The association between visual attention and body movement-controlled video games, balance and mobility in older adults
Bibliographic record
Abstract
BACKGROUND: Body movement-controlled video games involving physical motion and visual attention may have the potential to train both abilities simultaneously. Our purpose was to determine the associations between performance in these games and visual attention, balance and mobility in a group of older adults. The long-term goal is to identify the optimal type of interactive games with regards to training potential. METHODS: Fifty healthy adults aged 65+ years participated in this cross-sectional study. Visual attention was measured with static and dynamic versions of a useful field of view (UFV) and a multiple object tracking (MOT) test. Balance was measured with a force plate in bi-pedal quiet stance test (QST) and one-legged stance (OLST). Gait variability and walking speed were assessed with the Five Meter Walk Test (5MWT). Four Microsoft™ Xbox® 360 Kinect™ interactive video games were chosen based on the apparent levels of visual attention demand. RESULTS: Visual attention (UFV and MOT) was significantly associated with performance in Xbox® Kinect™ games that appeared to have a high visual attention demand (p < 0.05), while there was minimal or no significant association with games with apparent low visual attention demand. Balance and mobility show correlations with visual attention, and with Xbox games. CONCLUSION: The results suggest that there are relationships between visual attention, balance, mobility and Xbox® Kinect™ game performance. Since different Xbox® games were associated with different balance, mobility and visual attention scores, a variety of such games, rather than a single game, may be most effective for training for falls prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".