Video Games and Gamification for Assessing Mild Cognitive Impairment: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Early assessment of mild cognitive impairment (MCI) in older adults is crucial, as it enables timely interventions and decision-making. In recent years, researchers have been exploring the potential of gamified interactive systems (GISs) to assess pathological cognitive decline. However, effective methods for integrating these systems and designing GISs that are both engaging and accurate in assessing cognitive decline are still under investigation. OBJECTIVE: We aimed to comprehensively investigate GISs used to assess MCI. Specifically, we reviewed the existing systems to understand the different game types (including genres and interaction paradigms) used for assessment. In addition, we examined the cognitive functions targeted. Finally, we investigated the evidence for the performance of assessing MCI through GISs by looking at the quality of validation for these systems in assessing MCI and the diagnostic performance reported. METHODS: We conducted a scoping search in IEEE Xplore, ACM Digital Library, and Scopus databases to identify interactive gamified systems developed for assessing MCI. Game types were categorized according to genres and interaction paradigms. The cognitive functions targeted by the systems were compared with those assessed in the Montreal Cognitive Assessment (MoCA). Finally, we examined the quality of validation against the reference standard (ground truth), relevance of controls, and sample size. Where provided, the diagnostic performance on sensitivity, specificity, and area under the curve was reported. RESULTS: A total of 81 articles covering 49 GISs were included in this review. The primary game types used for MCI assessment were classified as casual games (30/49, 61%), simulation games (17/49, 35%), full-body movement games (4/49, 8%), and dedicated interactive games (3/49, 6%). Of the 49 systems, 6 (12%) assessed cognitive functions comprehensively, compared to those functions assessed via the MoCA. Of the 49 systems, 14 (29%) had validation studies, with sensitivities ranging from 70.7% to 100% and specificities ranging from 56.5% to 100%. The reported diagnostic performances of GISs were comparable to those of common screening instruments, such as Mini-Mental State Examination and MoCA, with some systems reporting near-perfect performance (area under the curve>0.98). However, these findings often stemmed from small samples and retrospective designs. Moreover, some of these systems' model training and validation exhibited substantial deficiencies. CONCLUSIONS: This review provides a comprehensive summary of GISs for assessing MCI, exploring the cognitive functions assessed by these systems and evaluating their diagnostic performance. The results indicate that current GISs hold promise for the assessment of MCI, with several systems demonstrating diagnostic performance comparable to established screening tools. Nevertheless, despite some systems reporting impressive performance, there is a need for improvement in validation, particularly concerning sample size and methodological rigor. Future work should prioritize prospective validation and present greater methodological consistency.
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 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 | 0,006 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,015 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».