Computer and Videogame Interventions for Older Adults' Cognitive and Everyday Functioning
Bibliographic record
Abstract
OBJECTIVE: This study compared older adults' gains in cognitive and everyday functioning after a 60-session home-based videogame intervention with gains seen under formal cognitive training and usual care/no intervention. MATERIALS AND METHODS: Participants were randomized to one of three groups: one group played an off-the-shelf videogame (i.e., Crazy Taxi), the second group engaged in a computerized training program focused on visual attention and processing speed (i.e., PositScience InSight), and the third group received no training. Training in the two intervention conditions consisted of 60 training sessions of 1 hour each, which were completed in 3 months (5 hours a week). Participants received a broad battery of cognitive and everyday functioning assessments immediately before (pretest), after (post-test), and 3 months after (follow-up) training. RESULTS: Both training conditions improved on direct assessments of trained outcomes. In the InSight-trained group, we found transfer to untrained measures of visual attention and processing speed that were similar to the trained tasks, and these gains endured for up to 3 months. Participants in the videogame condition showed small additional benefits, not emerging until 3 months after intervention completion, on a measure of both attention and mood. No trained groups showed gain on visuospatial skills or memory. CONCLUSION: Training effects were highly specific to the target of training. Training effects to visual attention and processing speed were, as expected, larger for InSight-trained participants but were also seen for videogame participants. Given that past research has shown that videogame training leads to greater engagement than cognitive training, videogame interventions may represent a choice for more modest gains in a more engaging context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".