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Depresyonda Tam Zamanında Uyarlanabilir Müdahaleler

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

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

-

Project Number

-

Thanks

-

References

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  • Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N (2010) Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: a meta-analysis. PLoS One, 5:e13196.
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  • Bardram JE, Frost M, Szántó K, Marcu G (2012) The MONARCA self-assessment system. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, 21–30.
  • Bennabi D, Vandel P, Papaxanthis C, Pozzo T, Haffen E (2013) Psychomotor retardation in depression: a systematic review of diagnostic, pathophysiologic, and therapeutic implications. BioMed Res Int, 2013:158746.
  • Ben-Zeev D, McHugo GJ, Xie H, Dobbins K, Young MA (2012) Comparing retrospective reports to real-time/real-place mobile assessments in individuals with schizophrenia and a nonclinical comparison group. Schizophr Bull, 38:396–404.
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  • González Valero G, Zurita Ortega F, Ubago Jiménez JL, Puertas Molero P (2019) Use of meditation and cognitive behavioral therapies for the treatment of stress, depression and anxiety in students. A systematic review and meta-analysis. Int J Environ Res Public Health, 16:4394.
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  • ICT4Depression Consortium (2013) ICT4Depression pilot in Sweden. Technical report. https://www.ict4depression.eu/wp/wp-content/uploads/2013/10/Results-Swedish-pilot.pdf (Accessed 04.11.2023).
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  • Klein B, Nguyen H, McLaren S, Andrews B, Shandley K (2023) A fully automated self-help biopsychosocial transdiagnostic digital ıntervention to reduce anxiety and/or depression and ımprove emotional regulation and well-being: pre–follow-up single-arm feasibility trial. JMIR Form Res, 7:e43385.
  • Kürümlüoğlugil R, Tanrıverdi D (2022) The effects of the psychoeducation on cognitive distortions, negative automatic thoughts and dysfunctional attitudes of patients diagnosed with depression. Psychol Health Med, 27:2085–2095.
  • Liao P, Klasnja P, Tewari A, Murphy SA (2016) Sample size calculations for micro-randomized trials in mHealth. Stat Med, 35:1944–1971.
  • Lobbestael J, Leurgans M, Arntz A (2011) Inter-rater reliability of the structured clinical ınterview for DSM-IV axis I disorders (SCID I) and axis II disorders (SCID II). Clin Psychol Psychother, 18:75–79.
  • Lopes RT, da Rocha GC, Svacina MA, Meyer B, Šipka D, Berger T (2023) Effectiveness of an ınternet-based self-guided program to treat depression in a sample of brazilian users: randomized controlled trial. JMIR Form Res, 7:e46326.
  • Mason MJ, Coatsworth JD, Zaharakis N, Russell M, Brown A, McKinstry S (2023) Testing mechanisms of change for text message–delivered cognitive behavioral therapy: randomized clinical trial for young adult depression. JMIR Mhealth Uhealth, 11:e45186.
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  • Osipov M (2019) Towards automated symptoms assessment in mental health (PhD thesis). Oxford, University of Oxford.
  • Paradiso R, Bianchi AM, Lau K, Scilingo EP (2010) PSYCHE: Personalised monitoring systems for care in mental health. Annu Int Conf IEEE Eng Med Biol Soc, 2010:3602–3605.
  • Ponzo S, Morelli D, Kawadler JM, Hemmings NR, Bird G, Plans D (2020) Efficacy of the digital therapeutic mobile app biobase to reduce stress and ımprove mental well-being among university students: randomized controlled trial. JMIR mHealth and uHealth, 8:e17767.
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Just-in-Time Adaptive Interventions for Depression

