iCogCA to Promote Cognitive Health Through Digital Group Interventions for Individuals Living With a Schizophrenia Spectrum Disorder: Protocol for a Nonrandomized Concurrent Controlled Trial (Preprint)
Notice bibliographique
Résumé
BACKGROUND Cognitive impairments are a key aspect of schizophrenia spectrum disorders (SSDs), significantly affecting clinical and functional outcomes. The COVID-19 pandemic has heightened concerns about mental health services and cognitive stimulation opportunities. Despite evidence-based interventions like action-based cognitive remediation (ABCR) and metacognitive training (MCT), a research-to-practice gap exists in their application across mental health settings. OBJECTIVE The iCogCA study aims to address this gap by implementing digital ABCR and MCT through a national Canadian collaborative effort using digital psychological interventions to enhance cognitive health in SSDs. METHODS The study involves 5 Canadian sites, with mental health care practitioners trained digitally through the E-Cog platform, which was developed by our research group. Over 2.5 years, participants with SSDs will undergo pre- and postintervention assessments for clinical symptoms, cognition, and functioning. Each site will run groups annually for both ABCR and MCT, totaling ~390 participants. A nonrandomized concurrent controlled design will assess effectiveness design, in which one intervention (eg, ABCR) acts as the active control for the other (eg, MCT) and vice versa, comparing cognitive and clinical outcomes between the interventions using generalized linear mixed effect modeling. Implementation strategy evaluation will consider the digital platform’s efficacy for mental health care practitioners’ training, contextual factors influencing implementation, and sustainability, using descriptive statistics for quantitative data and thematic analysis for qualitative data. RESULTS A pilot pragmatic trial has been conducted previously at the Montreal site, evaluating 3 early implementation outcomes: acceptability, feasibility, and engagement. Patient and therapist acceptability was deemed as high and feasible (21/28, 75% of recruited service users completed therapy, rated feasible by therapists). Technology did not appear to significantly impede program participation. Therapist-rated levels of engagement were also satisfactory. In the ongoing study, recruitment is underway (114 participants recruited as of winter 2024), and intervention groups have been conducted at all sites, with therapists receiving training via the E-Cog learning platform (32 enrolled as of winter 2024). CONCLUSIONS At least 3 significant innovations will stem from this project. First, this national effort represents a catalyst for the use of digital technologies to increase the adoption of evidence-based interventions and will provide important results on the effectiveness of digitally delivered ABCR and MCT. Second, the results of the implementation component of this study will generate the expertise needed to inform the implementation of similar initiatives. Third, the proposed study will introduce and validate our platform to train and supervise mental health care practitioners to deliver these interventions, which will then be made accessible to the broader mental health community. CLINICALTRIAL ClinicalTrials.gov NCT05661448; https://clinicaltrials.gov/study/NCT05661448 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/63269
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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 | 0,025 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,003 |
| Méta-épidémiologie (sens large) | 0,010 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,091 | 0,015 |
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 ».