Prospective Acceptability of Digital Therapy for Major Depressive Disorder in France: Multicentric Real-Life Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".