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

Rabia Kaya Veysel Kaplan Filiz Solmaz Yasemin Yılmaz Mehmet Emin Düken

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

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

-

References

  • Adams MA, Sallis JF, Norman GJ, Hovell MF, Hekler EB, Perata E (2013) An adaptive physical activity intervention for overweight adults: a randomized controlled trial. PLoS One, 8:e82901.
  • Aguilera A, Hernandez Ramos R, Haro Ramos AY, Boone CE, Luo TC, Xu J et al. (2021) A Text messaging ıntervention (staywell at home) to counteract depression and anxiety during covıd-19 social distancing: pre-post study. JMIR Mental Health, 8:e25298.
  • Andrade LH, Alonso J, Mneimneh Z, Wells JE, Al-Hamzawi A, Borges G et al. (2014) Barriers to mental health treatment: results from the WHO world mental health surveys. Psychol Med, 44:1303–1317.
  • Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N (2010) Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: a meta-analysis. PLoS One, 5:e13196.
  • Barak A, Klein B, Proudfoot JG (2009) Defining internet-supported therapeutic interventions. Ann Behav Med, 38:4–17.
  • Bardram JE, Frost M, Szántó K, Marcu G (2012) The MONARCA self-assessment system. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, 21–30.
  • Bennabi D, Vandel P, Papaxanthis C, Pozzo T, Haffen E (2013) Psychomotor retardation in depression: a systematic review of diagnostic, pathophysiologic, and therapeutic implications. BioMed Res Int, 2013:158746.
  • Ben-Zeev D, McHugo GJ, Xie H, Dobbins K, Young MA (2012) Comparing retrospective reports to real-time/real-place mobile assessments in individuals with schizophrenia and a nonclinical comparison group. Schizophr Bull, 38:396–404.
  • Bostan S, Sur H, Erdem R (2022) Dijital sağlık ve kişiselleştirilmiş tıp. 7.Uluslararası Sağlık Bilimi ve Yönetimi Kongresi, Üsküdar Üniversitesi, İstanbul.
  • Bögemann SA, Riepenhausen A, Puhlmann LMC, Bar S, Hermsen EJC, Mituniewicz J et al. (2023) Investigating two mobile just-in-time adaptive interventions to foster psychological resilience: research protocol of the DynaM-INT study. BMC Psychol, 11:245.
  • Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N et al. (2020) The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet, 395:912–920.
  • Burns D (2015) İyi Hissetmek- Yeni Duygudurum Tedavisi (Çev. Ed. HA Karaosmanoğlu, 19th ed.). İstanbul, Psikonet.
  • Caligiuri MP, Ellwanger J (2000) Motor and cognitive aspects of motor retardation in depression. J Affect Disord, 57:83–93.
  • Carlozzi NE, Sander AM, Choi SW, Wu Z, Miner JA, Lyden AK et al. (2022) Improving outcomes for care partners of persons with traumatic brain injury: protocol for a randomized control trial of a just-in-time-adaptive self-management intervention. PloS One, 17:e0268726.
  • Cinemre B, Coskun MN, Topcuoğlu M, Erdoğan A (2021) Attitude of psychiatry clinic patients towards digital health applications with internet and smartphone usage. Kocaeli Medical Journal, 10:147–155.
  • Cohn MA, Pietrucha ME, Saslow LR, Hult JR, Moskowitz JT (2014) An online positive affect skills intervention reduces depression in adults with type 2 diabetes. J Posit Psychol, 9: 523–534.
  • Coppersmith DDL, Dempsey W, Kleiman EM, Bentley KH, Murphy SA, Nock MK (2022) Just-in-time adaptive ınterventions for suicide prevention: promise, challenges, and future directions. Psychiatry, 85:317–333.
  • Digital 2023 (2023) Global Overview Report: The Essential Guide to the World’s Connected Behaviours. https://wearesocial.com/wp-content/uploads/2023/03/Digital-2023-Global-Overview-Report.pdf (Accessed 12.02.2024).
  • Durdu Akgün B, Aktaç A, Yorulmaz O (2019) Ruh sağlığında mobil uygulamalar: etkinliğe yönelik sistematik bir gözden geçirme. Psikiyatride Güncel Yaklaşımlar, 11:519–530.
  • Economides M, Bolton H, Male R, Cavanagh K (2022) Feasibility and preliminary efficacy of web-based and mobile ınterventions for common mental health problems in working adults: multi-arm randomized pilot trial. JMIR Form Res, 6:e34032.
  • Emre İE, Taş C, Erol Ç (2021) Psikiyatride makine öğrenmesi yöntemlerinin kullanımı. Psikiyatride Güncel Yaklaşımlar, 13:332–353.
  • Gautam M, Tripathi A, Deshmukh D, Gaur M (2020) Cognitive behavioral therapy for depression. Indian J Psychiatry, 62(Suppl 2):S223-S229.
  • Gonul S, Namli T, Huisman S, Laleci Erturkmen GB, Toroslu IH, Cosar A (2019) An expandable approach for design and personalization of digital, just-in-time adaptive interventions. J Am Med Inform Assoc, 26:198–210.
  • González Valero G, Zurita Ortega F, Ubago Jiménez JL, Puertas Molero P (2019) Use of meditation and cognitive behavioral therapies for the treatment of stress, depression and anxiety in students. A systematic review and meta-analysis. Int J Environ Res Public Health, 16:4394.
  • Güler S, Keklik B (2022) Dijital sağlık kapsamında giyilebilir sağlık teknolojileri. 7.Uluslararası Sağlık Bilimi ve Yönetimi Kongresi, Üsküdar Üniversitesi, İstanbul.
  • Heron KE, Smyth JM (2010) Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol, 15:1–39.
