Older Adults' Engagement During an Intervention Involving Off-the-Shelf Videogame
Bibliographic record
Abstract
OBJECTIVE: The overall goal of our current study was to examine older adults' experience of Flow (i.e., subjective engagement) during the course of a home-based cognitive training program. MATERIALS AND METHODS: In this study, participants took part in a home-based training program. They were randomized to one of the two training groups. One group played an off-the-shelf videogame (i.e., Crazy Taxi), and the other group played a brain training game (i.e., Insight). Training consisted of 60 training sessions of 1 hour each, which were completed in 3 months (5 hours a week). After each training session, participants completed a Flow questionnaire to measure their engagement with the training. RESULTS: The analysis was performed with a linear growth curve model. The results indicate that on average, there was no change in flow for the Insight group between time points. There was no difference between the initial flow status of the Insight group and the Crazy Taxi group. However, the interaction between group membership and time was statistically significant, indicating that the participants in the Crazy Taxi group increased their scores at each week at a rate that was 0.99 larger than those in the Insight group. CONCLUSION: The analyses revealed that both groups experienced increase in Flow over the period, but only participants in the Crazy Taxi group significantly improved in Flow. This has long-term implications since we would expect participation to go beyond 12 weeks in a real-world scenario.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".