Domain-Specific Evaluation of Exergame Metrics Among Older Adults With Mild Neurocognitive Disorder: Secondary Analysis of 2 Randomized Controlled Trials
Notice bibliographique
Résumé
BACKGROUND: Exergame-based training enhances physical and cognitive performance in older adults, including those with mild neurocognitive disorder (mNCD). In-game metrics generated from user interactions with exergames enable individualized adjustments. However, there is a need to systematically investigate how well such game metrics capture true cognitive and motor-cognitive performance to provide a more robust basis for personalized training. OBJECTIVE: The primary objective was to identify valid game metrics as indicators for in-game domain-specific cognitive performance during exergaming in individuals with mNCD. We also aimed to explore game metric performance changes over time during exergame-based training. METHODS: Data were analyzed from individuals with mNCD who completed a 12-week home-based, exergame-based intervention following the Brain-IT training concept. A cross-sectional analysis was conducted by correlating game metrics with standardized neurocognitive reference assessments. To confirm the alternative hypothesis, we predetermined the following criteria: (1) statistically significant correlation (P≤.05; uncorrected; 1-sided) with (2) a correlation coefficient (Pearson r or Spearman ρ) of ≥0.4. Visual and curve-fitting longitudinal analyses were conducted to explore game performance changes over time. RESULTS: Data were available from 31 participants (mean age 76.4, SD 7.5 y; n=9, 29% female). In total, 33% (6/18) of the game metrics were identified as valid indicators for in-game cognitive performance during exergaming. In the neurocognitive domain of learning and memory, these metrics included the mean reaction time (ρ=-0.747), the number of collected items (ρ=0.691), and the precision score (r=-0.607) for the game Shopping Tour (P<.001 in all cases), as well as the point rate (P=.008; r=0.471) for the game Simon. In addition, point rate was a valid indicator for executive function (P=.006; r=0.455) and visuospatial skills (P=.02; r=0.474) for the games Targets and Gears, respectively. The exploratory longitudinal analysis revealed high interindividual variability, with a general trend of the expected typical curvilinear curves of rapid initial improvements followed by a plateau in performance. CONCLUSIONS: This study demonstrated that metrics reflecting the precision of responses generally performed better than metrics reflecting the speed of responses. These observations highlight the importance of selecting valid game metrics for implementation in exergame designs. Further research is needed to explore the potential of game metrics and identify factors contributing to individual variability in in-game performance and performance progression, as well as identifying and adopting strategies that facilitate individual learning success and thus promote effectiveness in improving health outcomes.
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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 | 0,009 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».