Trunk balance control during beam walking improves with the haptic anchors without the interference of an auditory-cognitive task in older adults
Bibliographic record
Abstract
BACKGROUND: Prior studies have shown that older adults reduced trunk acceleration when walking on a balance beam with haptic inputs provided by anchors; however, it is unknown whether these benefits would remain in the presence of a concurrent cognitive task. RESEARCH QUESTION: This study aimed to evaluate the effect of a cognitive task on balance control when using the anchors while walking on a balance beam in older adults. METHODS: Thirty older adults participated in this study. They walked on a balance beam under four conditions combining haptic inputs (with and without anchors) and a cognitive task (present and absent). The anchors consisted of a flexible cable with a small load (125 g) attached at the end contacting the ground. Participants held one anchor in each hand and dragged the loads over the ground while walking. In the cognitive task, participants silently counted the number of times they heard a target number within a series of random numbers and provided their response at the end of each trial. Trunk acceleration and normalized step speed were assessed. RESULTS: The anchors reduced the normalized step speed and the trunk acceleration amplitude in the frontal plane when walking on the beam. The cognitive task also diminished the normalized step speed in the beam walking. The use of the anchors did not influence the cognitive task performance. SIGNIFICANCE: Even on a balance beam in the presence of a cognitive task, haptic anchors were able to reduce trunk acceleration in older adults to improve balance control. The cognitive task did not affect the use of haptic anchors.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".