1. Just-in-Time Adaptive Interventions for Depression
- Author
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Rabia Kaya, Veysel Kaplan, Filiz Solmaz, Yasemin Yılmaz, and Mehmet Emin Düken
- Subjects
just-in-time adaptive intervention ,depression ,internet-based intervention ,machine learning ,Psychiatry ,RC435-571 - Abstract
Mental problems are disorders whose incidence is increasing with the effect of the global crises experienced in the world today and which significantly reduce the functionality of the individual. Depression draws attention as the most common mental problem. An average of two-thirds of individuals diagnosed with depression cannot receive treatment due to treatment cost, transportation, stigma, lack of information, low perceived need for treatment, and barriers to seeking mental health help.Internet-based interventions can offer highly effective and advantageous suggestions to overcome the disadvantages created by these barriers. As an internet-based intervention, Just-in-Time Adaptive Interventions (JITAIs) is an intervention design that aims to provide the right type and intensity of support at the right time by adapting to the changing internal and contextual situation of the individual. This intervention has emerged from the need to use mobile health in general, to address situations of vulnerability for adverse health outcomes, and to take advantage of rapid, unexpected, ecologically emerging situations of opportunity. In general, the mechanisms of JITAIs include 6 key elements: vulnerability/opportunity situation, distal outcome, proximal outcomes, decision points, intervention options, adaptation of variables and decision rules. Considering the potential rise of depression, especially in relation to new global events (e.g., pandemics and economic downturns), this application, which can be considered as a scalable, fully automated self-administered biopsychosocial transdiagnostic digital intervention, can provide widespread benefits. In this study, we focus on the working principles and advantages of JITAIs in general.
- Published
- 2024
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