Differential Effects of Concurrent Tasks on Gait in Typically Developing Children: A Meta-Analysis
Bibliographic record
Abstract
The objective of this study was to systematically analyze the literature surrounding dual-task (DT) effects on gait in typically developing children (TDC) and to conduct meta-analyses where applicable. After reviewing the abstracts of 676 articles, a total of 22 studies were included. The outcomes of interest were relative change in gait speed, cadence, stride length, double support time, variability in stride length between single and DT walking; and the exposures were concurrent tasks used for DT gait assessment. DT significantly affected each gait parameter (point estimate (PE), ranged from PE, −0.10; 95% CI, −0.13 to −0.08; p < .001 to PE, −0.66; 95% CI, −0.94 to −0.38; p < .001). The strength of DT effects varied by the concurrent task used. The greatest DT effect on gait speed, which was the most commonly presented outcome, was reported when upper extremity complex functional tasks (PE, −0.36; 95% CI, −0.49 to −0.23; p < .001, fine motor tasks (PE, −0.35; 95% CI, −0.38 to −0.32; p < .001), and verbal fluency tasks (PE, −0.26; 95% CI, −0.30 to −0.21; p < .001) were completed concurrently with gait. Children and adolescents experience performance decrements when they walk under DT conditions. Concurrent tasks differentially affect the degree of DT interference for each gait parameter.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.040 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".