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Enregistrement W2766774404 · doi:10.2196/mental.7889

Transdiagnostic, Psychodynamic Web-Based Self-Help Intervention Following Inpatient Psychotherapy: Results of a Feasibility Study and Randomized Controlled Trial

2017· article· en· W2766774404 sur OpenAlexvenueno aff
Rüdiger Zwerenz, Jan Becker, Robert Johansson, Ronald J. Frederick, Gerhard Andersson, Manfred E. Beutel

Notice bibliographique

RevueJMIR Mental Health · 2017
Typearticle
Langueen
DomainePsychology
ThématiqueDigital Mental Health Interventions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychological interventionRandomized controlled trialMental healthAnxietyDescriptive statisticsMedicineCompetence (human resources)Clinical psychologyRepeated measures designPsychologyPsychiatry

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Mental disorders have become a major health issue, and a substantial number of afflicted individuals do not get appropriate treatment. Web-based interventions are promising supplementary tools for improving health care for patients with mental disorders, as they can be delivered at low costs and used independently of time and location. Although psychodynamic treatments are used frequently in the face-to-face setting, there has been a paucity of studies on psychodynamic Web-based self-help interventions. OBJECTIVE: The objective of this study was to determine the feasibility and preliminary efficacy of a transdiagnostic affect-focused psychodynamic Web-based self-help intervention designed to increase emotional competence of patients with mental disorders. METHODS: A total of 82 psychotherapy inpatients with mixed diagnoses were randomized into two groups. Following discharge, the intervention group (IG) got access to a guided version of the intervention for 10 weeks. After a waiting period of 10 weeks, the wait-list control group (WLCG) got access to an unguided version of the intervention. We reported the assessments at the beginning (T0) and at the end of the intervention, resp. the waiting period (T1). The primary outcome was satisfaction with the treatment at T1. Secondary outcome measures included emotional competence, depression, anxiety, and quality of life. Statistical analyses were performed with descriptive statistics (primary outcome) and analysis of covariance; a repeated measurement analysis of variance was used for the secondary outcomes. Effect sizes were calculated using Cohen d and data were analyzed as per protocol, as well as intention-to-treat (ITT). RESULTS: Patients were chronically ill, diagnosed with multiple diagnoses, most frequently with depression (84%, 58/69), anxiety (68%, 47/69), personality disorder (38%, 26/69), and depersonalization-derealization disorder (22%, 15/69). A majority of the patients (86%, 36/42) logged into the program, of which 86% (31/36) completed the first unit. Satisfaction with the units mastered was rated as good (52%, 16/31) and very good (26%, 9/31). However, there was a steady decline of participation over the course of the program; only 36% of the participants (13/36) participated throughout the trial completing at least 50% of the sessions. According to the ITT analysis, participants improved statistically significantly and with moderate effect sizes (Cohen d) compared with the WLCG regarding depression (d=0.60), quality of life (d=0.53), and emotional competence (d=0.49). Effects were considerably stronger for the completers with respect to depression (d=1.33), quality of life (d=0.83), emotional competence (d=0.68), and general anxiety (d=0.62). CONCLUSIONS: Although overall program satisfaction and benefit of the program were favorable with respect to the indicators of emotional disorders, the rate of completion was low. Our findings point to the need to target the intervention more specifically to the needs and capabilities of participants and to the context of the intervention. TRIAL REGISTRATION: Clinicaltrials.gov NCT02671929; https://clinicaltrials.gov/ct2/show/NCT02671929 (Archived by WebCite at http://www.webcitation.org/6ntWg1yWb).

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai randomisé · Signal consensuel: Essai randomisé
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,040
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,031
Tête enseignante GPT0,433
Écart entre enseignants0,402 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeEssai randomisé
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations50
Publié2017
Routes d'admission1
Résumé présentoui

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