The Making and Evaluation of Digital Games Used for the Assessment of Attention: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Serious games are now widely used in many contexts, including psychological research and clinical use. One area of growing interest is that of cognitive assessment, which seeks to measure different cognitive functions such as memory, attention, and perception. Measuring these functions at both the population and individual levels can inform research and indicate health issues. Attention is an important function to assess, as an accurate measure of attention can help diagnose many common disorders, such as attention-deficit/hyperactivity disorder and dementia. However, using games to assess attention poses unique problems, as games inherently manipulate attention through elements such as sound effects, graphics, and rewards, and research on adding game elements to assessments (ie, gamification) has shown mixed results. The process for developing cognitive tasks is robust, with high psychometric standards that must be met before these tasks are used for assessment. Although games offer more diverse approaches for assessment, there is no standard for how they should be developed or evaluated. OBJECTIVE: To better understand the field and provide guidance to interdisciplinary researchers, we aim to answer the question: How are digital games used for the cognitive assessment of attention made and measured? METHODS: We searched several databases for papers that described a digital game used to assess attention that could be deployed remotely without specialized hardware. We used Rayyan, a systematic review software, to screen the records before conducting a systematic review. RESULTS: The initial database search returned 49,365 papers. Our screening process resulted in a total of 74 papers that used a digital game to measure cognitive functions related to attention. Across the studies in our review, we found three approaches to making assessment games: gamifying cognitive tasks, creating custom games based on theories of cognition, and exploring potential assessment properties of commercial games. With regard to measuring the assessment properties of these games (eg, how accurately they assess attention), we found three approaches: comparison to a traditional cognitive task, comparison to a clinical diagnosis, and comparison to knowledge of cognition; however, most studies in our review did not evaluate the game's properties (eg, if participants enjoyed the game). CONCLUSIONS: Our review provides an overview of how games used for the assessment of attention are developed and evaluated. We further identified three barriers to advancing the field: reliance on assumptions, lack of evaluation, and lack of integration and standardization. We then recommend the best practices to address these barriers. Our review can act as a resource to help guide the field toward more standardized approaches and rigorous evaluation required for the widespread adoption of assessment games.
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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 | 0,020 | 0,119 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,008 |
| Bibliométrie | 0,019 | 0,016 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 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 ».