Effects of Balance-Based Exergame Training With Variable Difficulty on Balance and Spatiotemporal Gait Outcomes in Adults With Mild Cognitive Impairment: Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Exergame balance training integrates cognitive and motor challenges, potentially enhancing neuroplasticity, postural control, and gait stability in mild cognitive impairment (MCI). However, whether modulating the task difficulty of a balance-based exergame training may influence posture- and gait-related outcomes remains unknown. OBJECTIVE: We compared balance and gait improvements across exergame training groups performing exercise with different difficulty levels and a Wii Fit group in adults with MCI. METHODS: This 4-armed, parallel design, double-blinded, randomized clinical trial included 97 participants with MCI (Montreal Cognitive Assessment score=18-25). Participants were convenience-sampled from the Railway General Hospital, Rawalpindi, Pakistan, and randomized to one of 4 intervention groups: low-difficulty, moderate-difficulty, high-difficulty exergame training or a Wii Fit training group. Each participant completed 24 sessions (40 min, 3/week) supervised by physical therapists. Gait and balance were assessed using time up and go (TUG), cognitive time up and go (C-TUG), and the Gait & Balance mobile app at baseline and after 4 and 8 weeks. Although the calculated sample size was 80, 97 were recruited to offset attrition. Eighty-seven participants completed the study (94% adherence) (attrition: low-difficulty 1, moderate 3, high 2, Wii Fit 2; 10% total). Data were analyzed using mixed-model analysis of covariance with baseline values as covariates to assess time×group interactions. Bonferroni-adjusted post hoc comparisons revealed between-group differences. RESULTS: High-difficulty training showed the greatest TUG gains (-0.71, SD 0.32; P=.03, anteroposterior (AP) steadiness with eyes open (EO) on firm surface (0.04, SD 0.02; P=.04), step time variability head forward (HF; 0.06, SD 0.09; P=.02), walking speed HF (0.08, SD 0.04; P=.05), step time head turn (HT; -0.04, SD 0.02; P=.04), step time variability HT (-0.35, SD 0.09; P<.001), step length variability (-0.27, SD 0.13; P=.04), and walking speed HT (0.09, SD 0.04; P=.01) versus Wii Fit. Moderate-difficulty training improved AP steadiness EO firm (0.05, SD 0.02; P=.03) and reduced step time variability HT (-0.26, SD 0.09; P=.01). Low-difficulty training improved C-TUG (-1.61, SD 0.63; P=.01), AP steadiness EO firm (0.05, SD 0.02; P=.03), step time variability HF (-0.20, SD 0.09; P=.03), step time variability HT (-0.25, SD 0.09; P=.01), step length variability (-0.31, SD 0.12; P=.014), and walking speed HT (0.11, SD 0.04; P=.03). No significant differences observed between exergame difficulty groups (P>.05). CONCLUSIONS: Balance-based exergame training improves balance and gait in adults with MCI, with no significant differences across difficulty levels, while the high- and low-difficulty training outperformed Wii Fit in several outcomes. High-difficulty training yielded the most consistent improvements in TUG, postural steadiness, gait variability, and walking speed. These results support graded cognitive-motor exergaming as an effective strategy for enhancing postural control and walking stability in MCI, potentially aiding fall prevention and mobility preservation in aging populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT04959383; https://clinicaltrials.gov/study/NCT04959383.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,000 | 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 ».