MétaCan
Menu
Back to cohort
Record 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 on OpenAlexvenueno aff
Rüdiger Zwerenz, Jan Becker, Robert Johansson, Ronald J. Frederick, Gerhard Andersson, Manfred E. Beutel

Bibliographic record

VenueJMIR Mental Health · 2017
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionRandomized controlled trialMental healthAnxietyDescriptive statisticsMedicineCompetence (human resources)Clinical psychologyRepeated measures designPsychologyPsychiatry

Abstract

fetched live from 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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.433
Teacher spread0.402 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations50
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Mental HealthSame topicDigital Mental Health InterventionsFrench-language works237,207