Depresyonda Tam Zamanında Uyarlanabilir Müdahaleler

Year 2024, Volume: 16 Issue: 4, 585 - 594, 25.12.2024

Rabia Kaya , Veysel Kaplan , Filiz Solmaz , Yasemin Yılmaz , Mehmet Emin Düken

https://doi.org/10.18863/pgy.1407401

Abstract

Ruhsal sorunlar bugün, dünyada yaşanılan küresel krizlerinde etkisiyle görülme sıklığı artan ve bireyin işlevselliğini önemli ölçüde azaltan bozukluklardır. Depresyon ise en fazla görülen ruhsal sorun olarak dikkat çekmektedir. Depresyon tanılı bireylerin ortalama üçte ikisi tedavi maliyeti, ulaşım, damgalanma, bilgi eksikliği, düşük algılanan tedavi ihtiyacı ve ruhsal sağlık yardımı aramanın önündeki engeller nedeniyle tedavi edilememektedir. İnternet tabanlı müdahaleler bu engellerin yarattığı dezavantajları ortadan kaldırmak adına oldukça etkili ve avantajlı öneriler sunabilmektedir. Bir internet tabanlı müdahale olarak Tam Zamanında Uyarlanabilir Müdahaleler (TZUM) ise, bireyin değişen iç ve bağlamsal durumuna uyum sağlayarak doğru zamanda, doğru türde ve yoğunlukta destek sağlamayı amaçlayan bir müdahale tasarımıdır. Bu müdahale genel olarak mobil sağlığın kullanımı, olumsuz sağlık sonuçları için kırılganlık durumlarını ele alma ve hızlı, beklenmedik, ekolojik olarak ortaya çıkan fırsat durumlarından yararlanma ihtiyacından ortaya çıkmıştır. TZUM mekanizmaları genel olarak; kırılganlık/fırsat durumu, distal sonuç, proksimal sonuçlar, karar noktaları, müdahale seçenekleri, değişkenleri uyarlama ve karar kuralları şeklinde 6 temel unsur içermektedir. Özellikle yeni küresel olaylarla (örneğin, pandemi ve ekonomik gerileme) ilişkili olarak da depresyonun potansiyel yükselişi göz önüne alındığında, ölçeklenebilir, tam otomatik kendi kendine uygulanabilen biyopsikososyal transdiagnostik dijital müdahale olarak değerlendirilebilecek bu uygulanma, yaygın faydalar sağlayabilmektedir. Bu çalışmada da genel olarak TZUM’un çalışma prensiplerine ve avantajlarına odaklanılmaktadır.

Keywords

Tam Zamanında Uyarlanabilir Müdahale, Depresyon, İnternet Tabanlı Müdahale, Makine Öğrenimi

Ethical Statement

Çalışma için etik izin gerekli olmadığından alınmamıştır

Supporting Institution

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Project Number

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Thanks

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Just-in-Time Adaptive Interventions for Depression

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.

Keywords

Just-in-Time Adaptive Intervention, Depression, Internet-Based Intervention, Machine Learning

Project Number

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