The relationship of fear of falling and balance confidence with balance and dual tasking performance.
Bibliographic record
Abstract
According to traditional conceptualizations of the relationship between fear of falling and falls in older adults, fear of falling is considered to be predictive of falls because it leads to activity avoidance which, in turn, leads to de-conditioning that increases fall risk. The recent literature has begun to challenge such conceptualizations. Specifically, it has been argued that fear of falling and anxiety, in and of themselves, have a direct negative effect on balance. In this study we manipulated anxiety level by asking older research participants to walk either on the floor (low anxiety condition) or an elevated platform (high anxiety condition). Half the time participants carried a tray (dual tasking) and half the time they did not. Manipulation checks (involving heart rate, galvanic skin response, and self-reported anxiety measurement) confirmed that the experimental manipulation was successful in affecting anxiety level. The results demonstrate that the experimental manipulation (platform vs. floor) affected balance parameters and dual tasking performance with the platform condition resulting in a less stable gait. In addition, increased task demand (i.e., dual tasking) also had a negative effect on balance performance. Finally, the results demonstrate that the paper and pencil measures of fear can also predict balance performance (although the variance accounted for is small) even after controlling for medical risk factors for falling. Implications for models of fear of falling are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".