Usability and Concurrent Validity of the Gamified Brain Aging Monitor of Cognition (BAMCOG) for the Self-Monitoring of Perioperative Cognitive Function: A Pilot Study
Bibliographic record
Abstract
Objectives: An increasing number of older adults, including those with mild cognitive impairment, are undergoing cardiac surgery. Despite strong recommendations for preoperative cognitive screening and peri-operative monitoring, routine implementation faces challenges, such as limited time in busy outpatient clinics and lack of patient motivation. To address this issue, gamification and self-administration of cognitive screening using BAMCOG were explored in patients undergoing transcatheter aortic valve replacement (TAVR). Methods: A multi-methods prospective repeated-measures within-subject cohort study was conducted between January 2021 and December 2022 to assess usability and concurrent validity. The initial part after game development focused on qualitatively examining the usability of BAMCOG in eight patients, using the System Usability Scale (SUS). The second part, with 40 patients, evaluated concurrent validity by comparing BAMCOG with the widely used Montreal Cognitive Assessment (MoCA). Results: The average SUS score was 79.7, indicating good usability. In the preoperative period, the correlation between BAMCOG and MoCA scores was r = 0.33 (p < 0.05), which increased to r = 0.59 (p < 0.001) on the first postoperative day. Conclusions: In conclusion, peri-operative self-monitoring of cognition around a TAVR procedure is feasible, but the concurrent validity of the BAMCOG and the MoCA scores is moderate to low. This warrants further research on gamified cognitive screeners to optimize their use in perioperative cognitive monitoring.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".