Effectiveness of a Web- and Mobile-Guided Psychological Intervention for Depressive Symptoms in Turkey: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: In Turkey, there are serious deficiencies in mental health care. Although depression is highly prevalent, only a small number of people seek professional help. Innovative solutions are needed to overcome this treatment gap. Web-based problem-solving therapy (PST) is an intervention proven to be effective in the treatment of depression, although little is known about its clinical effects in Turkey. OBJECTIVE: This study aims to test the clinical effects of a Web and mobile app of an adapted PST for depressive symptoms among the general population in Turkey. METHODS: Participants will be recruited through announcements in social media and the Middle East Technical University. Adults (18-55 years) with mild to moderate depressive symptoms (Beck Depression Inventory-II [BDI-II] score between 10-29) will be included in the study. Participants with a medium-to-high suicidal risk (according to the Mini-International Neuropsychiatric Interview) will be excluded. A 3-armed randomized controlled trial with a waiting control group will be utilized. A sample size of 444 participants will be randomized across 3 groups. The first experimental group will receive direct access to the Web version of the intervention; the second experimental group will receive direct access to the mobile app of the intervention as well as automated supportive short message service text messages based on PST. The control group consists of a wait-list and will gain access to the intervention 4 months after the baseline. The intervention is based on an existing PST for the Turkish population, Her Şey Kontrol Altında (HŞKA), consisting of 5 modules each with a duration of 1 week and is guided by a clinical psychologist. The primary outcome is change in depressive symptoms measured by the BDI-II. Secondary outcomes include symptoms of anxiety, stress, worry, self-efficacy, and quality of life. Furthermore, satisfaction with, usability and acceptability of the intervention are important features that will be evaluated. All outcomes will take place online through self-assessment at posttest (6-8 weeks after baseline) and at follow-up (4 months after baseline). RESULTS: We will recruit a total of 444 participants with mild to moderate depressive symptoms from March 2018 to February 2019 or until the recruitment is complete. We expect the final trial results to be available by the end of May 2019. This trial is funded by the Scientific and Technological Research Council of Turkey (National Postdoctoral Research Fellowship Programme 2016/1). CONCLUSIONS: Results from this study will reveal more information about the clinical effects of HŞKA as well as its applicability in a Turkish setting through the Web and mobile platforms. On the basis of the results, a guided Web- and mobile-based PST intervention might become an appropriate alternative for treating mild to moderate depressive symptoms. TRIAL REGISTRATION: ClinicalTrials.gov NCT03754829; https://clinicaltrials.gov/ct2/show/NCT03754829 (Archived by WebCite at http://www.webcitation.org/74HugwLo7). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13239.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,014 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,063 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».