Training Multitasking Abilities in Cognitively Healthy Older Adults: Training Gains and Transfer Effects of a Digital Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Developing efficient cognitive training programs for the older population is a major public health goal due to its potential benefits on cognition and quality of life. A promising training approach is emphasis change, which can benefit executive/attention control and multitasking abilities. The aim of this digital pilot randomized controlled trial was to assess the effects of the emphasis change using a new web-based platform that simulates real-life multitasking demands, the Breakfast Game. METHOD: A community-based sample of 38 cognitively healthy participants (M = 65.8, SD = 3.6), highly educated (M = 16.4, SD = 2.0) were randomized between two conditions: 1) Emphasis Change (EC): participants were instructed to place particular emphasis on specific aspects of the game; and 2) Active Control (AC) - gameplay with standard instructions. Participants underwent 13 online sessions using the Breakfast Game. Each session lasted one hour and occurred 3 times a week at participants' homes. Five out of the 13 sessions were supervised via videoconference. Participants completed a neuropsychological assessment via videoconference at baseline and after the intervention (clinicaltrials.gov ID: NCT05506852). RESULT: At baseline, participants from both conditions showed similar levels of cognitive performance and computer literacy (p > .05). The trial presented high adherence (94.5%) and retention rates (89%), with a loss of 2 participants per group. Both groups show a learning curve, with time effects in the game outcomes (p < .05). A time-by-group interaction (p = .04) was observed for one game accuracy measure (e.g., range of stop times), indicating greater training gain in the EC group. Regarding the transfer effects, there were time-by-group interactions (p = .02; p = .03) in the primary outcome (Alphanumeric Task), showing greater improvement in the EC in modulating their attention allocation. There was a modest effect in the secondary transfer outcome, the executive functions composite score (p = .03), mainly driven by working memory and divided attention tasks. There were no changes in the self-efficacy and mood measures. CONCLUSION: Online emphasis change training using the Breakfast Game is feasible with modest effects and promising benefits on divided attention/executive control in older adults. Further research should improve the features of the Breakfast Game and clarify the dose-response relationship.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".