The Real-World Usability, Feasibility, and Performance Distributions of Deploying a Digital Toolbox of Computerized Assessments to Remotely Evaluate Brain Health: Development and Usability Study
Notice bibliographique
Résumé
BACKGROUND: An ongoing global challenge is managing brain health and understanding how performance changes across the lifespan. OBJECTIVE: We developed and deployed a set of self-administrable, computerized assessments designed to measure key indexes of brain health across the visual and auditory sensory modalities. In this pilot study, we evaluated the usability, feasibility, and performance distributions of the assessments in a home-based, real-world setting without supervision. METHODS: Potential participants were untrained users who self-registered on an existing brain training app called BrainHQ. Participants were contacted via a recruitment email and registered remotely to complete a demographics questionnaire and 29 unique assessments on their personal devices. We examined participant engagement, descriptive and psychometric properties of the assessments, associations between performance and self-reported demographic variables, cognitive profiles, and factor loadings. RESULTS: Of the 365,782 potential participants contacted via a recruitment email, 414 (0.11%) registered, of whom 367 (88.6%) completed at least one assessment and 104 (25.1%) completed all 29 assessments. Registered participants were, on average, aged 63.6 (SD 14.8; range 13-107) years, mostly female (265/414, 64%), educated (329/414, 79.5% with a degree), and White (349/414, 84.3% White and 48/414, 11.6% people of color). A total of 72% (21/29) of the assessments showed no ceiling or floor effects or had easily modifiable score bounds to eliminate these effects. When correlating performance with self-reported demographic variables, 72% (21/29) of the assessments were sensitive to age, 72% (21/29) of the assessments were insensitive to gender, 93% (27/29) of the assessments were insensitive to race and ethnicity, and 93% (27/29) of the assessments were insensitive to education-based differences. Assessments were brief, with a mean duration of 3 (SD 1.0) minutes per task. The pattern of performance across the assessments revealed distinctive cognitive profiles and loaded onto 4 independent factors. CONCLUSIONS: The assessments were both usable and feasible and warrant a full normative study. A digital toolbox of scalable and self-administrable assessments that can evaluate brain health at a glance (and longitudinally) may lead to novel future applications across clinical trials, diagnostics, and performance optimization.
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 | 0,005 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».