  • ICT4Depression Consortium (2013) ICT4Depression pilot in Sweden. Technical report. https://www.ict4depression.eu/wp/wp-content/uploads/2013/10/Results-Swedish-pilot.pdf (Accessed 04.11.2023).
  • Işık A, Güler İ (2010) Teletıpta mobil uygulama çalışması ve mobil iletişim teknolojilerinin analizi. Bilişim Teknolojileri Dergisi, 3:1-10.
  • Kaplan V, Düken ME, Kaya R, Almazan J (2023) Investigating the effects of cognitive-behavioral-therapy-based psychoeducation program on university students’ automatic thoughts, perceived stress, and self-efficacy levels. Journal of Research & Health, 13:87–98.
  • Khademian F, Aslani A, Bastani P (2021) The effects of mobile apps on stress, anxiety, and depression: overview of systematic reviews. Int J Technol Assess Health Care, 37:e4.
  • Klein B, Nguyen H, McLaren S, Andrews B, Shandley K (2023) A fully automated self-help biopsychosocial transdiagnostic digital ıntervention to reduce anxiety and/or depression and ımprove emotional regulation and well-being: pre–follow-up single-arm feasibility trial. JMIR Form Res, 7:e43385.
  • Kürümlüoğlugil R, Tanrıverdi D (2022) The effects of the psychoeducation on cognitive distortions, negative automatic thoughts and dysfunctional attitudes of patients diagnosed with depression. Psychol Health Med, 27:2085–2095.
  • Liao P, Klasnja P, Tewari A, Murphy SA (2016) Sample size calculations for micro-randomized trials in mHealth. Stat Med, 35:1944–1971.
  • Lobbestael J, Leurgans M, Arntz A (2011) Inter-rater reliability of the structured clinical ınterview for DSM-IV axis I disorders (SCID I) and axis II disorders (SCID II). Clin Psychol Psychother, 18:75–79.
  • Lopes RT, da Rocha GC, Svacina MA, Meyer B, Šipka D, Berger T (2023) Effectiveness of an ınternet-based self-guided program to treat depression in a sample of brazilian users: randomized controlled trial. JMIR Form Res, 7:e46326.
  • Mason MJ, Coatsworth JD, Zaharakis N, Russell M, Brown A, McKinstry S (2023) Testing mechanisms of change for text message–delivered cognitive behavioral therapy: randomized clinical trial for young adult depression. JMIR Mhealth Uhealth, 11:e45186.
  • Moritz S, Schilling L, Hauschildt M, Schröder J, Treszl A (2012) A randomized controlled trial of internet-based therapy in depression. Behav Res Ther, 50:513–521.
  • Muro A, Feliu-Soler A, Castellà J (2021) Psychological impact of COVID-19 lockdowns among adult women: the predictive role of individual differences and lockdown duration. Women Health, 61:668–679.
  • Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz, K, Tewari A et al. (2018) Just-in-Time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med, 52:446–462.
  • Nock MK, Kleiman EM, Abraham M, Bentley KH, Brent DA, Buonopane RJ et al. (2021) Consensus statement on ethical & safety practices for conducting digital monitoring studies with people at risk of suicide and related behaviors. Psychiatr Res Clin Pract, 3:57–66.
  • Osipov M (2019) Towards automated symptoms assessment in mental health (PhD thesis). Oxford, University of Oxford.
  • Paradiso R, Bianchi AM, Lau K, Scilingo EP (2010) PSYCHE: Personalised monitoring systems for care in mental health. Annu Int Conf IEEE Eng Med Biol Soc, 2010:3602–3605.
  • Ponzo S, Morelli D, Kawadler JM, Hemmings NR, Bird G, Plans D (2020) Efficacy of the digital therapeutic mobile app biobase to reduce stress and ımprove mental well-being among university students: randomized controlled trial. JMIR mHealth and uHealth, 8:e17767.
  • Santomauro DF, Mantilla Herrera AM, Shadid J, Zheng P, Ashbaugh C, Pigott DM et al. (2021) Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet, 398:1700–1712.
  • Shatte ABR, Hutchinson DM, Teague SJ (2019) Machine learning in mental health: a scoping review of methods and applications. Psychol Med, 49:1426–1448.
  • Taylor RW, Male R, Economides M, Bolton H, Cavanagh K (2023) Feasibility and preliminary efficacy of digital ınterventions for depressive symptoms in working adults: multiarm randomized controlled trial. JMIR Form Res, 7:e41590.
  • Teepe GW Da Fonseca A, Kleim B, Jacobson NC, Salamanca Sanabria A, Tudor Car L et al. (2021) Just-in-Time adaptive mechanisms of popular mobile apps for individuals with depression: systematic app search and literature review. J Med Internet Res, 23: e29412.
  • Sağlık Bakanlığı (2020) Ulusal Ruh Sağlığı Eylem Planı 2020-2023. Ankara, TC Sağlık Bakanlığı.
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Details

Primary Language English
Subjects Psychiatry, Mental Health Nursing
Journal Section Review
Authors

Rabia Kaya 0000-0003-3875-9437

Veysel Kaplan 0000-0001-9082-1379

Filiz Solmaz 0000-0001-8695-7492

Yasemin Yılmaz 0000-0001-5618-3668

Mehmet Emin Düken 0000-0002-1902-9669

Project Number -
Publication Date
Submission Date December 20, 2023
Acceptance Date March 12, 2024
Published in Issue Year 2024 Volume: 16 Issue: 4

Cite

AMA Kaya R, Kaplan V, Solmaz F, Yılmaz Y, Düken ME. Just-in-Time Adaptive Interventions for Depression. Psikiyatride Güncel Yaklaşımlar - Current Approaches in Psychiatry. 16(4):585-594. doi:10.18863/pgy.1407401

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Psikiyatride Güncel Yaklaşımlar - Current Approaches in Psychiatry is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.