Cognitive Load and Dual-Task Performance in Individuals with and without Forward Head Posture
Bibliographic record
Abstract
Background: Recent studies have found forward head posture (FHP) is associated with altered physiology. There is a lack of research into whether FHP is associated with altered gait parameters when cognitively challenged. Our hypothesis is that individuals with FHP and those without will demonstrate different responses when undergoing dual-task assessment. Methods: Forty-five asymptomatic participants with FHP, defined as a craniovertebral angle (CVA) < 50°, were matched to forty-five participants with normal head posture (NHP) with a CVA > 55°. Participants walked along a 10 m platform under a control condition (no cognitive load) while an optical motion-capture system assessed gait kinematics. Secondly, participants were assessed under a dual-task cognitive load condition to identify the impact on gait kinematics. Results: Under the single-task condition, there were no significant differences for any gait parameter. In the dual-task condition, 12/13 gait parameters were significantly altered for the FHP vs. NHP group (p < 0.01). A calculation of the dual-task cost (DTC) percentage showed significant increases in all gait parameters in participants with FHP (p < 0.02). Correlations between the CVA and gait parameters were not significant for the single-task condition, but all gait parameters were correlated to CVA for the dual-task condition (p < 0.01). The correlation between CVA and DTC for all gait variables was significant (p < 0.04). Conclusions: This study demonstrates that FHP significantly increases the cognitive cost during walking, highlighting the importance of proper postural alignment for maintaining cognitive function under a dual-task condition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".