Nonimmersive Brain Gaming for Older Adults With Cognitive Impairment: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Technological advances have allowed a variety of computerized cognitive training tools to be engineered in ways that are fun and entertaining yet challenging at a level that can maintain motivation and engagement. This revolution has created an opportunity for gerontological scientists to evaluate brain gaming approaches to improve cognitive and everyday function. The purpose of this scoping review is to provide a critical overview of the existing literature on nonimmersive, electronic brain gaming interventions in older adults with mild cognitive impairment or dementia. RESEARCH DESIGN AND METHODS: Systematic search was conducted using 7 electronic databases from inception through July 2017. A comprehensive 2-level eligibility process was used to identify studies for inclusion based on PRISMA guidelines. RESULTS: Seventeen studies met eligibility criteria. Majority of the studies were randomized controlled trials (n = 13) and incorporated an active control (n = 9). Intervention doses ranged from 4 to 24 weeks in duration with an average of 8.4 (±5.1 standard deviation [SD]) weeks. Session durations ranged from 30 to 100 min with an average of 54 (±25 SD) minutes. Nearly half of studies included a follow-up, ranging from 3 months to 5 years (n = 8). For most studies, brain gaming improved at least one cognitive outcome (n = 12); only one study reported improvement in activities of daily living. DISCUSSION AND IMPLICATIONS: This scoping review conveys the breadth of an emerging research field, which will help guide future research to develop standards and recommendations for brain gaming interventions which are currently lacking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".