M103. Treating Motivation Deficits in Schizophrenia With a Virtual Reality Motivation Training Program
Notice bibliographique
Résumé
Background: Motivation deficits have emerged as a critical determinant of functional disability in schizophrenia. Effective therapeutic strategies for motivation deficits, however, remain elusive. This has ultimately hindered our ability to promote recovery for affected individuals. To address this unmet therapeutic need, this open-label pilot study investigated a novel virtual reality-based (VR) training strategy for the treatment of motivation deficits in schizophrenia over the course of 8 weeks of training, along with concomitant effects on community functioning and brain structure and function. Methods: Stable adult outpatients with schizophrenia between the ages of 18 and 35, with prominent motivation deficits were recruited for this study. Participants underwent baseline and post-treatment clinical assessments, evaluation of community functioning with the Quality of Life Scale (QLS), as well as structural (diffusion tensor imaging [DTI]) and functional MRI. Treatment consisted of 8 weeks of VR motivation training using a progressive effort task in a virtual environment for 1 hour per week. Motivation deficits were evaluated using the Apathy Evaluation Scale (AES) every 2 weeks. Our primary outcome of interest was change in AES scores over time, with secondary outcomes consisting of change in overall community functioning (QLS), and frontostriatal white matter microstructural integrity and functioning during motivated behavior. Results: To date, 8 participants have completed this study. Preliminary analyses revealed a significant reduction in AES scores as a result of treatment (F(4,28) = 5.025, P = .004), with on average a 13% reduction in AES score. Participants also exhibited a mean improvement of 29% in QLS score, with 63% of the sample showing more than 20% improvement, although the difference for the overall sample was nonsignificant. Changes in DTI indices of frontostriatal white matter microstructural integrity, and brain function during motivated behavior, as a result of treatment will also be presented. Conclusion: This pilot study investigated the use of a novel VR-based training strategy to treat motivation deficits in young adults with schizophrenia. Preliminary findings suggest that this VR-based training resulted in a significant reduction in the severity of motivation deficits, along with a notable improvement in overall community functioning. With the ongoing search for effective treatments for motivation deficits and their functional consequences in schizophrenia, these findings provide an early promising signal for a potential novel treatment strategy for these deficits that may improve outcomes for patients.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».