Prospective Acceptability of Digital Therapy for Major Depressive Disorder in France: Multicentric Real-Life Study
Notice bibliographique
Résumé
BACKGROUND: Major depressive disorder is one of the leading causes of disability worldwide. Although most international guidelines recommend psychological and psychosocial interventions as first-line treatment for mild to moderate depression, access remains limited in France due to the limited availability of trained clinicians, high costs for patients in the context of nonreimbursement, and the fear of stigmatization. Therefore, online blended psychological treatment such as Deprexis could improve access to care for people with depression. It has several advantages, such as easy accessibility and scalability, and it is supported by evidence. OBJECTIVE: This study aims to evaluate the real-life acceptability of Deprexis for people with depression in France outside of a reimbursement pathway. METHODS: Deprexis Acceptability Study Measure in Real Life (DARE) was designed as a multicenter cross-sectional study in which Deprexis was offered to any patient meeting the inclusion criteria during the fixed inclusion period (June 2022-March 2023). Inclusion criteria were (1) depression, (2) age between 18 and 65 years, (3) sufficient French language skills, and (4) access to the internet with a device to connect to the Deprexis platform. Exclusion criteria were previous or current diagnoses of bipolar disorder, psychotic symptoms, and suicidal thoughts during the current episode. The primary objective was to measure the prospective acceptability of Deprexis, a new digital therapy. Secondary objectives were to examine differences in acceptability according to patient and clinician characteristics and to identify reasons for refusal. All investigators received video-based training on Deprexis before enrollment to ensure that they all had the same level of information and understanding of the program. RESULTS: A total of 245 patients were eligible (n=159, 64.9% were women and n=138, 56.3% were single). The mean age was 40.7 (SD 14.1) years. A total of 78% (n=191) of the patients had moderate to severe depression (according to the Patient Health Questionnaire-9 [PHQ-9]). More than half of the population had another psychiatric comorbidity (excluding bipolar disorder, psychotic disorders, and suicidal ideation). A total of 33.9% (n=83) of patients accepted the idea of using Deprexis; the main reason for refusal was financial at 83.3% (n=135). Multivariate logistic regression identified factors that might favor the acceptability of Deprexis. Among these, being a couple, being treated with an antidepressant, or having a low severity level favored the acceptance of Deprexis. CONCLUSIONS: DARE is the first French study aiming at evaluating the prospective acceptability of digital therapy in the treatment of depression. The main reason for the refusal of Deprexis was financial. DARE will allow better identification of factors influencing acceptability in a natural setting. This study highlights the importance of investigating factors that may be associated with the acceptability of digital interventions, such as marital status, medication use, and severity of depression.
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 | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».