Cognitive Computerized Training for Older Adults and Patients with Neurological Disorders: Do the Amount and Training Modality Count? An Umbrella Meta-Regression Analysis.


Journal

Games for health journal
ISSN: 2161-7856
Titre abrégé: Games Health J
Pays: United States
ID NLM: 101583709

Informations de publication

Date de publication:
Apr 2023
Historique:
medline: 5 4 2023
pubmed: 16 3 2023
entrez: 15 3 2023
Statut: ppublish

Résumé

Numerous applications have been created to train cognition and challenge the brain, a process known as computerized cognitive training (CCT). Despite potential positive results, important questions remain unresolved: the appropriate training duration, the efficacy of CCT depending on its type (commercial or developed in-house for the rehabilitation of specific patients) and delivery mode (at-home or on-site), and the patients most likely to benefit such intervention. This study aims to perform an umbrella meta-analysis and meta-regression to determine if the type of CCT, the delivery mode, the amount of training, and participants' age at inclusion influence the improvement of the cognitive function. To do so, we performed a umbrella meta-analysis. One hundred studies were included in this analysis representing 6407 participants. Statistical improvements were found for the different conditions after the training. We do not find statistical difference between the type of intervention or the delivery mode. No dose-response relationship between the total amount of training and the improvement of cognitive functions was found. CCT is effective in improving cognitive function in patients suffering from neurological conditions and in healthy aging. There is therefore an urgent need for health care systems to recognize its therapeutic potential and to evaluate at a larger scale their integration into the clinical pipeline as preventive and rehabilitation tool.

Identifiants

pubmed: 36920851
doi: 10.1089/g4h.2022.0120
doi:

Types de publication

Meta-Analysis Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

100-117

Auteurs

Bruno Bonnechère (B)

REVAL Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium.
Technology-Supported and Data-Driven Rehabilitation, Data Sciences Institute, Hasselt University, Diepenbeek, Belgium.

Malgorzata Klass (M)

Laboratory of Applied Biology and Neurophysiology, ULB Neuroscience Institute (UNI), Université Libre de Bruxelles (ULB), Brussels, Belgium.

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Classifications MeSH