluation of the Decision Tool Anxiety Disorders, OCD and PTSD (DTAOP), which aims to aid clinicians in the early identification of patients with an anxiety disorder, OCD, or PTSD in need of highly specialized mental healthcare.

Methods: A systematic literature review and a concept mapping procedure were carried out to inform the development of the DTAOP.

Development and psychometric evaluation of the Transdiagnostic Decision Tool: matched care for patients with a mental disorder in need of highly specialised care.

Background: Early identification of patients with mental health problems in need of highly specialised care could enhance the timely provision of appropriate care and improve the clinical and cost-effectiveness of treatment strategies. Recent research on the development and psychometric evaluation of diagnosis-specific decision-support algorithms suggested that the treatment allocation of patients to highly specialised mental healthcare settings may be guided by a core set of transdiagnostic patient factors.

Aims: To develop and psychometrically evaluate a transdiagnostic decision tool to facilitate the uniform assessment of highly specialised mental healthcare need in heterogeneous patient groups.

The decision tool unipolar depression (DTUD): a new measure to facilitate the early identification of patients with major depressive disorder in need of highly specialized care.

Background: Selection of the optimal initial treatment in patients with major depressive disorder (MDD) in need of highly specialized care has the potential to benefit treatment outcomes and cost-effectiveness of treatment strategies. However, to date, there is a paucity of measures that could guide the selection of the initial treatment, in particular to indicate which patients with MDD are in need of highly specialized care. Recognizing this gap, this paper reports on the development and psychometric evaluation of the Decision Tool Unipolar Depression (DTUD), aimed to facilitate the early identification of patients with MDD in need of highly specialized care.

Indicators of patients with major depressive disorder in need of highly specialized care: A systematic review.

Objectives: Early identification of patients with major depressive disorder (MDD) that cannot be managed by secondary mental health services and who require highly specialized mental healthcare could enhance need-based patient stratification. This, in turn, may reduce the number of treatment steps needed to achieve and sustain an adequate treatment response. The development of a valid tool to identify patients with MDD in need of highly specialized care is hampered by the lack of a comprehensive understanding of indicators that distinguish patients with and without a need for highly specialized MDD care.