Efficacy and effectiveness
Intervention studies seek to conduct efficacy trials which are examining the performance of an intervention under ideal and controlled circumstances, whereas effectiveness trials generally refer to an examination of the interventions performance under ‘real-world' conditions. The range includes methods that are experimental and quasi-experimental, open trialling and feasibility and acceptability studies.
Qualitative experiences of service users and supporters
The theme seeks to understand more fully the psychological experiences of users as they progress through an internet-delivered intervention. It seeks to employ methods such as significant events research and qualitative semi-structured interviews to examine the experience of users and clinicians/ supporters of Internet-delivered interventions.
Patients with chronic physical conditions, such as diabetes, heart disease, cancer, etc., have associated mental health symptoms. This should not be overlooked, rather, the comorbidity between mental and physical health should be taken into account to improve management of patients in the short and long term. Therefore, this area will focus precisely on research that examines the effect of long-term conditions on mental health as its objective.
Cultural adaptation and personalisation
Cultural adaptation of interventions employs a method that seeks to systematically review and revise intervention components based on and also informed from the literature on cross-cultural adaptation in psychotherapy and cross-cultural principles.
Implementation science & practice
How interventions are implemented is a key consideration in their success. Developing a greater understanding of the science and practice of implementation will advance the field.
Methodologies and designs
Several distinct trial designs and methodologies have been employed to test the effectiveness of e-mental health solutions. Pilot studies, feasibility studies, and RCTs all are relevant in testing new platforms and ideas. Furthermore, the methods are vital to assuring data gathered is scrutinised and reported appropriately. We aim to use a wide array of methodologies to test e-mental health solutions and strengthen the results.
Natural data analytics and data mining, performance and learning
Predictive analytics and data mining using Machine Learning algorithms to discover knowledge and find the best solutions. Data mining is a process based on algorithms to analyze and extract useful information and automatically discover hidden patterns and relationships from data. We seek to build and execute on algorithms to detect significant patterns in our data and make meaningful conclusions and recommendations from that data to inform development and service delivery.
Device integration and experimentation
Devices that can be integrated with technology platforms for mental health automatically collect and integrate patients' vital signs data generated from various devices to inform a needs assessment, delivery of accurate content and support as patients progress.