Virtual Reality Attention Task: Effectiveness in Predicting ADHD and Establishing Validity in an Adult Sample
Notice bibliographique
Résumé
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder with symptomatic ADHD suggested to be prevalent within 6.76% an adult population (Song et al., 2021). Research has identified an association between ADHD in adults and maintaining attention, with a specific focus on impaired sustained attention (Avisar, 2022). Sustained attention refers to prolonged focus on a task, and is measured through computerised neuropsychological tasks, such as the Continuous Performance Test (CPT; Fortenbaugh et al., 2017; Conners et al., 2003). One of the paradigms represented by the CPT is assessed through inhibition tasks, in which non-target stimuli are detected and target stimuli are ignored (Servera & Cardo, 2006). The CPT is incorporated clinically in the assessment of adult ADHD by measuring reaction time (RT) of correct responses, variability of RT, omission and commission errors (Conners et al., 2003). Despite the CPT being highly established, it possesses low ecological validity. Thus, performance on this test may not accurately represent attention functioning in every day life. To overcome this limitation, Virtual Reality (VR) has been implemented within clinical research over recent decades with immersive properties increasing ecological validity and optimising therapeutic outcomes (Bhugra et al., 2017). Immersion refers to the technical aspects of the virtual environment that increase feelings of 'presence' (Wilkinson et al., 2021). Research has identified that immersion falls along two dimensions, VR presence and VR sickness, with higher levels of VR presence and lower levels of VR sickness increasing performance (Maneuvrier et al., 2020). Immersive VR environments have been researched within the assessment of psychiatric disorders, with findings suggesting that VR attention tasks can predict ADHD in adults, as well as other highly prevalent disorders such as depression and anxiety (Ohnishi et al., 2019; Voinescu et al., 2021). Due to high comorbidity rates between these disorders, it is important than the VR attention task is able to predict ADHD after controlling for both depression and anxiety. Moreover, research on VR and ADHD has a primary focus on children and adolescent samples, with findings identifying the clinical utility of VR within assessment (Parsons et al., 2019). The small body of research currently available with a focus on ADHD in adults and VR assessment has reported similar findings providing evidence for the need for further research with an adult sample (Areces et al., 2019). In light of this information, validation of a novel VR task is required to ensure the clinical utility of the environment in the assessment of sustained, selective, divided and alternating attention in adults. This will be achieved through comparing the VR attention task results to the CPT to establish convergent validity, and the Montreal Cognitive Assessment/Victoria Stroop Task to establish divergent validity. Despite the CPT assessing sustained attention only, all four attention types within the VR task require validation through correlation with the CPT outcomes. Additionally, this means all four attention types will be compared to the CPT to determine effectiveness in predicting ADHD.
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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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».